The Atlantic Warm Pool (AWP) is located in the western tropical North Atlantic (TNA) and the Intra-Americas Sea (IAS) (the Gulf of Mexico and the Caribbean Sea). The AWP is an area of warm sea surface temperatures (SSTs) greater than 28.5°C that exhibits a strong seasonal cycle, with the AWP disappearing in boreal winter (December-January-February) and reaching a maximum in late boreal summer (August-September-October (ASO)). In addition, the AWP exhibits interannual variability, with large AWPs nearly three times larger in area than small AWPs. Because there is a lack of surface and subsurface observations in the IAS and the TNA, this study uses reanalysis products to examine the AWP. It is of interest to ask what is the quality of these reanalysis products in their examination of the AWP. Thus, the main objective of this study is to examine and intercompare different reanalysis' renditions of the AWP. Three ocean reanalysis products are examined in this study and they are the following: (1) the Global Ocean Data Assimilation System (GODAS), (2) the Climate Forecast System Reanalysis (CFSR), and (3) the Simple Ocean Data Assimilation (SODA). In addition, because GODAS and SODA are not coupled to the atmosphere whereas CFSR is, two atmospheric reanalysis products are also used and they are the following: (1) the NCEP–National Center for Atmospheric Research (NCAR) reanalysis (hereafter R1), and (2) the NCEP–Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis (hereafter R2). R1 is used in conjunction with SODA, and R2 is used in conjunction with GODAS. The period of interest for this study is 1980−2006. Each ocean reanalysis is used to examine the annual and interannual variability of the AWP in ASO. In addition, GODAS with R2, CFSR, and SODA with R1 are used to perform a detailed analysis of the SST tendency equation of the AWP. Furthermore, GODAS with R2, CFSR, and SODA with R1 are used to examine the impact of the AWP on the tropical tropospheric temperature and see if the impact is similar to the impact seen by the El Niño Southern Oscillation (ENSO). All three ocean reanalyses depict similar annual variability of the AWP. They all exhibit the AWP to be non-existent in boreal winter (December-January-February) and at a maximum in boreal summer. However, the reanalyses are inconsistent in their initial appearance of the AWP. All three ocean reanalyses depict similar interannual variability of the AWP; however, they are inconsistent in the relationship between the AWP area and subsurface temperatures. From the SST tendency equation, the fluxes contribute more to the AWP SST tendency in GODAS and CFSR, and the fluxes act to remove heat from the AWP. In SODA, the advective terms contribute more to the AWP SST tendency. Additionally, the reanalyses show that the AWP warms the tropical troposphere contemporaneously (ASO) but not three to six months after, as in the case with ENSO. / A Thesis Submitted to the the Department of Earth, Ocean and Atmospheric Science in Partial Fulfillment of the Requirements for the Degree of Masters of Science. / Summer Semester, 2011. / July 5, 2011. / Wang, lead-lag, Contemporaneously, Correlations, Tendency / Includes bibliographical references. / Vasubandhu Misra, Professor Directing Thesis; Robert Hart, Committee Member; Mark Bourassa, Committee Member.

Past binary logistic regression (BLR) and perfect prognosis schemes have shown skill for predicting the probability of one or more cloud-to-ground (CG) lightning flashes for regions such as Florida. This study examines the ability of the BLR and perfect prog techniques to forecast only those flashes at the daily onset of warm season convective activity. A statistical model is developed for two domains in the western United States in the vicinities of Amarillo, Texas and Albuquerque, New Mexico, to predict the probability of one or more CG flashes during the 1800 UTC to 2100 UTC time window. Warm season convection in these locales is influenced by factors such as the Southwest Monsoon, drylines, and topography. Each domain consisted of a 28 × 26 grid at a 10-km resolution. One equation for each domain was developed using 10-year climatology and predictors from the North American Regional Reanalysis (NARR) dataset for the dependent period May through September of 1994 – 2004. In our perfect prog scheme, the reanalysis data were treated as observations. Forecasts were made for the 2005 warm season, and evaluated using the Brier Score, Brier Skill Score, and reliability diagrams. Due to the relative rarity of the event being forecast, forecast probabilities tended to be small, rarely exceeding the 30% range in the Amarillo domain and 70% (with a majority below 50%) in the Albuquerque domain. Both domains typically exhibited skill over climatology and good reliability for low forecast probabilities, which constituted a majority of the forecasts. Sample forecasts for both domains are examined. The Amarillo model demonstrated competent performance on 10 June 2005, but poor performance on 1 September 2005. The Albuquerque model performed well on 1 September 2005, but performed poorly on 17 July 2005. Despite the low magnitude of probabilities forecast, both models showed promise, commonly placing their relative maxima in the vicinity of the verifying initiating flashes, with some obvious exceptions. / A Thesis Submitted to the Department of Earth, Ocean and Atmospheric Science in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Summer Semester, 2011. / August 24, 2007. / Statistical Modeling, Forecasting, Lightning / Includes bibliographical references. / Henry E. Fuelberg, Professor Directing Thesis; Jon Ahlquist, Committee Member; Paul Ruscher, Committee Member.

