[Objective]Precipitation events caused by Super Typhoon Doksuri in Fujian Province were simulated and evaluated based on the WRF model to provide a reference for typhoon precipitation simulation and forecasting in sou...[Objective]Precipitation events caused by Super Typhoon Doksuri in Fujian Province were simulated and evaluated based on the WRF model to provide a reference for typhoon precipitation simulation and forecasting in southeast coastal areas of China.[Methods]The next-generation mesoscale numerical weather prediction model WRF V4.3(The Weather Research and Forecasting Model)was used to simulate the precipitation caused by Typhoon Doksuri in Fujian Province in 2023.Observations from 86 meteorological stations with hourly rainfall records were used to evaluate the model’s performance.Six evaluation indices were used,including the correlation coefficient(R),root mean square error(RMSE),mean absolute error(MAE),equitable threat score(ETS),probability of detection(POD),and false alarm ratio(FAR).[Results](1)The temporal and spatial evolution of precipitation during Typhoon Doksuri was effectively captured by the WRF model.Precipitation intensity increased gradually from July 27 to 29,2023,with the heaviest rainfall concentrated in the northern and eastern coastal areas of Fujian Province.(2)Significant differences in model performance were observed in terms of R,RMSE,and MAE.The largest errors occurred in Putian City,while smaller errors were found in southwestern Fujian Province.The evaluation result of all six indices showed that the WRF model performed best in simulating daily precipitation compared to hourly,three-hourly,six-hourly,and twelve-hourly precipitation.(3)The R95p index indicated that the WRF model successfully captured the overall spatial distribution of extreme precipitation.However,extreme precipitation intensity was overestimated in certain coastal areas.(4)Despite accurately identifying the coastal regions of Fujian as being most affected,the WRF model failed to accurately simulate the spatial distribution and intensity of precipitation.The simulated precipitation centers showed discrepancies when compared with the observed centers.[Conclusion]Although the WRF model underestimated hourly precipitation,it successfully captured the temporal evolution and spatial distribution of rainfall caused by Typhoon Doksuri in Fujian Province.It reproduced the heavy rainfall centers in central Fujian Province,with daily precipitation peaks reaching up to 350 mm.This highlighted the severity of extreme rainfall caused by Typhoon Doksuri.展开更多
Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri'...Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts,global models and regional models with lead times varying from 1 to 5 days.The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration(CMA).The typhoon also caused significant wind and rainfall impacts,with precipitation at several stations reaching historical extremes,ranking eighth in terms of total rainfall impact during the event.The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method(SSTC)and Fengwu Model are the most effective for short-term track forecasts.Meanwhile,the forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)and United Kingdom Meteorological Office(UKMO)are optimal for long-term predictions.It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity.The objective forecast is terribly poor when there is a sudden change in intensity.CMA-National Digital Forecast System(CMA-NDFS)provides a better reference value for typhoon accumulated rainfall forecasts,and regional models perform well in forecasting extreme rainfall.The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model.This improves the effective application of model products.展开更多
After landfall,tropical cyclone(TC)remnants may maintain or even rejuvenate and incur catastrophic disasters.What leads to the revival of TC remnants over land remains elusive.In this study,the revival mechanism of Ty...After landfall,tropical cyclone(TC)remnants may maintain or even rejuvenate and incur catastrophic disasters.What leads to the revival of TC remnants over land remains elusive.In this study,the revival mechanism of Typhoon Doksuri(2023)remnants is extensively explored.Doksuri brought severe damage to the Chinese mainland after its landfall.The remnants vortex of Doksuri sustained an inland trajectory for 3 days and underwent a total maintenance of 60 h,with a revival of 18 h.Based on multi-source observations and ERA5 reanalysis data,by calculation of moist potential vorticity and analysis of slantwise vorticity development(SVD),this study unveils that while maintaining a significant warm-core structure over the course of maintenance and revival,the Doksuri remnants transported sufficient moisture in the mid–lower troposphere,which intensified the north–south temperature and humidity gradients,causing tilting of the isentropic surfaces remarkably.According to the SVD theory,the tilting gave rise to vorticity development and forced upward air motion on the northern side of the remnant vortex.Moreover,numerical sensitivity experiments based on the WRF model reveal that the topography of Taihang Mountains and the diabatic heating associated with surface and convective latent heat fluxes also played important roles in the revival of the Doksuri remnants.The dynamic and thermodynamic mechanisms derived by this study will help improve understanding and prediction of the disasters induced by TC remnants.展开更多
为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风...为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风过程降水量进行常规检验和空间检验,并与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报降水产品(以下简记为“ECMWF”)、福建省气象局最优TS(threat score)评分订正法(optimal TS,OTS)订正降水产品(以下简记为“FZECMOS”)结果进行对比。分析表明:(1)DSAEF_LTP模型对福建沿海强降水落区和东北部强降水中心的预报接近实况,100 mm及以上和250 mm及以上等极端降水量TS评分比ECMWF和FZECMOS提升明显,但DSAEF_LTP模型存在特大暴雨预报范围显著偏小等缺点。(2)在100 mm及以上和250 mm及以上量级,MODE(Method for Object-based Diagnostic Evaluation)空间检验显示,DSAEF_LTP模型在整体相似度上明显优于ECMWF和FZECMOS,尤其在对孤立小区域强降水的预报性能方面表现出色。(3)随着降水检验量级的增加,DSAEF_LTP模型预报产品与实况重叠面积之比也增大,表明DSAEF_LTP模型在极端降水方面的预报效果更加突出。(4)DSAEF_LTP模型还能够根据最新的相似路径实况和预报,调整筛选历史相似台风,合理保留相似台风及其降水分布,使得集合预报效果得以改善。展开更多
2023年第5号台风“杜苏芮”(2305)造成福建省莆田市罕见特大暴雨。本文利用福建省地面气象观测数据、雷达和卫星等多源观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)再分析资料,分析“杜...2023年第5号台风“杜苏芮”(2305)造成福建省莆田市罕见特大暴雨。本文利用福建省地面气象观测数据、雷达和卫星等多源观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)再分析资料,分析“杜苏芮”引发的莆田市特大暴雨的阶段性和强度特征。结果表明:本次过程由3个暴雨阶段以“无缝衔接”的形式组成,第一阶段为台风眼壁暴雨,具有短时雨强较强、空间分布均匀的特点;第二阶段为螺旋雨带暴雨,具有小时雨强差异显著、雨峰明显的特点;第三阶段为季风暴雨,具有暴雨范围广、持续时间长的特点。“杜苏芮”引发的莆田暴雨极端性显著,具体表现为:暴雨强度强、特大暴雨影响范围广、累计雨量大、短时强降水发生频次高且持续时间长。其中,莆田站24 h雨量达561.7 mm,突破福建省历史最高记录,极端性特征尤为突出。台风暖式切变线、低空南风急流和季风等系统持续维持,是三个阶段暴雨实现“无缝衔接”的重要天气背景,兴化平原“三面环山、向南开口”的地形对南风急流的抬升和收缩作用是暴雨中心位于兴化平原至东北部山区的重要因素。展开更多
This study delves into the multiple weather systems and their interaction mechanisms that caused the severe rainfall event in Northeast China in early August 2023. The analysis reveals that the atmospheric circulation...This study delves into the multiple weather systems and their interaction mechanisms that caused the severe rainfall event in Northeast China in early August 2023. The analysis reveals that the atmospheric circulation in the mid-to-high latitudes of the Eurasian continent exhibited a significant “two troughs and two ridges” structure, with Northeast China located precisely in the peripheral region of the subtropical high, significantly influenced by its marginal airflows. Additionally, the residual circulation of Typhoon “Doksuri” interacting with the subtropical high and upper-level troughs significantly increased the rainfall intensity and duration in the region. In particular, the continuous and powerful transport of the southwest jet provided the necessary moisture and unstable conditions for the generation and development of convective systems. The rainfall event resulted in nearly 40,000 people affected and crop damage covering an area of approximately 4000 hectares, demonstrating the severity of extreme weather. The study emphasizes that strengthening meteorological monitoring and early warning systems, as well as formulating and improving emergency response mechanisms, are crucial for reducing potential disaster losses caused by heavy rainfall. Future research can further explore the interaction mechanisms among weather systems, limitations of data sources, and the connection between long-term trends of heavy rainfall events and global climate change.展开更多
文摘[Objective]Precipitation events caused by Super Typhoon Doksuri in Fujian Province were simulated and evaluated based on the WRF model to provide a reference for typhoon precipitation simulation and forecasting in southeast coastal areas of China.[Methods]The next-generation mesoscale numerical weather prediction model WRF V4.3(The Weather Research and Forecasting Model)was used to simulate the precipitation caused by Typhoon Doksuri in Fujian Province in 2023.Observations from 86 meteorological stations with hourly rainfall records were used to evaluate the model’s performance.Six evaluation indices were used,including the correlation coefficient(R),root mean square error(RMSE),mean absolute error(MAE),equitable threat score(ETS),probability of detection(POD),and false alarm ratio(FAR).[Results](1)The temporal and spatial evolution of precipitation during Typhoon Doksuri was effectively captured by the WRF model.Precipitation intensity increased gradually from July 27 to 29,2023,with the heaviest rainfall concentrated in the northern and eastern coastal areas of Fujian Province.