Built-up areas represent an artifiial impait to natural environment with large spatial variability and speiifi meihaniit radiationt thermal and ihemiial properties. Despite of inireasing horizontal resolution of numeriial weather prediition modelst the impait of loial built-up area on mesosynoptiv weather phenomena is still not well resolved. Therefore it is neiessary to use some of urban environment modelst whiih were designed to parameterize speiifi urban prosiessest not expliiitly resolved inside the grid box. In the thesis main urban iharaiteristiis are explained (impait on the struiture of boundary layert radiation and heat balanie of urban environment or urban heat island)t basii priniiples of urbanization appliiation in the numeriial weather model are desiribedt as well as different urban parameterizations available in numeriial model WRFe (Weather Reseaih and Feoreiasting). Number of validation experiments were performed for summer and winter episode in non-hydrostatii mode at 3t3 km resolutiont where different urban parametrizationst antropogenii heat adjustment and impait of mosaii land-use were tested. April 2018 Prague weather foreiast was verifiated in ionsideration of urban heat island.

The work analyses contemporary weather broadcasting in Czech television (Česká televize). in the Czech Republic and abroad. Through an analysis of a selected broadcasting day of ČT24 like. On the basis of interviews with expert editors employed in Česká televize the work

The use of numerical weather prediction (NWP) has brought significant improvements to severe weather outbreak forecasting; however, determination of the primary mode of severe weather (in particular tornadic and nontornadic outbreaks) continues to be a challenge. Uncertainty in model runs contributes to forecasting difficulty; therefore it is beneficial to a forecaster to understand the sources and magnitude of uncertainty in a severe weather forecast. This research examines the impact of data assimilation, microphysics parameterizations, and planetary boundary layer (PBL) physics parameterizations on severe weather forecast accuracy and model variability, both at a mesoscale and synoptic-scale level. NWP model simulations of twenty United States tornadic and twenty nontornadic outbreaks are generated. In the first research phase, each case is modeled with three different modes of data assimilation and a control. In the second phase, each event is modeled with 15 combinations of physics parameterizations: five microphysics and three PBL, all of which were designed to perform well in convective weather situations. A learning machine technique known as a support vector machine (SVM) is used to predict outbreak mode for each run for both the data assimilated model simulations and the different parameterization simulations. Parameters determined to be significant for outbreak discrimination are extracted from the model simulations and input to the SVM, which issues a diagnosis of outbreak type (tornadic or nontornadic) for each model run. In the third phase, standard synoptic parameters are extracted from the model simulations and a k-means cluster analysis is performed on tornadic and nontornadic outbreak data sets to generate synoptically distinct clusters representing atmospheric conditions found in each type of outbreak. Variations among the synoptic features in each cluster are examined across the varied physics parameterization and data assimilation runs. Phase I found that conventional and HIRS-4 radiance assimilation performs best of all examined assimilation variations by lowering false alarm ratios relative to other runs. Phase II found that the selection of PBL physics produces greater spread in the SVM classification ability. Phase III found that data assimilation generates greater model changes in the strength of synoptic-scale features than either microphysics or PBL physics parameterization.

