Intercomparison of Different Sources of Precipitation Data in the Brazilian Legal Amazon

IF 3 Q2 METEOROLOGY & ATMOSPHERIC SCIENCES Climate Pub Date : 2023-12-09 DOI:10.3390/cli11120241
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
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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.
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巴西法定亚马逊河流域不同来源降水数据的相互比较
巴西法定亚马逊河流域(BLA)由地球上最大的热带雨林和最大的河流流域的大部分组成,对该地区降雨量的监测极为重要,但也极具挑战性。仅面积和土地覆盖就给雨量计网络的运行带来了困难。有鉴于此,我们对估算布拉格地区降雨量的九个数据库进行了性能评估,其中四个来自基于雨量测量的网格分析(Xavier、CPC、GPCC 和 CRU),四个来自遥感(CHIRPS、IMERG、CMORPH 和 PERSIANN-CDR),一个来自再分析(ERA5Land)。我们发现,所有基础数据都能有效描述布拉格地区累积降水的年平均周期,但主要存在负偏差。皮尔逊相关性(r)、均方根误差(RMSE)和泰勒图(SDE)等参数被应用于整个勃兰登堡地区以及六个水文均质区的空间分析中,结果表明,根据技能排序,来自泽维尔网格分析、CHIRPS、GPCC 和 ERA5Land 的数据最能代表勃兰登堡地区的月度、季节和年度降水量。PERSIANN-CDR 数据显示了中等水平的性能,而 IMERG、CMORPH、CRU 和 CPC 数据显示了最低的相关性和最高的误差,泰勒图也反映了这些特征。希望这种基于技能的分级方法将有助于该地区的气候研究,因为它与生物多样性、水资源和重要的气候调节器都有很大关系。
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来源期刊
Climate
Climate Earth 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.
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