Estimation of land surface temperature over the Tibetan Plateau using GMS data
Estimation of land surface temperature over the Tibetan Plateau using GMS data
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DOI：
10.1175/1520-0450(2004)043
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发表时间：
2004-04-01
期刊：
影响因子：
--
通讯作者：
Ishikawa, H
中科院分区：
文献类型：
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作者：
Oku, Y;Ishikawa, H
利用静止气象卫星可见光/红外自旋扫描辐射计（GMS VISSR）图像，对青藏高原陆面温度分布的日变化进行了估算。为NOAA高级甚高分辨率辐射计（AVHRR）开发的红外分裂窗口算法已被用于这一目的。通过辐射传输模拟，得到了分裂窗算法内部系数所涉及的大气透过率和温差。该算法所需的可蒸发水分布估计从6.7妈妈的亮度温度利用GMS水汽通道的光谱特性。云的去除在地表温度反演过程中起着重要的作用。为了识别对流云活动，许多研究人员使用卫星红外测量与固定阈值技术。在这项研究中，不仅需要去除对流云，而且还需要去除暖云。为此，提出了一种可变阈值技术。阈值随季节和日变化，其值根据地面观测确定。通过可变阈值，可以在夏季去除相对较热的云层，并在冬季夜间检测较冷的地面。使用该算法从GMS数据中估计的表面温度与现场表面测量值的比较结果显示出约0.8的相关性。
Geostationary Meteorological Satellite Visible/Infrared Spin-Scan Radiometer (GMS VISSR) images have been used to estimate diurnal variations of land surface temperature distributions over the Tibetan Plateau. The infrared split-window algorithm developed for NOAA Advanced Very High Resolution Radiometer (AVHRR) has been adapted for this purpose. Radiative transfer simulations are carried out to obtain the atmospheric transmittances and the difference temperatures that are involved in the internal coefficients of the split-window algorithm. Precipitable water distribution that is required by this algorithm is estimated from 6.7-mum brightness temperature utilizing spectral characteristics of the GMS water vapor channel. Cloud removal plays an important role in the surface temperature retrieval process. To identify convective cloud activity, many researchers use satellite infrared measurements with a fixed threshold technique. In this study, it is necessary to remove not only convective clouds but also warm clouds. For this purpose, a variable threshold technique is proposed. The threshold varies both seasonally and diurnally, and its value is determined on the basis of surface observations. With a variable threshold, it becomes possible to remove relatively warmer clouds in summer and detect colder ground surfaces at nighttime in the winter. The results of comparing estimated surface temperature from GMS data using this algorithm with in situ surface measurements show correlations around 0.8.