ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 51,Issue 11,2025 Table of Contents
SHEN Xueshun
,
SU Yong
,
LI Xingliang
,
HAN Wei
,
YANG Jun
,
MA Zhanshan
,
LIU Yongzhu
,
ZHANG Xu
,
TANG Jie
,
CHEN Xian
,
WENG Fuzhong
2025, 51(11):1293-1320.
DOI: 10.7519/j.issn.1000-0526.2025.093001
Abstract:
Numerical weather prediction (NWP) is the core technology for weather forecasting services and disaster prevention and mitigation, and it is also a key indicator for measuring the level of a nation’s meteorological modernization. In the course of independent innovation of China’s NWP, the establishment of a comprehensive operational system centered on the Global/Regional Assimilation and Prediction System (GRAPES) is a milestone event, marking a major leap from technology introduction to independent innovation. This article provides a comprehensive review of the original achievements in the core technologies during the development of the GRAPES since the 21st century. The key innovations include a prediction-correction semi-implicit semi-Lagrangian time integration scheme, the multi-moment constrained finite volume (MCV) method, a high-precision positive-definite shape-preserving scalar advection scheme, a double-moment microphysical cloud scheme, a scale-adaptive 3D turbulence parameterization scheme, the self-developed ARMS (advanced radiative transfer modeling system) model, the tangent linear and ADjoint models for non-hydrostatic global models, the constrained satellite data bias correction techniques, and the assimilation algorithms for FY-4 infrared hyperspectral data. These breakthroughs are the results from the close integration of fundamental researches and operational practices, and have comprehensively enhanced the forecast performance of China’s independently developed numerical weather prediction model.
Numerical weather prediction (NWP) is the core technology for weather forecasting services and disaster prevention and mitigation, and it is also a key indicator for measuring the level of a nation’s meteorological modernization. In the course of independent innovation of China’s NWP, the establishment of a comprehensive operational system centered on the Global/Regional Assimilation and Prediction System (GRAPES) is a milestone event, marking a major leap from technology introduction to independent innovation. This article provides a comprehensive review of the original achievements in the core technologies during the development of the GRAPES since the 21st century. The key innovations include a prediction-correction semi-implicit semi-Lagrangian time integration scheme, the multi-moment constrained finite volume (MCV) method, a high-precision positive-definite shape-preserving scalar advection scheme, a double-moment microphysical cloud scheme, a scale-adaptive 3D turbulence parameterization scheme, the self-developed ARMS (advanced radiative transfer modeling system) model, the tangent linear and ADjoint models for non-hydrostatic global models, the constrained satellite data bias correction techniques, and the assimilation algorithms for FY-4 infrared hyperspectral data. These breakthroughs are the results from the close integration of fundamental researches and operational practices, and have comprehensively enhanced the forecast performance of China’s independently developed numerical weather prediction model.
JIN Ronghua
,
CAO Yong
,
ZHAO Ruixia
,
DAI Kan
,
GUO Yunqian
,
XU Jun
,
ZENG Xiaoqing
,
WANG Yu
,
TANG Jian
,
WEI Qing
2025, 51(11):1321-1334.
DOI: 10.7519/j.issn.1000-0526.2025.063003
Abstract:
Intelligent digital weather forecasting is a key means to support meteorological service. Countries around the world are actively developing new-generation seamless forecasting technology systems and promoting the application of artificial intelligence (AI) in the meteorological field. China has established a relatively complete intelligent digital weather forecasting operation system, achieving seamless forecasting for the near-surface and three-dimensional meteorological elements with a resolution of 1 km across China and 5 km around the globe, covering a time range of 0-30 days. By developing adaptable technologies through the strategy of implementing different strategies at different time scales and integrating multi-source forecasts, China has made remarkable achievements. A unified, standardized, and modularly expandable intelligent digital general technology framework has been constructed, with more than 30 types of algorithms constructed and the “low-code” deployment able to be supported. This is very important in major event support and extreme weather forecasting. The in-depth application of AI technology has significantly improved forecast performance in short-time, short-term and medium-term precipitation forecasting, severe convective weather forecasting, and disastrous gale forecasting by leveraging deep learning models. Progress has also been made in refined downscaling technology. At the same time, the data fusion and integration technology has continued to be developed, and the intelligent integration of objective and subjective forecasts has developed to enhance the ability to predict disastrous weather. China’s intelligent digital forecasting system has improved the forecast accuracy by 10%-31% compared with the EC_IFS and CMA models, and has been widely applied in many fields. However, it still faces challenges. In the future, breakthroughs will be made in the technologies related to the forecasting of disastrous and transitional weather, the low-altitude hundreds-meter-resolution refined downscaling forecasting, the industry-specific weather and risk forecasting, and the integrated platform with the meteorological “intelligent brain” as the core.
Intelligent digital weather forecasting is a key means to support meteorological service. Countries around the world are actively developing new-generation seamless forecasting technology systems and promoting the application of artificial intelligence (AI) in the meteorological field. China has established a relatively complete intelligent digital weather forecasting operation system, achieving seamless forecasting for the near-surface and three-dimensional meteorological elements with a resolution of 1 km across China and 5 km around the globe, covering a time range of 0-30 days. By developing adaptable technologies through the strategy of implementing different strategies at different time scales and integrating multi-source forecasts, China has made remarkable achievements. A unified, standardized, and modularly expandable intelligent digital general technology framework has been constructed, with more than 30 types of algorithms constructed and the “low-code” deployment able to be supported. This is very important in major event support and extreme weather forecasting. The in-depth application of AI technology has significantly improved forecast performance in short-time, short-term and medium-term precipitation forecasting, severe convective weather forecasting, and disastrous gale forecasting by leveraging deep learning models. Progress has also been made in refined downscaling technology. At the same time, the data fusion and integration technology has continued to be developed, and the intelligent integration of objective and subjective forecasts has developed to enhance the ability to predict disastrous weather. China’s intelligent digital forecasting system has improved the forecast accuracy by 10%-31% compared with the EC_IFS and CMA models, and has been widely applied in many fields. However, it still faces challenges. In the future, breakthroughs will be made in the technologies related to the forecasting of disastrous and transitional weather, the low-altitude hundreds-meter-resolution refined downscaling forecasting, the industry-specific weather and risk forecasting, and the integrated platform with the meteorological “intelligent brain” as the core.
2025, 51(11):1335-1352.
