• DOI:10.1175/BAMS-D-13-00068.1
• Corpus ID: 7692021

PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

@article{Ashouri2015PERSIANNCDRDP,
  title={PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies},
  author={Hamed Ashouri and Kuo-lin Hsu and Soroosh Sorooshian and Dan Braithwaite and Kenneth R. Knapp and L. DeWayne Cecil and Brian R. Nelson and Olivier P. Prat},
  journal={Bulletin of the American Meteorological Society},
  year={2015},
  volume={96},
  pages={69-83}
}

• Hamed Ashouri, K. Hsu, +5 authors O. Prat
• Published 27 March 2015
• Environmental Science
• Bulletin of the American Meteorological Society

AbstractA new retrospective satellite-based precipitation dataset is constructed as a climate data record for hydrological and climate studies. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides daily and 0.25° rainfall estimates for the latitude band 60°S–60°N for the period of 1 January 1983 to 31 December 2012 (delayed present). PERSIANN-CDR is aimed at addressing the need for a consistent, long-term, high-resolution, and global precipitation dataset for studying the changes and trends in daily precipitation, especially extreme precipitation events, due to climate change and natural variability. PERSIANN-CDR is generated from the PERSIANN algorithm using GridSat-B1 infrared data. It is adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5° monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset ...