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Statistical Post-Processing of Multiple Meteorological Elements Using the Multimodel Integration Embedded Method

Xingxing Ma,Hongnian Liu, Qiushi Dong,Qizhi Chen, Ninghao Cai

Atmospheric Research(2024)SCI 2区

Nanjing Univ

Cited 2|Views11
Abstract
Statistical post-processing of systematic errors is required for numerical weather predictions to obtain accurate and credible forecasts. Traditionally, this is accomplished separately with different individual models and for one specific element of focus. Here, a promising new method is proposed for the post-processing of meteorological elements output by the European Centre for Medium-Range Weather Forecasts (ECMWF), based on the integration of several different models. For 24-h precipitation, 2-m temperature, and 10-m wind speed, our new method, called the Multimodel Integration Embedded Method (MMIEM), outperformed the single models in terms of several skill scores, while being computationally more convenient. The mean average error of the MMIEM post-processed daily maximum 2-m temperature, minimum 2-m temperature, and maximum 10-m wind speed, was 18%, 26% and 29% lower than that of the raw ECMWF forecast, respectively. Also, compared with ECMWF, the threat score of the rainstorm forecast was improved by 9%. Key attributions to this improvement were the use of multiple models containing different advantages, with help from the embedding process and the interaction of multiple features in the model training. Furthermore, MMIEM can be extended to other statistical and forecast problems. It is anticipated that, with ever-increasing amounts of data, machine learning methods can transform the post-processing of numerical weather forecasts by gaining insight into the importance of different meteorological elements.
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Post-processing,ECMWF,Multi-model method,Machine learning,MOS
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要点】:提出了一种新的气象元素统计后处理方法——多模型集成嵌入式方法(MMIEM),通过集成多个模型提高了天气预报的准确性,并降低了计算复杂度。

方法】:采用多模型集成的方式,将不同模型的输出进行整合,并利用嵌入式方法训练模型,提高气象元素预报的准确度。

实验】:使用ECMWF模型输出的24小时降水、2米气温和10米风速数据进行实验,MMIEM方法在多个技能评分上优于单一模型,且计算更为方便。实验结果表现为:MMIEM后处理的日最大2米气温、日最小2米气温和最大10米风速的平均误差分别比ECMWF原始预报低18%、26%和29%,雨storm预报的威胁分数提高了9%。