The capacity value (CV) of a power generation unit indicates the extent to which it contributes to the generation system adequacy of a region’s bulk power system. Given the capricious nature of the wind resource, determining wind generation’s CV is nontrivial, but can be understood simply as how well its power output temporally correlates with a region’s electricity load during times of system need. Both wind generation and load are governed by weather phenomena that exhibit variability across all timescales, including low frequency weather cycles that span decades. Thus, a data-driven determination of wind’s CV should involve the use of long-term (i.e., multiple decades) coincident load and wind data. In addition to the challenge of finding high-quality, long-term wind data, existing load data more than several years old is of limited utility due to shifting end usage patterns that alter a region’s electricity load profile. Due to a lack of long-term data, current industry practice does not adequately account for the effects of weather variability in CV calculations. To that end, the objective of this thesis is to develop a model to “hindcast” what the historic regional load in New England would have been if governed by the conjoined influence of historic weather and a more current load profile. Modeling focuses exclusively on summer weekdays since this period is typically the most influential on CV. The summer weekday model is developed using multiple linear regression (MLR), and features a separate hour-based model for eight sub-regions within New England. A total of eighty-four candidate weather predictors are made available to the model, including lagged temperature, humidity, and solar insolation variables. A reanalysis weather dataset produced by the National Aeronautics and Space Administration (NASA) – the Modern Era Retrospective-Analysis for Research and Applications (MERRA) dataset – is used since it offers data homogeneity throughout New England over multiple decades, and includes atmospheric fields that may be used for long-term wind resource characterization. Weather regressors are selected using both stepwise regression and a genetic algorithm(GA) based method, and the resulting models and their performance are compared. To avoid a tendency for overfitting, the GA-based method employs triple cross-validation as a fitness function. Results indicate a regional mean absolute percent error (MAPE) of less than 3% over all hours of the summer weekday period, suggesting that the modeling approach developed as part of this research has merit and that further development of the hindcasting model is warranted.

<p> It has been hypothesized that some measurable properties of $Z_{DR}$ arcs in supercells may change in the minutes prior to tornadogenesis and tornadogenesis failure, and that $Z_{DR}$ arc area will change with SRH and can be used as a real-time proxy to estimate SRH. Output form the Cloud Model 1 (CM1) along with a polarimetric emulator is used to simulate $Z_{DR}$ arcs in 9 tornadic and 9 non-tornadic supercells. A random forest algorithm is used to automatically identify the $Z_{DR}$ arcs. Finally the inflow sector SRH is calculated at times when $Z_{DR}$ arcs are identified. To analyze the change in intensity and area a comparison between the average $Z_{DR}$ value inside and outside of the arc, as well as the spatial size of the arc and storm was done. Model calculated SRH is then compared to these metrics.</p> <p> </p> <p> It has also been observed that hail fallout complicates the automatic identification of $Z_{DR}$ arcs. In this study, three experiments are run where the simulated $Z_{DR}$ arcs are produced. One using all categories of hydrometeors, one where wet growth and melting of hail is excluded, and one excluding the contribution to $Z_{DR}$ from the hail hydrometeor category. The same analysis as above is repeated for all three experiments. Finally observed $Z_{DR}$ arcs are analyzed to see if these results are applicable to the real world. </p>

The aim of this thesis is to investigate the use of weather information from an information science perspective. By using Everyday Life Information Seeking theories and a qualitative method this thesis takes a novel approach on how weather information is used and viewed by the everyday users and meteorologists. Thus the material, based on seven interviews with everyday users and two focus group interviews with meteorologists, manages to convey new aspects on how weather information is used in an everyday setting and how meteorologists view their role as information providers. The analysis show that for everyday users there is a difference in how weather information is used depending on age. While apps on mobile phones are used by both younger and older informants, other media types, such as TV and webpages, tend to be used by either older or younger age groups. The results also show that there are non-traditional sources used for weather information among everyday users, such as non-weather web cameras and social media. The results also show that there is a difference in how meteorological forecasters and researchers view different aspects of weather information. Both groups have an understanding of information as being dependent on how it is presented, though forecast meteorologists express a more nuanced view. The results from this study show that information science can be a vital tool for studying weather information habits. It is the firm belief of the author that using information science could gain new insights for the meteorological community in the future. This is a two-year master thesis in Archive, Library and Museum studies. / Syftet med denna uppsats är att undersöka väderinformation från ett informationsvetenskapligt perspektiv. Genom att använda Everyday Life Information Seeking teorier och en kvalitativ metod ger denna uppsats ett nydanande angreppsätt på hur väderinformation används och ses av vardagsanvändare och meteorologer. Således kan materialet, som baseras på sju intervjuer med vardagsanvändare och två fokusgruppsintervjuer med meteorologer, frambära nya aspekter på hur väderinformation används i vardagen och hur meteorologer ser på sin roll som informationsförmedlare. Analysen visar att det för vardagsanvändare finns en skillnad i hur väderinformation används beroende på åldersgrupp. Medan appar på mobiltelefoner används av både yngre och äldre informanter, så tenderar övriga media typer, som TV och hemsidor, att användas främst av endast en ålderskategori. Vidare visar resultaten på att icketraditionella källor för väderprognoser används av vardaganvändare, så som webkameror och sociala medier. Resultaten visar även på att det finns en skillnad i hur prognosmeteorologer och meteorologiska forskare ser på olika aspekter av väderinformation. Båda grupperna visar på en förståelse för att information är beroende på hur den presenteras, så ger prognosmeteorologer uttryck för en mer nyanserad bild. Resultaten från studien visa på att informationsvetenskap kan vara ett viktigt verktyg för att studera väderinformationsvanor. Författaren menar på att informationsvetenskap skulle kunna ge nya insikter inom det meteorologiska området i framtiden. Detta är en tvåårig masteruppsats inom Arkiv-, Bibliotek- och Museumstudier.

