ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 51,Issue 1,2025 Table of Contents
2025, 51(1):1-16.
DOI: 10.7519/j.issn.1000-0526.2024.072701
Abstract:
In view of the increasing global climate change and extreme weather events, the impact of marine thunderstorms on navigation and offshore operations has become more and more serious. However, due to the lack of understanding of the characteristics and mechanisms of marine thunderstorms, coupled with the scarcity of marine observation data, it is difficult to track and forecast marine thunderstorms. In response to the above problems, this article comprehensively summarizes the latest progress in the field of marine thunderstorm research at home and abroad from the perspective of meteorological forecasters, covering mar-ine thunderstorm monitoring methods and technologies, activity characteristics and formation mechanisms for the purpose of sorting and discussing the current status, development trends and key issues of global marine thunderstorm research. Based on the review, the future research direction of marine thunderstorms in China is discussed, including strengthening the construction of marine observation systems, building marine thunderstorm data sets, deepening the study of the formation mechanisms of marine thunderstorms and strengthening the application of artificial intelligence to build accurate prediction models. The content of this article can provide research results on marine thunderstorms and better serve the safety of navigation and offshore operations and the reduction of the adverse effects of marine thunderstorms.
In view of the increasing global climate change and extreme weather events, the impact of marine thunderstorms on navigation and offshore operations has become more and more serious. However, due to the lack of understanding of the characteristics and mechanisms of marine thunderstorms, coupled with the scarcity of marine observation data, it is difficult to track and forecast marine thunderstorms. In response to the above problems, this article comprehensively summarizes the latest progress in the field of marine thunderstorm research at home and abroad from the perspective of meteorological forecasters, covering mar-ine thunderstorm monitoring methods and technologies, activity characteristics and formation mechanisms for the purpose of sorting and discussing the current status, development trends and key issues of global marine thunderstorm research. Based on the review, the future research direction of marine thunderstorms in China is discussed, including strengthening the construction of marine observation systems, building marine thunderstorm data sets, deepening the study of the formation mechanisms of marine thunderstorms and strengthening the application of artificial intelligence to build accurate prediction models. The content of this article can provide research results on marine thunderstorms and better serve the safety of navigation and offshore operations and the reduction of the adverse effects of marine thunderstorms.
2025, 51(1):17-29.
DOI: 10.7519/j.issn.1000-0526.2024.092701
Abstract:
The causes and predictability of the high-impact large-scale cryogenic freezing rain and snow weather (CFRSW) in East China and Central China in February 2024 are investigated with the daily temperature and precipitation data from 2374 stations, the NCEP/NCAR atmospheric circulation reanalysis data and the HadISST data. The results show that the two CFRSW processes occurred in China in early and late February 2024, and the overlap of the disaster-hit regions was high so that East China and Central China suffered the most serious freezing rain and snow disaster. The subtropical and mid-high latitude circulation system configurations that impacted the two CFRSW processes were more consistent, the Northwest Pacific subtropical high was stronger and more westward. Moreover, the Siberian high, the South Branch trough and the Northwest Pacific anticyclone were stronger in the same period, forming a strong synergistic effect. The enhancement of the Siberian high led to cold air southward, and the synchronization of the enhancement of South Branch trough and western North Pacific anticyclone provided abundant water vapor conditions for East China and Central China. A moderate-intensity El Nino event occurred in the equatorial east-central Pacific from May 2023 to April 2024. In addition, the tropical Indian Ocean and the tropical North Atlantic were abnormally warm in winter. The abnormal SST of the three oceans jointly led to the continuous strengthening of the Northwest Pacific subtropical high. This was conductive to the periodic development and enhancement of the anticyclones in the Northwest Pacific Ocean, and provided abundant water vapor conditions for the two CFRSW processes in February 2024. The predictability of the sub-seasonal model for the two processes is about 1-2 weeks ahead of time, and the prediction skill within 1 week is relatively high. When the prediction is advanced by more than 2 weeks, the model is unable to accurately predict the anomalous characteristics of the Eurasian mid-high latitude circulation system, resulting in lower predictive ability for the two processes.
