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Juzovitski, Emil. "Wearable Weather Forecast : Creating a forecast through iOS and Smart Clothes." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229445.
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Smart Clothes refers to clothes that have been embedded with computer devices. Items of the category can be used to inform an individual about his health. The technology can likewise be used to track an individuals surroundings. There are many possible uses for Smart Clothes. This thesis is based on the idea of creating a Smart Clothes item that can create weather forecasts independently. In order to create such system unique problems have to be solved. One major problem to overcome is realizing what a mobile application a mobile application has to do in a Smart Clothes system that predicts weather to the user. This thesis tries to answer the problem by examining how an existing weather forecast model can be implemented into an application to later display a forecast to a user. The study is built on an inductive approach and has a purpose to explore the necessary steps in developing a mobile application that predicts the weather. Presenting an implemented prototype is the purpose of the thesis. As such, the purpose becomes to create a mobile application that implements a weather forecast model and translates the created weather forecast into a UI suited for a mobile application. The weather forecast model should use atmospheric pressure differences to create a weather forecast. The goal of the thesis is to find out how the purpose can be achieved, how a mobile application should behave in order to assure that the Smart Clothes system can create a weather forecast for the user. A partial goal is to understand how atmospheric pressure should be converted into a weather forecast and how the weather forecast should be translated into a UI. The result is presented in the form of a prototype, an iOS application. The created prototype uses atmospheric pressure differences to create a weather forecast. The weather forecast is shown to the user through a intuitive user interface. The prototype is tested through a user-test and is evaluated through a thorough model with criteria. The evaluation shows that the prototype is easy to use and understand. There are in the same time improvements to be done for the future. Proposals have been given for improvements. The user-test has shown that the interest in the prototype is higher in situations where the user lacks internet connectivity. There is thus, a link between the prototype's usability and internet connectivity. In other words, people find the prototype to be of a big benefit as it can be used without Internet connectivity.<br>”Smart Clothes” refererar till klädesplagg som har integrerats med dataenheter. Plagg i kategorin kan användas för att informera individen om hans hälsa. Teknologin kan likväl användas för att spåra individens omgivning. Det finns många möjliga användningsområden för Smart Clothes. Den här uppsatsen baseras på idén att skapa en Smart-klädesplagg som kan skapa väderprognoser självständigt. Uppsatsen försöker lösa problemet om vad en mobil applikation måste göra i ett Smart-klädessystem som förutspår väder för användaren. Studien bygger på en induktiv forskningsinsats och syftar på att utforska faktorer runt utvecklingen av en mobilapplikation, som är ansedd att integreras i klädesplagg. Att presentera en skapad prototyp är uppsatsens syfte. Detta ger att syftet blir att skapa en mobil applikation som implementerar en väderprognos-modell och omvandlar den skapade prognosen till ett användargränssnitt som passar en mobil-applikation. Väderprognos-modellen ska kunna konvertera skillnader i lufttryck till en väderprognos. Målet av uppsatsen är att finna hur syftet kan uppfyllas, hur en mobil-applikation ska agera för att säkerställa att Smart-klädessytemet kan skapa en väderprognos för användaren. Ett delmål är att förstå hur lufttryck ska omvandlas till en väderprognos och hur väderprognosen ska omvandlas till att användargränsnitt. I egenskap av resultat presenteras en prototyp iOS mobilapplikation. Den skapade prototypen använder skillnader i lufttryck för att skapa en väderprognos. Väderprognosen visas vidare till användaren genom ett intuitivt användargränssnitt. Prototypen testas genom ett användartest och utvärderas genom en utarbetad modell med kriterier. Evalueringen visar att den skapade prototypen är lätt att använda och förstå. Samtidigt finns fortfarande förbättringar att göra i framtiden. Rapporten ger förslag till förbättringar. Användartest indikerar att intresset att använda produkten är högre i situationer då användaren saknar internetanslutning. Således finns det en länk mellan produktens användbarhet och internetanslutning. Alltså, personer finner att prototypen är av stor nytta för att den kan användas även utan uppkoppling till internet.
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Mackie, Shona. "Exploiting weather forecast data for cloud detection." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/4350.
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Accurate, fast detection of clouds in satellite imagery has many applications, for example Numerical Weather Prediction (NWP) and climate studies of both the atmosphere and of the Earth’s surface temperature. Most operational techniques for cloud detection rely on the differences between observations of cloud and of clear-sky being more or less constant in space and in time. In reality, this is not the case - different clouds have different spectral properties, and different cloud types are more or less likely in different places and at different times, depending on atmospheric conditions and on the Earth’s surface properties. Observations of clear sky also vary in space and time, depending on atmospheric and surface conditions, and on the presence or absence of aerosol particles. The Bayesian approach adopted in this project allows pixel-specific physical information (for example from NWP) to be used to predict pixel-specific observations of clear sky. A physically-based, spatially- and temporally-specific probability that each pixel contains a cloud observation is then calculated. An advantage of this approach is that identification of ambiguously classed pixels from a probabilistic result is straightforward, in contrast to the binary result generally produced by operational techniques. This project has developed and validated the Bayesian approach to cloud detection, and has extended the range of applications for which it is suitable, achieving skills scores that match or exceed those achieved by operational methods in every case. High temperature gradients can make observations of clear sky around ocean fronts, particularly at thermal wavelengths, appear similar to cloud observations. To address this potential source of ambiguous cloud detection results, a region of imagery acquired by the AATSR sensor which was noted to contain some ocean fronts, was selected. Pixels in the region were clustered according to their spectral properties with the aim of separating pixels that correspond to different thermal regimes of the ocean. The mean spectral properties of pixels in each cluster were then processed using the Bayesian cloud detection technique and the resulting posterior probability of clear then assigned to individual pixels. Several clustering methods were investigated, and the most appropriate, which allowed pixels to be associated with multiple clusters, with a normalized vector of ‘membership strengths’, was used to conduct a case study. The distribution of final calculated probabilities of clear became markedly more bimodal when clustering was included, indicating fewer ambiguous classifications, but at the cost of some single pixel clouds being missed. While further investigations could provide a solution to this, the computational expense of the clustering method made this impractical to include in the work of this project. This new Bayesian approach to cloud detection has been successfully developed by this project to a point where it has been released under public license. Initially designed as a tool to aid retrieval of sea surface temperature from night-time imagery, this project has extended the Bayesian technique to be suitable for imagery acquired over land as well as sea, and for day-time as well as for night-time imagery. This was achieved using the land surface emissivity and surface reflectance parameter products available from the MODIS sensor. This project added a visible Radiative Transfer Model (RTM), developed at University of Edinburgh, and a kernel-based surface reflectance model, adapted here from that used by the MODIS sensor, to the cloud detection algorithm. In addition, the cloud detection algorithm was adapted to be more flexible, making its implementation for data from the SEVIRI sensor straightforward. A database of ‘difficult’ cloud and clear targets, in which a wide range of both spatial and temporal locations was represented, was provided by M´et´eo-France and used in this work to validate the extensions made to the cloud detection scheme and to compare the skill of the Bayesian approach with that of operational approaches. For night land and sea imagery, the Bayesian technique, with the improvements and extensions developed by this project, achieved skills scores 10% and 13% higher than M´et´eo-France respectively. For daytime sea imagery, the skills scores were within 1% of each other for both approaches, while for land imagery the Bayesian method achieved a 2% higher skills score. The main strength of the Bayesian technique is the physical basis of the differentiation between clear and cloud observations. Using NWP information to predict pixel-specific observations for clear-sky is relatively straightforward, but making such predictions for cloud observations is more complicated. The technique therefore relies on an empirical distribution rather than a pixel-specific prediction for cloud observations. To try and address this, this project developed a means of predicting cloudy observations through the fast forward-modelling of pixel-specific NWP information. All cloud fields in the pixel-specific NWP data were set to 0, and clouds were added to the profile at discrete intervals through the atmosphere, with cloud water- and ice- path (cwp, cip) also set to values spaced exponentially at discrete intervals up to saturation, and with cloud pixel fraction set to 25%, 50%, 75% and 100%. Only single-level, single-phase clouds were modelled, with the justification that the resulting distribution of predicted observations, once smoothed through considerations of uncertainties, is likely to include observations that would correspond to multi-phase and multi-level clouds. A fast RTM was run on the profile information for each of these individual clouds and cloud altitude-, cloud pixel fraction- and channel-specific relationships between cwp (and similarly cip) and predicted observations were calculated from the results of the RTM. These relationships were used to infer predicted observations for clouds with cwp/cip values other than those explicitly forward modelled. The parameters used to define the relationships were interpolated to define relationships for predicted observations of cloud at 10m vertical intervals through the atmosphere, with pixel coverage ranging from 25% to 100% in increments of 1%. A distribution of predicted cloud observations is then achieved without explicit forward-modelling of an impractical number of atmospheric states. Weights are applied to the representation of individual clouds within the final Probability Density Function (PDF) in order to make the distribution of predicted observations realistic, according to the pixel-specific NWP data, and to distributions seen in a global reference dataset of NWP profiles from the European Centre for Medium Range Weather Forecasting (ECMWF). The distribution is then convolved with uncertainties in forward-modelling, in the NWP data, and with sensor noise to create the final PDF in observation space, from which the conditional probability that the pixel observation corresponds to a cloud observation can be read. Although the relatively fast computational implementation of the technique was achieved, the results are disappointingly poor for the SEVIRI-acquired dataset, provided by M´et´eo-France, against which validation was carried out. This is thought to be explained by both the uncertainties in the NWP data, and the forward-modelling dependence on those uncertainties, being poorly understood, and treated too optimistically in the algorithm. Including more errors in the convolution introduces the problem of quantifying those errors (a non-trivial task), and would increase the processing time, making implementation impractical. In addition, if the uncertianties considered are too high then a PDF flatter than the empirical distribution currently used would be produced, making the technique less useful.
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Lanagan, Gareth Daniel Edward. "Weather forecast error decomposition using rearrangements of functions." Thesis, Aberystwyth University, 2012. http://hdl.handle.net/2160/b489892f-7607-4125-90fb-46d8376edf8f.
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This thesis applies rearrangement and optimal mass transfer theory to weather forecast error decomposition. Errors in weather forecasting are often due to displacement of key features; conventional error scores do not necessarily favour good forecasts, nor are they descriptive of how the forecast failed. We study forecast error decomposition, where error is split into an error due to displacement and an error due to differences in qualitative features. In its simple formulation, we seek re-arrangements of the forecast which are a best fit to the actual data, and then find the “least kinetic energy” of a notional velocity transporting the forecast to a best fit. In mathematical terms, we are characterising those elements of a set of rearrangements which are closest (in the sense of L2) to a prescribed square integrable function, and seeking the least 2-Wasserstein distance squared between the forecast and the closest displaced forecasts. We demonstrate that there are closest rearrangements, and characterise this set; the best fitting rearrangements are determined up to rearrangement on the level sets of positive size of the prescribed function. Displacement error is calculated by finding the minimum value of an optimal mass transfer problem; we review previous work, demonstrating the connection with transport of the forecast to the best fit. A problem with the simple formulation of forecast error decomposition is that because the qualitative features error is taken first, an error in qualitative features may be penalise as a large displacement error. We conclude this thesis by considering a formulation which minimises both errors simultaneously.
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Saffin, Leo John. "Linking weather forecast errors with the physical processes responsible." Thesis, University of Reading, 2017. http://centaur.reading.ac.uk/74257/.
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Progress in numerical weather prediction (NWP) is made through better understand¬ing of the physical processes represented in numerical models and their impacts on the dynamics of large-scal~ weather systems. Here, potential vorticity (PV) tracer diagnostics are used to investigate the representation of processes in the Met Office Unified Model (MetG:l1). An exact budget of the PV tracers is derived and a "dynamics-tracer inconsistency" diagnostic implemented to quantify non-conservation of PV by the dynamical core which was not previously accounted for. It is shown that non-conservation of PV by the dy¬namical core can have comparable tendencies to the dominant physical processes implying that non-conservation of PV by a dynamical core can, and should, be quantified alongside PV modification by physical processes. Recent work has shown that the sharpness of the extratropical tropopause declines with lead time in KWP models. In the MetUM, the advection scheme is shown to result in an exponential decay of tropopause sharpness and non-conservative processes are shown to sharpen the tropopause. The systematic errors in tropopause-level PV are comparable to the tendencies associated with physical processes, suggesting that the systematic error in tropopause sharpness could be significantly rednced through realistic adjustments to the model physics. I' Turbulent mixing within the boundary layer has been previously shown to produce positive PV anomalies that can be advected into cyclones and reduce growth rates through an increase in static stability; however, it is unclear whether N\VP models correctly represent this mechanism. In the MetUM, the generation of these positive PV anomalies is found to be less clear due to large cancellations with other physical processes in the cold sector. Front-relative compositing .is used to separate the cold and warm sectors, providing the basis for investigating PV generation in the boundary layer systematically by compositing over many fronts.
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McCollor, Douglas. "Improving hydrometeorologic numerical weather prediction forecast value via bias correction and ensemble analysis." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/979.
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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.
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Leffler, Ingela. "The Vertical Route Forecast : an Evaluation of a New Flight Path Based Weather Forecast Product with HARMONIE-AROME High Resolution Forecasts over Scandinavia." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-318805.