DOI: 10.7519/j.issn.1000-0526.2025.091801
Abstract:
This paper reviews the development history, key technologies, and application value of ensemble prediction, which originated from the understanding of atmospheric nonlinearity and chaotic characteristics. Since Lorenz proposed the “butterfly effect” in the 1960s, it has quantified forecast uncertainty by introducing multiple perturbation experiments into numerical weather prediction. Initial perturbation techniques have evolved from breeding vectors and singular vectors to ensemble Kalman filtering, while model perturbation techniques include stochastic kinetic energy backscatter, stochastic physics parameterization perturbations, and multi-physics ensembles. In the early 1990s, major international meteorological centers successively established global and regional ensemble prediction systems. Through statistical post-processing techniques, ensemble prediction systems have generated various probabilistic forecast products, significantly improving the accuracy and timeliness of extreme weather warnings. In recent years, artificial intelligence (AI)-based ensemble models represented by Google SEEDS etc. have achieved breakthroughs, delivering superior forecast performance at lower computational costs. Future ensemble prediction will develop toward a new paradigm combining physical models with AI to further enhance the forecasting capabilities.
This paper reviews the development history, key technologies, and application value of ensemble prediction, which originated from the understanding of atmospheric nonlinearity and chaotic characteristics. Since Lorenz proposed the “butterfly effect” in the 1960s, it has quantified forecast uncertainty by introducing multiple perturbation experiments into numerical weather prediction. Initial perturbation techniques have evolved from breeding vectors and singular vectors to ensemble Kalman filtering, while model perturbation techniques include stochastic kinetic energy backscatter, stochastic physics parameterization perturbations, and multi-physics ensembles. In the early 1990s, major international meteorological centers successively established global and regional ensemble prediction systems. Through statistical post-processing techniques, ensemble prediction systems have generated various probabilistic forecast products, significantly improving the accuracy and timeliness of extreme weather warnings. In recent years, artificial intelligence (AI)-based ensemble models represented by Google SEEDS etc. have achieved breakthroughs, delivering superior forecast performance at lower computational costs. Future ensemble prediction will develop toward a new paradigm combining physical models with AI to further enhance the forecasting capabilities.
2025, 51(11):1353-1366.
DOI: 10.7519/j.issn.1000-0526.2025.092401
Abstract:
Under the background of global warming, West Asia has emerged as a prominent hotspot of land surface warming. Observational records reveal significant long-term warming trends in land surface temperature (LST) over this region, accompanied by notable interdecadal variation and substantial interannual variability. Understanding the basic features, drivers and climatic effects of land surface thermal anomalies in West Asia is of great practical significance for addressing climate change chanllenges. This article reviews the recent studies on land surface thermal anomalies in West Asia. Beginning from observational evidence, the article examines the associated atmospheric circulation variations and external forcings, and then further explores the impacts of these thermal anomalies on regional climate, Indian monsoon and climate, large-scale circulation systems and atmospheric teleconnection patterns, and on climate anomalies in China as well. Nevertheless, the complex topography and high spatiotemporal heterogeneity of land surface thermal conditions in West Asia necessitate further investigation. Critical research gaps include identifying the dominant causes of LST anomalies, and the mechanisms of local land-atmosphere interactions. Advancing the understanding of global and regional climate change through the lens of land surface thermal anomalies can strengthen the scientific basis for reliable climate prediction.
Under the background of global warming, West Asia has emerged as a prominent hotspot of land surface warming. Observational records reveal significant long-term warming trends in land surface temperature (LST) over this region, accompanied by notable interdecadal variation and substantial interannual variability. Understanding the basic features, drivers and climatic effects of land surface thermal anomalies in West Asia is of great practical significance for addressing climate change chanllenges. This article reviews the recent studies on land surface thermal anomalies in West Asia. Beginning from observational evidence, the article examines the associated atmospheric circulation variations and external forcings, and then further explores the impacts of these thermal anomalies on regional climate, Indian monsoon and climate, large-scale circulation systems and atmospheric teleconnection patterns, and on climate anomalies in China as well. Nevertheless, the complex topography and high spatiotemporal heterogeneity of land surface thermal conditions in West Asia necessitate further investigation. Critical research gaps include identifying the dominant causes of LST anomalies, and the mechanisms of local land-atmosphere interactions. Advancing the understanding of global and regional climate change through the lens of land surface thermal anomalies can strengthen the scientific basis for reliable climate prediction.
2025, 51(11):1367-1382.
DOI: 10.7519/j.issn.1000-0526.2025.081001
Abstract:
China has established the world’s largest ground-based multi-band weather radar network and successfully launched its first active radar precipitation measurement satellite, the third of its kind globally, achieving world-class overall technical capabilities. Weather radars and precipitation satellites are crucial components of the integrated space-ground precipitation observation network, and are key technical support for accurately capturing precipitation dynamics and comprehensively analyzing precipitation characteristics. While weather radars offer high spatiotemporal resolution, their coverage is geographically constrained, limiting continuous global observation. Spaceborne precipitation radars can provide three-dimensional precipitation structure information over global mid-to-low latitudes, particularly over the regions difficult to be covered by ground-based equipment, such as in oceans and plateaus. The fusion application of space-ground integrated systems achieves an organic combination of continuous large-scale precipitation monitoring and refined detection of local precipitation features, which can provide more precise and comprehensive data support and decision-making basis for meteorological forecasting, disaster warning, and water resource management. This paper introduces the technical characteristics, operational quality, and data products of the multi-band weather radar network and the FY-3G precipitation satellite in China in detail. It also presents preliminary thoughts and prospects on collaborative observation and the fusion and application of the fundamental data from China’s weather radar network and FY-3G precipitation satellite in four aspects: (1) the cross validation of satellite-ground radar reflectivity factor, (2) data fusion of satellite-ground radar reflectivity factor, (3) the simulation of ground-based radar signals using geostationary satellite data, and (4) the fusion of active/passive microwave and geostationary satellite infrared data.
