Diagnostic Understanding of CWRF Performance over the Chesapeake Bay

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Sunday, 2 February 2014
Hall C3 (The Georgia World Congress Center )
Stephanie S. Rushley, NOAA/NCAS, Washington, DC; and X. Z. Liang

This study examines the capabilities of the Climate extension of the Weather Research and Forecast model (CWRF) over the Chesapeake Bay watershed with respect to temperature and precipitation. The Chesapeake Bay watershed encompasses the largest estuary in the United States and is responsible for draining a vast area of the North and Central East Coast. Understanding how climate change affects the temperature and precipitation distribution around the Bay is vital to agriculture, industry and the organisms that live in and around the Bay. The Chesapeake Bay has already seen changes in its climate and already organisms and agriculture are noting the change. The primary parameters that dictate change in climate over the Chesapeake Bay are precipitation which alters the runoff into the bay and the overall salinity of the bay, and temperature which has an effect on the evaporation rate and the rise in the sea surface temperature and thus the sea level rise.

The CWRF model is focused on climate prediction and weather forecasting, this study examines the accuracy of the CWRF when compared to observational data. The CWRF and observational data is examined on a monthly and daily bases, as well as on a seasonal and annual time scale. The data was examined spatially as well as temporally to not only examine the CWRF's accuracy across the Chesapeake Bay area but also to examine trends in time variance between the CWRF and the observational data. Precipitation showed the most variation and thus was examined further to examine the frequency distribution, the number of rainy days, the maximum consecutive dry days, and the 95th percentile. Temperature preformed better both spatially and temporally with large biases for temperature and precipitation occurring in the Northern edge of the study area, mountains and coastal areas. However, both precipitation and temperature were statistically significant.