Goff, J. A., & Arbic, B. K. (2010). Global prediction of abyssal hill roughness statistics for use in ocean models from digital maps of paleo-spreading rate, paleo-ridge orientation, and sediment thickness. Ocean Modelling , 32 (1-2), 36–43.
Zamudio, L., Metzger, E. J., & Hogan, P. J. (2010). Gulf of California response to Hurricane Juliette. Ocean Modelling , 33 (1-2), 20–32.
Scott, R. B., Arbic, B. K., Chassignet, E. P., Coward, A. C., Maltrud, M., Merryfield, W. J., et al. (2010). Total kinetic energy in four global eddying ocean circulation models and over 5000 current meter records. Ocean Modelling , 32 (3-4), 157–169.
Arbic, B. K., Wallcraft, A. J., & Metzger, E. J. (2010). Concurrent simulation of the eddying general circulation and tides in a global ocean model. Ocean Modelling , 32 (3-4), 175–187.
Boisserie, M. (2010). Generation of an empirical soil moisture initialization and its potential impact on subseasonal forecasting skill of continental precipitation and air temperature . Ph.D. thesis, Florida State University, Tallahassee, FL, FL.
Abstract: The effect of the PAR technique on the model soil moisture estimates is evaluated using the Global Soil Wetness Project Phase 2 (GSWP-2) multimodel analysis product (used as a proxy for global soil moisture observations) and actual in-situ observations from the state of Illinois. The results show that overall the PAR technique is effective; across most of the globe, the seasonal and anomaly variability of the model soil moisture estimates well reproduce the values of GSWP-2 in the top 1.5 m soil layer; by comparing to in-situ observations in Illinois, we find that the seasonal and anomaly soil moisture variability is also well represented deep into the soil. Therefore, in this study, we produce a new global soil moisture analysis dataset that can be used for many land surface studies (crop modeling, water resource management, soil erosion, etc.). Then, the contribution of the resulting soil moisture analysis (used as initial conditions) on air temperature and precipitation forecasts are investigated. For this, we follow the experimental set up of a model intercomparison study over the time period 1986-1995, the Global Land-Atmosphere Coupling Experiment second phase (GLACE-2), in which the FSU/COAPS climate model has participated. The results of the summertime air temperature forecasts show a significant increase in skill across most of the U.S. at short-term to subseasonal time scales. No increase in summertime precipitation forecasting skill is found at short-term to subseasonal time scales between 1986 and 1995, except for the anomalous drought year of 1988. We also analyze the forecasts of two extreme hydrological events, the 1988 U.S. Drought and the 1993 U.S. flood. In general, the comparison of these two extreme hydrological event forecasts shows greater improvement for the summertime of 1988 than that of 1993, suggesting that soil moisture contributes more to the development of a drought than a flood. This result is consistent with Dirmeyer and Brubaker [1999] and Weaver et al. [2009]. By analyzing the evaporative sources of these two extreme events using the back-trajectory methodology of Dirmeyer and Brubaker [1999], we find similar results as this latter paper; the soil moisture-precipitation feedback mechanism seems to play a greater role during the drought year of 1988 than the flood year of 1993. Finally, the accuracy of this soil moisture initialization depends upon the quality of the precipitation dataset that is assimilated. Because of the lack of observed precipitation at a high temporal resolution (3-hourly) for the study period (1986-1995), a reanalysis product is used for precipitation assimilation in this study. It is important to keep in mind that precipitation data in reanalysis sometimes differ significantly from observations since precipitation is often not assimilated into the reanalysis model. In order to investigate that aspect, a similar analysis to that we performed in this study could be done using the 3-hourly Tropical Rainfall Measuring Mission (TRMM) dataset available for a the time period 1998-present. Then, since the TRMM dataset is a fully observational dataset, we expect the soil moisture initialization to be improved over that obtained in this study, which, in turn, may further increase the forecast skill.
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
Winterbottom, H. (2010). The Development of a High-Resolution Coupled Atmosphere-Ocean Model and Applications Toward Understanding the Limiting Factors for Tropical Cyclone Intensity Prediction . Ph.D. thesis, Florida State University, Tallahassee, FL.
