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

Journal Article · · Scientific Data
 [1];  [1];  [1];  [1];  [1];  [2]
  1. Univ. of California, Irvine, CA (United States). The Henry Samueli School of Engineering. Dept. of Civil and Environmental Engineering. Center for Hydrometeorology and Remote Sensing (CHRS)
  2. Univ. of California, Irvine, CA (United States). The Henry Samueli School of Engineering. Dept. of Civil and Environmental Engineering. Center for Hydrometeorology and Remote Sensing (CHRS); Univ. of California, Irvine, CA (United States). Dept. of Earth System Science
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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° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°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.

Research Organization:
Univ. of California, Berkeley, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
IA0000018
OSTI ID:
1816288
Journal Information:
Scientific Data, Vol. 8, Issue 1; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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