China has established the world’s largest ground-based multi-band weather radar network and successfully launched its first active radar precipitation measurement satellite, the third of its kind globally, achieving world-class overall technical capabilities. Weather radars and precipitation satellites are crucial components of the integrated space-ground precipitation observation network, and are key technical support for accurately capturing precipitation dynamics and comprehensively analyzing precipitation characteristics. While weather radars offer high spatiotemporal resolution, their coverage is geographically constrained, limiting continuous global observation. Spaceborne precipitation radars can provide three-dimensional precipitation structure information over global mid-to-low latitudes, particularly over the regions difficult to be covered by ground-based equipment, such as in oceans and plateaus. The fusion application of space-ground integrated systems achieves an organic combination of continuous large-scale precipitation monitoring and refined detection of local precipitation features, which can provide more precise and comprehensive data support and decision-making basis for meteorological forecasting, disaster warning, and water resource management. This paper introduces the technical characteristics, operational quality, and data products of the multi-band weather radar network and the FY-3G precipitation satellite in China in detail. It also presents preliminary thoughts and prospects on collaborative observation and the fusion and application of the fundamental data from China’s weather radar network and FY-3G precipitation satellite in four aspects: (1) the cross validation of satellite-ground radar reflectivity factor, (2) data fusion of satellite-ground radar reflectivity factor, (3) the simulation of ground-based radar signals using geostationary satellite data, and (4) the fusion of active/passive microwave and geostationary satellite infrared data.
2025, 51(11):1383-1394.
DOI: 10.7519/j.issn.1000-0526.2025.070801
Abstract:
Extreme weather events have an important impact on economic and social development. The extreme weather events often result from superimposed influence of multi-factor and multi-scale abnormal signals of climate environment. Thus, extreme weather events embody the unity of weather and climate. This paper analyzes the difficulties and challenges of extreme weather event prediction from the perspective of the internal relationship between weather and climate, and also discusses the opportunities of extreme weather forecasting which are gained from the weather and climate integration models, multi-loop coupling assimilation and ensemble technology as well as the assimilation probability prediction technology based on deep learning and AI. Finally, the significance of the integration of weather and climate disciplines and the establishment of weather and climate integrated forecasting operation system is pointed out.
Extreme weather events have an important impact on economic and social development. The extreme weather events often result from superimposed influence of multi-factor and multi-scale abnormal signals of climate environment. Thus, extreme weather events embody the unity of weather and climate. This paper analyzes the difficulties and challenges of extreme weather event prediction from the perspective of the internal relationship between weather and climate, and also discusses the opportunities of extreme weather forecasting which are gained from the weather and climate integration models, multi-loop coupling assimilation and ensemble technology as well as the assimilation probability prediction technology based on deep learning and AI. Finally, the significance of the integration of weather and climate disciplines and the establishment of weather and climate integrated forecasting operation system is pointed out.
2025, 51(11):1395-1404.
DOI: 10.7519/j.issn.1000-0526.2025.012002
Abstract:
Tropical cyclones， known as typhoons in the Northwest Pacific Region, are one of the major natural disasters’ weather system. China is located on the west coast of the North Pacific， deeply affected by typhoons. China also is one of the countries with severe tropical cyclone disasters in the world. China attaches great importance to cooperation with the international community while focusing on improving its own typhoon monitoring, prediction, warning, and disaster prevention capabilities. Through long-term cooperation with the international typhoon community, China has benefited greatly and made special contributions to the development of international typhoon disaster prevention and mitigation, and China is a staunch supporter, active participant, and important contributor to international cooperation in typhoon disaster prevention and mitigation. In the past, China has achieved fruitful results in international cooperation on typhoon-related events, and has also displayed a series of animated scrolls that resonate with the quick development of typhoon disaster prevention and mitigation in China and internationally.
Tropical cyclones， known as typhoons in the Northwest Pacific Region, are one of the major natural disasters’ weather system. China is located on the west coast of the North Pacific， deeply affected by typhoons. China also is one of the countries with severe tropical cyclone disasters in the world. China attaches great importance to cooperation with the international community while focusing on improving its own typhoon monitoring, prediction, warning, and disaster prevention capabilities. Through long-term cooperation with the international typhoon community, China has benefited greatly and made special contributions to the development of international typhoon disaster prevention and mitigation, and China is a staunch supporter, active participant, and important contributor to international cooperation in typhoon disaster prevention and mitigation. In the past, China has achieved fruitful results in international cooperation on typhoon-related events, and has also displayed a series of animated scrolls that resonate with the quick development of typhoon disaster prevention and mitigation in China and internationally.
2025, 51(11):1405-1416.
DOI: 10.7519/j.issn.1000-0526.2025.072802
Abstract:
The torrential rain events in North China include regional or valley torrential rain and also contain local torrential rain only covering the area of several hundreds to thousands square kilometers. Essentially, these torrential rain events all result from the interaction of multi-scale synoptic systems with diversity of dynamical structures. However, it is not easy to clear up the interaction processes of every torrential rain event. From the views of the multi-scale synoptic systems and their interactions, the paper sums up the main mechanisms of torrential rain in North China by reconstructing interrelated research achievements. The findings are as follows. Synoptic scale systems act as controlling functions in large-area torrential rain events. Their dynamic properties constructed by various essential factors play leading roles on large-scale moisture convergence intensity, the ascending motion strength and the duration. As far as the rainfall intensity and the location of extreme precipitation during a torrential rain event are concerned, the mesoscale systems are decisive factors. They are possible to be derived from the interaction of synoptic scale systems, induced by the complex underlying surface such as terrains, urbans and interface between land and sea, and engendered by feedback effect of local severe precipitation. For the extreme precipitation intensity during torrential rain events over North China, the generation, development, structure evolution and organization of deep convective storms are the key factors. The exceptionally complex nonlinear interaction processes exist in torrential rain events over North China by various interactions among the different-scale synoptic systems and complex underlying surface forcing. It is actually difficult to separate any unitary system or isolated dynamical process perfectly from the complex interaction whether by actual observed data or by numerical simulation. In other words, the complex nonlinear interaction process itself is a weather life system built by diverse feedbacks to each other. Some scientific issues on torrential rain events in North China, which should be resolved in future, are discussed in last part of the paper.
The torrential rain events in North China include regional or valley torrential rain and also contain local torrential rain only covering the area of several hundreds to thousands square kilometers. Essentially, these torrential rain events all result from the interaction of multi-scale synoptic systems with diversity of dynamical structures. However, it is not easy to clear up the interaction processes of every torrential rain event. From the views of the multi-scale synoptic systems and their interactions, the paper sums up the main mechanisms of torrential rain in North China by reconstructing interrelated research achievements. The findings are as follows. Synoptic scale systems act as controlling functions in large-area torrential rain events. Their dynamic properties constructed by various essential factors play leading roles on large-scale moisture convergence intensity, the ascending motion strength and the duration. As far as the rainfall intensity and the location of extreme precipitation during a torrential rain event are concerned, the mesoscale systems are decisive factors. They are possible to be derived from the interaction of synoptic scale systems, induced by the complex underlying surface such as terrains, urbans and interface between land and sea, and engendered by feedback effect of local severe precipitation. For the extreme precipitation intensity during torrential rain events over North China, the generation, development, structure evolution and organization of deep convective storms are the key factors. The exceptionally complex nonlinear interaction processes exist in torrential rain events over North China by various interactions among the different-scale synoptic systems and complex underlying surface forcing. It is actually difficult to separate any unitary system or isolated dynamical process perfectly from the complex interaction whether by actual observed data or by numerical simulation. In other words, the complex nonlinear interaction process itself is a weather life system built by diverse feedbacks to each other. Some scientific issues on torrential rain events in North China, which should be resolved in future, are discussed in last part of the paper.
