Recent improvements in numerical weather model resolution open the possibility of producing forecasts for lightning using indirect lightning threat indicators well in advance of an event. This research examines the feasibility of a statistical machine-learning algorithm known as a support vector machine (SVM) to provide a probabilistic lightning forecast for Mississippi at 9 km resolution up to one day in advance of a thunderstorm event. Although the results indicate that SVM forecasts are not consistently accurate with single-day lightning forecasts, the SVM performs skillfully on a data set consisting of many forecast days. It is plausible that errors by the numerical forecast model are responsible for the poorer performance of the SVM with individual forecasts. More research needs to be conducted into the possibility of using SVM for lightning prediction with input data sets from a variety of numerical weather models.

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The work presented in this dissertation is driven by three fundamental questions in climate science: (1) What is the natural variability of our climate system? (2) What components of this variability are predictable? (3) How does climate change affect variability and predictability? Determining the variability and predictability of the chaotic and nonlinear climate system is an inherently challenging problem. Climate scientists face the additional complications from limited and error-filled observational data of the true climate system and imperfect dynamical climate models used to simulate the climate system. This dissertation contains four chapters, each of which explores at least one of the three fundamental questions by providing novel approaches to address the complications. Chapter 1 examines the uncertainty in the observational record. As surface temperature data is among the highest quality historical records of the Earth’s climate, it is a critical source of information about the natural variability and forced response of the climate system. However, there is still uncertainty in global and regional mean temperature series due to limited and inaccurate measurements. This chapter provides an assessment of the global and regional uncertainty in temperature from 1880-present in the NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP). Chapter 2 extends the work of Chapter 1 to the regional spatial scale and monthly time scale. An observational uncertainty ensemble of historical global surface temperature is provided for easy use in future studies. Two applications of this uncertainty ensemble are discussed. First, an analysis of recent global and Arctic warming shows that the Arctic is warming four times faster than the rest of the global, updating the oft-provided statistic that Arctic warming is double that of the global rate. Second, the regional uncertainty product is used to provide uncertainty on country-level temperature change estimates from 1950-present. Chapter 3 investigates the impacts of the El Niño-Southern Oscillation (ENSO) on seasonal precipitation globally. In this study, novel methodology is developed to detect ENSO-precipitation teleconnections while accounting for missing data in the CRU TS historical precipitation dataset. In addition, the predictability of seasonal precipitation is assessed through simple empirical forecasts derived from the historical impacts. These simple forecasts provide significant skill over climatological forecasts for much of the globe, suggesting accurate predictions of ENSO immediately provide skillful forecasts of precipitation for many regions. Chapter 4 explores the role of initialization shock in long-lead ENSO forecasts. Initialized predictions from the CMIP6 decadal prediction project and uninitialized predictions using an analogue prediction method are compared to assess the role of model biases in climatology and variability on long-lead ENSO predictability. Comparable probabilistic skill is found in the first year between the model-analogs and the initialized dynamical forecasts, but the initialized dynamical forecasts generally show higher skill. The presence of skill in the initialized dynamical forecasts in spite of large initialization shocks suggest that initialization of the subsurface ocean may be a key component of multi-year ENSO skill. Chapter 5 brings together ideas from the previous chapters through an attribution of historical temperature variability to various anthropogenic and natural sources of variability. The radiative forcing due to greenhouse gas emissions is necessary to explain the observed variability in temperature nearly everywhere on the land surface. Regional fingerprints of anthropogenic aerosols are detected as well as the impact of major sources of natural variability such as ENSO and Atlantic Multidecadal Variability (AMV).

Between 2011-2021, violent tornadoes accounted for an average of 65% of all tornado-related fatalities. The Violent Tornado Parameter (VTP), created in 2018, attempts to address this forecast problem but has issues with false alarms. Storm Relative Helicity has historically been used in tornado forecasting. Recent studies have shown the 0-500 m effective layer SRH (ESRH) has skill in discerning significantly tornadic events from those that are not. This study explored the effects of incorporating 0-500 m ESRH into the VTP and issues relating to the parameter’s false alarm rate by examining RUC/RAP forecast soundings for 302 U.S. tornadic events (83 violent, 122 strong, 97 weak) from 2011 to 2020, along with test data from 2021. Overall, the study found that 0-500 m ESRH has skill in forecasting violent tornadoes, and that both the 0-3 km MLCAPE and 0-3 km Lapse Rate terms raised the parameter’s false alarm rate.