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As a complement to existing weather forecast products for aviation, a prototype of a new product is presented and evaluated. It shows the atmosphere in a vertical cross section along the intended route. This Vertical Route Forecast introduces the possibility to examine the vertical distribution of cloud layers, wind, precipitation, turbulence and more along the flight path. Through a market research with 166 participating Swedish pilots it was found that the demand for the product is high and that 90 % of the participants would use it if available. The Vertical Route Forecast is inspired by the existing product GRAMET by Ogimet (Ballester Valor, n.d) but instead of using forecasts from the weather prediction model GFS (Global Forecast System) at 0.5° (56 km) resolution it uses data from the 2.5 km resolution model HARMONIE-AROME. The latter is operational at SMHI (Swedish Meteor-ological and Hydrological Institute) and because of its high resolution it enables more detailed structures of the weather to be presented. The product differs further from GRAMET by showing only the lower parts of the atmosphere so as to be of more use to small aircraft pilots flying at low levels. To assess the accuracy of the forecasts, a model evaluation of HARMONIE-AROME has been conducted through a case study in which the model was verified and compared to GFS over Sweden. The two models were verified against their own analyses at four different atmospheric pressure levels in terms of bias, root mean square error, standard deviation and correlation. HARMONIE-AROME performed best for temperature while GFS had the best forecasts of relative humidity. Wind speed and direction were also evaluated with insignificantly better results for GFS. However, the weather did not vary very much during the study as the two weeks were dominated by high pressure systems. Other evaluations made of HARMONIE-AROME by e.g. the HIRLAM consortium (2016a) have shown good or adequate performance of the model. It was concluded that HARMONIE-AROME would be well suited as the forecast producing model for this Vertical Route Forecast.<br>För att piloter ska kunna planera en säker flygning behöver de tillgång till bra och användbara väderprognoser. Med de prognosprodukter som finns tillgängliga idag kan det dock vara svårt att få en detaljerad uppfattning om hur vädret kommer vara längs med vägen. Här presenteras och utvärderas därför ett förslag till en ny prognosprodukt som visar atmosfären i en sidovy längs en valfri sträcka. Med den kan piloten granska utbredningen av bland annat molntäcken, vind, nederbörd och turbulens i höjdled längs den planerade färdvägen. Denna vertikala ruttprognos är inspirerad av den redan befintliga produkten GRAMET från Ogimet (Ballester Valor, n.d) men visar mer detaljerade prognoser som är bättre anpassade till flygningar på låga höjder. Vid en marknadsundersökning utförd med 166 medverkande svenska piloter stod det klart att efterfrågan på produkten är hög och 90 % av de medverkande påstod att de skulle använda den om den fanns tillgänglig. För att bedöma prognosernas precision har en utvärdering gjorts av den prognosmodell som använts till produkten. Modellen används annars hos SMHI (Sveriges Meteorologiska och Hydrologiska Institut) och kallas HARMONIE-AROME. I en fallstudie jämfördes den med modellen GFS som skapar prognoserna för GRAMET. Studien täckte Sverige och sträckte sig över 14 dagar i början av februari, 2017. HARMONIE-AROME visade bäst resultat för temperatur medan GFS gjorde de bästa fuktighetsprognoserna. Vindhastighet och vindriktning undersöktes också och för dem var modellerna ungefär lika bra. Vädret varierade dock inte så mycket under tvåveckorsperioden som dominerades av högtryck. Andra utvärderingar som gjorts av HARMONIE-AROME togs också i beaktande och modellen verkar generellt sett göra bra prognoser. Från samtliga resultat drogs slutsatsen att prognos-produkten skulle underlätta för småplanspiloter samt att HARMONIE-AROME är en lämplig modell att använda för att skapa dess prognoser.
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Hunemuller, Toby John. "Review and analysis of the National Weather Service river forecasts for the June 2008 eastern Iowa floods." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/821.
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The accuracy and quality of river forecasts are dependent on the nature of each flood. Less extreme , more common, floods may afford deviations between the predicted forecast and observed stage because the locals may be prepared, based on past experience to deal with the less extreme flood events. For less frequent, high flow events the flood forecasts and advanced warning time are more critical, because the locals need time to develop emergency response plans.
The National Weather Service River Forecast Centers (NWS RFC) develop the river forecasts and provide them to the National Weather Service Weather Forecast Office (NWS WFO) for dissemination. During flood events the RFC's are tasked with processing the observed data and running, reviewing and modifying the forecast models to provide reasonable river forecasts based on observed conditions and the forecasters' experience.
This thesis will discuss the personal experiences of the author, analyze the components of the National Weather Service river forecasting process, analyze June 2008 river and precipitation forecasts for several eastern Iowa watersheds, and discuss the results of the analysis as well as provide support to current calls to action to support forecast verification through the hindcasting process.
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Pagliaro, Daniel E. "Verification of the AFWA 3-Element Severe Weather Forecast Algorithm." Thesis, Monterey, Calif. : Naval Postgraduate School, 2008. http://bosun.nps.edu/uhtbin/hyperion-image.exe/08Mar%5FPagliaro.pdf.
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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2008.<br>Thesis Advisor(s): Nuss, Wendell A. "March 2008." Description based on title screen as viewed on May 5, 2008 Includes bibliographical references (p. 85-86). Also available in print.
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Korsos, Marianna. "Leap forward in Space Weather forecast : novel prediction of flares." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/22683/.
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Rodrigues, Eduardo Rocha. "Dynamic load-balancing : a new strategy for weather forecast models." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2011. http://hdl.handle.net/10183/34776.
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Weather forecasting models are computationally intensive applications and traditionally they are executed in parallel machines. However, some issues prevent these models from fully exploiting the available computing power. One of such issues is load imbalance, i.e., the uneven distribution of load across the processors of the parallel machine. Since weather models are typically synchronous applications, that is, all tasks synchronize at every time-step, the execution time is determined by the slowest task. The causes of such imbalance are either static (e.g. topography) or dynamic (e.g. shortwave radiation, moving thunderstorms). Various techniques, often embedded in the application’s source code, have been used to address both sources. However, these techniques are inflexible and hard to use in legacy codes. In this thesis, we explore the concept of processor virtualization for dynamically balancing the load in weather models. This means that the domain is over-decomposed in more tasks than the available processors. Assuming that many tasks can be safely executed in a single processor, each processor is put in charge of a set of tasks. In addition, the system can migrate some of them from overloaded processors to underloaded ones when it detects load imbalance. This approach has the advantage of decoupling the application from the load balancing strategy. Our objective is to show that processor virtualization can be applied to weather models as long as an appropriate strategy for migrations is used. Our proposal takes into account the communication pattern of the application in addition to the load of each processor. In this text, we present the techniques used to minimize the amount of change needed in order to apply processor virtualization to a real-world application. Furthermore, we analyze the effects caused by the frequency at which the load balancer is invoked and a threshold that activates rebalancing. We propose an automatic strategy to find an optimal threshold to trigger load balancing. These strategies are centralized and work well for moderately large machines. For larger machines, we present a fully distributed algorithm and analyze its performance. As a study case, we demonstrate the effectiveness of our approach for dynamically balancing the load in Brams, a mesoscale weather forecasting model based on MPI parallelization. We choose this model because it presents a considerable load imbalance caused by localized thunderstorms. In addition, we analyze how other effects of processor virtualization can improve performance.
