This dissertation describes research designed to enhance hydrometeorological forecasts. The objective of the research is to deliver an optimal methodology to produce reliable, skillful and economically valuable probabilistic temperature and precipitation forecasts. Weather plays a dominant role for energy companies relying on forecasts of watershed precipitation and temperature to drive reservoir models, and forecasts of temperatures to meet energy demand requirements. Extraordinary precipitation events and temperature extremes involve consequential water- and power-management decisions. This research compared weighted-average, recursive, and model output statistics bias-correction methods and determined optimal window-length to calibrate temperature and precipitation forecasts. The research evaluated seven different methods for daily maximum and minimum temperature forecasts, and three different methods for daily quantitative precipitation forecasts, within a region of complex terrain in southwestern British Columbia, Canada. This research then examined ensemble prediction system design by assessing a three-model suite of multi-resolution limited area mesoscale models. The research employed two different economic models to investigate the ensemble design that produced the highest-quality, most valuable forecasts. The best post-processing methods for temperature forecasts included moving-weighted average methods and a Kalman filter method. The optimal window-length proved to be 14 days. The best post-processing methods for achieving mass balance in quantitative precipitation forecasts were a moving-average method and the best easy systematic estimator method. The optimal window-length for moving-average quantitative precipitation forecasts was 40 days. The best ensemble configuration incorporated all resolution members from all three models. A cost/loss model adapted specifically for the hydro-electric energy sector indicated that operators managing rainfall-dominated, high-head reservoirs should lower their reservoir with relatively low probabilities of forecast precipitation. A reservoir-operation model based on decision theory and variable energy pricing showed that applying an ensemble-average or full-ensemble precipitation forecast provided a much greater profit than using only a single deterministic high-resolution forecast. Finally, a bias-corrected super-ensemble prediction system was designed to produce probabilistic temperature forecasts for ten cities in western North America. The system exhibited skill and value nine days into the future when using the ensemble average, and 12 days into the future when employing the full ensemble forecast.

This dissertation describes research designed to enhance hydrometeorological forecasts. The objective of the research is to deliver an optimal methodology to produce reliable, skillful and economically valuable probabilistic temperature and precipitation forecasts. Weather plays a dominant role for energy companies relying on forecasts of watershed precipitation and temperature to drive reservoir models, and forecasts of temperatures to meet energy demand requirements. Extraordinary precipitation events and temperature extremes involve consequential water- and power-management decisions. This research compared weighted-average, recursive, and model output statistics bias-correction methods and determined optimal window-length to calibrate temperature and precipitation forecasts. The research evaluated seven different methods for daily maximum and minimum temperature forecasts, and three different methods for daily quantitative precipitation forecasts, within a region of complex terrain in southwestern British Columbia, Canada. This research then examined ensemble prediction system design by assessing a three-model suite of multi-resolution limited area mesoscale models. The research employed two different economic models to investigate the ensemble design that produced the highest-quality, most valuable forecasts. The best post-processing methods for temperature forecasts included moving-weighted average methods and a Kalman filter method. The optimal window-length proved to be 14 days. The best post-processing methods for achieving mass balance in quantitative precipitation forecasts were a moving-average method and the best easy systematic estimator method. The optimal window-length for moving-average quantitative precipitation forecasts was 40 days. The best ensemble configuration incorporated all resolution members from all three models. A cost/loss model adapted specifically for the hydro-electric energy sector indicated that operators managing rainfall-dominated, high-head reservoirs should lower their reservoir with relatively low probabilities of forecast precipitation. A reservoir-operation model based on decision theory and variable energy pricing showed that applying an ensemble-average or full-ensemble precipitation forecast provided a much greater profit than using only a single deterministic high-resolution forecast. Finally, a bias-corrected super-ensemble prediction system was designed to produce probabilistic temperature forecasts for ten cities in western North America. The system exhibited skill and value nine days into the future when using the ensemble average, and 12 days into the future when employing the full ensemble forecast.

