摘要
基于欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts,ECMWF)2015年2月8日—2016年12月31日中国华东及华南地区24~168 h预报时效的逐日24 h累积降水集合预报资料,利用前馈神经网络建立NN(Neutral Network)模型及NN-GI(Neutral Network-Geographic Information)模型进行概率预报试验,并对两个模型输出的概率预报结果进行评估。结果表明,经NN模型和NN-GI模型订正后,降水概率预报结果得到明显改进,在168 h预报时效时,降水概率预报的CRPS值与原始集合预报相比分别下降了约16.00%、21.27%。与NN模型相比,NN-GI模型由于考虑到各格点的地理信息差异,在区域内预报技巧整体改进更优。这表明,在利用机器学习方法改进降水预报时,在模型中加入各个格点的地理信息非常重要。
With the increasing impact of human activities on climate change,the extreme weather events such as extreme precipitation occur more frequently and people pay more attention on probabilistic precipitation forecast.Since there is still a large error in precipitation ensemble forecast,it is of great significance to calibrate the forecast.Based on the daily 24 h accumulated precipitation forecasts obtained from the global ensemble forecast system of ECMWF(the European Centre for Medium-Range Weather Forecasts)with 24—168 h forecast lead times in East and South China from 8 February 2015 to 31 December 2016,NN(neutral network)model and NN-GI(neutral network-geographic information)model using feedforward neural network were established to improve probabilistic precipitation forecast and evaluate the results before and after calibration.Results show that after the correction of NN model and NN-GI model,the precipitation probabilistic forecasts are improved obviously.Compared with ECMWF raw ensemble forecasts,CRPSs of precipitation probabilistic forecasts from NN model and NN-GI model with 168 h forecast lead time decrease by around 16.00%and 21.27%,respectively.Meanwhile,compared with NN model,NN-GI model takes into account the geographic information difference of each grid point,and the overall improvement of forecasting skills in the region is better.However,NN-GI model has better performance,indicating that the machine leaning approach can improve the probabilistic forecast of the precipitation more significantly by taking into account the geographic information of each grid point in the model.
作者
智协飞
张珂珺
田烨
季焱
ZHI Xiefei;ZHANG Kejun;TIAN Ye;JI Yan(Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster,Ministry of Education (KLME),Nanjing University of Information Science and Technology,Nanjing 210044,China;Weather Online Institute of Meteorological Applications,Wuxi 214000,China)
出处
《大气科学学报》
CSCD
北大核心
2021年第3期381-393,共13页
Transactions of Atmospheric Sciences
基金
国家自然科学基金资助项目(41575104)
国家重点研发计划重点专项(2017YFC1502000)。
关键词
降水
概率预报
神经网络
地理信息
ECMWF集合预报
precipitation
probabilistic forecast
neural network
geographic information
ECMWF ensemble forecast
