暖季东北冷涡背景下吉林省降水预报偏差分析

Analysis of Precipitation Forecast Deviation of Numerical Models over Jilin Province in Warm Seasons under Background of Northeast Cold Vortex

摘要:

利用2021—2023年5—9月东北冷涡背景下吉林省1436站逐小时降水观测资料，对比检验欧洲中心中期天气预报模式（EC）、中国气象局中尺度天气数值预报系统（CMA-MESO）、中国气象局全球同化预报系统（CMA-GFS）、中国气象局台风模式（CMA-TYM）、上海数值预报模式系统（CMA-SH9）、北京快速更新循环数值预报系统（CMA-BJ）等6种业务数值模式降水预报产品，使用定量评估、分级评估与日变化预报对比等方法揭示不同模式在平原与山地丘陵区逐3 h降水预报及其偏差的特征差异。结果表明：①EC对平原与山地丘陵组的降水量日变化预报均最优，但对站次平均降水量峰值无预报能力，降水站次预报在中午前后偏高尤为明显。而CMA-MESO与CMA-SH9能够更好地预报出降水站次与站次平均降水量的日变化峰值或趋势特征。②各模式的弱降水站次预报偏多，存在湿偏差；在中等及以上强度降水预报中，CMA-MESO与CMA-TYM预报优于全球模式，全球模式存在明显干偏差；在不同强度降水预报中，除CMA-SH9的强降水外，各模式在山地丘陵组预报效果均优于平原组。③预报时钟环形图指出，EC中午前后降水站次预报偏多明显主要由弱降水预报偏多导致，其对于全天各时次的强降水预报误差较大主要由漏报导致；CMA-MESO强降水预报能力强于EC，对傍晚峰值有预报能力且能够预报出强降水，其误差由预报位置偏差导致。

Abstract:

Numerical models serve as critical reference bases for weather forecasting. Effective application of numerical models for forecasting first requires understanding the models’ forecasting performance, which is derived from verification. This study researches the forecasting capabilities of different numerical models in terms of precipitation associated with the Northeast Cold Vortex in Jilin Province. Utilising hourly precipitation observation data from 1436 stations in Jilin Province during the Northeast Cold Vortex period from May to September 2021-2023, a comparison analysis is conducted between the precipitation forecasting products of six numerical models, namely, EC, CMA-MESO, CMA-GFS, CMA-TYM, CMA-SH9, and CMA-BJ. The aim is to reveal the characteristic differences in forecasting capability and deviation contributions between three-hour-interval precipitation forecasts in plain and hilly areas using quantitative assessment, graded assessment, and diurnal variation forecast comparison. The results indicate that: (1) The observed precipitation associated with the Northeast Cold Vortex and the precipitation forecasting capability of numerical models are closely related to topographic distribution. In the plain group, both the precipitation amount and the number of observation stations are smaller, while the precipitation intensity is stronger, and the mean relative error (MRE) and mean absolute error (MAE) of the stations are larger. In contrast, the hill group shows opposite trends: larger precipitation amount, more station numbers, weaker intensity, and smaller MRE and MAE. (2) EC demonstrates the best performance in forecasting diurnal variations of precipitation in both plain and hilly groups; however, it lacks the ability to predict the peak of precipitation intensities, with a notable overestimation of precipitation frequency occurring around midday. In contrast, CMA-MESO and CMA-SH9 are more effective in capturing the diurnal peak or trend characteristics of precipitation frequency and intensities. (3) The weak precipitation forecasts under various models are overly frequent, while the forecasting capabilities for moderate and heavy precipitation are influenced by topographical differences, where the performance in hilly areas surpasses that in plain areas. Additionally, CMA-MESO and CMA-TYM outperform global models, with global models exhibiting significant dry deviations. (4) The forecasting skill clock plots indicate that the excessive forecast of frequency by EC around midday is mainly due to overforecasting weak precipitation, while the significant underforecast of strong precipitation throughout the day results from missed occurrences. Furthermore, CMA-MESO exhibits stronger forecasting capabilities for heavy precipitation compared to EC, successfully predicting evening peak values and the occurrence of heavy precipitation stations. Its errors arise from location deviation in the forecasts.