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基于ECMWF产品的站点气温预报集成学习误差订正

陈昱文 黄小猛 李熠 陈悦 徐挚仁 黄兴

陈昱文, 黄小猛, 李熠, 等. 基于ECMWF产品的站点气温预报集成学习误差订正. 应用气象学报, 2020, 31(4): 494-503. DOI: 10.11898/1001-7313.20200411..
引用本文: 陈昱文, 黄小猛, 李熠, 等. 基于ECMWF产品的站点气温预报集成学习误差订正. 应用气象学报, 2020, 31(4): 494-503. DOI: 10.11898/1001-7313.20200411.
Chen Yuwen, Huang Xiaomeng, Li Yi, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. DOI:  10.11898/1001-7313.20200411.
Citation: Chen Yuwen, Huang Xiaomeng, Li Yi, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. DOI:  10.11898/1001-7313.20200411.

基于ECMWF产品的站点气温预报集成学习误差订正

DOI: 10.11898/1001-7313.20200411
资助项目: 

国家重点研究发展计划 2018YFB0505000

国家重点研究发展计划 2017YFC1502200

国家自然科学基金项目 41776010

国家重点研究发展计划 2016YFB0201100

国家重点研究发展计划 2018YFB1502800

详细信息
    通信作者:

    黄小猛, hxm@tsinghua.edu.cn

Ensemble Learning for Bias Correction of Station Temperature Forecast Based on ECMWF Products

  • 摘要: 提出一种基于数值模式预报产品的气温预报集成学习误差订正方法,通过人工神经网络、长短期记忆网络和线性回归模型组合出新的集成学习模型(ALS模型),采用2013—2017年的欧洲中期天气预报中心数值天气预报模式2 m气温预报产品和中国部分气象站点数据,利用气象站点气温、风速、气压、相对湿度4个观测要素,挖掘观测数据的时序特征并结合模式2 m气温预报结果训练机器学习模型,对2018年模式2 m气温6~168 h格点预报产品插值到站点后的预报结果进行偏差订正。结果表明:ALS模型可将站点气温预报整体均方根误差由3.11℃降至2.50℃,降幅达0.61℃(19.6%),而传统的线性回归模型降幅为0.23℃(8.4%)。ALS模型对站点气温预报误差较大的区域和气温峰值预报的订正效果尤为显著,因此,集成学习方法在数值模式预报结果订正中具有较大的应用潜力。
  • 图  1  ALS模型结构

    Fig. 1  The structure of ALS model

    图  2  不同模型订正结果相对站点气温预报改善率分布

    (蓝色表示订正改善率为正,红色表示订正改善率为负)

    Fig. 2  average correction improvement rate of different models compared to station temperature forecast

    (the blue denotes a positive correction improvement rate, the red denotes a negative correction improvement rate)

    图  3  2018年6—8月贵阳站、原平站、福州站、台南站72 h气温预报与观测对比

    Fig. 3  Comparison of temperature forecast with lead time of 72 h to the observation at Guiyang, Yuanping, Fuzhou and Tainan from Jun to Aug in 2018

    图  4  2018年7月15—22日贵阳站、原平站、福州站和台南站气温预报整体均方根误差对比

    Fig. 4  Comparison of averaged root mean square error of temperature forecast at Guiyang, Yuanping, Fuzhou and Tainan from 15 Jul to 22 Jul in 2018

    表  1  2018年不同预报时效的气温预报均方根误差(单位:℃)

    Table  1  Root mean square error of temperature forecast with different lead times in 2018(unit:℃)

    预报时效/h站点气温预报LR模型ANN模型LSTM-FCN模型ALS模型
    6~242.732.111.841.861.81
    30~482.872.482.262.242.20
    54~722.982.642.452.432.38
    78~963.082.762.652.562.53
    102~1203.192.872.742.682.66
    126~1443.353.022.932.862.83
    150~1683.573.203.143.073.05
    下载: 导出CSV

    表  2  2018年6—8月4个站72 h预报时效气温预报均方根误差(单位:℃)

    Table  2  Root mean square error of temperature forecast with lead time of 72 h at 4 stations from Jun to Aug in 2018(unit:℃)

    站点站点气温预报LR模型ANN模型LSTM-FCN模型ALS模型
    贵阳站2.021.571.551.481.48
    原平站4.152.882.632.642.60
    福州站2.821.881.801.751.75
    台南站1.861.611.551.491.49
    下载: 导出CSV
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  • 收稿日期:  2020-01-15
  • 修回日期:  2020-04-09
  • 刊出日期:  2020-07-31

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