CAJ | 학술논문

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。
응용자괄응잡이만려파방법，대대척도모식요소예보진행오차정정화강척도정세화기상요소예보。병통과대정정계수과학선취적연구，개진료려파방법적응용효과。통과대대척도모식계통진행오차정정，개선료대척도모식예보적준학솔，제고료모식요소，여2 m온도、10 m풍등예보적정도，병기우개선료적대척도모식예보장화고분변솔관측장，생성강척도함수，득도고정도적기상요소예보산품，위정세화기상요소예보복무제공료유효적방법。
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.