Journal
BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
Volume 96, Issue 1, Pages 69-+Publisher
AMER METEOROLOGICAL SOC
DOI: 10.1175/BAMS-D-13-00068.1
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Funding
- NOAA/Cooperative Institute for Climate and Satellites (CICS)
- NOAA NCDC/Climate Data Record program [NA09NES440006]
- NOAA NCDC/Climate Data Record program (NCSU CICS) [2009-1380-01]
- NOAA Climate Change Data and Detection (CCDD) [NA10DAR4310122]
- NASA Energy and Water Cycle Study (NEWS) program [NNX06AF93G]
- NASA Earth and Space Science Fellowship (NESSF) Award [NNX12AO11H]
- NASA Decision Support System [NNX09A067G]
- NASA [NNX12AO11H, 12342] Funding Source: Federal RePORTER
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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 degrees rainfall estimates for the latitude band 60 degrees S-60 degrees 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 degrees monthly scale throughout the entire record. Three case studies for testing the efficacy of the dataset against available observations and satellite products are reported. The verification study over Hurricane Katrina (2005) shows that PERSIANN-CDR has good agreement with the stage IV radar data, noting that PERSIANN-CDR has more complete spatial coverage than the radar data. In addition, the comparison of PERSIANN-CDR against gauge observations during the 1986 Sydney flood in Australia reaffirms the capability of PERSIANN-CDR to provide reasonably accurate rainfall estimates. Moreover, the probability density function (PDF) of PERSIANN-CDR over the contiguous United States exhibits good agreement with the PDFs of the Climate Prediction Center (CPC) gridded gauge data and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) product. The results indicate high potential for using PERSIANN-CDR for long-term hydroclimate studies in regional and global scales.
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