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- CMIP5 projected changes in mean and extreme climate in the Belt and Road region
- Sub-seasonal to seasonal prediction of tropical cyclone activity in the Western North Pacific: a review
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- Subseasonal-to-seasonal(S2S) prediction using the spatial-temporal projection model(STPM)
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WANG Kaicun, WANG Can, LI Longhui, WANG Tao, WU Guocan, FU Yongshuo, MA Qian, ZHANG Jingyong, CAI Wenjia, CAO Jing, YU Chaoqing, ZHU Huasheng, NAN Zhuotong, CHEN Min, ZHANG Jing, JI Duoying, SHEN Miaogen, TANG Wenjun, HE Bin, ZHAN Wenfeng
2024,47(1): 1-22, DOI: 10.13878/j.cnki.dqkxxb.20240103001
Abstract:
With the support of the National Key Research and Development program,the project proposed a new method for the detection and correction of inhomogeneity of the observed land surface climate data,solved the problem of the detection and correction of the gradual inhomogeneity,and constructed the station and grid data set for the homogenized surface solar radiation,air temperature,ground temperature,wind speed and precipitation in China.The conclusions on the trend of surface wind speed,warming pattern in China and its formation mechanism have been revised.Multi-source data were integrated to construct and validate historical and future datasets of key anthropogenic factors affecting natural systems such as power plants,population,biomass energy,water withdrawal,nitrogen emissions,and carbon dioxide emissions at the km,watershed,or county level.Scenarios of future key anthropogenic factors were constructed,methane and nitrous oxide emission scenarios under carbon neutrality targets and future scenarios used to drive global models were developed,and the mitigation effects of China's carbon neutrality on global warming were estimated,and it was found that China's carbon neutrality had significant mitigation effects on long-term and medium-term global warming.The safety threshold and overshoot time of water nitrogen emission in each province of China are given,the relationship between grain yield and nitrogen fertilization in China is expounded,and effective ways to reduce water nitrogen emission under the premise of ensuring food security are proposed.It is pointed out that the reconstruction of urban and rural nutrient cycling system is a necessary way to ensure food security and restore water quality at the same time.It is found that the interannual change of global water vapor deficit is significantly related to the interannual change of atmospheric carbon dioxide concentration rising rate,which illustrates the important role of water vapor deficit change in regulating ecosystem productivity and the complex influence of multi-factor coupling on ecosystem productivity change.A more comprehensive and detailed assessment of the socio-economic and natural ecological impacts of China's various pathways to carbon neutrality is recommended to ensure that the goal of carbon neutrality is achieved in synergy with other sustainable development goals.
With the support of the National Key Research and Development program,the project proposed a new method for the detection and correction of inhomogeneity of the observed land surface climate data,solved the problem of the detection and correction of the gradual inhomogeneity,and constructed the station and grid data set for the homogenized surface solar radiation,air temperature,ground temperature,wind speed and precipitation in China.The conclusions on the trend of surface wind speed,warming pattern in China and its formation mechanism have been revised.Multi-source data were integrated to construct and validate historical and future datasets of key anthropogenic factors affecting natural systems such as power plants,population,biomass energy,water withdrawal,nitrogen emissions,and carbon dioxide emissions at the km,watershed,or county level.Scenarios of future key anthropogenic factors were constructed,methane and nitrous oxide emission scenarios under carbon neutrality targets and future scenarios used to drive global models were developed,and the mitigation effects of China's carbon neutrality on global warming were estimated,and it was found that China's carbon neutrality had significant mitigation effects on long-term and medium-term global warming.The safety threshold and overshoot time of water nitrogen emission in each province of China are given,the relationship between grain yield and nitrogen fertilization in China is expounded,and effective ways to reduce water nitrogen emission under the premise of ensuring food security are proposed.It is pointed out that the reconstruction of urban and rural nutrient cycling system is a necessary way to ensure food security and restore water quality at the same time.It is found that the interannual change of global water vapor deficit is significantly related to the interannual change of atmospheric carbon dioxide concentration rising rate,which illustrates the important role of water vapor deficit change in regulating ecosystem productivity and the complex influence of multi-factor coupling on ecosystem productivity change.A more comprehensive and detailed assessment of the socio-economic and natural ecological impacts of China's various pathways to carbon neutrality is recommended to ensure that the goal of carbon neutrality is achieved in synergy with other sustainable development goals.
CHEN Haishan, ZHANG Yaocun, ZHANG Wenjun, YIN Zhicong, HUA Wenjian, KUANG Xueyuan, CHEN Guosen, MA Hongyun, HAN Tingting
2024,47(1): 23-45, DOI: 10.13878/j.cnki.dqkxxb.20240110007
Abstract:
Under the background of global warming,extreme weather and climate events may be unprecedented when they occur with increased frequency,evident temporal and spatial clustering (concurrence),stronger persistence and new combinations (compound).This increases the difficulty of predicting extreme events.Extreme weather and climate events,such as persistent heavy rainfall,extreme low temperature,extreme high temperature and drought compound events,heat waves and strong typhoon,have produced a significant influence on economic,social and sustainable development.However,new features of extreme weather and climate events,coupled with associated key processes and relevant mechanism,remain unclear,and the prediction of extreme weather and climate events needs further improvement.This paper first gives a brief introduction of the National Key R&D Program of China for Earth System and Global Change;supported by this program,the new features of extreme weather and climate events causing serious influence over China under changing climate are investigated.Next,the paper explores the physical mechanisms of ocean-land-atmosphere coupling processes that affect extreme weather and climate events,and attempts to seek the precursors for sub-seasonal to seasonal predictions of extreme events.This program aims to develop a new statistical-dynamical combined prediction method of extreme events and establish a new generation of high-resolution numerical prediction in China,along with a detection and attribution system of extreme events.This paper also highlights recent main achievements of this program.
Under the background of global warming,extreme weather and climate events may be unprecedented when they occur with increased frequency,evident temporal and spatial clustering (concurrence),stronger persistence and new combinations (compound).This increases the difficulty of predicting extreme events.Extreme weather and climate events,such as persistent heavy rainfall,extreme low temperature,extreme high temperature and drought compound events,heat waves and strong typhoon,have produced a significant influence on economic,social and sustainable development.However,new features of extreme weather and climate events,coupled with associated key processes and relevant mechanism,remain unclear,and the prediction of extreme weather and climate events needs further improvement.This paper first gives a brief introduction of the National Key R&D Program of China for Earth System and Global Change;supported by this program,the new features of extreme weather and climate events causing serious influence over China under changing climate are investigated.Next,the paper explores the physical mechanisms of ocean-land-atmosphere coupling processes that affect extreme weather and climate events,and attempts to seek the precursors for sub-seasonal to seasonal predictions of extreme events.This program aims to develop a new statistical-dynamical combined prediction method of extreme events and establish a new generation of high-resolution numerical prediction in China,along with a detection and attribution system of extreme events.This paper also highlights recent main achievements of this program.
2024,47(1): 46-54, DOI: 10.13878/j.cnki.dqkxxb.20231201001
Abstract:
Earth System Models (ESM) are powerful tools for studying the earth system and play an indispensable role in conducting scientific research on disaster prevention and mitigation,climate change,and environmental governance.Traditional weather and climate models rapidly evolve towards ESM,including ocean,sea ice,biogeochemical,and atmospheric chemical processes.At the same time,an increasing number of applications are adopting ESM for weather,climate,and ecological prediction.The current international mainstream trend in developing numerical models is to achieve seamless simulation and prediction by constructing integrated models,simultaneously meeting the needs of weather-climate forecasts and predictions at varying temporal and spatial scales.With improved model complexity and resolution,traditional numerical weather models have rapidly progressed in climate change research and climate prediction.However,challenges remain regarding data assimilation,ensemble coupling,high-performance computing,and uncertainty analysis and evaluation.The combination of artificial intelligence (AI) and meteorology has recently attracted tremendous attention.Based on various deep learning architectures,deep learning models can be trained using powerful computing resources and massive data for weather forecasts in a new scientific paradigm independent of traditional numerical weather models.Some technology companies,such as Huawei,NVIDIA,DeepMind,Google,Microsoft,etc.,as well as domestic and international universities such as Tsinghua University,Fudan University,the University of Michigan,Rice University,etc.,have released several Large Weather Models (LWMs) covering from nowcasting,short-term forecast to medium-term forecast,and even extended-period forecast.For instance,FourCastNet,GraphCast,NowcastNet,Pangu Weather,Fengwu,Fuxi,etc.,show significant advantages and great potential in improving forecast accuracy and accelerating the forecast inference process.For accuracy,except in areas like extreme weather,LWMs have matched or even surpassed that of traditional numerical models.Moreover,with continuous development of deep learning methods,their forecasting precision is steadily increasing.For timeliness,LWMs,leveraging deep neural networks' powerful generalization capabilities,far exceed traditional numerical models' predictive abilities under the same resolution conditions.For computational speed,LWMs have significantly increased inference computation speed compared to traditional numerical models,gradually reduced the enormous computation times required by traditional numerical models.The emergence of LWMs signifies that the cross-fertilization between AI and meteorological fields has reached a new horizon.Although these LWMs have made significant breakthroughs at this stage,their development still faces many challenges,such as the interpretability problem,the generalization and migration challenge,and the over-smoothing problem.The advancement of numerical weather prediction is closely tied to developments in computational and data storage technology,as well as observational techniques.Its application requires interdisciplinary integration,combining insights from various scientific fields.A critical scientific challenge in this field is to foster a more profound integration of numerical weather prediction with emerging information technologies such as artificial intelligence,quantum computing,and digital twins.This challenge also involves tailoring complex and refined component models to meet diverse disciplinary demands and societal needs.Advancing numerical weather prediction within the broader context of earth system science requires a concerted effort to promote cross-disciplinary collaboration,addressing vital scientific questions at the intersection of multiple fields.
Earth System Models (ESM) are powerful tools for studying the earth system and play an indispensable role in conducting scientific research on disaster prevention and mitigation,climate change,and environmental governance.Traditional weather and climate models rapidly evolve towards ESM,including ocean,sea ice,biogeochemical,and atmospheric chemical processes.At the same time,an increasing number of applications are adopting ESM for weather,climate,and ecological prediction.The current international mainstream trend in developing numerical models is to achieve seamless simulation and prediction by constructing integrated models,simultaneously meeting the needs of weather-climate forecasts and predictions at varying temporal and spatial scales.With improved model complexity and resolution,traditional numerical weather models have rapidly progressed in climate change research and climate prediction.However,challenges remain regarding data assimilation,ensemble coupling,high-performance computing,and uncertainty analysis and evaluation.The combination of artificial intelligence (AI) and meteorology has recently attracted tremendous attention.Based on various deep learning architectures,deep learning models can be trained using powerful computing resources and massive data for weather forecasts in a new scientific paradigm independent of traditional numerical weather models.Some technology companies,such as Huawei,NVIDIA,DeepMind,Google,Microsoft,etc.,as well as domestic and international universities such as Tsinghua University,Fudan University,the University of Michigan,Rice University,etc.,have released several Large Weather Models (LWMs) covering from nowcasting,short-term forecast to medium-term forecast,and even extended-period forecast.For instance,FourCastNet,GraphCast,NowcastNet,Pangu Weather,Fengwu,Fuxi,etc.,show significant advantages and great potential in improving forecast accuracy and accelerating the forecast inference process.For accuracy,except in areas like extreme weather,LWMs have matched or even surpassed that of traditional numerical models.Moreover,with continuous development of deep learning methods,their forecasting precision is steadily increasing.For timeliness,LWMs,leveraging deep neural networks' powerful generalization capabilities,far exceed traditional numerical models' predictive abilities under the same resolution conditions.For computational speed,LWMs have significantly increased inference computation speed compared to traditional numerical models,gradually reduced the enormous computation times required by traditional numerical models.The emergence of LWMs signifies that the cross-fertilization between AI and meteorological fields has reached a new horizon.Although these LWMs have made significant breakthroughs at this stage,their development still faces many challenges,such as the interpretability problem,the generalization and migration challenge,and the over-smoothing problem.The advancement of numerical weather prediction is closely tied to developments in computational and data storage technology,as well as observational techniques.Its application requires interdisciplinary integration,combining insights from various scientific fields.A critical scientific challenge in this field is to foster a more profound integration of numerical weather prediction with emerging information technologies such as artificial intelligence,quantum computing,and digital twins.This challenge also involves tailoring complex and refined component models to meet diverse disciplinary demands and societal needs.Advancing numerical weather prediction within the broader context of earth system science requires a concerted effort to promote cross-disciplinary collaboration,addressing vital scientific questions at the intersection of multiple fields.
2024,47(1): 55-64, DOI: 10.13878/j.cnki.dqkxxb.20231009006
Abstract:
At present,precipitation forecasting mainly relies on numerical weather forecasting models.However,due to factors such as physical parameterization and computational resources,there remains significant uncertainty in precipitation forecasting based on numerical models.In recent years,deep learning has shown great advantages and potential in the field of weather forecasting.The present study constructs neural networks to predict daily precipitation distribution in the northeastern United States,to explore the capabilities of neural-network models in predicting high-resolution precipitation (CPC,0.25°) using low-resolution meteorological fields (ERA-Interim,0.7°).Next,the study compares the performance of three mainstream network frameworks (VGG,ResNet,and GoogleNet) in the aforementioned task.The results indicate that all three frameworks have certain capabilities for predicting the daily precipitation distribution in the northeastern United States,with VGG performing the best,but their root mean square error (RMSE) is higher than that of the ERA-Interim 24-hour (ERA24) prediction.The ensemble-mean results of the three neural networks are all superior to the ERA24 prediction,and combining these three with the ERA24 prediction results can significantly improve ERA24 prediction in different seasons and intensities.It is thus concluded that deep learning has great potential in improving the resolution and accuracy of precipitation prediction.
At present,precipitation forecasting mainly relies on numerical weather forecasting models.However,due to factors such as physical parameterization and computational resources,there remains significant uncertainty in precipitation forecasting based on numerical models.In recent years,deep learning has shown great advantages and potential in the field of weather forecasting.The present study constructs neural networks to predict daily precipitation distribution in the northeastern United States,to explore the capabilities of neural-network models in predicting high-resolution precipitation (CPC,0.25°) using low-resolution meteorological fields (ERA-Interim,0.7°).Next,the study compares the performance of three mainstream network frameworks (VGG,ResNet,and GoogleNet) in the aforementioned task.The results indicate that all three frameworks have certain capabilities for predicting the daily precipitation distribution in the northeastern United States,with VGG performing the best,but their root mean square error (RMSE) is higher than that of the ERA-Interim 24-hour (ERA24) prediction.The ensemble-mean results of the three neural networks are all superior to the ERA24 prediction,and combining these three with the ERA24 prediction results can significantly improve ERA24 prediction in different seasons and intensities.It is thus concluded that deep learning has great potential in improving the resolution and accuracy of precipitation prediction.
2024,47(1): 65-79, DOI: 10.13878/j.cnki.dqkxxb.20230922001
Abstract:
Prediction of tropical cyclone (TC) genesis at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions,which is a challenge for TC forecast.This study presents an extended-range hybrid dynamical-statistical prediction model and a statistical prediction model for TC frequency over the western North Pacific.The models are based on tropical intraseasonal oscillation signals and the TC clustering method.The fuzzy c-mean clustering method categorizes TCs over the western North Pacific into seven track patterns.Predicting anomalous TC counts in each week involves adding the observed climatological mean of weekly TC counts to obtain total genesis counts for each cluster.The probability of TC track distributions each week is derived by involving the climatology of each track probability.This model could not only predict TC number for each cluster but also the TC track distribution pattern each week.The hybrid dynamical-statistical model relies on contemporaneous statistical relationships between low-frequency variabilities and the output of the ECMWF dynamical model from the S2S dataset.The predictand is the TC genesis number over the western North Pacific during each week.Evaluation of prediction results indicates that the forecast skill of the hybrid dynamic-statistical forecast surpasses that of the statistical forecast model.The precursor signals associated with sub-seasonal TC changes dissipate rapidly,making stable forecasts challenging.In contrast,the dynamic model simulates the low-frequency background field (predictors) effectively,enhancing the hybrid model's forecast skill.While,the current forecast skill of the hybrid dynamic-statistical forecast model extends to six weeks,further improvement is possible.Evaluation of prediction skills and error analysis of different TC clusters reveal that interannual and interdecadal variabilities of background fields on the modulations of intraseasonal oscillations on TC activity cannot be ignored.Statistical relationships between TC counts and low-frequency variabilities differ in distinct ENSO phases,suggesting potential improvement by developing forecast models based on different ENSO phases.Additionally,extratropical intraseasonal signals (e.g.,Rossby wave breaking and westerly jet intensity) significantly impact TC frequency and trajectory,which may provide more source of predictability for TC extended-range prediction.
Prediction of tropical cyclone (TC) genesis at the extended-range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions,which is a challenge for TC forecast.This study presents an extended-range hybrid dynamical-statistical prediction model and a statistical prediction model for TC frequency over the western North Pacific.The models are based on tropical intraseasonal oscillation signals and the TC clustering method.The fuzzy c-mean clustering method categorizes TCs over the western North Pacific into seven track patterns.Predicting anomalous TC counts in each week involves adding the observed climatological mean of weekly TC counts to obtain total genesis counts for each cluster.The probability of TC track distributions each week is derived by involving the climatology of each track probability.This model could not only predict TC number for each cluster but also the TC track distribution pattern each week.The hybrid dynamical-statistical model relies on contemporaneous statistical relationships between low-frequency variabilities and the output of the ECMWF dynamical model from the S2S dataset.The predictand is the TC genesis number over the western North Pacific during each week.Evaluation of prediction results indicates that the forecast skill of the hybrid dynamic-statistical forecast surpasses that of the statistical forecast model.The precursor signals associated with sub-seasonal TC changes dissipate rapidly,making stable forecasts challenging.In contrast,the dynamic model simulates the low-frequency background field (predictors) effectively,enhancing the hybrid model's forecast skill.While,the current forecast skill of the hybrid dynamic-statistical forecast model extends to six weeks,further improvement is possible.Evaluation of prediction skills and error analysis of different TC clusters reveal that interannual and interdecadal variabilities of background fields on the modulations of intraseasonal oscillations on TC activity cannot be ignored.Statistical relationships between TC counts and low-frequency variabilities differ in distinct ENSO phases,suggesting potential improvement by developing forecast models based on different ENSO phases.Additionally,extratropical intraseasonal signals (e.g.,Rossby wave breaking and westerly jet intensity) significantly impact TC frequency and trajectory,which may provide more source of predictability for TC extended-range prediction.
2024,47(1): 80-94, DOI: 10.13878/j.cnki.dqkxxb.20210324001
Abstract:
In this study,a Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting (WRF) model,WRF Hybrid Data Assimilation system,and Ensemble Transform Kalman Filter (ETKF),while assimilating both Wind Profile Radar Detection (WPRD) and Microwave Radiometer (MWR) data.Experiments were performed on the impact of four important parameters on the system (that is,two tuning factors of static background error,localization scale and ensemble weighting factor),and contrast research was carried on to the results of the hybrid and 3DVAR schemes.Some encouraging conclusions were reached:Tuning these four parameters could improve performance of the Rapid Refresh Hybrid system,the analysis and forecast of the hybrid scheme with parameters not tuned were superior to those of 3DVAR,and the best results were those of the hybrid schemed with parameters tuned.
In this study,a Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting (WRF) model,WRF Hybrid Data Assimilation system,and Ensemble Transform Kalman Filter (ETKF),while assimilating both Wind Profile Radar Detection (WPRD) and Microwave Radiometer (MWR) data.Experiments were performed on the impact of four important parameters on the system (that is,two tuning factors of static background error,localization scale and ensemble weighting factor),and contrast research was carried on to the results of the hybrid and 3DVAR schemes.Some encouraging conclusions were reached:Tuning these four parameters could improve performance of the Rapid Refresh Hybrid system,the analysis and forecast of the hybrid scheme with parameters not tuned were superior to those of 3DVAR,and the best results were those of the hybrid schemed with parameters tuned.
