ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Volume 50,Issue 6,2024 Table of Contents
2024, 50(6):649-660.
DOI: 10.7519/j.issn.1000-0526.2024.031402
Abstract:
In this paper, the performance of the forecasting system of short-term multi-category convective phenomena in the event that happened on 13 June 2022 is analyzed first. Then, based on the objective probability forecasts of thunderstorm, short-time severe rainfall, thunderstorm gale and hail events in 2022 as well as available multi-category severe convective monitoring data, the performance of objective probability forecast products of the four types of severe convective weather provided by the short-term forecasting system is evaluated in detail by adopting the spatial test methods used in the severe convection forecast operations and the indices that indicate deterministic and probabilistic properties. The evaluated forecast period of the forecast products initiated at 08:00 BT from 1 April to 30 September 2022 is 96 h with interval of 12 h. Case studies show that the potential area of the four different convective phenomena could be well forecasted 24 h in advance. Statistical verification results show that the short-time severe rainfall forecast has the best performance among the four convective weather phenomena, followed by the forecast of thunderstorm. The forecast of the thunderstorm gale has certain applicability as well. There are obvious problems of overestimation in all the four convective weather phenomena compared to the observations. The diurnal variations of thunderstorm, short-time severe rainfall and thunderstorm gale forecasts are related to the forecast coverage time. These evaluation results are beneficial to subsequent improvement and development of forecast model and system, and could provide a useful reference for the operational application of multi-category severe convection forecast results based on the fusion of physical understanding and fuzzy logic artificial intelligence.
In this paper, the performance of the forecasting system of short-term multi-category convective phenomena in the event that happened on 13 June 2022 is analyzed first. Then, based on the objective probability forecasts of thunderstorm, short-time severe rainfall, thunderstorm gale and hail events in 2022 as well as available multi-category severe convective monitoring data, the performance of objective probability forecast products of the four types of severe convective weather provided by the short-term forecasting system is evaluated in detail by adopting the spatial test methods used in the severe convection forecast operations and the indices that indicate deterministic and probabilistic properties. The evaluated forecast period of the forecast products initiated at 08:00 BT from 1 April to 30 September 2022 is 96 h with interval of 12 h. Case studies show that the potential area of the four different convective phenomena could be well forecasted 24 h in advance. Statistical verification results show that the short-time severe rainfall forecast has the best performance among the four convective weather phenomena, followed by the forecast of thunderstorm. The forecast of the thunderstorm gale has certain applicability as well. There are obvious problems of overestimation in all the four convective weather phenomena compared to the observations. The diurnal variations of thunderstorm, short-time severe rainfall and thunderstorm gale forecasts are related to the forecast coverage time. These evaluation results are beneficial to subsequent improvement and development of forecast model and system, and could provide a useful reference for the operational application of multi-category severe convection forecast results based on the fusion of physical understanding and fuzzy logic artificial intelligence.
2024, 50(6):661-674.
DOI: 10.7519/j.issn.1000-0526.2024.032601
Abstract:
Using the FY-4A satellite water vapor images and geostationary interferometric infrared sounder (GIIRS) products combined with potential vorticity products, the cold wave in central and eastern China from 6 to 8 January in 2021 is analyzed, based on the interpretation principle of meteorological satellite images and basic theory of potential vorticity conservation. It is found that this cold wave event was affected mainly by the high-altitude cold vortex and the surface cold high. The low-level cold air moved from the south of Baikal Lake to southeast and strengthened at 08:00 BT 5 January. By 20:00 BT 8, it weakened. The comparison of 850 hPa 24 h temperature changes of FY-4A/GIIRS and ERA5 shows that the characteristics of the low troposphere cold air moving to southeast and the cold and warm temperature change regional distribution monitored by FY-4A/GIIRS are basically consistent with the ERA5 data. The 24 h negative temperature change center at 850 hPa and the location of 0℃ isallotherm by FY-4A/GIIRS were similar to the ERA5 data. The intensity of the 24 h temperature change center at 850 hPa by FY-4A/GIIRS was slightly higher than that of ERA5. The increase of high-level potential vorticity near the enhancement of the dark area of the water vapor image triggered the enhancement of the subsidence movement behind the high-level cyclonic circulation, and the dry air subsidence caused the enhancement of the surface high pressure. The increase of absolute vorticity near the center of 500 hPa cold vortex was one of the reasons for the enhancement of cold air. During the period of sharp strengthening of cold air, the increase of vortices in the middle and lower layers resulted in the accumulation of cold air on the ground and the enhancement of cold wave. The analysis result of potential vorticity theory on the enhancement mechanism of cold air is consistent with the development characteristic of cold wave movement of FY-4A/GIIRS temperature data. This indicates that the application of FY-4A/GIIRS temperature data can be effectively used to analyze the evolution characteristics of cold wave.
