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.
Highlights
Background & SummaryPrecipitation is widely recognized as the main driving component for the global hydrological cycle and has an essential role for regulating the climate system[1,2,3]
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), which provides 0.04° spatial and 3-hourly temporal resolution estimates from 1983 to present, has been explicitly designed to address the need for having a long term dataset with fine spatiotemporal resolution precipitation estimation which is reliable for extreme event detection
The International Satellite Cloud Climatology Project (ISCCP) B1 dataset consists of observations from different sensors launched by different countries, including United States [for the Geostationary Operational Environmental Satellite (GOES) series], Japan [for the Japanese Geostationary Meteorological Satellite (GMS) series and Multi-functional Transport Satellite (MTSAT)], Europe [for the European Meteorological satellite (Meteosat) series], and China [for the www.nature.com/scientificdata
Summary
Precipitation is widely recognized as the main driving component for the global hydrological cycle and has an essential role for regulating the climate system[1,2,3]. Mehran et al (2014) evaluated the performance of Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) and CPC MORPHing technique (CMORPH) for detecting heavy precipitation rates against Stage IV radar observations over the CONUS They showed that all these precipitation datasets miss a significant volume of rainfall. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR), which provides 0.04° spatial and 3-hourly temporal resolution estimates from 1983 to present, has been explicitly designed to address the need for having a long term dataset with fine spatiotemporal resolution precipitation estimation which is reliable for extreme event detection
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