Brolley, J. M. (2004).
Experimental Forest Fire Threat Forecast. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Climate shifts due to El Niño (warmer than normal ocean temperatures in the tropical Pacific Ocean) and La Niña (cooler than normal) are well known and used to predict seasonal temperature and precipitation trends up to a year in advance. These climate shifts are particularly strong in the Southeastern United States. During the winter and spring months, El Niño brings plentiful rainfall and cooler temperatures to Florida. Recent los Niños occurred in 1997-1998, one of the strongest on record, with another mild El Niño in 2002-2003. Conversely, La Niña is associated with warm and dry winter and spring seasons in Florida. Temperature and precipitation affect wildfire activity; interannual drivers of climate, like ENSO, have an influence on wildfire activity. Studies have shown a strong connection between wildfires in Florida and La Niña, with the more than double the average number of acres burned (O'Brien et al 2002; Jones et al. 1999). While this relationship is important and lends a degree of predictability to the relative activity of future wildfire seasons, human activities such as effective suppression, prescribed burns, and ignition can play an equally important role in wildfire risks. This study forecasts wildfire potential rather than actual burn statistics to avoid complications due to human interactions. This wildfire threat potential is based upon the Keetch-Byram Drought Index (KBDI). The KBDI is well suited as a seasonal forecast medium. It is based on daily temperature and rainfall measurements and responds to changing climate and weather conditions on time scales of days to months, and this index is high during dry warm weather patterns and low during wet cool patterns. The KBDI has been widely used in forestry in the Southeastern United States since its development in the 1970's, with foresters and firefighters have a good level of familiarity with the index and its applications. The KBDI is calculated daily and used as an index by wildfire managers. This study calculates wildfire potential using a statistical method known as bootstrapping. Many datasets contain approximately a half-century of data, and the limited dataset will introduce biases. Bootstrapping can remedy bias by simulating thousands of years of data, which retain the climatology for the past half-century. Bootstrapping preserves the mean but not the variance. By incorporating this method, this study will improve long-term forest fire risks that will become useful for those living or working near forests and assist in managing forests and wildfires. The Southeast Climate Consortium will also be issuing wildfire risk forecast for Florida and parts of Alabama and Georgia based on ENSO phase and the KBDI. Climate information and ENSO predictions are better served by incorporating them with known climate indices that are routinely used in the forestry sector. Wildfire managers and foresters operationally use the KBDI to monitor and predict wildfire activity (O'Brien et al. 2002). Meteorologists at the Florida Division of Forestry have demonstrated the validity of the KBDI as an indicator of potential wildfire activity in Florida (Long 2004). They showed that the value of the KBDI is not as important as the deviation from the monthly average. The wildfire risk forecast is based on the probabilities of KBDI anomalies and will present the probabilities associated with large deviations from the seasonal normal.
Brolley, J. M., O'Brien, J. J., Schoof, J., & Zierden, D. (2007). Experimental drought threat forecast for Florida.
Agricultural and Forest Meteorology, 145(1-2), 84–96.
Brunke, M. A., Zeng, X., Misra, V., & Beljaars, A. (2008). Integration of a prognostic sea surface skin temperature scheme into weather and climate models.
J. Geophys. Res., 113(D21).
Bruno-Piverger, R. E. (2019). Applying Neural Networks to Simulate Visual Inspection of Observational Weather Data.
Florida State University College of Arts and Sciences, Master's Thesis, .
Brzezinski, M. A., Krause, J. W., Bundy, R. M., Barbeau, K. A., Franks, P., Goericke, R., et al. (2015). Enhanced silica ballasting from iron stress sustains carbon export in a frontal zone within the California Current.
J. Geophys. Res. Oceans, 120(7), 4654–4669.
Buchanan, S., Misra, V., & Bhardwaj, A. (2018). https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.5450.
International Journal of Climatology, 38(6), 2651–2661.
Abstract: The integrated kinetic energy (IKE) of a tropical cyclone (TC), a volume integration of the surface winds around the centre of the TC, is computed from a comprehensive surface wind (National Aeronautics and Space Administrationís (NASA) cross‐calibrated multi‐platform [CCMP]) analysis available over the global oceans to verify against IKE from wind radii estimates of extended best‐track data maintained by NOAA for the North Atlantic TCs. It is shown that CCMP surface wind analysis severely underestimates IKE largely from not resolving hurricane force winds for majority of the Atlantic TCs, under sampling short‐lived and small‐sized TCs. The seasonal cycle of the North Atlantic TC IKE also verifies poorly in the CCMP analysis. In this article we introduce proxy IKE (PIKE) based on the kinetic energy of the winds at the radius of the last closed isobar (ROCI), which shows promise for a wide range of TC sizes including the smaller‐sized TCs unresolved in the CCMP data set.
Buijsman, M. C., Arbic, B. K., Richman, J. G., Shriver, J. F., Wallcraft, A. J., & Zamudio, L. (2017). Semidiurnal internal tide incoherence in the equatorial Pacific.
J. Geophys. Res. Oceans, 12(7), 5286–5305.
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.
Bunge, L., & Clarke, A. J. (2014). On the Warm Water Volume and Its Changing Relationship with ENSO.
J. Phys. Oceanogr., 44(5), 1372–1385.
Cabrera, V., D. Solis, G. Baigorria and D. Letson. (2009). Managing climate variability in agricultural analysis. In J.A. Long and D.S. Wells (Ed.),
Ocean Circulation and El Niño: New Research (pp. 163–179). Nova Publishing, Inc.