• CN 62-1175/P
• ISSN 1006-7639
• 双月刊
• 中国科技核心期刊
• 中国学术期刊综合评价数据库统计源期刊
• 中文科技期刊数据库收录期刊

• 业务技术应用 •

### 统计降尺度方法在天津小时降水和气温精细化预报中的应用

1. 1.天津市气象台,天津 300074
2.江苏省东台市气象局,江苏 东台 224200
• 收稿日期:2020-11-22 修回日期:2021-12-06 出版日期:2022-02-28 发布日期:2022-02-28
• 作者简介:田笑(1989— ),女,山西人,工程师,博士,主要研究方向为数值天气预报及东亚季风. E-mail: weiwu906098912@sina.com
• 基金资助:
天津市博士基金资助(201916bsjj02)

### Fine prediction of hourly precipitation and air temperature of Tianjin based on statistical downscaling in ECMWF model

1. 1. Tianjin Meteorological Observatory, Tianjin 300074, China
2. Dongtai Meteorological Bureau of Jiangsu Province, Dongtai 224200, Jiangsu, China
• Received:2020-11-22 Revised:2021-12-06 Online:2022-02-28 Published:2022-02-28

Abstract:

Statistical downscaling forecasting of precipitation and 2 m air temperature was obtained based on ECMWF model forecast data from March to November 2018. The interpolated precipitation was corrected using the frequency matching method firstly and then the threshold method, the interpolated temperature was corrected by using the Kalman filter-type decreasing average statistical downscaling technique, finally the hourly precipitation and temperature prediction were obtained. The results are as follows: (1) For the accuracy of rain probability forecast, it was obviously improved by using the frequency matching method and the threshold method for most forecasting time, and the maximum improvement range was more than 20% for the former. For the relative error, the threshold method had reduced the occurrence of false alarms considerably. For the short-term heavy rainfall with 1 h rainfall greater than or equal to 20 mm, the TS score was also improved significantly after using the frequency matching method. For the Typhoon “Amby” event in 2018, in addition to the above improvement effects, the frequency matching method improved the prediction capacity of the model about the amount and patterns of rainfalls, and the threshold method corrected false-alarm station completely. (2) For the test of temperature forecast of ECMWF model, the absolute error was the largest in March for almost forecast time. After using the Kalman filter-type decreasing average statistical downscaling technique, the absolute error of temperature in different months decreased to varying degrees. In general, the absolute error curve after correction still had the periodic fluctuation with the extension of forecast period, and the position of wave peak and trough was basically the same as those before correction, and the greater the absolute error, the greater the correction range was. For the temperature case, the accuracy of the spatial distribution of temperature prediction was retained, and the absolute error decreased significantly after correction.

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