This study deals with weather symbolism in DBZ Ntuli's literature. Chapter one describes the aim, biography of DBZ Ntuli, definition of important literary concepts, the scope and the methodology. Chapter two considers the symbolic use of mist, fog, overcast weather and clouds. Each of these aspects is defined and is studied under each genre, i.e. in terms of its use by Ntuli in prose, drama and poetry. Chapter three explores the symbolic usage of rain, thunder and the rainbow in all the genres in which Ntuli writes. Chapter four deals with the imagery of the sun. The sun is shown as exhibiting three distinct levels of heat: mild, hot and extremely hot. Chapter five deals with the symbol of cold weather. Its aspects can be perceived on two levels: cold weather and extremely cold weather. Chapter six is a general conclusion that reveals the outcome of the research, observations and the recommendations. / African Languages / M.A. (African Languages)

Elizabeth Hay, aujourd'hui auteure a succes au Canada anglais, publiait son premier roman en 2000, A Student of Weather, qui s'est attire une foule de critiques elogieuses. Malgre son succes du cote anglophone, le roman n'a jamais ete traduit en francais et reste inconnu du lectorat quebecois. Dans une prose d'une grande splendeur, Hay propose avec A Student of Weather un roman d'oppositions et de paradoxes, ou l'Est et l'Ouest du territoire canadien deviennent les symboles de poles opposes d'une histoire d'amour impossible. Ce qui fait la richesse de ce roman est sans contredit sa valeur implicite, ses non-dits, ses silences d'une grande eloquence du point de vue tant du recit que de la narration et des personnages. Dans l'optique d'une traduction en francais, le roman doit etre interprete en profondeur par le traducteur afin qu'il comprenne toute la portee de sa valeur implicite, pour ainsi effectuer la traduction la plus juste possible. Une recherche qui tente de construire des ponts entre differentes disciplines, notamment la traductologie, la litterature, la linguistique, la stylistique et la culture, dans l'objectif avoue de traduire l'implicite, presente des defis evidents. En effet, comment traduire ce qui n'est pas formellement exprime? Comment traduire l'implicite d'un texte comme A Student of Weather, qui en est charge? A cette fin, ma recherche s'effectuera en deux temps. Tout d'abord, j'offrirai une definition de la notion d'implicite ainsi que son interpretation dans le roman. Suivra une typologie des difficultes de traduction, identifiees et analysees en categories distinctes. En tenant compte des elements d'analyse etablis, j'effectuerai enfin une traduction en francais de la premiere partie du roman A Student of Weather.