Using the FY-4A satellite water vapor images and geostationary interferometric infrared sounder (GIIRS) products combined with potential vorticity products, the cold wave in central and eastern China from 6 to 8 January in 2021 is analyzed, based on the interpretation principle of meteorological satellite images and basic theory of potential vorticity conservation. It is found that this cold wave event was affected mainly by the high-altitude cold vortex and the surface cold high. The low-level cold air moved from the south of Baikal Lake to southeast and strengthened at 08:00 BT 5 January. By 20:00 BT 8, it weakened. The comparison of 850 hPa 24 h temperature changes of FY-4A/GIIRS and ERA5 shows that the characteristics of the low troposphere cold air moving to southeast and the cold and warm temperature change regional distribution monitored by FY-4A/GIIRS are basically consistent with the ERA5 data. The 24 h negative temperature change center at 850 hPa and the location of 0℃ isallotherm by FY-4A/GIIRS were similar to the ERA5 data. The intensity of the 24 h temperature change center at 850 hPa by FY-4A/GIIRS was slightly higher than that of ERA5. The increase of high-level potential vorticity near the enhancement of the dark area of the water vapor image triggered the enhancement of the subsidence movement behind the high-level cyclonic circulation, and the dry air subsidence caused the enhancement of the surface high pressure. The increase of absolute vorticity near the center of 500 hPa cold vortex was one of the reasons for the enhancement of cold air. During the period of sharp strengthening of cold air, the increase of vortices in the middle and lower layers resulted in the accumulation of cold air on the ground and the enhancement of cold wave. The analysis result of potential vorticity theory on the enhancement mechanism of cold air is consistent with the development characteristic of cold wave movement of FY-4A/GIIRS temperature data. This indicates that the application of FY-4A/GIIRS temperature data can be effectively used to analyze the evolution characteristics of cold wave.
2024, 50(6):675-685.
DOI: 10.7519/j.issn.1000-0526.2024.022002
Abstract:
Using the basic reflectivity factor PPI image data of 47 severe convective cases from 50 S-band Doppler weather radars in the central-eastern region of China from 2016 to 2020, the three-body scatter spike (TBSS) characteristics are analyzed based on the screening of 2626 TBSS samples. The results show that TBSS is most frequently observed at altitudes of 1.5 km and 5 km and at the elevation 2.4°. TBSS is mainly observed in the range from the edge of the radar static cone area to the radar radial distance of 210 km. It reaches its peak at the spot 115 km away from the radar. In the radar polar coordinate system, TBSS appears more in the south than in the north, and more in the west than in the east. It is better to observe TBSS when the angle between the moving direction of the convective storm and the radial direction of the radar is larger than 30°. The TBSS echo strength decreases rapidly to 5 dBz within 0-15 km radially outward from the beginning, and fluctuates in the range of -5 and 10 dBz after 15 km, with more than 70% of the TBSS zone being weak echoes weaker than 25 dBz. 99% of TBSS length is under 40 km, and the patterns of TBSSs with varying lengths follow a normal distribution. No significant correlation is found between the frequency and length of TBSS occurrences and the highest value of the high echo core and the area of the high echo.
