Hood, M., and 39 Coauthors(including S. R. Smith). (2010). Ship-Based Repeat Hydrography: A Strategy for a Sustained Global Program. In D.ll D.E. and Stammer Harrison J. Hall (Ed.),
Proceedings of OceanObs'09: Sustained Ocean Observations and Information for Society (Vol. 2).
Kopp, R. E., Mitrovica, J. X., Griffies, S. M., Yin, J., Hay, C. C., & Stouffer, R. J. (2010). The impact of Greenland melt on local sea levels: a partially coupled analysis of dynamic and static equilibrium effects in idealized water-hosing experiments: A letter.
Climatic Change, 103(3-4), 619–625.
Koster, R. D., Mahanama, S. P. P., Yamada, T. J., Balsamo, G., Berg, A. A., Boisserie, M., et al. (2010). Contribution of land surface initialization to subseasonal forecast skill: First results from a multi-model experiment.
Geophys. Res. Lett., 37(2), n/a-n/a.
Krishnamurthy, V., & Misra, V. (2010). Observed ENSO teleconnections with the South American monsoon system.
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Clim Dyn, 35(2-3), 449–471.
Lindstrom, E. J., M. A. Bourassa, L.-A. Breivik, C. J. Donlon, Lee-Lueng Fu, P. Hacker, G. Lagerloef, T. Lee, C. Le Quere, V. Swail, W. S. Wilson, V. Zlotnicki. (2010). Research Satellite Missions. In D. D.E. and Stammer Harrison J. Hall (Ed.),
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Mariani, P., MacKenzie, B. R., Iudicone, D., & Bozec, A. (2010). Modelling retention and dispersion mechanisms of bluefin tuna eggs and larvae in the northwest Mediterranean Sea. In
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Maue, R. (2010).
Warm Seclusion Extratropical Cyclones. Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: The warm seclusion or mature stage of the extratropical cyclone lifecycle often has structural characteristics reminiscent of major tropical cyclones including eye-like moats of calm air at the barotropic warm-core center surrounded by hurricane force winds along the bent-back warm front. Many extratropical cyclones experience periods of explosive intensification or deepening (bomb) as a result of nonlinear dynamical feedbacks associated with latent heat release. Considerable dynamical structure changes occur during short time periods of several hours in which lower stratospheric and upper-tropospheric origin potential vorticity combines with ephemeral lower-tropospheric, diabatically generated potential vorticity to form a coherent, upright tower circulation. At the center, anomalously warm and moist air relative to the surrounding environment is secluded and may exist for days into the future. Even with the considerable body of research conducted during the last century, many questions remain concerning the warm seclusion process. The focus of this work is on the diagnosis, climatology, and synoptic-dynamic development of the warm seclusion and surrounding flank of intense winds. To develop a climatology of warm seclusion and explosive extratropical cyclones, current long-period reanalysis datasets are utilized along with storm tracking procedures and cyclone phase space diagnostics. Limitations of the reanalysis products are discussed with special focus on tropical cyclone diagnosis and the recent dramatic decrease in global accumulated tropical cyclone energy. A large selection of case studies is simulated with the Weather Research and Forecasting (WRF) mesoscale model using full-physics and “fake dry” adiabatic runs in order to capture the very fast warm seclusion development. Results are presented concerning the critical role of latent heat release and the combination of advective and diabatically generated potential vorticity in the generation of the coherent tower circulation characteristic of the warm seclusion. To motivate future research, issues related to predictability are discussed with focus on medium-range forecasts of varying extratropical cyclone lifecycles. Additional work is presented relating tropical cyclones and large-scale climate variability with special emphasis on the abrupt and dramatic decline in recent global tropical cyclone accumulated cyclone energy.
May, J. (2010).
Quantifying Variance Due to Temporal and Spatial Difference Between Ship and Satellite Winds. Master's thesis, Florida State University, Tallahassee, FL.
Abstract: Ocean vector winds measured by the SeaWinds scatterometer onboard the QuikSCAT satellite can be validated with in situ data. Ideally the comparison in situ data would be collocated in both time and space to the satellite overpass; however, this is rarely the case because of the time sampling interval of the in situ data and the sparseness of data. To compensate for the lack of ideal collocations, in situ data that are within a certain time and space range of the satellite overpass are used for comparisons. To determine the total amount of random observational error, additional uncertainty from the temporal and spatial difference must be considered along with the uncertainty associated with the data sets. The purpose of this study is to quantify the amount of error associated with the two data sets, as well as the amount of error associated with the temporal and/or spatial difference between two observations. The variance associated with a temporal difference between two observations is initially examined in an idealized case that includes only Shipboard Automated Meteorological and Oceanographic System (SAMOS) one-minute data. Temporal differences can be translated into spatial differences by using Taylor's hypothesis. The results show that as the time difference increases, the amount of variance increases. Higher wind speeds are also associated with a larger amount of variance. Collocated SeaWinds and SAMOS observations are used to determine the total variance associated with a temporal (equivalent) difference from 0 to 60 minutes. If the combined temporal and spatial difference is less than 25 minutes (equivalent), the variance associated with the temporal and spatial difference is offset by the observational errors, which are approximately 1.0 m2s-2 for wind speeds between 4 and 7 ms-1 and approximately 1.5 m2s-2 for wind speeds between 7 and 12 ms-1. If the combined temporal and spatial difference is greater than 25 minutes (equivalent), then the variance associated with the temporal and spatial difference is no longer offset by the variance associated with observational error in the data sets; therefore, the total variance gradually increases as the time difference increases.