Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models

[EN] Meteorological events have always been of great interest because they have influenced everyday activities in critical areas, such as water resource management systems. Weather forecasts are solved with numerical weather prediction models. However, it sometimes leads to unsatisfactory performanc...

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Detalles Bibliográficos
Autores: Calvo Olivera, María Carmen, Guerrero Higueras, Ángel Manuel, Lorenzana Campillo, Jesús Ángel, García Ortega, Eduardo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/22455
Acceso en línea:https://link.springer.com/article/10.1007/s11269-024-03779-y
https://hdl.handle.net/10612/22455
Access Level:acceso abierto
Palabra clave:Matemáticas
Meteorología
Precipitation
Machine learning
Forecast
Uncertainty
Decision tree
2509.03 Previsión Meteorológica a largo Plazo
1203.04 Inteligencia Artificial
2509.11 Predicción Operacional Meteorológica
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oai_identifier_str oai:buleria.unileon.es:10612/22455
network_acronym_str ES
network_name_str España
repository_id_str
spelling Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based ModelsCalvo Olivera, María CarmenGuerrero Higueras, Ángel ManuelLorenzana Campillo, Jesús ÁngelGarcía Ortega, EduardoMatemáticasMeteorologíaPrecipitationMachine learningForecastUncertaintyDecision tree2509.03 Previsión Meteorológica a largo Plazo1203.04 Inteligencia Artificial2509.11 Predicción Operacional Meteorológica[EN] Meteorological events have always been of great interest because they have influenced everyday activities in critical areas, such as water resource management systems. Weather forecasts are solved with numerical weather prediction models. However, it sometimes leads to unsatisfactory performance due to the inappropriate setting of the initial state. Precipitation forecasting is essential for water resource management in semi-arid climate and seasonal rainfall areas such as the Ebro basin. This research aims to improve the estimation of the uncertainty associated with real-time precipitation predictions presenting a machine learning-based method to evaluate the uncertainty of a weather forecast obtained by the Weather Research and Forecasting model. We use a model trained with ground-truth data from the Confederación Hidrográfica del Ebro, and WRF forecast results to compute uncertainty. Experimental results show that Decision Tree-based ensemble methods get the lowest generalization error. Prediction models studied have above 90% accuracy, and root mean square error has similar results compared to those obtained with the ground truth data. Random Forest presents a difference of -0.001 concerning the 0.535 obtained with the ground truth data. Generally, using the ML-based model offers good results with robust performance over more traditional forms for uncertainty calculation and an effective alternative for real-time computation.SIThis work has been financially supported by the Ministry for Digital Transformation and of Civil Service of the Spanish Government through the QUANTUM ENIA project call - Quantum Spain project, and by the European Union through the Recovery, Transformation and Resilience Plan - NextGenerationEU within the framework of the Digital Spain 2026 Agenda.Ministerio para la Transformación Digital y de la Función PúblicaSpringerArquitectura y Tecnologia de ComputadoresEscuela de Ingenierias Industrial, Informática y Aeroespacial2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://link.springer.com/article/10.1007/s11269-024-03779-yhttps://hdl.handle.net/10612/22455reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/224552026-06-24T12:43:27Z
dc.title.none.fl_str_mv Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
title Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
spellingShingle Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
Calvo Olivera, María Carmen
Matemáticas
Meteorología
Precipitation
Machine learning
Forecast
Uncertainty
Decision tree
2509.03 Previsión Meteorológica a largo Plazo
1203.04 Inteligencia Artificial
2509.11 Predicción Operacional Meteorológica
title_short Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
title_full Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
title_fullStr Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
title_full_unstemmed Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
title_sort Real-Time Evaluation of the Uncertainty in Weather Forecasts Through Machine Learning-Based Models
dc.creator.none.fl_str_mv Calvo Olivera, María Carmen
Guerrero Higueras, Ángel Manuel
Lorenzana Campillo, Jesús Ángel
García Ortega, Eduardo
author Calvo Olivera, María Carmen
author_facet Calvo Olivera, María Carmen
Guerrero Higueras, Ángel Manuel
Lorenzana Campillo, Jesús Ángel
García Ortega, Eduardo
author_role author
author2 Guerrero Higueras, Ángel Manuel
Lorenzana Campillo, Jesús Ángel
García Ortega, Eduardo
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnologia de Computadores
Escuela de Ingenierias Industrial, Informática y Aeroespacial
dc.subject.none.fl_str_mv Matemáticas
Meteorología
Precipitation
Machine learning
Forecast
Uncertainty
Decision tree
2509.03 Previsión Meteorológica a largo Plazo
1203.04 Inteligencia Artificial
2509.11 Predicción Operacional Meteorológica
topic Matemáticas
Meteorología
Precipitation
Machine learning
Forecast
Uncertainty
Decision tree
2509.03 Previsión Meteorológica a largo Plazo
1203.04 Inteligencia Artificial
2509.11 Predicción Operacional Meteorológica
description [EN] Meteorological events have always been of great interest because they have influenced everyday activities in critical areas, such as water resource management systems. Weather forecasts are solved with numerical weather prediction models. However, it sometimes leads to unsatisfactory performance due to the inappropriate setting of the initial state. Precipitation forecasting is essential for water resource management in semi-arid climate and seasonal rainfall areas such as the Ebro basin. This research aims to improve the estimation of the uncertainty associated with real-time precipitation predictions presenting a machine learning-based method to evaluate the uncertainty of a weather forecast obtained by the Weather Research and Forecasting model. We use a model trained with ground-truth data from the Confederación Hidrográfica del Ebro, and WRF forecast results to compute uncertainty. Experimental results show that Decision Tree-based ensemble methods get the lowest generalization error. Prediction models studied have above 90% accuracy, and root mean square error has similar results compared to those obtained with the ground truth data. Random Forest presents a difference of -0.001 concerning the 0.535 obtained with the ground truth data. Generally, using the ML-based model offers good results with robust performance over more traditional forms for uncertainty calculation and an effective alternative for real-time computation.
publishDate 2024
dc.date.none.fl_str_mv 2024
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://link.springer.com/article/10.1007/s11269-024-03779-y
https://hdl.handle.net/10612/22455
url https://link.springer.com/article/10.1007/s11269-024-03779-y
https://hdl.handle.net/10612/22455
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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