Physical Sciences and Mathematics Commons^™

Comparison Of Spatial Precipitation Forecasts With A Satellite Dataset, Andrew C. Siebels

Theses and Dissertations

The purpose of this research is to analyze and compare global precipitation data from the Climate Forecast System Version 2 (CFSv2) with the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Climate Data Record (CDR) to improve long term precipitation forecasting. The CFSv2 has a 0.5-degree resolution which will provide model data for precipitation forecasts. The PERSIANN-CDR is a satellite derived daily 0.25-degree dataset with 37 years of global precipitation coverage 60 N to 60 S. The 0-to-10, 15-to-25, 55-to-65, and 80-to-90 day forecast time frames will then be analyzed for accuracy, and a quantile mapping (QM) technique …