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Ahmad, I., Singh, A., Fahad, M., & Waqas, M. M. (2020). Remote sensing-based framework to predict and assess the interannual variability of maize yields in Pakistan using Landsat imagery. Computers and Electronics in Agriculture, 178.
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Bastola, S., & Misra, V. (2015). Seasonal hydrological and nutrient loading forecasts for watersheds over the Southeastern United States. Environmental Modelling & Software, 73, 90–102.
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Chang, N. - B., Yang, Y. J., Imen, S., & Mullon, L. (2017). Multi-scale quantitative precipitation forecasting using nonlinear and nonstationary teleconnection signals and artificial neural network models. Journal of Hydrology, 548, 305–321.
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Elliott, J., Glotter, M., Best, N., Boote, K., Jones, J. W., Hatfield, J., et al. (2013). Predicting agricultural impacts of large-scale drought: 2012 and the case for better modeling.
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Elsner, J. B., & Widen, H. M. (2013). Predicting Spring Tornado Activity in the Central Great Plains By March 1st. Mon. Wea. Rev., 142(1), 259–267.
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Flower, H., Rains, M., Carl Fitz, H., Orem, W., Newman, S., Osborne, T. Z., et al. (2019). Shifting Ground: Landscape-Scale Modeling of Biogeochemical Processes under Climate Change in the Florida Everglades. Environ Manage, 64(4), 416–435.
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Ibanez, I., Gornish, E. S., Buckley, L., Debinski, D. M., Hellmann, J., Helmuth, B., et al. (2012). Moving forward in global-change ecology: capitalizing on natural variability. Ecol Evol, 3(1), 170–181.
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Jones, J. W., Hansen, J. W., Royce, F. S., & Messina, C. D. (2000). Potential benefits of climate forecasting to agriculture. Agriculture, Ecosystems & Environment, 82, 169–184.
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Kozar, M. E., & Misra, V. (2014). Statistical Prediction of Integrated Kinetic Energy in North Atlantic Tropical Cyclones. Monthly Weather Review, 142(12), 4646–4657.
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Kren,, Cucurull,, & Wang,. (2018). Impact of UAS Global Hawk Dropsonde Data on Tropical and Extratropical Cyclone Forecasts in 2016. Wea. Forecasting, 33(5), 1121–1141.
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Ma, S., Jiang, J., Huang, Y., Shi, Z., Wilson, R. M., Ricciuto, D., et al. (2017). Data-Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO2 and Warming. J. Geophys. Res. Biogeosci., 122(11), 2841–2861.
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Royce, F. S., Jones, J. W., & Hansen, J. W. (2001). Model-based optimization of crop management for climate forecast applications. Transactions of the ASAE, 44, 1319–1327.
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Tian, D., & Martinez, C. J. (2012). Forecasting reference evapotranspiration using retrospective forecast analogs in the southeastern United States. J. Hydrometeor, 13(6), 1874–1892.
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Tian, D., Martinez, C. J., & Graham, W. D. (2014). Seasonal Prediction of Regional Reference Evapotranspiration Based on Climate Forecast System Version 2. J. Hydrometeor, 15(3), 1166–1188.
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Woli, P., Jones, J., Ingram, K., & Paz, J. (2013). Forecasting Drought Using the Agricultural Reference Index for Drought (ARID): A Case Study. Wea. Forecasting, 28(2), 427–443.
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Xiao, H., Wang, D., Medeiros, S. C., Hagen, S. C., & Hall, C. R. (2018). Assessing sea-level rise impact on saltwater intrusion into the root zone of a geo-typical area in coastal east-central Florida. Sci Total Environ, 630, 211–221.
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Yang, K., V. A. Paramygin, & Y. Peter Sheng. (2020). A Rapid Forecasting and Mapping System of Storm Surge and Coastal Flooding. Wea. Forecasting, 35(4), 1663–1681.
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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.
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