Este trabalho é um estudo sobre a mistura que se estabelece entre informação meteorológica e entretenimento nos quadros da previsão do tempo de dois telejornais nacionais das Organizações Globo: Jornal Nacional JN (canal aberto Rede Globo) e Rural Notícias RN (canal fechado Canal Rural). A recepção dos programas sobre o tempo é analisada a partir de entrevistas abertas, com 40 espectadores de ambos os noticiários, habitantes da cidade e do campo. Também por meio de entrevistas, são avaliadas as representações da produção, privilegiando as denominadas moças do tempo, Flávia Freire (JN) e Lilian Lima (RN). Auxiliam na identificação do entretenimento, nos espaços da meteorologia, a observação do surgimento do consumidor moderno, a extensão do consumo ao lazer, o divertimento pela televisão, o tempo como assunto para conversa, as representações do corpo na cultura brasileira e traços da educação na transmissão meteorológica no Telejornalismo do país. Assim, são analisados os formatos dos quadros do tempo, suas funções aparentes, formatações, imagem corporal construída de suas apresentadoras e o público-alvo de cada telejornal. Esses principais pontos são relacionados com o pensamento de autores que estudam comunicação, tempo, hedonismo, utilitarismo, consumo, representações sociais, corpo, educação e pedagogia. A previsão da meteorologia aparece com várias funções, sendo dominantes a ênfase no cotidiano, em ambos os jornais; o foco no lazer, sobretudo no Jornal Nacional; e o destaque para o vínculo com a produção rural em sentido amplo, no caso do Rural Notícias. / This work is a study about a mixture between weather information and entertainment on the weather forecast board of two Brazilian newscasts from Globo Organizations: Jornal Nacional JN (broadcast by an open channel Rede Globo) and Rural Notícias RN (by paid channel Canal Rural). The reception of the weather program is assessed by open interviews with 40 viewers of both newscasts that live in cities and in the countryside. Also through interviews, the production representations are assessed with the weather girls Flávia Freire (JN) and Lilian Lima (RN), both of them well known by the audience. The observation of the emergence of the modern consumer, the extension of consumption to leisure, television as entertainment, the weather as a subject of conversation, the representations of the human body in Brazilian culture and education features in the transmission of weather reports by Brazilian TV are subjects that help identifying leisure on the weather board. Therefore, weather report formats, seeming functions, formatting, the built-up bodily image of their anchorwomen and the target audience of each newscast are subjected to analysis. These main themes are related to the visions of authors who study communication, weather, hedonism, utilitarianism, consumption, social representations, body, education and pedagogy. The weather forecast is shown as having several meanings, the emphasis being on everyday life, on both newscasts; the accent on leisure, above all on Jornal Nacional; and the emphasis on the broad meaning of the agricultural production, on Rural Notícias.