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Grimit, Eric P. "Probabilistic mesoscale forecast error prediction using short-range ensembles /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/10064.
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Elizalde, Eduardo. "WEATHER FORECAST CONTROL : Prestudy of installing a predictive heating control system." Thesis, University of Gävle, Department of Technology and Built Environment, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-628.
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<p>The forecast control for the heating system tries to maintain the buildings’ indoor</p><p>temperature at a desired level, by using technical building characteristics, data on its</p><p>mode of operation, data on the building location and meteorological parameters. By</p><p>forecasting the conditions of the building and calculating the free energies (solar radiation</p><p>and internal heat generation) it is possible to know the amount of additional heat that</p><p>should be supplied to the building every moment, avoiding heat surpluses or deficits.</p><p>By applying the theory developed by Roger Taesler to buildings 92911 and</p><p>92917 placed in Sandvik AB, it is seen that if the desired temperature was 21ºC it would</p><p>be possible to save a 16.3% of energy, 15518 SEK/year, and that the payback time for the</p><p>installation of such a system is 3 years.</p><p>Other efficiency measures as changing the ventilation schedule or installing a</p><p>heat exchanger are also discussed in the present report.</p>
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Yano, Jun-Ichi, Jean-François Geleyn, Martin Köller, et al. "Basic concepts for convection parameterization in weather forecast and climate models." Universitätsbibliothek Leipzig, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-177427.
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The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the
subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for
a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.
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Frame, Thomas H. A. "Methods of targeting observations for the improvement of weather forecast skill." Thesis, University of Reading, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494980.
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This thesis is a contribution to the subjects of midlatitude atmospheric dynamics and targeting observations for the improvement of weather forecasts. For the first time the full spectrum of singular vectors of the Eady model are considered. The importance and implications of the unshielding and modal unmasking mechanisms to the computed singular vectors are discussed. The computed singular vectors are used to analyse the vertical structure of the singular vector targeting function commonly used in observation targeting, in a vertical cross-section. Through comparison of this vertical cross-section to the dynamics of singular vectors, inferences about the scale and qualitative behaviour of the perturbations to which particular regions are 'sensitive' are made.
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Chan, San-wing Frederick. "Developing inquiry based learning in secondary geography education topic weather forecast : an action research /." Click to view the E-thesis via HKUTO, 2003. http://sunzi.lib.hku.hk/hkuto/record/B3984870X.
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Leblebici, Engin. "Terrain Modeling And Atmospheric Turbulent Flowsolutions Based On Meteorological Weather Forecast Data." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614103/index.pdf.
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In this study, atmospheric and turbulent flow solutions are obtained using meteorological flowfield and topographical terrain data in high resolution. The terrain topology of interest, which may be obtained in various resolution levels, is accurately modeled using structured or unstructured grids depending on whether high-rise building models are present or not.
Meteorological weather prediction software MM5, is used to provide accurate and unsteady boundary conditions for the solution domain. Unsteady turbulent flow solutions are carried out via FLUENT with the help of several User Defined Functions developed.
Unsteady flow solutions over topographical terrain of METU campus are computed with 25m x 25m x 15m resolution using structured grids. These FLUENT solutions are compared with the MM5 solutions. Also, the accuracy of the boundary layer velocity profiles is assessed. Finally, effects of surface roughness model extracted from MM5 for the region of interest is investigated.
In addition, unsteady flow solutions over METU campus are repeated in presence of high-rise building models using unstructured grids with resolution varying from 5 meters around buildings to 80 meters further away.
The study shows that unsteady, turbulent flow solutions can be accurately obtained using low resolution atmospheric weather prediction models and high resolution Navier-Stokes solutions over topographical terrains.
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Darnell, Karen M. "Analysis of weather forecast impacts on United States Air Force combat operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FDarnell.pdf.
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Thesis (M.S. in Meteorology)--Naval Postgraduate School, March 2006.<br>Thesis Advisor(s): Tom Murphree, David Smarsh. "March 2006." Includes bibliographical references (p. 97-99). Also available online.
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Gu, Shuqing, and Shuqing Gu. "An Autonomic Workflow Performance Manager for Weather Forecast and Research Modeling Workflows." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/621837.
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Parameter selection is a critical task in scientific workflows in order to maintain the accuracy of the simulation in an environment where physical conditions change dynamically such as in the case of weather research and forecast simulations. Currently, Numerical Weather Prediction (NWP) is the premier method for weather prediction, which is used by the National Oceanic and Atmospheric Administration (NOAA). It takes the current observations from observed sites as the input for numeric computer models and then produces the final prediction. Considering the large number of simulation parameters, the size of the configuration search space becomes prohibitive for rapidly evaluating and identifying the parameter configuration that leads to most accurate prediction. In this thesis, we develop an Autonomic Workflow Performance Manager (AWPM) for Hurricane Integrated Modeling System (HIMS). AWPM is implemented on top of the Apache Storm and ZooKeeper to handle multiple real-time data streams for weather forecast. AWPM can automatically manage model initialization and execution workflow and achieve better performance and efficiency. In our experiments, AWPM achieves better performance and efficiency for the model initialization and execution processes, by utilizing automatic computing, distributed computing and component-based development. We reduced the timescale of the configuration search workflow by a factor of 10 by using 20 threads with the full search method, and a factor of 20 by with the roofline method when compared to serial workflow execution as it is typically performed by domain scientists.
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Amezaga, Guillermo R. "Impact of GFO satellite and ocean nowcast/forecast systems on Naval antisubmarine warfare (ASW)." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Mar%5FAmezaga.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, March 2006.<br>Thesis Advisor(s): Peter C. Chu. Includes bibliographical references (p. 131-132). Also available online.
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Cooper, Grant Alexander. "Variational retrieval of Eastern Pacific atmospheric boundary layer parameters using ATOVS with the COAMPSi mesoscale forecast system /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FCooper%5FPhD.pdf.
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Thesis (Ph. D. in Meteorology)--Naval Postgraduate School, September 2002.<br>Dissertation co-advisors: Phillip A. Durkee, Nancy L. Baker. Includes bibliographical references (p. 235-242). Also available online.
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Moatshe, Peggy Seanokeng. "Verification of South African Weather Service operational seasonal forecasts." Diss., University of Pretoria, 2008. http://hdl.handle.net/2263/27145.