Using the basic reflectivity factor PPI image data of 47 severe convective cases from 50 S-band Doppler weather radars in the central-eastern region of China from 2016 to 2020, the three-body scatter spike (TBSS) characteristics are analyzed based on the screening of 2626 TBSS samples. The results show that TBSS is most frequently observed at altitudes of 1.5 km and 5 km and at the elevation 2.4°. TBSS is mainly observed in the range from the edge of the radar static cone area to the radar radial distance of 210 km. It reaches its peak at the spot 115 km away from the radar. In the radar polar coordinate system, TBSS appears more in the south than in the north, and more in the west than in the east. It is better to observe TBSS when the angle between the moving direction of the convective storm and the radial direction of the radar is larger than 30°. The TBSS echo strength decreases rapidly to 5 dBz within 0-15 km radially outward from the beginning, and fluctuates in the range of -5 and 10 dBz after 15 km, with more than 70% of the TBSS zone being weak echoes weaker than 25 dBz. 99% of TBSS length is under 40 km, and the patterns of TBSSs with varying lengths follow a normal distribution. No significant correlation is found between the frequency and length of TBSS occurrences and the highest value of the high echo core and the area of the high echo.
2024, 50(6):686-700.
DOI: 10.7519/j.issn.1000-0526.2024.033001
Abstract:
Using the detection results of Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) and microwave imager (GMI) from 2014 to 2021, the differences in observing snowfall processes between different precipitation products of DPR are compared. Moreover, the macro and micro characteristics of three types of snowfall systems (deep, shallow, and near surface) are clarified, and the structural characteristics of four typical snowfall clouds in Xinjiang are analyzed by means of equal frequency height statistical method. The results show that the probability of shallow snowfall clouds exceeds 60%, while deep snowfall clouds contribute the most to snowfall. The liquid water path (LWP) and ice water path (IWP) of the three types of clouds are mostly distributed in 80-450 g·m-2 and 100-380 g·m-2, with a positive correlation between the echo top height, LWP, and IWP and the near-surface snowfall rate as a whole. The four selected cases are located in the Ale Mount Taishan area (A), the Ili River Valley (B), the hinterland of the Gurban Tunggut Desert (C), and the northern foot of the Kunlun Mountains in southern Xinjiang (D).The snow system observation results of GMI’s 166 GHz high-frequency channel are more detailed, 〖JP2〗and the brightness temperature is concentrated in the range of 200-275 K.〖JP〗 Cases A and B are mainly ice clouds, and cases C and D are mostly ice water mixed clouds, and the snowfall intensity and area of the former are greater than the latter. The radar reflectivity factor (Z) of the cloud profile is concentrated in 16-25 dBz and 0.75-4.65 km height. Cloud clusters in cases A, B, and D exhibit a “left leaning” structure, with the highest echo intensity in the upper and middle parts of the cloud, belonging to a typical development stage of cloud cluster. The echo intensity of case C is mainly located in the lower part of the cloud, and the cloud cluster is in the mature stage. Dm (mass weighted average diameter) and dBNw (particle number concentration) produce more snowfall between 1.00-1.22 mm and 33-35, respectively. The snowfall intensity is not only related to the particle size of ice crystals or supercooled water, but also affected by the particle number concentration. This study is of significance for improving the level of snow monitoring in Xinjiang, understanding the formation mechanism of snowfall and evaluating the potential and effectiveness of artificial snow seeding and cloud seeding in different regions of northern and southern Xinjiang.
Using the detection results of Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) and microwave imager (GMI) from 2014 to 2021, the differences in observing snowfall processes between different precipitation products of DPR are compared. Moreover, the macro and micro characteristics of three types of snowfall systems (deep, shallow, and near surface) are clarified, and the structural characteristics of four typical snowfall clouds in Xinjiang are analyzed by means of equal frequency height statistical method. The results show that the probability of shallow snowfall clouds exceeds 60%, while deep snowfall clouds contribute the most to snowfall. The liquid water path (LWP) and ice water path (IWP) of the three types of clouds are mostly distributed in 80-450 g·m-2 and 100-380 g·m-2, with a positive correlation between the echo top height, LWP, and IWP and the near-surface snowfall rate as a whole. The four selected cases are located in the Ale Mount Taishan area (A), the Ili River Valley (B), the hinterland of the Gurban Tunggut Desert (C), and the northern foot of the Kunlun Mountains in southern Xinjiang (D).The snow system observation results of GMI’s 166 GHz high-frequency channel are more detailed, 〖JP2〗and the brightness temperature is concentrated in the range of 200-275 K.〖JP〗 Cases A and B are mainly ice clouds, and cases C and D are mostly ice water mixed clouds, and the snowfall intensity and area of the former are greater than the latter. The radar reflectivity factor (Z) of the cloud profile is concentrated in 16-25 dBz and 0.75-4.65 km height. Cloud clusters in cases A, B, and D exhibit a “left leaning” structure, with the highest echo intensity in the upper and middle parts of the cloud, belonging to a typical development stage of cloud cluster. The echo intensity of case C is mainly located in the lower part of the cloud, and the cloud cluster is in the mature stage. Dm (mass weighted average diameter) and dBNw (particle number concentration) produce more snowfall between 1.00-1.22 mm and 33-35, respectively. The snowfall intensity is not only related to the particle size of ice crystals or supercooled water, but also affected by the particle number concentration. This study is of significance for improving the level of snow monitoring in Xinjiang, understanding the formation mechanism of snowfall and evaluating the potential and effectiveness of artificial snow seeding and cloud seeding in different regions of northern and southern Xinjiang.
