在被雪覆盖的陆地表面和海岸带上空ShARP被动降雨反演的评价
On evaluation of ShARP passive rainfall retrievals over snow-covered land surfaces and coastal zones
摘要：
对于陆地降水反演，使用微波波段的卫星测量，重要的是要正确区分强和高度可变的背景地面发射的弱降雨信号。传统上，土地降雨反演方法往往依赖于一个微弱的信号，降雨散射的高频通道（85 GHz），并利用经验阈值和回归为基础的技术。由于地面信号干扰的增加，在辐射复杂的陆地表面，特别是在积雪覆盖的土地，沙漠和沿海地区的降水反演，是这类检索技术的特殊挑战。本文评估了最近提出的收缩局部线性嵌入算法检索降水（ShARP）的结果，在辐射复杂的地形和沿海地区使用的热带降雨测量任务（TRMM）卫星提供的数据。为此，在东南亚的一个地区，部分覆盖青藏高原，喜马拉雅山脉，恒河-雅鲁藏布江-梅克纳河流域及其三角洲的ShARP检索实验。我们阐明了有前途的结果ShARP在积雪覆盖的陆地表面和海岸线附近，与标准的TRMM-2A 12产品的陆地降雨检索。具体而言，使用TRMM-2A 25雷达产品作为参考，我们提供的证据表明，ShARP算法可以显着减少降雨超估计由于背景雪污染，并显着提高检测和检索降雨在海岸线附近。在2013日历年，我们证明，在研究领域的均方根差可以减少高达38%，每年，而减少可以达到70%，在寒冷的月份。
英文摘要：
For precipitation retrievals over land, using satellite measurements in microwave bands, it is important to properly discriminate the weak rainfall signals from strong and highly variable background surface emission. Traditionally, land rainfall retrieval methods often rely on a weak signal of rainfall scattering on high-frequency channels (85 GHz) and make use of empirical thresholding and regression-based techniques. Due to the increased ground surface signal interference, precipitation retrieval over radiometrically complex land surfaces, especially over snow-covered lands, deserts and coastal areas, is of particular challenge for this class of retrieval techniques. This paper evaluates the results by the recently proposed Shrunken locally linear embedding Algorithm for Retrieval of Precipitation (ShARP), over a radiometrically complex terrain and coastal areas using the data provided by the Tropical Rainfall Measuring Mission (TRMM) satellite. To this end, the ShARP retrieval experiments are performed over a region in Southeast Asia, partly covering the Tibetan Highlands, Himalayas, Ganges-Brahmaputra-Meghna river basins and its delta. We elucidate promising results by ShARP over snow covered land surfaces and at the vicinity of coastlines, in comparison with the land rainfall retrievals of the standard TRMM-2A12 product. Specifically, using the TRMM-2A25 radar product as a reference, we provide evidence that the ShARP algorithm can significantly reduce the rainfall over estimation due to the background snow contamination and markedly improve detection and retrieval of rainfall at the vicinity of coastlines. During the calendar year 2013, we demonstrate that over the study domain the root mean squared difference can be reduced up to 38% annually, while the reduction can reach up to 70% during the cold months.
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