This dissertation describes research designed to enhance hydrometeorological forecasts. The objective of the research is to deliver an optimal methodology to produce reliable, skillful and economically valuable probabilistic temperature and precipitation forecasts. Weather plays a dominant role for energy companies relying on forecasts of watershed precipitation and temperature to drive reservoir models, and forecasts of temperatures to meet energy demand requirements. Extraordinary precipitation events and temperature extremes involve consequential water- and power-management decisions. This research compared weighted-average, recursive, and model output statistics bias-correction methods and determined optimal window-length to calibrate temperature and precipitation forecasts. The research evaluated seven different methods for daily maximum and minimum temperature forecasts, and three different methods for daily quantitative precipitation forecasts, within a region of complex terrain in southwestern British Columbia, Canada. This research then examined ensemble prediction system design by assessing a three-model suite of multi-resolution limited area mesoscale models. The research employed two different economic models to investigate the ensemble design that produced the highest-quality, most valuable forecasts. The best post-processing methods for temperature forecasts included moving-weighted average methods and a Kalman filter method. The optimal window-length proved to be 14 days. The best post-processing methods for achieving mass balance in quantitative precipitation forecasts were a moving-average method and the best easy systematic estimator method. The optimal window-length for moving-average quantitative precipitation forecasts was 40 days. The best ensemble configuration incorporated all resolution members from all three models. A cost/loss model adapted specifically for the hydro-electric energy sector indicated that operators managing rainfall-dominated, high-head reservoirs should lower their reservoir with relatively low probabilities of forecast precipitation. A reservoir-operation model based on decision theory and variable energy pricing showed that applying an ensemble-average or full-ensemble precipitation forecast provided a much greater profit than using only a single deterministic high-resolution forecast. Finally, a bias-corrected super-ensemble prediction system was designed to produce probabilistic temperature forecasts for ten cities in western North America. The system exhibited skill and value nine days into the future when using the ensemble average, and 12 days into the future when employing the full ensemble forecast. / Science, Faculty of / Earth, Ocean and Atmospheric Sciences, Department of / Graduate

Studies show teenagers are influenced by television. This study tested the hypothesis that students can learn meteorology by viewing a weather forecast on Music Television (MTV). MTV was used because it is the network watched most by adolescents. Two surveys were administered to 175 high school students along with a DVD showing a weather forecast for MTV’s Spring Break. Half of the sample group was told the forecast was for MTV and the other was told it was for Channel One. Results showed that there was no statistically significant difference between MTV and Channel One in information recalled from the forecast. Results also showed the White student population recalled more information from the weather forecast format than the other races surveyed in the study. Additionally a series of concert forecasts was pilot-tested on 15 bands with very positive and promising feedback.

There are many unsolved questions about the future of climate, and most of them are due to lack of knowledgeabout the complex system of atmosphere, but still there are models that produce relatively realistic projectionsof the future although there are uncertainties in the presentation of them, and that's where statistical methodscould be of help. Here a short introduction is given to the projection of future climate with GCM ensembles andthe uncertainties about them, the emerging probabilistic approach, as well as the REA (Reliability EnsembleAverage) method for measuring the reliability of the model projections. In order to have an impression of theresults of the GCM ensemble results and their uncertainties the results of the weather forecast over a time periodof one year in three dierent cities of Sweden is studied as well.

This thesis analyses comparison and verification of three global numeric weather models, GFS, ECMWF, NEMS. The research subjects are make comparison of their 48-hour forecast with, for this thesis created, index correspondence of models and evaluate predictability of weather. Next, introduce basic verification methods and their application to forecast verification, from previously mentioned models, against surface observations with resolution 2 ř x 2 ř lat/lon between 1. 6. 2017-28. 2. 2018. Results show, that the worst predictability is at areas with continental glaciers, extensive world mountain ranges and at ITCZ area. The best predictability is observed in subtropical anticyclones over the oceans. Verification of temperature we find out significant smoothing of diurnal cycle in all three models. Biases of relative humidity are strongly negative corelated with temperature bias, skill score for relative humidity is worse than for temperature. Performance of mean sea level pressure is the best for all verification metrics from all analysed quantities. Wind speed is for most world overestimated. Results of 3-hour precipitation depends on treshold. Models overestimate frequency of low intensity precipitation, opposite results are observed for high intensity precipitation, break occur at interval...