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The South African Weather Service rainfall seasonal forecasts are verified for the period of January-February-March to October-November-December 1998-2004. These forecasts are compiled using different models from different institutions. Probability seasonal forecasts can be evaluated using different skill measures, but in this study the Ranked Probability Skill Score (RPSS), Reliability Diagram (RD) and Relative Operating Characteristics (ROC) are used. The RPSS is presented in the form of maps whereas the RD and ROC are analyses are presented in the form of graphs. The aim of the study is to present skill estimates of operational seasonal forecasts issued at South African Weather Service A limited number of forecasts show positive RPSS value throughout the validation period. From RD and ROC analysis, there is no skill in predicting the normal category as compared to below-normal and above-normal categories. Notwithstanding, the frequency diagrams show that the normal category was often given a large weight in the operational forecasts. The value of verifying seasonal forecast accuracy from the user’s perspective is important. The understanding of seasonal forecast performance helps decision makers to determine when and how to respond to expected climate anomalies. Therefore the frequent update of the seasonal forecast verification is important in order to help Users make better decisions. Copyright<br>Dissertation (MSc)--University of Pretoria, 2008.<br>Geography, Geoinformatics and Meteorology<br>Unrestricted
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Chan, San-wing Frederick, and 陳新榮. "Developing inquiry based learning in secondary geography education topic: weather forecast : an actionresearch." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B3984870X.
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Venkatesh, Chandrasekar. "Advanced Weather Monitoring for a Cable Stayed Bridge." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1535377315373168.
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Che, Jun. "Exploring value creation through web mining: acase study on the online weather forecast business." Thesis, Blekinge Tekniska Högskola, Institutionen för maskinteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-11102.
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Desouky, Azza Ahmed El. "Accurate fast weather dependent load forecast for optimal generation scheduling in real time application." Thesis, University of Bath, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.392211.
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Britten, Adrian J. "Memory for television weather forecast information : the role of presentation modality and content structure." Thesis, Cardiff University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293029.
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Sanabia, Elizabeth R. "Objective identification of environmental patterns related to tropical cyclone track forecast errors." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FSanabia.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Patrick A. Harr, Russell L. Elsberry. "September 2006." Includes bibliographical references (p. 43). Also available in print.
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Siegert, Stefan. "Rank statistics of forecast ensembles." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-102152.
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Ensembles are today routinely applied to estimate uncertainty in numerical predictions of complex systems such as the weather. Instead of initializing a single numerical forecast, using only the best guess of the present state as initial conditions, a collection (an ensemble) of forecasts whose members start from slightly different initial conditions is calculated. By varying the initial conditions within their error bars, the sensitivity of the resulting forecasts to these measurement errors can be accounted for. The ensemble approach can also be applied to estimate forecast errors that are due to insufficiently known model parameters by varying these parameters between ensemble members.
An important (and difficult) question in ensemble weather forecasting is how well does an ensemble of forecasts reproduce the actual forecast uncertainty. A widely used criterion to assess the quality of forecast ensembles is statistical consistency which demands that the ensemble members and the corresponding measurement (the ``verification\'\') behave like random independent draws from the same underlying probability distribution. Since this forecast distribution is generally unknown, such an analysis is nontrivial. An established criterion to assess statistical consistency of a historical archive of scalar ensembles and verifications is uniformity of the verification rank: If the verification falls between the (k-1)-st and k-th largest ensemble member it is said to have rank k. Statistical consistency implies that the average frequency of occurrence should be the same for each rank.
A central result of the present thesis is that, in a statistically consistent K-member ensemble, the (K+1)-dimensional vector of rank probabilities is a random vector that is uniformly distributed on the K-dimensional probability simplex. This behavior is universal for all possible forecast distributions. It thus provides a way to describe forecast ensembles in a nonparametric way, without making any assumptions about the statistical behavior of the ensemble data. The physical details of the forecast model are eliminated, and the notion of statistical consistency is captured in an elementary way. Two applications of this result to ensemble analysis are presented.
Ensemble stratification, the partitioning of an archive of ensemble forecasts into subsets using a discriminating criterion, is considered in the light of the above result. It is shown that certain stratification criteria can make the individual subsets of ensembles appear statistically inconsistent, even though the unstratified ensemble is statistically consistent. This effect is explained by considering statistical fluctuations of rank probabilities. A new hypothesis test is developed to assess statistical consistency of stratified ensembles while taking these potentially misleading stratification effects into account.
The distribution of rank probabilities is further used to study the predictability of outliers, which are defined as events where the verification falls outside the range of the ensemble, being either smaller than the smallest, or larger than the largest ensemble member. It is shown that these events are better predictable than by a naive benchmark prediction, which unconditionally issues the average outlier frequency of 2/(K+1) as a forecast. Predictability of outlier events, quantified in terms of probabilistic skill scores and receiver operating characteristics (ROC), is shown to be universal in a hypothetical forecast ensemble. An empirical study shows that in an operational temperature forecast ensemble, outliers are likewise predictable, and that the corresponding predictability measures agree with the analytically calculated ones.
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Carminatti, Henrique dos Santos. "Simulation and test of a solar domestic water heating system controlled by weather forecast information." reponame:Repositório Institucional da UFSC, 2017. https://repositorio.ufsc.br/xmlui/handle/123456789/182715.
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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2017.<br>Made available in DSpace on 2018-01-16T03:19:52Z (GMT). No. of bitstreams: 1
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Previous issue date: 2017<br>O Brasil possui uma vasta diversidade de fontes energéticas. De grandes hidrelétricas, as quais são sua fonte principal, até os mais recentes parques eólicos e fotovoltáicos. No entanto, a despeito desta variedade de recursos energéticos há uma verdade negativa: a maioria das habitações no país usa chuveiros elétricos. Esses dispositivos são responsáveis por 43% do pico de demanda de energia elétrica doméstico Brasileiro. Esse pico de demanda de energia causa problemas não só à companhia geradora de energia, a qual tem que reservar pelo menos quatro giga watts para fornecê-lo, mas também para à empresa de distribuição de energia elétrica devido à sobrecarga e/ou superdimensionamento de cabos e danos aos transformadores. Com o objetivo de transferir o uso de energia elétrica para aquecimento de água para o amanhecer, um novo Sistema de Aquecimento Solar de Água Doméstico foi simulado e testado. O objetivo deste sistema é planificar o perfil de consumo de energia elétrica de urna casa. Este sistema usa dois tanques e dados de previsão do tempo para prever a energia solar absorvida pelo coletor para o próximo dia, a fim de evitar possíveis condições climáticas desfavoráveis. A análise computacional foi realizada utilizando o software TRNSYS. O sistema apresentado foi capaz de diminuir o pico da demanda de energia elétrica. As simulações forneceram dados esperados e provaram que o sistema pode ser usado em muitas regiões brasileiras, um dando o pico de demanda de energia de uma habitação. No entanto, à medida que os erros de previsão simulados aumentaram, o sistema não conseguiu fornecer água quente ao usuário com mais frequência. Mais pesquisas são necessárias para rastrear uma tendência para justificar um ponto de parada para o aumento da precisão da previsão do tempo. Além disso, para validar a simulação, uma bancada de testes foi projetada e construída. A comparação de dados simulados e experimentais mostrou que os tanques utilizados na plataforma experimental possuem perdas muito altas. Portanto, a plataforma experimental não conseguiu fornecer os resultados esperados para o experimento a longo prazo. A simulação, por outro lado, pôde ser validada através de testes específicos na bancada experimental. Logo, os resultados simulados representam o verdadeiro comportamento do sistema.<br>Abstract : Brazil uses many renewable energy sources. From hydropower, which is its main source, to the recent photovoltaic and wind farms. However, beneath the Brazilian energy resources is a negative truth: most dwellings in the country uses electric shower heaters. These devices are responsible for 43% of the Brazilian domestic electric energy demand peak. This peak causes problems not only to the electric energy system, which has to reserve at least four gigawatts to supply it, but also to electrical energy distribution company due to cable overload and/or oversizing and transformers damage. In order to shift the electric energy use to dawn a novel Solar Domestic Water Heating system was simulated and tested. The objective of this system is to flatten the electrical energy consumption profile of a house. This system uses two tanks and weather forecast data to predict the solar energy absorbed by the collector for the next day in order to prevent for possible unfavorable climatic conditions. Computational analysis was carried using the TRNSYS software. Also a Test bed was built in order to compare results. The presented system design was able to shift and shrink the energy demand peak for hot water of a household. Simulations delivered expected data and proved the system may be used in many Brazilian regions shifting the energy demand peak of a dwelling. However, as the simulated forecast errors increased, the system failed to deliver hot water to the user more often. More research must be done in order to trace down a tendency that could justify a stopping point on increasing the accuracy of the weather forecast. In addition, to validate the simulation the author designed and built a test-bed. Comparing simulated and experimental data showed that the tanks used on the experimental rig were far from ideal. Therefore, the experimental rig could not deliver expected results for the long-term experiment. The simulation, on the other hand, could be validated and its results represent the true system behavior.
