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基于伴随敏感性的风廓线雷达和地基微波辐射计观测对模式预报的影响评估研究

唐兆康 鲍艳松 顾英杰 范水勇 齐亚杰 崔伟 陈强

唐兆康, 鲍艳松, 顾英杰, 等. 2022. 基于伴随敏感性的风廓线雷达和地基微波辐射计观测对模式预报的影响评估研究[J]. 大气科学, 46(4): 775−787 doi: 10.3878/j.issn.1006-9895.2107.20222
引用本文: 唐兆康, 鲍艳松, 顾英杰, 等. 2022. 基于伴随敏感性的风廓线雷达和地基微波辐射计观测对模式预报的影响评估研究[J]. 大气科学, 46(4): 775−787 doi: 10.3878/j.issn.1006-9895.2107.20222
TANG Zhaokang, BAO Yansong, GU Yingjie, et al. 2022. Using the Adjoint-Based Forecast Sensitivity Method to Evaluate the Observations of Wind Profile Radar and Microwave Radiometer Impacts on a Model Forecast [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 775−787 doi: 10.3878/j.issn.1006-9895.2107.20222
Citation: TANG Zhaokang, BAO Yansong, GU Yingjie, et al. 2022. Using the Adjoint-Based Forecast Sensitivity Method to Evaluate the Observations of Wind Profile Radar and Microwave Radiometer Impacts on a Model Forecast [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 775−787 doi: 10.3878/j.issn.1006-9895.2107.20222

基于伴随敏感性的风廓线雷达和地基微波辐射计观测对模式预报的影响评估研究

doi: 10.3878/j.issn.1006-9895.2107.20222
基金项目: 国家重点研发计划项目2017YFC1501704,上海航天科技创新基金资助项目 SAST2019-046,国家自然科学基金项目41975046
详细信息
    作者简介:

    唐兆康,男,1995年出生,硕士研究生,主要从事数值模式和资料同化研究。E-mail: tzk_nuist@foxmail.com

    通讯作者:

    鲍艳松,E-mail: ysbao@nuist.edu.cn

  • 中图分类号: P435

Using the Adjoint-Based Forecast Sensitivity Method to Evaluate the Observations of Wind Profile Radar and Microwave Radiometer Impacts on a Model Forecast

Funds: National Key Research and Development Program of China (Grant 2017YFC1501704), Shanghai Aerospace Science and Technology Innovation Foundation (Grant SAST2019-046), National Natural Science Foundation of China (Grant 41975046)
  • 摘要: 同化大量观测资料可以有效地改进模式预报结果,但不同观测对预报的影响有着显著差异,合理评估观测对预报的贡献是数值模式中最具挑战性的诊断之一。本文采用基于伴随的预报对观测的敏感性(Forecast Sensitivity to Observation,简称FSO)方法,构建WRFDA(Weather Research and Forecasting model’s Data Assimilation)框架下的WRFDA-FSO系统。基于2019年9月超大城市项目在北京地区获取的风廓线雷达(Wind Profile Radar,简称WPR)和地基微波辐射计(Microwave Radiometer,简称MWR)观测数据,利用WRFDA-FSO系统,开展观测对WRF模式12 h预报的影响试验,并分析风温湿观测对预报的贡献。结果表明:(1)同化的观测资料(MWR、WPR、Sound、Synop和Geoamv)均减小了WRF模式12 h预报误差,对预报为正贡献,其中MWR观测对预报的影响最大,WPR风场观测对预报的改进效果优于Sound的风场观测。(2)WPR的UV观测和MWR的TQ观测中,V观测和T观测对预报的正贡献值更高,对预报的改进效果更优。(3)WPR和MWR多数高度层的观测均减小了预报误差,对预报为正贡献,其中MWR的T观测对预报的正贡献主要位于近地面800 hPa以下。
  • 图  1  2019年9月(a)00时(协调世界时,下同)和(b)12时观测对预报的影响($ \Delta e $,单位:103 J kg−1)及其近似估计($ \delta e $,单位:103 J kg−1)的时间序列

