摘要
应用自适应卡尔曼滤波方法,对大尺度模式要素预报进行误差订正和降尺度精细化气象要素预报。并通过对订正系数科学选取的研究,改进了滤波方法的应用效果。通过对大尺度模式系统进行误差订正,改善了大尺度模式预报的准确率,提高了模式要素,如2 m温度、10 m风等预报的精度,并基于改善了的大尺度模式预报场和高分辨率观测场,生成降尺度函数,得到高精度的气象要素预报产品,为精细化气象要素预报服务提供了有效的方法。
Using self-adaption Kalman filter method, bias correction of surface parameter products of large- scale numerical prediction system are done. Through studying the appreciated method of obtaining bias correction coefficient, the filter method is improved and the forecasts of large-scale model parameters such as 2 m temperature and 10 m wind are improved accordingly. Based on corrected large-scale model forecast field and high resolution observatory field, downscaling vector function is obtained, and refined statistical downscaling meteorological parameter forecasts are created and it is an effective way to do high resolution meteorological parameter forecasts.
出处
《气象》
CSCD
北大核心
2014年第1期66-75,共10页
Meteorological Monthly
基金
公益性行业(气象)科研专项(GYHY201006017)资助
关键词
模式产品误差订正
卡尔曼滤波
统计降尺度
精细化要素预报
bias correction of numerical model products, Kalman filter, statistical downscaling, refinedmeteorological parameter forecast