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Zhu, Zhipeng. "Techniques to Evaluate the Modifier Process of National Weather Service Flood Forecasts." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613752950288287.
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Simmons, Cody Ryan. "Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7594.
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This work explores the development of a home energy management system (HEMS) that uses weather and market forecasts to optimize the usage of home appliances and to manage battery usage and solar power production. A Moving Horizon Estimation (MHE) application is used to find the unknown home model parameters. These parameters are then updated in a Model Predictive Controller (MPC) which optimizes and balances competing comfort and economic objectives. Combining MHE and MPC applications alleviates model complexity commonly seen in HEMS by using a lumped parameter model that is adapted to fit a high-fidelity model. HVAC on/off behaviors are simulated by using Mathematical Program with Complementary Constraints (MPCCs) and solved in near real-time with a nonlinear solver. Removing HVAC on/off as a discrete variable decreases potential solutions and consequently reduces solve time and increases the probability of reaching a more optimal solution. The results of this work indicate that energy management optimization significantly decreases energy costs and balances energy usage more effectively throughout the day compared to a home with regular temperature control. A case study for Phoenix, Arizona shows an energy reduction of 21% and a cost reduction of 40%. Homes using this home energy optimization will contribute less to the grid peak load and therefore, improve grid stability and reduce the amplitude of load following cycles for utilities. This case study combines renewable energy, energy storage, forecasts, cooling system, variable rate electricity plan and a multi-objective function allowing for a complete home energy optimization assessment. There remain several challenges, including improved forecast models, improved computational performance to allow the algorithms to run in real-time, and mixed empirical / first principles machine learning methods to guide the model structure.
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Shaw, Victoria Leigh. ""A PBS mind in an MTV world" teaching teenagers meteorology by placing a weather forecast on MTV and the creation of the concert forecast /." Master's thesis, Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-04032009-113347.
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Shaw, Victoria. "A PBS MIND IN A MTV WORLD: TEACHING TEENAGERS METEOROLOGY BY PLACING A WEATHER FORECAST ON MTV AND THE CREATION OF THE CONCERT FORECAST." MSSTATE, 2009. http://sun.library.msstate.edu/ETD-db/theses/available/etd-04032009-113347/.
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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 MTVs 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.
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Callahan, Jeremy. "Metrics of METOC forecast performance and operational impacts on carrier strike operations." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Sep%5FCallahan.pdf.
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Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, September 2006.<br>Thesis Advisor(s): Tom Murphree, Rebecca Stone. "September 2006." Includes bibliographical references (p. 61-62). Also available in print.
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Pincini, Giacomo. "Forecast of high-impact weather over Italy: performance of global and limited-area ensemble prediction systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16207/.
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The aim of this work is to assess the added value of the enhanced horizontal resolution in the probabilistic prediction of upper-level and surface fields.
In particular, the performance of three different ensemble prediction systems were compared: ECMWF-ENS (51 members, 18 km horizontal resolution), COSMO-LEPS (16 members, 7 km horizontal resolution) and COSMO-2I-EPS (10 members, 2.2 km horizontal resolution).
While the first 2 ensemble systems are operational, COSMO-2I-EPS is still in a development phase. Therefore, the intercomparison window covers a limited period, which ranges from 20 to 27 June 2016.
In this work, both upper-level and surface variables are analyzed.
As for upper-level, both temperature and the geopotential height at three different pressure levels are considered; the ensemble spread and the root mean square error are computed using the available Italian radiosounding data every 12/24 hours for verification.
As for the surface, 2-metre temperature and precipitation cumulated over six hours are verified against the non-conventional station network provided by the National Civil Protection Department. The ensemble spread and the root mean square error of 2-metre temperature are computed, while a number of probabilistic scores (Brier Skill Score, Ranked Probability Score, Roc-Area, Outliers Percentage and others) are considered for precipitation.
For both upper-level and surface verification, it turns out that the best scores are mainly obtained by the COSMO-based ensemble systems with higher horizontal resolution and lower ensemble size.
The added value of high resolution in mesoscale ensemble systems seems to play a crucial role in the probabilistic prediction of atmospheric fields at all levels. In particular, the more detailed description of mesoscale and orographic-related processes in COSMO-ensembles provides an added value for the prediction of localised High-Impact Weather events.
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Schraff, Christoph H. "Data assimilation and mesoscale weather prediction : a study with a forecast model for the Alpine region /." [S.l.] : [s.n.], 1996. http://e-collection.ethbib.ethz.ch/show?type=diss&nr=11627.
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Yano, Jun-Ichi, Jean-François Geleyn, Martin Köller, et al. "Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report." MDPI AG, 2014. https://ul.qucosa.de/id/qucosa%3A12408.
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The research network “Basic Concepts for Convection Parameterization in Weather Forecast and Climate Models” was organized with European funding (COST Action ES0905) for the period of 2010–2014. Its extensive brainstorming suggests how the
subgrid-scale parameterization problem in atmospheric modeling, especially for convection, can be examined and developed from the point of view of a robust theoretical basis. Our main cautions are current emphasis on massive observational data analyses and process studies. The closure and the entrainment–detrainment problems are identified as the two highest priorities for convection parameterization under the mass–flux formulation. The need for
a drastic change of the current European research culture as concerns policies and funding in order not to further deplete the visions of the European researchers focusing on those basic issues is emphasized.