Particle-laden buoyant jets (PBJ) are free-convective flows that contain particles. Many studies have examined pure thermal plumes, discussing a symmetric self-similar structure that is common in the plume literature. Other studies have studied the fallout properties of particles embedded in plumes. Many contaminant transport models use a Gaussian, self-similar plume profile and treat particles only as a passive tracer. There is however no evidence in the literature of the simultaneous examination of both particle loading and temperature on the flow regimes of a PBJ. Twenty-five numerical simulations were implemented using the LANL HIGRAD numerical model, to systematically study the resulting flow regimes as the temperature and particle loading of a PBJ were systematically varied. A supplemental experiment was carried out in the laboratory, with three simulations carried out that were dynamically similar to the experiment. The comparison between experiment and simulation indicated that the model did not create turbulence as close to the domain floor as in the experiment, and further tuning is required. With awareness of this inconsistency, the twenty-five simulation results produced results of significant interest. Only two of the twenty-five simulation results produced the self-similar, Gaussian profile that is observed in the plume literature. Four of the twenty-five cases produced a no collapse PBJ scenario. The remaining cases produced either a partial collapse or full collapse of the PBJ. The use of the Grashof and buoyant Richardson numbers to characterize the flow regimes did not produce consistency with the resulting flow regime. A modified multiphase Richardson number however, accounting for particle density within a control volume, showed consistency with the resulting flow regime and indicates promise for further research and perhaps operational use. PBJ are physically relevant to a variety of particle-laden convective flows including volcanoes, industrial emissions, and contaminant transport. These results are of interest to a number of stakeholders including the DOE, DOD, DHS, and emergency management communities. / A Dissertation Submitted to the Department of Meteorology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. / Summer Semester, 2010. / April 29, 2010. / Particles, Plumes, Buoyant Jets, Convective, HIGRAD / Includes bibliographical references. / Paul Ruscher, Professor Directing Dissertation; James B. Elsner, University Representative; Carol Anne Clayson, Committee Member; Henry E. Fuelberg, Committee Member; Guosheng Liu, Committee Member.

Coherent Tropopause Disturbances (CTD's) have been observed to play a central role in both extratropical cyclogenesis and the dynamics of jet streaks. Recent observational studies have quantified the existence of CTD's by locating potential temperature minima on a constant potential vorticity surface that is representative of the dynamic tropopause. This study applies a similar approach, but to a data set from a high-resolution global model (the Community Climate Model, Version 3). Application of an objective identification and tracking algorithm, along with several statistical calculations involving potential temperature on the 2.0 PVU surface, with statistics of outgoing longwave radiation, indicate that the CCM3 is a tool that may yield valuable insight into the dynamical interactions that determine the origin, growth and lifecycles of CTD's. / A Thesis Submitted to the Department of Meteorology in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Spring Semester, 2006. / March 28, 2006. / CTD, Coherent Tropopause Disturbances, Diabatic Potential Vorticity, Jet Amplification, Jet Dynamics, Tropopause, Potential Vorticity, Vortex / Includes bibliographical references. / Philip Cunningham, Professor Directing Thesis; Robert Hart, Committee Member; T. N. Krishnamurti, Committee Member; Paul Reasor, Committee Member.

This study uses the high-resolution infrared radiation AVHRR (Advanced Very High Resolution Radiometer)-only and microwave radiation AMSR (Advanced Microwave Scanning Radiometer)+AVHRR sea surface temperature (SST) datasets to analyze and compare non-Gaussian statistics and extreme events for SSTs. Since the primary difference between the two datasets is the lack of AVHRR data in regions of cloud cover, higher correlations between the datasets are expected in areas of low percent daily-averaged total cloud cover. These are regions where both sensors usually are capable of detecting SSTs and do not rely on the process of optimum interpolation to fill missing data. Probability density functions, skewness, kurtosis, autocorrelation time scale, and standard errors are used to reveal non-Gaussianity (i.e., statistically extreme events) in the datasets, while the correlation coefficient between the datasets is used to explore extreme events beyond a certain threshold. Non-Gaussianity is present in both SST datasets, and the highest correlations of extreme events between the datasets were within positive anomalies above a certain threshold for regions of low percent daily-averaged total cloud cover. / A Thesis Submitted to the Department of Earth, Ocean and Atmospheric Science in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Fall Semester, 2010. / August 3, 2010. / extreme events, non-Gaussian, skewness, kurtosis, SST, AVHRR, AMSR / Includes bibliographical references. / Philip Sura, Professor Directing Thesis; Carol Anne Clayson, Committee Member; Sharon Nicholson, Committee Member.

