PERSIANN-CDR

PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record) developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) provides daily rainfall estimates at 0.25 deg for the latitude band 60N-60S over the period of 01/01/1983 to 12/31/2015 (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 and adjusted using the Global Precipitation Climatology Project (GPCP) monthly product to maintain consistency of the two datasets at 2.5 deg monthly scale throughout the entire record.



References

Nguyen, P., E.J. Shearer, H. Tran, M. Ombadi, N. Hayatbini, T. Palacios, P. Huynh, G. Updegraff, K. Hsu, B. Kuligowski, W.S. Logan, and S. Sorooshian, The CHRS Data Portal, an easily accessible public repository for PERSIANN global satellite precipitation data, Nature Scientific Data, Vol. 6, Article 180296, 2019. doi: https://doi.org/10.1038/sdata.2018.296

Ashouri, H., K.L. Hsu, S. Sorooshian, D.K. Braithwaite, K.R. Knapp, L.D. Cecil, B.R. Nelson, and O.P. Prat, 2015: PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies. Bull. Amer. Meteor. Soc., 96, 6983. Doi: http://dx.doi.org/10.1175/BAMS-D-13-00068.1

Miao, C, H. Ashouri, K. Hsu, S. Sorooshian, and Q. Duan, Evaluation of the PERSIANN-CDR Daily Rainfall Estimates in Capturing the Behavior of Extreme Precipitation Events over China, Journal of Hydrometeorology, 16(3), 1387-1396, 2015. doi: http://dx.doi.org/10.1175/JHM-D-14-0174.1.

Sorooshian, S., X. Gao, K. Hsu, R.A. Maddox, Y. Hong, B. Imam, and H.V. Gupta, Diurnal Variability of Tropical Rainfall Retrived from Combined GOES and TRMM Satellite Information, Journal of Climate, Vol.15, 983-1001, 2002.

Hsu, K., H.V. Gupta, X. Gao, S. Sorooshian, and B. Imam, SOLO-An Artificial Neural Network Suitable for Hydrologic Modeling and Analysis, Water Resources Research, Vol.38, No.12. 1302, 2002.

Sorooshian, S., K. Hsu, X. Gao, H.V. Gupta, B. Imam, and Dan Braithwaite, Evaluation of PERSIANN System Satellite-Based Estimates of Tropical Rainfall, Bulletin of the American Meteorological Society, Vol. 81, No. 9, 2035-2046, 2000.

Hsu, K., H.V. Gupta, X. Gao, and S. Sorooshian, Rainfall Estimation from Satellite Imagery, Chapter 11 of Artificial Neural Networks in Hydrology, Edited by R.S. Govindaraju and A.R. Rao, Published by Kluwer Academic Publishers, P.O. Box 17, 3300 AA Dordrecht, The Netherlands, pp 209-234, 2000.

Hsu, K., H.V. Gupta, X. Gao, and S. Sorooshian, Estimation of Physical Variables from Multiple Channel Remotely Sensed Imagery Using a Neural Network: Application to Rainfall Estimation, Water Resources Research, 35(5), 1605-1618, 1999.

Hsu, K., X. Gao, S. Sorooshian, and H.V. Gupta, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks, Journal of Applied Meteorology, Vol. 36, No. 9, 1176-1190, 1997.


Variables
rainfall [mm/day]
Zonal Global by 0.25°
Meridional -60°to 60° by 0.25°
Vertical  
Temporal Jan 1983 to Jun 2022 by 1 day(s)
Static? no
Volume ~1gb/year
Server public:
Source https://chrsdata.eng.uci.edu/ 
Acquired Mar 14, 2019 (Updated October 2022)
APDRC contact
Supplements