A new water vapor algorithm for TRMM Microwave Imager (TMI) measurements based on a log linear relationship
A new water vapor algorithm for TRMM Microwave Imager (TMI) measurements based on a log linear relationship
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DOI：
10.1029/2008jd011057
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发表时间：
2009-11
影响因子：
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通讯作者：
Yu Wang;Yunfei Fu;Guosheng Liu;Qi Liu;Liang Sun
中科院分区：
文献类型：
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作者：
Yu Wang;Yunfei Fu;Guosheng Liu;Qi Liu;Liang Sun
[1] 提出了一种新算法，用于通过使用热带降雨测量任务 (TRMM) 微波成像仪 (TMI) 测量来在无雨的情况下反演海洋上空的大气柱状水蒸气 (CWV)。利用亮度温度与主要环境变量（包括CWV、海面温度、风速、云液态水路径和云温度）之间的对数线性关系，通过辐射传输模型模拟开发了这种基于五通道的算法。该算法的独特优势在于，反演的CWV仅由五个通道的亮度温度得出，无需其他辅助数据，几乎不受其他地球物理变量的影响。将检索结果与无线电探空仪观测结果进行比较，结果显示出良好的一致性，偏差小于 0.7 kg m ―2 ，均方根误差约为 2.5 kg m ―2 ，无论是否存在云。此外，与遥感系统CWV反演结果进行比较，两种算法一致性较高，平均差为0.021 kg m ―2 ，均方根差为2.076 kg m ―2 。最后，基于该算法的CWV全球分布与其他独立数据集的CWV全球分布的相似性表明该新算法可以应用于气候应用。
[1] A new algorithm is proposed for retrieving atmospheric columnar water vapor (CWV) over ocean in the absence of rain by using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) measurements. Applying a log linear relationship between the brightness temperatures and main environmental variables including CWV, sea surface temperature, wind speed, cloud liquid water path, and cloud temperature, this five-channel-based algorithm was developed through radiative transfer model simulations. The unique advantage of this algorithm is that the retrieved CWV, derived simply from only the five channel brightness temperatures without other ancillary data, is hardly influenced by other geophysical variables. Retrievals are compared against radiosonde observations, which showed a good agreement with a bias less than 0.7 kg m ―2 and a root mean square error about 2.5 kg m ―2 , regardless of the presence of clouds. Additionally, comparison is made with CWV retrievals from the Remote Sensing Systems, which showed a high consistency between the two algorithms with a mean difference of 0.021 kg m ―2 and a root mean square difference of 2.076 kg m ―2 . Finally, the similarity of CWV global distributions based on this algorithm to those from other independent data sets suggests that the new algorithm can be applied for climatic applications.