The causes and predictability of the high-impact large-scale cryogenic freezing rain and snow weather (CFRSW) in East China and Central China in February 2024 are investigated with the daily temperature and precipitation data from 2374 stations, the NCEP/NCAR atmospheric circulation reanalysis data and the HadISST data. The results show that the two CFRSW processes occurred in China in early and late February 2024, and the overlap of the disaster-hit regions was high so that East China and Central China suffered the most serious freezing rain and snow disaster. The subtropical and mid-high latitude circulation system configurations that impacted the two CFRSW processes were more consistent, the Northwest Pacific subtropical high was stronger and more westward. Moreover, the Siberian high, the South Branch trough and the Northwest Pacific anticyclone were stronger in the same period, forming a strong synergistic effect. The enhancement of the Siberian high led to cold air southward, and the synchronization of the enhancement of South Branch trough and western North Pacific anticyclone provided abundant water vapor conditions for East China and Central China. A moderate-intensity El Nino event occurred in the equatorial east-central Pacific from May 2023 to April 2024. In addition, the tropical Indian Ocean and the tropical North Atlantic were abnormally warm in winter. The abnormal SST of the three oceans jointly led to the continuous strengthening of the Northwest Pacific subtropical high. This was conductive to the periodic development and enhancement of the anticyclones in the Northwest Pacific Ocean, and provided abundant water vapor conditions for the two CFRSW processes in February 2024. The predictability of the sub-seasonal model for the two processes is about 1-2 weeks ahead of time, and the prediction skill within 1 week is relatively high. When the prediction is advanced by more than 2 weeks, the model is unable to accurately predict the anomalous characteristics of the Eurasian mid-high latitude circulation system, resulting in lower predictive ability for the two processes.
2025, 51(1):30-42.
DOI: 10.7519/j.issn.1000-0526.2024.120201
Abstract:
Using the 3 h densely-observed radiosonde and the 1 h automatic weather station data obtained from Baolian, Chaoyang and Daxing stations in Beijing from 28 August to 2 September 2016, based on the WRF model and the WRFDA three-dimensional variational assimilation system, this paper conducts assimi-lation experiments on only assimilating densely-observed radiosonde data (S-DA), only assimilating automatic weather station data (A-DA), and simultaneously assimilating the above two types of data (M-DA), to investigate the improvement effect of the three data assimilation schemes in the numerical simulation of the boundary layer in the Beijing Area. The results show that in the vertical direction, the densely-observed radiosonde data play a core role in improving simulation results, which can reduce the root mean square errors of temperature, relative humidity, and wind speed within the boundary layer range by 65%, 61%, and 22%, respectively. The automatic weather station data also contribute to the simulation results in the vertical direction, but the improvement is small and the impact range is low. The results of the M-DA experiment are similar to those of the S-DA experiment. In the horizontal direction, the improvement effect of the assimilated automatic weather station data is mainly reflected in a wide range of impact. The effect of densely-observed radiosonde data has strong improvement, but the impact range is small. The advantages of combining the two types of data in the M-DA experiment can make the simulation results closer to the observation results. In terms of assimilation timeliness, the assimilation experiment has a relatively long effect on improving the thermal state within the boundary layer, and a relatively short effect on improving the humidity state and dynamic structure. Among them, the M-DA experiment can extend its effect for the thermal state up to 6 h in forecast, and its effect for the humidity and dynamic structure up to 3 h in forecast. In summary, the simultaneous assimilation of densely-observed radiosonde and automatic weather station data is more effective than assimilating one of the data only. The two types of data can complement each other’s shortcomings after assimilation, which can greatly improve the initial field of the model and thus improve the accuracy of boundary layer simulation results to a certain extent.
Using the 3 h densely-observed radiosonde and the 1 h automatic weather station data obtained from Baolian, Chaoyang and Daxing stations in Beijing from 28 August to 2 September 2016, based on the WRF model and the WRFDA three-dimensional variational assimilation system, this paper conducts assimi-lation experiments on only assimilating densely-observed radiosonde data (S-DA), only assimilating automatic weather station data (A-DA), and simultaneously assimilating the above two types of data (M-DA), to investigate the improvement effect of the three data assimilation schemes in the numerical simulation of the boundary layer in the Beijing Area. The results show that in the vertical direction, the densely-observed radiosonde data play a core role in improving simulation results, which can reduce the root mean square errors of temperature, relative humidity, and wind speed within the boundary layer range by 65%, 61%, and 22%, respectively. The automatic weather station data also contribute to the simulation results in the vertical direction, but the improvement is small and the impact range is low. The results of the M-DA experiment are similar to those of the S-DA experiment. In the horizontal direction, the improvement effect of the assimilated automatic weather station data is mainly reflected in a wide range of impact. The effect of densely-observed radiosonde data has strong improvement, but the impact range is small. The advantages of combining the two types of data in the M-DA experiment can make the simulation results closer to the observation results. In terms of assimilation timeliness, the assimilation experiment has a relatively long effect on improving the thermal state within the boundary layer, and a relatively short effect on improving the humidity state and dynamic structure. Among them, the M-DA experiment can extend its effect for the thermal state up to 6 h in forecast, and its effect for the humidity and dynamic structure up to 3 h in forecast. In summary, the simultaneous assimilation of densely-observed radiosonde and automatic weather station data is more effective than assimilating one of the data only. The two types of data can complement each other’s shortcomings after assimilation, which can greatly improve the initial field of the model and thus improve the accuracy of boundary layer simulation results to a certain extent.