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Optimal operation of water reclamation facilities (WRFs) is critical for an indirect potable reuse (IPR) system, especially when the reclaimed water constitutes a major portion of the reservoir's safe yield. It requires timely and informed decision-making in response to the fluctuating operational conditions, e.g., weather patterns, plant performance, water demand, etc. Advanced integrated modeling techniques can be used to develop reliable operational strategies to mitigate future risks associated with water quality without needing high levels of financial investment. The Upper Occoquan Service Authority (UOSA) WRF, located in northern Virginia, discharges nitrified reclaimed water directly into a tributary of the Occoquan Reservoir, one of the major water supply sources for Fairfax County. Among the many operational challenges at UOSA, one is to regulate the nitrate concentration in its reclaimed water based on the denitrifying capacity of the reservoir. This study presents an integrated model that is used to predict future reservoir conditions based on the weather and streamflow forecasts obtained from the Climate Forecast System and the National Water Model. The application captures the dynamic transformations of the pollutant loadings in the streams, withdrawals by the water treatment plant, WRF effluent flows, and plant operations to manage the WRF performance. It provides plant operators with useful feedback for correctly targeting the effluent nitrates using an intelligent process simulator called IViewOps. The platform is powered by URUNME, a new software that fully automates the operation of the reservoir and process models integrating forecasting products, and data sources. URUNME was developed in C#.NET to provide out-of-the-box functionality for model coupling, data storage, analysis, visualization, scenario management, and decision support systems. The software automatically runs the entire integrated model and outputs data on user-friendly dashboards, displaying historical and forecasting trends, on a periodic basis. This decision support system can provide stakeholders with a holistic view for the design, planning, risk assessments, and potential improvements in various components of the water supply chain, not just for the Occoquan but for any reservoir augmentation type IPR system. / Doctor of Philosophy / In an indirect potable reuse (IPR) system, reclaimed water from an advanced wastewater treatment facility is blended with a natural water source, such as a reservoir, to augment drinking water supply. Reliable operation of such a system is critical, especially when the reclaimed water constitutes a major portion of the withdrawals from the reservoir for treatment and distribution. One example of such an IPR system is the Upper Occoquan Service Authority (UOSA) water reclamation facility (WRF) which discharges its reclaimed water into the Occoquan Reservoir, a key water resource for Fairfax County. Integrated environmental modeling (IEM) provides a comprehensive approach towards the design and operation of water resource systems in which water supply, drainage, and sanitation are simulated as a single entity rather than independent units. In IEM, different standalone models, each representing a single subsystem, are linked together to analyze the complex interactions between various components of the system. This approach can be used for developing operational support tools for an IPR system to ensure timely and informed decision-making in response to the fluctuating conditions, e.g., weather patterns, plant performance, water demand, etc. The overarching goal of this research was to integrate different models and the data sources and develop a decision support system (DSS) to manage the UOSA-WRF performance. This resulting integrated model is used to predict future reservoir conditions based on the weather and streamflow forecasts obtained from the National Weather Service. The application runs various future scenarios to capture the possible variations of the pollutant loadings in the streams, withdrawals by the water treatment plant, WRF effluent flows, and plant operations and provide feedback to plant operators. The entire integrated model is operated periodically to output data on user-friendly dashboards, displaying historical and forecasting trends. The DSS provides stakeholders with a holistic view for the design, planning, risk assessments, and potential improvements in various components of the water supply chain, not just for the Occoquan but for any reservoir augmentation type IPR system.

This thesis offers a new contextualisation of weather signs, naturally occurring terrestrial indicators of weather change (from, for example, animals, plants and atmospheric phenomena), in antiquity. It asks how the utility of this method of prediction was perceived and presented in ancient sources and studies the range of answers given across almost eight hundred years of Greek and Roman civilisation. The presentation of weather signs is compared throughout to that of another predictive method, astrometeorology, which uses the movement of the stars as markers of approaching weather. The first chapter deals with the presentation and discussion of weather signs in a range of Greek texts. It sees hesitant trust being placed in weather signs, lists of which were constructed so as to be underpinned by astronomical knowledge. The second chapter assesses how these Greek lists were received and assimilated into Roman intellectual discourse by looking to the strikingly similar practice of divining by portents. This lays the foundations for the final chapter, which describes and explains the Roman treatment of weather signs. Here, the perceived utility of weather signs can be seen to reduce rapidly as the cultural significance of astronomy reaches new heights. This thesis provides new readings and interpretations of a range of weather-based passages and texts, from the Pseudo-Theophrastan De Signis, to Lucan's Pharsalia, to Pliny's Natural History, many of which have previously been greatly understudied or oversimplified. It allows us to understand the social and scientific place of weather prediction in the ancient world and therefore how abstract and elaborate ideas and theories filtered in to the seemingly commonplace and everyday. I argue that between the 7th century BC and the end of the 1st century AD, the treatment of weather signs changes from being framed in fundamentally practical terms to one in which practical considerations were negligible or absent. As this occurred, astrometeorology comes to be seen as the only predictive method worthy of detailed attention. These two processes, I suggest, were linked.