2024, 50(6):701-710.
DOI: 10.7519/j.issn.1000-0526.2024.020201
Abstract:
In order to improve the understanding of precipitation characteristics of hailstorm in the Tibetan Plateau, based on the raindrop size distribution data observed by DSG5 disdrometer, radar reflectivity, FY-4A satellite and minutely precipitation data, etc., the precipitation microphysical characteristics of the hailstorm that occurred in Lhasa on 23 July 2020 is analyzed. The results show that there were some slightly differences at the time of beginning and end of the hailstorm, precipitation peak and total precipitation between rain gauge and DSG5 disdrometer although the minutely precipitation amounts of the two were similar. The fitted curves of the average spectrum distribution of raindrops and hailstones were monotonously decreasing. The raindrop size spectrum conformed to Γ distribution and the hail size spectrum accorded with M-P distribution. In the early stage of hail process, the precipitation particles were mostly small, which was mainly caused by the evaporation of the raindrops. In the later stage, the spectral width of precipitation particles became wider, the collision and fragmentation of the particles and the melting of graupel particles produced a large number of small raindrops, leading to a sharp increase in the number of particle concentration. The significant increase of number concentration and the widening of the droplet spectrum in this stage were the reason for the increased rainfall. The number concentration of hailstones only accounted for 1.6% of the total number concentration of precipitation particles, while the hailstones were the major contributors of the surface total precipitation. The empirical formula for the average terminal falling velocity and the diameter of the hails was fitted based on the observation.
In order to improve the understanding of precipitation characteristics of hailstorm in the Tibetan Plateau, based on the raindrop size distribution data observed by DSG5 disdrometer, radar reflectivity, FY-4A satellite and minutely precipitation data, etc., the precipitation microphysical characteristics of the hailstorm that occurred in Lhasa on 23 July 2020 is analyzed. The results show that there were some slightly differences at the time of beginning and end of the hailstorm, precipitation peak and total precipitation between rain gauge and DSG5 disdrometer although the minutely precipitation amounts of the two were similar. The fitted curves of the average spectrum distribution of raindrops and hailstones were monotonously decreasing. The raindrop size spectrum conformed to Γ distribution and the hail size spectrum accorded with M-P distribution. In the early stage of hail process, the precipitation particles were mostly small, which was mainly caused by the evaporation of the raindrops. In the later stage, the spectral width of precipitation particles became wider, the collision and fragmentation of the particles and the melting of graupel particles produced a large number of small raindrops, leading to a sharp increase in the number of particle concentration. The significant increase of number concentration and the widening of the droplet spectrum in this stage were the reason for the increased rainfall. The number concentration of hailstones only accounted for 1.6% of the total number concentration of precipitation particles, while the hailstones were the major contributors of the surface total precipitation. The empirical formula for the average terminal falling velocity and the diameter of the hails was fitted based on the observation.
2024, 50(6):711-722.
