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FY-4A AGRI辐射率资料偏差特征分析及订正试验

耿晓雯 闵锦忠 杨春 王元兵 许冬梅

耿晓雯, 闵锦忠, 杨春, 等. 2020. FY-4A AGRI辐射率资料偏差特征分析及订正试验[J]. 大气科学, 44(4): 679−694 doi:  10.3878/j.issn.1006-9895.1907.18254
引用本文: 耿晓雯, 闵锦忠, 杨春, 等. 2020. FY-4A AGRI辐射率资料偏差特征分析及订正试验[J]. 大气科学, 44(4): 679−694 doi:  10.3878/j.issn.1006-9895.1907.18254
GENG Xiaowen, MIN Jinzhong, YANG Chun, et al. 2020. Analysis of FY-4A AGRI Radiance Data Bias Characteristics and a Correction Experiment [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(4): 679−694 doi:  10.3878/j.issn.1006-9895.1907.18254
Citation: GENG Xiaowen, MIN Jinzhong, YANG Chun, et al. 2020. Analysis of FY-4A AGRI Radiance Data Bias Characteristics and a Correction Experiment [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 44(4): 679−694 doi:  10.3878/j.issn.1006-9895.1907.18254

FY-4A AGRI辐射率资料偏差特征分析及订正试验

doi: 10.3878/j.issn.1006-9895.1907.18254
基金项目: 国家重点研发计划项目2017YFC1502103,国家自然科学基金项目41430427、41805016、41805071,江苏省自然科学基金项目BK20160954,南京信息工程大学人才启动基金项目2017r058,江苏高校优势学科建设工程项目PAPD
详细信息
    作者简介:

    耿晓雯,女,1995年出生,硕士研究生,主要从事资料同化研究。E-mail: gengxw24@163.com

    通讯作者:

    闵锦忠,E-mail: minjz@nuist.edu.cn

  • 中图分类号: P405

Analysis of FY-4A AGRI Radiance Data Bias Characteristics and a Correction Experiment

Funds: National Key Research and Development Program of China (Grant 2017YFC1502103), National Natural Science Foundation of China (NSFC) (Grants 41430427, 41805016, 41805071), Natural Science Foundation of Jiangsu Province (Grant BK20160954), Talent Start Foundation of NUIST (Nanjing University of Information Science & Technology) (Grant 2017r058), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
  • 摘要: 风云四号A星(Fengyun-4A,简称FY-4A)作为我国最新一代静止气象卫星,各方面技术指标都体现了“高、精、尖”特色,处于国际领先地位。其上搭载的多通道扫描成像辐射计(Advanced Geosynchronous Radiation Imager,简称AGRI)较上一代静止卫星风云二号的可见光红外自旋扫描辐射仪观测精度更高、扫描时间更短,充分体现AGRI观测资料将有效提高“一带一路”沿线国家和地区的天气预报和灾害预警水平。偏差订正是卫星资料处理的重要环节之一,因此本文通过在WRFDA v3.9.1(Weather Research and Forecasting model’s Data Assimilation v3.9.1)搭建AGRI同化接口,利用RTTOV v11. 3辐射传输模式和GFS全球预报系统(Global Forecast System)分析场研究了FY-4A AGRI红外通道8~14晴空辐射率资料的偏差特征并进行偏差订正对比试验,分析了卫星天顶角对AGRI资料偏差订正的影响,为将来实现AGRI红外通道辐射率资料在中尺度模式中的同化应用奠定基础。结果表明:(1)通道8~10及14为正偏差,通道11~13为负偏差。水汽通道9和10偏差及其标准差相对较小,偏差海陆差异不明显。通道11~14探测高度较低,陆地上观测受地表发射率影响大,质量控制时可剔除这些通道陆地上的观测。(2)各通道偏差随卫星天顶角变化的拟合直线斜率都小于0.035,对比试验结果表明偏差与卫星天顶角的关系不明显,预报因子中无需考虑卫星天顶角的作用。(3)通道8及11~14的偏差随着目标亮温的变化比水汽通道9~10明显,偏差有较强的目标亮温依赖特征。(4)根据分析的偏差特征对2018年5月13日18时(协调世界时,下同)至15日18时进行变分偏差订正试验,系统性偏差得到了有效的订正。
  • 图  1  FY-4A AGRI(Advanced Geosynchronous Radiation Imager)通道8~14权重函数(Ch8~Ch14代表通道8~14,权重函数利用美国标准大气廓线基于RTTOV辐射传输模式计算得出)

    Figure  1.  FY-4A AGRI (Advanced Geosynchronous Radiation Imager) channels 8–14 weighting function, calculated using RTTOV model based on the U.S. standard atmospheric profiles (Ch8–Ch14 represent channels 8–14)

    图  2  2018年5月15日18时(协调世界时,下同)AGRI的(a)云检测(其中Clear为晴空,Pclear为可能晴空,Pcloud为可能有云,Cloud为有云观测)、(b)云类型(其中Clear为晴空,Water为液态水云,Scwater为过冷水云,Mixed为混合云,Ice为不透明冰云,Cirrus为卷云,Overlap为多层云)、(c)通道9和(d)通道12亮温(单位:K)的分布

    Figure  2.  Distributions of AGRI (a) cloud detection (Clear, Pclear, Pcloud and Cloud represent clear, probably clear, probably cloudy, and cloudy observations, respectively), (b) cloud types (Scwater represents super cooled water type), (c) channel 9 and (d) channel 12 brightness temperature (units: K) at 1800 UTC on 15 May 2018

    图  3  2018年5月7日00时至12日18时通道8~14观测与模拟亮温偏差海陆分布(0.5°×0.5°网格内平均,色标表示偏差的大小)

    Figure  3.  Sea and Land distribution of bias between observed and stimulated brightness temperature in channels 8–14 averaged from 0000 UTC 7 to 1800 UTC on 12 May 2018 within 0.5°×0.5°grid boxes (color labels represent bias)

    图  4  2018年5月7日00时至12日18时通道8~14观测与模拟亮温偏差及其标准差海陆差异柱状图(单位:K,其中Land_bias表示陆地上偏差,Land_std表示陆地上偏差标准差,Sea_bias表示海洋上偏差,Sea_std表示海洋上偏差标准差)

    Figure  4.  The bias and its standard deviation between observed and stimulated brightness temperature over land or sea in Channels 8–14 averaged from 0000 UTC 7 to 1800 UTC on 12 May 2018 (units: K, Land_bias and Sea_bias represent bias over land and sea respectively, Land_std and Sea_std represent standard deviation over land and sea, respectively)

    图  5  2018年5月7~12日通道8观测与模拟亮温偏差均值Mean及其标准差Stdv时间序列(单位:K)

    Figure  5.  Time series of mean bias and its standard deviation between observed and stimulated brightness temperature in channel 8 on 7–12 May 2018 (units: K)

    图  6  2018年5月7日00、06、12、18时通道8质量控制后的观测与模拟亮温偏差(阴影,在0.5°×0.5°网格内统计平均,单位:K)及太阳耀斑角分布 [等值线,单位:(°)]

    Figure  6.  Distribution of bias between observed and stimulated brightness temperature in Channel 8 within 0.5°×0.5° grid boxes after quality control (shadings, units: K) and sun glint angles [contours, units: (°)] at 0000, 0600, 1200, 1800 UTC 7 May 2018

    图  7  2018年5月7日00时至12日18时通道8~14观测与模拟亮温偏差(OMB Tb,单位:K)与卫星天顶角 [Satellite zenith angle,单位:(°)]的关系(阴影表示观测数目,方程和实线表示线性回归的拟合直线)

    Figure  7.  The dependence of the bias (OMB Tb, units:K) between observed and stimulated brightness temperature in channels 8–14 on satellite zenith angle [units: (°)] averaged from 0000 UTC 7 to 1800 UTC on 12 May 2018 (Observation counts are shaded, the equation and solid line show a linear regression line)

    图  8  2018年5月7日00时至12日18时通道8~14(a)观测与模拟亮温偏差Bias及其(b)标准差Std(单位:K)与卫星天顶角 [Satellite zenith angle,单位:(°)] 的关系。图a中柱状图为每2°天顶角内的观测总数(×104

    Figure  8.  The dependence of (a) bias and (b) its standard deviation between observed and stimulated brightness temperature (units: K) in channels 8–14 on satellite zenith angle [units: (°)] averaged from 0000 UTC 7 to 1800 UTC on 12 May 2018. The bar chart in Fig.a represents observation data counts at 2° interval (×104)

    图  9  2018年5月7日00时至12日18时通道8~14海洋上(a)观测与模拟亮温偏差Bias、(b)偏差标准差Std、(c)观测个数Count(×104)与目标亮温(OBS,单位:K)的关系

    Figure  9.  The dependence of (a) bias, (b) standard deviation between observed and stimulated brightness temperature, and (c) counts in channels 8–14 on the scene brightness temperature (units: K) over the ocean averaged from 0000 UTC 7 to 1800 UTC on 12 May 2018

