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何邓新, 赖安伟, 张文, 康兆萍, 王俊超, 王珊珊, 郭英莲, 马鹤翟, 王志斌. 2024. 同化ECMWF短时预报场对区域中尺度模式预报的影响研究[J]. 暴雨灾害, 43(3): 288-298. DOI: 10.12406/byzh.2022-258
引用本文: 何邓新, 赖安伟, 张文, 康兆萍, 王俊超, 王珊珊, 郭英莲, 马鹤翟, 王志斌. 2024. 同化ECMWF短时预报场对区域中尺度模式预报的影响研究[J]. 暴雨灾害, 43(3): 288-298. DOI: 10.12406/byzh.2022-258
HE Dengxin, LAI Anwei, ZHANG Wen, KANG Zhaoping, WANG Junchao, WANG Shanshan, GUO Yinglian, MA Hedi, WANG Zhibin. 2024. Study on the influence of ECMWF short-term forecast field assimilation on regional mesoscale model forecast[J]. Torrential Rain and Disasters, 43(3): 288-298. DOI: 10.12406/byzh.2022-258
Citation: HE Dengxin, LAI Anwei, ZHANG Wen, KANG Zhaoping, WANG Junchao, WANG Shanshan, GUO Yinglian, MA Hedi, WANG Zhibin. 2024. Study on the influence of ECMWF short-term forecast field assimilation on regional mesoscale model forecast[J]. Torrential Rain and Disasters, 43(3): 288-298. DOI: 10.12406/byzh.2022-258

同化ECMWF短时预报场对区域中尺度模式预报的影响研究

Study on the influence of ECMWF short-term forecast field assimilation on regional mesoscale model forecast

  • 摘要: 全球数值天气预报模式的预报场常作为背景场驱动区域中尺度天气模式业务系统,其数据质量对区域模式的预报结果具有重要的影响。华中区域中尺度业务模式使用美国环境预报中心的全球预报场(NCEP GFS)作为背景场,其预报精度有待提高。为提高模式的短期预报能力,提出一种利用三维变分方法同化高质量欧洲中期天气预报中心(EC-MWF)细网格预报场改善模式初始场的方法,将NCEP GFS预报场作为背景场,同化ECMWF三维格点预报要素,开展个例和批量同化试验。首先,利用探空观测对NCEP GFS和ECMWF的12 h预报产品进行误差特征统计,ECMWF 12 h细网格预报的温度、水平风场、相对湿度的均方根误差都小于NCEP GFS预报场,因此对其进行同化是可行的。然后,对2021年8月11—13日的强降水个例进行不同分辨率的ECMWF预报场同化敏感性试验。最后,基于不同敏感性试验的结果,选择1°×1°的ECMWF预报场对2021年8月进行批量同化试验。结果如下:(1) 2021年8月11—13日的强降水个例同化预报试验表明,要素场预报误差得到了较为显著的改善,尤其是在模式低层;对12~36 h、36~60 h、60~84 h累积降水的TS评分具有一定的改善能力,尤其是暴雨量级。(2) 不同分辨率的ECMWF预报场同化敏感性试验表明ECMWF预报场1°×1°的预报效果要优于0.5°×0.5°和0.25°×0.25°。(3) 2021年8月的批量试验结果显示,同化ECMWF 12 h 1°×1°预报要素后,12 h、36 h和60 h预报的温度、湿度和风场在垂直方向上比控制试验均方根误差有所降低,降水TS评分提高明显,特别是50 mm暴雨降水TS评分,提升了13.33%,可有效改善华中区域数值天气预报系统对强降水的预报能力。

     

    Abstract: The forecast of the global numerical weather prediction model is often used as the background field to drive the regional mesoscale weather models, and its forecast quality has an important impact on the prediction skill of regional models. The mesoscale operational model in Central China uses the Global Forecast Field (NCEP GFS) of the US Center for Environmental Prediction as the background field, and its forecast accuracy needs to be improved. This paper proposes a method to improve the initial field of the model by using the three-dimensional variational method to assimilate the high-quality European Centre for Medium-range Weather Forecasts (ECMWF) fine-grid forecast field to improve the short-term forecasting ability of the model. At first, the error characters of the 12 h forecast products of NCEP GFS and ECMWF were analyzed by using sounding observations. The RMSE (root mean square error) of temperature, horizontal wind field and relative humidity of ECMWF 12 h forecast are smaller than those of NCEP GFS forecast. Second, sensitivity experiments of ECMWF forecast field assimilation with different resolutions were conducted. Finally, 1°×1° resolution was selected based on the sensitivity experiments and a series of the data assimilation experiments with 1°×1° resolution was performed for August 2021. The results are as follows. (1) The assimilation forecast of heavy rain from 11 to 13 August 2021 shows that after 12 hours of spin-up, the forecast error of the element field has been significantly improved, especially at the bottom of the model. The TS score of 12-36 h, 36-60 h and 60-84 h cumulative precipitation has a certain improvement, especially the forecast of rainstorm magnitude. (2) ECMWF forecast field assimilation sensitivity experiments with different resolutions show that the forecast effect of ECMWF forecast field with 1°×1° resolution is better than 0.5°×0.5° and 0.25°×0.25°. (3) A series of the data assimilation experiments for August 2021 shows that the assimilation of ECMWF 12 h 1°×1° predicted variables has a lower RMSE of the 12 h, 36 h and 60 h forecasted temperature, humidity and wind field in the vertical direction than the control experiments, and the TS score was significantly improved, especially for 50 mm heavy rain precipitation, with an increase of 13.33%, which can effectively improve the forecast skills of the Wuhan Mesoscale Model.

     

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