DOI: 10.7519/j.issn.1000-0526.2023.092001
Abstract:
Based on observations and the 24 h mid-low level wind speed and temperature forecast data from the ECMWF deterministic model in the period of January to December 2021, a support vector machine regression method was employed to develop a gust forecast model for the offshore areas of China so as to enhance the capability of predicting gusts at sea. Independent sample verification was conducted using data from January to September 2022, and a comparative analysis was performed against the gust factor method. The following conclusions were drawn. Changes in wind speed and temperature at different heights or vertical wind speed and temperature variations can all have an impact on gust forecasts. Consequently, relying solely on the 10 m wind speed forecast from the model, as done in the gust factor method, may lead to overestimation or underestimation of gusts in certain situations. The forecast model that incorporates upper-level meteorological elements based on the gust factor method can achieve better forecast performance. For gust of scale 9, the accuracy of this model is 50%, significantly higher than the 30% accuracy of the gust factor method. It also demonstrates good performance for gusts of large scales in different sea areas. When there is a certain deviation between the 10 m wind speed forecast from the ECMWF deterministic model and the observed wind speed, the gust forecast results of the support vector machine regression model, which considers upper-level element information, are closer to the observation compared to the result by the gust factor method.
Based on observations and the 24 h mid-low level wind speed and temperature forecast data from the ECMWF deterministic model in the period of January to December 2021, a support vector machine regression method was employed to develop a gust forecast model for the offshore areas of China so as to enhance the capability of predicting gusts at sea. Independent sample verification was conducted using data from January to September 2022, and a comparative analysis was performed against the gust factor method. The following conclusions were drawn. Changes in wind speed and temperature at different heights or vertical wind speed and temperature variations can all have an impact on gust forecasts. Consequently, relying solely on the 10 m wind speed forecast from the model, as done in the gust factor method, may lead to overestimation or underestimation of gusts in certain situations. The forecast model that incorporates upper-level meteorological elements based on the gust factor method can achieve better forecast performance. For gust of scale 9, the accuracy of this model is 50%, significantly higher than the 30% accuracy of the gust factor method. It also demonstrates good performance for gusts of large scales in different sea areas. When there is a certain deviation between the 10 m wind speed forecast from the ECMWF deterministic model and the observed wind speed, the gust forecast results of the support vector machine regression model, which considers upper-level element information, are closer to the observation compared to the result by the gust factor method.
2024, 50(6):723-732.
DOI: 10.7519/j.issn.1000-0526.2024.031901
Abstract:
Addressing the limitations of traditional models in capturing long-term dependencies and generalization ability in meteorological sequences, we propose a novel air temperature forecasting model based on sparse attention and adaptive seasonal and trend decomposition using loess (ATFSAS) in this paper. ATFSAS employs an encoder-decoder architecture, and integrates a sparse attention mechanism to effectively capture long-term dependencies in meteorological observation data. An information distillation method is introduced to reduce redundancy during the encoding process. The model refines the periodic and trend components in the forecast signals by combining a multi-layer decoder with an adaptive timing decomposition unit, achieving precise air temperature forecast. Based on the climate dataset in Jena, Germany, 24 h refined air temperature forecast is performed by ATFSAS, abtaining a mean absolute error of 1.7108℃. Compared to the LSTM model, ATFSAS demonstrates superior performance in medium-short term daily average air temperature and multi-region single-day average air temperature forecasts based on the China ground climate daily dataset, and their mean absolute errors get improved by 35.56% and 23.66%, respectively.
Addressing the limitations of traditional models in capturing long-term dependencies and generalization ability in meteorological sequences, we propose a novel air temperature forecasting model based on sparse attention and adaptive seasonal and trend decomposition using loess (ATFSAS) in this paper. ATFSAS employs an encoder-decoder architecture, and integrates a sparse attention mechanism to effectively capture long-term dependencies in meteorological observation data. An information distillation method is introduced to reduce redundancy during the encoding process. The model refines the periodic and trend components in the forecast signals by combining a multi-layer decoder with an adaptive timing decomposition unit, achieving precise air temperature forecast. Based on the climate dataset in Jena, Germany, 24 h refined air temperature forecast is performed by ATFSAS, abtaining a mean absolute error of 1.7108℃. Compared to the LSTM model, ATFSAS demonstrates superior performance in medium-short term daily average air temperature and multi-region single-day average air temperature forecasts based on the China ground climate daily dataset, and their mean absolute errors get improved by 35.56% and 23.66%, respectively.