2025, 51(1):43-56.
DOI: 10.7519/j.issn.1000-0526.2024.113001
Abstract:
To study the characteristics of downburst under weak vertical wind shear, based on the SA Doppler weather radar, sounding and surface (10 m) maximum wind data and the cloud images from stationary meteorological satellites, we selected 381 downburst events that occurred under weak vertical wind shear from June to September in 2019-2020 in Jiangsu Province and divided them into three categories according to the scales of microburst, macroburst and downburst cluster. The statistical results show that 64.9% of thunderstorm days in Jiangsu from June to September are accompanied by downbursts, with an average of 8 downburst events per downburst day. The proportions of three types of downbursts are 21.7% for microbursts, 47.6% for macrobursts and 30.7% for downburst clusters, and they all tend to occur in the time period of 15:00-16:00 BT. According to radar radial velocity, the average duration of downbursts is 25.4 min, and the average intensity (radial velocity difference of divergence couplets or extreme radial velocity at low elevation) is 22 m·s-1. Only 7 downburst samples reach the standard of disaster gale (extreme radial velocity at low elevation reaches 25 m·s-1 or radial velocity difference of divergence couplets attains 40 m·s-1). The average surface maximum wind caused by downburst observed by surface meteorological stations is 15.5 m·s-1, indicating that disaster-causing potential of downburst is not significant. The low-elevation radial velocity modes of downburst are dominated by divergence vel-ocity couplet (73.3%), but the majorities are asymmetric while only 6.1% for symmetrical divergence velocity couplet. There are 26.7% of downburst characterized by strong wind at low elevation.
To study the characteristics of downburst under weak vertical wind shear, based on the SA Doppler weather radar, sounding and surface (10 m) maximum wind data and the cloud images from stationary meteorological satellites, we selected 381 downburst events that occurred under weak vertical wind shear from June to September in 2019-2020 in Jiangsu Province and divided them into three categories according to the scales of microburst, macroburst and downburst cluster. The statistical results show that 64.9% of thunderstorm days in Jiangsu from June to September are accompanied by downbursts, with an average of 8 downburst events per downburst day. The proportions of three types of downbursts are 21.7% for microbursts, 47.6% for macrobursts and 30.7% for downburst clusters, and they all tend to occur in the time period of 15:00-16:00 BT. According to radar radial velocity, the average duration of downbursts is 25.4 min, and the average intensity (radial velocity difference of divergence couplets or extreme radial velocity at low elevation) is 22 m·s-1. Only 7 downburst samples reach the standard of disaster gale (extreme radial velocity at low elevation reaches 25 m·s-1 or radial velocity difference of divergence couplets attains 40 m·s-1). The average surface maximum wind caused by downburst observed by surface meteorological stations is 15.5 m·s-1, indicating that disaster-causing potential of downburst is not significant. The low-elevation radial velocity modes of downburst are dominated by divergence vel-ocity couplet (73.3%), but the majorities are asymmetric while only 6.1% for symmetrical divergence velocity couplet. There are 26.7% of downburst characterized by strong wind at low elevation.
2025, 51(1):57-67.
