Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis
Blowing 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 Retro...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2073-4433/16/7/760 |
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| author | Surendra Bhatta Yuekui Yang |
| author_facet | Surendra Bhatta Yuekui Yang |
| author_sort | Surendra Bhatta |
| collection | DOAJ |
| description | Blowing 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 Pathfinder 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. |
| format | Article |
| id | doaj-art-e638ccc03d624f509e07c5d0bee3930d |
| institution | DOAJ |
| issn | 2073-4433 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Atmosphere |
| spelling | doaj-art-e638ccc03d624f509e07c5d0bee3930d2025-08-20T03:13:36ZengMDPI AGAtmosphere2073-44332025-06-0116776010.3390/atmos16070760Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth DiagnosisSurendra Bhatta0Yuekui Yang1Climate and Radiation Laboratory, NASA Goddard Space Flight Centre, Greenbelt, MD 20771, USAClimate and Radiation Laboratory, NASA Goddard Space Flight Centre, Greenbelt, MD 20771, USABlowing 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 Pathfinder 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.https://www.mdpi.com/2073-4433/16/7/760blowing snow height and optical depthCALIPSOmachine learningMERRA-2 |
| spellingShingle | Surendra Bhatta Yuekui Yang Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis Atmosphere blowing snow height and optical depth CALIPSO machine learning MERRA-2 |
| title | Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis |
| title_full | Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis |
| title_fullStr | Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis |
| title_full_unstemmed | Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis |
| title_short | Machine Learning Model Optimization for Antarctic Blowing Snow Height and Optical Depth Diagnosis |
| title_sort | machine learning model optimization for antarctic blowing snow height and optical depth diagnosis |
| topic | blowing snow height and optical depth CALIPSO machine learning MERRA-2 |
| url | https://www.mdpi.com/2073-4433/16/7/760 |
| work_keys_str_mv | AT surendrabhatta machinelearningmodeloptimizationforantarcticblowingsnowheightandopticaldepthdiagnosis AT yuekuiyang machinelearningmodeloptimizationforantarcticblowingsnowheightandopticaldepthdiagnosis |