WANG Shuo
,
ZHU Wengang
,
ZHANG Dianguo
,
WANG Hong
,
WANG Wenqing
,
YUAN Yahan
,
GAO Ronglu
,
LIU Quan
2024, 50(6):733-745.
DOI: 10.7519/j.issn.1000-0526.2024.041701
Abstract:
In order to analyze the distribution characteristics of ice crystals in cold clouds and reveal the evolution mechanism of their growth, ice crystals are classified into 8 categories and labeled according to their shapes and sizes. At the same time, one type of partition column is labeled for data quality control. Then, the 9 types of labeled images are integrated to build an image dataset. The transfer learning VGGNet16 model is adopted for identification training, and the classification accuracy of the trained model reaches 98%. The model is applied to the study of ice crystal characteristics of cold clouds in autumn. Three cumulus-stratiform mixed cloud precipitation processes and three stratiform cloud precipitation processes in 2019 are selected to analyze the proportion of ice crystal shapes in different temperature ranges and the change characteristics of ice crystal size distribution. The results show that the initial shape distribution of ice crystal is determined by the temperature’s affecting the ratio of base surface to edge surface of ice crystals. In the same temperature range, the proportion of spherical ice crystals and linear ice crystals in cumulus-stratiform mixed clouds is higher than that in stratiform cloud, and the proportion of various ice crystals is relatively fixed when the temperature is below -12℃. In cumulus-stratiform mixed clouds, the diameters of linear ice crystals are concentrated in 300-800 μm with the ice crystal size distribution being multimodal. The diameters of aggregated ice crystals are greater than 600 μm. The concentrations at the beginning and the end of the ice crystal spectrum are equal. The diameters of spherical ice crystals are concentrated in the range of 120-300 μm, and the ice crystal size distribution shows a monotonically decreasing trend.
In order to analyze the distribution characteristics of ice crystals in cold clouds and reveal the evolution mechanism of their growth, ice crystals are classified into 8 categories and labeled according to their shapes and sizes. At the same time, one type of partition column is labeled for data quality control. Then, the 9 types of labeled images are integrated to build an image dataset. The transfer learning VGGNet16 model is adopted for identification training, and the classification accuracy of the trained model reaches 98%. The model is applied to the study of ice crystal characteristics of cold clouds in autumn. Three cumulus-stratiform mixed cloud precipitation processes and three stratiform cloud precipitation processes in 2019 are selected to analyze the proportion of ice crystal shapes in different temperature ranges and the change characteristics of ice crystal size distribution. The results show that the initial shape distribution of ice crystal is determined by the temperature’s affecting the ratio of base surface to edge surface of ice crystals. In the same temperature range, the proportion of spherical ice crystals and linear ice crystals in cumulus-stratiform mixed clouds is higher than that in stratiform cloud, and the proportion of various ice crystals is relatively fixed when the temperature is below -12℃. In cumulus-stratiform mixed clouds, the diameters of linear ice crystals are concentrated in 300-800 μm with the ice crystal size distribution being multimodal. The diameters of aggregated ice crystals are greater than 600 μm. The concentrations at the beginning and the end of the ice crystal spectrum are equal. The diameters of spherical ice crystals are concentrated in the range of 120-300 μm, and the ice crystal size distribution shows a monotonically decreasing trend.
2024, 50(6):746-755.
DOI: 10.7519/j.issn.1000-0526.2024.030401
Abstract:
In view of the difficulties in monitoring summer rainy season in North China, the concept of the summer potential rainy season in North China is proposed, and a new monitoring method， i.e., the speci-fic humidity subtropical high method, is established. The main monitoring results are as follows. The average start date of the summer rainy season in North China from 1961 to 2022 is 10 July, the end date is 8 August, and the rainy season lasts for 28 days. The start and end time of the rainy season in North China has no obvious long-term change trend, but the interdecadal change is significant. The summer rainy season precipitation and the comprehensive intensity index of rainy season monitored by this method in North China are very consistent with the changes in summer precipitation, which is a high positive correlation. This indicates that the monitored rainy season parameters have a good indication of the changes in summer precipitation in North China. The start and end of the summer rainy season in North China are accompanied by significant changes in atmospheric circulation. The East Asian subtropical summer monsoon at 850 hPa strengthens and weakens, and the atmospheric specific humidity in North China north of 30°N rapidly increases and decreases. The 500 hPa height field presents positive and negative anomaly centers in North China, and the Northwest Pacific subtropical high begins to significantly lift northward or retreat eastward along the coast of East Asia. The upper westerly jet axis at 200 hPa moves north from 37.5°N to 41.5°N or south from 44.5°N to 40.5°N.
