ropical cyclone-induced tornadoes (TCIT) exacerbate the devastation that landfalling tropical cyclones have on the United States. This research applied machine learning techniques in conjunction with midlatitude severe weather parameters to create an artificial intelligence (AI) capable of predicting TCIT occurrence. Severe weather diagnostic variables were collected at thousands of gridpoints from the North American Regional Reanalysis (NARR) to characterize the environments within tropical cyclones between 1991 and 2011. A support vector machine (SVM) was generated in various configurations to obtain the most effective AI. This approach revealed many parameters that were ineffective at predicting TCITs (primarily those utilizing the effective inflow layer). In addition, the most highly configured AI were capable of predicting TCIT occurrence with a Heidke Skill Score around 0.48.

An association was tested between the presence of a television weather broadcaster on-screen and viewers’ likelihood to seek shelter, measured via risk perception and preventative behavior. Social networking websites were used to recruit respondents. Four clips of archived severe weather videos, one pair (on-screen and off-screen broadcaster) using the reflectivity product and another pair (on-screen and off-screen broadcaster) using velocity product, were presented to participants. Viewers’ trust and weather salience were also quantified for additional interactions. A relationship between viewers’ risk perception (preflectivity = 0.821, pvelocity = 0.625) and preventative behavior (preflectivity = 0.217, pvelocity = 0.236) and the presence of the broadcaster on-screen was not found. The reflectivity product was associated with higher risk perception and preventative behavior scores than the velocity product (prp = 0.000, ppb = 0.000).

The lee shores of the North American Great Lakes are subject to hazardous amounts of snowfall each winter as continental polar air masses are destabilized by the relatively warmer lakes which manifests as pronounced heat and moisture fluxes and subsequent convection and snow generation. This phenomenon, known as lake-effect snow (LES), has been studied by the atmospheric scientific community extensively as the local and mesoscale processes are becoming better understood through the implementation of in situ research projects and high-resolution numerical weather prediction models. However, considerably less research effort has inquired on what large-scale conditions are linked with lake-effect snow. The objective of this dissertation is to develop a more comprehensive understanding of the synoptic-scale conditions associated with lake-effect snowstorms and how they differentiate with non-LES winter storms. Chapter 1 provides a brief introduction to LES and reviews the basic dynamics of LES formation in the form of a comprehensive literature review. Chapter 2 consists of the first synoptic climatologies of lake-effect snowstorms off Lakes Michigan and Superior through statistical analysis of past lake-effect cases off those two lakes. Chapter 3 focuses on developing a synoptic climatology of wintertime cyclonic systems, specifically Alberta Clippers, that traversed the Great Lakes basin but did not result in lake-effect snow formation. Chapter 4 features the development of an objective classification model that differentiates between these two winter weather phenomena by using past LES and non-LES winter storm case repositories to train and test the model. This research effort will focus on wintertime Alberta Clipper systems and LES off Lakes Erie and Ontario. Finally, Chapter 5 reviews the primary results from this research and discusses their significance and implications regarding possible future research.

The climate of Mississippi has been considered to be ideally suited for the production of soybeans. Many studies have shown the influence of weather on crops, but none that specifically attempt to pinpoint which aspects of weather have an impact on the Mississippi soybean crop. The purpose of this study was to determine what affect weather has on the phenological period length and yield of Mississippi soybeans. Data on bean phenological periods and yield used in this research were recorded as a part of soybean variety testing conducted at Delta Research and Extension Center in Stoneville, Mississippi from 1976-2000. The major statistical procedure used in this study was Pearson?s Correlation Analysis that was used to determine any significant relationships between soybeans and weather. This study found that cooler temperatures and more moist conditions positively influence yield in Mississippi soybeans, while energy is more important in determining period lengths than water.

Alabama, also known as the Dixie state, is no stranger to severe weather. Severe weather can occur during much of the year. Experienced local forecasters have long suspected that North and Central Alabama has its own tornado alley. Many of these forecasters have noticed storm tracks as well as tornado tracks to be similar to past historic events. Many questions have risen about the exact influential factors that cause convective initiation and tornadic development. For example the effects of terrain, water, and population on tornado climatology will be discussed in this study. The sometimes unreliable climatology of tornadoes will be addressed as well as the history of storm reporting. Tornado clusters were found and further explained regarding relationships with terrain, water, and population. Through this research, it is concluded that there are two distinct tornado regions that exist in North and Central Alabama.

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No description available.