    Figure  1.  Time series of the impact of observation on the forecast ($ \Delta e $, units: 103 J kg−1) and its approximate estimation ($ \delta e $, units: 103 J kg−1) at (a) 0000 UTC and (b) 1200 UTC in September 2019

    图  2  2019年9月(a)00时和(b)12时不同观测的站点观测对预报的贡献及其位置分布(红色表示该站点观测对预报负贡献,蓝色表示该站点观测对预报正贡献;图例中“/”前后的数字分别表示该观测的正贡献站点数和总站点数)

    Figure  2.  Contribution of the station observation with various observations to the forecast and its location distribution at (a) 0000 UTC and (b) 1200 UTC in September 2019 (red indicates a negative contribution of the station observation to the forecast, and the blue indicates a positive contribution of the station observation to the forecast; the numbers before and after “/” in the legend represent the number of positive contribution stations and total stations of the observation respectively)

    图  3  2019年9月00时和12时的(a)不同观测对预报误差的贡献(单位:104 J kg−1)和(b)不同观测的有益观测百分比

    Figure  3.  (a) Contribution (units: 104 J kg−1) of various observations to the forecast error and (b) percentage of beneficial observations of various observations at 0000 UTC and 1200 UTC in September 2019

    图  4  2019年9月00时和12时北京地区7站点(HD、YQ、HR、MY、PG、DX和XYL)的(a)WPR观测和(b)MWR观测对预报误差的平均贡献(单位:102 J kg−1)。HD:海淀,YQ:延庆,HR:怀柔,MY:密云,PG:平谷,DX:大兴,XYL:霞云岭

    Figure  4.  Average contribution (units: 102 J kg−1) of (a) WPR observations and (b) MWR observations to the forecast error at seven stations, which are Haidian (HD), Yanqing (YQ), Huairou (HR), Miyun (MY), Pinggu (PG), Daxing (DX), and Xiayunling (XYL) in Beijing at 0000 UTC and 1200 UTC in September 2019

    图  5  2019年9月00时观测误差σ≤3且新息增量|δy|>4的WPR(a)U观测和(b)V观测对应的对预报误差的贡献(单位:102 J kg−1)与新息增量(单位:m s−1)的散点图(黑色表示该观测对预报负贡献,灰色表示该观测对预报正贡献)

    Figure  5.  Scatter plot of forecast error contribution (units: 102 J kg−1) and innovation increment (units: m s−1) corresponding to (a) U observations and (b) V observations of WPR with observation error less than or equal to 3 and innovation increment greater than 4 (σ≤ 3 and |δy|>4) at 0000 UTC in September 2019 (black indicates a negative contribution of the observation to the forecast, and gray indicates a positive contribution of the observation to the forecast)

    图  6  2019年9月00时MWR的T观测对应的(a)对预报误差的贡献(单位:103 J kg−1)、(b)分析增量(单位:K)、(c)新息增量(单位:K)以及(d)观测误差(单位:K)随高度的分布

    Figure  6.  The distribution of (a) a forecast error contribution (units: 103 J kg−1), (b) an analysis increment (units: K), (c) an innovation increment (units: K), and (d) an observation error (units: K) with a height corresponding to temperature observations of MWR at 0000 UTC in September 2019

    表  1  剔除近似结果不准确的时次前后各观测资料对预报误差的贡献统计(单位:104 J kg−1

    Table  1.   Statistics of contributing various observations to the forecast error before and after eliminating inaccurate approximate results (units: 104 J kg−1)

    观测资料00时对预报误差的贡献 12时对预报误差的贡献
    剔除前剔除后剔除前剔除后
    MWR−117.06−128.3811.89−62.56
    WPR5.88−12.75−125.16−26.30
    Sound−26.07−45.37−82.87−25.41
    Synop−14.90−23.14−26.71−42.30
    Geoamv−0.77−0.580.27−0.30
    下载: 导出CSV

    表  2  2019年9月00时和12时廓线观测对预报误差的平均贡献(单位:J kg−1)统计

    Table  2.   Statistics of the average contribution of profile observations to the forecast error (units: J kg−1) at 0000 UTC and 1200 UTC in September 2019

    观测时次观测对预报误差平均贡献/J kg−1
    UVTQ
    WPR00时15.09−80.49   −   −
    12时−94.35−127.96
    MWR00时−468.49−72.34
    12时−286.58−17.00
    Sound00时−8.73−38.06−75.78−88.89
    12时−30.75−44.42−30.36−8.55
    下载: 导出CSV

    表  3  2019年9月00时和12时不同高度层WPR观测对预报误差的平均贡献(单位:J kg−1)统计

    Table  3.   Statistics of the average contribution of WPR observations at various altitudes to the forecast error (units: J kg−1) at 0000 UTC and 1200 UTC in September 2019

    高度层00时对预报误差的
    平均贡献/J kg−1
    12时对预报误差的
    平均贡献/J kg−1
    UVUV
    <500 m−129.32−289.43105.27−497.88
    500~1000 m23.99−60.47−248.26−300.04
    1000~1500 m216.62−37.83−80.44−188.43
    1500~2000 m180.83−135.84−153.71−144.17
    2000~3000 m−49.26−103.49−99.29−102.72
    3000~4000 m−156.17−38.77−64.03−20.35
    ≥4000 m−61.11−21.52−33.3112.6
    下载: 导出CSV

    表  4  2019年9月00时和12时不同高度层MWR观测对预报误差的平均贡献(单位:J kg−1)统计

    Table  4.   Statistics of the average contribution of MWR observations at various altitudes to the forecast error (units: J kg−1) at 0000 UTC and 1200 UTC in September 2019

    高度层00时对预报误差的
    平均贡献/J kg−1
    12时对预报误差的
    平均贡献/J kg−1
    TQTQ
    >900 hPa−1949.85−77.67−919.90−51.68
    800~900 hPa−580.04−132.18−419.2223.19
    700~800 hPa356.33−1.47−163.18−90.48
    600~700 hPa144.00−41.4828.3790.92
    500~600 hPa−5.2155.1610.35−13.02
    400~500 hPa−7.08−97.082.40−21.19
    ≤400 hPa6.33−130.804.46−39.21
    下载: 导出CSV

    表  5  2019年9月00时WPR观测对预报误差的贡献(单位:J kg−1)分类统计

    Table  5.   Classified statistics of the contribution of WPR observations to the forecast error (units: J kg−1) at 0000 UTC in September 2019

    U观测
    负贡献(×103J/kg) / 观测数正贡献(×103J/kg) / 观测数
    |δy|>4|δy|≤4|δy|>4|δy|≤4
    σ≤3148.75 / 37157.99 / 730−76.35 / 74−195.55 / 969
    σ>30.12 / 40.92 / 36−3.16 / 17−2.43 / 80
    V观测
    负贡献(×103J/kg) / 观测数正贡献(×103J/kg) / 观测数
    |δy|>4|δy|≤4|δy|>4|δy|≤4
    σ≤361.40 / 28100.50 / 697−35.13 / 46−281.52 / 1039
    σ>30.14 / 30.87 / 49−1.89 / 10−1.98 / 75
    下载: 导出CSV

    表  6  2019年9月00时 MWR观测对预报误差的贡献(单位:J kg−1)分类统计

    Table  6.   Classified statistics of the contribution of MWR observations to the forecast error (units: J kg−1) at 0000 UTC in September 2019

    T观测
    负贡献(×103J/kg) / 观测数正贡献(×103J/kg) / 观测数
    |δy|>2|δy|≤2|δy|>2|δy|≤2
    σ≤150.81 / 39309.20 / 197−308.19/ 41−932.91 / 285
    σ>1101.56 / 41871.54 / 558−287.15/ 305−121.72 / 541
    Q观测
    负贡献(×103J/kg) / 观测数正贡献(×103J/kg) / 观测数
    |δy|>2|δy|≤2|δy|>2|δy|≤2
    σ≤119.76 / 1089.65 / 474−4.79 / 2−235.19 / 788
    σ>173.84 / 4555.41 / 384−49.06 / 85−116.53 / 516
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-11-02
  • 录用日期:  2021-09-02
  • 网络出版日期:  2021-09-09
  • 刊出日期:  2022-07-19

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