Muitos negócios possuem exposição às variações climáticas e com poucas alternativas para mitigar este tipo de risco. Nos últimos 20 anos o mercado de derivativos climáticos se desenvolveu principalmente em locais como Canadá, EUA e Europa para transferir os riscos relacionados às variações climáticas para investidores com maior capacidade de absorção, tais como seguradoras, resseguradoras e fundos de investimentos. Este trabalho implementou uma metodologia de precificação destes contratos para a variável temperatura média diária no Brasil. Foram utilizados os dados de 265 estações meteorológicas cadastras no site do BDMEP/INMET, utilizando-se observações diárias durante o período 1970-2012. Enquanto a maior parte dos trabalhos de precificação fora desenvolvida para um local específico, neste estudo buscou-se uma solução mais generalizada e que permitisse aos participantes deste novo mercado balizar suas expectativas de preço para qualquer ponto com uma estação meteorológica no país. O principal desafio para esta abordagem foram as falhas nas séries temporais e para isto desenvolveu-se uma metodologia de preenchimento utilizando as informações do projeto NCEP/NCAR. Cada estação foi submetida ao algoritmo de análise e modelagem das séries de temperatura. Considerou-se \"Sucesso\" (36.2% dos casos) as estações cujo processo de modelagem culminou em um resíduo ruído branco, estacionário e homoscedástico. Por \"Fracasso\" (63.8% das estações) entendem-se os casos que violaram pelo menos uma destas condições. Para a incorporação da tendência nos dados utilizou-se a Regressão Polinomial Local (LOESS). Para a estimação da sazonalidade foi empregada análise espectral e utilizada a série de Fourier. Para o tratamento da autocorrelação serial nos resíduos utilizou-se modelos ARFIMA, que contempla um parâmetro para memória longa do processo. A análise espacial dos resultados sugere uma maior taxa de \"Sucesso\" para a precificação de contratos na região Centro-Sul do país e piores para Norte e Nordeste. O método de preenchimento das falhas não deve ser utilizado indiscriminadamente por todo o país, uma vez que a correlação entre as séries do BDMEP/INMET e NCEP/NCAR não é constante, além de apresentar um claro padrão na dispersão espacial. A precificação dos contratos foi feita pelos métodos de \"Burning cost\", \"Modelagem do Índice\" e \"Modelagem da temperatura média diária\". Para este último caso as temperaturas simuladas apresentaram um viés ligeiramente acima dos dados históricos, podendo causar grandes distorções na precificação dos contratos. Deve-se realizar uma correção dos valores simulados antes da precificação dos contratos. A qualidade e consistência dos dados climáticos representam a maior ameaça para a utilização de derivativos climáticos no país, principalmente na região Cento-Oeste, aonde existem poucas estações meteorológicas, e Nordeste, com baixíssima taxa de \"Sucesso\", mesmo com um razoável número de estações. / Many business are exposed to weather variations and managers did not use to have a tool to avoid it. In the last twenty years, weather derivative markets has developed mainly in Canada, USA and Europe, transferring these risks to investors who are willing and able to assume it and receive a financial compensation for that, such as investment funds, insurance and reinsurance companies. This study developed a methodology to price weather contracts with daily average temperature as underlying. It was used 265 public weather stations from BDMEP/ INMET and data was collected from 1970 up to 2012. While the most part of studies in this area have focused in one or few stations, the goal of this study was to develop a more general pricing tool which would allow assessing weather risk and quoting it at any place in Brazil with an available weather station. The main issue was the gaps that occur so frequently in weather time series data and a methodology using interpolated data from NCEP/NCAR was proposed to deal with it. At the bottom of modelling process, weather stations were classified as \"Success\" (36.2%) or \"Failure\" (63.8%) according to the analysis of residuals. To be considered \"Success\", residuals of a time series must be stationary, homoscedastic and white-noise, i.e., free of autocorrelation. If at least one of these was not reached, the modelling process of this weather station was considered \"Failure\". Detrend data was done using Local Polynomial Regression (LOESS). Seasonality was estimated using spectral analysis and Fourier analysis. Autocorrelation of residuals was incorporated into the model using ARFIMA models, which have a parameter to deal with long memory process. Spatial analysis of results suggests a higher \"Success\" rate for contracts priced in the Center south region and worst results were obtained in North and Northeast. Methodology to fill the gaps should not be used in all situations, once correlation is not constant through the country and has a strong spatial pattern (clustering). Pricing was done using \"Burning cost\", \"Index modelling\" and \"Daily modelling of average temperature\". In this former case, simulated temperature has shown a slightly positive bias, which could create huge differences in prices compared with other models. A correction should be done to these values, to use it for pricing purposes. The quality and consistency of weather data is the main issue to develop a weather market in Brazil, mainly in Center-West region, where there is a small number of weather stations and Northeast with the lowest \"Success\" rate, even with a not so small number of weather stations.

