Studies On Application Of Products From Mesoscale NWP In Forecast Of Winter Temperature At LiaoNing Provience Based On Methods Of Mean Bias Removal And MOS

With the development of society, the people needed more high resolution elements forecasts, the requirements of meteorologists is higher. Development of Numerical Weather Prediction technology for meteorologists forecast provides a wealth of objective product, but the product still exists Numerical Weather Prediction bias is inevitable, in order to provide a reference forecast of higher value products, while scientists are working to improve the numerical model forecast accuracy, on the other meteorologists who are using the mean bias removal, statistical and other methods for numerical forecasting product release to use, produce higher-value objective elements reference forecast products.In this paper, Liaoning Province region numerical prediction system output products based on sliding cycle of mean bias removal revised 24 hours Liaoning winter temperature patterns interpolation results were revised forecast, analyzed the results of fixed and sliding cycle mean bias removal、MOS method forecast results, the following aspects of the conclusions:(1)The result of comparative analysis and site observations show that the bilinear interpolation method mesoscale forecasting system to predict winter temperature treatment site 24 hours maximum temperature forecast is low, minimum temperature forecast is high.(2)Statistical analysis showed that, after mean bias removal, MOS methods are superior to the results of the numerical model predictions interpolation products; Liaoning eastern mountains, western, central plains, the effect of the error revised fixed numerical model is better than the revised forecast of product interpolation results; slide revised results for correction of errors in Liaoning province are better than the results of the numerical model predictions interpolation of products, and superior to the effect of the fixed cycle mean bias removal.(3)24 hours of Liaoning winter minimum temperature forecast, the temperature forecast accuracy MOS method of making the highest ratio and direct interpolation error revised production forecast accuracy are high; but the 08 highest temperature forecast accuracy method for the production of MOS is relatively low.(4)The mean bias removal forecast products are mainly used in Liaoning mesoscale numerical prediction business systems and results of actual observation; MOS method requires a large accumulation of information, for the purposes of grassroots meteorologists the applicability of sliding cycle of mean bias removal methods is wider.