期刊
SCIENTIFIC DATA
卷 8, 期 1, 页码 -出版社
NATURE RESEARCH
DOI: 10.1038/s41597-021-00940-9
关键词
-
资金
- U.S. Department of Energy (DOE Prime Award) [DE-IA0000018]
- California Energy Commission (CEC) [300-15-005]
- University of California [4600010378 TO15 Am 22]
- Fariborz Maseeh fellowship
- NSF [EAR-1928724]
- NASA [80NSSC19K0726]
- UK Research and Innovation Global Challenges Research Fund (GCRF) [NES0089261]
- NVIDIA Corporation
Accurate long-term global precipitation estimates, especially for heavy precipitation rates, are essential for climatological studies. The PERSIANN-CCS-CDR dataset provides reliable precipitation estimates with high spatiotemporal resolution and a longer period of record, particularly for extreme events.
Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04 degrees spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60 degrees S to 60 degrees N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.
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