1. Archive of Adjusted PERSIANN 3 Hourly data
Full monthly archives of PERSIANN's final 0.25° are available at 3 hourly temporal resolution. To obtain data for each month, just click on the x in the table below.
NOTE: adjusted PERSIANN data after September 2009 will be available later in 2011.
4-byte binary float (little-endian byte order).
Spatial coverage is:
60° to -60° lat
0° to 360° long
.25° x .25° resolution
480 rows x 1440 cols
The data is stored in C style row centric format. with the
first value centered at 59.875,.125,
the second value at 59.875,.375,
and the last 2 values are centered at: -59.875,359.625 & -59.875,359.875
sample matlab code:
function om = loadbfn(fn,dim,sz)
% loadbfn -- loads a binary file fn of dim
% ([nrows ncols]) of sz 'float','int', etc.
% by DKB on 2/5/96
if(nargin < 3)
sz = 'float32';
f = fopen(fn,'r','l');
if(nargout > 0)
om = fread(f,flipud(dim(:))',sz)';
This bias corrected PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks)
precipitation maintains total monthly precipitation estimates, consistent with GPCP (Global Precipitation Climatology
Project) product. The newly released data set retains the spatial and temporal features of precipitation estimates
provided by the original PERSIANN algorithm at 0.25-degree spatial and 3-hourly temporal resolution.
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 PERSAINN 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.