2024,47(1): 95-107, DOI: 10.13878/j.cnki.dqkxxb.20210415001
Abstract:
In this study,NCEP/FNL reanalysis and WRFV3.9.1 are used to investigate the influence of the intensity of the monsoon depression on the rainstorm process in Guangdong from 27 August to 1 September,2018.On August 27,the rainstorm was in the development stage,the precipitation distribution was relatively scattered,and the maximum value of the daily precipitation was 80 mm/day.Starting from August 28,the heavy rainfall was concentrated and tended to shift southward to the Pearl River Delta region,with a maximum daily precipitation exceeding 100 mm/day.The rainstorm reached a mature phase between 29 and 30 August,with a maximum of daily precipitation more than 200 mm/day,and the location of the precipitation area was concentrated in the southeast coastal area of Guangdong,with a northeast-southwest spatial distribution.On 31 August,the rainfall began to weaken,but the daily precipitation remained strong.Above all,this rainstorm process involved much precipitation,a long duration,and a concentrated area,making it a rare extraordinary heavy rainstorm event.Using the physical decomposition method indicates that the monsoon depression and low-level jet provided sufficient water vapor and energy for the rainstorm process.In the mature phase of the rainstorm process,there was a negative entropy anomaly to the south of the rainstorm area,and a positive one to the north.The zero line of entropy anomaly indicates that the tendency of entropy was zero,that is,the value of entropy reached its peak,which means that the accumulation of energy had a maximum value in this area.On the north side of the rainstorm,the increase of the entropy means that the unstable energy continued to grow,while the precipitation on the south side released the unstable energy,that is,the entropy decreased and tended to be stable.The location of the zero line of the entropy change corresponded closely with the spatial distribution of the precipitation.Energy accumulating at the zero line of the entropy change was favorable to the development of the rainstorm.To further verify the influence of the monsoon depression,sensitivity experiments (EXP_500,EXP_700,and EXP_1000) were constructed.Sensitivity experiments were conducted by filtering out the perturbation component in the monsoon depression to change the intensity of the monsoon depression.The results show that when the monsoon depression was strong,the rainstorm was likely to occur and the precipitation was strong,and when the monsoon depression was weak,the precipitation was reduced or even absent.The diagnostic analysis shows that the energy helicity had implications for the development of the rainfall:when the energy helicity was greater,the intensity of precipitation was stronger,and when the intensity of the monsoon depression weakened,the energy helicity decreased and the precipitation weakened.
In this study,NCEP/FNL reanalysis and WRFV3.9.1 are used to investigate the influence of the intensity of the monsoon depression on the rainstorm process in Guangdong from 27 August to 1 September,2018.On August 27,the rainstorm was in the development stage,the precipitation distribution was relatively scattered,and the maximum value of the daily precipitation was 80 mm/day.Starting from August 28,the heavy rainfall was concentrated and tended to shift southward to the Pearl River Delta region,with a maximum daily precipitation exceeding 100 mm/day.The rainstorm reached a mature phase between 29 and 30 August,with a maximum of daily precipitation more than 200 mm/day,and the location of the precipitation area was concentrated in the southeast coastal area of Guangdong,with a northeast-southwest spatial distribution.On 31 August,the rainfall began to weaken,but the daily precipitation remained strong.Above all,this rainstorm process involved much precipitation,a long duration,and a concentrated area,making it a rare extraordinary heavy rainstorm event.Using the physical decomposition method indicates that the monsoon depression and low-level jet provided sufficient water vapor and energy for the rainstorm process.In the mature phase of the rainstorm process,there was a negative entropy anomaly to the south of the rainstorm area,and a positive one to the north.The zero line of entropy anomaly indicates that the tendency of entropy was zero,that is,the value of entropy reached its peak,which means that the accumulation of energy had a maximum value in this area.On the north side of the rainstorm,the increase of the entropy means that the unstable energy continued to grow,while the precipitation on the south side released the unstable energy,that is,the entropy decreased and tended to be stable.The location of the zero line of the entropy change corresponded closely with the spatial distribution of the precipitation.Energy accumulating at the zero line of the entropy change was favorable to the development of the rainstorm.To further verify the influence of the monsoon depression,sensitivity experiments (EXP_500,EXP_700,and EXP_1000) were constructed.Sensitivity experiments were conducted by filtering out the perturbation component in the monsoon depression to change the intensity of the monsoon depression.The results show that when the monsoon depression was strong,the rainstorm was likely to occur and the precipitation was strong,and when the monsoon depression was weak,the precipitation was reduced or even absent.The diagnostic analysis shows that the energy helicity had implications for the development of the rainfall:when the energy helicity was greater,the intensity of precipitation was stronger,and when the intensity of the monsoon depression weakened,the energy helicity decreased and the precipitation weakened.
2024,47(1): 108-123, DOI: 10.13878/j.cnki.dqkxxb.20230616001
Abstract:
During the period of 1979—2019,there was a significant negative correlation between the interannual variation in precipitation anomalies over South China (SC) and those in the eastern Tibetan Plateau (ETP) in July,with a correlation coefficient of -0.60 between the area-averaged precipitation over the two regions.It is statistically significant at the 99％ confidence level.Therefore,a precipitation dipole index (PDI) is defined as the difference in the standardized area-averaged precipitation between SC (20°—26°N,108°—123°E) and the ETP (28°—35°N,90°—108°E) in July.The positive phase of the July precipitation dipole is characterized by increased (decreased) precipitation over SC (the ETP),and vice versa.Based on this,we utilize ERA5 reanalysis data and station precipitation data from the period of 1979—2019 to investigate the influence of soil moisture anomalies in May on the interannual variation of the July precipitation dipole,along with its underlying physical processes.The study results show that higher soil moisture in the TP and lower soil moisture in Central China (CC) in May tend to lead to increased (decreased) precipitation over SC (the ETP) in July,displaying a dipole pattern of precipitation over the two regions.Further analysis reveals that the positive (negative) soil moisture anomalies in the TP (CC) may persist from May to July,resulting in positive surface turbulent heat flux anomalies in the Northern China (NC) region in July.This in turn causes local middle to lower troposphere warming,thus intensifying the meridional temperature gradient and atmospheric baroclinicity between NC and Lake Baikal.Correspondingly,enhanced synoptic-scale transient wave activity is observed,which creates an anomalous high pressure with an equivalent barotropic structure and Rossby wave sources over NC-Mongolia by transient vorticity forcing.The associated Rossby waves propagate southeastward to SC.Subsequently,an anomalous low pressure appears over SC with an equivalent barotropic structure,which facilitates the eastward shift of the western Pacific subtropical high and westward shift of the South Asian high.This anomalous circulation respectively corresponds to anticyclone and cyclone anomalies in the middle and lower troposphere over Mongolia and SC,and consequently enhanced (suppressed) precipitation occurs in SC (the ETP).In addition,the positive feedback process between the anomalous high over Mongolia and local low soil moisture is favorable to the formation of the July precipitation dipole by intensifying and maintaining the above physical process,and vice versa.
During the period of 1979—2019,there was a significant negative correlation between the interannual variation in precipitation anomalies over South China (SC) and those in the eastern Tibetan Plateau (ETP) in July,with a correlation coefficient of -0.60 between the area-averaged precipitation over the two regions.It is statistically significant at the 99％ confidence level.Therefore,a precipitation dipole index (PDI) is defined as the difference in the standardized area-averaged precipitation between SC (20°—26°N,108°—123°E) and the ETP (28°—35°N,90°—108°E) in July.The positive phase of the July precipitation dipole is characterized by increased (decreased) precipitation over SC (the ETP),and vice versa.Based on this,we utilize ERA5 reanalysis data and station precipitation data from the period of 1979—2019 to investigate the influence of soil moisture anomalies in May on the interannual variation of the July precipitation dipole,along with its underlying physical processes.The study results show that higher soil moisture in the TP and lower soil moisture in Central China (CC) in May tend to lead to increased (decreased) precipitation over SC (the ETP) in July,displaying a dipole pattern of precipitation over the two regions.Further analysis reveals that the positive (negative) soil moisture anomalies in the TP (CC) may persist from May to July,resulting in positive surface turbulent heat flux anomalies in the Northern China (NC) region in July.This in turn causes local middle to lower troposphere warming,thus intensifying the meridional temperature gradient and atmospheric baroclinicity between NC and Lake Baikal.Correspondingly,enhanced synoptic-scale transient wave activity is observed,which creates an anomalous high pressure with an equivalent barotropic structure and Rossby wave sources over NC-Mongolia by transient vorticity forcing.The associated Rossby waves propagate southeastward to SC.Subsequently,an anomalous low pressure appears over SC with an equivalent barotropic structure,which facilitates the eastward shift of the western Pacific subtropical high and westward shift of the South Asian high.This anomalous circulation respectively corresponds to anticyclone and cyclone anomalies in the middle and lower troposphere over Mongolia and SC,and consequently enhanced (suppressed) precipitation occurs in SC (the ETP).In addition,the positive feedback process between the anomalous high over Mongolia and local low soil moisture is favorable to the formation of the July precipitation dipole by intensifying and maintaining the above physical process,and vice versa.
2024,47(1): 124-135, DOI: 10.13878/j.cnki.dqkxxb.20231220010
Abstract:
A heat wave defined as several consecutive days with positive temperature anomalies.Since the 1950s,heat waves have increased in number and intensified globally,exerting significant impacts on human health,theecosystem,and the social economy.The increase in heat waves,especially extreme events with longer duration and higher amplitude over North China after the late 1990s,is of particular note.However,relevant existing studies have mainly focused on unsorted events or typical cases,and the atmospheric backgrounds of heat waves with different severities are unclear.Therefore,using the CN05.1 daily maximum temperature and NCEP/NCAR reanalysis data for the period of 1961—2020,this study identifies severe and mild heat wave events over North China,then selects the years with high frequencies of such events.On this basis,the spatiotemporal variation and atmospheric background of severe and mild heat waves events over North China are investigated through composite analysis.The results show that high frequencies of severe and mild heat wave events mainly appear in the northern part and the middle and southern parts of North China,respectively.For both types of events,their frequencies averaged over North China have shown a significant increasing trend since the 1960s,with a higher occurrence rate of severe heat waves (0.09 events per decade) than mild ones (0.03 events per decade).Corresponding to high frequencies of severe heat waves in North China,an anomalous anticyclonic circulation with the quasi-equivalent barotropic structure dominates the mid-latitudes of East Asia,and the North China domain is located in its southeast flank.Against this background,anomalous subsidence and hence reduction of cloud cover and humidity appear in North China.In addition to the adiabatic heating of the subsidence,the clear-dry air over North China is conducive to stronger solar radiation reaching the land surface.Accordingly,the outgoing longwave radiation and sensible heat from the land surface are enhanced,thus warming the overlying atmosphere over the region.Incomparison,associated with high frequencies of mild heat wave events in North China is an anomalous dipole pattern with the anticyclonic and cyclonic anomalies respectively residing on the southeast and northwest sides of North China,and the target region is under the control of southwesterly anomalies.The southwesterly anomaly can heat the atmosphere through warm advection transport toward North China.At the same time,it also increases humidity over North China by transporting water vapor from the ocean to the region.The increased humidity may capture and reflect more longwave radiation onto the surface,thus favoring the warming of overlying atmosphere.Ingeneral,the occurrence of severe heat waves is affected by the joint contribution of adiabatic heating due to abnormal subsidence,solar radiation and sensible heating,exhibiting a dry-hot nature.The occurrence of mild heat waves is dominated by the greenhouse effect induced by abundant water vapor,and horizontal temperature advection,exhibiting a wet-hot nature.There is a discernible difference in the atmospheric background of severe and mild heat waves.
A heat wave defined as several consecutive days with positive temperature anomalies.Since the 1950s,heat waves have increased in number and intensified globally,exerting significant impacts on human health,theecosystem,and the social economy.The increase in heat waves,especially extreme events with longer duration and higher amplitude over North China after the late 1990s,is of particular note.However,relevant existing studies have mainly focused on unsorted events or typical cases,and the atmospheric backgrounds of heat waves with different severities are unclear.Therefore,using the CN05.1 daily maximum temperature and NCEP/NCAR reanalysis data for the period of 1961—2020,this study identifies severe and mild heat wave events over North China,then selects the years with high frequencies of such events.On this basis,the spatiotemporal variation and atmospheric background of severe and mild heat waves events over North China are investigated through composite analysis.The results show that high frequencies of severe and mild heat wave events mainly appear in the northern part and the middle and southern parts of North China,respectively.For both types of events,their frequencies averaged over North China have shown a significant increasing trend since the 1960s,with a higher occurrence rate of severe heat waves (0.09 events per decade) than mild ones (0.03 events per decade).Corresponding to high frequencies of severe heat waves in North China,an anomalous anticyclonic circulation with the quasi-equivalent barotropic structure dominates the mid-latitudes of East Asia,and the North China domain is located in its southeast flank.Against this background,anomalous subsidence and hence reduction of cloud cover and humidity appear in North China.In addition to the adiabatic heating of the subsidence,the clear-dry air over North China is conducive to stronger solar radiation reaching the land surface.Accordingly,the outgoing longwave radiation and sensible heat from the land surface are enhanced,thus warming the overlying atmosphere over the region.Incomparison,associated with high frequencies of mild heat wave events in North China is an anomalous dipole pattern with the anticyclonic and cyclonic anomalies respectively residing on the southeast and northwest sides of North China,and the target region is under the control of southwesterly anomalies.The southwesterly anomaly can heat the atmosphere through warm advection transport toward North China.At the same time,it also increases humidity over North China by transporting water vapor from the ocean to the region.The increased humidity may capture and reflect more longwave radiation onto the surface,thus favoring the warming of overlying atmosphere.Ingeneral,the occurrence of severe heat waves is affected by the joint contribution of adiabatic heating due to abnormal subsidence,solar radiation and sensible heating,exhibiting a dry-hot nature.The occurrence of mild heat waves is dominated by the greenhouse effect induced by abundant water vapor,and horizontal temperature advection,exhibiting a wet-hot nature.There is a discernible difference in the atmospheric background of severe and mild heat waves.
2024,47(1): 136-147, DOI: 10.13878/j.cnki.dqkxxb.20230916001
Abstract:
Atmospheric rivers (ARs) are characterized as long,narrow,and transient channels of strong horizontal water vapor transport.Previous studies have primarily focused on their impact on mid-latitudes,emphasizing the potential risks of deleterious hazards and financial losses.However,less attention has been given to ARs in the Antarctic region,despite they account for over 90％ of moisture transport into the high latitudes.ARs typically originate in the robust poleward meridional transport flank within ridges (blocking highs) and explosive extratropical cyclones in the Antarctic region,facilitating the substantial moisture transport through a vigorous low-level jet.Three widely-used metrics for characterizing the moisture intrusion state are integrated water vapor,v-component of integrated water vapor and integrated water vapor.An AR is detected when the enclosed shape of the extremely high moisture intrusion path is adequately elongated.The frequency of ARs varies across different AR detection algorithms based on diverse metrics and distinct extremity thresholds.The annual frequency of ARs decreases with latitude,exhibiting a zonally asymmetric pattern that show higher seasonal frequencies in austral winter and spring.These spatial and temporal features are shaped by the geographical environment and the distribution of synoptic systems in the Antarctic region.The annual variability of AR frequency appears to be associated with dominant atmospheric modes in southern high latitudes,such as the Southern Annular Mode.Additionally,it is also modulated by the natural variability of sea surface temperature modes,including El Niño Southern Oscillation and Indian Ocean Dipole.ARs have significant impacts on the Antarctic ice sheet and sea ice.ARs contribute both positively and negatively to the Antarctic ice sheet.On one hand,the intense snowfall during ARs constitutes a major portion of the total precipitation over the ice sheet,favoring its mass gain.Conversely,warm-moist air intrusions accompanying ARs induce surface melting and extremely high temperatures due to foehn winds and anomalously high net surface energy flux.Moreover,surface meltwater during ARs promotes hydraulic fracturing on the ice shelves and trigger their disintegration by removing sea ice through strong winds.These processes pose a substantial threat to the ice sheet mass balance.Meanwhile,the warm-moist air and strong winds during ARs thermodynamically and dynamically reduce sea ice.ARs lead to anomalously high temperatures and net surface heat flux,intensifying sea ice thermodynamically melting,especially in winter.The strong winds dynamically drift the sea ice onshore,accelerating ice breaking through powerful waves and further enhancing lateral melting.Though ARs in the Antarctic region have been subject to various analyses,certain questions persist.The evaluation on ARs' impact on Antarctic ice sheet and sea ice remain contingent on the chosen detection algorithm,necessitating the development of a more universal and robust approach.Implementing machine learning to extract the spatial and temporal features of ARs could offer such an approach.Moreover,although the fact that most liquid precipitation is attributed to ARs,its influence on the ice sheet and sea ice is often overlooked,despite its potential to enhance melting and destabilize ice shelves.In addition,ARs may exert a profound influence on the ocean,subsequently providing feedback to the atmosphere.However,the interaction between ARs and the Southern Ocean is not well understood.Therefore,further research imperative to elucidate these mechanisms and evaluate the future changes in Antarctic ice sheet mass balance and sea ice influenced by ARs.
Atmospheric rivers (ARs) are characterized as long,narrow,and transient channels of strong horizontal water vapor transport.Previous studies have primarily focused on their impact on mid-latitudes,emphasizing the potential risks of deleterious hazards and financial losses.However,less attention has been given to ARs in the Antarctic region,despite they account for over 90％ of moisture transport into the high latitudes.ARs typically originate in the robust poleward meridional transport flank within ridges (blocking highs) and explosive extratropical cyclones in the Antarctic region,facilitating the substantial moisture transport through a vigorous low-level jet.Three widely-used metrics for characterizing the moisture intrusion state are integrated water vapor,v-component of integrated water vapor and integrated water vapor.An AR is detected when the enclosed shape of the extremely high moisture intrusion path is adequately elongated.The frequency of ARs varies across different AR detection algorithms based on diverse metrics and distinct extremity thresholds.The annual frequency of ARs decreases with latitude,exhibiting a zonally asymmetric pattern that show higher seasonal frequencies in austral winter and spring.These spatial and temporal features are shaped by the geographical environment and the distribution of synoptic systems in the Antarctic region.The annual variability of AR frequency appears to be associated with dominant atmospheric modes in southern high latitudes,such as the Southern Annular Mode.Additionally,it is also modulated by the natural variability of sea surface temperature modes,including El Niño Southern Oscillation and Indian Ocean Dipole.ARs have significant impacts on the Antarctic ice sheet and sea ice.ARs contribute both positively and negatively to the Antarctic ice sheet.On one hand,the intense snowfall during ARs constitutes a major portion of the total precipitation over the ice sheet,favoring its mass gain.Conversely,warm-moist air intrusions accompanying ARs induce surface melting and extremely high temperatures due to foehn winds and anomalously high net surface energy flux.Moreover,surface meltwater during ARs promotes hydraulic fracturing on the ice shelves and trigger their disintegration by removing sea ice through strong winds.These processes pose a substantial threat to the ice sheet mass balance.Meanwhile,the warm-moist air and strong winds during ARs thermodynamically and dynamically reduce sea ice.ARs lead to anomalously high temperatures and net surface heat flux,intensifying sea ice thermodynamically melting,especially in winter.The strong winds dynamically drift the sea ice onshore,accelerating ice breaking through powerful waves and further enhancing lateral melting.Though ARs in the Antarctic region have been subject to various analyses,certain questions persist.The evaluation on ARs' impact on Antarctic ice sheet and sea ice remain contingent on the chosen detection algorithm,necessitating the development of a more universal and robust approach.Implementing machine learning to extract the spatial and temporal features of ARs could offer such an approach.Moreover,although the fact that most liquid precipitation is attributed to ARs,its influence on the ice sheet and sea ice is often overlooked,despite its potential to enhance melting and destabilize ice shelves.In addition,ARs may exert a profound influence on the ocean,subsequently providing feedback to the atmosphere.However,the interaction between ARs and the Southern Ocean is not well understood.Therefore,further research imperative to elucidate these mechanisms and evaluate the future changes in Antarctic ice sheet mass balance and sea ice influenced by ARs.
