地球科学进展

地球科学进展 ›› 2019, Vol. 34 ›› Issue (11): 1120 -1130. doi: 10.11867/j.issn.1001-8166.2019.11.1120

综述与评述 上一篇    下一篇

全天候卫星微波观测资料变分同化研究进展
窦芳丽 1, 2, 3( ),陆其峰 3( ),郭杨 3   
  1. 1. 中国气象科学研究院，北京 100081
    2. 中国科学院大学，北京 100049
    3. 国家卫星气象中心 中国气象局中国遥感卫星辐射测量和定标重点开放实验室，北京 100081
  • 收稿日期:2019-07-01 修回日期:2019-10-15 出版日期:2019-11-10
  • 通讯作者: 陆其峰 E-mail:doufl@cma.gov.cn;luqf@cma.gov.cn
  • 基金资助:
    国家自然科学基金项目“全天候同化中高频微波散射辐射传输模式的改进研究”(41905034)

Overview of Researches on All-Sky Satellite Microwave Data Variational Assimilation

Fangli Dou 1, 2, 3( ),Qifeng Lu 3( ),Yang Guo 3   

  1. 1. Chinese Academy of Meteorological Sciences, Beijing 100081, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, Beijing 100081, China
  • Received:2019-07-01 Revised:2019-10-15 Online:2019-11-10 Published:2019-12-31
  • Contact: Qifeng Lu E-mail:doufl@cma.gov.cn;luqf@cma.gov.cn
  • About author:Dou Fangli （1986-）， female， Binzhou City, Shandong Province， Assistant professor. Research areas include microwave remote sensing, data assimilation. E-mail: doufl@cma.gov.cn
  • Supported by:
    the National Natural Science Foundation of China ”Research on improving the high-frequency microwave scattering radiative transfer model in the microwave all-sky assimilation”(41905034)
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云和降水区微波观测包含大量与天气系统，特别是台风、暴雨等灾害性天气系统发生发展密切相关的大气信息，因此微波资料的全天候同化应用成为当前数值预报领域的热点研究问题。过去20年间，全球几大数值预报中心逐步开展了全天候同化技术的研究和业务应用，证实了全天候卫星微波观测资料能够改进模式中的质量、风场、湿度以及云和降水场的初始信息，从而改进数值预报模式的预报效果。通过梳理和评述全天候卫星微波观测资料同化方法，分析其中的关键技术问题和目前存在的困难和挑战，为未来在我国数值天气预报领域开展全天候同化研究提供依据。随着我国新一代数值天气预报模式的发展应用，加强我国全天候资料同化技术的研究将会在业务中发挥更大的科学效益和应用效益。

关键词: 全天候, 云和降水, 微波观测, 资料同化, 一维变分

As the important components of the earth’s atmospheric system, cloud and precipitation strongly affect the global hydrology and energy cycles through the interaction of solar and infrared radiation with cloud droplets and the release of latent heat in precipitation development. The microwave observations in cloudy and rainy conditions have a large amount of information closely related to the development of weather systems, especially the severe weather systems like typhoon and rainstorm. Nevertheless, satellite microwave observations are usually only assimilated in clear-sky above the ocean and their cloud and precipitation content is discarded. Over the past two decades, several Numerical Weather Prediction (NWP) centers have gradually developed the “all-sky” approach to make use of the cloud- and precipitation-affected microwave radiances. It’s been proved that the all-sky assimilation can be used to improve the first guessed mass, wind, humidity, cloud and precipitation through the tracer effect. For providing an investigated reference for the future research of all-weather assimilation in domestic numerical weather forecast, this paper reviewed the all-sky assimilation methods using microwave observation data, analyzed the advantages and disadvantages of each method, and discussed the key technical problems and the existing difficulties and challenges in this field. With the development and application of the new generation of NWP model in China, advancing the domestic research of all-weather data assimilation technology will bring more scientific and practical benefits in the future.

Key words: All-sky, Cloud and precipitation, Microwave, Data assimilation, 1 DVar.

中图分类号: 

图 1 “1D+4DVar”两步同化（实线）和直接4DVar全天候同化（虚线）方法流程图
Fig.1 Flowchart describing the “1D+4DVar”(solid lines) and the direct 4DVar(dotted lines) methods for the all-sky assimilation
表1 全球数值天气预报中心的全天候同化业务状态
Table 1 The status of all-sky assimilation operation in global NWP centers
机构 业务同化的微波资料 预备同化的微波资料 同化方案 湿控制变量
ECMWF SSMIS，GMI，AMSR-2，MHS,MWHS-2和SAPHIR ATMS，AMSU-A和ATMS 混合四维变分 相对湿度
JMA GMI，AMSR-2和SSMIS MWRI，MHS，ATMS，MWHS-2，SAPHIR和AMSU-A 混合四维变分 对数绝对湿度
NCEP AMSU-A GMI，AMSR-2，MHS和ATMS 混合四维变分 相对湿度、总水混合比
Met Office MHS，ATMS，SSMIS，MWHS-2，SAPHIR，GMI，AMSU-A和ATMS 混合四维变分 水汽、云冰、云水混合比
Météo-France MHS，ATMS，SSMIS，MWHS-2，SAPHIR和GMI 混合四维变分 绝对湿度
DWD MHS，ATMS，SSMIS，MWHS-2，SAPHIR和GMI 混合三维变分 水汽、云冰、云水混合比
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