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Statistical Downscaling Models for Precipitation Climate Change Studies

This is one of 6 past projects focusing on climate change research that was funded in 2010 as part of FAU’s Research Priorities. For more information about this project, contact the research scientist.

Dr. Ramesh Teegavarapu,  Department of Civil, Environmental and Geomatics Engineering 

Dr. Aneesh Goly (Ph.D. Student, graduated in August 2013)

Spatial and temporal downscaling of precipitation from general circulation model (GCM) outputs is one of the most challenging tasks encountered by climate change researchers. In general, skill of any climate change model is considered to be lower for precipitation compared to temperature. Recent evaluation of downscaled projections for 20th century for temperature and precipitation available at 1/8° resolution for Florida led to similar conclusions. The projections are based on Bias-Correction and Spatial Disaggregation (BCSD) methodology which have been used in several successful applications related to climate change impact studies in the U.S. in the past. The downscaled precipitation data when, evaluated against historical climatology, showed several deficiencies of BCSD downscaling model especially for Florida conditions. The proposed study will develop a new statistical downscaling model that will use GCM variables to initially develop projections at a monthly scale and then to a finer temporal scale of a day, using an appropriate weather generator. The downscaled precipitation data sets from this study are intended for use in water resource assessment using conceptually simple water-budget models.

Contact Dr. Teegavarapu - rteegava@fau.edu

 Last Modified 11/8/16