Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento. / The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.

Avaliou-se o desempenho de um modelo numérico de previsão do tempo (GFS - Global Forecast System – antigo AVN – AViatioN model - do Centro Nacional para Previsão Ambiental – NCEP) no prognóstico de variáveis meteorológicas temperatura, déficit de pressão de vapor do ar, saldo de radiação e velocidade do vento, e da evapotranspiração de referência calculada pelos métodos de Thornthwaite (1948) e de Penman-Monteith (Allen et al., 1998). O desempenho foi avaliado por comparação com dados provenientes de uma estação meteorológica, situada em Piracicaba, São Paulo. A temperatura e o déficit de pressão de vapor do ar foram os elementos melhor prognosticados, com desempenho "muito bom" e "bom", de acordo com o índice de desempenho proposto por Camargo e Sentelhas (1997), para no máximo quatro e três dia de antecedência, respectivamente, durante o período seco. Para o período úmido, somente o prognóstico do déficit de pressão de vapor do ar para o primeiro dia mostrou-se "bom". Os prognósticos de saldo de radiação e velocidade do vento foram ruins para ambos os períodos. Em decorrência do bom desempenho do modelo para prognosticar a temperatura, verificou-se que a estimativa de ETo pelo método de Thornthwaite teve boa concordância com o calculado a partir dos dados da estação meteorológica, com antecedência de até três dias para o período seco. Para o úmido, este fato foi observado apenas para o primeiro dia de antecedência. A concordância entre os valores estimados pelo modelo e a partir da estação para o método de Penman-Monteith foi muito baixa, em conseqüência do desempenho do modelo de previsão do tempo em prognosticar o saldo de radiação e a velocidade do vento. / The performance of a numeric weather forecast model (GFS- Forecast System, former AVN - AvatioN model, National Center for Environmental Prediction-NCEP) was evaluated for predicting weather variables, like air temperature and vapor pressure deficit, net radiation and wind speed, as well as reference evapotranspiration calculated by Thornthwaite (1948) and Penman-Monteith (Allen et al., 1948) methods, by the comparison with data obtained by an automatic weather station, in Piracicaba, State of São Paulo, Brazil. Temperature and vapor pressure deficit were the variables predicted with the best accuracy, with a "very good" and "good" performance, according to the index of confidence proposed by Camargo and Sentelhas (1997), for the maximum of four and three days in advance, respectively, during the dry season. For the wet season, only vapor pressure deficit was predicted with a "good" performance of the model. The predictions of net radiation and wind speed were very poor for both seasons. As the weather forecast model predicted temperature well, ETo estimated by Thornthwaite method showed a good agreement with ETo values estimated by observed data from the weather station, with till three days in advance for the dry season. For the wet season, such agreement was observed just for one day in advance. When ETo estimated by Penman-Monteith method with data from the weather forecast model and from weather station were compared any agreement was observed, which was caused by the poor performance of the numeric weather forecast model to predict net radiation and wind speed.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / An investigation of the atmospheric environments prone to severe convective weather is conducted for the subtropics of South America, east of the Andes Mountain Range. Upper air soundings valid at 00Z and 12Z for six sounding sites in subtropical South America (namely, Porto Alegre/BRA, Florianópolis/BRA, Curitiba/BRA, Foz do Iguaçu/BRA, Resistência/ARG e Buenos Aires/ARG) are employed to generate a short 12-yr climatology (from January 1998 to December 2009) of some of the main necessary ingredients for severe thunderstorm development: moisture availability, conditional instability, and vertical wind shear (VWS). The goal of the climatology is to document the typical magnitudes and seasonal variability of those ingredients with the aid of convective parameters. Threshold magnitudes for such parameters were objectively determined (via quantile analysis) and tested as cut-off criteria utilized to discriminate the severe weather environments. To that end, classic North-American threshold values extracted from the literature were also tested and the results compared vis-à-vis the South-American counterparts. Distinct combinations of such thresholds were employed to select atmospheric profiles theoretically conducive to severe thunderstorms and tornadoes. Atmospheric profiles obtained from the NCEP-NCAR Reanalysis data valid at 18Z were also used. The time and space distribution of the selected severe weather profiles were examined, emphasizing the seasonal variation and geographic distribution. From these sampled profiles, composite analysis were built for and Principal Component Analysis were applied to relevant meteorological variables at distinct vertical levels in order to search for the associated synoptic-scale patterns. The climatology succesfully reproduced the expected seasonal behavior of parameters that are indicative of conditional instability and VWS. Compared to the North-American climatology, the magnitudes found for the 700-500hPa lapse rates, 0-6km bulk shear and height of the LCL were lower in South America. The seasonal variability and space distribution of the severe weather profiles suggest that there exists an equatorward [poleward] displacement of the severe weather activity as winter [summer] approaches. The synoptic pattterns associated with the severe weather environments is different from those traditionally recognized for North America, particularly at the surface. / Neste trabalho é realizada uma investigação dos ambientes favoráveis ao desenvolvimento de convecção severa nos subtrópicos da América do Sul (AS) à leste dos Andes. Sondagens de ar superior das 00Z e 12Z de seis estações distribuídas na região subtropical da AS (quais sejam: Porto Alegre/BRA, Florianópolis/BRA, Curitiba/BRA, Foz do Iguaçu/BRA, Resistência/ARG e Buenos Aires/ARG), foram utilizadas para gerar uma pequena climatologia (12 anos; de janeiro de 1998 a dezembro de 2009) de alguns dos principais ingredientes necessários para o desenvolvimento de convecção severa: oferta de umidade, instabilidade condicional termodinâmica e cisalhamento vertical do vento (CVV). A climatologia visou documentar as magnitudes típicas e a variação sazonal destes parâmetros para a região de interesse. Valores significativos dos parâmetros foram objetivamente identificados via análise de quantis e testados como limiares combinados para salientar ambientes favoráveis ao desenvolvimento de tempestades severas. Limiares para a América do Norte documentados na literatura também foram testados para este fim e confrontados com os respectivos valores da amostragem sul-americana. Diferentes combinações destes limiares foram empregadas para se extrair os perfis atmosféricos considerados teoricamente favoráveis a tempestades severas e tornados. Perfis de tempo severo também foram extraídos dos dados da Reanálise do NCEP-NCAR válidos às 18Z. Foi examinada a distribuição temporal e espacial destes ambientes de tempo severo explorando sua sazonalidade e distribuição geográfica. Partindo destes perfis, foram analisados os padrões sinóticos predominantes na região através da composição média e Análise de Componentes Principais para as variáveis meteorológicas mais relevantes em diferentes níveis verticais. A climatologia reproduziu bem o comportamento sazonal esperado dos parâmetros de instabilidade termodinâmica e CVV. Em comparação com a climatologia da América do Norte encontrou-se para a América do Sul valores mais baixos de lapse rates na média troposfera, CVV na camada entre 0-6km, e altura do NCL. A distribuição sazonal e espacial dos perfis de tempo severo sugere uma migração geográfica das condições de tempo severo das latitudes mais baixas [altas] para as latitudes mais altas [baixas] à medida que nos aproximamos dos meses de verão [inverno]. O padrão sinótico de tempo severo na América do Sul é diferente daquele tradicionalmente documentado para a América do Norte, especialmente em superfície.

