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|Cammarano, D., Basso, B., Stefanova, L., & Grace, P. (2012). Adapting wheat sowing dates to projected climate change in the Australian subtropics: analysis of crop water use and yield. Crop Pasture Sci., 63(10), 974.|
|Cammarano, D., Stefanova, L., Ortiz, B. V., Ramirez-Rodrigues, M., Asseng, S., Misra, V., et al. (2013). Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US. Reg Environ Change, 13(S1), 101–110.|
|Cammarano, D., Zierden, D., Stefanova, L., Asseng, S., O'Brien, J. J., & Jones, J. W. (2016). Using historical climate observations to understand future climate change crop yield impacts in the Southeastern US. Climatic Change, 134(1-2), 311–326.|
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.
Keywords: *Climate Change/statistics & numerical data; Florida; Forecasting; Humans; Models, Theoretical; Public Health/*trends; United States; adaptation; attributable fraction; climate modeling; project disease burden; public health
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.
Keywords: Regression analysis; Forecast verification/skill; Forecasting techniques; Probability forecasts/models/distribution; Statistical forecasting
|Krishnamurti, T. N., Stefanova, L., &, M., V. (2013). Tropical Meteorology: An Introduction. Springer.|
|LaRow, T. E., Stefanova, L., Shin, D. - W., & Cocke, S. (2010). Seasonal Atlantic tropical cyclone hindcasting/forecasting using two sea surface temperature datasets. Geophys. Res. Lett., 37(2).|
|Lim, Y. - K., Stefanova, L. B., Chan, S. C., Schubert, S. D., & O'Brien, J. J. (2011). High-resolution subtropical summer precipitation derived from dynamical downscaling of the NCEP/DOE reanalysis: how much small-scale information is added by a regional model? Clim Dyn, 37(5-6), 1061–1080.|
|Mirhosseini, G., Srivastava, P., & Stefanova, L. (2013). The impact of climate change on rainfall Intensity-Duration-Frequency (IDF) curves in Alabama. Reg Environ Change, 13(S1), 25–33.|
|Misra, V., Moeller, L., Stefanova, L., Chan, S., O'Brien, J. J., Smith III, T. J., et al. (2011). The influence of the Atlantic Warm Pool on the Florida panhandle sea breeze: FLORIDA SEA BREEZE VARIATIONS. J. Geophys. Res., 116(D21).|