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Saide, Peralta Pablo Enrique. "Aerosol predictions and their links to weather forecasts through online interactive atmospheric modeling and data assimilation." Diss., University of Iowa, 2013. https://ir.uiowa.edu/etd/1744.
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Atmospheric particles represent a component of air pollution that has been identified as a major contributor to adverse health effects and mortality. Aerosols also interact with solar radiation and clouds perturbing the atmosphere and generating responses in a wide range of scales, such as changes to severe weather and climate. Thus, being able to accurately predict aerosols and its effects on atmospheric properties is of upmost importance.
This thesis presents a collection of studies with the global objective to advance in science and operations the use of WRF-Chem, a regional model able to provide weather and atmospheric chemistry predictions and simultaneously representing aerosol effects on climate. Different strategies are used to obtain accurate predictions, including finding an adequate model configuration for each application (e.g., grid resolution, parameterizations choices, processes modeled), using accurate forcing elements (e.g., weather and chemical boundary conditions, emissions), and developing and applying data assimilation techniques for different observational sources. Several environments and scales are simulated, including complex terrain at a city scale, meso-scale over the southeast US for severe weather applications, and regional simulations over the three subtropical persistent stratocumulus decks (off shore California and southeast Pacific and Atlantic) and over North America. Model performance is evaluated against a large spectrum of observations, including field experiments and ground based and satellite measurements.
Overall, very positive results were obtained with the WRF-Chem system once it had been configured properly and the inputs chosen. Also, data assimilation of aerosol and cloud satellite observations contributed to improve model performance even further. The model is proven to be an excellent tool for forecasting applications, both for local and long range transported pollution. Also, advances are made to better understand aerosol effects on climate and its uncertainties. Aerosols are found to generate important perturbations, ranging from changes in cloud properties over extensive regions, up to playing a role in increasing the likelihood of tornado occurrence and intensity. Future directions are outline to keep advancing in better predictions of aerosols and its feedbacks.
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Jaklovsky, Simon. "Drag based forecast for CME arrival." Thesis, Uppsala universitet, Institutionen för fysik och astronomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-415153.
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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.
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Hawkins, Samuel Lennon. "High resolution re-analysis of wind speeds over the British Isles for wind energy integration." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/7898.
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The UK has highly ambitious targets for wind development, particularly offshore, where over 30GW of capacity is proposed for development. Integrating such a large amount of variable generation presents enormous challenges. Answering key questions depends on a detailed understanding of the wind resource and its temporal and spatial variability. However, sources of wind speed data, particularly offshore, are relatively sparse: satellite data has low temporal resolution; weather buoys and met stations have low spatial resolution; while the observations from ships and platforms are affected by the structures themselves. This work uses a state-of-the art mesoscale atmospheric model to produce a new high-resolution wind speed dataset over the British Isles and surrounding waters. This covers the whole region at a resolution of 3km for a period of eleven consecutive years, from 2000 to 2010 inclusive, and is thought to be the first high resolution re-analysis to represent a true historic time series, rather than a statistically averaged climatology. The results are validated against observations from met stations, weather buoys, offshore platforms and satellite-derived wind speeds, and model bias is reduced offshore using satellite derived wind speeds. The ability of the dataset to predict power outputs from current wind farms is demonstrated, and the expected patterns of power outputs from future onshore and offshore wind farms are predicted. Patterns of wind production are compared to patterns of electricity demand to provide the first conclusive combined assessment of the ability of future onshore and offshore wind generation meet electricity demand and contribute to secure energy supplies.
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Mudelsee, Manfred. "XTREND: A computer program for estimating trends in the occurrence rate of extreme weather and climate events." Universitätsbibliothek Leipzig, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-217157.
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XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.
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Ichard, Cécile. "Random media and processes estimation using non-linear filtering techniques : application to ensemble weather forecast and aircraft trajectories." Thesis, Toulouse 3, 2015. http://www.theses.fr/2015TOU30153/document.
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L'erreur de prédiction d'une trajectoire avion peut être expliquée par différents facteurs. Les incertitudes associées à la prévision météorologique sont l'un d'entre-eux. Qui plus est, l'erreur de prévision de vent a un effet non négligeable sur l'erreur de prédiction de la position d'un avion. En regardant le problème sous un autre angle, il s'avère que les avions peuvent être utilisés comme des capteurs locaux pour estimer l'erreur de prévision de vent. Dans ce travail nous décrivons ce problème d'estimation à l'aide de plusieurs processus d'acquisition d'un même champ aléatoire. Quand ce champ est homogène, nous montrons que le problème est équivalent à plusieurs processus aléatoires évoluant dans un même environnement aléatoire pour lequel nous donnons un modèle de Feynman-Kac. Nous en dérivons une approximation particulaire et fournissons pour les estimateurs obtenus des résultats de convergence. Quand le champ n'est pas homogène mais qu'une décomposition en sous-domaine homogène est possible, nous proposons un modèle différent basé sur le couplage de plusieurs processus d'acquisition. Nous en déduisons un modèle de Feynman-Kac et suggérons une approximation particulaire du flot de mesure. Par ailleurs, pour pouvoir traiter un trafic aérien, nous développons un modèle de prédiction de trajectoire avion. Finalement nous démontrons dans le cadre de simulations que nos algorithmes peuvent estimer les erreurs de prévisions de vent en utilisant les observations délivrées par les avions le long de leur trajectoire<br>Aircraft trajectory prediction error can be explained by different factors. One of them is the weather forecast uncertainties. For example, the wind forecast error has a non negligible impact on the along track accuracy for the predicted aircraft position. From a different perspective, that means that aircrafts can be used as local sensors to estimate the weather forecast error. In this work we describe the estimation problem as several acquisition processes of a same random field. When the field is homogeneous, we prove that they are equivalent to random processes evolving in a random media for which a Feynman-Kac formulation is done. Then we give a particle-based approximation and provide convergence results of the ensuing estimators. When the random field is not homogeneous but can be decomposed in homogeneous sub-domains, a different model is proposed based on the coupling of different acquisition processes. From there, a Feynman-Kac formulation is derived and its particle-based approximation is suggested. Furthermore, we develop an aircraft trajectory prediction model. Finally we demonstrate on a simulation set-up that our algorithms can estimate the wind forecast errors using the aircraft observations delivered along their trajectory
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Yu, Wansik. "Ensemble Flood Forecasting using High-Resolution Ensemble Numerical Weather Prediction with Radar Based Prediction Considering Rainfall Forecast Uncertainty." 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/192164.
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Law, Kevin T. "A statistical model to forecast short-term Atlantic hurricane intensity." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1149013191.
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Isaksson, Robin. "Reduction of Temperature Forecast Errors with Deep Neural Networks." Thesis, Uppsala universitet, Luft-, vatten och landskapslära, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-350264.
