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基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究

钟波 王云峰 马刚 马新园

钟波, 王云峰, 马刚, 马新园. 基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究[J]. 大气科学, 2018, 42(1): 164-177. doi: 10.3878/j.issn.1006-9895.1704.16281
引用本文: 钟波, 王云峰, 马刚, 马新园. 基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究[J]. 大气科学, 2018, 42(1): 164-177. doi: 10.3878/j.issn.1006-9895.1704.16281
Bo ZHONG, Yunfeng WANG, Gang MA, Xinyuan MA. A New Variational Assimilation Method for Numerical Typhoon Simulation Based on Gradient Information of Satellite Data[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(1): 164-177. doi: 10.3878/j.issn.1006-9895.1704.16281
Citation: Bo ZHONG, Yunfeng WANG, Gang MA, Xinyuan MA. A New Variational Assimilation Method for Numerical Typhoon Simulation Based on Gradient Information of Satellite Data[J]. Chinese Journal of Atmospheric Sciences, 2018, 42(1): 164-177. doi: 10.3878/j.issn.1006-9895.1704.16281

基于卫星资料梯度信息的新型变分同化方法对于台风数值模拟的研究

doi: 10.3878/j.issn.1006-9895.1704.16281
基金项目: 

国家自然科学基金项目 41375106

国家自然科学基金项目 11271195

国家自然科学基金项目 41230421

详细信息
    作者简介:

    钟波, 男, 1991年出生, 硕士研究生, 研究方向为资料同化与数值模拟。E-mail:308557251@qq.com

    通讯作者:

    王云峰, E-mail:wangyf@mail.iap.ac.cn

  • 中图分类号: P457

A New Variational Assimilation Method for Numerical Typhoon Simulation Based on Gradient Information of Satellite Data

Funds: 

National Natural Science Foundation of China 41375106

National Natural Science Foundation of China 11271195

National Natural Science Foundation of China 41230421

  • 摘要: 卫星资料凭着卫星遥感的全球性、连续性和高频次观测等优势,成为一种重要的非常规资料源,但卫星观测仍然存在各种各样的观测误差,其中包含由于观测偶然性所造成的统计学上的随机误差及仪器本身和辐射传输模式等造成的系统性偏差,这些误差在很大程度上影响了卫星资料的质量。文中提出了一种能有效订正卫星观测资料系统性偏差的梯度信息同化算法,该方法用一个梯度算子进行模式变量与观测变量的梯度变换,从而达到订正系统性偏差的目的。本文利用WRF(Weather Research Forecast)模式及其同化模式WRFDA(WRF Data Assimilation system),以及AIRS(Atmospheric Infrared Sounder)资料,对台风“圆规”进行了实际的数值模拟和同化试验,数值结果表明,梯度信息同化方法能明显改善台风路径的模拟,在处理可信度较低的资料时仍然适用。另外,通过同化诊断分析,发现卫星资料的系统性偏差对于台风数值模拟有较大影响,而文中提出的梯度信息同化方法能较好的解决此类问题。
  • 图  1  各试验台风模拟路径与CMA-STI西北太平洋热带气旋最佳路径(OBS)数据集:(a)不考虑系统性偏差;(b)考虑系统性偏差

    Figure  1.  Tracks of typhoon from experiments and best track (OBS) provided by CMA-STI (China Meteorological Administration-Shanghai Typhoon Institute): (a) Not considering the systematic errors; (b) considering the systematic errors. The CTRL experiment indicates that numerical simulation is performed directly without variational assimilation method. The AIRS experiment indicates that numerical simulation is performed with conventional variational assimilation method, and data is not superimposed system bias. The AIRS_GRD experiment indicates that numerical simulation is performed with gradient information assimilation method, and data is not superimposed system bias. The AIRS_SYB experiment indicates that numerical simulation is performed with conventional variational assimilation method, and data is superimposed system bias. The AIRS_SYB_GRD experiment indicates that numerical simulation is performed with gradient information assimilation method, and data is superimposed system bias.

    图  2  各试验66小时模拟的台风路径与实况偏差情况(单位:km):(a)不考虑系统性偏差;(b)考虑系统性偏差

    Figure  2.  Track errors (units: km) of typhoon during the 66-h simulation by each experiment: (a) Not considering the systematic errors; (b) considering the systematic errors

    图  3  台风路径改善比(单位:km):(a)AIRS_GRD试验与CTRL试验;(b)AIRS_GRD试验与AIRS试验;(c)AIRS_SYB试验与AIRS试验;(d)AIRS_SYB_GRD试验与AIRS_GRD试验

