Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

• Published in: Earth and Space Science
• Main Authors: Abishek Adhikari, Mohammad Reza Ehsani, Yang Song, Ali Behrangi
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union (AGU) 2020
• Subjects: global snow map; satellite remote sensing of falling snow; machine learning; passive microwave snow retrieval; MHS snow; Astronomy; QB1-991; Geology; QE1-996.5; Greenland; Merra; Alaska
• Online Access: https://doi.org/10.1029/2020EA001357; https://doaj.org/article/d00c3f243c4745b680b5c8c694d25dad

Abstract 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...