Les erreurs des modèles de prévision numérique du temps (PNT) peuvent être réduites par des méthodes de post-traitement (dites d'adaptation statistique ou AS) construisant une relation statistique entre les observations et les prévisions. L'objectif de cette thèse est de construire des AS de prévisions de vent pour la France sur la grille de plusieurs modèles de PNT, pour les applications opérationnelles de Météo-France en traitant deux problèmes principaux. Construire des AS sur la grille de modèles de PNT, soit plusieurs milliers de points de grille sur la France, demande de développer des méthodes rapides pour un traitement en conditions opérationnelles. Deuxièmement, les modifications fréquentes des modèles de PNT nécessitent de mettre à jour les AS, mais l'apprentissage des AS requiert un modèle de PNT inchangé sur plusieurs années, ce qui n'est pas possible dans la majorité des cas.Une nouvelle analyse du vent moyen à 10 m a été construite sur la grille du modèle local de haute résolution (2,5 km) de Météo-France, AROME. Cette analyse se compose de deux termes: une spline fonction de la prévision la plus récente d'AROME plus une correction par une spline fonction des coordonnées du point considéré. La nouvelle analyse obtient de meilleurs scores que l'analyse existante, et présente des structures spatio-temporelles réalistes. Cette nouvelle analyse, disponible au pas horaire sur 4 ans, sert ensuite d'observation en points de grille pour construire des AS.Des AS de vent sur la France ont été construites pour ARPEGE, le modèle global de Météo-France. Un banc d'essai comparatif désigne les forêts aléatoires comme meilleure méthode. Cette AS requiert un long temps de chargement en mémoire de l'information nécessaire pour effectuer une prévision. Ce temps de chargement est divisé par 10 en entraînant les AS sur des points de grille contigü et en les élaguant au maximum. Cette optimisation ne déteriore pas les performances de prévision. Cette approche d'AS par blocs est en cours de mise en opérationnel.Une étude préalable de l'estimation du « continuous ranked probability score » (CRPS) conduit à des recommandations pour son estimation et généralise des résultats théoriques existants. Ensuite, 6 AS de 4 modèles d'ensemble de PNT de la base TIGGE sont combinées avec les modèles bruts selon plusieurs méthodes statistiques. La meilleure combinaison s'appuie sur la théorie de la prévision avec avis d'experts, qui assure de bonnes performances par rapport à une prévision de référence. Elle ajuste rapidement les poids de la combinaison, un avantage lors du changement de performance des prévisions combinées. Cette étude a soulevé des contradictions entre deux critères de choix de la meilleure méthode de combinaison : la minimisation du CRPS et la platitude des histogrammes de rang selon les tests de Jolliffe-Primo. Il est proposé de choisir un modèle en imposant d'abord la platitude des histogrammes des rangs. / Errors of numerical weather prediction (NWP) models can be reduced thanks to post-processing methods (model output statistics, MOS) that build a statistical relationship between the observations and associated forecasts. The objective of the present thesis is to build MOS for windspeed forecasts over France on the grid of several NWP models, to be applied on operations at Météo-France, while addressing the two main issues. First, building MOS on the grid of some NWP model, with thousands of grid points over France, requires to develop methods fast enough for operational delays. Second, requent updates of NWP models require updating MOS, but training MOS requires an NWP model unchanged for years, which is usually not possible.A new windspeed analysis for the 10 m windspeed has been built over the grid of Météo-France's local area, high resolution (2,5km) NWP model, AROME. The new analysis is the sum of two terms: a spline with AROME most recent forecast as input plus a correction with a spline with the location coordinates as input. The new analysis outperforms the existing analysis, while displaying realistic spatio-temporal patterns. This new analysis, now available at an hourly rate over 4, is used as a gridded observation to build MOS in the remaining of this thesis.MOS for windspeed over France have been built for ARPEGE, Météo-France's global NWP model. A test-bed designs random forests as the most efficient MOS. The loading times is reduced by a factor 10 by training random forests over block of nearby grid points and pruning them as much as possible. This time optimisation goes without reducing the forecast performances. This block MOS approach is currently being made operational.A preliminary study about the estimation of the continuous ranked probability score (CRPS) leads to recommendations to efficiently estimate it and to generalizations of existing theoretical results. Then 4 ensemble NWP models from the TIGGE database are post-processed with 6 methods and combined with the corresponding raw ensembles thanks to several statistical methods. The best combination method is based on the theory of prediction with expert advice, which ensures good forecast performances relatively to some reference forecast. This method quickly adapts its combination weighs, which constitutes an asset in case of performances changes of the combined forecasts. This part of the work highlighted contradictions between two criteria to select the best combination methods: the minimization of the CRPS and the flatness of the rank histogram according to the Jolliffe-Primo tests. It is proposed to choose a model by first imposing the flatness of the rank histogram.