<p>  </p> <p>Rapid intensification of low-level rotation in non-classic tornadic storms in southeastern United States, often at time scales shorter than the volume updates from existing opera- tional radars, calls for a deeper understanding of storm-scale processes. There is growing evidence that the highly nonlinear interactions between vertical wind shear and cold pools regulate the intensity of downdrafts, low- and mid-level updrafts, and thus tornadic poten- tial in supercells. Tornado-strength circulations are more likely associated with cold pools of intermediate strength. The microphysical pathway leading to storm electrification also plays a major role in the regulation of cold pool intensity. Storm electrification and subsequent lightning initiation are a by-product of charging of ice hydrometeors in the mixed-phase updrafts. Lightning flashes frequently initiate along the periphery of turbulent updrafts and total flash rate is controlled by the updraft speed and volume.</p> <p><br></p> <p>In the first part of this work, polarimetric fingerprints like ZDR and KDP columns (proxies for mixed-phase updraft strength) are objectively identified to track rapid fluctuations in updraft intensity. We quantify the volume of ZDR and KDP columns to evaluate their utility in predicting temporal variability in lightning flash characteristics and the onset of severe weather. Using observational data from KTLX radar and Oklahoma Lightning Mapping Array, we had previously found evidence of temporal covariance between ZDR column volume and the total lightning flash rate in a tornadic supercell in Oklahoma. </p> <p><br></p> <p> Here, we extend our analysis to three high-shear low-CAPE (HSLC) cases observed during the 2016-17 VORTEX-SE field campaign in Northern Alabama. In all three scenarios (one tornadic and one nontornadic supercell, and a quasi-linear convective system), the KDP column volume had a stronger correlation with total flash rates than the ZDR column volume. We also found that all three storms maintained a normal tripole charge structure, with majority of the cloud-to-ground (CG) strikes lowering negative charge to the ground. The tornadic storm’s CG polarity changed from negative to positive at the same time it entered a region with higher surface equivalent potential temperature. In contrast to the Oklahoma storm, lightning flash initiations in HSLC storms occurred primarily outside the footprint of ZDR and KDP column objects.</p> <p><br></p> <p>Storm dynamics coupled with microphysical processes such as diabatic heating/cooling and advection/sedimentation of hydrometeors also plays a significant role in electrification of thunderstorms. Simulation of deep convection, therefore, needs to account for the feedback of microphysics to storm dynamics. In the second part of this work, the NSSL microphysics scheme is used to simulate ice mass fluxes, cold pool intensity, and noninductive charging rates. The scheme is run in its triple-moment configuration in order to provide a more realis- tic size-sorting process that avoids pathologies that arise in double-moment representations.</p> <p><br></p> <p>We examine the possible tertiary linkage between noninductive charging rates and cold pool through their dependence on mixed-phase microphysical processes. The Advanced Re- gional Prediction System (ARPS) model is used to simulate the same three HSLC cases from VORTEX-SE 2016-17 IOPs. WSR-88D radar reflectivity and Doppler velocity observations are assimilated in a 40-member ensemble using an ensemble Kalman filter (EnKF) filter.</p> <p><br></p> <p>In all three cases, the simulated charge separation is consistent with the observed normal tripole. Greater updraft mass flux, supercooled liquid water concentration, and nonprecip- itation mass flux explain the nontornadic supercell’s higher total flash rate compared to the tornadic supercell. Positive and negative graupel charging rates were found to have the greatest linear correlation with updraft mass flux, followed by precipitation mass flux in all three cases. At zero time lag, horizontal buoyancy gradients associated with a surface cold pool were not found to be correlated with either the charging rates or the updraft and precipitation mass flux. Total flash rate based on empirical relationships between simulated ice mass fluxes was lower than the observed values.</p>

In this thesis we study measures of verification, accuracy determination, and implementation of limited-area research and operational weather prediction models. We give a complete description of the implementation of the multivariate autoregressive research model. This includes a FORTRAN program and three order selection routines. We conclude with a discussion on possible extensions of our results and other applications. / M.S.

L’objectif principal de cette thèse est de s’intéresser aux fortes pluies dans les Monts Métallifères (OM) et les Vosges (VG) en Europe centrale. La méthode Weather Extremity Index a été appliquée sur les données de précipitation journalière de 167 stations dans les OM et 168 stations dans les VG et a permis de sélectionner les 54 plus forts évenements des précipitations extrêmes (EPEs) dans OM et VG. Plusieurs aspects des EPEs ont été examinés. Les résultats ont montré que les EPEs sont le plus souvent de courte durée (1—2 jours) dans les deux régions. Ils affectent une plus grande partie des OM que des VG. Les EPEs dans les VG apparaissent majoritairement lors de la situation synoptique d’un front froid ondulant ; dans les OM lors des cyclones générés par une goutte d’air froid isolé et dont le trajet est souvent qualifié de « Vb » (c.a.d. allant de la Méditerranée vers le nord-est). Toutefois deux des dix plus forts EPEs des VG sont apparus lors de situations de cyclones Vb. / The thesis focuses on extreme precipitation in the Ore Mountains (OM) and the Vosges Mountains (VG) in Central Europe. The Weather Extremity Index (WEI) was employed on daily precipitation totals from 167 stations in OM and 168 stations in VG. The WEI enabled to select the 54 strongest extreme precipitation events (EPEs) in OM and VG. Many characteristics of the EPEs were investigated in the thesis. The results showed that the EPEs lasted mostly 1—2 days in both regions, whereas affected a larger part of OM as compared to VG. Stationary fronts occurred most frequently during EPEs in VG, while lows in OM. Lows in OM during EPEs often originated from cold air cut-off and most of them had Vb track from Mediterranean towards the northeast. Even during two of the ten strongest EPEs in VG, the extreme precipitation was related to Vb lows, this time strongly deflected westwards.