PERSIANN precipitation estimation relies on cloud texture information from longwave infrared images (~10.2-11.2 µm) obtained
from geostationary satellites and updated using the higher quality rainfall estimates from low-orbit passive microwave sensors
(Hsu et al., 2007; Soroosh et al., 2000). To reduce bias while preserving spatial and temporal patterns in high resolution,
PERSIANN precipitation is adjusted based on GPCP rainfall (Version 2.1) at 2.5o monthly resolution (Adler et al., 2003; Huffman et al., 2009).
Before applying bias adjustment, missing data in PERSIANN estimation is filled with passive microwave rainfall estimation at each
30 minutes time step. In the subsequent step, the data is aggregated to 2.5o monthly scale and a correction factor is computed
based on the ratio of GPCP rainfall and PERSIANN rainfall at a grid of 2.5o monthly scale. This ratio is then used to calculate
the PERSIANN rainfall fine spatial (0.25o) and temporal scale (hourly) within the 2.5o coverage. The final product is provided to
users at 3-hour and 0.25o resolution.
Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber,
J. Susskind, and P. Arkin, 2003: The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis
(1979-Present). J. Hydrometeor., 4,1147-1167.
George J. Huffman, Robert F. Adler, David T. Bolvin, Guojun Gu. (2009) Improving the global precipitation record: GPCP
Version 2.1. Geophysical Research Letters 36:17,
Hsu, K., X. Gao, S. Sorooshian, and H.V. Gupta, Precipitation Estimation from Remotely Sensed Information Using Artificial
Neural Networks, Journal of Applied Meteorology, 36(9), 1176-1190, 1997.
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, 81(9), 2035-2046, 2000.