2024,47(1): 148-159, DOI: 10.13878/j.cnki.dqkxxb.20230512003
Abstract:
Atmospheric detection lidar has been widely used in air pollutant monitoring,aerosol detection,cloud parameter retrieval,boundary layer height inversion and other important fields due to its high detection accuracy,fine time resolution and far vertical detection ability.In recent years,many new ground-based lidars have been applied to observation networks in China,providing many continuous vertical-profiling observations.However,the quality of ground-based lidar data is inconsistent,thus the quality control of lidar detection data is important.Specifically,unrealistic near-surface aerosol profiles can be found in some lidar products,indicating that the calibration of overlap function is crucial to improving the data quality of ground lidars.The experimental method proposed by Wandinger and Ansmann (2002),based on observations from two channels,is widely used to determine the overlap function of Raman lidars when external calibration instruments are unavailable.This method assumes that the overlap function in the elastic channel is approximately equal to that in the nitrogen channel.This assumption is valid when the lidar system is well aligned,thus Wandinger and Ansmann (2002) did not create a method to determine it.However,misalignments often occur in practice.After a lidar is initially calibrated after production,slight vibrations in the process of transportation,handling and installation may lead to misalignments,which may result in the precondition for the overlap function calibration method being invalid.It is unclear whether the method proposed by Wandinger and Ansmann (2002) can be used to calibrate the overlap function in reality,thus this paper introduces a telecover test to evaluate the validity of precondition for the calibration method.A ray tracing method is applied to the Raman lidar at the Nanjing Meteorological Bureau,and simulations are conducted to determine the overlap functions of both the elastic and nitrogen channels under various alignment conditions and during different telecover tests.The simulation results reveal that the precondition of the method proposed by Wandinger and Ansmann (2002) is valid when the ratio of elastic signals to nitrogen signals remains consistent across different quadrants of the telescope aperture.However,the precondition can be invalid when the ratio of elastic signals to nitrogen signals differs during different telecover tests.Using this method,the Raman lidar of Nanjing Meteorological Bureau was evaluated and adjusted.After the Raman lidar was initially installed,the results from telecover tests show that the signal strength of elastic scattering channel differs greatly from that of nitrogen channel.A test using the CCD-lidar (charge-coupled device lidar) system was applied to verify the overlap function determined by the dual-channel experimental method,and the results suggest that the error is high due to the invalidity of the precondition.To solve this problem,we adjusted the lidar optical system to improve its collimation.After this,we performed the telecover test again,and the signal strength of elastic scattering channel was basically the same as that of nitrogen channel.Our numerical simulations suggest that the preconditions of Raman radar overlapping function calibration method can be basically satisfied after the adjustment.The CCD-lidar test was performed again,and this time the overlap function determined by the dual-channel experimental calibration method was constant with CCD-lidar.The method proposed in this paper can effectively verify the applicability of the dual-channel experimental calibration method,and has good operability,thus this method could be applied to other Raman lidars in the future.Compared with the CCD-lidar observation calibration method which requires outdoor operations at clear-sky nighttime,the dual-channel experimental calibration method has greater advantages when the preconditions are met.
Atmospheric detection lidar has been widely used in air pollutant monitoring,aerosol detection,cloud parameter retrieval,boundary layer height inversion and other important fields due to its high detection accuracy,fine time resolution and far vertical detection ability.In recent years,many new ground-based lidars have been applied to observation networks in China,providing many continuous vertical-profiling observations.However,the quality of ground-based lidar data is inconsistent,thus the quality control of lidar detection data is important.Specifically,unrealistic near-surface aerosol profiles can be found in some lidar products,indicating that the calibration of overlap function is crucial to improving the data quality of ground lidars.The experimental method proposed by Wandinger and Ansmann (2002),based on observations from two channels,is widely used to determine the overlap function of Raman lidars when external calibration instruments are unavailable.This method assumes that the overlap function in the elastic channel is approximately equal to that in the nitrogen channel.This assumption is valid when the lidar system is well aligned,thus Wandinger and Ansmann (2002) did not create a method to determine it.However,misalignments often occur in practice.After a lidar is initially calibrated after production,slight vibrations in the process of transportation,handling and installation may lead to misalignments,which may result in the precondition for the overlap function calibration method being invalid.It is unclear whether the method proposed by Wandinger and Ansmann (2002) can be used to calibrate the overlap function in reality,thus this paper introduces a telecover test to evaluate the validity of precondition for the calibration method.A ray tracing method is applied to the Raman lidar at the Nanjing Meteorological Bureau,and simulations are conducted to determine the overlap functions of both the elastic and nitrogen channels under various alignment conditions and during different telecover tests.The simulation results reveal that the precondition of the method proposed by Wandinger and Ansmann (2002) is valid when the ratio of elastic signals to nitrogen signals remains consistent across different quadrants of the telescope aperture.However,the precondition can be invalid when the ratio of elastic signals to nitrogen signals differs during different telecover tests.Using this method,the Raman lidar of Nanjing Meteorological Bureau was evaluated and adjusted.After the Raman lidar was initially installed,the results from telecover tests show that the signal strength of elastic scattering channel differs greatly from that of nitrogen channel.A test using the CCD-lidar (charge-coupled device lidar) system was applied to verify the overlap function determined by the dual-channel experimental method,and the results suggest that the error is high due to the invalidity of the precondition.To solve this problem,we adjusted the lidar optical system to improve its collimation.After this,we performed the telecover test again,and the signal strength of elastic scattering channel was basically the same as that of nitrogen channel.Our numerical simulations suggest that the preconditions of Raman radar overlapping function calibration method can be basically satisfied after the adjustment.The CCD-lidar test was performed again,and this time the overlap function determined by the dual-channel experimental calibration method was constant with CCD-lidar.The method proposed in this paper can effectively verify the applicability of the dual-channel experimental calibration method,and has good operability,thus this method could be applied to other Raman lidars in the future.Compared with the CCD-lidar observation calibration method which requires outdoor operations at clear-sky nighttime,the dual-channel experimental calibration method has greater advantages when the preconditions are met.
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在线获得:March 06, 2024, DOI: 10.13878/j.cnki.dqkxxb.20240212001
Abstract:
In the field of artificial precipitation enhancement, aircraft observation is still the only effective ways to directly obtain the phase state of cloud particles. Because the liquid supercooled water content in the cloud is an important parameter to judge the microphysical structure characteristics of the cloud system, based on the layered vertical detection data of Par-ticle Measurement System (PMS) in the Sanjiangyuan National Nature Reserve, we analyzed the microphysical charac-teristics of a typical layered cloud system in this area in spring. The results show that: (1) the typical stratiform cloud system in spring cloud system is mainly composed of three layers, the uppermost Cs (cirrostratus) is ice-phase cloud, and the AS (altostratus) in upper layer (high-level cloud) and the lower layer are mixed clouds. Moreover, the high super-cooled water area is mainly distributed in the middle, upper and lower parts of the low-level As, where the cloud particle concentration and supercooled water content are the highest, with obvious regional characteristics. (2) The liquid cloud particles in the As layer are concentrated in the median diameter range of 3.5 ~ 27.5 μm, and the cloud particles larger than 30.5 μm are ice-phase. In the middle and upper part of the low As layer, there is an obvious growth phenomenon in the high supercooled water area, and there is obvious ice crystals effect in the middle and lower part. (3) In the low su-percooled water area, the average content ratio of supercooled water is 90.8±10.9%, and the middle to lower parts (95.6±5.6%) is significantly higher than the middle and upper parts (79.8±12.1%). The accurate understanding and judgment of the microphysical structure of the layered cloud system in Sanjiangyuan will provide a reliable observation basis for the catalysis of artificial precipitation enhancement in this area.
In the field of artificial precipitation enhancement, aircraft observation is still the only effective ways to directly obtain the phase state of cloud particles. Because the liquid supercooled water content in the cloud is an important parameter to judge the microphysical structure characteristics of the cloud system, based on the layered vertical detection data of Par-ticle Measurement System (PMS) in the Sanjiangyuan National Nature Reserve, we analyzed the microphysical charac-teristics of a typical layered cloud system in this area in spring. The results show that: (1) the typical stratiform cloud system in spring cloud system is mainly composed of three layers, the uppermost Cs (cirrostratus) is ice-phase cloud, and the AS (altostratus) in upper layer (high-level cloud) and the lower layer are mixed clouds. Moreover, the high super-cooled water area is mainly distributed in the middle, upper and lower parts of the low-level As, where the cloud particle concentration and supercooled water content are the highest, with obvious regional characteristics. (2) The liquid cloud particles in the As layer are concentrated in the median diameter range of 3.5 ~ 27.5 μm, and the cloud particles larger than 30.5 μm are ice-phase. In the middle and upper part of the low As layer, there is an obvious growth phenomenon in the high supercooled water area, and there is obvious ice crystals effect in the middle and lower part. (3) In the low su-percooled water area, the average content ratio of supercooled water is 90.8±10.9%, and the middle to lower parts (95.6±5.6%) is significantly higher than the middle and upper parts (79.8±12.1%). The accurate understanding and judgment of the microphysical structure of the layered cloud system in Sanjiangyuan will provide a reliable observation basis for the catalysis of artificial precipitation enhancement in this area.
Zhou Xiaotian, Zhang Qianru, Guo Qingyan, Chen Yiling, Zhou Xuesong, Li Changjun, Feng Yong, Zhang Ping
在线获得:November 17, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221130001
Abstract:
As the most basic physical quantity for studying on climate evolution, the integrity and accuracy of daily temperature series are of great significance for climate analysis and assessment. In recent years, with the deployment of a large number of unmanned ground intensified automatic weather stations, missing data with double random characteristics such as random distribution of stations and random lengths of series, which poses significant obstacles to climate analysis and operational applications. In view of the shortcomings of the existing methods for meteorological data interpolation, a new twice interpolation method of daily temperature data based on dynamic time warping (DTW) is proposed in this paper. The method adopts a real-time interpolation strategy, which mainly includes: (1) The method decomposes the temperature observation time series into a fitted straight line and a residual curved line by using univariate linear regression equation, and recomposes new temperature series by combining the two lines; (2) The method provides the definition and interpolation conditions of temperature interpolation areas; (3)The method proposes a new model for calculating the distance between stations by DTW . The collecting temperature data from Shandong Province in 2021 is used to test the method, and the test results show that the method can meet the interpolation needs of daily temperature data with double random characteristics, and the combination method of DTW distance and twice interpolation in the interpolation process can achieve a better effect than any of the other combination based on site geographical proximity relationships; the method is sensitive to terrain, and the interpolation effect in plain or hilly area is better than that in mountainous area.
As the most basic physical quantity for studying on climate evolution, the integrity and accuracy of daily temperature series are of great significance for climate analysis and assessment. In recent years, with the deployment of a large number of unmanned ground intensified automatic weather stations, missing data with double random characteristics such as random distribution of stations and random lengths of series, which poses significant obstacles to climate analysis and operational applications. In view of the shortcomings of the existing methods for meteorological data interpolation, a new twice interpolation method of daily temperature data based on dynamic time warping (DTW) is proposed in this paper. The method adopts a real-time interpolation strategy, which mainly includes: (1) The method decomposes the temperature observation time series into a fitted straight line and a residual curved line by using univariate linear regression equation, and recomposes new temperature series by combining the two lines; (2) The method provides the definition and interpolation conditions of temperature interpolation areas; (3)The method proposes a new model for calculating the distance between stations by DTW . The collecting temperature data from Shandong Province in 2021 is used to test the method, and the test results show that the method can meet the interpolation needs of daily temperature data with double random characteristics, and the combination method of DTW distance and twice interpolation in the interpolation process can achieve a better effect than any of the other combination based on site geographical proximity relationships; the method is sensitive to terrain, and the interpolation effect in plain or hilly area is better than that in mountainous area.
在线获得:November 01, 2023, DOI: 10.13878/j.cnki.dqkxxb.20231016001
Abstract:
Based on the hourly maximum wind speed data of Shanghai automatic stations and real-time gale disaster data provided by Shanghai Emergency Response Coordination Center from 2008 to 2019,?characteristics of spatiotemporal distributions and correlations between urban gales and disasters are analyzed in order to explore the impacts of strong winds in Shanghai. The results are as follows: (1) The gales and disasters present significant seasonal variations with force 8 gale days, maximum wind speeds, and disasters all exhibiting a summer single peak pattern. Summer gale days account for 41% of the year, and gale disasters account for over 80% of the year. The inter-annual fluctuations of disasters are significant, mainly influenced by extreme weathers. (2)The gales and disasters show significant differences between urban and suburban areas: Ⅰ) Wind speeds in downtown areas are much lower due to the impact of urbanization. Ⅱ) The density of disaster cases ranks the highest in central city (up to 37 cases/km2) and relatively high in regional centers and sub-centers. It is also found that the number of disasters in each region is positively correlated with its population and GDP. III) The housing related damages are relatively prominent in downtown and its surrounding regions, but the least significant in Chongming District. (3) The disastrous gale weather in Shanghai can be divided into four categories: Jiang-huai cyclone gales, thunderstorm gales, tropical cyclone gales and cold air gales, with thunderstorm gales the most frequent and tropical cyclone gales the most severe. (4) Trees and vehicles are the most common disaster-bearing bodies of strong wind disasters in Shanghai, followed by power lines and rain shelters. Gales cause disasters with obvious chain reactions. The study on the characteristics and impacts of urban gales are of great significance for the gale risk warning development and disaster prevention and mitigation.
Based on the hourly maximum wind speed data of Shanghai automatic stations and real-time gale disaster data provided by Shanghai Emergency Response Coordination Center from 2008 to 2019,?characteristics of spatiotemporal distributions and correlations between urban gales and disasters are analyzed in order to explore the impacts of strong winds in Shanghai. The results are as follows: (1) The gales and disasters present significant seasonal variations with force 8 gale days, maximum wind speeds, and disasters all exhibiting a summer single peak pattern. Summer gale days account for 41% of the year, and gale disasters account for over 80% of the year. The inter-annual fluctuations of disasters are significant, mainly influenced by extreme weathers. (2)The gales and disasters show significant differences between urban and suburban areas: Ⅰ) Wind speeds in downtown areas are much lower due to the impact of urbanization. Ⅱ) The density of disaster cases ranks the highest in central city (up to 37 cases/km2) and relatively high in regional centers and sub-centers. It is also found that the number of disasters in each region is positively correlated with its population and GDP. III) The housing related damages are relatively prominent in downtown and its surrounding regions, but the least significant in Chongming District. (3) The disastrous gale weather in Shanghai can be divided into four categories: Jiang-huai cyclone gales, thunderstorm gales, tropical cyclone gales and cold air gales, with thunderstorm gales the most frequent and tropical cyclone gales the most severe. (4) Trees and vehicles are the most common disaster-bearing bodies of strong wind disasters in Shanghai, followed by power lines and rain shelters. Gales cause disasters with obvious chain reactions. The study on the characteristics and impacts of urban gales are of great significance for the gale risk warning development and disaster prevention and mitigation.
在线获得:May 08, 2023, DOI: 10.13878/j.cnki.dqkxxb.20230501007
Abstract:
From March to April in 2023, 9 dust weather processes occurred in China.The processes from 19 to 24 March and from 9 to 13 April reached the level of strong sandstorm and sandstorm respectively, bringing adverse impacts on population health, ecological envi-ronment and transportation.The anomalous Eurasian atmospheric circulations showed a zonal distribution from the late winter to early spring, the Siberian high and the East Asian trough were weak, and the area around Mongolia was controlled by the high pressure anom-aly, which resulted in the continuous warm surface air temperature and soil temperature in the dust source area (the second highest since 1980).In addition, the lower troposphere also lacked water vapor transporting to the sand source, resulting in significantly less precipita-tion in the winter and spring of 2023 (the lowest in history since 1980).The continuous warm and dry climate conditions in winter and spring resulted in a large number of dry and loose gravel in the dust source area from February to March in 2023, which provided ex-tremely favorable dust source conditions for the occurrence of sandstorms.The synoptic disturbance systems that triggered the sandstorms in March 19—24 and April 9—13 were both Mongolian cyclones.However, the two Mongolian cyclones and their configuration with the rear cold high were different, which directly caused the differences in the intensity, path and duration of the two sandstorms.From 19 to 24 March, the dust process reached the level of strong sandstorm, causing PM10 concentrations in Beijing and Harbin to exceed 2000 and 3600 μg·m?3 respectively.The sandstorm from 9 to 13 April not only affected Northeast and North China, but also caused a significant increase in PM10 concentration in cities south of the Yangtze River, such as Hangzhou and Fuzhou.Moreover, the research emphases of dust events from the aspects of climate cumulative effect and synoptic disturbance mechanism are analyzed, and the necessity and key points of conducting subseasonal-seasonal and interdecadal prediction of dust events are discussed.Furthermore, severe sandstorms in 2021 and 2023 also raise the scientific question, "Have dust events entered a new period of activity?".To answer this question, it is neces-sary to study the interdecadal mechanism and prediction of dust change, and also to predict the future change of dust event trend to a cer-tain extent.
From March to April in 2023, 9 dust weather processes occurred in China.The processes from 19 to 24 March and from 9 to 13 April reached the level of strong sandstorm and sandstorm respectively, bringing adverse impacts on population health, ecological envi-ronment and transportation.The anomalous Eurasian atmospheric circulations showed a zonal distribution from the late winter to early spring, the Siberian high and the East Asian trough were weak, and the area around Mongolia was controlled by the high pressure anom-aly, which resulted in the continuous warm surface air temperature and soil temperature in the dust source area (the second highest since 1980).In addition, the lower troposphere also lacked water vapor transporting to the sand source, resulting in significantly less precipita-tion in the winter and spring of 2023 (the lowest in history since 1980).The continuous warm and dry climate conditions in winter and spring resulted in a large number of dry and loose gravel in the dust source area from February to March in 2023, which provided ex-tremely favorable dust source conditions for the occurrence of sandstorms.The synoptic disturbance systems that triggered the sandstorms in March 19—24 and April 9—13 were both Mongolian cyclones.However, the two Mongolian cyclones and their configuration with the rear cold high were different, which directly caused the differences in the intensity, path and duration of the two sandstorms.From 19 to 24 March, the dust process reached the level of strong sandstorm, causing PM10 concentrations in Beijing and Harbin to exceed 2000 and 3600 μg·m?3 respectively.The sandstorm from 9 to 13 April not only affected Northeast and North China, but also caused a significant increase in PM10 concentration in cities south of the Yangtze River, such as Hangzhou and Fuzhou.Moreover, the research emphases of dust events from the aspects of climate cumulative effect and synoptic disturbance mechanism are analyzed, and the necessity and key points of conducting subseasonal-seasonal and interdecadal prediction of dust events are discussed.Furthermore, severe sandstorms in 2021 and 2023 also raise the scientific question, "Have dust events entered a new period of activity?".To answer this question, it is neces-sary to study the interdecadal mechanism and prediction of dust change, and also to predict the future change of dust event trend to a cer-tain extent.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220805002
Abstract:
Southeast Asia has a complex topography and a large population. The response of extreme precipitation events to global warming is sensitive and its future evolutionary characteristics are widely valued. In this study, 26 global climate models (GCMs) are used to present projected change in precipitation extremes over Southeast Asia at the end of 21st century based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The thermodynamic and dynamic contributions to future changes in precipitation are analyzed by decomposition of moisture budget equation. The multi-model ensemble medians show that, compared with the historical reference period (1985—2014), the frequency and intensity of climatological precipitation and precipitation extremes in most of Southeast Asia are obviously increased at the end of 21st century (2070—2099) under SSP2-4.5 and SSP5-8.5 scenarios. The variation of precipitation extremes shows significant spatial differences under the global warming. Precipitation extremes with short duration and high intensity will occur in the Kalimantan. In southern Sumatra, the total precipitation of wet days (PRCPTOT) tends to decrease significantly and the occurrence of consecutive dry days (CDD) become more frequent. Except for heavy precipitation days (R10mm), the change of extreme precipitation indices with more pronounced magnitudes under SSP5-8.5 scenario than SSP2-4.5 scenario. The contribution rate of heavy precipitation (R95pTOT) increased by 22% (41%) under SSP2-4.5 (SSP5-8.5) scenario. Quantitative analysis of moisture budget equation shows that, thermodynamic and dynamic effects mainly result in the precipitation climatological changes at the end of 21st century. Compared with the large-scale circulation changes, the thermodynamic component with higher inter-model consistency is the main contribution term. Under SSP2-4.5 (SSP5-8.5) scenario, the contribution of thermodynamic effects accounts for 65% (64%) of the P-E (precipitation minus evaporation) changes. However, the dynamic effects show a counteracting trend to the changes of P-E, which contributes 35% (36%). Moisture convergence caused by atmospheric specific humidity changes is considered as the dominating factor of projected precipitation increase.
