A Península Antártica (PA) é uma das regiões no planeta que apresentam as mais adversas condições do tempo devido à constante passagem de ciclones. O conhecimento das condições meteorológicas futuras é fundamental para o desenvolvimento de atividades operacionais e de pesquisa na região. Nos últimos anos a implantação e melhoramento dos modelos numéricos, que tem como foco a previsão do tempo na Antártica, têm sido alvo de diversos estudos pela comunidade acadêmica. O objetivo principal deste trabalho foi avaliar o desempenho do modelo BRAMS na simulação de parâmetros meteorológicos durante a passagem de ciclones na Península Antártica. Diversas simulações, que envolveram diferentes configurações estruturais e físicas do modelo foram realizadas para dois casos de passagem de ciclones na PA, um que ocorreu em fevereiro e outro em julho de 2009. A avaliação do desempenho do modelo BRAMS foi feita através de duas análises, uma qualitativa, analisado o comportamento de cada variável simulada pelo modelo em comparação com os dados de estações meteorológicas, e a outra uma análise de sensibilidade baseada em índices estatísticos. O desempenho do modelo BRAMS se mostrou altamente dependente das condições iniciais adotadas. A pressão ao nível médio do mar foi a variável melhor representada, mas o modelo não conseguiu prever adequadamente os aumentos de pressão que ocorrem após a passagem do ciclone pela PA, o que ficou evidente no evento de julho. Por outro lado, o BRAMS se mostrou ineficiente em representar as variações de temperatura que ocorrem durante o período de simulação, principalmente no evento de fevereiro. As temperaturas simuladas pelo BRAMS foram mais elevadas que aquelas observadas nas estações meteorológicas para os dois casos (fevereiro e julho). Além disso, o modelo não conseguiu prever as quedas abruptas de temperatura, observadas durante o avanço do ciclone no mês de julho, devido em grande parte à ausência de gelo marinho nas regiões onde, de fato, as observações mostravam que ele estava presente. O modelo BRAMS, de forma geral, não obteve bom desempenho na simulação do vento, principalmente em relação às variações de direção. O modelo capta as principais variações da componente zonal do vento no caso de verão, porém em algumas estações, quando o escoamento tornou-se meridional, o BRAMS simulou um vento de leste, demonstrando uma forte dependência das condições iniciais. Já no caso de inverno, após o ciclone cruzar a PA, os experimentos simulam um vento de oeste que não condiz com o observado nas estações meteorológicas. Já em se tratando do vento meridional notou-se que o BRAMS intensifica os fluxos de sul, principalmente após a passagem do ciclone pela PA. / The Antarctic Peninsula is one of the regions of the earth, which have the most adverse weather conditions due to the constant movement of cyclones. The knowledge of future meteorological conditions is essential for the operational activities and research developments on the region. In the last years, implantation and improvement of the numeric models, which focus is the weather forecasting on Antarctic, has been the subject of several studies of academic community. The main objective of this study is evaluating the model BRAMS performance, on the simulation of meteorological parameters in events of cyclones on Antarctic Peninsula. Several simulations with different structural and physics configurations on the model were performed in two events of cyclones on AP. One of them occurred in February e the other one in July of 2009. The evaluation of BRAMS model was performed by means of two analyses. The first analysis was qualitative, which analyzed the behavior of each variable simulated by the model in compared to weather stations data. The second analysis was related with the sensibility based on statistical indexes. The BRAMS model performance seems to be dependent on the initial conditions. The pressure at mean sea level was a well represented variable, however the model did not forecasted properly the pressure increase, which occurred after the cyclone event on the AP and it was more evident on event of July. Otherwise, the BRAMS seems to be inefficient for variations on temperatures during the simulation period, especially on February event. Temperatures simulated by BRAMS were higher than that observed on weather station for both cases (February and July). Furthermore, the model did not predicted the abrupt decrease in temperature, observed during the cyclone in July, due to the absence of ice sea in regions where, in fact, the observations showed that he was present. In general, the BRAMS model did not achieved good performance simulating winds, especially on changes of direction. The model captures the major variations of zonal wind during summer, however, in some stations, when the flow direction was changed to meridional, the BRAMS simulated an easterly wind, showing a strong dependence on initial conditions. During winter events, after the cyclone cross the AP, the experiments simulated a west wind, which is not consistent with that observed at meteorological stations. In the case of meridional wind, BRAMS intensified the south flows, especially after the cyclone on AP.

