Autori
Hamed Ashouri, Kuo-Lin Hsu, Soroosh Sorooshian, Dan K Braithwaite, Kenneth R Knapp, L Dewayne Cecil, Brian R Nelson, Olivier P Prat
Data pubblicazione
2015/1
Pubblicazione
Bulletin of the American Meteorological Society
Volume
96
Numero
1
Pagine
69-83
Descrizione
A 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° …
Articoli in Scholar
H Ashouri, KL Hsu, S Sorooshian, DK Braithwaite… - Bulletin of the American Meteorological Society, 2015