Southeast Asia has a complex topography and a large population. The response of extreme precipitation events to global warming is sensitive and its future evolutionary characteristics are widely valued. In this study, 26 global climate models (GCMs) are used to present projected change in precipitation extremes over Southeast Asia at the end of 21st century based on the Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. The thermodynamic and dynamic contributions to future changes in precipitation are analyzed by decomposition of moisture budget equation. The multi-model ensemble medians show that, compared with the historical reference period (1985—2014), the frequency and intensity of climatological precipitation and precipitation extremes in most of Southeast Asia are obviously increased at the end of 21st century (2070—2099) under SSP2-4.5 and SSP5-8.5 scenarios. The variation of precipitation extremes shows significant spatial differences under the global warming. Precipitation extremes with short duration and high intensity will occur in the Kalimantan. In southern Sumatra, the total precipitation of wet days (PRCPTOT) tends to decrease significantly and the occurrence of consecutive dry days (CDD) become more frequent. Except for heavy precipitation days (R10mm), the change of extreme precipitation indices with more pronounced magnitudes under SSP5-8.5 scenario than SSP2-4.5 scenario. The contribution rate of heavy precipitation (R95pTOT) increased by 22% (41%) under SSP2-4.5 (SSP5-8.5) scenario. Quantitative analysis of moisture budget equation shows that, thermodynamic and dynamic effects mainly result in the precipitation climatological changes at the end of 21st century. Compared with the large-scale circulation changes, the thermodynamic component with higher inter-model consistency is the main contribution term. Under SSP2-4.5 (SSP5-8.5) scenario, the contribution of thermodynamic effects accounts for 65% (64%) of the P-E (precipitation minus evaporation) changes. However, the dynamic effects show a counteracting trend to the changes of P-E, which contributes 35% (36%). Moisture convergence caused by atmospheric specific humidity changes is considered as the dominating factor of projected precipitation increase.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220715001
Abstract:
Based on the dataset of precipitation phenomenon instrument in Zhengzhou, the dual polarization radars, the conventional observation and the China’s First Generation Global Atmosphere and Land Reanalysis data, the microphysical characteristics of “7·20” extremely heavy rainfall in Zhengzhou were analyzed. The extremely heavy rainfall was affected by multiscale weather systems, providing favorable environmental conditions for the formation of complex microphysical characteristics. The results showed that the raindrop size distributions changed significantly with time, and values of the raindrop size distribution parameters were widely distributed, covering the distribution area from continental convective precipitation to maritime convective precipitation. The number density of small drops was much higher than that of common convective precipitation in East Asia and the average value in South China in summer, and a large number of large drops also existed, leading to the higher precipitation efficiency during the strongest precipitation period from 16:00 to 17:00 on the 20th July. The low-centroid structure of convective storm was observed by dual polarization radar, showing the typical characteristics of warm cloud. The intense warm-rain processes below the 0℃ layer played an important role in the formation of the extremely heavy rainfall because of the melting of lots of ice particles and high efficiency for the growth of raindrops.
Based on the dataset of precipitation phenomenon instrument in Zhengzhou, the dual polarization radars, the conventional observation and the China’s First Generation Global Atmosphere and Land Reanalysis data, the microphysical characteristics of “7·20” extremely heavy rainfall in Zhengzhou were analyzed. The extremely heavy rainfall was affected by multiscale weather systems, providing favorable environmental conditions for the formation of complex microphysical characteristics. The results showed that the raindrop size distributions changed significantly with time, and values of the raindrop size distribution parameters were widely distributed, covering the distribution area from continental convective precipitation to maritime convective precipitation. The number density of small drops was much higher than that of common convective precipitation in East Asia and the average value in South China in summer, and a large number of large drops also existed, leading to the higher precipitation efficiency during the strongest precipitation period from 16:00 to 17:00 on the 20th July. The low-centroid structure of convective storm was observed by dual polarization radar, showing the typical characteristics of warm cloud. The intense warm-rain processes below the 0℃ layer played an important role in the formation of the extremely heavy rainfall because of the melting of lots of ice particles and high efficiency for the growth of raindrops.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220715002
Abstract:
In order to understand the causes of summer precipitation anomalies in North China and improve climate monitoring and prediction technology, based on the summer precipitation data in North China and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data from 1961 to 2021, using the methods of correlation, synthesis and circulation anomaly reconstruction, the circulation background of summer (June -August) precipitation anomaly in North China and the relationship between East Asian subtropical summer monsoon, North China dynamic rise index and summer precipitation in North China are analyzed. The results show that: (1) the circulation conditions of abnormal precipitation in North China in summer are that the high ridge of Ural Mountain at 500hPa in summer is stronger, the Baikal Lake trough is deeper, the subtropical high in the Northwest Pacific is north by west near the Korean Peninsula, and North China is in the circulation situation of "high in the East and low in the west", so the dynamic rising conditions are favorable. At the same time, the 850hPa Indian monsoon is stronger, the East Asian subtropical summer monsoon is significantly stronger, and the water vapor source in North China is sufficient. (2) The East Asian subtropical summer monsoon index and North China dynamic rise index defined in this paper have a good indication of summer precipitation anomalies in North China. When the two indexes are stronger, the summer precipitation in North China is abnormally more. If both indexes are weak, the summer precipitation in North China is abnormally less. If the two indexes are inconsistent, the summer precipitation in North China is basically normal. (3) The abnormally more or less summer rainfall in North China is the result of the synergy of the East Asian subtropical summer monsoon and the dynamic upward movement in North China. The subtropical summer monsoon in East Asia is stronger (the dynamic upward movement in North China is stronger), the Baikal trough in the 500hPa layer is deepened, the subtropical high in the Northwest Pacific is north by west, and North China is under the control of the circulation of "high in the East and low in the West", so the dynamic upward conditions are favorable. At the same time, the Indian summer monsoon at 850hPa and the subtropical summer monsoon in East Asia are relatively strong, which will strengthen the water vapor transmission of the west wind in the tropical Indian Ocean and the south wind in the subtropical region of East Asia, and the water vapor source in North China is sufficient. The above situation will cause abnormally more summer precipitation in North China. On the contrary, the summer precipitation in North China will be abnormally less. (4) From April to May in the early stage, the Baikal trough in the 500hPa layer deepened, the subtropical high in the Northwest Pacific was northerly, and the southerly wind in East Asia in the 850hPa layer was significantly stronger, which can be used as a climate monitoring and prediction index for the abnormally more summer precipitation in North China.
In order to understand the causes of summer precipitation anomalies in North China and improve climate monitoring and prediction technology, based on the summer precipitation data in North China and NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data from 1961 to 2021, using the methods of correlation, synthesis and circulation anomaly reconstruction, the circulation background of summer (June -August) precipitation anomaly in North China and the relationship between East Asian subtropical summer monsoon, North China dynamic rise index and summer precipitation in North China are analyzed. The results show that: (1) the circulation conditions of abnormal precipitation in North China in summer are that the high ridge of Ural Mountain at 500hPa in summer is stronger, the Baikal Lake trough is deeper, the subtropical high in the Northwest Pacific is north by west near the Korean Peninsula, and North China is in the circulation situation of "high in the East and low in the west", so the dynamic rising conditions are favorable. At the same time, the 850hPa Indian monsoon is stronger, the East Asian subtropical summer monsoon is significantly stronger, and the water vapor source in North China is sufficient. (2) The East Asian subtropical summer monsoon index and North China dynamic rise index defined in this paper have a good indication of summer precipitation anomalies in North China. When the two indexes are stronger, the summer precipitation in North China is abnormally more. If both indexes are weak, the summer precipitation in North China is abnormally less. If the two indexes are inconsistent, the summer precipitation in North China is basically normal. (3) The abnormally more or less summer rainfall in North China is the result of the synergy of the East Asian subtropical summer monsoon and the dynamic upward movement in North China. The subtropical summer monsoon in East Asia is stronger (the dynamic upward movement in North China is stronger), the Baikal trough in the 500hPa layer is deepened, the subtropical high in the Northwest Pacific is north by west, and North China is under the control of the circulation of "high in the East and low in the West", so the dynamic upward conditions are favorable. At the same time, the Indian summer monsoon at 850hPa and the subtropical summer monsoon in East Asia are relatively strong, which will strengthen the water vapor transmission of the west wind in the tropical Indian Ocean and the south wind in the subtropical region of East Asia, and the water vapor source in North China is sufficient. The above situation will cause abnormally more summer precipitation in North China. On the contrary, the summer precipitation in North China will be abnormally less. (4) From April to May in the early stage, the Baikal trough in the 500hPa layer deepened, the subtropical high in the Northwest Pacific was northerly, and the southerly wind in East Asia in the 850hPa layer was significantly stronger, which can be used as a climate monitoring and prediction index for the abnormally more summer precipitation in North China.
在线获得:April 06, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221212001
Abstract:
During the Dragon-boat precipitation period in 2022 (from May 21 to June 21), the average precipitation in South China is 472.5 mm, which is the second largest in the same period in history, it is the third largest in Guangdong，Guangxi is the largest in the same period since there are meteorological records history in 1951. Under its influence, River basin flood occurs in the Pearl River basin, and catastrophic floods occurs in the Beijiang River basin. Using the daily precipitation data of 192 national meteorological observation stations in South China, NCEP/NCAR atmospheric circulation，atmospheric outward long wave radiation(OLR) data and NOAA monthly scale SST reanalysis data, the characteristics of South China Dragon-boat precipitation anomaly in 2022 and its relationship with atmospheric circulation and sea surface temperature are studied by using correlation and composite analysis methods. The results show that the East Asia Trough and the Northeast Cold Vortex are significantly strengthened, the cold air affecting South China is strong. The Western Pacific Subtropical High and the South Branch Trough are both relatively strong, and there is significant water vapor convergence in South China during the Dragon-boat precipitation period in 2022. The atmospheric response to the La Nina event is obvious in 2022, and the Walker circulation is enhanced. The convection in South China strengthened obviously. An abnormal anticyclone circulation was in the east of the Philippines. The Western Pacific Subtropical High becomes stronger, the southwest wind on its northwest side is stronger. Water vapor transmission to South China has increased significantly, Water vapor transmission to South China increased significantly. the significant enhancement of water vapor transport to South China. A significant upward movement at the same time in South China. The abnormality of atmospheric circulation and SST lead to the extreme Dragon-boat precipitation in South China in 2022.
During the Dragon-boat precipitation period in 2022 (from May 21 to June 21), the average precipitation in South China is 472.5 mm, which is the second largest in the same period in history, it is the third largest in Guangdong，Guangxi is the largest in the same period since there are meteorological records history in 1951. Under its influence, River basin flood occurs in the Pearl River basin, and catastrophic floods occurs in the Beijiang River basin. Using the daily precipitation data of 192 national meteorological observation stations in South China, NCEP/NCAR atmospheric circulation，atmospheric outward long wave radiation(OLR) data and NOAA monthly scale SST reanalysis data, the characteristics of South China Dragon-boat precipitation anomaly in 2022 and its relationship with atmospheric circulation and sea surface temperature are studied by using correlation and composite analysis methods. The results show that the East Asia Trough and the Northeast Cold Vortex are significantly strengthened, the cold air affecting South China is strong. The Western Pacific Subtropical High and the South Branch Trough are both relatively strong, and there is significant water vapor convergence in South China during the Dragon-boat precipitation period in 2022. The atmospheric response to the La Nina event is obvious in 2022, and the Walker circulation is enhanced. The convection in South China strengthened obviously. An abnormal anticyclone circulation was in the east of the Philippines. The Western Pacific Subtropical High becomes stronger, the southwest wind on its northwest side is stronger. Water vapor transmission to South China has increased significantly, Water vapor transmission to South China increased significantly. the significant enhancement of water vapor transport to South China. A significant upward movement at the same time in South China. The abnormality of atmospheric circulation and SST lead to the extreme Dragon-boat precipitation in South China in 2022.
在线获得:March 22, 2023, DOI: 10.13878/j.cnki.dqkxxb.20221025001
Abstract:
Based on the observational and reanalysis data during 1979—2020, this study investigated the basic features of the month-to-month reversal of air temperature anomalies in Northeast Asia in early summer (May to June) on the interannual timescales, as well as the effects of soil moisture anomalies in eastern Europe and the possible physical processes. The results show that the predominant mode of the intermonthly variation of air temperature anomalies in Northeast Asia in early summer is the reversal mode, that is warmer (colder) in May and colder (warmer) in June; and its formation is directly derived from the reversal of circulation anomalies over Northeast Asia. Further analysis indicates that the lower soil moisture in eastern Europe in May tends to lead to a warmer May and colder June in Northeast Asia. With the possible physical processes as following: lower soil moisture in May leads to local soil temperature and lower troposphere warming, which in turn causes a weakening (enhancement) of the lower tropospheric meridional temperature gradient and baroclinicity in the Mediterranean region (northern Europe). Correspondingly, high-frequency transient wave activity weakens (enhances) and facilitates the formation of an anomalous high over central and eastern Europe and hence Rossby wave source via transient vorticity forcing. The associated Rossby wave travels eastward along the polar front jet, resulting in a barotropic anomalous high over Northeast Asia and resultant surface warming. The soil moisture anomalies can persist until June but weaken in intensity. Similarly, it favors the formation of an anomalous high and Rossby wave source, but their center shifts westward to western Europe. The related Rossby wave activity leads to a barotropic anomalous low and hence cooling over Northeast Asia. There are obvious differences in the activity characteristics of Rossby waves (wave source, activity centers, and propagation pathway) associated with the soil moisture anomalies in eastern Europe in May and June, which are closely related to the difference in atmospheric climatology over northern Eurasia. When the soil moisture in eastern Europe is high in May, the above physical processes are roughly reversed.
Based on the observational and reanalysis data during 1979—2020, this study investigated the basic features of the month-to-month reversal of air temperature anomalies in Northeast Asia in early summer (May to June) on the interannual timescales, as well as the effects of soil moisture anomalies in eastern Europe and the possible physical processes. The results show that the predominant mode of the intermonthly variation of air temperature anomalies in Northeast Asia in early summer is the reversal mode, that is warmer (colder) in May and colder (warmer) in June; and its formation is directly derived from the reversal of circulation anomalies over Northeast Asia. Further analysis indicates that the lower soil moisture in eastern Europe in May tends to lead to a warmer May and colder June in Northeast Asia. With the possible physical processes as following: lower soil moisture in May leads to local soil temperature and lower troposphere warming, which in turn causes a weakening (enhancement) of the lower tropospheric meridional temperature gradient and baroclinicity in the Mediterranean region (northern Europe). Correspondingly, high-frequency transient wave activity weakens (enhances) and facilitates the formation of an anomalous high over central and eastern Europe and hence Rossby wave source via transient vorticity forcing. The associated Rossby wave travels eastward along the polar front jet, resulting in a barotropic anomalous high over Northeast Asia and resultant surface warming. The soil moisture anomalies can persist until June but weaken in intensity. Similarly, it favors the formation of an anomalous high and Rossby wave source, but their center shifts westward to western Europe. The related Rossby wave activity leads to a barotropic anomalous low and hence cooling over Northeast Asia. There are obvious differences in the activity characteristics of Rossby waves (wave source, activity centers, and propagation pathway) associated with the soil moisture anomalies in eastern Europe in May and June, which are closely related to the difference in atmospheric climatology over northern Eurasia. When the soil moisture in eastern Europe is high in May, the above physical processes are roughly reversed.
在线获得:February 17, 2023, DOI: 10.13878/j.cnki.dqkxxb.20220324001
Abstract:
Based on the daily observations collected by National Meteorological Information Centre since 1919, the homogenized daily maximum and minimum temperature series during the period of 1912-2019 were established for Baoding in Hebei Province. Firstly, the error data due to manual observation or recording, instrument malfunctions and digital inputs were removed by quality control. Then, the missing observations were interpolated by standardized series method using the homogenized daily temperature data in Tianjin over century-long scale. Lastly, several significant breakpoints caused by changes in interpolation, station relocation and instrument manufacturers were detected by penalized maximal t test (PMT) with annual and monthly reference series constructed by two means, and adjusted by Quantile Matching (QM) adjustment with daily reference series from Berkeley Earth-daily data. It was found that the characteristics of inter-annual, decadal and trend changes were consistent with those fromBerkeley Earth-monthly、CRUTS4.03 and GHCNV3. And the characteristics of warming change induced by the rapid urban development in Baoding region have well been reflected, simultaneously compared with the whole Beijing-Tianjin-Hebei region. In addition, the warming change of annual and seasonal temperature extremes in Baoding have also increased significantly since 1912, the warming trends of annual and autumn lowest minimum temperature (TNn) were 0.340℃/10a and 0.404℃/10a (95% significance level), respectively, and the corresponding diurnal temperature range (DTR) are -0.118℃/10a and -0.215℃/10a (95% significance level).
Based on the daily observations collected by National Meteorological Information Centre since 1919, the homogenized daily maximum and minimum temperature series during the period of 1912-2019 were established for Baoding in Hebei Province. Firstly, the error data due to manual observation or recording, instrument malfunctions and digital inputs were removed by quality control. Then, the missing observations were interpolated by standardized series method using the homogenized daily temperature data in Tianjin over century-long scale. Lastly, several significant breakpoints caused by changes in interpolation, station relocation and instrument manufacturers were detected by penalized maximal t test (PMT) with annual and monthly reference series constructed by two means, and adjusted by Quantile Matching (QM) adjustment with daily reference series from Berkeley Earth-daily data. It was found that the characteristics of inter-annual, decadal and trend changes were consistent with those fromBerkeley Earth-monthly、CRUTS4.03 and GHCNV3. And the characteristics of warming change induced by the rapid urban development in Baoding region have well been reflected, simultaneously compared with the whole Beijing-Tianjin-Hebei region. In addition, the warming change of annual and seasonal temperature extremes in Baoding have also increased significantly since 1912, the warming trends of annual and autumn lowest minimum temperature (TNn) were 0.340℃/10a and 0.404℃/10a (95% significance level), respectively, and the corresponding diurnal temperature range (DTR) are -0.118℃/10a and -0.215℃/10a (95% significance level).
在线获得:December 06, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20210324001
Abstract:
A Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting(WRF) Model,WRF Hybrid Data Assimilation system and Ensemble Transform Kalman Filter(ETKF),with assimilating both Wind Profile Radar Detection(WPRD) data and Microwave Radiometer(MWR) data.Experiments were performed for the study of the impact of 4 important parameters on the system,which are 2 tuning factors of static background error, localization scale and ensemble weighting factor,and contrast research was carried on to the results of Hybrid and 3DVAR schemes.Some encouraged conclusions were given as follows:Tuning these 4 parameters could improve performance of the Rapid Refresh Hybrid system.The analysis and forecast of Hybrid with pa-rameters not tuned were better than those of 3DVAR, and the results of Hybrid with parameters tuned were the best.
A Rapid Refresh Hybrid system was constructed based on the Weather Research and Forecasting(WRF) Model,WRF Hybrid Data Assimilation system and Ensemble Transform Kalman Filter(ETKF),with assimilating both Wind Profile Radar Detection(WPRD) data and Microwave Radiometer(MWR) data.Experiments were performed for the study of the impact of 4 important parameters on the system,which are 2 tuning factors of static background error, localization scale and ensemble weighting factor,and contrast research was carried on to the results of Hybrid and 3DVAR schemes.Some encouraged conclusions were given as follows:Tuning these 4 parameters could improve performance of the Rapid Refresh Hybrid system.The analysis and forecast of Hybrid with pa-rameters not tuned were better than those of 3DVAR, and the results of Hybrid with parameters tuned were the best.
在线获得:December 06, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20220306001
Abstract:
Based on the Global Ensemble Prediction System in China Meteorological Administration (CMA-GEPS), the extreme Meiyu process over China in 2020 was evaluated. Results show that, during the Meiyu season, a strong and stable western Pacific subtropical high (WPSH) and a gradually strengthened East Asian summer monsoon provide favorable dynamic and moisture conditions for strong rainfall. For the western Pacific subtropical high, CMA-GEPS could skillfully forecast the evolution trend of WPSH index with 7-9 leading days; the CMA-GEPS prediction skills of WPSH strength and area are about the same level as the results from the NCEP ensemble prediction system, and the WPSH strength presents the weaker bias compared with the observation; the prediction skills of ridge line and western boundary index with CMA-GEPS are comparable with the results from ECMWF ensemble prediction system, and the forecasting bias is mainly attributed to the more southward location of ridge line and more eastward center position of western boundary. For the East Asian summer monsoon, CMA-GEPS could skillfully predict the index with 9 leading days, which was two days earlier than the control forecast. The CMA-GEPS control forecasting bias is mainly attributed to the weaker precipitation intensity and more southward location of strong rainfall belt, and also the control forecasting fails to predict the heavy rain in some regions in the middle and lower reaches of Yangtze river. With the time-spatial weight probability (TSWP) neighborhood scheme, the prediction skills related to the heavy rain with CMA-GEPS are improved obviously, and the scheme reduced the occurrence of precipitation missing report. The results of precipitation probability prediction were verified by observation and Brier Scores, which show that: the TSWP neighborhood scheme is superior to the original single point ensemble probability forecast method and the control forecast, and also has a well application values for the heavy storm prediction in the Meiyu period.
Based on the Global Ensemble Prediction System in China Meteorological Administration (CMA-GEPS), the extreme Meiyu process over China in 2020 was evaluated. Results show that, during the Meiyu season, a strong and stable western Pacific subtropical high (WPSH) and a gradually strengthened East Asian summer monsoon provide favorable dynamic and moisture conditions for strong rainfall. For the western Pacific subtropical high, CMA-GEPS could skillfully forecast the evolution trend of WPSH index with 7-9 leading days; the CMA-GEPS prediction skills of WPSH strength and area are about the same level as the results from the NCEP ensemble prediction system, and the WPSH strength presents the weaker bias compared with the observation; the prediction skills of ridge line and western boundary index with CMA-GEPS are comparable with the results from ECMWF ensemble prediction system, and the forecasting bias is mainly attributed to the more southward location of ridge line and more eastward center position of western boundary. For the East Asian summer monsoon, CMA-GEPS could skillfully predict the index with 9 leading days, which was two days earlier than the control forecast. The CMA-GEPS control forecasting bias is mainly attributed to the weaker precipitation intensity and more southward location of strong rainfall belt, and also the control forecasting fails to predict the heavy rain in some regions in the middle and lower reaches of Yangtze river. With the time-spatial weight probability (TSWP) neighborhood scheme, the prediction skills related to the heavy rain with CMA-GEPS are improved obviously, and the scheme reduced the occurrence of precipitation missing report. The results of precipitation probability prediction were verified by observation and Brier Scores, which show that: the TSWP neighborhood scheme is superior to the original single point ensemble probability forecast method and the control forecast, and also has a well application values for the heavy storm prediction in the Meiyu period.
在线获得:September 15, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211121001
Abstract:
At present, a Regional Ensemble Prediction System has been developed by Kelamayi Meteorological Bureau. The system has only adopted initial condition perturbation of Ensemble Transform Kalman Filter(ETKF), and the system is lack of spread. In order to improve the skill of this Ensemble Prediction System, the model perturbation method of Stochastic Physics Parameterization Tendency(SPPT) is adopted and tested. This paper conducted sensitivity test on critical parameter of SPPT and parameter setting of SPPT is determined. Ensemble forecast experiment test is conducted and compared for both ETKF scheme and ETKF-SPPT scheme. The results show that the ETKF method can generate initial condition perturbation with dynamic structure, but the spread will saturated within short forecast lead time and will decrease due to the constraint of identical LBC for all members. With SPPT model perturbation method adopted, the ensemble spread can significantly improved. Ensemble verification scores indicate that the reliability of ETKF without model perturbation is small, and the root mean square error(RMSE) is relatively large, while add model perturbation to initial condition perturbation will improve the probabilistic forecast skill with larger forecast reliability and smaller(RMSE) . The results of gale forecast show that the adoption of model perturbation method can significantly improve the ensemble forecast that it has more accurate forecast on the magnitude and time period of local gale.
At present, a Regional Ensemble Prediction System has been developed by Kelamayi Meteorological Bureau. The system has only adopted initial condition perturbation of Ensemble Transform Kalman Filter(ETKF), and the system is lack of spread. In order to improve the skill of this Ensemble Prediction System, the model perturbation method of Stochastic Physics Parameterization Tendency(SPPT) is adopted and tested. This paper conducted sensitivity test on critical parameter of SPPT and parameter setting of SPPT is determined. Ensemble forecast experiment test is conducted and compared for both ETKF scheme and ETKF-SPPT scheme. The results show that the ETKF method can generate initial condition perturbation with dynamic structure, but the spread will saturated within short forecast lead time and will decrease due to the constraint of identical LBC for all members. With SPPT model perturbation method adopted, the ensemble spread can significantly improved. Ensemble verification scores indicate that the reliability of ETKF without model perturbation is small, and the root mean square error(RMSE) is relatively large, while add model perturbation to initial condition perturbation will improve the probabilistic forecast skill with larger forecast reliability and smaller(RMSE) . The results of gale forecast show that the adoption of model perturbation method can significantly improve the ensemble forecast that it has more accurate forecast on the magnitude and time period of local gale.
在线获得:September 07, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211228001
Abstract:
As an important part of the cryosphere, snow cover has an important impact on atmospheric circulation and the hydrologic cycle. 98% of the seasonal snow in the world is located in the northern hemisphere. Eurasia continent is the main snow area in the Northern Hemisphere. China has a vast territory with nearly 50 degrees latitude across from north to south, and snow is widely distributed, which is important for studying spatial and temporal dynamics of snow cover. This paper proposes a trend extraction method based on empirical mode decomposition (EMD) based on the Snow Cover (SNC) data of FY-3 meteorological satellite and discusses the temporal and spatial characteristics and variation trends of Snow Cover in China during the past decade (2010-2019). The results show that: 1) China"s Snow Cover Frequency (SCF) has significant seasonal characteristics, which increases first and then decreases, and reaches the maximum SCF in February and March each year. The inter-annual SCF in northeast China has a significant trend of decline, but there is little change in other regions. 2) Snow Cover Rate (SCR) in Inner Mongolia and Xinjiang has decreased by 1.6% and 1.5% in the last decade, respectively. SCR in other areas has not changed significantly. SCR in main snow covered areas changed from increase to decrease in 2016.This study is of great significance to understand the spatial and temporal dynamics of typical snow cover areas in China and to further explore the relationship between snow cover and air temperature, precipitation, and other influential factors. At the same time, relevant results are obtained from the data of the Wind Cloud Meteorological Satellite, and the application of the observation data of the FY-3 satellite is strengthened.
As an important part of the cryosphere, snow cover has an important impact on atmospheric circulation and the hydrologic cycle. 98% of the seasonal snow in the world is located in the northern hemisphere. Eurasia continent is the main snow area in the Northern Hemisphere. China has a vast territory with nearly 50 degrees latitude across from north to south, and snow is widely distributed, which is important for studying spatial and temporal dynamics of snow cover. This paper proposes a trend extraction method based on empirical mode decomposition (EMD) based on the Snow Cover (SNC) data of FY-3 meteorological satellite and discusses the temporal and spatial characteristics and variation trends of Snow Cover in China during the past decade (2010-2019). The results show that: 1) China"s Snow Cover Frequency (SCF) has significant seasonal characteristics, which increases first and then decreases, and reaches the maximum SCF in February and March each year. The inter-annual SCF in northeast China has a significant trend of decline, but there is little change in other regions. 2) Snow Cover Rate (SCR) in Inner Mongolia and Xinjiang has decreased by 1.6% and 1.5% in the last decade, respectively. SCR in other areas has not changed significantly. SCR in main snow covered areas changed from increase to decrease in 2016.This study is of great significance to understand the spatial and temporal dynamics of typical snow cover areas in China and to further explore the relationship between snow cover and air temperature, precipitation, and other influential factors. At the same time, relevant results are obtained from the data of the Wind Cloud Meteorological Satellite, and the application of the observation data of the FY-3 satellite is strengthened.
在线获得:September 01, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220623001
Abstract:
The observational stability, observational ability, and rationality of both base data and products of millimeter cloud radar (MCR for short) installed at Shanghai Meteorological Service during “The study and experiment of vertical integrated meteorological observation technology in mega-cities” (MCME for short) have been evaluated using observations from ground-based Lidar distrometer, micro-rain radar, radiosonde, FY-4A’s products as well as surface rain gage. The results show that: the observational stability of MCR is considerably high that only one-time software failure happened during the MCME and the data acquisition rate is larger than 95%. The minimal detectable reflectivity of MCR is generally distributed within -40 to -20 dBZ and has an exponential distribution in the vertical direction which fits well with the theory. The minimal detectable reflectivity of MCR changes little (smaller than 2 dB for heights lower than 9 km) during the MCME. A false “No Cloud” region will be seen in the MCR’s observation when the rain rate is larger than 4-5 mm/h. Although the raw data of the two MCRs have certain differences, the pattern of reflectivity in the time-height cross-section is highly reasonable indicated by the comparisons between the reflectivity from MCR’s observation, the reflectivity calculated with Lidar distrometer’s observation, and the reflectivity from micro-rain radar’s observation. The cloud top height and cloud base height retrieved by MCR are also evaluated by the cloud top/base height calculated by radiosonde and cloud top height retrieved by FY-4A satellite, and the results show certainly a degree of consistency among the three observations. The inconsistency of reflectivity due to the merge of multiple pulses with different pulse widths used in MCR’s base data processing and the false “clear sky area” due to the strong attenuation are two obvious issues that may have significant impacts on the operational usage of MCR. The advice on how to improve the current MCR from the perspective of the demand for the development of the Shanghai Meteorological Service has been given at the end of the paper.
The observational stability, observational ability, and rationality of both base data and products of millimeter cloud radar (MCR for short) installed at Shanghai Meteorological Service during “The study and experiment of vertical integrated meteorological observation technology in mega-cities” (MCME for short) have been evaluated using observations from ground-based Lidar distrometer, micro-rain radar, radiosonde, FY-4A’s products as well as surface rain gage. The results show that: the observational stability of MCR is considerably high that only one-time software failure happened during the MCME and the data acquisition rate is larger than 95%. The minimal detectable reflectivity of MCR is generally distributed within -40 to -20 dBZ and has an exponential distribution in the vertical direction which fits well with the theory. The minimal detectable reflectivity of MCR changes little (smaller than 2 dB for heights lower than 9 km) during the MCME. A false “No Cloud” region will be seen in the MCR’s observation when the rain rate is larger than 4-5 mm/h. Although the raw data of the two MCRs have certain differences, the pattern of reflectivity in the time-height cross-section is highly reasonable indicated by the comparisons between the reflectivity from MCR’s observation, the reflectivity calculated with Lidar distrometer’s observation, and the reflectivity from micro-rain radar’s observation. The cloud top height and cloud base height retrieved by MCR are also evaluated by the cloud top/base height calculated by radiosonde and cloud top height retrieved by FY-4A satellite, and the results show certainly a degree of consistency among the three observations. The inconsistency of reflectivity due to the merge of multiple pulses with different pulse widths used in MCR’s base data processing and the false “clear sky area” due to the strong attenuation are two obvious issues that may have significant impacts on the operational usage of MCR. The advice on how to improve the current MCR from the perspective of the demand for the development of the Shanghai Meteorological Service has been given at the end of the paper.
在线获得:July 07, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220503001
Abstract:
Based on the satellite derived snow depth over the Tibetan Plateau (TP), daily rainfall data of 261 meteorological stations in South China (SC) and the ERA5 reanalysis dataset during 1979-2018, relationships between the TP winter snow depth and precipitation during the first rainy season (FRS) in SC have been investigated. Results show that: (1) Connections between snow depth over the western TP and precipitation during the FRS in SC are the most robust and the TP snow depth mainly affects frontal precipitation of the FSR, whereas it shows less impact on summer monsoonal rainfall. (2) Onset date of the FRS in above normal TP snow years is about 20 days earlier than that in below normal snow years, leading to more rainy days, longer of the FRS and more rainfall during the FRS. However, rainfall intensity during the FRS shows small difference between different types of the TP snow years. (3) The TP is colder in above normal snow year and the cooling effect stimulates an abnormal anticyclonic circulation over the TP. However, tripole anomaly patterns of 500 hPa geopotential height occur at East Asian coastal region. Configurations of the circulation facilitate cold air in middle-high latitude regions invade SC, which makes a colder SC. And the enhanced northwest Pacific subtropical high intensifies the low-level southerly flow and water vapor supplement. The front swings northward-southward in northern part of the SC during March-April. The FRS has been established once the dry cold northerly and the warm-wet southerly flow invade the SC in early April. In below normal TP snow years, both the cold northerly and warm southerly flow are weak and inactivity and the front in northern part of the SC is interrupted in early April. When the northerly and southerly flow invades the SC in mid-to-late April, the front is reverted and the FRS occurs later.
Based on the satellite derived snow depth over the Tibetan Plateau (TP), daily rainfall data of 261 meteorological stations in South China (SC) and the ERA5 reanalysis dataset during 1979-2018, relationships between the TP winter snow depth and precipitation during the first rainy season (FRS) in SC have been investigated. Results show that: (1) Connections between snow depth over the western TP and precipitation during the FRS in SC are the most robust and the TP snow depth mainly affects frontal precipitation of the FSR, whereas it shows less impact on summer monsoonal rainfall. (2) Onset date of the FRS in above normal TP snow years is about 20 days earlier than that in below normal snow years, leading to more rainy days, longer of the FRS and more rainfall during the FRS. However, rainfall intensity during the FRS shows small difference between different types of the TP snow years. (3) The TP is colder in above normal snow year and the cooling effect stimulates an abnormal anticyclonic circulation over the TP. However, tripole anomaly patterns of 500 hPa geopotential height occur at East Asian coastal region. Configurations of the circulation facilitate cold air in middle-high latitude regions invade SC, which makes a colder SC. And the enhanced northwest Pacific subtropical high intensifies the low-level southerly flow and water vapor supplement. The front swings northward-southward in northern part of the SC during March-April. The FRS has been established once the dry cold northerly and the warm-wet southerly flow invade the SC in early April. In below normal TP snow years, both the cold northerly and warm southerly flow are weak and inactivity and the front in northern part of the SC is interrupted in early April. When the northerly and southerly flow invades the SC in mid-to-late April, the front is reverted and the FRS occurs later.
在线获得:June 22, 2022, DOI: 10.13878/j.cnki.dqkxxb.20220406001
Abstract:
The key dynamic forcing factors and sources of water vapor influencing the remote heavy rainfall over North China produced by Typhoon Lekima on 10 August 2019 are investigated using the surface rain gauge data, ERA5 reanalysis data, and ECWMF ensemble forecasts data. Ensemble sensitivity analysis shows that low-level relative vorticity over the remote typhoon precipitation (TRP) area is the most significant dynamic factor facilitating TRP. The stronger low-level relative vorticity is closely associated with the enhancement of northerly winds in the north caused by the deepening of the low-level short-wave trough and jointly related to the strengthening low-level southerly winds between the TRP and typhoon precipitation (TP) area. The divergence term of relative vorticity is the crucial dynamic process that dominates the strengthening and weakening period of TRP. During the TRP enhancement phase, the positive vorticity is mainly contributed by the positive divergence term, while the negative divergence term leads to the decrease of relative vorticity and the weakening of TRP intensity. The water vapor in the TRP region at 500 hPa is mainly contributed by local area and Typhoon Lekima, while 60% of the water vapor at 700 hPa is contributed by Typhoon Lekima. 40% of the water vapor at 850 hPa is contributed by Typhoon Rosa, and the remaining 60% of water vapor is transported from Typhoon Lekima and the local area.
The key dynamic forcing factors and sources of water vapor influencing the remote heavy rainfall over North China produced by Typhoon Lekima on 10 August 2019 are investigated using the surface rain gauge data, ERA5 reanalysis data, and ECWMF ensemble forecasts data. Ensemble sensitivity analysis shows that low-level relative vorticity over the remote typhoon precipitation (TRP) area is the most significant dynamic factor facilitating TRP. The stronger low-level relative vorticity is closely associated with the enhancement of northerly winds in the north caused by the deepening of the low-level short-wave trough and jointly related to the strengthening low-level southerly winds between the TRP and typhoon precipitation (TP) area. The divergence term of relative vorticity is the crucial dynamic process that dominates the strengthening and weakening period of TRP. During the TRP enhancement phase, the positive vorticity is mainly contributed by the positive divergence term, while the negative divergence term leads to the decrease of relative vorticity and the weakening of TRP intensity. The water vapor in the TRP region at 500 hPa is mainly contributed by local area and Typhoon Lekima, while 60% of the water vapor at 700 hPa is contributed by Typhoon Lekima. 40% of the water vapor at 850 hPa is contributed by Typhoon Rosa, and the remaining 60% of water vapor is transported from Typhoon Lekima and the local area.
在线获得:June 06, 2022, DOI: 10.13878/j.cnki.dqkxxb.20220215001
Abstract:
In recent years, O3 pollution in Nanjing has gradually intensified. In order to understand the impact of surrounding cities on O3 in Nanjing, we simulated the air quality in the Yangtze River Delta(YRD) using the source-oriented WRF/CMAQ regional air quality model from March to October 2018.By tracking the emissions from 41 cities in the YRD and outside the YRD, the characteristics of the transportation of O3 in Nanjing were analyzed under different winds. The results show that from March to October 2018, Nanjing contributes 56.8% of local O3 on polluted days, and contributed 49.5% on polluted days. When pollution occurs, the contribution of surrounding cities to Nanjing increased from 36.4% to 46.3%, and the contribution from outside the YRD to Nanjing decreased from 6.8% to 4.7%. From the perspective of daily changes, Nanjing O3 accounts for more than 70% of the local contribution during the daytime, and at night, most of the transport contributions come from surrounding cities and areas outside the YRD (more than 95%) .On all O3 pollution days, the contribution in Jiangsu Province accounts for 73.1%, 10.8% in Zhejiang Province, 10.5% in Anhui Province and 0.95% in Shanghai. During the pollution period, the contribution ratio of O3 (O3N) produced by NOx as a precursor in Nanjing to O3 (O3V) produced by VOCs as a precursor was about 7:13; During all polluting periods, the proportion of southeast and northeast winds was 38.0% and 35.9% respectively, when the dominant wind direction is northeast, Nanjing has the highest concentration of non-background O3, and the contribution of upwind to Yangzhou reached 13.6%. When the dominant wind direction is southeast wind, the upwind to Changzhou (7.3%), Zhenjiang (7.0%) and Wuxi (6.5%) contributed more to Nanjing O3. Therefore, Nanjing O3 pollution control should consider the transportation of upwind to cities.
In recent years, O3 pollution in Nanjing has gradually intensified. In order to understand the impact of surrounding cities on O3 in Nanjing, we simulated the air quality in the Yangtze River Delta(YRD) using the source-oriented WRF/CMAQ regional air quality model from March to October 2018.By tracking the emissions from 41 cities in the YRD and outside the YRD, the characteristics of the transportation of O3 in Nanjing were analyzed under different winds. The results show that from March to October 2018, Nanjing contributes 56.8% of local O3 on polluted days, and contributed 49.5% on polluted days. When pollution occurs, the contribution of surrounding cities to Nanjing increased from 36.4% to 46.3%, and the contribution from outside the YRD to Nanjing decreased from 6.8% to 4.7%. From the perspective of daily changes, Nanjing O3 accounts for more than 70% of the local contribution during the daytime, and at night, most of the transport contributions come from surrounding cities and areas outside the YRD (more than 95%) .On all O3 pollution days, the contribution in Jiangsu Province accounts for 73.1%, 10.8% in Zhejiang Province, 10.5% in Anhui Province and 0.95% in Shanghai. During the pollution period, the contribution ratio of O3 (O3N) produced by NOx as a precursor in Nanjing to O3 (O3V) produced by VOCs as a precursor was about 7:13; During all polluting periods, the proportion of southeast and northeast winds was 38.0% and 35.9% respectively, when the dominant wind direction is northeast, Nanjing has the highest concentration of non-background O3, and the contribution of upwind to Yangzhou reached 13.6%. When the dominant wind direction is southeast wind, the upwind to Changzhou (7.3%), Zhenjiang (7.0%) and Wuxi (6.5%) contributed more to Nanjing O3. Therefore, Nanjing O3 pollution control should consider the transportation of upwind to cities.
在线获得:June 02, 2022, DOI: 10.13878/j.cnki.dqkxxb.20210811002
Abstract:
Based on the daily precipitation of 24 global climate models from the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6) multimodel simulations, the generalized extreme value distribution (GEV) is introduced to study the risks of precipitation extremes that expected to occur every 20, 50 and 100 years over China under 1.5/2°C global warming. Compared to the historical period (1995-2014), the changes in the probability risks of extreme precipitation under global warming of 1.5/2℃ present an overall increasing trend. Although their spatial distributions show similar characteristics, the additional half a degree global warming will lead to a higher risk. For example, extreme precipitation that occurred once every 50 years will become once about every 17/14years under the warming of 1.5/2°C, respectively, and extreme precipitation will become more frequent. There are regional differences in the responses of different regions to climate warming, among which the middle and upper reaches of the Yangtze and Yellow Rivers and the Qinghai-Tibet Plateau region in the Western China, and the middle and lower reaches of Yangtze and Yellow Rivers and its south in Eastern China, are highly sensitive response regions to climate change, and their probability ratios reach 3 or even 5 or more. Furthermore, the influence and contribution measures of location and scale parameters on the probability ratios are explored from a theoretical perspective based on the probability distributions, and are used to explore the influences of the climate means and variability changes on the risks of extreme precipitation. The results show that there are significant differences between Eastern China and Western China in the incremental changes of location and scale parameters, and in the rates of probability risk changes, which lead to differences of the factors that influencing the risk of extreme precipitation. In the Western China, although the changes in climate means and variabilities of extreme precipitation are small, the probability ratios increase significantly due to the high rates of probability risk changes. In contrast, the change rates are small, but the climate means and variabilities present a high increase, which also lead to an increase in the Eastern China. Moreover, compared to the location parameters, the increase risks in most of regions over China are mainly due to the changes of scale parameters of extreme precipitation in the future.
