Evaluation of Summer Rainfall Estimation by Satellite Data using the ANN Model for the GCM Subgrid Distribution.
Committee ChairSorooshian, Soroosh
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PublisherThe University of Arizona.
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AbstractA system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) is under development at The University of Arizona. The current core of this system is an adaptive Artificial Neural Network (ANN) model that estimates rainfall rates using infrared satellite imagery and ground-surface information. In this thesis, the model is validated for both the Florida peninsula and the Southwestern United States (using GOES-8 and ground-based data). The PERSIANN System dramatically improves estimation performance in response to the diverse rainfall characteristics of different geographical regions and time of year. Another important feature investigated in this thesis is that the coverage of rainfall of an area is strongly dependent on its size (window size), and this dependence exhibits a scaling law over a range of sizes. Secondly, this coverage is dependent on the resolution at which it is measured (pixel size). Therefore, an improved disaggregation scheme for GCMs is proposed which incorporates the previous findings so as to allow the coverage to be obtained for any area and any mean rainfall depth.
Degree ProgramHydrology and Water Resources