The emergence of the exascale era necessitates development of new techniques to efficiently perform high-performance scientific simulations, online data analysis and on-the-fly visualization. Critical applications like cyclone tracking and earthquake modeling require high-fidelity and high- performance simulations involving large-scale computations and generate huge amounts of data. Faster simulations and simultaneous online data analysis and visualization enable scientists provide real-time guidance to policy makers. In this thesis, we present a set of techniques for efficient high-fidelity simulations, online data analysis and visualization in environments with varying resource configurations. First, we present a strategy for improving throughput of weather simulations with multiple regions of interest. We propose parallel execution of these nested simulations based on partitioning the 2D process grid into disjoint rectangular regions associated with each subdomain. The process grid partitioning is obtained from a Huffman tree which is constructed from the relative execution times of the subdomains. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. We observe up to 33% gain over the default strategy in weather models. Second, we propose a processor reallocation heuristic that minimizes data redistribution cost while reallocating processors in the case of dynamic regions of interest. This algorithm is based on hierarchical diffusion approach that uses a novel tree reorganization strategy. We have also developed a parallel data analysis algorithm to detect regions of interest within a domain. This helps improve performance of detailed simulations of multiple weather phenomena like depressions and clouds, thereby in- creasing the lead time to severe weather phenomena like tornadoes and storm surges. Our method is able to reduce the redistribution time by 25% over a simple partition from scratch method. We also show that it is important to consider resource constraints like I/O bandwidth, disk space and network bandwidth for continuous simulation and smooth visualization. High simulation rates on modern-day processors combined with high I/O bandwidth can lead to rapid accumulation of data at the simulation site and eventual stalling of simulations. We show that formulating the problem as an optimization problem can deter- mine optimal execution parameters for enabling smooth simulation and visualization. This approach proves beneficial for resource-constrained environments, whereas a naive greedy strategy leads to stalling and disk overflow. Our optimization method provides about 30% higher simulation rate and consumes about 25-50% lesser storage space than a naive greedy approach. We have then developed an integrated adaptive steering framework, InSt, that analyzes the combined e ect of user-driven steering with automatic tuning of application parameters based on resource constraints and the criticality needs of the application to determine the final parameters for the simulations. It is important to allow the climate scientists to steer the ongoing simulation, specially in the case of critical applications. InSt takes into account both the steering inputs of the scientists and the criticality needs of the application. Finally, we have developed algorithms to minimize the lag between the time when the simulation produces an output frame and the time when the frame is visualized. It is important to reduce the lag so that the scientists can get on-the- y view of the simulation, and concurrently visualize important events in the simulation. We present most-recent, auto-clustering and adaptive algorithms for reducing lag. The lag-reduction algorithms adapt to the available resource parameters and the number of pending frames to be sent to the visualization site by transferring a representative subset of frames. Our adaptive algorithm reduces lag by 72% and provides 37% larger representativeness than the most-recent for slow networks.

Approved for public release, distribution is unlimited / This study investigates severe weather events occurring in the Midwest, Central, and Northeastern United States from May through September 2004. Severe weather events are pinpointed using tornado and hail reports and correlating them with NEXRAD radar data to determine maximum intensity of the event. Severe storms that occur within 30 minutes of a model forecast hour are catalogued for further investigation. Once these events are diagnosed, ETA-212 and MM5 model data is regridded, centered on the storm. Divergence values at 300 hPa are extracted from the model data for each storm event. These storms are then grouped in three ways: all storms, tornadic storms, and hail producing storms. The averaged maximum divergence values from the ETA-212 for each group are examined from the 0 hour analysis through the 21 hour forecast. From these averaged divergence values, a matrix of recommended divergence threshold values is derived. For the MM5 data, a subset of storms is examined. The MM5 and ETA-212 are run on an identical set of storms, and the divergence forecasts are compared. / Captain, United States Air Force

