Bunge, L., & Clarke, A. J. (2014). On the Warm Water Volume and Its Changing Relationship with ENSO. J. Phys. Oceanogr. , 44 (5), 1372–1385.
Glazer, R. H. (2014). The Influence of Mesoscale Sea Surface Temperature Gradients on Tropical Cyclones . Master's thesis, Florida State University, Tallahassee, FL.
Griffin, J. (2009). Characterization of Errors in Various Moisture Roughness Length Parameterizations . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Often the parameterization of the moisture roughness length is not seen as being important, as long as the parameterization seems reasonable; that is, it is within the rather considerable bounds of error for the data sets used to determine the parameterization. However, the choice of parameterization does influence height adjustments of humidity and calculations of turbulent heat fluxes. This paper focuses on the calculation of the turbulent heat fluxes using different parameterizations of roughness length. Five roughness length parameterizations are examined herein. These parameterizations include wall theory; the Clayson, Fairall, Curry parameterization; the Liu, Katsaros, Businger parameterization; Zilitinkevich et al. parameterization; and the COARE3.0 parameterization. Turbulent heat fluxes are calculated from each parameterization of the roughness length and are compared to observed turbulent heat flux values. The bulk latent heat flux estimates have a much better signal to noise ratio than the sensible heat fluxes, and are therefore the focus of the comparison to observations. This comparison indicates how to improve the proportionality in the above roughness length parameterizations, which are causing modeled turbulent heat flux magnitudes to be too large in four of the five parameterizations. The modeled turbulent heat fluxes are evaluated again after the modification of the parameterizations. Significant improvements in both the bias and the root mean square error (RMSE) are seen. Three parameterizations see roughly the same improvements of around 17Wm^-2 in the bias and roughly 10Wm^-2 in the RMSE. The largest improvements are in the Liu, Katsaros, Businger parameterization with bias improvements of over 45Wm^-2 and a RMSE reduction of nearly 32Wm^-2.
McNaught, C. (2014). The Increasing Intensity and Frequency of ENSO and its Impacts to the Southeast U.S. Bachelor's thesis, Florida State University, Tallahassee, FL.
Misra, V., & Dirmeyer, P. A. (2009). Air, Sea, and Land Interactions of the Continental U.S. Hydroclimate. J. Hydrometeor , 10 (2), 353–373.
Nielsen, E. R., Schumacher, R. S., & Keclik, A. M. (2016). The Effect of the Balcones Escarpment on Three Cases of Extreme Precipitation in Central Texas. Mon. Wea. Rev. , 144 (1), 119–138.
Smith, S. R., Briggs, K., Bourassa, M. A., Elya, J., & Paver, C. R. (2018). Shipboard automated meteorological and oceanographic system data archive: 2005-2017. Geosci Data J , 5 (2), 73–86.
Abstract: Since 2005, the Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative has been collecting, quality-evaluating, distributing, and archiving underway navigational, meteorological, and oceanographic observations from research vessels. Herein we describe the procedures for acquiring ship and instrumental metadata and the one-minute interval observations from 44 research vessels that have contributed to the SAMOS initiative from 2005 to 2017. The overall data processing workflow and quality control procedures are documented along with data file formats and version control procedures. The SAMOS data are disseminated to the user community via web, FTP, and Thematic Real-time Environmental Distributed Data Services from both the Marine Data Center at the Florida State University and the National Centers for Environmental Information, which serves as the long-term archive for the SAMOS initiative. They have been used to address topics ranging from air-sea interaction studies, the calibration, evaluation, and development of satellite observational products, the evaluation of numerical atmospheric and ocean models, and the development of new tools and techniques for geospatial data analysis in the informatics community. Maps provide users the geospatial coverage within the SAMOS dataset, with a focus on the Essential Climate/Ocean Variables, and recommendations are made regarding which versions of the dataset should be accessed by different user communities.
Smith, S. R., Lopez, N., & Bourassa, M. A. (2016). SAMOS air-sea fluxes: 2005-2014. Geosci. Data J. , 3 (1), 9–19.
Williams, M. (2010). Characterizing Multi-Decadal Temperature Variability in the Southeastern United States . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Prior studies of the long-term temperature record in the Southeastern United States (SE US) mostly discuss the long-term cooling trend, and the inter-annual variability produced by the region's strong ties to El Niño Southern Oscillation (ENSO). An examination of long-term temperature records in the SE US show clear multi-decadal variations in temperature, with relative warm periods in the 1920's through the mid 1950's and a cool period in the late 1950's through the late 1990's. This substantial shift in multi-decadal variability is not well understood and has not been fully investigated. It appears to account for the long-term downward trend in temperatures. An accurate characterization of this variability could lead to improved interannual and long-term forecasts, which would be useful for agricultural planning, drought mitigation, water management, and preparation for extreme temperature events. Statistical methods are employed to determine the spatial coherence of the observed variability on seasonal time scales. The goal of this study is to characterize the nature of this variability through the analysis of National Weather Service Cooperative Observer Program (COOP) station data in Florida, Georgia, Alabama, North Carolina, and South Carolina. One finding is a shift in the temperature Probability Distribution Function (PDF) between warm regimes and cool regimes.