Numerous factors play a role in the development and maintenance of North Atlantic tropical cyclones as they originate and cross the Main Development Region. These factors include sea-surface temperatures (SSTs), relative humidity, vertical wind shear, etc. One key player in many of these factors is the Saharan Air Layer (SAL) which has been a source for study for nearly five decades. The interplay between dust loading within the SAL and the development of African Easterly Waves (AEWs) has been repeatedly noted in many of the studies in this field. The cumulative indirect effect of the dust on AEWs however remains unknown (Evan et al., 2006a). On a case by case basis, the SAL has been shown to negatively influence the development of AEWs, i.e. entrainment of dry air into the low to mid-levels, enhanced vertical wind shear and suppression of convection within the storm (Dunion & Velden, 2004). Positive influences on AEW development have also been attributed to the SAL, namely its enhancement of the African Easterly Jet (AEJ) which in turn helps produce positive vorticity along its southern edge that AEWs tap into for energy (Karyampudi & Pierce, 2002). Further study is indeed warranted to try to fully understand whether or not the SAL has a positive or negative influence on the development of AEWs. A polarized view may be inadequate, as the SAL’s role could very well be positive, negative or somewhere in between depending on the storm characteristics and environmental conditions present at that unique time. This study looked into the role dust loading has on the mixing between the SAL and the moist marine boundary layer directly beneath the base of the SAL, which can range from 500 – 1500m and revealed a dynamic and varying relationship. It also demonstrated, through a decrease in cloud top temperatures, that dust levels are associated with the convective strength of AEWs by acting as cloud condensation nuclei (CCNs). However this association can be nullified through other parameters unique to each individual storm; SSTs, vertical wind shear, dry-air entrainment, etc.

Extratropical cyclones (ETCs) are major features of the weather in the mid- and high-latitudes and are often associated with hazardous conditions such as heavy precipitation, high winds, blizzard conditions, and flooding. Additionally, severe coastal damage and major local impacts, including inundation and erosion, can result from high waves and storm surge due to cyclone interaction with the ocean. Consequently, ETCs can have serious detrimental socio-economic impacts. The west and east coasts of North America are strongly influenced by ETC storm activity. These coastal regions are also host to many land-based, coastal, and maritime socio-economic sectors, all of which can experience strong adverse impacts from extratropical storm activity. Society would therefore benefit if variations in ETC storm activity could be predicted skilfully for the upcoming season. Skilful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to variations in storm activity and the associated risks. The overall objective of this dissertation is to determine the seasonal predictability of North American coastal extratropical storm activity during the cold months (3-month rolling seasons – OND, NDJ, DJF, JFM – during which storm activity is most frequent and intense) using Environment and Climate Change Canada’s Canadian Seasonal to Interannual Prediction System (CanSIPS). This dissertation describes research focused on three themes: 1.) reanalysis representation of North American coastal storm activity, 2.) potential predictability of storm activity and climate signal-storm activity relationships for the North American coastal regions, and 3.) seasonal prediction of storm activity in CanSIPS. Research Theme 1 evaluates six global reanalysis datasets to determine which best reproduces observed storm activity in the North American coastal regions, annually and seasonally, during the 1979-2010 time period using single-station surface pressure-based proxies; ERA-Interim is found to perform best overall. Research Theme 2, using ERA-Interim, investigates the potential predictability of extratropical storm activity (represented by mean sea level pressure [MSLP], absolute pressure tendency, and 10-m wind speed) during the 1979-2015 time period using analysis of variance. The detected potential predictability provides observation-based evidence showing that it may be possible to predict storm activity on the seasonal timescale. Additionally, using composite analysis, the El Niño-Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation are identified as possible sources of predictability in the North American coastal regions. Research Theme 2 provides a basis upon which seasonal forecasting of extratropical storm activity can be developed. Research Theme 3 investigates the seasonal prediction of North American coastal storm activity using the CanSIPS multi-model ensemble mean hindcasts (1981-2010). Quantitative deterministic, categorical deterministic, and categorical probabilistic forecasts are constructed using the three equiprobable category framework (below-, near-, and above-normal conditions) and the parametric Gaussian method for determining probabilities. These forecasts are then evaluated against ERA-Interim using the correlation skill score, percent correct score, and Brier skill score to determine forecast skill. Baseline forecast skill is found for the seasonal forecasts of all three storm activity proxies, with MSLP forecasts found to be most skilful and 10-m wind speed forecasts the least skilful. Skilful seasonal forecasting of North American coastal extratropical storm activity is, therefore, possible in CanSIPS. / Graduate