    图  10  2018年5月13日18时至15日18时no_satzen和satzen试验通道(a,b)9、(c,d)12偏差均值Mean,偏差标准差Stdv时间序列(单位:K)(图中NOMBnb(虚线)表示no_satzen试验偏差订正前观测与背景场亮温偏差,NOMBwb(点虚线)表示no_satzen试验偏差订正后观测与背景场亮温偏差,NOMA(实线)表示no_satzen试验偏差订正后观测与分析场亮温偏差;SOMBnb(虚线)表示satzen试验偏差订正前观测与背景场亮温偏差,SOMBwb(点虚线)表示satzen试验偏差订正后观测与背景场亮温偏差,SOMA(实线)表示satzen试验偏差订正后观测与分析场亮温偏差)

    Figure  10.  The mean and stand deviation of bias in channels (a, b) 9 and (c, d) 12 before and after no_satzen and satzen experiments during 1800 UTC on 13–15 May, 2018 (units: K). NOMBnb (dashed line), NOMBwb (dash and dot line), NOMA (solid line) represent observed minus stimulated brightness temperature and minus analyzed brightness temperature before and after no_satzen experiment, respectively. SOMBnb (dashed line), SOMBwb (dash and dot line), SOMA (solid line) represent observed minus stimulated brightness temperature and observed minus analyzed brightness temperature before and after satzen experiments, respectively

    图  11  no_satzen试验2018年5月15日18时偏差订正前通道9、12观测残差(各点观测与模拟亮温差值)水平分布

    Figure  11.  Spatial distribution of the difference between observed and stimulated brightness temperature in channels 9 and 12 at 1800 UTC 15 May 2018 (before no_satzen experiment), where the colors represent the difference between observed and stimulated brightness temperature

    图  12  no_satzen试验2018年5月15日18时偏差订正后通道9、12观测残差水平分布

    Figure  12.  Spatial distribution of the difference between observed and stimulated brightness temperature in channels 9 and 12 at 1800 UTC 15 May 2018(after no_satzen experiment), where the colors represent the difference between observed and stimulated brightness temperature

    图  13  no_satzen试验2018年5月15日18时通道9、12的观测与模拟亮温对比散点图(图中mean代表平均偏差,stdv代表偏差标准差,rms代表均方根误差,OBS Tb表示观测,BAK Tb表示背景场模拟亮温,ANA Tb表示分析场模拟亮温。单位:K。no BC和with BC分别表示偏差订正前后

    Figure  13.  Scatter plots of the observed versus stimulated brightness temperature in channels 9 and 12 at 1800 UTC 15 May 2018 (Mean represents the mean value of bias. Stdv represents the standard deviation. Rms represents the root mean square error. OBS Tb,BAK Tb and ANA Tb represent the observed, stimulated, and analyzed brightness temperature, respectively (units:K).The no BC and with BC represent before and after the no_satzen experiment)

    表  1  AGRI(Advanced Geosynchronous Radiation Imager)各通道特征(陆风等,2017

    Table  1.   Characteristics of AGRI(Advanced Geosynchronous Radiation Imager)channels (Lu et al., 2017)

    通道号中心波长/μm分辨率/km主要探测目的
    10.471.0昼间云、沙尘、气溶胶
    20.650.5昼间云、沙尘、积雪
    30.8251.0白天云、气溶胶、植被和海洋特性
    41.3752.0卷云(冰晶粒子)
    51.612.0低云/雪识别和水云/冰云识别
    62.252.0卷云、气溶胶粒子大小观测;夜晚可用于火点识别
    73.75H2.0高温端,用于火点高温及白天强的太阳反射监测
    83.75L4.0低温/常温端,低云和雾的监测
    96.254.0大气对流层高层的水汽
    107.14.0大气对流层中层的水汽
    118.54.0沙尘信息判别
    1210.74.0大气窗区,观测地球表面和云顶温度
    1312.04.0窗区边缘,弱吸收
    1413.54.0CO2吸收带,探测云、对流层中低层及地表信息
    下载: 导出CSV

    表  2  通道8~14 2018年5月7日00时至12日18时观测与模拟亮温平均偏差及其标准差(单位:K)

    Table  2.   The bias and standard deviation between observed and stimulated brightness temperature in channels 8–14 averaged from 0000 UTC 7 to 1800 UTC 12 May 2018 (units: K)

    通道号891011121314
    偏差/K0.9660.0981.182−1.163−1.359−1.1950.215
    偏差标准差/K4.1791.7311.6592.6232.7722.7981.972
    下载: 导出CSV

    表  3  AGRI变分偏差订正试验方案

    Table  3.   The scheme of AGRI variational bias correction experiment

    预报因子
    试验名称常数1000~300 hPa大气厚度200~50 hPa大气厚度模式地表温度模式初始场水汽总量卫星天顶角的正弦值及其平方、立方
    no_satzen×
    satzen
    注:表中√表示试验时选择该预报因子,×表示不选择该预报因子
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-11-12
  • 网络出版日期:  2019-10-31
  • 刊出日期:  2020-07-25

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