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PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies.
- Source :
- Scientific Data; 6/23/2021, Vol. 8 Issue 1, p1-11, 11p
- Publication Year :
- 2021
-
Abstract
- 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. Measurement(s) volume of hydrological precipitation • spatial resolution • spatial coverage • temporal resolution • temporal coverage Technology Type(s) digital curation Sample Characteristic - Location 60°S - 60°N Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14656452 [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20524463
- Volume :
- 8
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Data
- Publication Type :
- Academic Journal
- Accession number :
- 151044245
- Full Text :
- https://doi.org/10.1038/s41597-021-00940-9