Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However,...

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Authors: Ghada, Wael, Casellas, Enric, Herbinger, Julia, Garcia Benadi, Albert, Bothmann, Ludwig, Estrella, Nicole, Bech, Joan, Menzel, Annette
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Universidad de Barcelona
Repository:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/191843
Online Access:https://hdl.handle.net/2445/191843
Access Level:Open access
Keyword:Temps (Meteorologia)
Radar
Aprenentatge automàtic
Weather
Machine learning
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spelling Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain RadarGhada, WaelCasellas, EnricHerbinger, JuliaGarcia Benadi, AlbertBothmann, LudwigEstrella, NicoleBech, JoanMenzel, AnnetteTemps (Meteorologia)RadarAprenentatge automàticWeatherRadarMachine learningRain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain.MDPI2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/191843Articles publicats en revistes (Física Aplicada)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3390/rs14184563Remote Sensing, 2022, vol. 14, num. 18, p. 1-23https://doi.org/10.3390/rs14184563cc-by (c) Ghada, Wael et al., 2022https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1918432026-05-27T06:46:51Z
dc.title.none.fl_str_mv Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
title Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
spellingShingle Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
Ghada, Wael
Temps (Meteorologia)
Radar
Aprenentatge automàtic
Weather
Radar
Machine learning
title_short Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
title_full Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
title_fullStr Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
title_full_unstemmed Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
title_sort Stratiform and Convective Rain Classification Using Machine Learning Models and Micro Rain Radar
dc.creator.none.fl_str_mv Ghada, Wael
Casellas, Enric
Herbinger, Julia
Garcia Benadi, Albert
Bothmann, Ludwig
Estrella, Nicole
Bech, Joan
Menzel, Annette
author Ghada, Wael
author_facet Ghada, Wael
Casellas, Enric
Herbinger, Julia
Garcia Benadi, Albert
Bothmann, Ludwig
Estrella, Nicole
Bech, Joan
Menzel, Annette
author_role author
author2 Casellas, Enric
Herbinger, Julia
Garcia Benadi, Albert
Bothmann, Ludwig
Estrella, Nicole
Bech, Joan
Menzel, Annette
author2_role author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Temps (Meteorologia)
Radar
Aprenentatge automàtic
Weather
Radar
Machine learning
topic Temps (Meteorologia)
Radar
Aprenentatge automàtic
Weather
Radar
Machine learning
description Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain.
publishDate 2022
dc.date.none.fl_str_mv 2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/191843
url https://hdl.handle.net/2445/191843
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/rs14184563
Remote Sensing, 2022, vol. 14, num. 18, p. 1-23
https://doi.org/10.3390/rs14184563
dc.rights.none.fl_str_mv cc-by (c) Ghada, Wael et al., 2022
https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Ghada, Wael et al., 2022
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Física Aplicada)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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