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dc.contributor.authorAdhikari, Abishek
dc.contributor.authorEhsani, Mohammad Reza
dc.contributor.authorSong, Yang
dc.contributor.authorBehrangi, Ali
dc.identifier.citationAdhikari, A., Ehsani, M. R., Song, Y., & Behrangi, A. (2020). Comparative assessment of snowfall retrieval from Microwave Humidity Sounders using machine learning methods. Earth and Space Science, 7(11), e2020EA001357.en_US
dc.description.abstractAccurate 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 to be 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 United States verifies that the RF-MHS estimated snowfall agrees well with the ground-based National Center for Environmental Prediction (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.en_US
dc.rights© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.en_US
dc.subjectglobal snow mapen_US
dc.subjectsatellite remote sensing of falling snowen_US
dc.subjectmachine learningen_US
dc.subjectpassive microwave snow retrievalen_US
dc.subjectMHS snowen_US
dc.titleComparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methodsen_US
dc.contributor.departmentUniv Arizona, Dept Hydrol & Atmospher Scien_US
dc.identifier.journalEARTH AND SPACE SCIENCEen_US
dc.description.noteOpen access articleen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleEarth and Space Science

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© 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License.