Conlon, K. C., Kintziger, K. W., Jagger, M., Stefanova, L., Uejio, C. K., & Konrad, C. (2016). Working with Climate Projections to Estimate Disease Burden: Perspectives from Public Health. Int J Environ Res Public Health , 13 (8).
Abstract: There is interest among agencies and public health practitioners in the United States (USA) to estimate the future burden of climate-related health outcomes. Calculating disease burden projections can be especially daunting, given the complexities of climate modeling and the multiple pathways by which climate influences public health. Interdisciplinary coordination between public health practitioners and climate scientists is necessary for scientifically derived estimates. We describe a unique partnership of state and regional climate scientists and public health practitioners assembled by the Florida Building Resilience Against Climate Effects (BRACE) program. We provide a background on climate modeling and projections that has been developed specifically for public health practitioners, describe methodologies for combining climate and health data to project disease burden, and demonstrate three examples of this process used in Florida.
Keclik, A. (2014). The Accuracy of the National Hurricane Center's United States Tropical Cyclone Landfall Forecasts in the Atlantic Basin (2004-2012) . Bachelor's thesis, Florida State University, Tallahassee, FL.
Michael, J. - P. (2014). On Initializing CGCMs for Seasonal Predictability of ENSO . Ph.D. thesis, Florida State University, Tallahassee, FL.
Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States. Environmental Modelling & Software , 73 , 90–102.
Yatagai, A., Krishnamurti, T. N., Kumar, V., Mishra, A. K., & Simon, A. (2014). Use of APHRODITE Rain Gauge-Based Precipitation and TRMM 3B43 Products for Improving Asian Monsoon Seasonal Precipitation Forecasts by the Superensemble Method. J. Climate , 27 (3), 1062–1069.
Devanas, A., & Stefanova, L. (2018). Statistical Prediction Of Waterspout Probability For The Florida Keys. Wea. Forecasting , 33 , 389–410.
Abstract: A statistical model of waterspout probability was developed for wet-season (June–September) days over the Florida Keys. An analysis was performed on over 200 separate variables derived from Key West 1200 UTC daily wet-season soundings during the period 2006–14. These variables were separated into two subsets: days on which a waterspout was reported anywhere in the Florida Keys coastal waters and days on which no waterspouts were reported. Days on which waterspouts were reported were determined from the National Weather Service (NWS) Key West local storm reports. The sounding at Key West was used for this analysis since it was assumed to be representative of the atmospheric environment over the area evaluated in this study. The probability of a waterspout report day was modeled using multiple logistic regression with selected predictors obtained from the sounding variables. The final model containing eight separate variables was validated using repeated fivefold cross validation, and its performance was compared to that of an existing waterspout index used as a benchmark. The performance of the model was further validated in forecast mode using an independent verification wet-season dataset from 2015–16 that was not used to define or train the model. The eight-predictor model was found to produce a probability forecast with robust skill relative to climatology and superior to the benchmark waterspout index in both the cross validation and in the independent verification.
Nielsen, E. R., Schumacher, R. S., & Keclik, A. M. (2016). The Effect of the Balcones Escarpment on Three Cases of Extreme Precipitation in Central Texas. Mon. Wea. Rev. , 144 (1), 119–138.
Shin, D. W. (2003). Short- to medium-range superensemble precipitation forecasts using satellite products: 2. Probabilistic forecasting. J. Geophys. Res. , 108 (D8).
Shin, D. W. (2003). Short- to medium-range superensemble precipitation forecasts using satellite products: 1. Deterministic forecasting. J. Geophys. Res. , 108 (D8).
