4.7 Article

PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

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

  1. U.S. Department of Energy (DOE Prime Award) [DE-IA0000018]
  2. California Energy Commission (CEC) [300-15-005]
  3. University of California [4600010378 TO15 Am 22]
  4. Fariborz Maseeh fellowship
  5. NSF [EAR-1928724]
  6. NASA [80NSSC19K0726]
  7. UK Research and Innovation Global Challenges Research Fund (GCRF) [NES0089261]
  8. NVIDIA Corporation

向作者/读者索取更多资源

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。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据