Doppler processing in weather radar using deep learning

Autores
Collado Rosell, Arturo; Cogo, Jorge; Areta, Javier Alberto; Pascual, Juan Pablo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.
Fil: Collado Rosell, Arturo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Universidad Nacional de Cuyo; Argentina
Fil: Cogo, Jorge. Universidad Nacional de Río Negro; Argentina
Fil: Areta, Javier Alberto. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Pascual, Juan Pablo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Cuyo; Argentina
Materia
radar meteorológico
estimación
momentos espectrales
redes neuronales
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/126655
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Doppler processing in weather radar using deep learningCollado Rosell, ArturoCogo, JorgeAreta, Javier AlbertoPascual, Juan Pabloradar meteorológicoestimaciónmomentos espectralesredes neuronaleshttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.Fil: Collado Rosell, Arturo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Universidad Nacional de Cuyo; ArgentinaFil: Cogo, Jorge. Universidad Nacional de Río Negro; ArgentinaFil: Areta, Javier Alberto. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; ArgentinaFil: Pascual, Juan Pablo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Cuyo; ArgentinaInstitution of Engineering and Technology2020-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/126655Collado Rosell, Arturo; Cogo, Jorge; Areta, Javier Alberto; Pascual, Juan Pablo; Doppler processing in weather radar using deep learning; Institution of Engineering and Technology; Iet Signal Processing; 14; 9; 12-2020; 672-6821751-9675CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0095info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2020.0095info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-11-05T09:45:56Zoai:ri.conicet.gov.ar:11336/126655instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-11-05 09:45:57.232CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Doppler processing in weather radar using deep learning
title Doppler processing in weather radar using deep learning
spellingShingle Doppler processing in weather radar using deep learning
Collado Rosell, Arturo
radar meteorológico
estimación
momentos espectrales
redes neuronales
title_short Doppler processing in weather radar using deep learning
title_full Doppler processing in weather radar using deep learning
title_fullStr Doppler processing in weather radar using deep learning
title_full_unstemmed Doppler processing in weather radar using deep learning
title_sort Doppler processing in weather radar using deep learning
dc.creator.none.fl_str_mv Collado Rosell, Arturo
Cogo, Jorge
Areta, Javier Alberto
Pascual, Juan Pablo
author Collado Rosell, Arturo
author_facet Collado Rosell, Arturo
Cogo, Jorge
Areta, Javier Alberto
Pascual, Juan Pablo
author_role author
author2 Cogo, Jorge
Areta, Javier Alberto
Pascual, Juan Pablo
author2_role author
author
author
dc.subject.none.fl_str_mv radar meteorológico
estimación
momentos espectrales
redes neuronales
topic radar meteorológico
estimación
momentos espectrales
redes neuronales
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.
Fil: Collado Rosell, Arturo. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Universidad Nacional de Cuyo; Argentina
Fil: Cogo, Jorge. Universidad Nacional de Río Negro; Argentina
Fil: Areta, Javier Alberto. Universidad Nacional de Río Negro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina
Fil: Pascual, Juan Pablo. Comision Nacional de Energia Atomica. Gerencia D/area Invest y Aplicaciones No Nucleares. Gerencia de Des. Tec. y Proyectos Especiales. Departamento de Ingenieria En Telecomunicaciones; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte; Argentina. Universidad Nacional de Cuyo; Argentina
description A deep learning approach to estimate the mean Doppler velocity and spectral width in weather radars is presented. It can operate in scenarios with and without the presence of ground clutter. The method uses a deep neural network with two branches, one for velocity and the other for spectral width estimation. Different network architectures are analysed and one is selected based on its validation performance, considering both serial and parallel implementations. Training is performed using synthetic data covering a wide range of possible scenarios. Monte Carlo realisations are used to evaluate the performance of the proposed method for different weather conditions. Results are compared against two standard methods, pulse-pair processing (PPP) for signals without ground clutter and Gaussian model adaptive processing (GMAP) for signals contaminated with ground clutter. Better estimates are obtained when comparing the proposed algorithm against GMAP and comparable results when compared against PPP. The performance is also validated using real weather data from the C-band radar RMA-12 located in San Carlos de Bariloche, Argentina. Once trained, the proposed method requires a moderate computational load and has the advantage of processing all the data at once, making it a good candidate for real-time implementations.
publishDate 2020
dc.date.none.fl_str_mv 2020-12
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/126655
Collado Rosell, Arturo; Cogo, Jorge; Areta, Javier Alberto; Pascual, Juan Pablo; Doppler processing in weather radar using deep learning; Institution of Engineering and Technology; Iet Signal Processing; 14; 9; 12-2020; 672-682
1751-9675
CONICET Digital
CONICET
url http://hdl.handle.net/11336/126655
identifier_str_mv Collado Rosell, Arturo; Cogo, Jorge; Areta, Javier Alberto; Pascual, Juan Pablo; Doppler processing in weather radar using deep learning; Institution of Engineering and Technology; Iet Signal Processing; 14; 9; 12-2020; 672-682
1751-9675
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0095
info:eu-repo/semantics/altIdentifier/doi/10.1049/iet-spr.2020.0095
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institution of Engineering and Technology
publisher.none.fl_str_mv Institution of Engineering and Technology
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.084122

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