Weather affects agriculture more than any other variable. For centuries, growers had to depend upon small bits and pieces of local climatological data collected and passed down in almanacs. Over the last 100 years, however, scientists have developed complex Numerical Weather Prediction (NWP) models that are able to forecast weather with increasing accuracy. The objective of this work was to use a probabilistic NWP model (the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS)) as a component to couple with agricultural decision-making tools and models. First, customized ECMWF EPS forecasts were used as an irrigation scheduling aid for a field trial. Next, the CROPGRO Cotton Model was used to simulate the field experiment as well as an additional irrigation scheduling strategy. Finally, a cotton canopy temperature model was developed and coupled with customized ECMWF EPS forecasts to generate hourly canopy temperature forecasts. These forecasts were used to create a heat stress warning system. Results from the field trial indicate that using precipitation forecasts to schedule irrigation could provide a convenient alternative relative to a standard method. Results from the simulated field trial suggest using precipitation forecasts issued on the day of irrigation could be more efficient than using forecasts issued one to two days prior. Last, results from the heat stress project indicate forecasts were skillful to 10 days, allowing enough time for growers to protect crops if needed. In light of the above, implications for the agricultural community could be significant. Coupled atmospheric-agricultural models have the ability to put weather forecasts in terms producers can understand and can quickly use to make strategic on-farm decisions, therefore, possessing the potential to make a large positive global impact.

The global climate is changing due to the accumulation of greenhouse gases (GHGs) in the atmosphere, primarily due to anthropogenic activity. The dominant GHG is CO₂ which originates from combustion of fossil fuels, land use change and management. The terrestrial biosphere is a key driver of climate and biogeochemical cycles at regional and global scales. Furthermore, the response of the Earth system to future drivers of climate change will depend on feedbacks between biogeochemistry and climate. Therefore, understanding these processes requires a mechanistic approach in any model simulation framework. However ecosystem processes are complex and nonlinear and consequently models need to be validated against observations at multiple spatial scales. In this thesis the weather research and forecasting model (WRF) has been coupled to the mechanistic terrestrial ecosystem model soil-plant-atmosphere (SPA), creating WRF-SPA. The thesis is split into three main chapters: i. WRF-SPA model development and validation at multiple spatial scales, scaling from surface fluxes of CO₂ and energy to aircraft profiles and tall tower observations of atmospheric CO₂ concentrations. ii. Investigation of ecosystem contributions to observations of atmospheric CO₂ concentrations made at tall tower Angus, Dundee, Scotland using ecosystem specific CO₂ tracers at seasonal and interannual time scales. iii. An assessment of detectability of a policy relevant national scale afforestation by observations made at a tall tower. Detectability of changes in atmospheric CO₂ concentrations was assessed through a comparison of a control simulation, using current day forest extent, and an experimentally afforested simulation using WRF-SPA. WRF-SPA performs well at both site and regional scales, accurately simulating aircraft profiles of CO₂ concentration magnitudes (error <+- 4 ppm), indicating appropriate source sink distribution and realistic atmospheric transport. Hourly observations made at tall tower Angus were also well simulated by WRF-SPA (R² = 0.67, RMSE = 3.5 ppm, bias = 0.58 ppm). Analysis of CO₂ tracers at tall tower Angus show an increase in the seasonal error between WRF-SPA simulated atmospheric CO₂ and observations, which coincides with simulated cropland harvest. WRF-SPA does not simulate uncultivated land associated with agriculture, which in Scotland represents 36 % of agricultural holdings. Therefore, uncultivated land components may provide an explanation for the increase in model-data error. Interannual variation in weather is indicated to have a greater impact on ecosystem specific contributions to atmospheric CO₂ concentrations at Angus than variation in surface activity. In a model experiment, afforestation of Scotland was simulated to test the impact on Scotland’s carbon balance. The changes were shown to be potentially detectable by observations made at tall tower Angus. Afforestation results in a reduction in atmospheric CO₂ concentrations by up to 0.6 ppm at seasonal time scales at tall tower Angus. Detection of changes in forest surface net CO₂ uptake flux due to afforestation was improved through the use of a network of tall towers (R² = 0.83) compared to tall tower Angus alone (R² = 0.75).