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.

Aus der Einleitung: The calculation and prediction of river flow is a very old problem. Especially extremely high values of the runoff can cause enormous economic damage. A system which precisely predicts the runoff and warns in case of a flood event can prevent a high amount of the damages. On the basis of a good flood forecast, one can take action by preventive methods and warnings. An efficient constructional flood retention can reduce the effects of a flood event enormously.With a precise runoff prediction with longer lead times (>48h), the dam administration is enabled to give order to their gatekeepers to empty dams and reservoirs very fast, following a smart strategy. With a good timing, that enables the dams later to store and retain the peak of the flood and to reduce all effects of damage in the downstream. A warning of people in possible flooded areas with greater lead time, enables them to evacuate not fixed things like cars, computers, important documents and so on. Additionally it is possible to use the underlying rainfall-runoff model to perform runoff simulations to find out which areas are threatened at which precipitation events and associated runoff in the river. Altogether these methods can avoid a huge amount of economic damage.:List of Symbols and Abbreviations S. III 1 Introduction S. 1 2 Process based Rainfall-Runoff Modelling S. 5 2.1 Basics of runoff processes S. 5 2.2 Physically based rainfall-runoff and hydrodynamic river models S. 15 3 Portraying Rainfall-Runoff Processes with Neural Networks S. 21 3.1 The Challenge in General S. 22 3.2 State-of-the-art Approaches S. 24 3.3 Architectures of neural networks for time series prediction S. 26 4 Requirements specification S. 33 5 The PAI-OFF approach as the base of the system S. 35 5.1 Pre-Processing of the Input Data S. 37 5.2 Operating and training the PoNN S. 47 5.3 The PAI-OFF approach - an Intelligent System S. 52 6 Design and Implementation S. 55 6.1 Design S. 55 6.2 Implementation S. 58 6.3 Exported interface definition S. 62 6.4 Displaying output data with involvement of uncertainty S. 64 7 Results and Discussion S. 69 7.1 Evaluation of the Results S. 69 7.2 Discussion of the achieved state S. 75 8 Conclusion and FutureWork S. 77 8.1 Access to real-time meteorological input data S. 77 8.2 Using further developed prediction methods S. 79 8.3 Development of a graphical user interface S. 80 Bibliography S. 83