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The real time determination and future of evapotranspiration (ET) can contribute to more efficient management of irrigation. The objective of this study was: (i) to evaluate the accuracy of the estimate of the reference evapotranspiration (ETo) calculated on daily data of the weather forecast for seven future days (EToest), to different locations, growing seasons and periods with and without and rain; (Ii) evaluate the impact of using the EToest in the water balance of a soil cultivated with corn. The estimate ETo was performed to Santa Maria - RS and Crystalline and Morrinhos - GO. The collection of weather forecast data was carried out between June 26, 2011 to March 31 2014 was collected-daily (6:00 AM), the site: http://www.tempoagora.com.br/, the forecast data maximum temperature (Tmax) and minimum (Tmin) air, maximum relative humidity (RHmax) and minimum (RHmin) and wind speed (U2) for the next seven days. The EToest calculation was carried out by FAO-Penman-Monteith (FAO PM) and Hargreaves methods. The ETo observed day (EToday) was calculated by the FAO- PM method with data measured by automatic weather station of the National Institute of Meteorology (INMET). The values of EToest (est+1 the est+7) were compared with the values observed daily (ETodia). The accuracy of the estimated EToest was evaluated using statistical indicators (coefficient of determination - R2, slope - b, mean square error of the square root -RMSE and average error - ME). For the analysis of the water balance of a soil cultivated with corn, a field experiment was accomplished in the year 2011/12, in an area of UFSM. The experimental design was a completely randomized factorial design with four replications. The factor "A" consisted of two methods of calculation to estimate crop evapotranspiration (ETc): (i) using crop coefficient (Kc) and simple; (Ii) using dual Kc. The "D" factor consisted of four irrigation strategies: (i) 100% replacement of ETc; (ii) 75% replacement of ETc; (iii) 50% of ETc and replacement; (iv) 25% of replacement etc. The volumetric water content and available soil were simulated is compared with the measured (100% tartamento simple Kc). The results are an overestimation of EToest (>0,04 and <0,72 mm day-1) for the three sites, attributed to overestimation of Tmax by the weather forecast, a variable that showed high association with EToest (R2 0,60). This overestimation of EToest is reduced as the estimation approximates the observed daily. The differences between the ETodia and EToest, has small inlfuência the results of the soil water balance (RMSE less than 10 mm for the available water). Thus, EToest can be an alternative for determining the need for crop irrigation in future time, contributing to the management of irrigated activity and for the most efficient use of natural resources and other agricultural. / A determinação em tempo real e futuro da evapotranspiração (ET) pode contribuir para a maior eficiência no gerenciamento da irrigação. Assim, o objetivo do presente trabalho foi: (i) avaliar a precisão da estimativa da evapotranspiração de referência (ETo) calculada com dados diários da previsão meteorológica para sete dias futuros (EToest), para diferentes locais, anos agrícolas e períodos com e sem chuvas e; (ii) avaliar o impacto da utilização da EToest no balanço hídrico de um solo cultivado com milho. A estimativa da ETo foi realizada para Santa Maria - RS e para Cristalina e Morrinhos - GO. A coleta de dados da previsão meteorológica foi realizada entre 26 de junho de 2011 a 31 de março de 2014. Coletou-se, diariamente (6:00 AM), do site: http://www.tempoagora.com.br/, os dados de previsão de temperatura máxima (Tmax) e mínima (Tmin) do ar, umidade relativa máxima (RHmax) e mínima (RHmin) do ar e velocidade do vento (U2), para os próximos sete dias. O cálculo da EToest foi realizado pelos métodos FAO-Penman-Monteith (FAO PM) e Hargreaves. A ETo do dia observado (ETodia) foi calculada pelo método FAO PM, com dados medidos pela estação meteorológica automática do Instituto Nacional de Meteorologia (INMET). Os valores de EToest (est+1 a est+7) foram comparados com os valores do dia observado (ETodia). A precisão da estimativa da EToest foi avaliada a partir de indicadores estatísticos (coeficiente de determinação - R2, coeficiente angular - b, raiz quadrada do erro quadrático médio -RMSE e erro médio - ME). Para a análise do balanço hídrico de um solo cultivado com milho, um experimento de campo foi realizado no ano agrícola 2011/12, em área experimental da UFSM. O delineamento experimental utilizado foi o inteiramente casualizado, bifatorial, com quatro repetições. O fator A constituiu de duas metodologias de cálculo para a estimativa da evapotranspiração da cultura (ETc): (i) utilizando coeficiente de cultivo (Kc) simples e; (ii) utilizando Kc dual. O fator D foi constituído de quatro estratégias de irrigação: (i) 100% de reposição da ETc; (ii) 75% de reposição da ETc; (iii) 50% de reposição da ETc e; (iv) 25% de reposição da ETc. O conteúdo volumétrico e a água disponível no solo simulados foram comparados com os medidos (tratamento 100% com Kc simples). Os resultados são de uma superestimativa média da EToest (>0,04 e <0,72 mm dia-1) para os três locais, atribuída a superestimativa da Tmax pela previsão meteorológica, variável que apresentou elevada associação com a EToest (R2 de 0,60). Essa superestimativa da EToest é reduzida à medida que a estimativa se aproxima do dia observado. As diferenças entre a ETodia e a EToest, tem pequena inlfuência nos resultados do balanço hídrico do solo (RMSE inferior a 10 mm para a água disponível). Assim, a EToest pode ser uma alternativa para a determinação da necessidade de irrigação das culturas em tempo futuro, contribuindo para o gerenciamento da atividade irrigada e para a maior eficiência na utilização dos recursos naturais e demais insumos agrícolas.

This thesis is focused on assessing the impact of meteorological phenomena on transport safety. Analysis, questionnaire survey and meteorological forecasts were used for the evaluation. Part of the theoretical part of the thesis includes basic legislative regulations, meteorology and accident theory, playing an important role in the general transport.

Die zu erwartenden Temperaturen und Regenmengen der folgenden Tage bis Stunden sind heutzutage eine der wichtigsten Informationen. Diese Kenntnis ist nicht nur von allgemeinem Interesse. Insbesondere Bereiche wie die Landwirtschaft und Forstwirtschaft sind die zu erwartenden Regenmengen selbst über einen langen Zeitraum von Wochen von besonderen Interesse um zum Beispiel die Ernte oder den Schutz von Pflanzen zu planen. Daher ist die Fähigkeit, das Wetter zuverlässig und schnell für ausreichend lange Zeiträume vorher zu sagen, wesentlich. Die Zuverlässigkeit der Wettervorhersage, oder genau genommen der numerischen Wettervorhersage, hängt von mehreren Faktoren ab. Einer dieser Faktoren ist die Detailliertheit der Atmosphärenmodelle. Während die ersten numerischen Experimente die Atmosphäre als eine Schicht trockenen idealen Gases betrachteten, beinhalten aktuelle Modelle die Feuchte, Wolken, Niederschlag und Strahlung. Mit jedem zusätzlichen Detail steigt natürlich der Simulationsaufwand. Daher müssen parallel zur verbesserten Modellierung auch die numerischen Verfahren erweitert werden. Im allgemeinen sind die Atmosphärenmodelle Systeme nichtlinearer hyperbolischer Differentialgleichungen (PDEs). Insbesondere beinhalten die Modelle Wellen unterschiedlicher Ausbreitungsgeschwindigkeit, welche nahezu nicht gedämpft werden. Diese unterschiedlichen Geschwindigkeiten sind die Grundlage für den Mehrskalencharakter der Atmosphärenmodelle. Eine effektive numerische Methode muss daher die unterschiedlichen Skalen adäquat behandeln. Die Entwicklung und Analyse numerischer Mehrskalenverfahren zur Lösung von Systemen hyperbolischer Differentialgleichungen ist