PERSIANN-Cloud Classification System (PERSIANN-CCS) is a real-time global high resolution (0.04° x 0.04° or 4km x 4km;) satellite precipitation product developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI). PERSIANN-CCS system enables the categorization of cloud-patch features based on cloud height, areal extent, and variability of texture estimated from satellite imagery. At the heart of PERSIANN-CCS is the variable threshold cloud segmentation algorithm. In contrast with the traditional constant threshold approach, the variable threshold enables the identification and separation of individual patches of clouds. The individual patches can then be classified based on texture, geometric properties, dynamic evolution, and cloud top height. These classifications help in assigning rainfall values to pixels within each cloud based on a specific curve describing the relationship between rain-rate and brightness temperature.


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:

Nguyen, P., A. Thorstensen, S. Sorooshian, K. Hsu, and A. AghaKouchak, 2015: Flood Forecasting and Inundation Mapping Using HiResFlood-UCI and Near-Real-Time Satellite Precipitation Data: The 2008 Iowa Flood. J. Hydrometeor, 16, 11711183. DOI

Nguyen, P., S. Sellars, A. Thorstensen, Y. Tao, H. Ashouri, D. Braithwaite, K. Hsu and S. Sorooshian. 2014. Satellite Track Precipitation of Super Typhoon Haiyan. AGU EOS, 95 (16), 133&135.

Sorooshian, S., P. Nguyen, S. Sellars, D. Braithwaite, A. AghaKouchak, and K. Hsu, 2014: Satellite-based remote sensing estimation of precipitation for early warning systems, Extreme Natural Hazards, Disaster Risks and Societal Implications, A. Ismail-Zadeh, J.U. Fucugauchi, A. Kijko, K. Takeuchi, and I. Zaliapin, Cambridge University Press, 99-111.

Hong, Y., Hsu, K., Sorooshian, S. and Gao, X. (2004). Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43, 1834-1852.

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.

rainfall [mm/day]
Zonal Global by 0.04°
Meridional -60° to 60° by 0.04°
Temporal Jan 2003 to Present by 1 day
Static? no
Volume ~37gb/year
Server public:
Acquired Mar 14, 2019 (updated daily
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