Goto, Y. (2008). Improved Vegetation Characterization and Freeze Statistics in a Regional Spectral Model for the Florida Citrus Farming Region . Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: This study focused on the effective use of a numerical climate model for agriculture in Florida, especially in the citrus farming region of the Florida peninsula, because of the impact of agriculture to Florida's economy. For the analyses of the ensemble, the climate models used in this study were the FSU/COAPS Global Spectral Model and FSU/COAPS Regional Spectral Model (FSU/COAPS RSM) coupled with a land-surface model. The multi-convective scheme method and variable initial conditions were used for the ensembles. Severe freezes impacting agriculture in Florida were associated with some major climate patterns, such as El Niño and Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO). In the first part of this study, seasonal ensemble integrations of the regional model were examined for the tendencies of freezes in the Florida peninsula during each ENSO or NAO phase is examined. Mean excess values of minimum temperatures from thresholds on the basis of the Generalized Pareto Distribution (GPD), which represents the extreme data in a dataset, were used to analyze the freezes in the regional model. According to some previous studies, El Niño winters obtain fewer freezes than the other ENSO phases. Although the ensemble comprised only 19 winters, the ensemble found variability patterns in minimum temperatures in each climate phase similar to the findings in the previous studies which were based on the observed data. The FSU/COAPS RSM was coupled with Community Land Model 2.0 (CLM2), to represent the land-surface conditions. Although the coupling improved the temperature forecast of the RSM, it still has a cold bias and simulates smaller diurnal temperature changes than actually occur in southern Florida. Among the prescribed surface data, Leaf Area Index (LAI) for southern Florida in the CLM2 is lower than those observed by MODIS (Moderate Resolution Imaging Spectroradiometer). In the first experiment of this part, the sensitivity of the temperature forecast to the LAI in the climate models was investigated, by modifying the LAI data in the CLM2 based on the monthly MODIS observations. In the second experiment, newly created prescribed datasets of LAI and plant functional types for the CLM2 based on the MODIS observations were applied to the RSM. The substitution increased the diurnal temperature change in southern Florida slightly but almost consistently.
Ford, K. M. (2008). Uncertainty in Scatterometer-Derived Vorticity . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: A more versatile and robust technique is developed for determining area averaged surface vorticity based on vector winds from the SeaWinds scatterometer on the QuikSCAT satellite. This improved technique is discussed in detail and compared to two previous studies by Sharp et al. (2002) and Gierach et al. (2007) that focused on early development of tropical systems. The error characteristics of the technique are examined in detail. Specifically, three independent sources of error are explored: random observational error, truncation error and representation error. Observational errors are due to random errors in the wind observations, and determined as a worst-case estimate as a function of averaging spatial scale. The observational uncertainty in vorticity averaged for a roughly circular shape with a 100 km diameter, expressed as one standard deviation, is approximately 0.5 x 10 -5 s-1 for the methodology described herein. Truncation error is associated with the assumption of linear changes between wind vectors. For accurate results, it must be estimated on a case-by-case basis. An attempt is made to determine a lower bound of truncation errors through the use of composites of tropical disturbances. This lower bound is calculated as 10-7 s-1 for the composites, which is relatively small compared to the tropical disturbance detection threshold set at 5 x 10-5 s-1, used in an earlier study. However, in more realistic conditions, uncertainty related to truncation errors is much larger than observational uncertainty. The third type of error discussed is due to the size of the area being averaged. If the wind vectors associated with a vorticity maximum are inside the perimeter of this area (away from the edges), it will be missed. This type of error is analogous to over-smoothing. Tropical and sub-tropical low pressure systems from three months of QuikSCAT observations are used to examine this error. This error results in a bias of approximately 1.5 x 10-5 s-1 for area averaged vorticity calculated on a 100 km scale compared to vorticity calculated on a 25 km scale. The discussion of these errors will benefit future projects of this nature as well as future satellite missions.
Langland, R. H., Maue, R. N., & Bishop, C. H. (2008). Uncertainty in atmospheric temperature analyses. Tellus A , 60 (4), 598–603.
Kalnay, E., Cai, M., Nunez, M., & Lim, Y. - K. (2008). Impacts of urbanization and land surface changes on climate trends. Urban Climate News , 27 , 5–9.
Bourassa, M. A., Hughes, P. J., & Smith, S. R. (2008). Surface Turbulent Flux Product Comparison. Flux News , 5 , 22–24.
Zamudio, L., Metzger, E. J., & Hogan, P. J. (2008). A note on coastally trapped waves generated by the wind at the Northern Bight of Panama. Atmosfera , 21 (3), 241–248.
Bellow, J., A. Mokssit, J. O'Brien, and R. Sebbari. (2008). Building national and specialised climate services. In A. Troccoli, M. Harrison, D. L. T. Anderson, & S. Mason (Eds.), Seasonal Climate: Forecasting and Managing Risk (pp. 315–349). Springer.
Bellow, J. G., Nair, P. K. R., & Martin, T. A. (2008). Tree-Crop Interactions in Fruit Tree-based Agroforestry Systems in the Western Highlands of Guatemala: Component Yields and System Performance. In S. Jose, & A. M. Gordon (Eds.), Toward Agroforestry Design. Advances in Agroforestry (Vol. 4). Dordrecht: Springer.
Bourassa, M. A., & Gille, S. (2008). U.S. CLIVAR working groups on high latitude surface fluxes. U.S. CLIVAR Variations , 6 (1), 8–11.