herausfordernd. Beispiele für hyperbolische Systeme beginnen bei der einfachen skalaren linearen Advektionsgleichung, der Wellengleichung und enden bei nichtlinearen Systemen wie den Flachwassergleichungen oder den (reibungsfreien) Eulergleichungen. Letztere sind die Grundlage für alle Atmosphärenmodelle. Viele hyperbolische PDEs besitzen eine additive Struktur, wobei die Aufteilung gerade den Zeitskalen entsprechen. Wir gehen von einer angepassten Diskretisierung im Raum, in der Regel eine Finite-Volumen Diskretisierung, aus. Diese Diskretisierung erhält die additive Struktur des kontinuierlichen Problems in der (ortsdiskretisierten) gewöhnlichen Differentialgleichung (ODE). Daher entwickeln wir eine neue numerische Methode zur Lösung gewöhnlicher Differentialgleichungen, welche die additive Struktur und gleichzeitig die zugehörigen Zeitskalen ausnutzt. Die Analyse von Splittingverfahren ist herausfordernd sowohl in der Entwicklung der Ordnungsbedingungen als auch der Stabilitätskriterien. Jeder Mehrskalenansatz kombiniert die unterschiedlichen Zeitskalen auf unterschiedliche Art und Weise. Daher gibt es keine einheitliche Ordnungs- und Stabilitätstheorie. Wir entwickeln die Ordnungsbedingungen auf klassischem Wege, durch Differentiation der numerischen Lösung. Die Aufteilung der rechten Seite in schnelle und langsame Terme führt auf zusätzliche Koeffizienten und Kombinationen der elementaren Differentiale. Im Vergleich zu klassischen Verfahren hat jedes elementare Differential unterschiedliche nicht-klassische Koeffizienten, ohne erkennbare Struktur. Dieser Strukturverlust erschwert die numerische Lösung zusätzlich. Analytische Lösungen gibt es nur in Sonderfällen. Wir entwickeln und analysieren eine neue Klasse von Mehrskalen methoden, welche mit den Integrator der schnellen Skale parametriert ist. Dieser neue Ansatz erlaubt die Verallgemeinerung der Ausgangsmethode und vereinfacht etliche Schritte in der Herleitung der Ordnungsbedinungen. Zusätzlich hat die Verallgemeinerung auch den Vorteil, die Ordnungsbedingungen des Gesamtverfahrens und die Struktur des darunter liegenden Lösers der schnellen Zeitskale zu assoziieren. Wir untersuchen ebenfalls die lineare Stabilität der neuen Methoden. Aufgrund der Aufteilung in langsame und schnelle Terme gibt es viele verschiedene Modellprobleme. Wir leiten ein Modellproblem auf Basis eines vereinfachten hyperbolischer PDEs her. Auf Basis dieses Stabilitsproblems konstruieren wir die neuen Methoden und untersuchen ihre Effizienz anhand zweier nichtlineare Benchmarkprobleme. Analog zur Herleitung der Ordnungsbedingungen vereinheitlichen wir die Konstruktion der Stabilitätsfunktionen und heben im nachhinein die Unterschiede aufgrund des fast-scale integrators hervor. Gute numerische Methoden führen nicht nur zu einem kleinen Fehler, sondern haben auch ein großes Stabilitätsgebiet. Daher optimizieren wir die Methodenparameter im Hinblick auf die Größe des Stabilitätsgebiets. Unsere neuen Methoden besitzen sowohl reelle, als auch rationale Parameter. Die Lösung des gemischten ganzzahligen-reellen Optimierungsproblem vereinfachen wir durch die Auswahl einzelner rationaler Parameter. Dadurch erhalten wir allerdings einige tausend unabhängige Teilprobleme. Zum Abschluss analysieren wir die Effizienz der neuen Methoden anhand zweier nichtlinearer Benchmarkprobleme und vergleichen die Genauigkeit und Stabilität mit Referenzverfahren. / The expected temperatures and rainfall in the next days to hours is one of the most important information nowadays. This knowledge is not only of general interest. Disciplines like agriculture and forestry the knowledge of the rain is even more important for a time span of weeks to plan the harvest or protect the plants. Therefore, the possibility to forecast the weather reliably and fast is very important nowadays. The reliability of weather forecast, or more accurate the numerical weather forecast, depends on several factors. One factor is the complexity of atmosphere