主办单位:中国气象局沈阳大气环境研究所
国际刊号:ISSN 1673-503X
国内刊号:CN 21-1531/P

气象与环境学报 ›› 2025, Vol. 41 ›› Issue (2): 11-19.doi: 10.3969/j.issn.1673-503X.2025.02.002

• 论文 • 上一篇    下一篇

1982—2022年中国东北地区夏季降水量MODES数据产品预报结果评估

曲美慧1,2, 涂钢1,2, 刘长征3, 李尚锋1,2, 李玉鹏1,2, 路增鑫1,2, 任航1,2, 赵淑红4   

  1. 1. 吉林省气象科学研究所, 吉林长春 130062;
    2. 长白山气象与气候变化吉林省重点实验室, 吉林长春 130062;
    3. 国家气候中心, 北京 100081;
    4. 长白县气象局, 吉林白山 134400
  • 收稿日期:2023-11-14 修回日期:2024-01-12 出版日期:2025-04-28 发布日期:2025-06-20
  • 通讯作者: 涂钢,女,研究员,E-mail:shenxintu@aliyun.com。 E-mail:shenxintu@aliyun.com
  • 作者简介:曲美慧,女,1989年生,工程师,主要从事数值模式应用研究,E-mail:279995024@qq.com。
  • 基金资助:
    国家重点研发计划(2023YFC3007700、2023YFC3007705、2023YFC3007702)、吉林省气象局技术发展专项(202315、202101)、吉林省科技发展计划项目(20180101016JC)、中国气象局省级气象科研所科技创新发展项目(SSFZ201806)和吉林省科技发展重点研发计划项目(20220203199SF)共同资助。

Evaluation of MODES data products for summer precipitation forecasts in Northeast China during 1982-2022

QU Meihui1,2, TU Gang1,2, LIU Changzheng3, LI Shangfeng1,2, LI Yupeng1,2, LU Zengxin1,2, REN Hang1,2, ZHAO Shuhong4   

  1. 1. Jilin Meteorological Sciences Institute, Changchun 130062, China;
    2. Jilin Province Key Laboratory of Changbai Mountain Meteorology & Climate Change, Changchun 130062, China;
    3. National Climate Centre, Beijing 100081, China;
    4. Changbai Meteorological Service, Baishan 134400, China
  • Received:2023-11-14 Revised:2024-01-12 Online:2025-04-28 Published:2025-06-20

摘要: 应用趋势异常综合检验评分(Ps)、距平符号一致率(Pc)、距平相关系数(ACC)、时间相关系数(TCC)等指标评估中国东北地区夏季降水量MODES的4种模式数据产品预报结果,分析各模式的预测值与观测值距平的空间分布。结果表明:MODES各模式数据产品的东北地区夏季降水量异常趋势预报结果较好。MODESv2_NCC夏季降水量预测值与观测值显著正相关的站点数最多,主要分布在松花江流域和辽河流域,预报结果好于其他模式。各模式区域平均Ps为63.0分、平均Pc为50.0%,其中,MODESv2_ECMWF平均Ps、Pc分别为66.4分、51.8%,预报结果好于其他模式。各模式东北地区夏季降水量偏少预报的评估指标均大于偏多,其中,MODESV2_NCC、MODESv2_JMA、MODESv2_NCEP在松辽流域南部地区的夏季降水量偏少预报结果较好。

关键词: 综合检验评分, 降水量距平, 降水量预报, 松辽流域

Abstract: This study evaluates the forecast performance of summer precipitation in Northeast China from four MODES model data products,comprehensive trend anomaly test score (Ps),anomaly sign consistency rate (Pc),anomaly correlation coefficient (ACC),and temporal correlation coefficient (TCC).The spatial distribution of anomalies between predicted and observed values from each model was analyzed.The results show that MODES data products performed well in predicting anomalous trends of summer precipitation in Northeast China.MODESv2_NCC had the highest number of stations with statistically significant positive correlations between predicted and observed summer precipitation,mainly in the Songhua River and Liao River basins,and outperformed other models.The average regional Ps and Pc for all models were 63.0 points and 50.0%,respectively,with MODESv2_ECMWF achieving the best performance (average Ps:66.4 points,Pc:51.8%).All models performed better in forecasting precipitation deficits than surpluses,with MODESv2_NCC,MODESv2_JMA,and MODESv2_NCEP showing good performance for below-normal summer precipitation forecasts in the southern region of the Songliao Basin.

Key words: Comprehensive evaluation score, Precipitation anomaly, Precipitation forecast, Songliao Basin

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