Este estudo tem como objetivo a verificação das previsões diárias, das temperaturas máxima e mínima e precipitação acumulada, realizadas pelo modelo operacional de previsão numérica do tempo WRF (Weather Research Forecasting) para o estado de São Paulo. As condições iniciais e de fronteira fornecidas pela análise e previsão das 00UTC do modelo Global Forecast System (GFS), são usados no processamento do WRF, para previsões de 72 horas, em duas grades aninhadas (espaçamentos horizontais de grade de 50 km, D1, e 16,6 km, D2). O período avaliado foi de abril de 2010 a março de 2011. As comparações diárias das temperaturas máxima e mínima foram realizadas entre os valores preditos e observados nas estações de superfície de Registro, São Paulo, Paranapanema, Campinas, Presidente Prudente e Votuporanga (dados da CIIAGRO); através do erro médio (EM) e raiz do erro médio quadrático (REQM), para os prognósticos das 36, 60 e 72 horas. A precipitação acumulada diária é avaliada com relação ao produto MERGE, pela aplicação da ferramenta MODE, na previsão das 36 horas, para um limiar de 0,3 mm, no domínio espacial abrangendo o Estado de São Paulo e vizinhanças. Primeiramente, fez-se uma análise, comparando os pares de grade dos campos previsto e observado, utilizando os índices estatísticos de verificação tradicional de probabilidade de acerto (PA); índice crítico de sucesso (ICS); viés (VIÉS); probabilidade de detecção (PD) e razão de falso alarme (RFA). Posteriormente, foram analisados os campos de precipitação com relação à razão de área (RA); distância dos centroides (DC); razões de percentil 50 (RP50) e 90 (RP90). Os resultados evidenciaram que as saídas numéricas do modelo WRF com D2 tiveram desempenho melhor comparado à grade de menor resolução (maior espaçamento de grade horizontal, D1), tanto no prognóstico diário das temperaturas (máxima e mínima) quanto da precipitação acumulada. A temperatura apresentou um padrão de amortecimento, com temperaturas diárias máxima subestimada e mínima superestimada. Com relação à precipitação, as saídas numéricas do modelo GFS e WRF com D2 mostraram desempenho semelhante, com o D2 apresentando índices ligeiramente melhores, enquanto que as saídas numéricas do modelo WRF com D1 exibiram pior desempenho. Verificou-se um padrão de superestimativa, tanto em termos de abrangência espacial quanto em intensidade, para o modelo GFS e WRF em ambos os domínios simulados, ao longo de todo o período analisado. O percentil 50 é, geralmente, maior que o observado; entretanto, o percentil 90 é mais próximo ao observado. Os resultados também indicam que o viés dos modelos varia ao longo do ano analisado. Os melhores índices tanto com relação à precipitação quanto à temperatura foram obtidos para a estação de verão, com o modelo WRF com D2 apresentando melhores prognósticos. Entretanto, os modelos apresentam os maiores erros no inverno e no outono. Estes erros foram decorrentes de subestimativas das temperaturas máximas e superestimativas de área e intensidade de precipitação. / Forecasts of daily maximum and minimum temperatures and rainfall performed by the operational numerical weather prediction WRF (Weather Research Forecasting) model in the São Paulo are evaluated. Initial and boundary conditions provided by the 00UTC Global Forecast System (GFS) Model and WRF run for 72 hours, with two nested grids (with horizontal grid spacing of 50 km, D1, and 16.6 km, D2). The study was made for April 2010 to March 2011 period. Daily maximum and minimum temperatures comparisons were made, between predicted and observed data of the surface weather stations of Registro, São Paulo, Paranapanema, Campinas, Presidente Prudente and Votuporanga (CIIAGRO Data), through the mean error (ME) and root mean square error(RMSE), for the 36, 60 and 72 hours forecasts. The daily accumulated rainfall is evaluated using MODE with respect to the MERGE product, for the 36 hours forecast, with threshold of 0.3 mm over the spatial domain covering the State of São Paulo and neighborhoods. First, an analysis was made comparing grid pairs of predicted and observed fields, through the traditional statistical verification indexes: accuracy (PA), critical success index (ICS), bias (VIES), probability of detection (PD) and false alarm ratio (RFA). Subsequently, we analyzed the precipitation field with respect to area ratio (AR), distance from the centroids (DC), ratio of the 50th percentile (RP50) and ratio of the 90th percentile (RP90). The WRF, with D2 nested grid, had better performance compared to the grid of lower space resolution (higher horizontal grid spacing, D1) for both, daily temperatures (maximum and minimum) and the accumulated rainfall forecasts. The temperature forecast presented a damped pattern, with underestimated maximum and overestimated minimum values. Rainfall was overall overestimated spatially and in intensity for the three models throughout the analized period. The forecasted 50th percentile is generally higher than that observed, however, the 90th percentile is closer to observations. The results also indicate that the bias of the models varies annually. The best performances for both rainfall and temperature were obtained for the summer season, with the D2 showing slightly better results. However, the models had the biggest errors during the winter and autumn seasons. These errors were due to underestimation of maximum temperatures and overestimation in area and intensity of precipitation.