In view of the difficulties in monitoring summer rainy season in North China, the concept of the summer potential rainy season in North China is proposed, and a new monitoring method， i.e., the speci-fic humidity subtropical high method, is established. The main monitoring results are as follows. The average start date of the summer rainy season in North China from 1961 to 2022 is 10 July, the end date is 8 August, and the rainy season lasts for 28 days. The start and end time of the rainy season in North China has no obvious long-term change trend, but the interdecadal change is significant. The summer rainy season precipitation and the comprehensive intensity index of rainy season monitored by this method in North China are very consistent with the changes in summer precipitation, which is a high positive correlation. This indicates that the monitored rainy season parameters have a good indication of the changes in summer precipitation in North China. The start and end of the summer rainy season in North China are accompanied by significant changes in atmospheric circulation. The East Asian subtropical summer monsoon at 850 hPa strengthens and weakens, and the atmospheric specific humidity in North China north of 30°N rapidly increases and decreases. The 500 hPa height field presents positive and negative anomaly centers in North China, and the Northwest Pacific subtropical high begins to significantly lift northward or retreat eastward along the coast of East Asia. The upper westerly jet axis at 200 hPa moves north from 37.5°N to 41.5°N or south from 44.5°N to 40.5°N.
2024, 50(6):756-769.
DOI: 10.7519/j.issn.1000-0526.2024.040802
Abstract:
In order to determine the sections with potential wind disasters along the high-speed railway and reduce the occurrence of floating objects invasion disasters caused by strong winds, based on the observation data of meteorological stations along the high-speed railway in Tianjin, high-resolution series of satellite remote sensing image data, historical disaster of high-speed railway and basic geographic information, this article analyzes the impact of strong wind on the invasion disaster of light floating objects and proposes the wind speed threshold range and dominant wind direction of disasters in different regional sections of the Tianjin High-Speed Railway. Then, based the risk assessment theory of natural disasters, the meteorological risk analysis model of the wind-induced light floating objects invasion disaster of high-speed railway is established, and the risk zoning is formed. The results show that the invasion disasters of light floating objects along the high-speed railway mainly occur in spring and winter, accounting for 73.75% of the total invasion disasters of the whole year. The most concentrated occurrence time is from 12:00 BT to 15:00 BT in the afternoon, with most seen in the central and eastern parts of Tianjin and then in the northwestern and the southwestern parts in order. Disasters mainly occur in the wind speed range with the maximum wind speed of 8-17 m·s-1 and extreme wind speed of 13-21 m·s-1. When the wind direction is from west to north and the wind direction is at the angle of more than 45 ° with the line, disasters are more likely to occur. By means of satellite remote sensing technology, the distribution of hidden dangers along the Tianjin High-Speed Railway could be well identified, and the highest identification accuracy could reach 72.7%. Based on the characteristics of wind speed and wind direction and the distribution of hidden dangers, the meteorological risk analysis model of the Tianjin High-Speed Railway wind disaster is established, and the zoning results show that the Beijing-Shanghai High-Speed Railway and the Beijing-Tianjin Intercity Railway have higher risks, followed by the Tianjin-Qinhuangdao High-Speed Railway, and the Tianjin-Bazhou Passenger Dedicated-Line has the lowest risk. High risk sections are mainly concentrated in first, the north of the Wuqing District, Beichen District and Xiqing District of the Beijing-Shanghai High-Speed Railway, second, the Beichen District, the east of the Dongli District and the Binhai New Area section of the Beijing-Tianjin Intercity, and third, the east of the Dongli District of the Tianjin-Qinhuangdao High-Speed Railway.