DOI: 10.7519/j.issn.10000526.2024.080501
Abstract:
In the complex terrain of Sichuan Region, although the frequency of thunderstorm gale is relatively low, its impact is significant. There are few objective forecast products of the thunderstorm gale, and even if there are, the time resolution is lower. In order to further improve the accuracy of thunderstorm gale forecasting under complex terrain in Sichuan, in this article we comprehensively consider terrain factors, model physical quantity factors and time factors. According to the altitude, Sichuan is divided into highaltitude and low altitude areas. Based on the data from 2018 to 2021 and three machine learning methods of random forest, adaptive boosting and extreme random tree, we construct a thunderstorm gale prediction model and make a forecast for the 2022 thunderstorm gales obtaining a 3 h thunderstorm gale potential forecast. Then, based on the climate background, we downscale the 3 h forecast time to 1 h, and make a 0-12 h hourly thunderstorm gale forecast. At the same time, the forecasting effect is tested. The results show that, the adaptive boosting method of 3 h thunderstorm gale forecast has the best effect. The longtime and individual case tests show that the 0-12 h hourly thunderstorm gale forecast product obtained by the adaptive boosting method is superior to the forecasts of National Meteorological Centre with the TS score increased from 0.0104 to 0.0595, and the false alarm rate decreased from 0.988 to 0.808. This indicates that the adaptive boosting method has a higher application value in forecasting operation application value.
In the complex terrain of Sichuan Region, although the frequency of thunderstorm gale is relatively low, its impact is significant. There are few objective forecast products of the thunderstorm gale, and even if there are, the time resolution is lower. In order to further improve the accuracy of thunderstorm gale forecasting under complex terrain in Sichuan, in this article we comprehensively consider terrain factors, model physical quantity factors and time factors. According to the altitude, Sichuan is divided into highaltitude and low altitude areas. Based on the data from 2018 to 2021 and three machine learning methods of random forest, adaptive boosting and extreme random tree, we construct a thunderstorm gale prediction model and make a forecast for the 2022 thunderstorm gales obtaining a 3 h thunderstorm gale potential forecast. Then, based on the climate background, we downscale the 3 h forecast time to 1 h, and make a 0-12 h hourly thunderstorm gale forecast. At the same time, the forecasting effect is tested. The results show that, the adaptive boosting method of 3 h thunderstorm gale forecast has the best effect. The longtime and individual case tests show that the 0-12 h hourly thunderstorm gale forecast product obtained by the adaptive boosting method is superior to the forecasts of National Meteorological Centre with the TS score increased from 0.0104 to 0.0595, and the false alarm rate decreased from 0.988 to 0.808. This indicates that the adaptive boosting method has a higher application value in forecasting operation application value.
2025, 51(1):68-84.
DOI: 10.7519/j.issn.1000-0526.2024.111201
Abstract:
In order to improve the monitoring and early warning capabilities for autumn hail, based on the dual-polarization radar and FY-4A satellite data, this paper analyzed a rare autumn severe hail process that occurred in Hunan Province in November 2023, and discussed its early warning characteristics. The findings are as follows: (1) The severe hail process was produced by supercell hailstorms. In the hail stage, the bottom of the strong echo center with horizontal reflectivity factor ZH ≥65 dBz was close to the ground, the correlation coefficient (CC) was lower than 0.9, the specific differential phase (KDP) was a cavity, and the differential reflectivity factor (ZDR) was -3-0 dB. These corresponded to the condition for the fall of large hailstone with diameter ≥ 5 cm, and matched with the observation. The rise and fall of the ZDR column can characterize the strengthening and weakening of updraft, and the ZDR with CC can identify three body scatter signature (TBSS) and side lobe characteristics. (2) The area of strong echo and the height of centroid were good indicators for different development stages of supercell. Combined with monitoring, the lead time of early warning of hail can be improved by more than 12 min. During this severe hail process, the length of TBSS caused by the supercell reached 73 km which is rarely seen. The emergence of TBSS and the divergence of the storm top showed a significant weakening trend, which was 17 min more earlier than the occurrence time of hail. (3) Hail and thunderstorm gale appeared in areas with large gradients of hail cloud blackbody brightness temperature (TBB), cloud top temperature (CTT), cloud top height (CTH), and cloud top pressure (CTP). TBB≤-58℃, CTT≤-56℃, CTH≥13 km, CTP≤180 hPa and the continuous increase of TBB≤-52℃ area can be used as monitoring and early warning indicators of the characteristic parameters of autumn hail cloud by FY-4A satellite. (4) The TBB maximum lapse rate can provide at least 11 min of advance warning for impending hail. The brightness temperature difference of 13.3 μm and 10.8 μm channel is the most sensitive to the development and weakening of hail clouds. The brightness temperature difference of 6.5 μm and 10.8 μm channels is more sensitive to the related conditions before and after the hail passes through the hit area than other brightness temperature differences among the single-station convection monitoring indicators.
