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PERSIANN-CDR Daily Precipitation Used for High Resolution Long-term Trend Analysis of Global Precipitation Extreme

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Wednesday, 7 January 2015
Hamed Ashouri, University of California, Irvine, CA; and K. L. Hsu, S. Sorooshian, and D. Braithwaite

A new historical precipitation climate database, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network Climate Data Record (PERSIANN-CDR), has been developed. This CDR data set covers more than 30 years (01/01/1983 - 03/31/2014 as of now) of near global daily precipitation estimations at 0.25 x 0.25 grid boxes for the latitude band 60S60N. PERSIANN-CDR is generated using International Satellite Cloud Climatology Project (ISCCP) GridSat-B1 IRWIN data as input to the PERSIANN algorithm. The resulting rainfall estimates are then bias-corrected using the GPCP monthly product in the monthly scale. PERSIANN-CDR was developed to address the need for a consistent, long-term and high spatiotemporal resolution precipitation dataset for studying the variability and trends in historical daily precipitation due to natural variability and/or climate change. In this study, the ability of PERSIANN-CDR to accurately represent abnormal wetness (flood) and dryness (drought) conditions is demonstrated. The Standard Precipitation Index (SPI) method was applied to PERSIANN-CDR data to categorize rainfall conditions from extremely wet to extremely dry. SPI indices from PERSIANN-CDR were evaluated over the United States using CPC unified precipitation data, and over Australia using gridded daily precipitation data. The results show good agreement between PERSIANN-CDR estimates and those gauge observations.