CloudSense: A model for cloud type identification using machine learning from radar data
The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG)...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Applied Computing and Geosciences |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590197424000569 |
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| author | Mehzooz Nizar Jha K. Ambuj Manmeet Singh S.B. Vaisakh G. Pandithurai |
| author_facet | Mehzooz Nizar Jha K. Ambuj Manmeet Singh S.B. Vaisakh G. Pandithurai |
| author_sort | Mehzooz Nizar |
| collection | DOAJ |
| description | The knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG. |
| format | Article |
| id | doaj-art-1586e06bed624a9baa7976dc332bce8b |
| institution | DOAJ |
| issn | 2590-1974 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Applied Computing and Geosciences |
| spelling | doaj-art-1586e06bed624a9baa7976dc332bce8b2025-08-20T02:48:58ZengElsevierApplied Computing and Geosciences2590-19742024-12-012410020910.1016/j.acags.2024.100209CloudSense: A model for cloud type identification using machine learning from radar dataMehzooz Nizar0Jha K. Ambuj1Manmeet Singh2S.B. Vaisakh3G. Pandithurai4Cochin University of Science and Technology, Kochi, India; Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; Corresponding author. Cochin University of Science and Technology, Kochi, India.Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, IndiaIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India; The University of Texas at Austin, Austin, TX, USAIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, IndiaIndian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, IndiaThe knowledge of type of precipitating cloud is crucial for radar based quantitative estimates of precipitation. We propose a novel model called CloudSense which uses machine learning to accurately identify the type of precipitating clouds over the complex terrain locations in the Western Ghats (WG) of India. CloudSense uses vertical reflectivity profiles collected during July–August 2018 from an X-band radar to classify clouds into four categories namely stratiform, mixed stratiform-convective, convective and shallow clouds. The machine learning (ML) model used in CloudSense was trained using a dataset balanced by Synthetic Minority Oversampling Technique (SMOTE), with features selected based on physical characteristics relevant to different cloud types. Among various ML models evaluated Light Gradient Boosting Machine (LightGBM) demonstrate superior performance in classifying cloud types with a BAC (Balanced Accuracy) of 0.79 and F1-Score of 0.8. CloudSense generated results are also compared against conventional radar algorithms and we find that CloudSense performs better than radar algorithms. For 200 samples tested, the radar algorithm achieved a BAC of 0.69 and F1-Score of 0.68, whereas CloudSense achieved a BAC of 0.8 and F1-Score of 0.79. Our results show that ML based approach can provide more accurate cloud detection and classification which would be useful to improve precipitation estimates over the complex terrain of the WG.http://www.sciencedirect.com/science/article/pii/S2590197424000569Machine learningPrecipitating cloudsDoppler weather radarWestern ghatsLightGBM |
| spellingShingle | Mehzooz Nizar Jha K. Ambuj Manmeet Singh S.B. Vaisakh G. Pandithurai CloudSense: A model for cloud type identification using machine learning from radar data Applied Computing and Geosciences Machine learning Precipitating clouds Doppler weather radar Western ghats LightGBM |
| title | CloudSense: A model for cloud type identification using machine learning from radar data |
| title_full | CloudSense: A model for cloud type identification using machine learning from radar data |
| title_fullStr | CloudSense: A model for cloud type identification using machine learning from radar data |
| title_full_unstemmed | CloudSense: A model for cloud type identification using machine learning from radar data |
| title_short | CloudSense: A model for cloud type identification using machine learning from radar data |
| title_sort | cloudsense a model for cloud type identification using machine learning from radar data |
| topic | Machine learning Precipitating clouds Doppler weather radar Western ghats LightGBM |
| url | http://www.sciencedirect.com/science/article/pii/S2590197424000569 |
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