A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021

Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMER...

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Detalles Bibliográficos
Autores: Tapiador Fuentes, Francisco Javier, Villalba Pradas, Anahí, Navarro Martínez de la Casa, Andrés, Martín Martín, Raúl, Merino, Andrés, García Ortega, Eduardo, Sánchez, José Luis, Kim, Kwonil, Lee, Gyuwon
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/33782
Acceso en línea:https://www.mdpi.com/2072-4292/13/14/2702
https://hdl.handle.net/10578/33782
Access Level:acceso abierto
Palabra clave:Storm Filomena
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spelling A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021Tapiador Fuentes, Francisco JavierVillalba Pradas, AnahíNavarro Martínez de la Casa, AndrésMartín Martín, RaúlMerino, AndrésGarcía Ortega, EduardoSánchez, José LuisKim, KwonilLee, GyuwonStorm FilomenaEvaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1° × 0.1° V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses.MDPI202420242021info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://www.mdpi.com/2072-4292/13/14/2702https://hdl.handle.net/10578/33782reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésinfo:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/337822026-05-27T07:36:41Z
dc.title.none.fl_str_mv A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
title A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
spellingShingle A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
Tapiador Fuentes, Francisco Javier
Storm Filomena
title_short A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
title_full A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
title_fullStr A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
title_full_unstemmed A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
title_sort A Satellite View of an Intense Snowfall in Madrid (Spain): The Storm ‘Filomena’ in January 2021
dc.creator.none.fl_str_mv Tapiador Fuentes, Francisco Javier
Villalba Pradas, Anahí
Navarro Martínez de la Casa, Andrés
Martín Martín, Raúl
Merino, Andrés
García Ortega, Eduardo
Sánchez, José Luis
Kim, Kwonil
Lee, Gyuwon
author Tapiador Fuentes, Francisco Javier
author_facet Tapiador Fuentes, Francisco Javier
Villalba Pradas, Anahí
Navarro Martínez de la Casa, Andrés
Martín Martín, Raúl
Merino, Andrés
García Ortega, Eduardo
Sánchez, José Luis
Kim, Kwonil
Lee, Gyuwon
author_role author
author2 Villalba Pradas, Anahí
Navarro Martínez de la Casa, Andrés
Martín Martín, Raúl
Merino, Andrés
García Ortega, Eduardo
Sánchez, José Luis
Kim, Kwonil
Lee, Gyuwon
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Storm Filomena
topic Storm Filomena
description Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1° × 0.1° V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses.
publishDate 2021
dc.date.none.fl_str_mv 2021
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://www.mdpi.com/2072-4292/13/14/2702
https://hdl.handle.net/10578/33782
url https://www.mdpi.com/2072-4292/13/14/2702
https://hdl.handle.net/10578/33782
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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