北京地区中尺度非静力数值预报产品释用技术研究

在国家气象中心与北京市气象局联合开发的“北京地区中尺度数值预报业务系统”基础上，充分利用高分辨率中尺度数值预报产品和地面加密气象观测资料，分别使用统计和动力释用方法进行局地温度、风和北京市区空气污染状况的预报。试预报结果表明，用卡尔曼滤波方法建立的温度和风的预报效果高于中尺度数值预报直接输出结果；使用中尺度数值预报结果作为一个简单的污染预报动力模式的气象背景场，进行了污染预报试验，结果表明该预报方法具有一定的预报能力，其中SO₂、NO₂、CO浓度的预报准确率在夏、秋、冬三季中均可达到65%以上。上述研究成果已用于业务预报。

Interpretation of the Nonhydrostatic Mesoscale NWP Products in Terms of Local Weather Phenomena and Air Pollution in Beijing Area

The statistical and dynamical methods are used to interpret the mesoscale NWP products produced by the Beijing Area Mesoscale NWP System (which has been developed under a joint project of the National Meteorological Center and the Beijing Meteorological Bureau) in terms of the local weather elements and air pollution. First, the Kalman filtering method is applied to the mesoscale NWP products, generating the 3 h interval wind, temperature, and the daily maximum/minimum temperature forecasts over nine automatic weather stations in the Beijing area. The results of verification indicate that the forecasts of wind and temperature with Kalman filtering are much better than direct model outputs. Second, a simple dynamical air pollution model is connected to the Mesoscale NWP system, making daily air pollution forecasts. For the SO 2, NO 2 and CO, the predicted pollution levels are correct on more than 65% days during summer, fall and winter; for RSP, the forecasts are right on more than 65% days during fall and winter, but only on 50% days during summer. Both methods have been put into operational use.