In order to determine the sections with potential wind disasters along the high-speed railway and reduce the occurrence of floating objects invasion disasters caused by strong winds, based on the observation data of meteorological stations along the high-speed railway in Tianjin, high-resolution series of satellite remote sensing image data, historical disaster of high-speed railway and basic geographic information, this article analyzes the impact of strong wind on the invasion disaster of light floating objects and proposes the wind speed threshold range and dominant wind direction of disasters in different regional sections of the Tianjin High-Speed Railway. Then, based the risk assessment theory of natural disasters, the meteorological risk analysis model of the wind-induced light floating objects invasion disaster of high-speed railway is established, and the risk zoning is formed. The results show that the invasion disasters of light floating objects along the high-speed railway mainly occur in spring and winter, accounting for 73.75% of the total invasion disasters of the whole year. The most concentrated occurrence time is from 12:00 BT to 15:00 BT in the afternoon, with most seen in the central and eastern parts of Tianjin and then in the northwestern and the southwestern parts in order. Disasters mainly occur in the wind speed range with the maximum wind speed of 8-17 m·s-1 and extreme wind speed of 13-21 m·s-1. When the wind direction is from west to north and the wind direction is at the angle of more than 45 ° with the line, disasters are more likely to occur. By means of satellite remote sensing technology, the distribution of hidden dangers along the Tianjin High-Speed Railway could be well identified, and the highest identification accuracy could reach 72.7%. Based on the characteristics of wind speed and wind direction and the distribution of hidden dangers, the meteorological risk analysis model of the Tianjin High-Speed Railway wind disaster is established, and the zoning results show that the Beijing-Shanghai High-Speed Railway and the Beijing-Tianjin Intercity Railway have higher risks, followed by the Tianjin-Qinhuangdao High-Speed Railway, and the Tianjin-Bazhou Passenger Dedicated-Line has the lowest risk. High risk sections are mainly concentrated in first, the north of the Wuqing District, Beichen District and Xiqing District of the Beijing-Shanghai High-Speed Railway, second, the Beichen District, the east of the Dongli District and the Binhai New Area section of the Beijing-Tianjin Intercity, and third, the east of the Dongli District of the Tianjin-Qinhuangdao High-Speed Railway.
2024, 50(6):770-776.
DOI: 10.7519/j.issn.1000-0526.2024.051101
Abstract:
The main characteristics of the general atmospheric circulation in March 2024 are as follows. There was one polar vortex center in the Northern Hemisphere, which was stronger than usual. The circulation in mid-high latitudes showed an anomalous four-wave pattern, and the Asian circulation was relatively flat and straight. The northern part of China was basically in a negative anomaly zone of the 500 hPa geopotential height field. The strength of Western Pacific subtropical high was stronger and more westward than in normal years, while the south branch trough was a little weaker than usual. The monthly mean temperature was 6.0℃ across China, which is 1.2℃ warmer than normal. The monthly mean precipitation amount was 24.0 mm, 18.4% less than in normal period (29.4 mm). However，the precipitation in the north of Inner Mongolia and the east of Northwest China was significantly more than that in normal years. During this month, one large-scale precipitation process occurred, and severe convection weather appeared frequently with three regional convection events with strong localized intensities. In addition, there were two cold air processes without causing large range of drop in temperature. Three sand-dust weather processes occurred in northern China, and the drought condition continued in winter and spring in the southwestern region of China.
The main characteristics of the general atmospheric circulation in March 2024 are as follows. There was one polar vortex center in the Northern Hemisphere, which was stronger than usual. The circulation in mid-high latitudes showed an anomalous four-wave pattern, and the Asian circulation was relatively flat and straight. The northern part of China was basically in a negative anomaly zone of the 500 hPa geopotential height field. The strength of Western Pacific subtropical high was stronger and more westward than in normal years, while the south branch trough was a little weaker than usual. The monthly mean temperature was 6.0℃ across China, which is 1.2℃ warmer than normal. The monthly mean precipitation amount was 24.0 mm, 18.4% less than in normal period (29.4 mm). However，the precipitation in the north of Inner Mongolia and the east of Northwest China was significantly more than that in normal years. During this month, one large-scale precipitation process occurred, and severe convection weather appeared frequently with three regional convection events with strong localized intensities. In addition, there were two cold air processes without causing large range of drop in temperature. Three sand-dust weather processes occurred in northern China, and the drought condition continued in winter and spring in the southwestern region of China.
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ISSN:1000-0526 CN:11-2282/P