Stallard, A. (2017). Comparing SAMOS Document Search Performance Between Apache Solr and Neo4j . Master's thesis, Florida State University, Tallahassee, FL.
Stewart, M. L. (2007). Cyclogenesis and Tropical Transition in Frontal Zones . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Tropical cyclones can form from many different precursors, including baroclinic systems. The process of an extratropical system evolving into a warm core tropical cyclone is defined by Davis and Bosart (2004) as a Tropical Transition (TT) with further classification of systems into Weak Extratropical Cylclones (WEC) and Strong Extratropical Cyclones (SEC). It is difficult to predict which systems will make the transition and which will not, but the description of a common type of TT occurring along a front will aid forecasters in identifying systems that might undergo TT. A wind speed and SST relationship thought to be necessary for this type of transition is discussed. QuikSCAT and other satellite data are used to locate TT cases forming along fronts and track their transformation into tropical systems. Frontal TT is identified as a subset of SEC TT and the evolution from a frontal wave to a tropical system is described in five stages. A frontal wave with stronger northerly wind and weaker southerly wind is the first stage in the frontal cyclogenesis. As the extratropical cyclogenesis continues in the next two stages, bent back warm front stage and instant occlusion stage, the warmer air of the bent back front becomes surrounded by cooler air . Next, in the subtropical stage the latent heat release energy from the ocean surface begins ascent and forms a shallow warm core. As the energy from surface heat fluxes translates to convection within the system, the warm core extends further into the upper levels of the atmosphere in the final, tropical stage of TT. Model data from MM5 simulations of three storms, Noel (2001), Peter (2003) and Gaston (2004) are analyzed to illustrate the five stages of frontal TT. Noel is found to have the most baroclinic origin of the three and Gaston the least.
Tartaglione, C. A., Smith, S. R., & O'Brien, J. J. (2003). ENSO Impact on Hurricane Landfall Probabilities for the Caribbean. J. Climate , 16 (17), 2925–2931.
Taylor, J. P. (2006). Comparison of ECMWF and Quikscat-Derived Surface Pressure Gradients . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: A technique based solely on QuikSCAT data is developed for determining suspect differences between QSCAT and ECMWF pressure gradients. Pressure fields are computed from scatterometer winds using a variational method that applies a gradient wind conversion. Kinematic analysis of the satellite wind field is performed in order to determine which parameters are physically related to the suspect pressure gradients. It is discovered that the likelihood of these suspect occurrences has the greatest dependence on relative vorticity, total deformation, and the curvature Rossby number. A broad range of these values is tested and a single assessment criterion is derived based upon the value of several skill scores. Overall, the assessment criterion is able to correctly identify the majority of suspect pressure gradients; yet considerable over-flagging does occur in many instances. However, the over-flagging is not random: the false alarms are tightly clustered around the suspect areas, resulting in flagged regions that are too large. Identification of the location of suspect areas in pressure products should be useful to forecasters.
Weissman, D. E., & Bourassa, M. A. (2008). Measurements of the Effect of Rain-Induced Sea Surface Roughness on the QuikSCAT Scatterometer Radar Cross Section. IEEE Trans. Geosci. Remote Sensing , 46 (10), 2882–2894.
Weissman, D. E., Bourassa, M. A., O'Brien, J. J., & Tongue, J. S. (2003). Calibrating the quikscat/seawinds radar for measuring rainrate over the oceans. IEEE Trans. Geosci. Remote Sensing , 41 (12), 2814–2820.
Yu, P. (2006). Development of New Techniques for Assimilating Satellite Altimetry Data into Ocean Models . Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: State of the art fully three-dimensional ocean models are very computationally expensive and their adjoints are even more resource intensive. However, many features of interest are approximated by the first baroclinic mode over much of the ocean, especially in the lower and mid latitude regions. Based on this dynamical feature, a new type of data assimilation scheme to assimilate sea surface height (SSH) data, a reduced-space adjoint technique, is developed and implemented with a three-dimensional model using vertical normal mode decomposition. The technique is tested with the Navy Coastal Ocean Model (NCOM) configured to simulate the Gulf of Mexico. The assimilation procedure works by minimizing the cost function, which generalizes the misfit between the observations and their counterpart model variables. The “forward” model is integrated for the period during which the data are assimilated. Vertical normal mode decomposition retrieves the first baroclinic mode, and the data misfit between the model outputs and observations is calculated. Adjoint equations based on a one-active-layer reduced gravity model, which approximates the first baroclinic mode, are integrated backward in time to get the gradient of the cost function with respect to the control variables (velocity and SSH of the first baroclinic mode). The gradient is input to an optimization algorithm (the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) method is used for the cases presented here) to determine the new first baroclinic mode velocity and SSH fields, which are used to update the forward model variables at the initial time. Two main issues in the area of ocean data assimilation are addressed: 1. How can information provided only at the sea surface be transferred dynamically into deep layers? 2. How can information provided only locally, in limited oceanic regions, be horizontally transferred to ocean areas far away from the data-dense regions, but dynamically connected to it? The first problem is solved by the use of vertical normal mode decomposition, through which the vertical dependence of model variables is obtained. Analyses show that the first baroclinic mode SSH represents the full SSH field very closely in the model test domain, with a correlation of 93% in one of the experiments. One common way to solve the second issue is to lengthen the assimilation window in order to allow the dynamic model to propagate information to the data-sparse regions. However, this dramatically increases the computational cost, since many oceanic features move very slowly. An alternative solution to this is developed using a mapping method based on complex empirical orthogonal functions (EOF), which utilizes data from a much longer period than the assimilation cycle and deals with the information in space and time simultaneously. This method is applied to map satellite altimeter data from the ground track observation locations and times onto a regular spatial and temporal grid. Three different experiments are designed for testing the assimilation technique: two experiments assimilate SSH data produced from a model run to evaluate the method, and in the last experiment the technique is applied to TOPEX/Poseidon and Jason-1 altimeter data. The assimilation procedure converges in all experiments and reduces the error in the model fields. Since the adjoint, or “backward”, model is two-dimensional, the method is much more computationally efficient than if it were to use a fully three-dimensional backward model.
Yu, P., Morey, S. L., & Zavala-Hidalgo, J. (2004). New mapping method to observe propagating features. Sea Technology , 45 (5), 20–24.
Zamudio, L., Hurlburt, H. E., Metzger, E. J., Morey, S. L., O'Brien, J. J., Tilburg, C., et al. (2006). Interannual variability of Tehuantepec eddies. J. Geophys. Res. , 111 (C5).
Zavala-Hidalgo, J., Gallegos-García, A., Martínez-López, B., Morey, S. L., & O'Brien, J. J. (2006). Seasonal upwelling on the Western and Southern Shelves of the Gulf of Mexico. Ocean Dynamics , 56 (3-4), 333–338.
