定量降水预报技术进展

现有的降水预报后处理方法多倾向于对整个区域开展日降水量统一订正，忽视了降水偏差的区域差异，且无法满足精细化预报业务需求。在综合评估中国气象局区域中尺度天气数值预报系统CMA-MESO 3 km (China Meteorological Administration Mesoscale Model at 3 km resolution)在2020年山西汛期降水预报偏差的基础上，基于频率匹配方法，对小时降水预报开展分时段、分区域订正试验，并对订正结果进行了多角度检验分析。结果表明：(1)模式对山西日降水预报空报突出，其中盆地降水预报量级明显偏大。(2)不同区域、不同时段小时降水预报偏差不同，其中弱降水在白天低海拔地区预报偏多，夜间高海拔地区预报偏少。(3)分区域、分时段频率匹配订正后，日累计降水平均误差较订正前降低76.8%，强降水过度预报问题显著改善。(4)通过降低过度预报的降水频率和强度，降水量峰值提前、上午峰值虚报等问题得以解决，可更好再现降水日变化特征。(5)订正后有效改善了模式降水预报的地形分布特征，主要体现在强度预报改进上，但对模式降水预报频率的地形分布偏差调整有限。

Advances in techniques of quantitative precipitation forecast

The existing post-processing methods of precipitation forecast tend to correct the 24 h precipitation averaged in the whole region, ignoring the regional differences of precipitation, and cannot meet the needs of refined forecasting. Based on the evaluation of CMA-MESO 3 km (China Meteorological Administration Mesoscale Model at 3km resolution) precipitation prediction during the flooding season of 2020 in Shanxi province, the frequency matching method was applied to correct the hourly precipitation forecast for specific periods and regions. The correction results were also analyzed in detail. The results showed that: (1) 24-hour precipitation forecast of CMA-MESO 3 km showed consistent false alarm, and the positive errors in the basin were prominent in Shanxi Province. (2) The forecast biases are inconsistent over different periods and altitudes. The precipitation frequency showed large wet biases during the daytime, especially in the area with lower altitude, but had dry bias in the region with higher altitude, for light precipitation at night. (3) After correction for different periods and regions, the mean errors decreased by 76.8% for 24-hour precipitation, the overestimates of heavy rain in the forecast were significantly reduced. (4) By reducing the over-predicted frequency and intensity, the problems of earlier peak of precipitation and false prediction of precipitation peak in the morning were also solved. The diurnal variation characteristics of precipitation was reproduced well. (5) The correction could improve the forecast of precipitation intensity, which could further effectively improve the forecast of terrain distribution characteristics of precipitation. However, the improvement of forecast of terrain distribution deviation of precipitation was limited.