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.
DiNapoli, S. (2010). Determining the Error Characteristics of H*WIND . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: The HRD Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA's Hurricane Research Division to create a gridded tropical cyclone wind analysis based on a wide range of observations. One application of H*Wind fields is calibration of scatterometers for high wind speed environments. Unfortunately, the accuracy of the H*Wind product has not been studied extensively, and therefore the accuracy of scatterometer calibrations in these environments is also unknown. This investigation seeks to determine the uncertainty in the H*Wind product and estimate the contributions of several potential error sources. These error sources include random observation errors, relative bias between different data types, temporal drift resulting from combining non-simultaneous measurements, and smoothing and interpolation errors in the H*Wind software. The effects of relative bias between different data types and random observation errors are determined by performing statistical calculations on the observed wind speeds. We show that in the absence of large biases, the total contribution of all error sources results in an uncertainty of approximately 7% near the storm center, which increases to nearly 15% near the tropical storm force wind radius. The H*Wind analysis algorithm is found to introduce a positive bias to the wind speeds near the storm center, where the analyzed wind speeds are enhanced to match the highest observations. In addition, spectral analyses are performed to ensure that the filter wavelength of the final analysis product matches user specifications. With increased knowledge of these error sources and their effects, researchers will have a better understanding of the uncertainty in the H*Wind product, and can then judge the suitability of H*Wind for various research applications
DiNapoli, S. M., Bourassa, M. A., & Powell, M. D. (2012). Uncertainty and Intercalibration Analysis of H*Wind. J. Atmos. Oceanic Technol. , 29 (6), 822–833.
DiNapoli, S. M., & Misra, V. (2012). Reconstructing the 20th century high-resolution climate of the southeastern United States. J. Geophys. Res. , 117 (D19), n/a-n/a.
Dombrowsky, E., Bertino, L., Brassington, G., Chassignet, E., Davidson, F., Hurlburt, H., et al. (2009). GODAE Systems in Operation. Oceanog. , 22 (3), 80–95.
Domingues, R., Kuwano-Yoshida, A., Chardon-Maldonado, P., Todd, R. E., Halliwell, G., Kim, H. - S., et al. (2019). Ocean Observations in Support of Studies and Forecasts of Tropical and Extratropical Cyclones. Front. Mar. Sci. , 6 , 446.
Abstract: Over the past decade, measurements from the climate-oriented ocean observing system have been key to advancing the understanding of extreme weather events that originate and intensify over the ocean, such as tropical cyclones (TCs) and extratropical bomb cyclones (ECs). In order to foster further advancements to predict and better understand these extreme weather events, a need for a dedicated observing system component specifically to support studies and forecasts of TCs and ECs has been identified, but such a system has not yet been implemented. New technologies, pilot networks, targeted deployments of instruments, and state-of-the art coupled numerical models have enabled advances in research and forecast capabilities and illustrate a potential framework for future development. Here, applications and key results made possible by the different ocean observing efforts in support of studies and forecasts of TCs and ECs, as well as recent advances in observing technologies and strategies are reviewed. Then a vision and specific recommendations for the next decade are discussed.
Dukhovskoy, D. S., Morey, S. L., & O'Brien, J. J. (2006). Baroclinic topographic waves on the Nicaragua Shelf generated by tropical cyclones (J. Cote, Ed.). Research Activities in Atmospheric and Ocean Modeling, Report No. 36. Geneva, Switzerland: World Meteorological Organization.
Dukhovskoy, D. S., Morey, S. L., & O'Brien, J. J. (2005). Topographic Rossby Waves in a Z-Level Ocean Model (J. Cote, Ed.). Research Activities in Atmospheric and Ocean Modeling, Report No. 35. Geneva, Switzerland: World Meteorological Organization.
Dukhovskoy, D. S., Morey, S. L., & O'Brien, J. J. (2005). Topographic Rossby waves in a z-level ocean model. Eos Trans. AGU , 86 (18), Jt. Assem. Suppl., Abstract OS22A–06.
Dukhovskoy, D., Johnson, M., & Proshutinsky, A. (2006). Arctic decadal variability from an idealized atmosphere-ice-ocean model: 2. Simulation of decadal oscillations. J. Geophys. Res. , 111 (C6).
