Abstract
Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program.
Highlights
Precipitation is widely recognized as the driving component of the global water cycle, and has a significant impact on climatic patterns [1]
The Global Precipitation Climatology Project (GPCP) dataset is available via the Earth System Science Interdisciplinary Center (ESSIC) and Cooperative Institute for Climate and Satellites (CICS), University of Maryland College Park
In the second part of the analysis, we evaluated the performance of both PERSIANN-CDR V2.2 and V2.3 over global land areas with respect to Climate Prediction Center (CPC) unified gauge-based analysis and over the ocean with respect to Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 at monthly and daily scales
Summary
Precipitation is widely recognized as the driving component of the global water cycle, and has a significant impact on climatic patterns [1]. The Climate Prediction Center at the National Oceanic and Atmospheric Administration (NOAA) developed a product named NOAA-CPC morphing technique (CMORPH Version 1.0), which provides precipitation estimates with three spatial and temporal resolutions (8 km—30 min, 0.25◦—3 hourly, and 0.25◦—daily) starting from 1998 [30]. These two products are among the most well-known satellite-based precipitation estimation algorithms; their precipitation estimation records are too short to be utilized for climatological studies.
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