    Figure  3.  Improvement of simulated typhoon track (the difference between the result of the one experiment compared to the actual result and the result of the other experiment compared to the actual result, units: km): (a) Expts (experiments) AIRS_GRD-CTRL; (b) Expts AIRS_GRD-AIRS); (c) Expts AIRS_SYB-AIRS; (d) Expts AIRS_SYB_GRD-AIRS_GRD

    图  4  初始时刻各试验500 hPa风场偏差分布(单位:m s-1):(a)AIRS试验与CTRL试验;(b)AIRS_GRD试验与AIRS试验;(c)AIRS_SYB试验与AIRS试验;(d)AIRS_SYB_GRD试验与AIRS_GRD试验

    Figure  4.  500-hPa wind field deviation (units: m s-1) at the initial time: (a) Expts AIRS-CTRL; (b) Expts AIRS_GRD-AIRS; (c) Expts AIRS_SYB-AIRS; (d) Expts AIRS_SYB_GRD-AIRS_GRD

    图  5  初始时刻各试验850 hPa相对湿度偏差分布(单位:%):(a)AIRS试验与CTRL试验;(b)AIRS_GRD试验与AIRS试验;(c)AIRS_SYB试验与AIRS试验;(d)AIRS_SYB_GRD试验与AIRS_GRD试验

    Figure  5.  Bias of 850-hPa relative humidity (units: %) at the initial time: (a) Expts AIRS-CTRL; (b) Expts AIRS_GRD-AIRS; (c) Expts AIRS_SYB-AIRS; (d) Expts AIRS_SYB_GRD-AIRS_GRD

    图  6  初始时刻各试验垂直剖面温度偏差分布(单位:K):(a)AIRS试验与CTRL试验;(b)AIRS_GRD试验与AIRS试验;(c)AIRS_SYB试验与AIRS试验;(d)AIRS_SYB_GRD试验与AIRS_GRD试验

    Figure  6.  Vertical cross sections of temperature biases (units: K) at the initial time: (a) Expts AIRS-CTRL; (b) AIRS_GRD-AIRS; (c) Expts AIRS_SYB-AIRS; (d) Expts AIRS_SYB_GRD-AIRS_GRD

    图  7  初始时刻各试验垂直剖面位势高度偏差分布(单位:m2 s-2):(a)AIRS试验与CTRL试验;(b)AIRS_GRD试验与AIRS试验;(c)AIRS_SYB试验与AIRS试验;(d)AIRS_SYB_GRD试验与AIRS_GRD试验

    Figure  7.  Vertical cross sections of geopotential height biases (units: m2 s-2) at the initial time: (a) Expts AIRS-CTRL; (b) Expts AIRS_GRD-AIRS; (c) Expts AIRS_SYB-AIRS; (d) Expts AIRS_SYB_GRD-AIRS_GRD

    图  8  (a)AIRS试验与(b)AIRS_SYB试验第186、196、206、216通道的背景场与观测场的差值(OMB)分布(单位:K)。灰色区域为模式剔除掉的视场点区域

    Figure  8.  The OMB (Observation field Minus Background field) distributions (units: K) at channels 186, 196, 206, 216 in experiments (a) AIRS and (b) AIRS_SYB. The gray areas represent view fields removed by quality control

    图  9  第186、196、206、216通道背景场(BAD_TB)与观测场亮温(Obs_TB)散点分布(单位:K):(a)AIRS试验;(b)AIRS_SYB试验。Np表示同化的视场点的个数

    Figure  9.  Scatter plots of brightness temperature (TB, units: K) at channels 186, 196, 206, 216 between background field (BAD_TB) and observation field (Obs_TB): (a) AIRS; (b) AIRS_SYB. Np represents the number of assimilated view field

    图  10  各组试验OMA(观测场与分析场的差值)与OMB(观测场与背景场之间的差值)亮温对比(单位:K):(a)AIRS试验;(b)AIRS_GRD试验;(c)AIRS_SYB试验;(d)AIRS_SYB_GRD试验

    Figure  10.  Comparison of TB (units: K) between OMA (Observation field Minus Analysis field) and OMB (Observation field Minus Background field): (a) AIRS; (b) AIRS_GRD; (c) AIRS_SYB; (d) AIRS_SYB_GRD

    表  1  数值试验方案

    Table  1.   Configuration of numerical experiment

    试验方案 初始场 是否加入AIRS资料系统性偏差(-1 K) 是否基于梯度信息的同化方法
    CTRL NCEP NO NO
    AIRS NCEP+AIRS NO NO
    AIRS_GRD NCEP+AIRS NO YES
    AIRS_SYB NCEP+AIRS YES NO
    AIRS_SYB_GRD NCEP+AIRS YES YES
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-12-06
  • 网络出版日期:  2017-04-13
  • 刊出日期:  2018-01-15

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