In order to improve the monitoring and early warning capabilities for autumn hail, based on the dual-polarization radar and FY-4A satellite data, this paper analyzed a rare autumn severe hail process that occurred in Hunan Province in November 2023, and discussed its early warning characteristics. The findings are as follows: (1) The severe hail process was produced by supercell hailstorms. In the hail stage, the bottom of the strong echo center with horizontal reflectivity factor ZH ≥65 dBz was close to the ground, the correlation coefficient (CC) was lower than 0.9, the specific differential phase (KDP) was a cavity, and the differential reflectivity factor (ZDR) was -3-0 dB. These corresponded to the condition for the fall of large hailstone with diameter ≥ 5 cm, and matched with the observation. The rise and fall of the ZDR column can characterize the strengthening and weakening of updraft, and the ZDR with CC can identify three body scatter signature (TBSS) and side lobe characteristics. (2) The area of strong echo and the height of centroid were good indicators for different development stages of supercell. Combined with monitoring, the lead time of early warning of hail can be improved by more than 12 min. During this severe hail process, the length of TBSS caused by the supercell reached 73 km which is rarely seen. The emergence of TBSS and the divergence of the storm top showed a significant weakening trend, which was 17 min more earlier than the occurrence time of hail. (3) Hail and thunderstorm gale appeared in areas with large gradients of hail cloud blackbody brightness temperature (TBB), cloud top temperature (CTT), cloud top height (CTH), and cloud top pressure (CTP). TBB≤-58℃, CTT≤-56℃, CTH≥13 km, CTP≤180 hPa and the continuous increase of TBB≤-52℃ area can be used as monitoring and early warning indicators of the characteristic parameters of autumn hail cloud by FY-4A satellite. (4) The TBB maximum lapse rate can provide at least 11 min of advance warning for impending hail. The brightness temperature difference of 13.3 μm and 10.8 μm channel is the most sensitive to the development and weakening of hail clouds. The brightness temperature difference of 6.5 μm and 10.8 μm channels is more sensitive to the related conditions before and after the hail passes through the hit area than other brightness temperature differences among the single-station convection monitoring indicators.
7 Formation of Boundary Layer Convergence Lines in Hetao Area and Their Convection Triggering Effect
2025, 51(1):85-96.
DOI: 10.7519/j.issn.1000-0526.2024.100801
Abstract:
Based on dense ball sound data, dense field surface observation, ERA5 and the Linhe New Generation Weather Radar data, detailed analysis is conducted on the boundary layer convergence lines (BLCL) formed by the surface differences between the Hetao irrigation area and the Kubuqi Desert in Hetao Area of the Yellow River in July and August 2022, and also on the role of BLCL in convection triggering. The results indicate that the BLCL in the Hetao Area was formed by the combined influence of surface differences, boundary layer atmospheric circulation and complex terrain, and mainly occurred in the Kubuqi Desert which lies in the southeast of the Hetao irrigation area, and the length of BLCL ranged from 100 to 200 km. The BLCL was a shallow system with vertical thickness of approximately 1000 to 1100 m. The daily variation of BLCL was significant, with a high incidence from 12:00 BT to 17:00 BT, accounting for 80%. The probability of BLCL formation in July and August was as high as 60%, and 39% of BLCL could trigger convection. The prediction indicators about the formation of BLCL are as follows.The temperature in Hangjinhou Banner in Hetao irrigation area is 2.5 ℃ lower than that in Habailaigeng in Kubuqi Desert, with dew point temperature higher than 6℃ and relative humidity higher than 20%. Continuous strong southerly winds blow in the Kubuqi Desert and Mu Us Sandy Land in Hetao Area, with maximum wind of 10 m above the surface being stronger than 4 m·s-1. The sea level pressure is low in the west and high in the east. A dense isobaric zone is in the curved section of Yellow River and the pressure gradient is 3-4 hPa·100 km-1. The formation of BLCL is of great significance to the convection triggering in this area, which is manifested in convection triggering, strengthening, and organization. Under different environmental conditions and weather system superposition configurations, the BLCL could trigger local convection, organized severe convection, torrential rain in Hetao Area. The generation and convective triggering of Hetao BLCL in this specific area are closely related to the distribution characteristics of rapid increase in precipitation from west to east in Hetao Area.