Estuaries are dynamic on many spatial and temporal scales. Distinguishing effects of unpredictable events from cyclical patterns can be challenging but important to predict the influence of press and pulse drivers in the face of climate change. Diatom assemblages respond rapidly to changing environmental conditions and characterize change on multiple time scales. The goals of this research were to 1) characterize diatom assemblages in the Charlotte Harbor watershed, their relationships with water quality parameters, and how they change in response to climate; and 2) use assemblages in sediment cores to interpret past climate changes and tropical cyclone activity. Diatom assemblages had strong relationships with salinity and nutrient concentrations, and a quantitative tool was developed to reconstruct past values of these parameters. Assemblages were stable between the wet and dry seasons, and were more similar to each other than to assemblages found following a tropical cyclone. Diatom assemblages following the storm showed a decrease in dispersion among sites, a pattern that was consistent on different spatial scales but may depend on hydrological management regimes. Analysis of sediment cores from two southwest Florida estuaries showed that locally-developed diatom inference models can be applied with caution on regional scales. Large-scale climate changes were suggested by environmental reconstructions in both estuaries, but with slightly different temporal pacing. Estimates of salinity and nutrient concentrations suggested that major hydrological patterns changed at approximately 5.5 and 3 kyrs BP. A highly temporally-resolved sediment core from Charlotte Harbor provided evidence for past changes that correspond with known climate records. Diatom assemblages had significant relationships with the three-year average index values of the Atlantic Multidecadal Oscillation and the El Niño Southern Oscillation. Assemblages that predicted low salinity and high total phosphorus also had the lowest dispersion and corresponded with some major storms in the known record, which together may provide a proxy for evidence of severe storms in the paleoecological record.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1981. / Microfiche copy available in Archives and Science. / Vita. / Bibliography: leaves 219-224. / by John Richard Gyakum. / Ph.D.

An analysis to determine physical and spatial patterns of the surface latent heat flux (LHF) and near surface (5m) salinity (NSS) beneath tropical cyclones (TCs) in the North Atlantic and eastern North Pacific basins during the first 24 hours of rapid intensification (RI) was conducted using empirical orthogonal function (EOF) analysis. To determine if these patterns were unique to RI, TC RI cases were compared to three non-RI intensification thresholds, 10 kt, 15 kt and 20 kt, for both LHF and NSS. Though similarities exist between non-RI and RI cases physical and spatial patterns unique to the RI cases did exist. Sea surface temperatures associated with statistically identified TC groups were assessed for their potential influence on RI. While inconclusive in the eastern North Pacific, NSS in the Atlantic may play a role for RI TCs in areas affected by river discharge from South America.

Dense medium cyclones (DMCs) have become the workhorse of the coal preparation industry due to their high efficiency, large capacity, small footprint and low maintenance requirements. Although the advantages of DMCs make them highly desirable, size-by-size partitioning data collected from industrial operations suggest that DMC performance can suffer in response to fluctuations in feed coal quality. In light of this problem, a multi-stream monitoring system that simultaneously measures the densities of the feed, overflow and underflow medium around a DMC circuit was designed, installed and evaluated at an industrial plant site. The data obtained from this real-time data acquisition system indicated that serious shortcomings exist in the methods commonly used by industry to monitor and control DMC circuits. This insight, together with size-by-size partition data obtained from in-plant sampling campaigns, was used to develop an improved control algorithm that optimizes DMC performance over a wide range of feed coal types and operating conditions. This document describes the key features of the multi-stream monitoring system and demonstrates how this approach may be used to potentially improve DMC performance. / Master of Science

Forecasting rapid intensification (RI) of tropical cyclones (TCs) is considered one of the most challenging problems for the TC operational and research communities and remains a top priority for the National Hurricane Center. Upon landfall, these systems can have detrimental impacts to life and property. To support continued improvement of TC RI forecasts, this study investigated large-scale TC environments undergoing RI in the North Atlantic basin, specifically identifying important diagnostic variables in three-dimensional space. These results were subsequently used in the development of prognostic machine learning algorithms designed to predict RI 24 hours prior to occurrence. Using three RI definitions, this study evaluated base-state and derived meteorological parameters through S-mode and T-mode rotated principal component analysis, hierarchical compositing analysis, and hypothesis testing. Additionally, nine blended intelligence ensembles were developed using three RI definitions trained on data from the Statistical Hurricane Intensity Prediction Scheme- Rapid Intensification Index, Global Ensemble Forecast System Reforecast, and Final Operational Global Analysis. Performance metrics for the intelligence ensembles were compared against traditional linear methods. Additionally, a tenth ensemble was created using forecast data generated from Weather Research and Forecasting model simulations of TC RI events in the open North Atlantic and compared against linear methods. Results revealed modest classification ability of machine learning algorithms in predicting the onset of RI 24 hours in advance by including TC environmental spatial information of temperature and moisture variables, as well as variables indicative of ambient environmental interactions.

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