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Retrieval of cloud liquid water content over global oceans using FY-3C/3D microwave imager

  • role： First author第一作者
  • Affiliation：

    Chinese Academy of Meteorological Sciences, Beijing 100081, China

    University of Chinese Academy of Sciences, Beijing 100049, China

    Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, Beijing 100081, China

  • Email：doufl@cma.gov.cn
  • Introduction：窦芳丽，1986年生, 女, 助理研究员, 研究方向为微波定量遥感、卫星资料同化的理论和应用。E-mail: doufl@cma.gov.cn
DOU Fangli123,  
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, Beijing 100081, China

SHANG Jian3,  
  • Affiliation：

    University of Chinese Academy of Sciences, Beijing 100049, China

    Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, Beijing 100081, China

WU Qiong23,  
  • role： Corresponding author通信作者
  • Affiliation：

    Key Laboratory of Radiometric Calibration and Validation for Environment Satellites, National Satellite Meteorological Center, Beijing 100081, China

  • Email：gusy@cma.gov.cn
  • Introduction：谷松岩，1963年生, 女, 研究员, 研究方向为卫星微波遥感相关理论和应用。E-mail: gusy@cma.gov.cn
GU Songyan3*

résumé

Cloud Liquid Water Content (CLW) links the hydrological and radiative components of the climate system and is an important parameter for research in climate and cloud microphysics. Cloud liquid water is a highly variable target and depends on cloud type. The different cloud types exist on different levels and vary in the satellite sensor view.Cloud liquid water is one the most uncertain factors in climate change research. CLW can be directly measured from the passive microwave measurements on the basis of its spectral and polarization signatures. The all-sky CLW retrieval algorithms for FY-3C and FY-3D microwave imagers (MWRI) are presented in this study. The CRTM rapid radiative transfer and various cloud models, as well as ECMWF short-range forecast profile datasets are utilized for training the retrieval coefficients. Hence, the physical-based algorithm could ensure the adaptability of the CLW products for different seasons and regions. To prevent matching errors between visible and microwave pixels, a novel clear-sky detection method based on O-B (observations minus backgrounds) errors of FY-3 MWRI brightness temperatures is given and proved effective to adjust the coefficients and the scale factor of the retrieval equation. Then, the retrievals under rainy conditions based on the climate statistical features are added in the FY-3 CLW all-sky algorithms.As the validation of satellite derived CLWs is difficult to carry out, the statistical histogram method raised by Remote Sensing System (RSS) is used to estimate the accuracy of FY-3 CLW daily products and DMSP-F16 SSM/I CLW products. Results showed that the RMSE of FY-3C CLWs is 0.028 mm and FY-3D is 0.025 mm. As the RMSE of SSM/I CLWs is 0.025 mm according to the same method, the accuracy of FY-3 CLWs is comparable to that of the RSS operational products. We selected 15 days of FY-3 CLW orbital product data in March 2015 to compare with the GPM GMI orbital products from RSS under strict matching constraint. The comparison result shows that the two kinds of products are of good consistency, and the correlation coefficient reached 0.9061. The mean deviation and RMSE are 0.0075±0.0325 mm.The global distribution of FY-3 daily CLW was analyzed and compared with the distributions of clouds observed by SSM/I and FY-3D MERSI. According to the analysis, the cloud distribution observed by FY-3 is consistent with SSM/I and MERSI observations. FY-3 CLWs were more sensitive to thin clouds with smaller particles and less water content. The CLW values of thick clouds were slightly underestimated than SSM/I. FY-3 CLWs could depict the detailed structure of typhoons, including eye area, inner cloud wall, and outer spiral rain belt. At present, FY-3C/D CLW products are used in operations. The networking of morning and afternoon orbit satellites could achieve global coverage in one day.

mots-clés

remote sensing;FY-3 MWRI;cloud liquid water;radiative transfer;cloud model;statistical histogram;validation

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