基于机器学习的多源实况分析产品和观测数据融合应用试验
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1.太原市气象局;2.山西省气象台;3.山西省气象信息中心

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P457

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Application experiment of machine learning based multi source real situation analysis products and observation data fusion
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1.Taiyuan Meteorological Bureau,Taiyuan Shanxi;2.Shanxi Meteorological Observatory;3.Shanxi Province Meteorological Information Center

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    摘要:

    利用中国气象局公共气象服务中心地面实况专业服务产品(CAR)、国家气象信息中心多源融合实况分析数据(ART)、全国雷达反演降水产品(RAD)、风云四号卫星反演降水产品(SAT)以及全国气象观测站逐小时资料,应用机器学习方法建立了基于选定位置气温、降水、风向、风速要素的实况融合应用模型(简称GBDT)。 15天逐时GBDT融合产品的全国分区域检验结果表明:GBDT气温融合产品在东北、华北、西北、华中、新疆、西藏六个区域较CAR和ART均有改进,在西藏的改进最明显,在华东和西南GBDT优于ART而逊于CAR,在华南和内蒙GBDT误差较ART、CAR略有增加。GBDT降水融合产品在样本偏少的内蒙较ART、CAR误差略有增加,其他区域有改进或基本相当;GBDT风速、风向融合产品较ART、CAR均有较大改进。试验结果表明机器学习方法可应用于融合多源实况分析产品和观测数据开展选定位置气温、降水、风向、风速要素的实况气象信息服务。

    Abstract:

    Based on the machine learning, an application model(GBDT) of real-time fusion on temperature, precipitation, wind direction, and wind speed elements at selected locations is developed by using the professional service product (CAR) of the public meteorological service center of the China Meteorological Administration, the multi-source fusion observation analysis data (ART) of the National Meteorological Information Center, the national radar retrieval precipitation product (RAD), the Fengyun 4 satellite retrieval precipitation product (SAT), and the hourly data of the national meteorological observatory. A national and regional error analysis was conducted on the fusion products output by GBDT, CAR, and ART, respectively, with hourly observation data. The national and regional inspection results of the 15 day hourly GBDT fusion product show that: the GBDT temperature fusion product has improved compared to CAR and ART in six regions: Northeast China, North China, Northwest China, Central China, Xinjiang, and Tibet, with the most significant improvement in Tibet. GBDT is superior to ART and inferior to CAR in East and Southwest China, and GBDT error slightly increases compared to ART and CAR in South China and Inner Mongolia. The GBDT precipitation fusion product has a slight increase in error compared to ART and CAR in Inner Mongolia, where there are fewer samples, while other areas have improvements or are basically equivalent. The GBDT wind speed and direction fusion products have significant improvements compared to ART and CAR. The experimental results indicate that machine learning methods can be applied to fuse multi-source real-time analysis products and observation data to provide real-time meteorological information services for selected location temperature, precipitation, wind direction, and wind speed elements.

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  • 收稿日期:2023-03-30
  • 最后修改日期:2023-08-02
  • 录用日期:2023-09-06
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