Based on the daily precipitation of 24 global climate models from the sixth phase of the Coupled Model Intercomparison Project 6 (CMIP6) multimodel simulations, the generalized extreme value distribution (GEV) is introduced to study the risks of precipitation extremes that expected to occur every 20, 50 and 100 years over China under 1.5/2°C global warming. Compared to the historical period (1995-2014), the changes in the probability risks of extreme precipitation under global warming of 1.5/2℃ present an overall increasing trend. Although their spatial distributions show similar characteristics, the additional half a degree global warming will lead to a higher risk. For example, extreme precipitation that occurred once every 50 years will become once about every 17/14years under the warming of 1.5/2°C, respectively, and extreme precipitation will become more frequent. There are regional differences in the responses of different regions to climate warming, among which the middle and upper reaches of the Yangtze and Yellow Rivers and the Qinghai-Tibet Plateau region in the Western China, and the middle and lower reaches of Yangtze and Yellow Rivers and its south in Eastern China, are highly sensitive response regions to climate change, and their probability ratios reach 3 or even 5 or more. Furthermore, the influence and contribution measures of location and scale parameters on the probability ratios are explored from a theoretical perspective based on the probability distributions, and are used to explore the influences of the climate means and variability changes on the risks of extreme precipitation. The results show that there are significant differences between Eastern China and Western China in the incremental changes of location and scale parameters, and in the rates of probability risk changes, which lead to differences of the factors that influencing the risk of extreme precipitation. In the Western China, although the changes in climate means and variabilities of extreme precipitation are small, the probability ratios increase significantly due to the high rates of probability risk changes. In contrast, the change rates are small, but the climate means and variabilities present a high increase, which also lead to an increase in the Eastern China. Moreover, compared to the location parameters, the increase risks in most of regions over China are mainly due to the changes of scale parameters of extreme precipitation in the future.
在线获得:May 23, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220503002
Abstract:
A wind profiler radar is a new type of Doppler wind radar designed to measure the horizontal wind vectors, and are able to provide wind profiles at various elevations with high temporal and spatial resolution. It plays an increasingly important role in China"s Meteorological business. However, the quality of wind profiler radar data varies widely consider-ing the different types of radars deployed in China. To better utilize these data in numerical weather prediction (NWP), this study assesses the data quality from five different types of wind profiler radars (i.e., CFL-06, GLC-24, TWP8-L, CFL-03 and CLC-11-D) at 20 stations in North China from June to September 2019. Results show that each type of profiler has strong detection ability, but different profilers have great differences in data acquisition rate and effective detection height. Without considering the influence of precipitation, the data quality of v-wind is better than that of u-wind for all types of profilers. In all air conditions, the quality of u-wind data from TWP8-L profiler is relatively the best, followed by CFL-03 radar, the quality of u-wind data from GLC-24 radar is the worst. The differences in v-wind data is not noticea-ble, while bias correction and quality control are necessary for u-wind data. The data quality of wind profile radar is quite sensitive to precipitation, which reduces the data acquisition rates at low levels and increases the data acquisition rates at the middle and high levels, with the maximum increase of 53%. The statistical results indicate precipitation is responsible for the large mean errors and root mean square errors (RMSEs) of u wind data. GLC-24 and CLC-11-D profilers are the most sensitive to precipitation. The RMSEs are increased by 5.5 m/s in precipitation regions as compared to non-precipitation regions. The u-wind and v-wind data in precipitation regions requires further quality control procedure.
A wind profiler radar is a new type of Doppler wind radar designed to measure the horizontal wind vectors, and are able to provide wind profiles at various elevations with high temporal and spatial resolution. It plays an increasingly important role in China"s Meteorological business. However, the quality of wind profiler radar data varies widely consider-ing the different types of radars deployed in China. To better utilize these data in numerical weather prediction (NWP), this study assesses the data quality from five different types of wind profiler radars (i.e., CFL-06, GLC-24, TWP8-L, CFL-03 and CLC-11-D) at 20 stations in North China from June to September 2019. Results show that each type of profiler has strong detection ability, but different profilers have great differences in data acquisition rate and effective detection height. Without considering the influence of precipitation, the data quality of v-wind is better than that of u-wind for all types of profilers. In all air conditions, the quality of u-wind data from TWP8-L profiler is relatively the best, followed by CFL-03 radar, the quality of u-wind data from GLC-24 radar is the worst. The differences in v-wind data is not noticea-ble, while bias correction and quality control are necessary for u-wind data. The data quality of wind profile radar is quite sensitive to precipitation, which reduces the data acquisition rates at low levels and increases the data acquisition rates at the middle and high levels, with the maximum increase of 53%. The statistical results indicate precipitation is responsible for the large mean errors and root mean square errors (RMSEs) of u wind data. GLC-24 and CLC-11-D profilers are the most sensitive to precipitation. The RMSEs are increased by 5.5 m/s in precipitation regions as compared to non-precipitation regions. The u-wind and v-wind data in precipitation regions requires further quality control procedure.
在线获得:May 06, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20220207001
Abstract:
We proposed a novel method to predict the meteorological risk for important events, and successfully applied it to the 70th anniversary celebration of the People"s Republic of China. The method based on fine meteorological factors analysis, considered risk resources(weather risk) and vulnerability(impact on events), so that efficient measures can be taken in advance to make sure the success of the events. A risk matrix method was used to perform the analysis and prediction. Meteorological risk levels were evaluated and risk maps were drawn of major celebration events based on the investigation and analysis of meteorological factors that may affected the successful holding of the celebration events and the occurrence ratio of different meteorological events. For the 70th anniversary celebration of the People"s Republic of China, we recognized the major weather risks (from high to low) were precipitation/overcast and rain, day force eight wind, frog-haze, night force eight wind, high temperature and direct sunlight, thunder and lightning, low temperature, etc.. Risk control measures and countermeasures were put forward to the risk caused by different meteorological factors, which provided important reference for the scientific formulation and implementation of safety work plan and the successful development of activities. In the meteorological support work of major activities, a major change has been realized from considering only the occurrence probability of high impact weather to risk assessment based on weather impact and we got a good service effect for the events.
We proposed a novel method to predict the meteorological risk for important events, and successfully applied it to the 70th anniversary celebration of the People"s Republic of China. The method based on fine meteorological factors analysis, considered risk resources(weather risk) and vulnerability(impact on events), so that efficient measures can be taken in advance to make sure the success of the events. A risk matrix method was used to perform the analysis and prediction. Meteorological risk levels were evaluated and risk maps were drawn of major celebration events based on the investigation and analysis of meteorological factors that may affected the successful holding of the celebration events and the occurrence ratio of different meteorological events. For the 70th anniversary celebration of the People"s Republic of China, we recognized the major weather risks (from high to low) were precipitation/overcast and rain, day force eight wind, frog-haze, night force eight wind, high temperature and direct sunlight, thunder and lightning, low temperature, etc.. Risk control measures and countermeasures were put forward to the risk caused by different meteorological factors, which provided important reference for the scientific formulation and implementation of safety work plan and the successful development of activities. In the meteorological support work of major activities, a major change has been realized from considering only the occurrence probability of high impact weather to risk assessment based on weather impact and we got a good service effect for the events.
在线获得:May 06, 2022, DOI: :10.13878/j.cnki.dqkxxb. 20211230001
Abstract:
Nowadays, ensemble forecasting has become the main support for operational weather forecasting. However, due to the limitations and imperfections of the numerical model itself, the forecast results are generally systematically biased. In addition, different forecasting models usually have different physical parameterization schemes, initial conditions, etc., resulting in different forecasting capabilities. Therefore, how to eliminate systematic biases and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention. In recent years, using statistical theory and forecasting diagnosis, multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed to effectively eliminate systematic biases and improve weather forecasting skills. For the three most basic surface meteorological variables (i.e., temperature, precipitation, wind), the widely used multimodel ensemble technologies such as ensemble mean (EM), bias-removed ensemble mean (BREM), superensemble (SUP), Bayesian model averaging(BMA), and ensemble model output statistics(EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting. Finally, the issues that need to be paid attention to when using and developing multimodel ensemble technologies are discussed, including the consideration the number of participating models, the development of categorized precipitation and wind speed forecast models. Meanwhile, the combination of multimodel ensemble with machine learning deserves more investigation.
Nowadays, ensemble forecasting has become the main support for operational weather forecasting. However, due to the limitations and imperfections of the numerical model itself, the forecast results are generally systematically biased. In addition, different forecasting models usually have different physical parameterization schemes, initial conditions, etc., resulting in different forecasting capabilities. Therefore, how to eliminate systematic biases and how to make full and effective use of forecast information from different models to obtain more accurate weather forecasts has received extensive attention. In recent years, using statistical theory and forecasting diagnosis, multimodel ensemble forecasting technologies based on multiple ensemble prediction systems have been rapidly developed to effectively eliminate systematic biases and improve weather forecasting skills. For the three most basic surface meteorological variables (i.e., temperature, precipitation, wind), the widely used multimodel ensemble technologies such as ensemble mean (EM), bias-removed ensemble mean (BREM), superensemble (SUP), Bayesian model averaging(BMA), and ensemble model output statistics(EMOS) are first introduced from the perspective of deterministic forecasting and probabilistic forecasting. Finally, the issues that need to be paid attention to when using and developing multimodel ensemble technologies are discussed, including the consideration the number of participating models, the development of categorized precipitation and wind speed forecast models. Meanwhile, the combination of multimodel ensemble with machine learning deserves more investigation.
在线获得:March 23, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20211229001
Abstract:
To study the differences and similarities between fine observation and conventional observation in fog microstructure, a systematic field observation of fog was conducted and lasted 58 days in Donghai, Lianyungang during the winter of 2020. Two fog monitors with different frequencies (5Hz and 1Hz) were applied simultaneously for measuring a radiation fog on December 28, 2020. Based on this observation, we found that compared with the 1Hz data, the 5Hz measurement observed more extreme values. In terms of the whole fog process, the results of the 5Hz fog droplet spectrometer averaged into 1Hz are less similar to the original results of 1Hz in the fog generation and dissipation stages, and more similar in the fog maturation and development stages. In terms of the spectral patterns, the spectral patterns of 5 Hz and 1 Hz are similar, while the main differences appear at the peak. The measurements of 5 Hz and 1 Hz can both reflect the relationship between the microphysics at different stages of this fog process, and the main differences appear at the fog generation stage. This may be due to the relatively small number of activation and condensation growth of new fog droplets observed at 5Hz at this stage.
To study the differences and similarities between fine observation and conventional observation in fog microstructure, a systematic field observation of fog was conducted and lasted 58 days in Donghai, Lianyungang during the winter of 2020. Two fog monitors with different frequencies (5Hz and 1Hz) were applied simultaneously for measuring a radiation fog on December 28, 2020. Based on this observation, we found that compared with the 1Hz data, the 5Hz measurement observed more extreme values. In terms of the whole fog process, the results of the 5Hz fog droplet spectrometer averaged into 1Hz are less similar to the original results of 1Hz in the fog generation and dissipation stages, and more similar in the fog maturation and development stages. In terms of the spectral patterns, the spectral patterns of 5 Hz and 1 Hz are similar, while the main differences appear at the peak. The measurements of 5 Hz and 1 Hz can both reflect the relationship between the microphysics at different stages of this fog process, and the main differences appear at the fog generation stage. This may be due to the relatively small number of activation and condensation growth of new fog droplets observed at 5Hz at this stage.
Accounting the carbon sequestration rate of terrestrial ecosystems: methods, progress and challenges
在线获得:March 14, 2022, DOI: 10.13878/j.cnki.dqkxxb. 20220303002
Abstract:
Global warming, caused by the rapid increase of atmospheric CO2 has become an urgent problem for sustainable development of human beings. Terrestrial ecosystems have played an important carbon sink role in the past decades by absorbing about 30% of the CO2 emitted by human activities. This paper reviewed the estimation methods of carbon sequestration rate of terrestrial ecosystems, including sampling inventory, flux monitoring, model simulation, remote sensing, etc., and analyzed the progresses and challenges of the current approaches for accounting terrestrial carbon sequestration. Sampling inventory and flux observations can provide direct measurement of plot-scale carbon sequestration rate, but there are many problems, such as limited observation samples and insufficient spatial representation. Model simulation methods can describe the terrestrial carbon cycles and simulate the state and change of carbon sequestration rates in terrestrial ecosystems. However, the approximating and simplifying processes in model, together with the uncertainties introduced by model-driven data, it is very challenging to accurately modeling the carbon sequestration rate of terrestrial ecosystems. Satellite remote sensing, holding the advantages of global coverage, fine resolution, and time-series observations, combined with machine learning methods, can provide a new approach for the estimation of the carbon sequestration rate of terrestrial ecosystems. So far, various accounting methods for carbon sequestration rates have not yet met the needs of monitoring carbon sequestration in terrestrial ecosystems due to the high spatial and temporal heterogeneity. In the future, it is extremely important to integrate various accounting approaches, such as ground observations, model simulations, and satellite remote sensing, to provide accurate estimation of terrestrial ecosystem carbon sinks at the regional and global scales.
Global warming, caused by the rapid increase of atmospheric CO2 has become an urgent problem for sustainable development of human beings. Terrestrial ecosystems have played an important carbon sink role in the past decades by absorbing about 30% of the CO2 emitted by human activities. This paper reviewed the estimation methods of carbon sequestration rate of terrestrial ecosystems, including sampling inventory, flux monitoring, model simulation, remote sensing, etc., and analyzed the progresses and challenges of the current approaches for accounting terrestrial carbon sequestration. Sampling inventory and flux observations can provide direct measurement of plot-scale carbon sequestration rate, but there are many problems, such as limited observation samples and insufficient spatial representation. Model simulation methods can describe the terrestrial carbon cycles and simulate the state and change of carbon sequestration rates in terrestrial ecosystems. However, the approximating and simplifying processes in model, together with the uncertainties introduced by model-driven data, it is very challenging to accurately modeling the carbon sequestration rate of terrestrial ecosystems. Satellite remote sensing, holding the advantages of global coverage, fine resolution, and time-series observations, combined with machine learning methods, can provide a new approach for the estimation of the carbon sequestration rate of terrestrial ecosystems. So far, various accounting methods for carbon sequestration rates have not yet met the needs of monitoring carbon sequestration in terrestrial ecosystems due to the high spatial and temporal heterogeneity. In the future, it is extremely important to integrate various accounting approaches, such as ground observations, model simulations, and satellite remote sensing, to provide accurate estimation of terrestrial ecosystem carbon sinks at the regional and global scales.
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2019,42(2): 161-173, DOI: 10.13878/j.cnki.dqkxxb.20170504012
Abstract:
The eyewall replacement cycle plays an important role in changes regarding typhoon intensity and inner-core structure.In this study,in order to investigate the influences of large-eddy simulation(LES) on eyewall replacement,two ideal numerical experiments were conducted,of which one was coupled with LES.The study results indicate that the typhoon intensity of the LES experiment was stronger with larger inflow in the boundary layers.It took the two typhoon approximately 20-22 hours to complete the entire eyewall replacement,but the typhoon in the LES experiment had a faster enclosing of the outer eyewall.At the same time,the intensity and updraft in the outer eyewall were also greater.After the eyewall replacement,the typhoon in the LES experiment continued to intensify,and its intensity became greater than it was before the eyewall replacement.Of more importance is that the LES can more effectively simulate the downdraft within the moat region which is at the outside of the inner eyewall.In addition,the downward motion can more effectively induce the formation and development of convections near the outer eyewall regions,and is in line with the observational features found by previous studies.
The eyewall replacement cycle plays an important role in changes regarding typhoon intensity and inner-core structure.In this study,in order to investigate the influences of large-eddy simulation(LES) on eyewall replacement,two ideal numerical experiments were conducted,of which one was coupled with LES.The study results indicate that the typhoon intensity of the LES experiment was stronger with larger inflow in the boundary layers.It took the two typhoon approximately 20-22 hours to complete the entire eyewall replacement,but the typhoon in the LES experiment had a faster enclosing of the outer eyewall.At the same time,the intensity and updraft in the outer eyewall were also greater.After the eyewall replacement,the typhoon in the LES experiment continued to intensify,and its intensity became greater than it was before the eyewall replacement.Of more importance is that the LES can more effectively simulate the downdraft within the moat region which is at the outside of the inner eyewall.In addition,the downward motion can more effectively induce the formation and development of convections near the outer eyewall regions,and is in line with the observational features found by previous studies.
2022,45(2): 280-291, DOI: 10.13878/j.cnki.dqkxxb.20200719017
Abstract:
Based on the sea surface temperature (SST) data from NOAA in USA, the asymmetric characteristics of interannual relationship between ENSO and Victoria mode (VM;EOF2 of North Pacific SST anomalies in winter (DJF)) were emphatically analyzed.Results show that the correlation between VM and ENSO is weak on the decadal scale, but strong on the interannual scale.VM has significant negative correlation with ENSO in the same year, and has strong positive correlation with ENSO in the following year.However, there is a certain asymmetry in the relationship between the positive/negative VM events and ENSO warm/cold phases on the interannual scale.The relationship between the positive VM events and the SST anomalies in the tropical central and eastern Pacific in the same winter is weak, but El Niño events often occur in the following year.In contrast, the negative VM events are usually accompanied by El Niño events in the same years, but there is no significant relationship between the negative VM events and the SST anomalies in the tropical central and eastern Pacific in the following winter and there are few ENSO events.It can be seen that the positive VM event seems to promote the occurrence and development of El Niño in the next year and can be used as one of the early prediction factors of ENSO, while the negative VM event cannot be used as the early prediction factor of ENSO.
Based on the sea surface temperature (SST) data from NOAA in USA, the asymmetric characteristics of interannual relationship between ENSO and Victoria mode (VM;EOF2 of North Pacific SST anomalies in winter (DJF)) were emphatically analyzed.Results show that the correlation between VM and ENSO is weak on the decadal scale, but strong on the interannual scale.VM has significant negative correlation with ENSO in the same year, and has strong positive correlation with ENSO in the following year.However, there is a certain asymmetry in the relationship between the positive/negative VM events and ENSO warm/cold phases on the interannual scale.The relationship between the positive VM events and the SST anomalies in the tropical central and eastern Pacific in the same winter is weak, but El Niño events often occur in the following year.In contrast, the negative VM events are usually accompanied by El Niño events in the same years, but there is no significant relationship between the negative VM events and the SST anomalies in the tropical central and eastern Pacific in the following winter and there are few ENSO events.It can be seen that the positive VM event seems to promote the occurrence and development of El Niño in the next year and can be used as one of the early prediction factors of ENSO, while the negative VM event cannot be used as the early prediction factor of ENSO.
2014,37(5): 642-652, DOI: 10.13878/j.cnki.dqkxxb.20121017006
Abstract:
In this paper,the Weather Research and Forecast Model(WRF) is coupled with Surface-Layer Scheme,Single-Layer Urban Canopy Model and Mingle-Layer Urban Canopy Model respectively to evaluate the simulation effect of various parameterizations on the weather conditions on 1 August 2007 in Nanjing.The best urban parameterization scheme is coupled into WRF to study the impact of land cover change on the Urban Heat Island(UHI) effect in Nanjing.Results show that the Mingle-Layer Urban Canopy Model shows the best simulation effect for surface temperature and 10m wind field.Urbanization makes surface air temperature increase over the region,especially at night and thus intensifies the UHI effect.After urbanization,the wind speed in the downtown area decreases obviously while the Urban Heat Circulation occurs more apparently.There also exists the downstream effect of UHI in Nanjing.
