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|Ali, M. M., Bourassa, M. A., Bhowmick, S. A., Sharma, R., & Niharika, K. (2016). Retrieval of Wind Stress at the Ocean Surface From AltiKa Measurements. IEEE Geosci. Remote Sensing Lett., 13(6), 821–825.|
|Allende-Arandía, M. E., Zavala-Hidalgo, J., Romero-Centeno, R., Mateos-Jasso, A., Vargas-Hernández, J. M., & Zamudio, L. (2016). Analysis of Ocean Current Observations in the Northern Veracruz Coral Reef System, Mexico: 2007-12. Journal of Coastal Research, 317, 46–55.|
|Buijsman, M. C., Ansong, J. K., Arbic, B. K., Richman, J. G., Shriver, J. F., Timko, P. G., et al. (2016). Impact of Parameterized Internal Wave Drag on the Semidiurnal Energy Balance in a Global Ocean Circulation Model. J. Phys. Oceanogr., 46(5), 1399–1419.|
|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.|
|Campagnolo, M. L., Sun, Q., Liu, Y., Schaaf, C., Wang, Z., & Román, M. O. (2016). Estimating the effective spatial resolution of the operational BRDF, albedo, and nadir reflectance products from MODIS and VIIRS. Remote Sensing of Environment, 175, 52–64.|
|Cintra, R., Campos Velho, H., & Cocke, S. (2016). Multilayer Perceptron on data assimilation system applied to FSU global model..|
|Cintra, R., Velho, H. D., & Cocke, S. (2016). Tracking the model: data assimilation by artificial neural network. In IEEE International Joint Conference on Neural Networks (IJCNN) (pp. 403–410).|
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
|Conroy, B. J., Steinberg, D. K., Stukel, M. R., Goes, J. I., & Coles, V. J. (2016). Meso- and microzooplankton grazing in the Amazon River plume and western tropical North Atlantic. Limnol. Oceanogr., 61(3), 825–840.|
|Danabasoglu, G., Yeager, S. G., Kim, W. M., Behrens, E., Bentsen, M., Bi, D., et al. (2016). North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability. Ocean Modelling, 97, 65–90.|