2025, 51(11):1417-1432.
DOI: 10.7519/j.issn.1000-0526.2025.032401
Abstract:
This paper reviews the meteorological service work for major events in China over the past two decades, categorizing it into three types: conventional “fixed-time and fixed-location” forecast, special “fixed-time and fixed-location” forecast, and special forecast for major overseas event. It explores the difficulties and challenges in providing forecasting services for different types of meteorological service needs, and provides prospects for future meteorological service and its forecasting support system for major events. The main conclusions are as follows. In conventional “fixed-time and fixed-location” weather element forecasting scenarios, the forecast challenges primarily focus on convection generation and dissipation or the occurrence of weak precipitation. Over short-term periods, the weather situation forecast provided by numerical models remains an important basis for decision-making, and the service from high-resolution numerical model products is indispensable. However, in short-time forecasting or nowcasting, the observation data and the comprehensive analytical capabilities of forecasters are more important. Due to inadequate observational coverage, limited forecast capabilities of numerical models and the lack of targeted objective forecast methods, in special “fixed-time and fixed-location” forecast scenarios involving clouds, localized winds, and localized visibility, the experience of forecasters, combined with observations and weather situation analysis, is crucial for successful service, especially in overseas on-site service support, regardless of whether it is short-time or nowcasting. With the increased observational system coverage, the improved accurate forecast capabilities of numerical models, and the development of targeted objective forecast methods including AI, the special element forecasting scenarios will gradually transition into conventional element forecast scenarios, and the leading role of the forecasting team will also gradually shift to short-time forecasting or nowcasting. To meet the meteorological service needs for various major events in the future, it is necessary to strengthen the construction of a three-dimensional full-element observation system, enhance the accurate forecasting capabilities of numerical models, promote the development of targeted objective forecasting technologies, and build a forecasting team with continuous self-learning, high generalization abilities, and strong communication skills.
This paper reviews the meteorological service work for major events in China over the past two decades, categorizing it into three types: conventional “fixed-time and fixed-location” forecast, special “fixed-time and fixed-location” forecast, and special forecast for major overseas event. It explores the difficulties and challenges in providing forecasting services for different types of meteorological service needs, and provides prospects for future meteorological service and its forecasting support system for major events. The main conclusions are as follows. In conventional “fixed-time and fixed-location” weather element forecasting scenarios, the forecast challenges primarily focus on convection generation and dissipation or the occurrence of weak precipitation. Over short-term periods, the weather situation forecast provided by numerical models remains an important basis for decision-making, and the service from high-resolution numerical model products is indispensable. However, in short-time forecasting or nowcasting, the observation data and the comprehensive analytical capabilities of forecasters are more important. Due to inadequate observational coverage, limited forecast capabilities of numerical models and the lack of targeted objective forecast methods, in special “fixed-time and fixed-location” forecast scenarios involving clouds, localized winds, and localized visibility, the experience of forecasters, combined with observations and weather situation analysis, is crucial for successful service, especially in overseas on-site service support, regardless of whether it is short-time or nowcasting. With the increased observational system coverage, the improved accurate forecast capabilities of numerical models, and the development of targeted objective forecast methods including AI, the special element forecasting scenarios will gradually transition into conventional element forecast scenarios, and the leading role of the forecasting team will also gradually shift to short-time forecasting or nowcasting. To meet the meteorological service needs for various major events in the future, it is necessary to strengthen the construction of a three-dimensional full-element observation system, enhance the accurate forecasting capabilities of numerical models, promote the development of targeted objective forecasting technologies, and build a forecasting team with continuous self-learning, high generalization abilities, and strong communication skills.
CHEN Mingxuan
,
SONG Linye
,
YANG Lu
,
CHENG Conglan
,
CAO Weihua
,
WU Jiankun
,
LIU Hongjun
,
MA Chao
2025, 51(11):1433-1454.
DOI: 10.7519/j.issn.1000-0526.2025.102501
Abstract:
This study presents the development and implementation of the Rapid-Refresh Integrated Seamless Ensemble (RISE) forecasting system, an innovative multi-source data fusion system designed to provide “100-meter-scale, minute-level-update” weather forecasts. It was originally created to support meteorological services during the Beijing 2022 Olympic and Paralympic Winter Games. Here, this article demonstrates the significant improvements over several years after the games in short-time forecasting and nowcasting capabilities, which have been achieved through the integration of key technologies and the application of machine learning and deep learning methods within the high-resolution forecasting framework of the RISE system. The system’s novel key features are reflected in the refined short-time forecasting and nowcasting for heavy precipitation and thunderstorm gale， as well as in the nowcasting for the initiation of severe convection. The technologies integrated into the RISE system include: a bias-corrected, high-resolution gridded precipitation analysis scheme, the machine learning-based gridded precipitation short-time forecasting and nowcasting algorithms, a novel dynamic-statistical ensemble method for gust prediction using multi-source data fusion, an interpretable deep learning model for nowcasting convectively high winds or thunderstorm gale, a nowcasting method for convective initiation that integrates satellite observations and storm tracking, and an integration scheme for multiple numerical models. Then, comprehensive verification analyses confirm that these methodologies have significantly enhanced the forecast accuracy for precipitation and thunderstorm gale, particularly for the 0-6 h short-time forecasting and nowcasting during flood seasons. Finally, this study concludes with a critical discussion of existing challenges and potential future directions for “100-meter-scale, minute-level-update” weather forecasting.