models. Whereas the first numerical experiments treat the atmosphere as dry ideal gas with one layer, recent models incorporate the humidity, clouds, precipitation and radiation. But every higher detail in the model come at higher costs for simulation. Hence the development of finer grained models also require more advanced numerical methods to solve them. The atmosphere models are in general a nonlinear hyperbolic set of partial differential equations (PDEs). In particular the models consist of several waves, traveling with different speeds with nearly no damping. Roughly speaking these varying velocities lead to the multiscale nature of the atmosphere models and a suitable numerical method should respect the different time scales. The development and analysis of multirate methods for hyperbolic systems remains a challenging problem. Examples for class of hyperbolic systems of PDEs range from the scalar and linear advection equation, the wave equation to nonlinear systems like the shallow water equations or the (inviscid) Euler equations, which are the basis for the atmosphere models. The hyperbolic PDEs often have an additive split structure, which in turn account for the different time scales. We assume a suitable, often finite volume, discretization in space. Hence we retain the additive splitting from the continuous problem in the semi-discretized ordinary differential equation (ODE). Hence we develop a new numerical method which accounts for the additive split structure and the multiscale nature. The development of splitting methods is challenging in the analysis of the order conditions and the stability criteria. In particular the interaction between the fast and slow scales render the order conditions often complicated and unstructured. Furthermore every multiscale approach combines the scales in a different way, which is why there is no unified order condition theory. With these challenges in mind we derive the order conditions in a classical way by differentiation of the numerical method. The splitting in a fast and a slow right hand side leads to several combinations of elementary differentials. And every differential has different non-standard coefficients, without any structure between these combinations. This loss in structure renders the numerical solutions of the order conditions quite complicated, and the analytical solutions are only possible in rare cases. We develop a new class of multirate methods, which is parameterized by the fast scale solver. That new approach allows for a better generalization and simplifies several steps by unification. Nevertheless this new type of generalization has the advantage to associate the order conditions of the complete (macro scale) method with the structure of the underlying (micro scale) integrator. The second challenge is the analysis of the (linear) stability of multirate methods. We also analyze the (linear) stability of the newly developed methods. Due to the splitting structure there are many different model problems possible. We deduce a model problem from a simplified system of hyperbolic PDEs. On top of these stability model problems we will construct the new methodss. In analogy to the analysis of the order conditions, we unify the construction of the stability functions and highlight the differences due to the different fast scale integrators afterwards. A good method does not only lead to low errors, but also has a large stability area. Hence we optimize the method parameters with respect to the stability area. In our case, the parameter set contains rational and real parameters. We circumvent the solution of a mixed-integer optimization problem by considering only some rational parameters and optimize for them independently. Nevertheless, we obtain several thousand sub problems. Finally we consider two nonlinear benchmark problems. With these problems we analyze the accuracy and stability again and compare the efficiency with two reference multiscale methods.

