基于ECMWF高分辨率数值模式物理量诊断产品, 利用逻辑回归法开展江西定量降水客观预报试验, 并进行检验和改进。结果表明: （1）初始方案中直接提取预报因子单站建模所得到的预报结果较数值模式原始输出降水改进效果有限, 但在经过降水分区优化、 多倍数暴雨样本扩充、 本地气候频率降水订正等改进步骤后, 各等级降水预报均较初始方案TS有不同程度的提高。（2）2018年降水试验结果表明, 改进方案的晴雨、 各等级降水预报TS均高于EC模式降水和预报员, 其中大雨、 暴雨以上量级相对数值模式以及预报员的订正提高率达到1/4~1倍。（3）本方案预报产品对强天气尺度强迫下、 落区相对集中的暴雨天气有较好的识别能力; 而在暖区暴雨、 盛夏副热带高压边缘暴雨、 高架对流等强降水落区分散且范围较小的情况下, 或是当模式对天气形势、 主雨带预报有明显系统性偏差时, 无法有明显改进。
Based on ECMWF model, quantitative precipitation forecasting experiment was carried out with logistic model.The results show that: (1) Compared with EC model, precipitation forecasting modeled by single-station will provide limited benefits.After optimized precipitation division、 enlarged rainstorm samples、 bias correction for local climatic precipitation, TS scores has been improved.(2) The objective quantitative precipitation has been applied in grid forecasting in Jiangxi in 2018.Results show that the TS scores of rainfall and different-class precipitation are much better than EC model and forecasters, especially for heavy rain and torrential rain, which are 25%~1 times higher.(3) The products of this scheme has better recognition for rainstorm which are concentrated and forced by strong synoptic-scale systems.While it can't be accurate predicted in the situation of dispersive precipitation, which happened in warm sector、 edged of sub-tropical high in midsummer, etc.When the model has obvious systematic deviation to weather situation and main rain region, the objective correction can't improve the model results either.
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