In this paper,the Weather Research and Forecast Model(WRF) is coupled with Surface-Layer Scheme,Single-Layer Urban Canopy Model and Mingle-Layer Urban Canopy Model respectively to evaluate the simulation effect of various parameterizations on the weather conditions on 1 August 2007 in Nanjing.The best urban parameterization scheme is coupled into WRF to study the impact of land cover change on the Urban Heat Island(UHI) effect in Nanjing.Results show that the Mingle-Layer Urban Canopy Model shows the best simulation effect for surface temperature and 10m wind field.Urbanization makes surface air temperature increase over the region,especially at night and thus intensifies the UHI effect.After urbanization,the wind speed in the downtown area decreases obviously while the Urban Heat Circulation occurs more apparently.There also exists the downstream effect of UHI in Nanjing.
Abstract:
Based on the hourly precipitation observed by automatic weather stations(AWS) in China and retrieved from CMORPH(CPC MORPHing technique) satellite data,the merged precipitation product at hourly/0.1°lat/0.1°lon temporal-spatial resolution in China is developed through the two-step merging algorithm of PDF(probability density function) and OI(optimal interpolation).In this paper,the quality of merged precipitation product is assessed from the points of temporal-spatial characteristics of error,accuracy at different precipitation rates and cumulative times,merging effect at three station network densities and monitoring capability of the heavy rainfall.Results indicate that:1)The merged precipitation product effectively uses the advantages of AWS observations and satellite product of CMORPH,so it is more reasonable both at the precipitation amount and spatial distribution;2)The regional mean bias and root-mean-square error of the merged precipitation product are decreased remarkably,and they have a little change with time;3)The relative bias of merged precipitation product is -1.675%,less than 15% and about 30% for the medium(1.0—2.5 mm/h),medium to large(1.0—8.0 mm/h) and heavy rainfall(≥8.0 mm/h),respectively,and the product quality is improved further with the cumulative time increases.The merged precipitation product can capture the precipitation process very well and have a definite advantage in the quantitatively rainfall monitoring.
Based on the hourly precipitation observed by automatic weather stations(AWS) in China and retrieved from CMORPH(CPC MORPHing technique) satellite data,the merged precipitation product at hourly/0.1°lat/0.1°lon temporal-spatial resolution in China is developed through the two-step merging algorithm of PDF(probability density function) and OI(optimal interpolation).In this paper,the quality of merged precipitation product is assessed from the points of temporal-spatial characteristics of error,accuracy at different precipitation rates and cumulative times,merging effect at three station network densities and monitoring capability of the heavy rainfall.Results indicate that:1)The merged precipitation product effectively uses the advantages of AWS observations and satellite product of CMORPH,so it is more reasonable both at the precipitation amount and spatial distribution;2)The regional mean bias and root-mean-square error of the merged precipitation product are decreased remarkably,and they have a little change with time;3)The relative bias of merged precipitation product is -1.675%,less than 15% and about 30% for the medium(1.0—2.5 mm/h),medium to large(1.0—8.0 mm/h) and heavy rainfall(≥8.0 mm/h),respectively,and the product quality is improved further with the cumulative time increases.The merged precipitation product can capture the precipitation process very well and have a definite advantage in the quantitatively rainfall monitoring.
Abstract:
Based on the multiple type observational data,this paper preliminarily analyses the meso scale convective systems(MCSs) and weather background producing an extremely heavy rain along the Mei yu front in Hubei and Anhui provinces during 29—30 June 2009,and investigates the multi scale structure features of the Mei yu frontal rainstorm system.Then the meso scale numerical model WRF with large domain and 9 km horizontal resolution is used to carry out a 3 domain nested fine simulation for the heavy rain process.Morlet wavelet transformation is carried out to do spatial band passing filter for the model outputs,and the meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scale systems are separated out,in such a way that the three dimensional spatial dynamic and thermodynamic characteristics of the meso scale systems with different scales are studied.The results are as follows.The extremely Mei yu frontal heavy rain is directly resulted from several MCSs with different scales,which are of different features on satellite cloud images and radar echoes.On meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scales,the Mei yu frontal heavy rain system has obvious different dynamic and thermodynamic structure features in horizontal and vertical directions.The meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale systems have obvious vertical circulation,while meso 〖WTBX〗γ〖WTB1〗 scale system has some features of inertial gravity waves and usually develops in meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale system.Lastly,a physic conceptual model is advanced for the typical Mei yu frontal rainstorm system.
Based on the multiple type observational data,this paper preliminarily analyses the meso scale convective systems(MCSs) and weather background producing an extremely heavy rain along the Mei yu front in Hubei and Anhui provinces during 29—30 June 2009,and investigates the multi scale structure features of the Mei yu frontal rainstorm system.Then the meso scale numerical model WRF with large domain and 9 km horizontal resolution is used to carry out a 3 domain nested fine simulation for the heavy rain process.Morlet wavelet transformation is carried out to do spatial band passing filter for the model outputs,and the meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scale systems are separated out,in such a way that the three dimensional spatial dynamic and thermodynamic characteristics of the meso scale systems with different scales are studied.The results are as follows.The extremely Mei yu frontal heavy rain is directly resulted from several MCSs with different scales,which are of different features on satellite cloud images and radar echoes.On meso 〖WTBX〗α, β〖WTB1〗 and 〖WTBX〗γ〖WTB1〗 scales,the Mei yu frontal heavy rain system has obvious different dynamic and thermodynamic structure features in horizontal and vertical directions.The meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale systems have obvious vertical circulation,while meso 〖WTBX〗γ〖WTB1〗 scale system has some features of inertial gravity waves and usually develops in meso 〖WTBX〗α〖WTB1〗 and 〖WTBX〗β〖WTB1〗 scale system.Lastly,a physic conceptual model is advanced for the typical Mei yu frontal rainstorm system.
2023,46(3): 332-344, DOI: 10.13878/j.cnki.dqkxxb.20230303001
Abstract:
The summer of 2022 exhibits significant characteristics of high temperature,low humidity,and rainfall in South China.Previous studies have focused on extreme events of high temperature and low rainfall in summer,whereas attention to near-ground relative humidity,which is closely related to human comfort and crop growth,has been relatively insufficient.In this study,we define events of positive temperature anomaly,negative precipitation anomaly,and negative relative humidity anomaly exceeding one time of the interannual standard deviation between 1959 and 2022 are as compound events of summer high temperature,low humidity,and rainfall.Monthly ERA5 atmospheric reanalysis data of 1959—2022 are used in this study.We study the effect of spring soil moisture on the compound events in summer by composite analysis and a dynamic adjustment approach based on constructed circulation analogs,and the physical mechanism is analyzed.The results show that:1) The hot spots of the coupling between spring soil moisture and summer climate in south China are basically consistent with the high variability of summer temperature,precipitation,and relative humidity in 2022.2) When the soil in the Yangtze River Basin and Huang-Huai area is dry in spring and the southeast area is wet,the compound events of drying and heat will occur in summer.3) The effect of spring soil moisture on summer climate variability is mainly realized by adjusting the distribution of local evapotranspiration and net radiation energy.The study of the compound extreme events of high temperature,low humidity,and rainfall is of great significance in effectively preventing all kinds of disasters and safety accidents caused by them,protecting people's lives and property,and maintaining social production order.
The summer of 2022 exhibits significant characteristics of high temperature,low humidity,and rainfall in South China.Previous studies have focused on extreme events of high temperature and low rainfall in summer,whereas attention to near-ground relative humidity,which is closely related to human comfort and crop growth,has been relatively insufficient.In this study,we define events of positive temperature anomaly,negative precipitation anomaly,and negative relative humidity anomaly exceeding one time of the interannual standard deviation between 1959 and 2022 are as compound events of summer high temperature,low humidity,and rainfall.Monthly ERA5 atmospheric reanalysis data of 1959—2022 are used in this study.We study the effect of spring soil moisture on the compound events in summer by composite analysis and a dynamic adjustment approach based on constructed circulation analogs,and the physical mechanism is analyzed.The results show that:1) The hot spots of the coupling between spring soil moisture and summer climate in south China are basically consistent with the high variability of summer temperature,precipitation,and relative humidity in 2022.2) When the soil in the Yangtze River Basin and Huang-Huai area is dry in spring and the southeast area is wet,the compound events of drying and heat will occur in summer.3) The effect of spring soil moisture on summer climate variability is mainly realized by adjusting the distribution of local evapotranspiration and net radiation energy.The study of the compound extreme events of high temperature,low humidity,and rainfall is of great significance in effectively preventing all kinds of disasters and safety accidents caused by them,protecting people's lives and property,and maintaining social production order.
2019,42(4): 631-640, DOI: 10.13878/j.cnki.dqkxxb.20170815015
Abstract:
Imperative quality control methods for Doppler radar data,such as ground clutter elimination,range folding elimination and velocity dealiasing,should be adopted before being used for quantitative analyses,due to the serious impacts originating from certain non-meteorological factors.In this study,in order to precisely identify the ground clutter and precipitous echo,an automatic algorithm based on the Support Vector Machine(SVM) is performed,based on the observational CINRAD/SA Doppler weather radar data in the areas of Anqing and Changzhou from June to August,2013,and the results are compared with the recognition effect based on the Artificial Neural Networks(ANNs) method.Statistical learning theory(SLT) is favorable for small samples,which focuses on the statistical law and nature of small-sample learning.As a new machine learning based on SLT,the basic principle of the SVM is to possess an optimal separating hyperplane which is able to satisfy the requirement of the classification accuracy by introducing the largest classification intervals on either side of the hyperplane.In the first step,the dataset used in the experiment will be establised by empirically distinguishing the ground clutter and precipitous points at each bin.Next,several characteristic parameters,which are used to quantify the possibility affected by the ground clutter,such as reflectivity vertical variation (GDBZ),reflectivity horizontal texture (TDBZ),velocity regional average (MDVE),and spectrum regional average (MDSW),will be derived from the reflectivity,radaial velocity,spectrum width and spatial variance information of the ground clutter and precipitous echo.The statistical results of the above characteristic parameters show the following:a large portion of these parameters vary in terms of ground clutter and precipitous echo,which indicates that the seven parameters (GDBZ,TDBZ,SPIN,SIGN,MDVE,MDSW and SDVE) contribute to the identifiable recognition of the ground clutter and precipitous echo.Based on the above conclusions,seven parameters,which are regarded as the trigger (the training factor of SVM) to establish the SVM's training model,can be randomly extracted from the database.Finally,the training model is used to automatically recognize the ground clutter and precipitation using the random data from the database.The recognition effect of the SVM method will be examined by comparing the model output with the empirical identifications,and the examination of the ANNs algorithm is the same as that of the SVM method.The comparison of the recognition effect between the SVM and ANNs methods reveals the following:(1) The statistically identifiable recognition parameter for the sSVM and ANNs methods appears to be steady,despite the fact that the Doppler radar data vary in shape and position between Anqing and Changzhou;(2) An identifying threshold must be determined for the ANNs method before the ground clutter and precipitous echo are identified,which will lead to a differently identifiable accuracy with the unlike threshold;and (3) Overall,the SVM method works better than the ANNs method in terms of radar echo identification.Moreover,the identifiable recognition accuracy of the latter increases significantly with the increasing total number of training samples,while the identifiable recognition accuracy of the former performs at a highly accurate level,which remains relatively stable with the changes in the training samples.In terms of the identification accuracy of the total samples (ground clutter and precipitous echo) and identification accuracy of the ground clutter echo,the SVM method presents better results than the ANNs method.As for the precipitous echo erroneous recognition,the ANNs method performs slightly better than the SVM,but both methods control the erroneous recognition rate at a low level.
Imperative quality control methods for Doppler radar data,such as ground clutter elimination,range folding elimination and velocity dealiasing,should be adopted before being used for quantitative analyses,due to the serious impacts originating from certain non-meteorological factors.In this study,in order to precisely identify the ground clutter and precipitous echo,an automatic algorithm based on the Support Vector Machine(SVM) is performed,based on the observational CINRAD/SA Doppler weather radar data in the areas of Anqing and Changzhou from June to August,2013,and the results are compared with the recognition effect based on the Artificial Neural Networks(ANNs) method.Statistical learning theory(SLT) is favorable for small samples,which focuses on the statistical law and nature of small-sample learning.As a new machine learning based on SLT,the basic principle of the SVM is to possess an optimal separating hyperplane which is able to satisfy the requirement of the classification accuracy by introducing the largest classification intervals on either side of the hyperplane.In the first step,the dataset used in the experiment will be establised by empirically distinguishing the ground clutter and precipitous points at each bin.Next,several characteristic parameters,which are used to quantify the possibility affected by the ground clutter,such as reflectivity vertical variation (GDBZ),reflectivity horizontal texture (TDBZ),velocity regional average (MDVE),and spectrum regional average (MDSW),will be derived from the reflectivity,radaial velocity,spectrum width and spatial variance information of the ground clutter and precipitous echo.The statistical results of the above characteristic parameters show the following:a large portion of these parameters vary in terms of ground clutter and precipitous echo,which indicates that the seven parameters (GDBZ,TDBZ,SPIN,SIGN,MDVE,MDSW and SDVE) contribute to the identifiable recognition of the ground clutter and precipitous echo.Based on the above conclusions,seven parameters,which are regarded as the trigger (the training factor of SVM) to establish the SVM's training model,can be randomly extracted from the database.Finally,the training model is used to automatically recognize the ground clutter and precipitation using the random data from the database.The recognition effect of the SVM method will be examined by comparing the model output with the empirical identifications,and the examination of the ANNs algorithm is the same as that of the SVM method.The comparison of the recognition effect between the SVM and ANNs methods reveals the following:(1) The statistically identifiable recognition parameter for the sSVM and ANNs methods appears to be steady,despite the fact that the Doppler radar data vary in shape and position between Anqing and Changzhou;(2) An identifying threshold must be determined for the ANNs method before the ground clutter and precipitous echo are identified,which will lead to a differently identifiable accuracy with the unlike threshold;and (3) Overall,the SVM method works better than the ANNs method in terms of radar echo identification.Moreover,the identifiable recognition accuracy of the latter increases significantly with the increasing total number of training samples,while the identifiable recognition accuracy of the former performs at a highly accurate level,which remains relatively stable with the changes in the training samples.In terms of the identification accuracy of the total samples (ground clutter and precipitous echo) and identification accuracy of the ground clutter echo,the SVM method presents better results than the ANNs method.As for the precipitous echo erroneous recognition,the ANNs method performs slightly better than the SVM,but both methods control the erroneous recognition rate at a low level.
Abstract:
Wind shear in the atmosphere is a serious threat to the safety of aircraft,especially the low-level wind shear which is an important factor affecting the aircraft taking off and landing.By using the Doppler radar velocity data to calculate the one-dimension tangential,one-dimensional radial and two-dimension composite shear,accurately judging the dangerous area of wind shear could provide timely warning for flight,taking off and landing.In this study,as the wind shear automatic identification product on the principal user processor(PUP) for Doppler radar applications has the shortcomings such as weak edge recognition and larger location errors,according to Doppler radar velocity distributions and taking advantage of least square fitting method,"fitting window" suitable for airborne radar parameters are chosen,and the several cases have been identified and analyzed.For the performance in wind shear's identification,location and edge discerning,the least square method could provide better reference of wind shear and warnings than PUP's identification products.
Wind shear in the atmosphere is a serious threat to the safety of aircraft,especially the low-level wind shear which is an important factor affecting the aircraft taking off and landing.By using the Doppler radar velocity data to calculate the one-dimension tangential,one-dimensional radial and two-dimension composite shear,accurately judging the dangerous area of wind shear could provide timely warning for flight,taking off and landing.In this study,as the wind shear automatic identification product on the principal user processor(PUP) for Doppler radar applications has the shortcomings such as weak edge recognition and larger location errors,according to Doppler radar velocity distributions and taking advantage of least square fitting method,"fitting window" suitable for airborne radar parameters are chosen,and the several cases have been identified and analyzed.For the performance in wind shear's identification,location and edge discerning,the least square method could provide better reference of wind shear and warnings than PUP's identification products.
2023,46(6): 950-960, DOI: 10.13878/j.cnki.dqkxxb.20230313001
Abstract:
Northern China experienced four sandstorms or severe sandstorms in spring 2021, contrasting with just one event in the corresponding period of 2022. Utilizing air quality and multi-source meteorological data spanning 2015 to 2022, we applied the Lamb Jenkinson classification and Mann-Whitney U test methods to analyze similarities and differences in the sand source areas' conditions and meteorological factors during the spring of 2021 and 2022. Our findings reveal that the sand and dust weather (SDW) in northern China is frequently categorized into NW-N (cyclone type) and E-NE (high-pressure type), with the NW-N type leading to higher PM10 extreme values and a broader range of high concentrations. In terms of meteorological factors, synoptic conditions favorable for SDW in spring 2022 occur more frequently, with the differences in daily PM10 concentration predominantly associated with the NW-N type when compared to spring 2021. The frequency of NW-N type events and cyclone intensity remains comparable between the two periods, along with similar dynamic uplift conditions conducive to SDW are similar. Regarding sand source area conditions, the soil temperature in Mongolia's sand source area displayed a “cold before and warm after” pattern in the pre-winter of 2021, resulting in an early peak of snowmelt and other water content. In addition, a widespread decrease in precipitation and a relatively strong cyclone in Mongolia's sand source area in March contributed to the high incidence of sand and dust in spring 2021. Conversely, during the pre-winter of 2022, the soil temperature in Mongolia's sand source area followed a “warm before and cold after” trend, leading to a delayed peak of water content and soil moisture content during the snowmelt period. These conditions, characterized by thicker and moisture soil, were less conducive to sand formation. Therefore, the disparities in Mongolian sand source area conditions represent the primary factor behind the significant differences in SDW between the two periods.
Northern China experienced four sandstorms or severe sandstorms in spring 2021, contrasting with just one event in the corresponding period of 2022. Utilizing air quality and multi-source meteorological data spanning 2015 to 2022, we applied the Lamb Jenkinson classification and Mann-Whitney U test methods to analyze similarities and differences in the sand source areas' conditions and meteorological factors during the spring of 2021 and 2022. Our findings reveal that the sand and dust weather (SDW) in northern China is frequently categorized into NW-N (cyclone type) and E-NE (high-pressure type), with the NW-N type leading to higher PM10 extreme values and a broader range of high concentrations. In terms of meteorological factors, synoptic conditions favorable for SDW in spring 2022 occur more frequently, with the differences in daily PM10 concentration predominantly associated with the NW-N type when compared to spring 2021. The frequency of NW-N type events and cyclone intensity remains comparable between the two periods, along with similar dynamic uplift conditions conducive to SDW are similar. Regarding sand source area conditions, the soil temperature in Mongolia's sand source area displayed a “cold before and warm after” pattern in the pre-winter of 2021, resulting in an early peak of snowmelt and other water content. In addition, a widespread decrease in precipitation and a relatively strong cyclone in Mongolia's sand source area in March contributed to the high incidence of sand and dust in spring 2021. Conversely, during the pre-winter of 2022, the soil temperature in Mongolia's sand source area followed a “warm before and cold after” trend, leading to a delayed peak of water content and soil moisture content during the snowmelt period. These conditions, characterized by thicker and moisture soil, were less conducive to sand formation. Therefore, the disparities in Mongolian sand source area conditions represent the primary factor behind the significant differences in SDW between the two periods.
2021,44(1): 39-49, DOI: 10.13878/j.cnki.dqkxxb.20201113007
Abstract:
The Arctic climate,an important component of the global climate system,has moved into a new state over the past 20 years.Scientific questions and possible consequences related to these changes are now front in the midst of many important issues that the world needs to deal with in the future.These changes,including prominent atmospheric and oceanic warming and sea ice melting have been largely attributed to a combined effect of anthropogenic forcing and internal variability of the climate system.This review highlights some findings from a number of studies conducted by my research group in the past few years.The studies collectively suggest that the high latitude atmospheric circulation that is sensitive to tropical SST forcing related to the interdecadal Pacific oscillation (IPO) plays a vital role in driving the interannual and interdecadal variability of Arctic sea ice by affecting the atmospheric temperature,moisture,clouds and radiative fluxes over sea ice.In particular,the teleconnection excited by a SST cooling over the tropical Pacific is suggested to cause an enhanced melting from 2007 to 2012.In addition,it suggests that a similar internal process may also play a role to cause strong sea ice melting in summer 2020.Furthermore,the model evaluation focusing on CMIP5 models finds that most climate models have a limitation to replicate this IPO-related teleconnection,raising awareness on an urgent need to investigate the cause of this bias in models.Thus,this review is meant to offer priorities for future Arctic research so that more efforts are targeted on critical scientific questions raised in this study.
