Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate
[EN] Gridded precipitation datasets have been developed for data assimilation and evaluation tasks of weather and climate models and for climate analyses. Gridded data uncertainty evaluation is crucial to understand the limitations and feasibility. The development of high-resolution daily gridded pr...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/23945 |
| Acceso en línea: | https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7003 https://hdl.handle.net/10612/23945 |
| Access Level: | acceso abierto |
| Palabra clave: | Física Meteorología Daily observations Gridded precipitation Spatial resolution Station density Uncertainties 2502.01 Climatología Analítica 2502.06 Climatología Física 2501.22 Física de las Precipitaciones |
| id |
ES_7814b75c04a8a197fed7ea18a35b026d |
|---|---|
| oai_identifier_str |
oai:buleria.unileon.es:10612/23945 |
| network_acronym_str |
ES |
| network_name_str |
España |
| repository_id_str |
|
| spelling |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climateMerino Suances, AndrésGarcía Ortega, EduardoNavarro Martínez de la Casa, AndrésFernández González, SergioTapiador Fuentes, Francisco JavierSánchez Gómez, José LuisFísicaMeteorologíaDaily observationsGridded precipitationSpatial resolutionStation densityUncertainties2502.01 Climatología Analítica2502.06 Climatología Física2501.22 Física de las Precipitaciones[EN] Gridded precipitation datasets have been developed for data assimilation and evaluation tasks of weather and climate models and for climate analyses. Gridded data uncertainty evaluation is crucial to understand the limitations and feasibility. The development of high-resolution daily gridded precipitation datasets is desirable, but several factors need to be considered, namely rain gauge station availability, their spatial distribution, and orographic and climate characteristics of a study area. Quality assessment of gridded datasets can present difficulties when the influence of these factors is not thoroughly analysed. The main objective of this study was a detailed validation of precipitation grids based on four factors, that is, station density used for grid construction, grid spatial resolution, station altitude, and climate type. To this end, 18 grids were built using six spatial resolutions (0.01°, 0.025°, 0.05°, 0.1°, 0.2° and 0.4°) and three station densities (25, 50 and 75% of all available stations). Results indicate larger differences among the grids as a function of analysed factors. Station density was found to be the main factor, whereas grid spatial resolution had minor importance. However, the latter factor becomes more relevant in areas with strong altitude gradients and when a high station density is available. In addition, weak and moderate precipitation is overestimated on daily grids, whereas heavy precipitation cells are less frequent, reducing data variability. On the contrary, monthly and annual aggregates present less deviation from the observed distribution than daily comparisons. These findings question the applicability of the daily grid datasets for validation studies and climate analysis on a grid cell levelSIFunding came from projects LE240P18 (Consejería de Educación, Junta de Castilla y León) and CGL2016-78702-C2-1-R, PID2019-108470RB-C22, CGL2016-80609-R and PID2019-108470RB-C21 (Ministerio de Economía y Competitividad)WileyRoyal Meteorological SocietyFisica AplicadaFacultad de Ciencias Biologicas y Ambientales2021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7003https://hdl.handle.net/10612/23945reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónInglésinfo:eu-repo/grantagreemen/Junta de Castilla y León//LE240P18/ES/Predicción numérica por conjuntos y nowcasting aplicados a las predicciones severasinfo:eu-repo/grantagreement/AEI/Programa Estatal de I+D+i Orientado a los Retos de la Sociedad/CGL2016-78702-C2-1-R/ES/Modelos meteorológicos de alta resolución para la predicción de ondas de montaña y condiciones de engelamiento: aplicación a la mejora de la seguridad aéreainfo:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108470RB-C22info:eu-repo/grantagreemen/AEI/Programa estatal de investigación, desarrollo e innovación orientada a los retos de la sociedad/CGL2016-80609-R/ES/Medidas multifuente de precipitación en 4d para mejorar la cuantificación precisa de los cambios en el ciclo hidrológico en el marco de la misión GDM de la NASAinfo:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108470RB-C21http://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/239452026-06-24T12:43:27Z |
| dc.title.none.fl_str_mv |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| title |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| spellingShingle |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate Merino Suances, Andrés Física Meteorología Daily observations Gridded precipitation Spatial resolution Station density Uncertainties 2502.01 Climatología Analítica 2502.06 Climatología Física 2501.22 Física de las Precipitaciones |
