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Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth DiagnosisBlowing snow is a common phenomenon over the Antarctic ice sheet and sea ice regions, playing a crucial role in the Antarctic climate system. Previous research developed an optimized machine learning (ML) model to diagnose blowing snow occurrence using meteorological fields from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). This paper extends that work by optimizing an ML model to estimate blowing snow height and optical depth for operational data production. Observations from the Cloud–Aerosol Lidar and Infrared Path-finder Satellite Observation (CALIPSO) serve as ground truth for training. The optimization process involves selecting relevant input features and identifying the most effective ML regressor. As a result, 21 MERRA-2 fields were identified as key input features, and Extreme Gradient Boosting emerged as the most effective regressor. Feature importance analysis highlights wind components and surface pressure as the most significant predictors for blowing snow height and optical depth. Individual models were developed for each month. Using 10 years of CALIPSO data (2007–2016) for training, these optimized models can be applied across the full MERRA-2 dataset, spanning from 1980 to the present. This enables the generation of hourly blowing snow height and optical depth data on the MERRA-2 grid for the entire MERRA-2 time span.
Document ID
20250006629
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
Authors
Surendra Bhatta ORCID
(Morgan State University Baltimore, United States)
Yuekui Yang ORCID
(Goddard Space Flight Center Greenbelt, United States)
Date Acquired
June 30, 2025
Publication Date
June 21, 2025
Publication Information
Publication: Atmosphere
Publisher: Multidisciplinary Digital Publishing Institute (Switzerland)
Volume: 16
Issue: 7
Issue Publication Date: July 1, 2025
e-ISSN: 2073-4433
Subject Category
Meteorology and Climatology
Funding Number(s)
CONTRACT_GRANT: 80NSSC22M0001
WBS: 281945.02.04.04.36
Distribution Limits
Public
Copyright
Use by or on behalf of the US Gov. Permitted.
Technical Review
External Peer Committee
Keywords
MERRA-2
machine learning
CALIPSO
blowing snow height and optical depth
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