Fabrício Daniel dos Santos Silva, Claudia Priscila Wanzeler da Costa, Vânia dos Santos Franco, H. Gomes, Maria Cristina Lemos da Silva, Mário Henrique Guilherme dos Santos Vanderlei, R. L. Costa, Rodrigo Lins da Rocha Júnior, J. B. Cabral Júnior, J. D. dos Reis, R. Cavalcante, R. Tedeschi, Naurinete de Jesus da Costa Barreto, A. V. Nogueira Neto, Edmir dos Santos Jesus, Douglas Batista da Silva Ferreira
{"title":"巴西法定亚马逊河流域不同来源降水数据的相互比较","authors":"Fabrício Daniel dos Santos Silva, Claudia Priscila Wanzeler da Costa, Vânia dos Santos Franco, H. Gomes, Maria Cristina Lemos da Silva, Mário Henrique Guilherme dos Santos Vanderlei, R. L. Costa, Rodrigo Lins da Rocha Júnior, J. B. Cabral Júnior, J. D. dos Reis, R. Cavalcante, R. Tedeschi, Naurinete de Jesus da Costa Barreto, A. V. Nogueira Neto, Edmir dos Santos Jesus, Douglas Batista da Silva Ferreira","doi":"10.3390/cli11120241","DOIUrl":null,"url":null,"abstract":"Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.","PeriodicalId":37615,"journal":{"name":"Climate","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon\",\"authors\":\"Fabrício Daniel dos Santos Silva, Claudia Priscila Wanzeler da Costa, Vânia dos Santos Franco, H. Gomes, Maria Cristina Lemos da Silva, Mário Henrique Guilherme dos Santos Vanderlei, R. L. 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Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon
Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.
ClimateEarth and Planetary Sciences-Atmospheric Science
CiteScore
5.50
自引率
5.40%
发文量
172
审稿时长
11 weeks
期刊介绍:
Climate is an independent, international and multi-disciplinary open access journal focusing on climate processes of the earth, covering all scales and involving modelling and observation methods. The scope of Climate includes: Global climate Regional climate Urban climate Multiscale climate Polar climate Tropical climate Climate downscaling Climate process and sensitivity studies Climate dynamics Climate variability (Interseasonal, interannual to decadal) Feedbacks between local, regional, and global climate change Anthropogenic climate change Climate and monsoon Cloud and precipitation predictions Past, present, and projected climate change Hydroclimate.