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

Coronal Mass Ejections (CMEs) are considered to be one of the most energetic events in the heliosphere. Capable of inducing geomagnetic storms on Earth that can cause damage to electronics, a pillar which the modern society we live in leans heavily upon. Being able to accurately predict the arrival of CMEs would present us with the ability to issue timely warnings to authorities and commercial actors, allowing for protective measures to be put in place minimizing the damage. In this study the predicted arrival times and speeds from the Drag Based Model (DBM) and Drag Based Ensemble Model (DBEM) were compared to observational data from a set of 12 events containing fast, Earth-directed Halo CMEs and their corresponding shocks. Although DBM was developed to model CME propagation, varying some parameters allow it to be used for estimating shock/sheath arrival. The results presented in this study indicate that on average DBM performs best when the drag-parameter γ is in the range 0.2 ≤ γ ≤ 0.3. However the variability in the results show that determining a universal value of γ for fast CMEs does not increase the consistency in the model's performance. For completeness, further investigation is needed to account for not only halo CMEs. This will allow to test broader range of variation in the DBEM input parameters.

The present work reports a study related to the potential improvement of the energy performances of a heat pump based heating system for a Swedish single-family house. The analysis is focused on the design of new rule-based control strategies which employ perfect predictions of weather forecast and human behaviour information. In particular, the considered signals are the outdoor temperature, the solar radiation, the internal gain due to inhabitants’ activities and the Domestic Hot Water (DHW) consumption. The study is performed by means of the TRNSYS® simulation software in which the model of the heating system is implemented. More specifically, it is composed by a Ground Source Heat Pump (GSHP) unit, a stratified storage tank of three hundred litres and the building element. The performances of the developed control logics are evaluated using a degree-minute on/off controller as reference case. The results show that the improved control logics yield to an increase of the energy efficiency of the system as well as an enhancement of the indoor and DHW temperatures stability. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems

The present work deals with a study related to the analysis of weather data for heat pump system control improvement based on both instantaneous and forecasted measurements. In particular, the analysis is firstly focused on the comparison of multiple weather sources for the assessment of weather forecast uncertainties, based on the evaluation of errors in prediction with respect to measured values. Afterwards, the results are compared with the ones related to persistent predictions methods that assumes the state of the atmosphere to be stationary over the considered time interval. The development and testing of a new preliminary “predictive” control logic is also performed, thanks to TRNSYS numerical simulations, considering a typical Swedish single-family house located in Stockholm, with the aim of optimizing the operation of a heat pump heating system based on solar radiation prediction to yield energy and cost savings. With the crucial points of accuracy and precision by which the local weather processes can be predicted, the same TRNSYS model is run accounting for perfect predictions and solar radiation forecasted values. From this perspective, given the fact that forecast of solar radiation are usually absent within most of the weather forecast datasets, a deep analysis is also performed on hourly measurements of solar radiation to define a simple and effective methods to calculate hourly solar radiation predictions. The results show that, when a short-time horizon is considered, persistent predictions allow to provide forecasts with a sufficient accuracy, whereas, when longer horizon time are considered, significantly higher errors are calculated when persistent prediction techniques are adopted. Independently of the uncertainties considered for weather forecasts, the improved control logics demonstrated a potential for energy savings and improvements in indoor temperature stability when compared with a reference case of variable speed compressor with PID controller. / EffSys Expand P18: Smart Cotnrol Strategies for Heat Pump Systems