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中国北方冬季降水的多源资料产品评估和融合优化

潘旸 谷军霞 师春香 王正

潘旸,谷军霞,师春香,王正. 2022. 中国北方冬季降水的多源资料产品评估和融合优化. 气象学报,80(6):953-966 doi: 10.11676/qxxb2022.069
引用本文: 潘旸,谷军霞,师春香,王正. 2022. 中国北方冬季降水的多源资料产品评估和融合优化. 气象学报,80(6):953-966 doi: 10.11676/qxxb2022.069
Pan Yang, Gu Junxia, Shi Chunxiang, Wang Zheng. 2022. Assessment and merged optimization of multi-source winter precipitation products over northern China. Acta Meteorologica Sinica, 80(6):953-966 doi: 10.11676/qxxb2022.069
Citation: Pan Yang, Gu Junxia, Shi Chunxiang, Wang Zheng. 2022. Assessment and merged optimization of multi-source winter precipitation products over northern China. Acta Meteorologica Sinica, 80(6):953-966 doi: 10.11676/qxxb2022.069

中国北方冬季降水的多源资料产品评估和融合优化

doi: 10.11676/qxxb2022.069
基金项目: 国家重点研发计划项目(2018YFC1506601、2018YFC1506604)
详细信息
    作者简介:

    潘旸,主要从事气象资料多源数据融合方面的研究。E-mail:pany@cma.gov.cn

  • 中图分类号: P468

Assessment and merged optimization of multi-source winter precipitation products over northern China

  • 摘要: 为了考察不同来源降水产品在中国北方冬季(特别是固态降水)的精度和可用性,优化融合降水产品质量,利用2019年12月—2020年2月美国CMORPH和IMERGE卫星反演降水、日本GSMaP、中国气象局雷达定量估测降水(MOC-QPE)、CMA-MESO模式预报以及地面观测插值等不同来源分析的降水产品,以地面站观测逐小时降水量数据为基准,从KGE评分、相关系数、平均误差和均方根误差等精度统计指标以及命中率(FOD)、虚警率(FAR)和TS评分等降水事件发生角度开展评估,结果表明:中国区域单源降水产品中地面插值分析产品对冬季降水描述精度最高也最稳定,但存在明显的系统偏低;其次是MOC-QPE和IMERG卫星产品,对中国北方偏南部地区的降水有一定的描述能力,但对北方高纬度地区固态降水的反映能力较差;卫星产品中IMERG精度最高,CMORPH则基本没有反演能力;CMA-MSEO模式产品虽然误差较大但与地面站观测的降水特别是固态降水存在较高相关,明显优于雷达和IMERG、GSMaP等卫星产品。采用BMA技术融合雷达、模式、卫星降水形成优化背景场,评估逐步引入不同的数据源对融合降水在冬季的精度影响,引入IMERG卫星和CMA-MESO模式产品均能提升高分辨率融合产品的质量,其中模式产品的改进效果最显著。

     

  • 图 1  不同降水产品2021年1月累计降水的空间分布 (a. GGA,b. MOC-QPE,c. IMERG,d. CMA-MESO,e—g. 对应数据源(b—d)的偏差订正结果)

    Figure 1.  Distribution of accumulated rainfall in January 2021 (a. GGA,b. MOC-QPE,c. IMERG,d. CMA-MESO,e—g. the bias-corrected product of multi-source preducts (b—d))

    图 2  不同数据源中国北方地区平均的贝叶斯模型融合权重百分比的时间序列

    Figure 2.  Time series of averaged BMA merged weight percentage of different sources of precipitation in northern China

    图 3  不同来源降水产品检验站KGE值的空间分布 (a. GGA,b. MOC-QPE,c.IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO,g. 融合试验1,h. 融合试验2,i. 融合试验3)

    Figure 3.  Spatial distributions of KGE values from different precipitation products (a. GGA,b. MOC-QPE,c. IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO,g. Merge_test 1,h. Merge_test 2,i. Merge_test 3)

    图 5  不同来源降水产品相关系数的空间分布 (a. GGA, b. MOC-QPE, c. IMERG, d. GSMaP, e. CMORPH, f. CMA-MESO)

    Figure 5.  Spatial distributions of correlation coefficients from different precipitation products (a. GGA, b. MOC-QPE, c. IMERG, d. GSMaP, e. CMORPH, f. CMA-MESO)

    图 4  不同来源降水产品相对偏差 (单位:%)的空间分布 (a. GGA,b. MOC-QPE,c. IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO)

    Figure 4.  Spatial distributions of relative bias (unit:%) from different precipitation products (a. GGA,b. MOC-QPE,c. IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO)

    图 6  不同来源降水产品对固态降水的KGE评分的空间分布 (a. GGA,b. MOC-QPE,c. IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO)

    Figure 6.  Spatial distributions of KGE values of solid precipitation from different products (a. GGA,b. MOC-QPE,c. IMERG,d. GSMaP,e. CMORPH,f. CMA-MESO)

    图 8  北方地区误差指标不同等级区间的样本百分率分布 (a. KGE评分,b. 相关系数,c. 相对偏差 ,d. 均方根误差)

    Figure 8.  KGE values sample percentages of different precipitation products (a. KGE value,b. Correlation coefficient,c. Relative bias,d. Root of mean square error)

    图 7  偏差订正后 (a. MOC-QPE,b. IMERG,c. CMA-MESO) 和融合 (d. 试验1,e. 试验2,f. 试验3) 降水产品对固态降水的KGE评分空间分布