In 2006 a Numerical Weather Prediction (NWP) model known as the Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) was installed at the South African Weather Service (SAWS). Since then it has been used operationally at SAWS, replacing the Eta model that was previously used. The research documented in this dissertation was inspired by the need to verify the performance of the UM in simulating and predicting weather over South Africa. To achieve this aim, three model configurations of the UM were compared against each other and against observations. Verification of rainfall as well as minimum and maximum temperature for the year 2008 was therefore done to achieve this. 2008 is the first year since installation, where all the configurations of the UM used in the study are present. For rainfall verification the model was subjectively verified using the eyeball verification for the entire domain of South Africa, followed by objective verification of categorical forecasts for rainfall regions grouped according to standardized monthly rainfall totals obtained by cluster analysis and finally objective verification using continuous variables for selected stations over South Africa. Minimum and maximum temperatures were subjectively verified using the eyeball verification for the entire domain of South Africa, followed by objective verification of continuous variables for selected stations over South Africa, grouped according to different heights above mean sea level (AMSL). Both the subjective and objective verification of the three model configurations of the UM (for both rainfall as well as the minimum and maximum temperatures) suggests that 12km UM simulation with DA gives better and reliable results than the 12km and 15km UM simulations without DA. It was further shown that although there was no significant difference between the model outputs from the 12km and the 15km UM without DA, the 15km UM simulation without DA, proved to me more reliable and accurate than the 12km UM simulation without DA in simulating minimum and maximum temperatures over South Africa, on the other hand the 12km UM simulation without DA is more reliable and accurate than the 15km UM simulation without DA in simulating rainfall over South Africa. / Dissertation (MSc)--University of Pretoria, 2013. / gm2014 / Geography, Geoinformatics and Meteorology / unrestricted

The world revolves around weather information - whether the need is to understand, prepare for, or navigate weather. Although traditional television viewership of weather forecasts are on the decline, mobile weather applications are becoming the new media medium for weather information. In fact, the total number of app downloads in the weather market reached 69.5 million at the end of 2021 (Statista, 2021). As a result, understanding the characteristics of weather app users and what those users are looking for is pertinent to research regarding weather communication. While weather is an ongoing phenomenon sought to be understood by people for many decades, the uses and gratifications of engaging in weather communication are under-researched in literature. Thus, this study aimed to identify the characteristics of weather app users and their motivations for checking mobile weather apps. Using substantial literature to build a concourse of opinions about weather apps (Armstrong & Towery, 2021; Eachus & Keim, 2019; Hoang, 2015; Liu, et al., 2020), subjects participated in a Q sort, a quantitative and qualitative method that reveals the subjectivity of audiences (Brown, 1993; Stephenson, 1995). Findings indicate that managing expectations, fulfilling practical needs, and satisfying personal concerns were strong motivators for using a weather app. Other weather app user qualities included being optimistic, controlling, reasonable, or private. Practical implications for weather communicators, as well as for app developers are also discussed.

Multimedia environmental monitoring involves capturing valuable visual and audio information from the field station. This will permit the environmentalists and researchers to analyze the habitat and vegetation of a region with respect to other environmental specifics like temperature, soil moisture, etc. This thesis deals with the development of a test bed for multimedia monitoring by capturing image information and making it available for the public. A USB camera and a Single board computer are used to capture images at a specified frequency. A web-client is designed to display the image data and establish a secured remote access to reconfigure the field station. The development includes two modes of image acquisition including a basic activity recognition algorithm. Good quality images are captured with the cost for development of the system being less than 2 hundred dollars.

Thesis. 1978. M.S.--Massachusetts Institute of Technology. Dept. of Meteorology. / Microfiche copy available in Archives and Science. / Bibliography: leaves 177-178. / by John P. Sheldon. / M.S.

Indiana University-Purdue University Indianapolis (IUPUI) / The US weather enterprise includes academia, the private weather industry, and government-funded forecasting, research, and dissemination agencies. While not an organization in its own right, the enterprise behaves like an organization of organizations. This thesis applies the communicative constitution of organizations, and McPhee and Zaug’s four flows model in particular, to the US weather enterprise. Each organization in the weather enterprise behaves like individual members of an organization would, which extends this theory to a conceptualization of organization that increases innovation, collaboration, and coordination. The weather is a constitutive force which calls the US weather enterprise into being. Finally, CCO is extended to other collaborative, coordinated efforts among the public and private sectors, indicating the possibilities of CCO as an attractive answer to the great organizational questions of the 21st century and beyond. Future research areas are considered, including how the US weather enterprise manages the unexpected and reduces uncertainty organizationally. Also, considerations as to how CCO can be applied to the incident command structure, often called forward during high-impact weather events, will be made.

No description available.

No description available.

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.