(2)Significant differences in model performance were observed in terms of R,RMSE,and MAE.The largest errors occurred in Putian City,while smaller errors were found in southwestern Fujian Province.The evaluation result of all six indices showed that the WRF model performed best in simulating daily precipitation compared to hourly,three-hourly,six-hourly,and twelve-hourly precipitation.(3)The R95p index indicated that the WRF model successfully captured the overall spatial distribution of extreme precipitation.However,extreme precipitation intensity was overestimated in certain coastal areas.(4)Despite accurately identifying the coastal regions of Fujian as being most affected,the WRF model failed to accurately simulate the spatial distribution and intensity of precipitation.The simulated precipitation centers showed discrepancies when compared with the observed centers.[Conclusion]Although the WRF model underestimated hourly precipitation,it successfully captured the temporal evolution and spatial distribution of rainfall caused by Typhoon Doksuri in Fujian Province.It reproduced the heavy rainfall centers in central Fujian Province,with daily precipitation peaks reaching up to 350 mm.This highlighted the severity of extreme rainfall caused by Typhoon Doksuri.
基金supported jointly by Innovation and Development Special Program of China Meteorological Administration (Grant Nos.CXFZ2024J006)National Natural Science Foundation of China (Grant Nos.42075056)+4 种基金Research Program from Science and Technology Committee of Shanghai (Grant Nos.23DZ204700,22ZR1476400)Shanghai Science and Technology Commission Project (Grant Nos.23DZ1204701)Ningbo Key R&D Program (Grant Nos.2023Z139)East China Regional Meteorological Science and Technology Collaborative Innovation Fund (Grant Nos.QYHZ202318)Special Fund Project of Basic Scientific Research Business Expenses of Shanghai Typhoon Institute, (Grant Nos.2024JB03).
文摘Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts,global models and regional models with lead times varying from 1 to 5 days.The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration(CMA).The typhoon also caused significant wind and rainfall impacts,with precipitation at several stations reaching historical extremes,ranking eighth in terms of total rainfall impact during the event.The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method(SSTC)and Fengwu Model are the most effective for short-term track forecasts.Meanwhile,the forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)and United Kingdom Meteorological Office(UKMO)are optimal for long-term predictions.It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity.The objective forecast is terribly poor when there is a sudden change in intensity.CMA-National Digital Forecast System(CMA-NDFS)provides a better reference value for typhoon accumulated rainfall forecasts,and regional models perform well in forecasting extreme rainfall.The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model.This improves the effective application of model products.
基金Supported by the National Key Research and Development Program of China(2022YFC3004200 and 2022YFC3003901).
文摘After landfall,tropical cyclone(TC)remnants may maintain or even rejuvenate and incur catastrophic disasters.What leads to the revival of TC remnants over land remains elusive.In this study,the revival mechanism of Typhoon Doksuri(2023)remnants is extensively explored.Doksuri brought severe damage to the Chinese mainland after its landfall.The remnants vortex of Doksuri sustained an inland trajectory for 3 days and underwent a total maintenance of 60 h,with a revival of 18 h.Based on multi-source observations and ERA5 reanalysis data,by calculation of moist potential vorticity and analysis of slantwise vorticity development(SVD),this study unveils that while maintaining a significant warm-core structure over the course of maintenance and revival,the Doksuri remnants transported sufficient moisture in the mid–lower troposphere,which intensified the north–south temperature and humidity gradients,causing tilting of the isentropic surfaces remarkably.According to the SVD theory,the tilting gave rise to vorticity development and forced upward air motion on the northern side of the remnant vortex.Moreover,numerical sensitivity experiments based on the WRF model reveal that the topography of Taihang Mountains and the diabatic heating associated with surface and convective latent heat fluxes also played important roles in the revival of the Doksuri remnants.The dynamic and thermodynamic mechanisms derived by this study will help improve understanding and prediction of the disasters induced by TC remnants.
