期刊
HYDROLOGY AND EARTH SYSTEM SCIENCES
卷 22, 期 11, 页码 5801-5816出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/hess-22-5801-2018
关键词
-
资金
- ICIWaRM of the US Army Corps of Engineering, UNESCO's G-WADI program
- Cooperative Institute for Climate and Satellites (CICS) program (NOAA prime) [NA14NES4320003, 2014-2913-03]
- Army Research Office [W911NF-11-1-0422]
- National Science Foundation (NSF) [1331915]
- Department of Energy (DoE prime) [DE-IA0000018]
- California Energy Commission [300-15-005]
- University of California [4600010378, 15 Am 22]
- Direct For Computer & Info Scie & Enginr [1331915] Funding Source: National Science Foundation
- Division of Computing and Communication Foundations [1331915] Funding Source: National Science Foundation
Over the past 2 decades, a wide range of studies have incorporated Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely PERSIANN, PERSIANN-CCS, and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the contiguous US (CONUS) at different spatial and temporal scales using Climate Prediction Center (CPC) unified gauge-based analysis as a benchmark. Due to limitations of the baseline dataset (CPC), daily scale is the finest temporal scale used for the evaluation over CONUS. Additionally, we provide a comparison of the available products at a quasi-global scale. Finally, we highlight the strengths and limitations of the PERSIANN products and briefly discuss expected future developments.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据