An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data
An Improved Parameterization for Retrieving Clear-Sky Downward Longwave Radiation from Satellite Thermal Infrared Data
复制标题
从卫星热红外数据中检索晴空下行长波辐射的改进参数化
DOI：
10.3390/rs11040425
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发表时间：
2019-02
期刊：
影响因子：
5
通讯作者：
Li Li
中科院分区：
文献类型：
--
作者：
Shanshan Yu;Xiaozhou Xin;Qiang Liu;Hailong Zhang;Li Li
地表下行长波辐射(DLR)是地球表面能量平衡的重要组成部分。Yu等人。(2013)开发了一种结合使用卫星热红外数据和柱积分水汽数据以高空间分辨率反演晴空DLR的参数。我们将YU2013的参数化扩展到基于大气辐射模拟的中分辨率成像光谱仪(MODIS)数据，并对该参数化进行了修改，以减少大IWV时的系统负偏差。与YU2013算法相比，新的参数化方案将IWV≥3 cm的DLR精度提高了1.9W/m2至3.1W/m2。我们还将新的参数化与四种算法进行了比较，其中两种是基于大气层顶(TOA)辐射，另两种是基于近地表气象参数和水汽。这些算法首先使用模拟数据进行评估，然后应用于MODIS数据，并使用全球14个站的地表测量进行验证。结果表明，在地面温度与皮肤温度相差较大(足够相差20K)的地区，新的参数化方法优于基于TOA辐射度的算法。这种参数化在高海拔地区也很有效，因为基于大气参数的算法往往有很大的偏差。此外，比较不同来源的大气输入数据，我们发现，与MODIS大气产品相比，使用从大气再分析数据中内插的参数，新的参数化方案的DLR估计提高了7.8W/m2，而其他算法在高海拔地点的DLR估计提高了19.1W/m2。
Surface downward longwave radiation (DLR) is a crucial component in Earth’s surface energy balance. Yu et al. (2013) developed a parameterization for retrieving clear-sky DLR at high spatial resolution by combined use of satellite thermal infrared (TIR) data and column integrated water vapor (IWV). We extended the Yu2013 parameterization to Moderate Resolution Imaging Spectroradiometer (MODIS) data based on atmospheric radiative simulation, and we modified the parameterization to decrease the systematic negative biases at large IWVs. The new parameterization improved DLR accuracy by 1.9 to 3.1 W/m2 for IWV ≥3 cm compared to the Yu2013 algorithm. We also compared the new parameterization with four algorithms, including two based on Top-of-Atmosphere (TOA) radiance and two using near-surface meteorological parameters and water vapor. The algorithms were first evaluated using simulated data and then applied to MODIS data and validated using surface measurements at 14 stations around the globe. The results suggest that the new parameterization outperforms the TOA-radiance based algorithms in the regions where ground temperature is substantially different (enough that the difference between them is as large as 20 K) from skin air temperature. The parameterization also works well at high elevations where atmospheric parameter-based algorithms often have large biases. Furthermore, comparing different sources of atmospheric input data, we found that using the parameters interpolated from atmospheric reanalysis data improved the DLR estimation by 7.8 W/m2 for the new parameterization and 19.1 W/m2 for other algorithms at high-altitude sites, as compared to MODIS atmospheric products.
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