The Arctic climate,an important component of the global climate system,has moved into a new state over the past 20 years.Scientific questions and possible consequences related to these changes are now front in the midst of many important issues that the world needs to deal with in the future.These changes,including prominent atmospheric and oceanic warming and sea ice melting have been largely attributed to a combined effect of anthropogenic forcing and internal variability of the climate system.This review highlights some findings from a number of studies conducted by my research group in the past few years.The studies collectively suggest that the high latitude atmospheric circulation that is sensitive to tropical SST forcing related to the interdecadal Pacific oscillation (IPO) plays a vital role in driving the interannual and interdecadal variability of Arctic sea ice by affecting the atmospheric temperature,moisture,clouds and radiative fluxes over sea ice.In particular,the teleconnection excited by a SST cooling over the tropical Pacific is suggested to cause an enhanced melting from 2007 to 2012.In addition,it suggests that a similar internal process may also play a role to cause strong sea ice melting in summer 2020.Furthermore,the model evaluation focusing on CMIP5 models finds that most climate models have a limitation to replicate this IPO-related teleconnection,raising awareness on an urgent need to investigate the cause of this bias in models.Thus,this review is meant to offer priorities for future Arctic research so that more efforts are targeted on critical scientific questions raised in this study.
2015,38(1): 27-36, DOI: 10.13878/j.cnki.dqkxxb.20130626001
Abstract:
The high-resolution numerical simulations of Hurricane Bonnie(1998) are used to analyze its intensity and structure changes in relation to its associated inertial stability under the influence of intense vertical wind shear during three different stages of its life cycle.Results show that Bonnie has high asymmetry and experiences an eyewall displacement cycle during its rapid intensifying stage.During its rapid structure change stage,the development of high inertial stability is consistent with the change in hurricane inner core size.The inertially stable region,which is usually present inside the eyewall,provides resistance to radial motions,and plays an important role in reducing the influence of vertical wind shear.The inertially stable region reduces the Rossby radius of deformation,and concentrates the latent heating,which is beneficial to the enhancing of the hurricane.This is an important factor in the development of inner core region of the hurricane.
The high-resolution numerical simulations of Hurricane Bonnie(1998) are used to analyze its intensity and structure changes in relation to its associated inertial stability under the influence of intense vertical wind shear during three different stages of its life cycle.Results show that Bonnie has high asymmetry and experiences an eyewall displacement cycle during its rapid intensifying stage.During its rapid structure change stage,the development of high inertial stability is consistent with the change in hurricane inner core size.The inertially stable region,which is usually present inside the eyewall,provides resistance to radial motions,and plays an important role in reducing the influence of vertical wind shear.The inertially stable region reduces the Rossby radius of deformation,and concentrates the latent heating,which is beneficial to the enhancing of the hurricane.This is an important factor in the development of inner core region of the hurricane.
2016,39(6): 722-734, DOI: 10.13878/j.cnki.dqkxxb.20161028003
Abstract:
The present paper has mainly analysed the process and mechanisms of genesis and development of the 2014-2016 mega El Niño event.It is shown that the entire lifecycle of the event is about 2 years(from April 2014 to May 2016),with four stages identified for its evolutive process:(1)Early and continuous westerly wind bursts(December 2013 to April 2014).The continuous three westerly wind burstsnot only changed the state of the easterly trade wind prevailing tropical central and easterly in the Pacific for long period of time,but also changed the cold water state in this region for the most recent 12 years,thus leading to SST rise and warming.Until early spring 2014,the SSTA exceeded 0.5℃,marking the possible occurrence of a new El Niño event.(2)Alternative weakening period(June 2014 to August 2015).Six westerly wind bursts continued to occur,thus maintaining and enhancing the warming of the equatorial central and eastern Pacific,while at the same time overcoming two periods of SST warming decrease or barrier,so that the initial development of El Niño was not aborted,and even changed into the stage of strong El Niño.Correspondingly,in the sub layer of the equatorial central and eastern Pacific,six warm Kelvin waves were observed to propagate eastward.The heat contents of these oceanic waves not only maintained the continuous warming in the equatorial central and eastern Pacific,but also caused El Niño to change from CP to EP type.(3)Peak period of development (September 2015 to February 2016).Two stronger westerly wind bursts were observed,which corresponded to very vigorous convective activity on the equatorial central and eastern Pacific.Rapid warming occurred in the Niño3.4 region,with 3℃observed in November 2015,classified as the mega-El Niño event.(4)Accelerating weakening stage(March to May 2016).The intensity of the El Niño rapidly weakened from 2 to 0.5℃ in the Niño3.4 region,then accelerated the transition to the cold water phase.In July to August 2016,the SSTA in the Niño3.4 region already approached -0.5℃.This rapid phase shift is a manifestation of the theory of delayed oscillation.From the above results,it is concluded that the development and shift of warm and cold phases is observationally consistent with the mechanism derived from the paradigm of the current theory of recharge oscillation and/or delayed oscillation theory.This clearly demonstrates that the results of the El Niño theory effectively underpin the development of related operational prediction.
The present paper has mainly analysed the process and mechanisms of genesis and development of the 2014-2016 mega El Niño event.It is shown that the entire lifecycle of the event is about 2 years(from April 2014 to May 2016),with four stages identified for its evolutive process:(1)Early and continuous westerly wind bursts(December 2013 to April 2014).The continuous three westerly wind burstsnot only changed the state of the easterly trade wind prevailing tropical central and easterly in the Pacific for long period of time,but also changed the cold water state in this region for the most recent 12 years,thus leading to SST rise and warming.Until early spring 2014,the SSTA exceeded 0.5℃,marking the possible occurrence of a new El Niño event.(2)Alternative weakening period(June 2014 to August 2015).Six westerly wind bursts continued to occur,thus maintaining and enhancing the warming of the equatorial central and eastern Pacific,while at the same time overcoming two periods of SST warming decrease or barrier,so that the initial development of El Niño was not aborted,and even changed into the stage of strong El Niño.Correspondingly,in the sub layer of the equatorial central and eastern Pacific,six warm Kelvin waves were observed to propagate eastward.The heat contents of these oceanic waves not only maintained the continuous warming in the equatorial central and eastern Pacific,but also caused El Niño to change from CP to EP type.(3)Peak period of development (September 2015 to February 2016).Two stronger westerly wind bursts were observed,which corresponded to very vigorous convective activity on the equatorial central and eastern Pacific.Rapid warming occurred in the Niño3.4 region,with 3℃observed in November 2015,classified as the mega-El Niño event.(4)Accelerating weakening stage(March to May 2016).The intensity of the El Niño rapidly weakened from 2 to 0.5℃ in the Niño3.4 region,then accelerated the transition to the cold water phase.In July to August 2016,the SSTA in the Niño3.4 region already approached -0.5℃.This rapid phase shift is a manifestation of the theory of delayed oscillation.From the above results,it is concluded that the development and shift of warm and cold phases is observationally consistent with the mechanism derived from the paradigm of the current theory of recharge oscillation and/or delayed oscillation theory.This clearly demonstrates that the results of the El Niño theory effectively underpin the development of related operational prediction.
2020,43(4): 663-672, DOI: 10.13878/j.cnki.dqkxxb.20190330001
Abstract:
In this paper,using conventional observation data,NCEP 1°×1° reanalysis data,FY-2G satellite hourly TBB data,radar and AWS data,the potential and triggering characteristics of short-term heavy precipitation in southeastern Shanxi Province on the night of July 13,2018 were analyzed.The results reveal that the strong southwest airflow around subtropical high provide abundant water vapor conditions for the short-term heavy precipitation process.In addition,the stratification structure of "dry and cold under warm and wet" and the temperature differential advection of "high-level cold advection and low-level warm advection" provides the energy conditions required for the development of strong convection.The formation and maintenance of ascending motion are conducive to the release and enhancement of unstable energy.The meso-β scale convergence line on the ground develops into a meso-β scale vortex,thereby stimulating the consolidation and strengthening of the mid-β scale convective cloud mass,which in turn stimulates the merging and strengthening of meso-βscale convective clouds.The meso-γscale convective monomer embedded in the meso-βscale band echo of ≥ 35 dBZ,under the guidance of the 500 hPa southwest airflow,forms a slowly moving,highly organized multi-cell linear echo,which was the direct cause of the formation of short-duration heavy rainfall.The short-term heavy precipitation is located between 5 880 gpm and 5 840 gpm on the 500 hPa map,between the 850 hPa and 700 hPa shear line,and overlaps with 850 hPa and 700 hPa wet tongue,ground trunk line and mesoscale convergence line (near the 10 km range),as well as the cold air inflow side of the convective cloud mass TBB gradient high value area and TBB ≤ -60℃.
In this paper,using conventional observation data,NCEP 1°×1° reanalysis data,FY-2G satellite hourly TBB data,radar and AWS data,the potential and triggering characteristics of short-term heavy precipitation in southeastern Shanxi Province on the night of July 13,2018 were analyzed.The results reveal that the strong southwest airflow around subtropical high provide abundant water vapor conditions for the short-term heavy precipitation process.In addition,the stratification structure of "dry and cold under warm and wet" and the temperature differential advection of "high-level cold advection and low-level warm advection" provides the energy conditions required for the development of strong convection.The formation and maintenance of ascending motion are conducive to the release and enhancement of unstable energy.The meso-β scale convergence line on the ground develops into a meso-β scale vortex,thereby stimulating the consolidation and strengthening of the mid-β scale convective cloud mass,which in turn stimulates the merging and strengthening of meso-βscale convective clouds.The meso-γscale convective monomer embedded in the meso-βscale band echo of ≥ 35 dBZ,under the guidance of the 500 hPa southwest airflow,forms a slowly moving,highly organized multi-cell linear echo,which was the direct cause of the formation of short-duration heavy rainfall.The short-term heavy precipitation is located between 5 880 gpm and 5 840 gpm on the 500 hPa map,between the 850 hPa and 700 hPa shear line,and overlaps with 850 hPa and 700 hPa wet tongue,ground trunk line and mesoscale convergence line (near the 10 km range),as well as the cold air inflow side of the convective cloud mass TBB gradient high value area and TBB ≤ -60℃.
JIANG Tong, ZHAI Jianqing, LUO Yong, SU Buda, CHAO Qingchen, WANG Yanjun, WANG Guojie, HUANG Jinlong, XU Runhong, GAO Miaoni, MIAO Lijuan
2022,45(4): 502-511, DOI: 10.13878/j.cnki.dqkxxb.20220529013
Abstract:
The second working group of the IPCC Sixth Assessment Report (IPCC AR6 WGⅡ) focuses on the impact,risk,adaptation and vulnerability of climate change.The report quantitatively assesses the impact of climate change on natural and human systems with the latest data,detailed evidence and diverse methods.Compared to AR5,the following progress has been made:Firstly,The content clarifies that the impact of climate change is attributable to three categories:anthropogenic climate forcing,non-climate factor action and weather sensitivity identification,127 key risks from climate change will become widespread or irreversible,and limiting global warming to 1.5 ℃ can greatly reduce climate change loss and damage to natural and human systems,pointing to the importance of adapting to transition.Secondly,AR6 WGⅡ adopts the latest combination of SSPs and RCPS in terms of evaluation method,which is more comprehensive.Thirdly,AR6 WGⅡ has focus on risks and solutions,and on the basis of AR5 WGⅡ,it is clarified that under different future warming scenarios,the risk level of the key risks facing the five “reasons for concern (RFCs)” will be relied on lower to very high levels of global warming.Finally,AR6 WGⅡ clarifies the urgency of climate action,combining adaptation and mitigation to support sustainable development is essential for climate resilience development pathways,pointing to the importance of immediate action to address climate risks.
The second working group of the IPCC Sixth Assessment Report (IPCC AR6 WGⅡ) focuses on the impact,risk,adaptation and vulnerability of climate change.The report quantitatively assesses the impact of climate change on natural and human systems with the latest data,detailed evidence and diverse methods.Compared to AR5,the following progress has been made:Firstly,The content clarifies that the impact of climate change is attributable to three categories:anthropogenic climate forcing,non-climate factor action and weather sensitivity identification,127 key risks from climate change will become widespread or irreversible,and limiting global warming to 1.5 ℃ can greatly reduce climate change loss and damage to natural and human systems,pointing to the importance of adapting to transition.Secondly,AR6 WGⅡ adopts the latest combination of SSPs and RCPS in terms of evaluation method,which is more comprehensive.Thirdly,AR6 WGⅡ has focus on risks and solutions,and on the basis of AR5 WGⅡ,it is clarified that under different future warming scenarios,the risk level of the key risks facing the five “reasons for concern (RFCs)” will be relied on lower to very high levels of global warming.Finally,AR6 WGⅡ clarifies the urgency of climate action,combining adaptation and mitigation to support sustainable development is essential for climate resilience development pathways,pointing to the importance of immediate action to address climate risks.
Abstract:
利用IAEA\WMO\GNIP的降水稳定同位素资料，分析了中国降水稳定同位素的时空分布特征及其影响因素。结果表明，整体来看我国降水稳定同位素有明显的大陆效应和高度效应。各地大气降水线存在地域差异，内陆地区同一站点冬、夏半年也有明显差异，显示出水汽团特性的不同。不同地区降水稳定同位素（δ和过量氘）的季节变化特征明显不同，表明主要水汽来源存在季节性差异。通过对比长序列降水稳定同位素的年际变化与季风和ENSO指数的关系，发现ENSO与降水稳定同位素有显著的正相关，但不一定通过影响降水量来引起降水稳定同位素（stable isotope in precipitation, SIP）的变化。重点分析了我国降水量效应、温度效应的特点，指出沿海和西南等季风区主要受降水量的影响，北方非季风区温度效应起主要作用，交叉地带则两种效应都有影响。
利用IAEA\WMO\GNIP的降水稳定同位素资料，分析了中国降水稳定同位素的时空分布特征及其影响因素。结果表明，整体来看我国降水稳定同位素有明显的大陆效应和高度效应。各地大气降水线存在地域差异，内陆地区同一站点冬、夏半年也有明显差异，显示出水汽团特性的不同。不同地区降水稳定同位素（δ和过量氘）的季节变化特征明显不同，表明主要水汽来源存在季节性差异。通过对比长序列降水稳定同位素的年际变化与季风和ENSO指数的关系，发现ENSO与降水稳定同位素有显著的正相关，但不一定通过影响降水量来引起降水稳定同位素（stable isotope in precipitation, SIP）的变化。重点分析了我国降水量效应、温度效应的特点，指出沿海和西南等季风区主要受降水量的影响，北方非季风区温度效应起主要作用，交叉地带则两种效应都有影响。
2014,37(5): 653-664, DOI: 10.13878/j.cnki.dqkxxb.20111230001
Abstract:
Studies have shown that large-scale monsoon gyre activity is closely associated with tropical cyclogenesis over the western North Pacific.In this study,two cases of monsoon gyre activities in 2002 and 2009 were first examined.It was found that a monsoon gyre can be linked to the formation of one or more tropical cyclones,which usually occur near or to the east of the gyre center.Further analysis of the monsoon gyre activity during the period of 2000—2009 indicates that tropical cyclogenesis mainly occurs near or to the east of the gyre center,although the definition of a monsoon gyre depends on its duration and the circulation intensity.It is suggested that the tropical cyclogensis may be associated with the Rossby wave energy dispersion of monsoon gyres.
Studies have shown that large-scale monsoon gyre activity is closely associated with tropical cyclogenesis over the western North Pacific.In this study,two cases of monsoon gyre activities in 2002 and 2009 were first examined.It was found that a monsoon gyre can be linked to the formation of one or more tropical cyclones,which usually occur near or to the east of the gyre center.Further analysis of the monsoon gyre activity during the period of 2000—2009 indicates that tropical cyclogenesis mainly occurs near or to the east of the gyre center,although the definition of a monsoon gyre depends on its duration and the circulation intensity.It is suggested that the tropical cyclogensis may be associated with the Rossby wave energy dispersion of monsoon gyres.
Abstract:
A new atmospheric correction algorithm based on dark object method and the look up table developed from MODTRAN model was introduced for Landsat images in the paper.The infomation of the satellite remote sensing images was used to support the atmospheric correction.The algorithm was applied to the Landsat ETM+imagery and comparisons show that the influence on Landsat imagery caused by molecules,water vapor,ozone,and aerosol particles in the atmosphere was effectively reduced after the correction.The surface reflectivity was more precisely,which is beneficial for remote sensing information extraction and thematic interpretation.
A new atmospheric correction algorithm based on dark object method and the look up table developed from MODTRAN model was introduced for Landsat images in the paper.The infomation of the satellite remote sensing images was used to support the atmospheric correction.The algorithm was applied to the Landsat ETM+imagery and comparisons show that the influence on Landsat imagery caused by molecules,water vapor,ozone,and aerosol particles in the atmosphere was effectively reduced after the correction.The surface reflectivity was more precisely,which is beneficial for remote sensing information extraction and thematic interpretation.
2015,38(2): 184-194, DOI: 10.13878/j.cnki.dqkxxb.20140508002
Abstract:
The observed SST data and CMIP5 data are used to analyze climate state and interdecadal variation of sea surface temperature(SST) over Northwest Pacific(20—60°N,120°E—120°W).Results indicate that the selected 22 models can simulate the climate state perfectly.More importantly,the selected models can simulate the annual and interdecadal variations of SST over Northwest Pacific.Total standard deviation of SST simulted by the models is the largest in Kuroshio extension region.The majority of models have an ability to simulate the first EOF mode of SST.The SST over Northwest Pacific has a significant interdecadal oscillation phenomenon.SSTs simulated by the 13/22 models have obvious interdecadal oscillations.Meanwhile,the simulated deviation of SST climate state has a great effect on the periodic oscillation of SST,especially in Kuroshio extension region.
The observed SST data and CMIP5 data are used to analyze climate state and interdecadal variation of sea surface temperature(SST) over Northwest Pacific(20—60°N,120°E—120°W).Results indicate that the selected 22 models can simulate the climate state perfectly.More importantly,the selected models can simulate the annual and interdecadal variations of SST over Northwest Pacific.Total standard deviation of SST simulted by the models is the largest in Kuroshio extension region.The majority of models have an ability to simulate the first EOF mode of SST.The SST over Northwest Pacific has a significant interdecadal oscillation phenomenon.SSTs simulated by the 13/22 models have obvious interdecadal oscillations.Meanwhile,the simulated deviation of SST climate state has a great effect on the periodic oscillation of SST,especially in Kuroshio extension region.
Abstract:
超级单体风暴常伴随着冰雹、雷雨大风等强对流天气，最本质的特征是有一持久深厚的几千米尺度的涡旋———中气旋。利用2003-2009年福建龙岩新一代天气雷达观测到的32次超级单体风暴，分析了超级单体风暴中气旋的时空分布、结构特征以及旋转速度大小、中气旋顶和底的高度、伸长厚度以及切变值等特征量。结果表明：90%以上的超级单体中尺度气旋是与冰雹、雷雨大风、短时强降水等强对流天气相联系的。统计8次有详细灾情的雷雨大风或冰雹天气过程发现，中气旋强度不断加强，中气旋厚度加大，最强切变中心突降时将产生大风或冰雹等强对流天气
超级单体风暴常伴随着冰雹、雷雨大风等强对流天气，最本质的特征是有一持久深厚的几千米尺度的涡旋———中气旋。利用2003-2009年福建龙岩新一代天气雷达观测到的32次超级单体风暴，分析了超级单体风暴中气旋的时空分布、结构特征以及旋转速度大小、中气旋顶和底的高度、伸长厚度以及切变值等特征量。结果表明：90%以上的超级单体中尺度气旋是与冰雹、雷雨大风、短时强降水等强对流天气相联系的。统计8次有详细灾情的雷雨大风或冰雹天气过程发现，中气旋强度不断加强，中气旋厚度加大，最强切变中心突降时将产生大风或冰雹等强对流天气
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Ranking List
- 1APPLICATION OF WAVELET TRANSFORMATION TO ANALYSIS OF MULTIPLE TIME SCALE CLIMATE CHANGE
- 2Major Achievements in Meteorology in Ancient China
- 3The impact of the Himalayan-Tibet Plateau on the monsoon region
- 4Discussion on calculation method of flowering date meeting of hybrid rice parents
- 5CMIP5 projected changes in mean and extreme climate in the Belt and Road region
- 6Quality assessment of hourly merged precipitation product over China
- 7Analysis and comparison of four kinds of circulation indices of Aleutian Low
- 8The differences in characteristics and applicability among three types of Rossby wave activity flux in atmospheric dynamics
- 9Multiscale structure features of a typical Meiyu frontal rainstorm process
- 10Evaluation of the four PBL schemes in WRF Model over complex topographic areas
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