Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale
[1];
- Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering. The Henry Samueli School of Engineering. Center for Hydrometeorology and Remote Sensing (CHRS)
- Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering. The Henry Samueli School of Engineering. Center for Hydrometeorology and Remote Sensing (CHRS); Universities Space Research Association, Mountain View, CA (United States)
- Univ. of California, Irvine, CA (United States). Dept. of Civil and Environmental Engineering. The Henry Samueli School of Engineering. Center for Hydrometeorology and Remote Sensing (CHRS); Univ. of California, Irvine, CA (United States). Dept. of Earth System Science
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.
- Research Organization:
- Univ. of California, Oakland, CA (United States)
- Sponsoring Organization:
- USDOE Office of International Affairs (IA)
- Grant/Contract Number:
- IA0000018
- OSTI ID:
- 1801040
- Journal Information:
- Remote Sensing, Vol. 11, Issue 23; ISSN 2072-4292
- Publisher:
- MDPICopyright Statement
- Country of Publication:
- United States
- Language:
- English
Similar Records
The Global Precipitation Climatology Project (GPCP) combined precipitation dataset
PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies