The “GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System” has done a free software to convert the binary files of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) product at 0.25º spatial resolution and time step 3 Hourly into ASCII Grid format of ESRI. The original data can be downloaded via FTP at the following CHRS link:
The software can be downloaded in the following link of the Non-Profit association: “GH2MF2– Association for a Participated Global Hydrological Monitoring and Flood Forecasting System“
The current operational PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites. An adaptive training feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. The PERSIANN system was based on geostationary infrared imagery and later extended to include the use of both infrared and daytime visible imagery. The PERSIANN algorithm used here is based on the geostationary longwave infrared imagery to generate global rainfall. Rainfall product covers 60°S to 60°N globally.
Convert PERSIANN product to ASCII Grid ESRI
The “GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System” has done a free software to convert the binary files of PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) product at 0.25º spatial resolution and time step 3 Hourly into ASCII Grid format of ESRI. The original data can be downloaded via FTP at the following CHRS link:
ftp://persiann.eng.uci.edu/CHRSdata/PERSIANN/3hrly
The software can be downloaded in the following link of the Non-Profit association: “GH2MF2 – Association for a Participated Global Hydrological Monitoring and Flood Forecasting System“
https://drive.google.com/open?id=1SKnIb22cq7DHi1PD6VXAq179a40G9_oa
The current operational PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system developed by the Center for Hydrometeorology and Remote Sensing (CHRS) at the University of California, Irvine (UCI) uses neural network function classification/approximation procedures to compute an estimate of rainfall rate at each 0.25° x 0.25° pixel of the infrared brightness temperature image provided by geostationary satellites. An adaptive training feature facilitates updating of the network parameters whenever independent estimates of rainfall are available. The PERSIANN system was based on geostationary infrared imagery and later extended to include the use of both infrared and daytime visible imagery. The PERSIANN algorithm used here is based on the geostationary longwave infrared imagery to generate global rainfall. Rainfall product covers 60°S to 60°N globally.
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