A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） data, has been...A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction （NWP） information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre （CMC）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the UK Met Office （UKMO）, and the US National Centers for Environmental Prediction （NCEP）. The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance, 展开更多
In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC a...In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.展开更多
针对B08RDP(The Beijing 2008 Olympics Research and Development Project)5套区域集合预报资料,系统分析了各套集合预报温度场的预报质量。在此基础上运用集合预报的综合偏差订正方法对温度场进行偏差订正,并对其效果进行了分析讨论...针对B08RDP(The Beijing 2008 Olympics Research and Development Project)5套区域集合预报资料,系统分析了各套集合预报温度场的预报质量。在此基础上运用集合预报的综合偏差订正方法对温度场进行偏差订正,并对其效果进行了分析讨论。结果显示:5套B08RDP区域集合预报中,美国国家环境预报中心(NCEP)区域集合预报温度场的整体预报质量最高,平均预报误差最小,离散度也最为合理,预报可信度和可辨识度均较优;而中国气象科学研究院(CAMS)的温度预报误差过大,预报质量最差。整体上看,除NCEP之外的4套集合预报的温度场均存在集合离散度偏小的问题;综合偏差订正能有效减小各集合预报温度场的集合平均均方根误差,改善集合离散度的质量,显示出综合偏差订正方案对集合预报温度场偏差订正的良好能力。展开更多
基金Supported by the China Meteorological Administration Special Public Welfare Research Fund (GYHY201006037,GYHY200906007,and GYHY(QX)2007-6-1)National Natural Science Foundation of China (41105068)
文摘A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model, driven by The Observing System Research and Predictability Experiment （THORPEX） Interactive Grand Global Ensemble （TIGGE） data, has been developed for flood forecasting over the Huaihe River. The incorporation of numerical weather prediction （NWP） information into flood forecasting systems may increase forecast lead time from a few hours to a few days. A single NWP model forecast from a single forecast center, however, is insufficient as it involves considerable non-predictable uncertainties and leads to a high number of false alarms. The availability of global ensemble NWP systems through TIGGE offers a new opportunity for flood forecast. The Xinanjiang model used for hydrological rainfall-runoff modeling and the one-dimensional unsteady flow model applied to channel flood routing are coupled with ensemble weather predictions based on the TIGGE data from the Canadian Meteorological Centre （CMC）, the European Centre for Medium-Range Weather Forecasts （ECMWF）, the UK Met Office （UKMO）, and the US National Centers for Environmental Prediction （NCEP）. The developed ensemble flood forecasting model is applied to flood forecasting of the 2007 flood season as a test case. The test case is chosen over the upper reaches of the Huaihe River above Lutaizi station with flood diversion and retarding areas. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The Muskingum method is used for flood routing in the flood diversion area. A probabilistic discharge and flood inundation forecast is provided as the end product to study the potential benefits of using the TIGGE ensemble forecasts. The results demonstrate satisfactory flood forecasting with clear signals of probability of floods up to a few days in advance,
基金Supported by the National Key Research and Development Program of China(2018YFC1506801)National Natural Science Foundation of China(41505117)Special Funds for Basic Research and Operation in Government Level Research Institutes of Public Welfare Nature(IUMKY201904)
文摘In August 2018, the Institute of Urban Meteorology(IUM) in Beijing co-organized with Sinovation Ventures a Weather Forecasting Contest(WFC)—one of the AI(artificial intelligence) Challenger Global Contests. The WFC aims to take advantage of the AI techniques to improve the quality of weather forecast. Across the world, more than1000 teams enrolled in the WFC and about 250 teams completed real-time weather forecasts, among which top 5 teams were awarded in the final contest. The contest results show that the AI-based ensemble models exhibited improved skill for forecasts of surface air temperature and relative humidity at 2-m and wind speed at 10-m height.Compared to the IUM operational analog ensemble weather model forecast, the most notable improvements of 24.2%and 17.0% in forecast accuracy for surface 2-m air temperature are achieved by two teams using the AI techniques of time series model, gradient boosting tree, depth probability prediction, and so on. Meanwhile, it is found that reasonable data processing techniques and model composite structure are also important for obtaining better forecasts.
文摘针对B08RDP(The Beijing 2008 Olympics Research and Development Project)5套区域集合预报资料,系统分析了各套集合预报温度场的预报质量。在此基础上运用集合预报的综合偏差订正方法对温度场进行偏差订正,并对其效果进行了分析讨论。结果显示:5套B08RDP区域集合预报中,美国国家环境预报中心(NCEP)区域集合预报温度场的整体预报质量最高,平均预报误差最小,离散度也最为合理,预报可信度和可辨识度均较优;而中国气象科学研究院(CAMS)的温度预报误差过大,预报质量最差。整体上看,除NCEP之外的4套集合预报的温度场均存在集合离散度偏小的问题;综合偏差订正能有效减小各集合预报温度场的集合平均均方根误差,改善集合离散度的质量,显示出综合偏差订正方案对集合预报温度场偏差订正的良好能力。