This study presents the development and implementation of the Rapid-Refresh Integrated Seamless Ensemble (RISE) forecasting system, an innovative multi-source data fusion system designed to provide “100-meter-scale, minute-level-update” weather forecasts. It was originally created to support meteorological services during the Beijing 2022 Olympic and Paralympic Winter Games. Here, this article demonstrates the significant improvements over several years after the games in short-time forecasting and nowcasting capabilities, which have been achieved through the integration of key technologies and the application of machine learning and deep learning methods within the high-resolution forecasting framework of the RISE system. The system’s novel key features are reflected in the refined short-time forecasting and nowcasting for heavy precipitation and thunderstorm gale， as well as in the nowcasting for the initiation of severe convection. The technologies integrated into the RISE system include: a bias-corrected, high-resolution gridded precipitation analysis scheme, the machine learning-based gridded precipitation short-time forecasting and nowcasting algorithms, a novel dynamic-statistical ensemble method for gust prediction using multi-source data fusion, an interpretable deep learning model for nowcasting convectively high winds or thunderstorm gale, a nowcasting method for convective initiation that integrates satellite observations and storm tracking, and an integration scheme for multiple numerical models. Then, comprehensive verification analyses confirm that these methodologies have significantly enhanced the forecast accuracy for precipitation and thunderstorm gale, particularly for the 0-6 h short-time forecasting and nowcasting during flood seasons. Finally, this study concludes with a critical discussion of existing challenges and potential future directions for “100-meter-scale, minute-level-update” weather forecasting.
GUO Jianping
,
ZHANG Wei
,
ZHOU Bowen
,
YAN Chao
,
ZHANG Xuelin
,
SUN Yuping
,
DENG Weilong
,
CHEN Tianmeng
,
YANG Honglong
,
QIU Zongxu
,
LI Zhibo
,
TAO Fa
,
LIANG Haihe
,
ZHANG Chaolin
2025, 51(11):1455-1476.
DOI: 10.7519/j.issn.1000-0526.2025.082601
Abstract:
As an emerging form of new quality productive forces, the development of low-altitude economy relies heavily on flight operations within non-controlled airspace categories, such as Class G and Class W. This airspace predominantly resides in the lower atmospheric boundary layer, an area highly prones to aviation hazardous weather phenomena like turbulence and wind shear. Meteorological conditions, together with communication, navigation, and surveillance systems, constitute the critical foundational support for the high-quality development of the low-altitude economy. However, there remains a severe deficiency in the high spatiotemporal resolution monitoring and early warning capabilities for aviation hazardous weather within this airspace, posing significant challenges to achieving efficient and safe low-altitude flight operations. To address this, this paper systematically reviews domestic and international research progresses in low-altitude economy meteorology and analyzes the key core challenges currently faced in key scientific and technological domains, including coherent structures in boundary layer turbulence, low-altitude wind shear, monitoring and early warning of turbulence and microbursts, and large eddy simulation. Furthermore, this paper explores several frontier scientific and technological issues in low-altitude meteorology, which include new theoretical frameworks for near-neutral boundary layer turbulence, moist boundary layer processes, development of intelligent meteorological sensing equipment for low-altitude aircraft safety, early warning powered by artificial intelligence for low-altitude aviation hazardous weather, development of intelligent computational fluid dynamics models, and the synergistic optimization of low-altitude meteorological condition and unmanned aerial vehicle path planning. Overall, this study aims to deepen the understanding of the underlying mechanisms governing the interaction between low-altitude aviation hazardous weather and aircraft, and seeks to provide theoretical foundations and technical pathways for enhancing high-precision intelligent sensing, as well as rapid forecasting and early warning capabilities for low-altitude aviation hazardous weather. Ultimately, this work endeavors to deliver crucial meteorological science and technology support for the safety and sustainable development of the low-altitude economy.
As an emerging form of new quality productive forces, the development of low-altitude economy relies heavily on flight operations within non-controlled airspace categories, such as Class G and Class W. This airspace predominantly resides in the lower atmospheric boundary layer, an area highly prones to aviation hazardous weather phenomena like turbulence and wind shear. Meteorological conditions, together with communication, navigation, and surveillance systems, constitute the critical foundational support for the high-quality development of the low-altitude economy. However, there remains a severe deficiency in the high spatiotemporal resolution monitoring and early warning capabilities for aviation hazardous weather within this airspace, posing significant challenges to achieving efficient and safe low-altitude flight operations. To address this, this paper systematically reviews domestic and international research progresses in low-altitude economy meteorology and analyzes the key core challenges currently faced in key scientific and technological domains, including coherent structures in boundary layer turbulence, low-altitude wind shear, monitoring and early warning of turbulence and microbursts, and large eddy simulation. Furthermore, this paper explores several frontier scientific and technological issues in low-altitude meteorology, which include new theoretical frameworks for near-neutral boundary layer turbulence, moist boundary layer processes, development of intelligent meteorological sensing equipment for low-altitude aircraft safety, early warning powered by artificial intelligence for low-altitude aviation hazardous weather, development of intelligent computational fluid dynamics models, and the synergistic optimization of low-altitude meteorological condition and unmanned aerial vehicle path planning. Overall, this study aims to deepen the understanding of the underlying mechanisms governing the interaction between low-altitude aviation hazardous weather and aircraft, and seeks to provide theoretical foundations and technical pathways for enhancing high-precision intelligent sensing, as well as rapid forecasting and early warning capabilities for low-altitude aviation hazardous weather. Ultimately, this work endeavors to deliver crucial meteorological science and technology support for the safety and sustainable development of the low-altitude economy.
2025, 51(11):1477-1494.
DOI: 10.7519/j.issn.1000-0526.2025.110701
Abstract:
Digital intelligent weather forecasting has grown rapidly in global meteorological operations in recent years, yet conventional methods face inherent limitations in nonlinear error correction, spatial and temporal downscaling, and multi-source data integration. Deep learning, with its exceptional nonlinear approximation and pattern recognition capabilities, has emerged as a transformative tool in digital intelligent forecasting workflows. This paper presents representative advances and achievements of deep learning in five key areas: numerical weather prediction (NWP) bias correction, high-resolution downscaling, heterogeneous multi-source data fusion, hazardous weather prediction (exemplified by typhoons), and datadriven weather forecasting. The results show that deep learning models can substantially enhance forecast accuracy by learning complex forecast-observation relationships end-to-end to correct systematic biases, employing generative adversarial networks to refine precipitation structures and improve heavy rainfall prediction skill, integrating radar, satellite, and model data to extend severe convective weather nowcasting lead times, and developing data-driven models that generate forecasts in seconds with accuracy comparable to numerical models. Notably, the Fenglei model demonstrates the superiority of generative approaches in convective nowcasting (achieving about 30% improvement in hit rate for radar reflectivity ≥50 dBz), while the Fengqing model has been operationally implemented for 15 d global forecasting. Deep learning integration has been advancing intelligent forecast products in spatial and temporal resolution and uncertainty quantification. However, its current applications have persistent challenges including limited training samples, model interpretability, extreme event prediction, cross-scale consistency, and computational efficiency, all of which require further investigation in future’s researches. The research priorities in the future should be put on the following aspects: expand large-sample and reforecast datasets; incorporate physical constraints to enhance model interpretability and robustness; implement tail-weighted loss functions to improve the reliability of extreme weather forecasts; design cross-scale coherent frameworks to ensure consistency across scales; and optimize training and inference efficiency for operational requirements. The synergistic integration of deep learning and numerical modeling, with their complementary strengths, represents a pivotal pathway for advancing intelligent numerical weather prediction.