    Figure 7.  Spatial distributions of KGE values of solid precipitation from different products (a. MOC-QPE,b. IMERG,c. CMA-MESO,d. Merge_test 1, e. Merge_test 2, f. Merge_test 3)

    图 9  不同源降水产品和融合试验产品不同小时强度降水的 (a) 命中率、(b) 虚警率和 (c) TS评分

    Figure 9.  (a) POD,(b) FAR and (c) TS scores of different products for different hourly precipitation intensity

    图 10  不同源降水产品和融合试验产品不同强度降雪事件 (12 h累计降水量) 的 (a) 命中率、(b) 虚警率和 (c) TS评分

    Figure 10.  (a) POD,(b) FAR,and (c) TS scores of different products for different 12 h-accumulated precipitation intensity grades

    表  1  降水产品列表

    Table  1.   List of precipitation products

    产品(文中简称)来源机构时空分辨率数据源时效(滞后)
    地面降水网格分析产品(GGA)中国气象局气象信息中心1 h/0.05°×0.05°地面约4万个考核自动站观测 5 min
    雷达定量降水估计产品(MOC-QPE)中国气象局气象探测中心1 h/0.01°×0.01°全国200多部雷达组网 10 min
    CMA-MESO降水预报产品(CMA-MESO)中国气象局国家气象中心1 h/0.03°×0.03°CMA-MESO模式预报 0 min
    GSMaP卫星降水近实时(NRT)产品(GSMaP)日本JAXA1 h/0.1°×0.1°卫星(多卫星集成) 3.5 h
    IMERG卫星Lately降水(IMERG)美国NASA30 min /0.1°×0.1°卫星(多卫星集成) 14.5 h
    CMORPH卫星降水(CMORPH)美国NOAA/CPC30 min /0.073°×0.073°卫星(多卫星集成) 20 h
    下载: 导出CSV

    表  2  数据源优化融合试验方案

    Table  2.   Optimized merged schemes of different precipitation sources

    试验名称数据源融合分析技术
    试验1地面观测、雷达PDF+OI
    试验2地面观测、雷达、IMERGPDF+BMA+OI
    试验3地面观测、雷达、IMERG、
    CMA-MESO模式
    PDF+BMA+OI
    下载: 导出CSV

    表  3  2019年12月—2020年2月中国北方地区各类降水产品的误差统计

    Table  3.   Statistical errors of different precipitation products over northern China from December 2019 to February 2020

    产品相关系数均方根误差(mm/h)相对偏差(%)KGE评分平均降水率(mm/h)样本数
    GGA0.6240.1220−52.20.3190.0093469751
    CMORPH0.1130.2558−14.4−0.1210.0167
    GSMaP0.2010.3337−15.0−0.7000.0166
    IMERG0.3850.1804−15.50.2980.0165
    MOC-QPE0.4590.2065−45.7−0.8220.0106
    CMA-MESO0.4810.2531205.3−1.1540.0597
    背景场(试验2)0.5550.1495−22.70.3930.0151
    背景场(试验3)0.5850.1441−19.30.4560.0158
    融合降水(试验1)0.6060.1356−38.80.1960.0120
    融合降水(试验2)0.6330.1253−37.00.3600.0123
    融合降水(试验3)0.6450.1222−38.00.3740.0121
    下载: 导出CSV

    表  4  不同降水产品在不同KGE值区间的检验站数与总检验站数的占比百分率 (单位:%)

    Table  4.   KGE values sample percentages of different precipitation products (unit:%)

    产品0.6≤KGE<1.00.4≤KGE<1.00.2≤KGE<1.00.0≤KGE<1.0
    GGA27.548.059.872.5
    CMORPH0.00.02.013.7
    GSMaP2.02.02.911.8
    IMERG2.911.823.544.1
    MOC-QPE5.98.828.450.0
    CMA-MESO4.97.89.812.7
    背景场(试验2)7.818.638.257.8
    背景场(试验3)19.630.451.070.6
    融合降水(试验1)21.643.161.873.5
    融合降水(试验2)28.445.165.774.5
    融合降水(试验3)24.550.068.681.4
    下载: 导出CSV

    表  5  不同来源降水产品对固态降水误差统计

    Table  5.   Statistical errors of solid precipitation from different products over northern China

    产品相关系数均方根误差(mm/h)相对偏差 (%)KGE评分平均降水率(mm/h)样本数
    GGA0.5930.0930−50.40.3020.0072203909
    CMORPH0.0470.2139−29.5−0.6300.0102
    GSMaP0.1930.2143−46.8−1.5120.0077
    IMERG0.2590.1454−32.9−0.0080.0097
    MOC-QPE0.2930.1365−75.5−2.2120.0035
    CMA-MESO模式0.5150.2107240.7−1.4860.0493
    背景场(试验2)0.4080.1138−48.40.0770.0075
    背景场(试验3)0.5110.1043−41.60.2610.0085
    融合降水(试验1)0.5490.0980−56.90.0610.0062
    融合降水(试验2)0.5450.0980−50.40.2300.0072
    融合降水(试验3)0.6150.0911−47.30.3410.0076
    下载: 导出CSV
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  • 收稿日期:  2021-03-17
  • 录用日期:  2022-11-14
  • 修回日期:  2022-07-21
  • 网络出版日期:  2022-07-25

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