Date of Award

3-2021

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Engineering Physics

First Advisor

Robert C. Tournay, PhD

Abstract

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 will be applied to correct precipitation amounts for the CFSv2. The QM procedure requires both training and test datasets from the CFSv2 and PERSIANN-CDR. Finally, the forecast correction results for the CFSv2 may be used to improve medium range precipitation forecasts by the operational meteorological community.

AFIT Designator

AFIT-ENP-MS-21-M-136

DTIC Accession Number

AD1166680

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