Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boos...Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boosting machine (LightGBM) algorithm using C-band radar echo products and ground observations, to identify and classify three major types of severe convective weather (</span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;">, hail, short-term heavy rain (STHR), convective gust (CG)). The model evaluations show the LightGBM model performs well in the training set (2011-2017) and the testing set (2018) with the overall false identification ratio (FIR) of only 4.9% and 7.0%, respectively. Furthermore, the average probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR) for the three types of severe convective weather in two sample sets are over 85%, 65% and lower than 30%, respectively. The LightGBM model and the storm cell identification and tracking (SCIT) product are then used to forecast the severe convective weather 15 - 60 minutes in advance. The average POD, CSI and FAR for the forecasts of the three types of severe convective weather are 57.4%, 54.7% and 38.4%, respectively, which are significantly higher than those of the manual work. Among the three types of severe convective weather, the STHR has the highest POD and CSI and the lowest FAR, while the skill scores for the hail and CG are similar. Therefore, the LightGBM model constructed in this paper is able to identify, classify and forecast the three major types of severe convective weather automatically with relatively high accuracy, and has a broad application prospect in the future automatic meteorological operation.展开更多
Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for c...Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.展开更多
On August 1,2022,a rainstorm process occurred in Bayannur City,Inner Mongolia.An extreme precipitation event occurred in Wuyuan County,Urat Middle Banner and Urat Front Banner,causing rainstorm,flood,strong convective...On August 1,2022,a rainstorm process occurred in Bayannur City,Inner Mongolia.An extreme precipitation event occurred in Wuyuan County,Urat Middle Banner and Urat Front Banner,causing rainstorm,flood,strong convective wind and other disasters,thereby resulting in crop damage,livestock death and other losses.Meteorological departments made a series of forecast and early warning,meteorological service and basin joint prevention for the rainstorm process,set an example for dealing with the rainstorm disaster,and accumulated experience for the forecast service of rainstorm in future.展开更多
Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time...Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time based on the end-user's own risk assessment and decision threshold. The presented results of an interview study,conducted with 27 members of German civil protection authorities, show that developments in meteorology and weather forecasting do not necessarily fit the current practices of German emergency services. These practices are mostly carried out based on alarms and ground truth in a superficial reactive manner, rather than on anticipation based on prognoses or forecasts. Emergency managers cope with uncertainty by collecting, comparing, and blending different information about an uncertain event and its uncertain outcomes within the situation assessment to validate the information. Emergency managers struggle most with an increase of emergency calls and missions due to the impacts of severe weather. Because of the additional expenditures, the weather event makes it even harder for them to fulfill their core duties. These findings support the need for impact-based warnings.展开更多
文摘Severe convective weather can lead to a variety of disasters, but they are still difficult to be pre-warned and forecasted in the meteorological operation. This study generates a model based on the light gradient boosting machine (LightGBM) algorithm using C-band radar echo products and ground observations, to identify and classify three major types of severe convective weather (</span><i><span style="font-family:Verdana;">i.e.</span></i><span style="font-family:Verdana;">, hail, short-term heavy rain (STHR), convective gust (CG)). The model evaluations show the LightGBM model performs well in the training set (2011-2017) and the testing set (2018) with the overall false identification ratio (FIR) of only 4.9% and 7.0%, respectively. Furthermore, the average probability of detection (POD), critical success index (CSI) and false alarm ratio (FAR) for the three types of severe convective weather in two sample sets are over 85%, 65% and lower than 30%, respectively. The LightGBM model and the storm cell identification and tracking (SCIT) product are then used to forecast the severe convective weather 15 - 60 minutes in advance. The average POD, CSI and FAR for the forecasts of the three types of severe convective weather are 57.4%, 54.7% and 38.4%, respectively, which are significantly higher than those of the manual work. Among the three types of severe convective weather, the STHR has the highest POD and CSI and the lowest FAR, while the skill scores for the hail and CG are similar. Therefore, the LightGBM model constructed in this paper is able to identify, classify and forecast the three major types of severe convective weather automatically with relatively high accuracy, and has a broad application prospect in the future automatic meteorological operation.
基金Supported by Huzhou Science and Technology Program(2013GY06)Research Project of Huzhou Municipal Meteorological Bureau(hzqx201602)
文摘Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.
基金Supported by the Natural Science Foundation of Inner Mongolia Autonomous Region,China(2019BS04001,2021MS04019)。
文摘On August 1,2022,a rainstorm process occurred in Bayannur City,Inner Mongolia.An extreme precipitation event occurred in Wuyuan County,Urat Middle Banner and Urat Front Banner,causing rainstorm,flood,strong convective wind and other disasters,thereby resulting in crop damage,livestock death and other losses.Meteorological departments made a series of forecast and early warning,meteorological service and basin joint prevention for the rainstorm process,set an example for dealing with the rainstorm disaster,and accumulated experience for the forecast service of rainstorm in future.
基金funded by the BMVI (Federal Ministry of Transport and Digital Infrastructures)
文摘Communicating meteorological uncertainty allows earlier provision of information on possible future events. The desired benefit is to enable the end-user to start with preparatory protective actions at an earlier time based on the end-user's own risk assessment and decision threshold. The presented results of an interview study,conducted with 27 members of German civil protection authorities, show that developments in meteorology and weather forecasting do not necessarily fit the current practices of German emergency services. These practices are mostly carried out based on alarms and ground truth in a superficial reactive manner, rather than on anticipation based on prognoses or forecasts. Emergency managers cope with uncertainty by collecting, comparing, and blending different information about an uncertain event and its uncertain outcomes within the situation assessment to validate the information. Emergency managers struggle most with an increase of emergency calls and missions due to the impacts of severe weather. Because of the additional expenditures, the weather event makes it even harder for them to fulfill their core duties. These findings support the need for impact-based warnings.