| title_short |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| title_full |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| title_fullStr |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| title_full_unstemmed |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| title_sort |
Evaluation of gridded rain‐gauge‐based precipitation datasets: Impact of station density, spatial resolution, altitude gradient and climate |
| dc.creator.none.fl_str_mv |
Merino Suances, Andrés García Ortega, Eduardo Navarro Martínez de la Casa, Andrés Fernández González, Sergio Tapiador Fuentes, Francisco Javier Sánchez Gómez, José Luis |
| author |
Merino Suances, Andrés |
| author_facet |
Merino Suances, Andrés García Ortega, Eduardo Navarro Martínez de la Casa, Andrés Fernández González, Sergio Tapiador Fuentes, Francisco Javier Sánchez Gómez, José Luis |
| author_role |
author |
| author2 |
García Ortega, Eduardo Navarro Martínez de la Casa, Andrés Fernández González, Sergio Tapiador Fuentes, Francisco Javier Sánchez Gómez, José Luis |
| author2_role |
author author author author author |
| dc.contributor.none.fl_str_mv |
Fisica Aplicada Facultad de Ciencias Biologicas y Ambientales |
| dc.subject.none.fl_str_mv |
Física Meteorología Daily observations Gridded precipitation Spatial resolution Station density Uncertainties 2502.01 Climatología Analítica 2502.06 Climatología Física 2501.22 Física de las Precipitaciones |
| topic |
Física Meteorología Daily observations Gridded precipitation Spatial resolution Station density Uncertainties 2502.01 Climatología Analítica 2502.06 Climatología Física 2501.22 Física de las Precipitaciones |
| description |
[EN] Gridded precipitation datasets have been developed for data assimilation and evaluation tasks of weather and climate models and for climate analyses. Gridded data uncertainty evaluation is crucial to understand the limitations and feasibility. The development of high-resolution daily gridded precipitation datasets is desirable, but several factors need to be considered, namely rain gauge station availability, their spatial distribution, and orographic and climate characteristics of a study area. Quality assessment of gridded datasets can present difficulties when the influence of these factors is not thoroughly analysed. The main objective of this study was a detailed validation of precipitation grids based on four factors, that is, station density used for grid construction, grid spatial resolution, station altitude, and climate type. To this end, 18 grids were built using six spatial resolutions (0.01°, 0.025°, 0.05°, 0.1°, 0.2° and 0.4°) and three station densities (25, 50 and 75% of all available stations). Results indicate larger differences among the grids as a function of analysed factors. Station density was found to be the main factor, whereas grid spatial resolution had minor importance. However, the latter factor becomes more relevant in areas with strong altitude gradients and when a high station density is available. In addition, weak and moderate precipitation is overestimated on daily grids, whereas heavy precipitation cells are less frequent, reducing data variability. On the contrary, monthly and annual aggregates present less deviation from the observed distribution than daily comparisons. These findings question the applicability of the daily grid datasets for validation studies and climate analysis on a grid cell level |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021 |
| 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://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7003 https://hdl.handle.net/10612/23945 |
| url |
https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7003 https://hdl.handle.net/10612/23945 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
info:eu-repo/grantagreemen/Junta de Castilla y León//LE240P18/ES/Predicción numérica por conjuntos y nowcasting aplicados a las predicciones severas info:eu-repo/grantagreement/AEI/Programa Estatal de I+D+i Orientado a los Retos de la Sociedad/CGL2016-78702-C2-1-R/ES/Modelos meteorológicos de alta resolución para la predicción de ondas de montaña y condiciones de engelamiento: aplicación a la mejora de la seguridad aérea info:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108470RB-C22 info:eu-repo/grantagreemen/AEI/Programa estatal de investigación, desarrollo e innovación orientada a los retos de la sociedad/CGL2016-80609-R/ES/Medidas multifuente de precipitación en 4d para mejorar la cuantificación precisa de los cambios en el ciclo hidrológico en el marco de la misión GDM de la NASA info:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108470RB-C21 |
| dc.rights.none.fl_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc/4.0/ |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Wiley Royal Meteorological Society |
| publisher.none.fl_str_mv |
Wiley Royal Meteorological Society |
| 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 |
|
| _version_ |
1869411182403649536 |
| score |
15.812429 |