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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >An Active–Passive Microwave Land Surface Database From GPM
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An Active–Passive Microwave Land Surface Database From GPM

机译：来自GPM的主动被动微波陆地表面数据库

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A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM's dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10-166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25 degrees gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.
机译：微波发射率检索适用于陆地和海冰的五年全球降水测量（GPM）微波成像仪（GMI）观察。发射性与GPM的双频降水雷达（DPR）表面反向散射测量结合，在清晰的天空条件下。发射率 - Backsfatter数据库用于在GPM轨道内的曲面进行分析以进行降水检索算法和其他应用。使用最佳估计检索全10-166-GHz发射率矢量。由于GMI包括水蒸气探测通道，因此同时进行大气和表面状态的检索。使用Merra2重新分析作为先验的大气状态并具有正确的表征其误差，我们能够有效地筛选云和降水影响的发射性。与Colocated CloudSAT数据的比较表明，基于GMI的屏幕能够检测单独DPR的降水;然而，GMI仍未发现大约10％的沉淀发生的降水发生。然后将无监督的kohonen分类技术应用于多年月0.25度网格式的均匀的发射率和反散射，以截然不是无雪，积雪和海冰表面，以便根据主动和被动微波特性对表面进行分类。该类对应于植被覆盖和型，淹没区域，土壤成分和地形粗糙度。雪和海冰表面显示清晰的季节性周期，代表雪和冰空间范围的增加，春季减少。还探讨了GPM降水检索算法和对累积雨雪和降雪的敏感性。

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