På uppdrag av JAADAB AB har en undersökning huruvida prognosstyrning kan användas för att effektivt reglera värme i en byggnad samt spara energi och ge jämnare innetemperatur genom att använda SMHIs öppna API. I uppdraget ingick även att skapa en modell för att kunna implementera detta i en befintlig byggnad med okända där exakt kännedom om byggandens konstruktion och egenskaper saknas.   Arbetet inleddes med en teoristudie gällande byggnadens värmeöverföring samt hur vädret påverkar detta samt där det framgår att byggnaden kan tillgodogöra sig tillförd energi från solinstrålning samt att vind leder till ökade förluster via transmission, ventilation samt läckage.  En studie gjordes även gällande prognosstyrning som ledde fram till en väderkompenserad ekvivalent temperatur.   Modellen skapades sedan först teoretiskt innan den programmerades med ett koncept där byggnaden beskrivs via parametrar gällande utformning och material där bedömningar kan göras i de fall där exakt kunskap saknas. Modellen använde sedan dessa parametrar och SMHIs API för beräkningar gällande byggandens värmebalans under de nästkommande två dygnen och ta fram en ekvivalent temperatur som ersätter utetemperaturen som ingående temperatur till styrsystemet. Beräkningar gällande vind finns förberedda men är inte inkluderade då inget tillräckligt pålitligt samband mellan vindhastighet och förluster kunde fastställas innan simuleringsstart.    Modellen simulerades för en lätt och en tung byggnadskonstruktion via en DUC där utetemperaturen given av SMHI samt den ekvivalenta temperaturen loggades under fyra dygn.   Resultatet av simuleringen visade att medeltemperaturen var högre i båda fallen för den ekvivalenta temperaturen.  Gällande den tunga byggnadskonstruktionen var topptemperaturen lägre och bottentemperaturen högre för den ekvivalenta temperaturen. Gällande den lätta byggnadskonstruktionen var både topp och bottentemperaturen lägre för den ekvivalenta temperaturen. Arbetet visar tecken på att energibesparingar kan göras men kan inte fastställa att detta kan göras på lång sikt på grund av den korta testperioden som medför att ett begränsat antal förhållanden testats.   Förbättringsförslag finns lämnade via mera komplexa beräkningar gällande byggnadens värmeöverföring och påverkan av närmiljö samt flera och längre simuleringar. Förslag på framtida arbeten har lämnats gällande slutförande av vindberäkningar, ytterligare simuleringar, test på verklig byggnad och adderandet av en självlärande loop. / On behalf of JAADAB AB, a study has been conducted on whether forecast control can be used to effectively regulate heat in a building to save energy and provide a more even indoor temperature by using SMHI's open API. The assignment also included creating a model to be able to implement this in an existing building where details regarding the construction and properties of the building are unknown. The work began with a theoretical study regarding the building's heat transfer and how the weather affects the heat transfer where it appears that the building can utilize the supplied energy from solar radiation and that wind leads to increased losses via transmission, ventilation and leakage. A study was also done regarding forecast control which led to a weather-compensated equivalent temperature. The model was then first created theoretically before it was programmed with a concept where the building is described via parameters regarding design and materials where assessments can be made in cases where exact knowledge of the values is lacking. The model then used the submitted parameters and SMHI's API for calculations regarding the construction's heat balance during the next two days to produce an equivalent temperature that replaces the outdoor temperature as the input temperature to the control system. Calculations regarding wind are prepared but are not included as no sufficiently reliable connection between wind speed and losses could be established before the start of the simulation. The model was simulated for a light and a heavy building construction via the use of a DUC where the outdoor temperature was given by SMHI and the equivalent temperature were logged for four days. The result of the simulation showed that the average temperature was higher in both cases for the equivalent temperature. Regarding the heavy building construction, the highest temperature was lower and the bottom temperature higher for the equivalent temperature. Regarding the lightweight building construction, both the top and bottom temperatures were lower for the equivalent temperature. The work shows signs that energy savings can be made but it cannot be established that this can be done in the long term due to the short test period which means that a limited number of conditions have been tested. Suggestions for improvement have been submitted via more complex calculations regarding the building's heat transfer and impact on the local environment around the building as well as several further simulations with over more extended periods of time. Suggestions for future work have been submitted regarding the completion of wind calculations, additional simulations, tests on real building and the addition of a self-learning loop.