收稿：2018-08-14，
纸质出版：2020-06-07
移动端阅览
云液态水含量是气候和水循环研究的重要云微物理参数，也是目前气候变化研究中的最不确定因素之一。通过极轨气象卫星被动微波观测的光谱和极化特征能够实现对云液态水含量的直接测量，本文介绍了一种基于风云三号卫星微波成像仪（MWRI）观测亮温的全天候云液态水含量反演算法，利用快速辐射传输模式、云模型和大气廓线库建立MWRI模拟亮温库并训练反演系数的宽气候态物理算法可以保证算法系数在不同季节和不同地区的适应性。同时提出了一种基于观测增量（O-B）筛选晴空像元并对算法系数及比例因子进行订正的方法。利用统计直方图方法和卫星间交叉比对方法对反演产品精度进行了检验，统计直方图方法检验结果表明，FY-3C云水反演误差为0.028 mm，FY-3D为0.025 mm，与国外同类产品的精度相当；与低轨卫星微波辐射计GMI云水产品的交叉比对结果表明，两者具有较高一致性，均方根误差为0.0325 mm。FY-3C/3D CLW产品目前已经投入业务应用，上下午星组网能够一天内基本覆盖全球，实现全球云水分布监测。
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.
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