Digital intelligent weather forecasting has grown rapidly in global meteorological operations in recent years, yet conventional methods face inherent limitations in nonlinear error correction, spatial and temporal downscaling, and multi-source data integration. Deep learning, with its exceptional nonlinear approximation and pattern recognition capabilities, has emerged as a transformative tool in digital intelligent forecasting workflows. This paper presents representative advances and achievements of deep learning in five key areas: numerical weather prediction (NWP) bias correction, high-resolution downscaling, heterogeneous multi-source data fusion, hazardous weather prediction (exemplified by typhoons), and datadriven weather forecasting. The results show that deep learning models can substantially enhance forecast accuracy by learning complex forecast-observation relationships end-to-end to correct systematic biases, employing generative adversarial networks to refine precipitation structures and improve heavy rainfall prediction skill, integrating radar, satellite, and model data to extend severe convective weather nowcasting lead times, and developing data-driven models that generate forecasts in seconds with accuracy comparable to numerical models. Notably, the Fenglei model demonstrates the superiority of generative approaches in convective nowcasting (achieving about 30% improvement in hit rate for radar reflectivity ≥50 dBz), while the Fengqing model has been operationally implemented for 15 d global forecasting. Deep learning integration has been advancing intelligent forecast products in spatial and temporal resolution and uncertainty quantification. However, its current applications have persistent challenges including limited training samples, model interpretability, extreme event prediction, cross-scale consistency, and computational efficiency, all of which require further investigation in future’s researches. The research priorities in the future should be put on the following aspects: expand large-sample and reforecast datasets; incorporate physical constraints to enhance model interpretability and robustness; implement tail-weighted loss functions to improve the reliability of extreme weather forecasts; design cross-scale coherent frameworks to ensure consistency across scales; and optimize training and inference efficiency for operational requirements. The synergistic integration of deep learning and numerical modeling, with their complementary strengths, represents a pivotal pathway for advancing intelligent numerical weather prediction.
ZHENG Yongguang
,
YANG Bo
,
LAN Yu
,
SHENG Jie
,
ZHANG Xiaowen
,
TIAN Fuyou
,
CAO Yancha
,
ZHOU Kanghui
2025, 51(11):1495-1522.
DOI: 10.7519/j.issn.1000-0526.2025.082501
Abstract:
The disastrous nature of severe convective weather and the need for its accurate monitoring and forecasting have garnered widespread attention. This paper summarizes the main characteristics, environments, formation mechanisms, and dual-polarization Doppler weather radar observation features of various typical types of convective storms and severe convective weather. It presents fundamental concepts and understanding, briefly outlines forecasting approaches and monitoring techniques for severe convective weather, and provides prospects for future work. In recent years, main advances are as follows. Gravity waves are an important type of trigger mechanisms of nocturnal convective storms. The key formation mechanisms of severe convective weather in low convective available potential energy environments with strong vertical wind shear are on meso-γ scale vortices, which have been significantly understood. The sorting effect of supercell updrafts on precipitation particles results in highly distinctive dual-polarization radar signatures, and the signatuers such as ZDR column, ZDR arc, KDP foot have revealed more charatericstics of microphysical processes and dynamical structure of supercells. Most bow echoes in South China are formed through the merger of quasi-linear convective systems with pre-existing convective cells ahead of them. Mesovortices develop through multiple complex mechanisms. The intensification of updrafts by meso-γ scale vortices such as mesocylones and mesovortices is an important aspect of heavy rainfall formation mechanism. The meso-γ scale vortices, combined with rear-inflow jets of bow echoes, play a crucial role in extreme severe thunderstorm gale. A great many convective storms producing short-duration heavy rainfall often exhibit hybrid characteristics between continental and tropical maritime types, and it has been found that the heavier the instaneous extreme rainfall intensity in South China, the more the liquid and ice water content in convective storms. Hailstones with diameters ≥5.0 cm are primarily generated by supercells, and their growth rarely follows spiral trajectories or cyclic growth paths. Tornadogenesis hinges on the formation, concentration, and intensification of near-surface vertical vorticity. High-resolution numerical forecasting and deep learning techniques have significantly improved the accuracy of severe convective weather monitoring, forecasting, and warning systems. Future efforts should focus on the “about-100-m” fine-scale mechanisms and super-high-resolution numerical models, and deep learning models with fully integrating physical laws for comprehensive severe convective weather forecasting.
The disastrous nature of severe convective weather and the need for its accurate monitoring and forecasting have garnered widespread attention. This paper summarizes the main characteristics, environments, formation mechanisms, and dual-polarization Doppler weather radar observation features of various typical types of convective storms and severe convective weather. It presents fundamental concepts and understanding, briefly outlines forecasting approaches and monitoring techniques for severe convective weather, and provides prospects for future work. In recent years, main advances are as follows. Gravity waves are an important type of trigger mechanisms of nocturnal convective storms. The key formation mechanisms of severe convective weather in low convective available potential energy environments with strong vertical wind shear are on meso-γ scale vortices, which have been significantly understood. The sorting effect of supercell updrafts on precipitation particles results in highly distinctive dual-polarization radar signatures, and the signatuers such as ZDR column, ZDR arc, KDP foot have revealed more charatericstics of microphysical processes and dynamical structure of supercells. Most bow echoes in South China are formed through the merger of quasi-linear convective systems with pre-existing convective cells ahead of them. Mesovortices develop through multiple complex mechanisms. The intensification of updrafts by meso-γ scale vortices such as mesocylones and mesovortices is an important aspect of heavy rainfall formation mechanism. The meso-γ scale vortices, combined with rear-inflow jets of bow echoes, play a crucial role in extreme severe thunderstorm gale. A great many convective storms producing short-duration heavy rainfall often exhibit hybrid characteristics between continental and tropical maritime types, and it has been found that the heavier the instaneous extreme rainfall intensity in South China, the more the liquid and ice water content in convective storms. Hailstones with diameters ≥5.0 cm are primarily generated by supercells, and their growth rarely follows spiral trajectories or cyclic growth paths. Tornadogenesis hinges on the formation, concentration, and intensification of near-surface vertical vorticity. High-resolution numerical forecasting and deep learning techniques have significantly improved the accuracy of severe convective weather monitoring, forecasting, and warning systems. Future efforts should focus on the “about-100-m” fine-scale mechanisms and super-high-resolution numerical models, and deep learning models with fully integrating physical laws for comprehensive severe convective weather forecasting.