文摘为了评估改进的动力统计相似集合预报登陆台风降水模型(Dynamical-Statistical-Analog Ensemble Forecast model for Landfalling Typhoon Precipitation,DSAEF_LTP)在2023年第5号超强台风“杜苏芮”影响福建地区的表现,对其预报的台风过程降水量进行常规检验和空间检验,并与欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报降水产品(以下简记为“ECMWF”)、福建省气象局最优TS(threat score)评分订正法(optimal TS,OTS)订正降水产品(以下简记为“FZECMOS”)结果进行对比。分析表明:(1)DSAEF_LTP模型对福建沿海强降水落区和东北部强降水中心的预报接近实况,100 mm及以上和250 mm及以上等极端降水量TS评分比ECMWF和FZECMOS提升明显,但DSAEF_LTP模型存在特大暴雨预报范围显著偏小等缺点。(2)在100 mm及以上和250 mm及以上量级,MODE(Method for Object-based Diagnostic Evaluation)空间检验显示,DSAEF_LTP模型在整体相似度上明显优于ECMWF和FZECMOS,尤其在对孤立小区域强降水的预报性能方面表现出色。(3)随着降水检验量级的增加,DSAEF_LTP模型预报产品与实况重叠面积之比也增大,表明DSAEF_LTP模型在极端降水方面的预报效果更加突出。(4)DSAEF_LTP模型还能够根据最新的相似路径实况和预报,调整筛选历史相似台风,合理保留相似台风及其降水分布,使得集合预报效果得以改善。
文摘2023年第5号台风“杜苏芮”(2305)造成福建省莆田市罕见特大暴雨。本文利用福建省地面气象观测数据、雷达和卫星等多源观测资料及欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)再分析资料,分析“杜苏芮”引发的莆田市特大暴雨的阶段性和强度特征。结果表明:本次过程由3个暴雨阶段以“无缝衔接”的形式组成,第一阶段为台风眼壁暴雨,具有短时雨强较强、空间分布均匀的特点;第二阶段为螺旋雨带暴雨,具有小时雨强差异显著、雨峰明显的特点;第三阶段为季风暴雨,具有暴雨范围广、持续时间长的特点。“杜苏芮”引发的莆田暴雨极端性显著,具体表现为:暴雨强度强、特大暴雨影响范围广、累计雨量大、短时强降水发生频次高且持续时间长。其中,莆田站24 h雨量达561.7 mm,突破福建省历史最高记录,极端性特征尤为突出。台风暖式切变线、低空南风急流和季风等系统持续维持,是三个阶段暴雨实现“无缝衔接”的重要天气背景,兴化平原“三面环山、向南开口”的地形对南风急流的抬升和收缩作用是暴雨中心位于兴化平原至东北部山区的重要因素。
文摘This study delves into the multiple weather systems and their interaction mechanisms that caused the severe rainfall event in Northeast China in early August 2023. The analysis reveals that the atmospheric circulation in the mid-to-high latitudes of the Eurasian continent exhibited a significant “two troughs and two ridges” structure, with Northeast China located precisely in the peripheral region of the subtropical high, significantly influenced by its marginal airflows. Additionally, the residual circulation of Typhoon “Doksuri” interacting with the subtropical high and upper-level troughs significantly increased the rainfall intensity and duration in the region. In particular, the continuous and powerful transport of the southwest jet provided the necessary moisture and unstable conditions for the generation and development of convective systems. The rainfall event resulted in nearly 40,000 people affected and crop damage covering an area of approximately 4000 hectares, demonstrating the severity of extreme weather. The study emphasizes that strengthening meteorological monitoring and early warning systems, as well as formulating and improving emergency response mechanisms, are crucial for reducing potential disaster losses caused by heavy rainfall. Future research can further explore the interaction mechanisms among weather systems, limitations of data sources, and the connection between long-term trends of heavy rainfall events and global climate change.