Based on dense ball sound data, dense field surface observation, ERA5 and the Linhe New Generation Weather Radar data, detailed analysis is conducted on the boundary layer convergence lines (BLCL) formed by the surface differences between the Hetao irrigation area and the Kubuqi Desert in Hetao Area of the Yellow River in July and August 2022, and also on the role of BLCL in convection triggering. The results indicate that the BLCL in the Hetao Area was formed by the combined influence of surface differences, boundary layer atmospheric circulation and complex terrain, and mainly occurred in the Kubuqi Desert which lies in the southeast of the Hetao irrigation area, and the length of BLCL ranged from 100 to 200 km. The BLCL was a shallow system with vertical thickness of approximately 1000 to 1100 m. The daily variation of BLCL was significant, with a high incidence from 12:00 BT to 17:00 BT, accounting for 80%. The probability of BLCL formation in July and August was as high as 60%, and 39% of BLCL could trigger convection. The prediction indicators about the formation of BLCL are as follows.The temperature in Hangjinhou Banner in Hetao irrigation area is 2.5 ℃ lower than that in Habailaigeng in Kubuqi Desert, with dew point temperature higher than 6℃ and relative humidity higher than 20%. Continuous strong southerly winds blow in the Kubuqi Desert and Mu Us Sandy Land in Hetao Area, with maximum wind of 10 m above the surface being stronger than 4 m·s-1. The sea level pressure is low in the west and high in the east. A dense isobaric zone is in the curved section of Yellow River and the pressure gradient is 3-4 hPa·100 km-1. The formation of BLCL is of great significance to the convection triggering in this area, which is manifested in convection triggering, strengthening, and organization. Under different environmental conditions and weather system superposition configurations, the BLCL could trigger local convection, organized severe convection, torrential rain in Hetao Area. The generation and convective triggering of Hetao BLCL in this specific area are closely related to the distribution characteristics of rapid increase in precipitation from west to east in Hetao Area.
2025, 51(1):97-109.
DOI: 10.7519/j.issn.1000-0526.2024.100901
Abstract:
In order to solve the problem of meteorological service for transportation operation in areas with a few observation stations, three typical regions in northern China (Beijing-Tianjin, Shaanxi-Gansu and Qinghai) are selected. The data used include the transportation weather observation data, the surface data from China Meteorological Administration Land Data Assimilation System (CLDAS V2.0) and Multi-source Precipitation Analysis System (CMPAS), as well as surface short wave and long wave radiation retrieval products from FY-4A. The change characteristics of road surface temperature and its relationship with environmental meteorological factors are analyzed. Three methods (linear regression, random forest and deep neural network) are employed to construct a 1 h updated real-time road surface temperature model, and the effects of model results from different methods and different data combinations are tested. Besides, the spatial generalization ability of the model is also explored. The results show that the road surface temperature is significantly correlated to the environmental meteorological factors, but show different feactures in different regions, seasons and time periods. The independent test shows that there is no much difference among the model results based on different methods. All the model results can well reproduce the daily changes of road surface high temperature in summer and road surface low temperature in winter. The mean error in winter is significantly lower than that in summer. The application of satellite radiation products has a significant improvement effect on the road surface high temperature observation model results in summer. The model has good spatial adaptability. However, compared with the model constructed according to local observations, the error of the model using data from nearby traffic stations in the same climate region shows an increasing trend at different degrees, of which the error increasing in Beijing-Tianjin region is the smallest.
In order to solve the problem of meteorological service for transportation operation in areas with a few observation stations, three typical regions in northern China (Beijing-Tianjin, Shaanxi-Gansu and Qinghai) are selected. The data used include the transportation weather observation data, the surface data from China Meteorological Administration Land Data Assimilation System (CLDAS V2.0) and Multi-source Precipitation Analysis System (CMPAS), as well as surface short wave and long wave radiation retrieval products from FY-4A. The change characteristics of road surface temperature and its relationship with environmental meteorological factors are analyzed. Three methods (linear regression, random forest and deep neural network) are employed to construct a 1 h updated real-time road surface temperature model, and the effects of model results from different methods and different data combinations are tested. Besides, the spatial generalization ability of the model is also explored. The results show that the road surface temperature is significantly correlated to the environmental meteorological factors, but show different feactures in different regions, seasons and time periods. The independent test shows that there is no much difference among the model results based on different methods. All the model results can well reproduce the daily changes of road surface high temperature in summer and road surface low temperature in winter. The mean error in winter is significantly lower than that in summer. The application of satellite radiation products has a significant improvement effect on the road surface high temperature observation model results in summer. The model has good spatial adaptability. However, compared with the model constructed according to local observations, the error of the model using data from nearby traffic stations in the same climate region shows an increasing trend at different degrees, of which the error increasing in Beijing-Tianjin region is the smallest.