We will describe the development of a high-resolution, gridded forecast guidance product for warm season cloud-to-ground (CG) lightning in Florida. Four warm seasons of analysis data from the 20-km Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used to examine relationships between observed atmospheric parameters and the spatial and temporal patterns of CG lightning over Florida. The most important RUC-derived parameters then are used in a perfect prognosis (PP) technique to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial (NB) model is used to predict the amount of lightning, conditional on one or more flashes occurring. When applied to the dependent sample of RUC analyses, the equations show forecast skill over a model containing only persistence and climatology (L-CLIPER). We also evaluate the lightning forecast scheme when applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from NCEP's 13-km RUC, the NCEP 12-km NAM-WRF, and local runs of WRF for a domain over South Florida that were initialized with NCEP 1/12th degree sea-surface temperatures (SST) and data from the Local Analysis and Prediction System (LAPS) (WRF-LAPS). During the most active lightning period (1800-2059 UTC), the three models forecast between 80-90% of the lightning events having one or more flashes, and between 30-60% of the events with flash counts meeting or exceeding the 95th percentile. Of the three mesoscale models, WRF-LAPS generally produces the best verification scores during 1800-2059 UTC. Forecasts from all three mesoscale models generally show positive skill with respect to L-CLIPER and persistence through the 2100-2359 UTC period, demonstrating that the PP scheme is model independent. Although the exact timing and placement of forecast lightning is not perfect, there generally is good agreement between the forecasts and their verification, with most of the observed lightning occurring within the higher forecast probability contours. / A Dissertation Submitted to the Department of Meteorology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. / Spring Semester, 2007. / March 22, 2007. / Weather Research, Rapid Update Cycle, Negative Binomial, Logistic Regression, Perfect Prognosis, Lightning Forecasting, Forecasting Model / Includes bibliographical references. / Henry E. Fuelberg, Professor Directing Dissertation; James B. Elsner, Outside Committee Member; Paul H. Ruscher, Committee Member; Jon E. Ahlquist, Committee Member; Robert Hart, Committee Member.

Anthropogenic and biomass burning emissions impact atmospheric chemistry and many other natural processes that affect air quality and human health. Biomass burning emissions released in the boundary layer can be quickly lofted to the free troposphere by deep convection. Accurately simulating this process in chemical transport models (CTMs) will improve our understanding of the link between local pollution sources and global scale transport. This study investigated the convective transport of biomass burning emissions during the summer phase of NASA's Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) campaign using the Weather Research and Forecasting (WRF) model with chemistry (WRF-Chem). Three cumulus parameterization schemes were tested to identify which performs best: Kain-Fritsch (KF), Betts-Miller-Janjic (BMJ), and Grell-Devenyi (GD). To test the cumulus parameterizations, simulated meteorological parameters were quantitatively compared against point observations, and daily precipitation fields were compared against the Global Precipitation Climatology Project (GPCP) dataset using the Method for Object-Based Diagnostic Evaluation (MODE), an object-based verification tool. CO vertical mass fluxes were evaluated at various altitudes and times during the simulation period. Daily averaged total column CO and mixing ratios at three altitudes were quantitatively compared against daily averaged values from the Atmospheric InfraRed Sounder (AIRS) using MODE. Results show that the choice of cumulus parameterization is critical when simulating the convective transport of biomass burning emissions using WRF-Chem. Although spatial differences are not great at most individual times, they accumulate over time leading to large magnitude differences in precipitation, upward CO mass flux, and long-range CO plume transport. The KF cumulus parameterization scheme vertically transports more CO than the BMJ and GD schemes, and outperforms the other schemes when compared to GPCP and AIRS dataset. In situations similar to this study, not using KF cumulus parameterization may underestimate the convective transport of CO and subsequently its long-range transport. The current results demonstrate that the choice of a cumulus parameterization scheme in a CTM can affect many aspects of its output. / A Thesis Submitted to the Department of Earth, Ocean, and Atmospheric Science in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Spring Semester, 2011. / March 10, 2011. / Numerical weather prediction, Cumulus parameterization, Pollution transport, Forecast verification / Includes bibliographical references. / Henry Fuelberg, Professor Directing Thesis; Guosheng Liu, Committee Member; Vasubandhu Misra, Committee Member.

