Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning
Precipitation type prediction is crucial for various sectors, including aviation, agriculture, and public safety. For instance, freezing rain and sleet can severely disrupt transportation, while heavy rainfall may lead to flash floods. Using METAR weather reports, we sought to classify precipitation...
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Cluj University Press
2025-03-01
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| Series: | Aerul şi Apa: Componente ale Mediului |
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| Online Access: | https://aerapa.conference.ubbcluj.ro/2025/pdf/109_114_Varga_Balogh_etal_AWC_2025.pdf |
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| author | Adrienn VARGA-BALOGH Ádám LEELŐSSY Róbert MÉSZÁROS |
| author_facet | Adrienn VARGA-BALOGH Ádám LEELŐSSY Róbert MÉSZÁROS |
| author_sort | Adrienn VARGA-BALOGH |
| collection | DOAJ |
| description | Precipitation type prediction is crucial for various sectors, including aviation, agriculture, and public safety. For instance, freezing rain and sleet can severely disrupt transportation, while heavy rainfall may lead to flash floods. Using METAR weather reports, we sought to classify precipitation types based on surface variables such as temperature (°C), dew point deficit (°C), wind speed (knots). The dataset was divided into training and testing subsets. Known precipitation types in the training set allowed us to fit classifying algorithms. We evaluated several classification models. The k-nearest neighbors (k-NN) method was initially applied, with parameter optimization performed to enhance accuracy. Precipitation types were first categorized into two groups: liquid and non-liquid types. Liquid included rain, fog and drizzle, excluding convective precipitation. The non-liquid category included solid and supercooled types like snow, sleet, freezing rain, graupel, and supercooled fog. Further refinements classified precipitation into six categories: liquid precipitation, convective precipitation, snow, sleet, ice pellets, and supercooled water (including freezing rain and rime fog). Additionally, classifications into 5 and 4 categories were analyzed. Evaluation metrics, such as sensitivity, precision, specificity, and accuracy, were employed to assess model performance in classifying precipitation types. This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary's National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union.. |
| format | Article |
| id | doaj-art-4ae7fa8b7d044062a91448aaeb3ba77e |
| institution | DOAJ |
| issn | 2344-4401 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Cluj University Press |
| record_format | Article |
| series | Aerul şi Apa: Componente ale Mediului |
| spelling | doaj-art-4ae7fa8b7d044062a91448aaeb3ba77e2025-08-20T03:09:19ZengCluj University PressAerul şi Apa: Componente ale Mediului2344-44012025-03-01202510911410.24193/AWC2025_10Identifying Precipitation Types From Surface Meteorological Variables With Machine LearningAdrienn VARGA-BALOGH0Ádám LEELŐSSY1Róbert MÉSZÁROS2Department of Meteorology, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, HungaryDepartment of Meteorology, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, HungaryDepartment of Meteorology, Institute of Geography and Earth Sciences, Eötvös Loránd University, Budapest, HungaryPrecipitation type prediction is crucial for various sectors, including aviation, agriculture, and public safety. For instance, freezing rain and sleet can severely disrupt transportation, while heavy rainfall may lead to flash floods. Using METAR weather reports, we sought to classify precipitation types based on surface variables such as temperature (°C), dew point deficit (°C), wind speed (knots). The dataset was divided into training and testing subsets. Known precipitation types in the training set allowed us to fit classifying algorithms. We evaluated several classification models. The k-nearest neighbors (k-NN) method was initially applied, with parameter optimization performed to enhance accuracy. Precipitation types were first categorized into two groups: liquid and non-liquid types. Liquid included rain, fog and drizzle, excluding convective precipitation. The non-liquid category included solid and supercooled types like snow, sleet, freezing rain, graupel, and supercooled fog. Further refinements classified precipitation into six categories: liquid precipitation, convective precipitation, snow, sleet, ice pellets, and supercooled water (including freezing rain and rime fog). Additionally, classifications into 5 and 4 categories were analyzed. Evaluation metrics, such as sensitivity, precision, specificity, and accuracy, were employed to assess model performance in classifying precipitation types. This work has been implemented by the National Multidisciplinary Laboratory for Climate Change (RRF-2.3.1-21-2022-00014) project within the framework of Hungary's National Recovery and Resilience Plan supported by the Recovery and Resilience Facility of the European Union.. https://aerapa.conference.ubbcluj.ro/2025/pdf/109_114_Varga_Balogh_etal_AWC_2025.pdfprecipitation classificationmachine learningmetarhungary |
| spellingShingle | Adrienn VARGA-BALOGH Ádám LEELŐSSY Róbert MÉSZÁROS Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning Aerul şi Apa: Componente ale Mediului precipitation classification machine learning metar hungary |
| title | Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning |
| title_full | Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning |
| title_fullStr | Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning |
| title_full_unstemmed | Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning |
| title_short | Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning |
| title_sort | identifying precipitation types from surface meteorological variables with machine learning |
| topic | precipitation classification machine learning metar hungary |
| url | https://aerapa.conference.ubbcluj.ro/2025/pdf/109_114_Varga_Balogh_etal_AWC_2025.pdf |
| work_keys_str_mv | AT adriennvargabalogh identifyingprecipitationtypesfromsurfacemeteorologicalvariableswithmachinelearning AT adamleelossy identifyingprecipitationtypesfromsurfacemeteorologicalvariableswithmachinelearning AT robertmeszaros identifyingprecipitationtypesfromsurfacemeteorologicalvariableswithmachinelearning |