CHEN Xianyan
,
ZHANG Yingxian
,
GAO Ge
,
MEI Mei
,
ZHANG Fang
,
SONG Yafang
,
SUN Xiaoting
,
DING Yihui
2025, 51(11):1523-1534.
DOI: 10.7519/j.issn.1000-0526.2025.110101
Abstract:
The development of the integrated intelligent service system project for transportation meteorological disaster early warning, risk assessment, and emergency response refers to the establishment of an intelligent service system by integrating advanced information technologies and data resources from meteorology, transportation, emergency management and other related fields so as to respond to various potential meteorological disasters and other emergency situations in transportation. This paper systematically integrates the research and practical progress of China’s intelligent service systems for transportation meteorological disaster early warning, risk assessment, and emergency response, and analyzes the development process of scientific research and operational applications in the aspects of transportation meteorological disaster early warning models, risk assessment methods, and emergency response intelligent services in the current fields of highway, railway, and waterway transportation. At the same time, this paper examines the existing core problems and puts forward relevant countermeasures and suggestions, which could serve as a support for promoting the development of China’s integrated transportation meteorological system project.
The development of the integrated intelligent service system project for transportation meteorological disaster early warning, risk assessment, and emergency response refers to the establishment of an intelligent service system by integrating advanced information technologies and data resources from meteorology, transportation, emergency management and other related fields so as to respond to various potential meteorological disasters and other emergency situations in transportation. This paper systematically integrates the research and practical progress of China’s intelligent service systems for transportation meteorological disaster early warning, risk assessment, and emergency response, and analyzes the development process of scientific research and operational applications in the aspects of transportation meteorological disaster early warning models, risk assessment methods, and emergency response intelligent services in the current fields of highway, railway, and waterway transportation. At the same time, this paper examines the existing core problems and puts forward relevant countermeasures and suggestions, which could serve as a support for promoting the development of China’s integrated transportation meteorological system project.
2025, 51(11):1535-1546.
DOI: 10.7519/j.issn.1000-0526.2025.070501
Abstract:
This paper describes a quality control (QC) algorithm for reflectivity factor data of dual-channel millimeter-wave cloud radar (MMCR) in different regions. The data used for test sample are from the 15 MMCR stations which are the first batch of MMCR approved for operational use in China. The algorithm provides a method of automatically identifying the QC threshold parameters of reflectivity factor （Z） and linear depolarization ratio (LDR), combined with filtering check and continuity check, etc., and can effectively eliminate non-meteorological echoes. The method is based on the distribution characteristics between the cloud or rain echoes and suspended matter clutter in the MMCR data. It classifies and labels the cloud or rain echoes and suspended matter clutter samples from the 15 stations in 2023. Based on the intersection points of the frequency curves of the two types of echoes, the QC threshold parameters for Z and LDR at each station can be got rapidly. In addition, this paper compares the correlation coefficient, average deviation and root mean square error of cloud heights calculated from the MMCR data before and after QC and radiosonde data at different stations and during different observation periods, and discusses the effectiveness of the QC method. The results show that non-meteorological echoes in the data can be effectively removed after QC, especially the low-level suspended matter clutter. Its correlation coefficient increases from 0.47 to 0.91 with radiosonde-identified cloud base height, and increases from 0.80 to 0.87 with cloud top height. The calculated cloud heights after QC are more reasonable. So, the data after QC can enhance the consistency of the cloud height data between MMCR and radiosonde.
This paper describes a quality control (QC) algorithm for reflectivity factor data of dual-channel millimeter-wave cloud radar (MMCR) in different regions. The data used for test sample are from the 15 MMCR stations which are the first batch of MMCR approved for operational use in China. The algorithm provides a method of automatically identifying the QC threshold parameters of reflectivity factor （Z） and linear depolarization ratio (LDR), combined with filtering check and continuity check, etc., and can effectively eliminate non-meteorological echoes. The method is based on the distribution characteristics between the cloud or rain echoes and suspended matter clutter in the MMCR data. It classifies and labels the cloud or rain echoes and suspended matter clutter samples from the 15 stations in 2023. Based on the intersection points of the frequency curves of the two types of echoes, the QC threshold parameters for Z and LDR at each station can be got rapidly. In addition, this paper compares the correlation coefficient, average deviation and root mean square error of cloud heights calculated from the MMCR data before and after QC and radiosonde data at different stations and during different observation periods, and discusses the effectiveness of the QC method. The results show that non-meteorological echoes in the data can be effectively removed after QC, especially the low-level suspended matter clutter. Its correlation coefficient increases from 0.47 to 0.91 with radiosonde-identified cloud base height, and increases from 0.80 to 0.87 with cloud top height. The calculated cloud heights after QC are more reasonable. So, the data after QC can enhance the consistency of the cloud height data between MMCR and radiosonde.
WANG Fei
,
WANG Sihan
,
WU Rihan
,
GENG Yi
,
GE Xudong
,
SUN Qing
,
LIU Li
,
LIN Dawei
,
SUN Fengbin
,
ZHAO Zhiqiang
,
CHEN Baojun
2025, 51(11):1547-1558.
DOI: 10.7519/j.issn.1000-0526.2025.101701
Abstract:
The essence of weather modification (WM) lies in understanding and applying the principles of atmospheric physics and cloud microphysics to steer local weather processes toward desired outcomes through targeted intervention. Scientifically evaluating the effectiveness of WM operations and establishing objective assessment frameworks are crucial for optimizing intervention strategies. To provide both a theoretical foundation and practical guidance to support future research innovation in the field of WM benefit evaluation, this paper reviews the recent progresses in evaluating the benefits of WM in five key dimensions: hydrology, agriculture, ecology, environment and economy. Besides, current research challenges and limitations across multiple scales are analyzed and a paradigm shift from a “physical-effect-oriented” approach to one that is centered on “comprehensive value optimization” is proposed.