Fourteen years of cloud-to-ground lightning data from the National Lightning Detection Network, and radiosonde releases from Miami and West Palm Beach, are used to develop statistical guidance equations that forecast the amount of warm season afternoon and evening lightning that is expected over two areas of South Florida that are serviced by Florida Power and Light Corporation (FP&L)-- the eastern halves of Miami-Dade and Broward Counties. A total of 54 parameters are calculated from the soundings to serve as candidate predictors for the equations. These include parameters that describe wind direction and speed in various layers, moisture, temperature, and stability. Day number, persistence, and same day morning lightning also are included as potential predictors of afternoon lightning. A variety of statistical modeling techniques is attempted initially, but many are found to be inappropriate. The best results are obtained by creating four quartile groups of flash count based on climatology, and then using binary logistic regression to develop three prediction equations for each domain, one giving the conditional probability of a quartile one (Q1) lightning event, another for the probability of a quartile three (Q3) or greater event, and a third equation giving the probability of a quartile four (Q4) lightning event. Principal component analysis is used to select a subset of non-redundant predictors that have the greatest physical relevance to convection and lightning in South Florida. The final candidate sounding predictors are the vector mean 1000-700 hPa cross-shore wind component and speed, the K-index, modified Lifted Index, and the temperature at 900 hPa. Non-linear effects are considered by including second, third, and fourth order terms as additional candidate predictors. A combination of stepwise screening and cross-validation is used to select the variables that best generalize to independent data. To determine the most likely quartile of lightning activity, a decision tree scheme is constructed using probability thresholds for the three equations. Finally, the resulting prediction schemes are tested independently using k-fold cross-validation. The dominant effect in each of the equations is the component of the wind perpendicular to the coastline which is found to have a significant non-linear relationship with lightning activity. Other important variables are the K-index and modified Lifted Index. Day number, persistence, and same day morning activity also are selected as important indicators of afternoon lightning in the two domains. When each year is treated independently, the Miami-Dade scheme correctly forecasts the quartile ~ 37% of the time and is correct to within one quartile of the observed ~ 79% of the time. The scheme for eastern Broward County forecasts the correct quartile ~ 36% of the time and is correct to within one quartile ~ 77% of the time. The prediction schemes generally are superior to persistence and climatology for both the dependent data and during k-fold cross-validation. Thus, they possess real forecast skill. For example, when forecasting the correct quartile, these results are a ~ 4-6 percentage point improvement over persistence, and ~ 11-12 percentage point improvement over climatology. In terms of correctly predicting to within one quartile of the observed, the two schemes are an improvement over persistence by ~ 6-8 percentage points and over climatology by ~ 14-17 percentage points. Further analysis shows that the two schemes rarely forecast the upper two quartiles when no activity is observed. Additionally, correct predictions of Q4 events are shown to increase with flash count within the Q4 category. Overall, the cross-validation results show only a 1-2% reduction in skill from what is obtained for the fourteen years of dependent data, demonstrating that the two schemes are statistically robust, and can be expected to achieve similar results when implemented operationally. / A Thesis Submitted to the Department of Meteorology in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Summer Semester, 2004. / June 22, 2004. / South Florida, lightning climatology, statistical forecasting, logistic regression / Includes bibliographical references. / Henry E. Fuelberg, Professor Directing Thesis; Jon Ahlquist, Committee Member; Paul Ruscher, Committee Member; Andrew I. Watson, Committee Member.

The Weather Research and Forecasting Model (WRF) was developed by the National Center for Atmospheric Research as the next generation mesoscale meteorology model. The inclusion of a chemistry module (WRF-Chem) allows transport simulations of chemical and aerosol species such as those observed during NASA's Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) during 2008. The ARCTAS summer deployment phase during June and July coincided with large boreal wildfires in Saskatchewan and Eastern Russia. One of the most important aspects of simulating wildfire plume transport is the height at which emissions are injected. WRF-Chem contains an integrated one-dimensional plume rise model to determine the appropriate injection layer. The plume rise model accounts for thermal buoyancy associated with fires and the local meteorological stability. This study compares results from the plume model against those of more traditional injection methods such as filling the planetary boundary layer or a layer 3-5 km above ground level (AGL). Fire locations are satellite-derived from the GOES Wildfire Automated Biomass Burning Algorithm (WF_ABBA) and the MODIS thermal hotspot detection. Two preprocessing methods for these fires are compared: the prep_chem_sources method included with WRF-Chem, and the Naval Research Laboratory's Fire Locating and Monitoring of Burning Emissions (FLAMBE). Satellite products from the AIRS, MISR and CALIOP sensors provide data for verifying the simulations. Observed near-source plume heights from MISR's stereo-height product are compared with the plume rise model's simulated injection heights. Long range plume transport is evaluated qualitatively in the horizontal using AIRS's total column carbon monoxide product. Qualitative vertical evaluation uses CALIOP's high vertical resolution and aerosol identification algorithm. Horizontal plume structures are further tested quantitatively using an object-based methodology. The plume rise model produces the best agreement with satellite-observed injection heights. Filling the planetary boundary layer or the 3-5 km AGL layer with emissions exhibit less agreement with the observational plume heights. Results indicate that WRF-Chem can accurately transport chemical plumes throughout the ten-day simulation. However, differences in injection heights produce different transport pathways. Small differences in injection height are ameliorated when synoptic scale features such as warm conveyor belts quickly loft the emissions to higher altitudes. In scenarios where large scale lofting is delayed, the plume rise simulations creates the most accurate simulated plumes. / A Thesis Submitted to the Department of Meteorology in Partial Fulfillment of the Requirements for the Degree of Master of Science. / Summer Semester, 2010. / April 15, 2010. / Numerical Weather Prediction, ARCTAS / Includes bibliographical references. / Henry Fuelberg, Professor Directing Thesis; Guosheng Liu, Committee Member; Robert Hart, Committee Member.

The role of mesovortices in the eyewalls of sheared unstable, high-Rossby number vortices is investigated. A high-resolution numerical model is used to simulate dry vortices in an attempt to unite ideas from previous works. The simulations are used to investigate the dynamical, adiabatic interactions between potential vorticity (PV) mixing dynamics and shear forcings of barotropically unstable, high-Rossby number barotropic vortices. Previous work has investigated barotropic vortices in shear, while other previous work has studied barotropically unstable ring vortices. This work will combine those two avenues of research by shearing barotropically unstable barotropic ring vortices because ring vortices are more representative of tropical cyclones. Quantitative and qualitative analysis of the tilt and of the internal dynamics are presented. Using such as metrics as PV power spectra, PV palinstrophy, and a linear energy equation that incorporates the effects of the shear forcing, it is found that impact of the shear forcing on the initial breakdown of the ring is merely slight; however, the breakdown of the ring of high PV into smaller mesovortices – and the subsequent rearrangement of PV into a monopolar structure – is quite significant when considering the tilt evolution. As the vortex mixes, the storm weakens. This acts as a detriment to the ability of the vortex to keep itself upright and resistant to the shear forcing, as the penetration depth of each layer of the vortex decreases to below the scale height after mixing. In terms of the energetics, it is found that the barotropic energy conversion term is consistently the largest, which is expected. When sheared, the shear forcing acts to generally counteract the effects of mixing and reduce eddy kinetic energy. Additionally, it is found that the shear forcing induces a trochoidal oscillation at levels of highest background flow. The sensitivity of the results is investigated by comparing and contrasting two different centroid metrics - a pressure-ring centroid and a PV-cubed centroid. PV centroid metrics historically have been used to investigate inner-core tilt while geopotential centroid metrics have been to used to investigate larger-scale tilt. For the first time, these two approaches are being compared and contrasted. It is found that during a dynamical mixing event, the PV centroid is not very resistant to the rapidly changing inner-core PV field, and that this has nontrivial effects on the calculations of center-sensitive fields such as Fourier decompositions in the azimuth and determining radial and tangential wind structures. When using a pressure-ring centroid centeredon a pressure contour that resides far enough outside the core yet radially inward enough not to be impacted by the environment, it is found that this method is much more resistant to inner core processes. / A Dissertation Submitted to the Department of Earth, Oceanic, and Atmospheric Science in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy. / Spring Semester, 2011. / March 22, 2011. / dynamics, hurricane, adiabatic, vortex in shear / Includes bibliographical references. / Robert Hart, Professor Directing Dissertation; T. N. Krishnamurti, Professor Co-Directing Dissertation; William Dewar, University Representative; Vasu Misra, Committee Member; Robert Ellingson, Committee Member.