摘要
利用2014年2月—2015年9月中尺度模式(INCA、WRF)、全球模式(ECMWF、JMA)预报数据,结合巢湖湖泊周边区域站降水实况数据,应用加权平均法建立方程,开展多模式集成预报实验,得到不同时效最佳集成预报方程,并对各时效的方程预报能力进行了对比检验。结果表明:在最佳集成方程中,ECMWF模式预报稳定性较好,权重最大。随着预报时效的延长和降水量等级增大,集成预报和各单一模式预报的误差逐渐增大,评分逐渐降低。集成预报比单一模式预报的误差明显降低,预报准确率有所提高,开展多模式集成预报具有明显优越性。
Based on the data from INCA,WRF（mesoscale model）,ECMWF,JMA（Global model）and observed data around Chaohu Lake,multimodel test equations are established access the weighted average method.The best integrated forecast equation is obtained for different ageing conditions.Results show that ECMWF prediction stability is the best,and the weight coefficient is the largest under different forecast time scale.With prolonged aging time and rainfall level increasing,the errors of ensemble prediction and single model prediction increase gradually,and the forecast scores reduce gradually.The error of ensemble forecast is significantly less than that of single model,and the forecast accuracy is improved.There are obvious advantages to carry out multimodel integrated forecasting.
出处
《气象与减灾研究》
2016年第4期283-289,共7页
Meteorology and Disaster Reduction Research
基金
安徽省预报员专项科研基金项目(编号:KY201504)
关键词
集成预报
多模式
权重
检验
巢湖
ensemble prediction
multimodel
weighting
validation
Chaohu lake