Abstract: The prediction of tropical cyclone (TC) motion has improved greatly in recent decades. However, similar trends remain absent with respect to TC intensity prediction. Several hypotheses have been proposed attempting to explain why dynamical NWP models struggle to predict TC intensity. The leading candidates are as follows: (1) the lack of an evolving ocean (i.e., sea-surface temperature) boundary condition which responds as a function of the atmosphere (e.g., TC) forcing, (2) inappropriate initial conditions for the TC vortex (e.g., lack of data assimilation methods), (3) NWP model grid-length resolutions which are unable to resolve the temporal and length scale for the features believed responsible for TC vortex intensity. modulations (i.e., eye-wall dynamics, momentum transport, vortex Rossby wave interactions, etc.), and (4) physical parametrization which do not adequately represent the air-sea interactions observed during TC passage. In this study, a coupling algorithm for two independent, high-resolution, and state-of-the-art atmosphere and ocean models is developed. The atmosphere model -- the Advanced Weather Research and Forecasting (WRF-ARW) model is coupled to the HYbrid Coordinate Ocean Model (HYCOM) using a (UNIX) platform independent and innovative coupling methodology. Further, within the WRF-ARW framework, a dynamic initialization algorithm is developed to specify the TC vortex initial condition while preserving the synoptic-scale environment. Each of the tools developed in this study is implemented for a selected case-study: TC Bertha (2008) and TC Gustav (2008) for the coupled-model and TC vortex initialization, respectively. The experiment results suggest that the successful prediction (with respect to the observations) for both the ocean response and the TC intensity cannot be achieved by simply incorporating (i.e., coupling) an ocean model and/or by improving the initial structure for the TC. Rather the physical parametrization governing the air-sea interactions is suggested as the one of the weaknesses for the NWP model. This hypothesis is (indirectly) supported through a diagnostic evaluation of the synoptic-scale features (e.g., sea-level pressure and the deep-layer mean wind beyond the influence of the TC) while the assimilated TC vortex is nudged toward the observed intensity value. It is found -- in the case of TC Gustav (2008) using WRF-ARW, that as the assimilated TC vortex intensity approaches that of the observed, the balance between the mass and momentum states for WRF-ARW is compromised leading to unrealistic features for the environmental sea-level pressure and deep-layer (800- to 200-hPa) mean wind surrounding the TC. Forcing WRF-ARW to assimilate a TC vortex of the observed maximum wind-speed intensity may ultimately compromise the prediction for the TC's motion and subsequently mitigate any gains for the corresponding intensity prediction.Suggestions for additions to the coupled atmosphere-ocean model include a wave-model (WAVEWATCH3), the assimilation of troposphere thermodynamic observations, and modifications to the existing atmospheric boundary-layer parametrization. The current suite of atmosphere model parametrizations do not accurately simulate the observed azimuthal and radial variations for the exchange coefficients (e.g., drag and enthalpy) that have been indicated as potentialpredictor variables for TC intensity modulation. However, these modifications should be implemented only after the limitations for the current coupled-model and TC vortex initialization methods are fully evaluated.
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.
Pantina, P. (2010). Characterizing the Variability of the Indian Monsoon: Changes in Evaporative Sources for Summertime Rainfall Events . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study focuses on the interannual and intraseasonal variability of evaporative sources for rainfall events during the Indian monsoon. The monsoon is an important part of the economy and lifestyle in India, thus, any improvements in our understanding of its mechanisms would be directly beneficial to society. We first discuss the use of evaporative sources for rainfall events as an important tool to help increase our knowledge of the variations of the monsoon. We then outline the variability of the monsoon on an interannual (wet and dry years) and intraseasonal (active and break periods) time scale. We use three reanalyses (NCEP-R2, CFSR, and MERRA) and an IMD gridded rainfall dataset to trace the location and strength of evaporative sources via a quasi-isentropic back trajectory program. The program uses reanalysis winds and evaporation, among other parameters, to estimate these sources back in time. We discuss the differences in parameters between the datasets on a seasonal, interannual, and intraseasonal time scale. We then thoroughly investigate the strength and location of evaporative sources between datasets on interannual and intraseasonal time scales, and we attempt to explain the variations by analyzing the differences in the input parameters and circulation mechanisms themselves. The study finds that the evaporative sources for given interannual or intraseasonal rainfall events do vary in strength and location. Interannually, the strongest change in evaporative source occurs over central India and the Arabian Sea, suggesting that the overall monsoon flow contributes moisture for Indian rainfall on this time scale. Intraseasonally, the strongest change in evaporative source occurs over the Bay of Bengal, suggesting that low pressure systems contribute moisture for Indian rainfall on this time scale. All three reanalyses yield similar fields of evaporative source. We conclude that accurate prediction of the Indian monsoon requires improved understanding of both interannual and intraseasonal oscillations since the sources of moisture for these events are unique.
Michael, J. - P. (2010). ENSO Fidelity in Two Coupled Models . Master's thesis, Florida State University, Tallahassee, FL.
Abstract: This study examines the fidelity of the ENSO simulation in two coupled model integrations and compares this with available global ocean data assimilation. The two models are CAM-HYCOM coupled model developed by the HYCOM Consortium and CCSM3.0. The difference between the two climate models is in the use of different ocean general circulation model (OGCM). The hybrid isopycnal-sigma-pressure coordinate ocean model Hybrid Coordinate Ocean Model (HYCOM) replaces the ocean model Parallel Ocean Program (POP) of the CCSM3.0. In both, the atmospheric general circulation model (AGCM) Community Atmosphere Model (CAM) is used. In this way the coupled systems are compared in a controlled setting so that the effects of the OGCM may be obtained. Henceforth the two models will be referred to as CAM-HYCOM and CAM-POP respectively. Comparison of 200 years of model output is used discarding the first 100 years to account for spin-up issues. Both models (CAM-HYCOM and CAM-POP) are compared to observational data for duration, intensity, and global impacts of ENSO. Based on the analysis of equatorial SST, thermocline depth, wind stress and precipitation, ENSO in the CAM-HYCOM model is weaker and farther east than observations while CAM-POP is zonal and extends west of the international dateline. CAM-POP also has an erroneous biennial cycle of the equatorial pacific SSTs. The analysis of the subsurface ocean advective terms highlights the problems of the model simulations.
