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A Prototype Precipitation Retrieval Algorithm over Land for ATMS

机译:用于ATMS的陆上原型降水检索算法

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A prototype precipitation algorithm for the Advanced Technology Microwave Sounder (ATMS) was developed by using 3-yr coincident ground radar and ATMS observations over the continental United States (CONUS). Several major improvements to a previously published algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS) include 1) considering the different footprint size of ATMS pixels, 2) calculating the uncertainty associated with the precipitation estimation, and 3) extending the algorithm to the 60 degrees S-60 degrees Nregion using only CONUS observations to construct the database. It is found that the retrieved and radar-observed rain rates agree well (e.g., correlation 0.66) and the one-standard-deviation error bar provides valuable retrieval uncertainty information. The geospatial pattern from the retrieved rain rate is largely consistent with that from radar observations. For the snowfall performance, the ATMS-retrieved results clearly capture the snowfall events over the Rocky Mountain region, while radar observations almost entirely miss the snowfall events over this region. Further, this algorithm is applied to the 60 degrees S-60 degrees N land region. The representative nature of rainfall over CONUS permitted the application of this algorithm to 60 degrees S-60 degrees N for rainfall retrieval, evidenced by the progress and retreat of the major rainbands. However, an artificially large snowfall rate is observed in several regions (e.g., Tibetan Plateau and Siberia) because of frequent false detection and overestimation caused by much colder brightness temperatures.
机译:通过使用美国大陆(CONUS)上的3年重合地面雷达和ATMS观测数据,开发了先进技术微波探测仪(ATMS)的原型降水算法。以前发布的特殊传感器微波成像仪/声波(SSMIS)算法的几项重大改进包括:1)考虑ATMS像素的足迹尺寸不同,2)计算与降水量估计相关的不确定性,以及3)将算法扩展到仅使用CONUS观测值构建数据库的N区域60度至N 60度。已经发现,取回的和雷达观测的降雨率吻合得很好(例如,相关系数为0.66),并且一标准偏差误差条提供了有价值的取回不确定性信息。从反演的降雨率中得出的地理空间格局与雷达观测结果基本一致。对于降雪性能,ATMS检索的结果清楚地捕获了落基山地区的降雪事件,而雷达观测几乎完全错过了该地区的降雪事件。此外,该算法应用于60度S-60度N陆地区域。 CONUS上的降雨具有代表性,这使得该算法可以在60°S-60°N的范围内应用以进行降雨检索,主要雨带的前进和后退证明了这一点。然而,由于频繁的错误检测和高得多的亮度温度导致的高估,在几个地区(例如青藏高原和西伯利亚)观察到了人为的大降雪率。

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