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首页> 外文期刊>International journal of remote sensing >Assessing the performance of near real-time rainfall products to represent spatiotemporal characteristics of extreme events: case study of a subtropical catchment in south-eastern Brazil
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Assessing the performance of near real-time rainfall products to represent spatiotemporal characteristics of extreme events: case study of a subtropical catchment in south-eastern Brazil

机译：评估近实时降雨产品的性能以表示极端事件的时空特征：巴西东南部亚热带流域的案例研究

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摘要

This study evaluates the performance of four Near Real-Time (NRT) satellite rainfall products in estimating the spatiotemporal characteristics of different extreme rainfall events in a subtropical catchment in south-eastern Brazil. The Climate Prediction Centre Morphing algorithm (CMORPH), Tropical Rainfall Measuring Mission, Multisatellite Precipitation Analysis in real time (TMPA-RT), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Global Cloud Classification System (PERSIANN-GCCS), and the Hydro-Estimator are evaluated for monsoon seasons, based on their capability to represent four types of rainfall events distinguished for: (1) local and short duration, (2) long-lasting event, (3) short and spatial extent, and (4) spatial extent and long lasting. Since the events are defined relative to a percentile, the relative performance variation at different threshold levels (75th, 90th, and 95th) is also evaluated. The data from the 13 Automatic Weather Stations (AWSs) for the period from 2007 to 2014 are used as the reference. The results show that the product performance highly depends on the spatiotemporal characteristics of rainfall events. All four products tend to overestimate intense rainfall in the study area, especially in high altitude zones. CMORPH had the best overall performance to estimate different types of extreme spatiotemporal events. The results allow for developing a better understanding of the accuracy of the NRT products for the estimation of different types of rainfall events.
机译：这项研究评估了四种近实时（NRT）卫星降雨产品在估算巴西东南部亚热带流域不同极端降雨事件的时空特征方面的性能。气候预测中心变形算法（CMORPH），热带雨量测量任务，实时多卫星降水分析（TMPA-RT），使用人工神经网络-全球云分类系统（PERSIANN-GCCS）从遥感信息中进行降水估算以及根据估算季风雨季的能力，对水力预估器进行了评估，其表现方式分为以下四种类型：（1）局部和短期持续时间，（2）持续时间较长，（3）短期和空间范围，以及（ 4）空间范围长且持久。由于事件是相对于百分位定义的，因此还评估了不同阈值水平（第75、90和95）的相对性能变化。来自13个自动气象站（AWS）的2007年至2014年期间的数据用作参考。结果表明，产品性能在很大程度上取决于降雨事件的时空特征。这四种产品往往会高估研究区域的强降雨，尤其是在高海拔地区。 CMORPH在估计不同类型的极端时空事件方面具有最佳的整体性能。结果有助于更好地了解NRT产品的准确性，以估算不同类型的降雨事件。

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  • 来源
    《International journal of remote sensing》 |2018年第22期|7568-7586|共19页
  • 作者

    Laverde-Barajas M.; Corzo Perez G. A.; Dalfre Filho J. G.; Solomatine D. P.;

  • 作者单位

    IHE Delft Inst Water Educ, Integrated Water Syst & Governance Dept, Delft, Netherlands|Delft Univ Technol, Water Resources Sect, Delft, Netherlands;

    IHE Delft Inst Water Educ, Integrated Water Syst & Governance Dept, Delft, Netherlands;

    Univ Estadual Campinas, UNICAMP, Sch Civil Engn Architecture & Urbanism, Campinas, SP, Brazil;

    IHE Delft Inst Water Educ, Integrated Water Syst & Governance Dept, Delft, Netherlands|Delft Univ Technol, Water Resources Sect, Delft, Netherlands;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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