The essence of weather modification (WM) lies in understanding and applying the principles of atmospheric physics and cloud microphysics to steer local weather processes toward desired outcomes through targeted intervention. Scientifically evaluating the effectiveness of WM operations and establishing objective assessment frameworks are crucial for optimizing intervention strategies. To provide both a theoretical foundation and practical guidance to support future research innovation in the field of WM benefit evaluation, this paper reviews the recent progresses in evaluating the benefits of WM in five key dimensions: hydrology, agriculture, ecology, environment and economy. Besides, current research challenges and limitations across multiple scales are analyzed and a paradigm shift from a “physical-effect-oriented” approach to one that is centered on “comprehensive value optimization” is proposed.
2025, 51(11):1559-1570.
DOI: 10.7519/j.issn.1000-0526.2025.052601
Abstract:
Significant tornadoes associated with supercells can cause severe disasters, but issuing tornado warnings is challenging. Therefore, it is essential to study the detailed echo structure of supercells. Based on the observations of S-band Doppler polarimetric radar, 9 significant tornadic supercells are compared with 9 non-tornaic supercells. The results demonstrate the significant differences between them. The detailed results are as follows. In significant tornadic supercells, the differential reflectivity ZDR arc extends toward the hook echo, and the area of hail in the mid-to-low levels is smaller than in non-tornadic supercells. The average separation distance between the specific differential phase KDP foot centroid and ZDR arc centroid in low level is much larger in tornadic supercells than in non-tornadic supercells. In significant tornadic supercells, the low-level mesocyclone intensifies markedly. Additionally, 24 min and 6 min before the tornado occurs, the mid-level mesocyclone core remains at relatively low heights, averaged at the height of 4.2 km and 3.1 km, respectively. The aforementioned differences require further confirmation of more cases so as to provide additional evidence for in-depth research on the relationship between the echo structure characteristics of supercells and the mechanisms of tornado formation.
Significant tornadoes associated with supercells can cause severe disasters, but issuing tornado warnings is challenging. Therefore, it is essential to study the detailed echo structure of supercells. Based on the observations of S-band Doppler polarimetric radar, 9 significant tornadic supercells are compared with 9 non-tornaic supercells. The results demonstrate the significant differences between them. The detailed results are as follows. In significant tornadic supercells, the differential reflectivity ZDR arc extends toward the hook echo, and the area of hail in the mid-to-low levels is smaller than in non-tornadic supercells. The average separation distance between the specific differential phase KDP foot centroid and ZDR arc centroid in low level is much larger in tornadic supercells than in non-tornadic supercells. In significant tornadic supercells, the low-level mesocyclone intensifies markedly. Additionally, 24 min and 6 min before the tornado occurs, the mid-level mesocyclone core remains at relatively low heights, averaged at the height of 4.2 km and 3.1 km, respectively. The aforementioned differences require further confirmation of more cases so as to provide additional evidence for in-depth research on the relationship between the echo structure characteristics of supercells and the mechanisms of tornado formation.
2025, 51(11):1571-1580.
DOI: 10.7519/j.issn.1000-0526.2025.111201
Abstract:
In August 2025, the polar vortex in the Northern Hemisphere exhibited an eccentric distribution pattern with above-normal intensity. The mid-to-high latitude circulation over Eurasia displayed a “two-trough-one-ridge” configuration, with the high-pressure ridge east of the Ural Mountains being anomalously strong. The western Pacific subtropical high (WPSH) got significantly intensified, extending westward, and stably dominated the central and southern parts of China. As a result, the national mean temperature reached 22.0℃, being 0.9℃ above the climatological normal and ranking as the fourth highest since 1961 for the same period. Moreover, one persistent high-temperature event occurred during this month, with the number of high-temperature days exceeding the climatological normal by 1.7 days, ranking as the fifth most heat days in the historical record for the same period. The national average precipitation amounted to 114.5 mm, 7.0% above normal. During this month, there were a total of nine heavy precipitation events with severe precipitation repeatedly affecting South China, North China, the region from the Sichuan Basin to Northeast China, and some other places. Five typhoons were generated in Northwest Pacific and the South China Sea, but only one (Typhoon Podul) made landfall in China, thus the formation and landfall number of typhoon were both below the climatological normals. Typhoon Podul attained a maximum sustained wind speed of 42 m·s-1 prior to its landfall in southern Taiwan Island, China, at 08:00 BT on the 13th, exhibiting a typical feature of rapid intensification just before landfall. The anomalously strong WPSH led to precipitation deficits of 50%-80% in the central and eastern regions in south of the Yangtze River (Jiangnan Region) and eastern South China, so potential meteorological drought seemed to begin and develop. However, rainfall was 20%-100% above the normal amount across most of North China, central and eastern Northwest China, and the Pearl River Delta Region. Additionally, there were also regional droughts, severe convective weather, and hailstorm disasters during the month.
In August 2025, the polar vortex in the Northern Hemisphere exhibited an eccentric distribution pattern with above-normal intensity. The mid-to-high latitude circulation over Eurasia displayed a “two-trough-one-ridge” configuration, with the high-pressure ridge east of the Ural Mountains being anomalously strong. The western Pacific subtropical high (WPSH) got significantly intensified, extending westward, and stably dominated the central and southern parts of China. As a result, the national mean temperature reached 22.0℃, being 0.9℃ above the climatological normal and ranking as the fourth highest since 1961 for the same period. Moreover, one persistent high-temperature event occurred during this month, with the number of high-temperature days exceeding the climatological normal by 1.7 days, ranking as the fifth most heat days in the historical record for the same period. The national average precipitation amounted to 114.5 mm, 7.0% above normal. During this month, there were a total of nine heavy precipitation events with severe precipitation repeatedly affecting South China, North China, the region from the Sichuan Basin to Northeast China, and some other places. Five typhoons were generated in Northwest Pacific and the South China Sea, but only one (Typhoon Podul) made landfall in China, thus the formation and landfall number of typhoon were both below the climatological normals. Typhoon Podul attained a maximum sustained wind speed of 42 m·s-1 prior to its landfall in southern Taiwan Island, China, at 08:00 BT on the 13th, exhibiting a typical feature of rapid intensification just before landfall. The anomalously strong WPSH led to precipitation deficits of 50%-80% in the central and eastern regions in south of the Yangtze River (Jiangnan Region) and eastern South China, so potential meteorological drought seemed to begin and develop. However, rainfall was 20%-100% above the normal amount across most of North China, central and eastern Northwest China, and the Pearl River Delta Region. Additionally, there were also regional droughts, severe convective weather, and hailstorm disasters during the month.
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