2025, 51(1):110-121.
DOI: 10.7519/j.issn.10000526.2024.111301
Abstract:
In the summer of 2024, the overall climate of China was characterized by high temperature with more rainfall. The national average summer temperature ranked the first highest since 1961. The high temperature processes occurred earlier than normal, influenced more regions and had strong extremes. The overall precipitation was generally above normal, with a distribution of more in eastern China and less in western China. The precipitation was more than the normal by 50% to 100% in the southern part of Northeast China, central Inner Mongolia, eastern part of North China, northern part of East China, northern and southwestern parts of Central China, and eastern part of Northwest China. During this summer time, both the generated typhoons and the landfall typhoons were less than normal. The moderate intensity El Nino which began in May 2023 ended in May 2024 and transitioned to La Nina. At the same time, since the winter of 2023/2024, sea surface temperatures in the tropical Indian Ocean and North Atlantic have been at historical highs. The unusual distribution of tropical sea temperatures has been conducive to strengthening the Western Pacific subtropical high, which transported abundant moisture from Pacific Ocean and Indian Ocean to eastern China, resulting in widespread precipitation and frequent heavy precipitation processes in the eastern monsoon region of China.
In the summer of 2024, the overall climate of China was characterized by high temperature with more rainfall. The national average summer temperature ranked the first highest since 1961. The high temperature processes occurred earlier than normal, influenced more regions and had strong extremes. The overall precipitation was generally above normal, with a distribution of more in eastern China and less in western China. The precipitation was more than the normal by 50% to 100% in the southern part of Northeast China, central Inner Mongolia, eastern part of North China, northern part of East China, northern and southwestern parts of Central China, and eastern part of Northwest China. During this summer time, both the generated typhoons and the landfall typhoons were less than normal. The moderate intensity El Nino which began in May 2023 ended in May 2024 and transitioned to La Nina. At the same time, since the winter of 2023/2024, sea surface temperatures in the tropical Indian Ocean and North Atlantic have been at historical highs. The unusual distribution of tropical sea temperatures has been conducive to strengthening the Western Pacific subtropical high, which transported abundant moisture from Pacific Ocean and Indian Ocean to eastern China, resulting in widespread precipitation and frequent heavy precipitation processes in the eastern monsoon region of China.
2025, 51(1):122-128.
DOI: 10.7519/j.issn.1000-0526.2024.120301
Abstract:
The main characteristics of the general atmospheric circulation in October 2024 are that the polar vortex was distributed in a multipolar pattern. The mid- and high- latitude circulation in the Northern Hemisphere presented a multi-wave distribution, while in the Eurasian region it presented a “two troughs and one ridge” pattern. The Western Pacific subtropical high was stronger and the location was more westward and northward. In October, the monthly mean temperature was 11.6℃, 1.0℃ higher than that of the same period in normal years (10.6℃). The monthly average precipitation was 39.0 mm, 10% more than the normal (35.6 mm). During this month, there were six cold air processes, five major precipitation processes, and three typhoons affecting China. From September 29 to October 3, the cold air process was a national cold wave, and the three main precipitation processes were related to typhoons.
The main characteristics of the general atmospheric circulation in October 2024 are that the polar vortex was distributed in a multipolar pattern. The mid- and high- latitude circulation in the Northern Hemisphere presented a multi-wave distribution, while in the Eurasian region it presented a “two troughs and one ridge” pattern. The Western Pacific subtropical high was stronger and the location was more westward and northward. In October, the monthly mean temperature was 11.6℃, 1.0℃ higher than that of the same period in normal years (10.6℃). The monthly average precipitation was 39.0 mm, 10% more than the normal (35.6 mm). During this month, there were six cold air processes, five major precipitation processes, and three typhoons affecting China. From September 29 to October 3, the cold air process was a national cold wave, and the three main precipitation processes were related to typhoons.
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