Accurate quantification of snowfall rate from space is important, but has remained difficult. Four years (2007‐2010) of NOAA‐18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF‐MHS) is found the best for both detection and estimation of global snowfall. The RF‐MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern‐Era Retrospective analysis for Research and Applications Version 2 (MERRA‐2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF‐MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF‐MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA‐2, AIRS, and GPROF products. A case study over the US verifies that the RF‐MHS estimated snowfall agrees well with the ground‐based NCEP Stage‐IV and MERRA‐2 product whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow‐covered surfaces (e.g., Greenland, Alaska, and Northern Russia), where improvements through new sensors or retrieval techniques are needed.

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机器学习方法对微波湿度探测器降雪的比较评估

从太空准确降雪率很重要，但仍然很困难。使用来自几种CloudSat云剖析雷达（CPR）观测值的降雪估计值，使用几种机器学习方法对NOAA-18微波湿度探测仪（MHS）数据进行了四年（2007-2010）的培训和测试。在研究的方法中，使用MHS（RF-MHS）的随机森林被认为是检测和估算全球降雪的最佳方法。使用独立年份的同时CPR降雪估计值对RF‐MHS估计值进行测试，并与研究和应用版本2（MERRA-2），现代红外测深仪（AIRS）和MHS Goddard分析算法的现代时代回顾分析的降雪率进行比较（GPROF）。研究发现，与独立的CloudSat样本相比，RF‐MHS算法可以检测到大约90％的准确度和0.48的Heidke技能得分的全球降雪。发现湿球表面温度，190 GHz和157 GHz通道的亮度温度是描绘降雪区域的最重要特征。将RF‐MHS检索到的全球降雪率与CPR估算值进行了很好的比较，并显示出比MERRA‐2，AIRS和GPROF产品更好的统计数据。在美国进行的一项案例研究证明，RF-MHS估计的降雪量与地面NCEP Stage-IV和MERRA-2产品相吻合，而当前的GPROF产品（V05）却被低估了。但是，根据RF算法估算的MHS降雪量表明，在寒冷和积雪覆盖的地面（例如格陵兰岛，