PERSIANNPrecipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
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To make GridSat data compatible with the input structure of the PERSIANN model, these data were averaged and upscaled to a 0.
2) was used for correcting the biases of the PERSIANN rain-rate estimates.
In this study, the PERSIANN algorithm is applied to the historical archive of GridSat-B1 infrared window observations from GEO satellites to generate 3-hourly rain-rate estimates (1980-2012) at 0.
The existing PERSIANN algorithm provides global precipitation estimates using combined IR and PMW information from multiple GEO and LEO satellites.
In this CDR product, in order to eliminate the need for PMW observations, the nonlinear regression parameters of the ANN model are trained and remain fixed when PERSIANN is used for retrospective estimation of rainfall rates using the 3-hourly GridSat-B1 IRWIN data.
Adjusting daily PERSIANN data using monthly GPCP data.