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dc.contributor.authorSalles, Leandro de Almeida-
dc.contributor.authorSatgé, Frédéric-
dc.contributor.authorRoig, Henrique-
dc.contributor.authorAlmeida, Tati-
dc.contributor.authorOlivetti, Diogo-
dc.contributor.authorFerreira, Welber-
dc.date.accessioned2020-02-18T12:23:44Z-
dc.date.available2020-02-18T12:23:44Z-
dc.date.issued2019-
dc.identifier.citationSALLES, Leandro et al. Seasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Region. Water, v. 11, n. 4, 668, 2019. DOI: https://doi.org/10.3390/w11040668. Disponível em: https://www.mdpi.com/2073-4441/11/4/668. Acesso em: 18 fev. 2020.pt_BR
dc.identifier.urihttps://repositorio.unb.br/handle/10482/36921-
dc.language.isoInglêspt_BR
dc.publisherMDPIpt_BR
dc.rightsAcesso Abertopt_BR
dc.titleSeasonal effect on spatial and temporal consistency of the new GPM-based IMERG-v5 and GSMaP-v7 satellite precipitation estimates in Brazil’s Central Plateau Regionpt_BR
dc.typeArtigopt_BR
dc.subject.keywordPrecipitação (Meteorologia)pt_BR
dc.subject.keywordSatélites artificiaispt_BR
dc.subject.keywordCerradospt_BR
dc.rights.license© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).pt_BR
dc.identifier.doihttps://doi.org/10.3390/w11040668pt_BR
dc.description.abstract1This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1 spatial resolution and for a 0.25 grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0002-6056-7055pt_BR
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