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Deep artificial neural networks is a type of machine learning which can be used to find and utilize patterns in data. One of their many applications is as method for regression analysis. In this thesis deep artificial neural networks were implemented in the application of estimating the error of surface temperature forecasts as produced by a numerical weather prediction model. An ability to estimate the error of forecasts is synonymous with the ability to reduce forecast errors as the estimated error can be offset from the actual forecast. Six years of forecast data from the period 2010--2015 produced by the European Centre for Medium-Range Weather Forecasts' (ECMWF) numerical weather prediction model together with data from fourteen meteorological observational stations were used to train and evaluate error-predicting deep neural networks. The neural networks were able to reduce the forecast errors for all the locations that were tested to a varying extent. The largest reduction in error was by 83.0\% of the original error or a 16.7\degcs decrease in the mean-square error. The performance of the neural networks' error reduction ability was compared with that of a contemporary Kalman filter as implemented by the Swedish Meteorological and Hydrological Institute (SMHI). It was shown that the neural network implementation had superior performance for six out of seven of the evaluated stations where the Kalman filter had marginally better performance at one station.
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Sorooshian, Soroosh, Hoshin Gupta, Terri S. Hogue, Andrea Holz, and Dean Braatz. "A multi-step automatic calibration scheme (MACS) for river forecasting models utilizing the national weather service river forecast system (NWSRFS)." Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ), 1999. http://hdl.handle.net/10150/615749.
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Final Report to the Hydrologic Research Laboratory of the National Weather Service Grant NA #87WHO582<br>Traditional model calibration by National Weather Service (NWS) River Forecast Center (RFC)
hydrologists involves a laborious and time -consuming manual estimation of numerous
parameters. The National Weather Service River Forecasting System (NWSRFS), a software
system used by the RFCs for hydrologic forecasting, includes an automatic optimization program
(OPT3) to aid in model calibration. The OPT3 program is not used operationally by the majority
of RFC hydrologists who perform calibration studies. Lack of success with the traditional single -
step, single-criterion automatic calibration approach has left hydrologists more comfortable
employing a manual step-by-step process to estimate parameters. This study develops a Multistep
Automatic Calibration Scheme (MACS), utilizing OPT3, for the river forecasting models
used by the RFCs: the Sacramento Soil Moisture Accounting (SAC-SMA). and SNOW-17
models. Sixteen parameters are calibrated in three steps, replicating the progression of manual
calibration steps used by NWS hydrologists. MACS is developed by minimizing different
objective functions for different parameters in a step -wise manner. Model runs are compared
using the MACS optimized parameters and the manually estimated parameters for six basins in
the North Central River Forecast Center (NCRFC) forecast area. Results demonstrate that the
parameters obtained via the MACS procedure generally yield better model performance than
those obtained by manual calibration. The MACS methodology is a time-saving approach that
can provide prompt model forecasts for NWS watersheds.
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White, Megan L. "ASSOCIATING SEVERE THUNDERSTORM WARNINGS WITH DEMOGRAPHIC AND LANDSCAPE VARIABLES: A GEOGRAPHICALLY WEIGHTED REGRESSION-BASED MAPPING OF FORECAST BIAS." UKnowledge, 2014. http://uknowledge.uky.edu/geography_etds/20.
Full textAbstract:
Severe thunderstorm warnings (SVTs) are released by meteorologists in the local forecast offices of the National Weather Service (NWS). These warnings are issued with the intent of alerting areas in the path of severe thunderstorms that human and property risk are elevated, and that appropriate precautionary measures should be taken. However, studies have shown that the spatial distribution of severe storm warnings demonstrates bias. Greater numbers of severe thunderstorm warnings sometimes are issued where population is denser. By contrast, less populated areas may be underwarned. To investigate the spatial patterns of these biases for the central and southeastern United States, geographically weighted regression was implemented on a set of demographic and land cover descriptors to ascertain their patterns of spatial association with counts of National Weather Service severe thunderstorm warnings. GWR was performed for each our independent variables (total population, median income, and percent impervious land cover) and for all three of these variables as a group. Global R2 values indicate that each individual variable as well as all three collectively explain approximately 60% of the geographical variation in severe thunderstorm warning counts. Local R2 increased in the vicinity of several urban regions, notably Atlanta, Washington, D.C., St. Louis, and Nashville. However, the independent variables did not exhibit the same spatial patterning of R2. Some cities had high local R2 for all variables. Other cities exhibited high local R2 for only one or two of these independent variables. Median income had the highest local R2 values overall. Standardized residuals confirmed significant differences among several NWS forecast offices in the number and pattern of severe thunderstorm warnings. Overall, approximately half of the influences on the distribution of severe thunderstorm warnings across the study area are related to underlying land cover and demographics. Future studies may find it productive to investigate the extent to which the spatial bias mapped in this study is an artifact of forecast culture, background thunderstorm regime, or a product of urban anthropogenic weather modification.
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Mudelsee, Manfred. "XTREND: A computer program for estimating trends in the occurrence rate of extreme weather and climate events." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 26 = Meteorologische Arbeiten aus Leipzig ; 7 (2002), S. 149-196, 2002. https://ul.qucosa.de/id/qucosa%3A15228.
Full textAbstract:
XTREND consists of the following methodical Parts. Time interval extraction (Part 1) to analyse different parts of a time series; extreme events detection (Part 2) with robust smoothing; magnitude classification (Part 3) by hand; occurrence rate estimation (Part 4) with kernel functions; bootstrap simulations (Part 5) to estimate confidence bands around the occurrence rate. You work interactively with XTREND (parameter adjustment, calculation, graphics) to acquire more intuition for your data. Although, using “normal” data sizes (less than, say, 1000) and modern machines, the computing time seems to be acceptable (less than a few minutes), parameter adjustment should be done carefully to avoid spurious results or, on the other hand, too long computing times. This Report helps you to achieve that. Although it explains the statistical concepts used, this is generally done with less detail, and you should consult the given references (which include some textbooks) for a deeper understanding.
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Vasconi, Matteo. "Sensitivity of forecast skill to the parameterisation of moist convection in the COSMO model." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14566/.
Full textAbstract:
The parameterisation of convection in limited-area models is an important source of uncertainty as regards the spatio-temporal forecast of precipitation.
The development and implementation of ensemble systems in which different convection schemes are used provides an opportunity to upgrade state-of-the-art probabilistic systems at the convection-parameterised scale. As for the limited-area model COSMO, the sensitivity of the forecast skill to the use of different convection schemes is assessed by performing different sets of experiments.
For one case of heavy precipitation over Italy, the performance of COSMO model run with the Bechtold scheme or with the Tiedtke scheme is investigated in both deterministic and ensemble modes with particular attention to the types of forecast errors (e.g. location, timing, intensity) provided by the different convection schemes in terms of total precipitation.
In addition to this, a 10-member ensemble has been run for approximately 2 months with the Bechtold scheme, using the same initial and boundary conditions as members 1-10 of the operational COSMO-LEPS ensemble system (which has 20 members, all run with the Tiedtke scheme). The performance of these members is assessed and compared to that of the system made of members 1-10 of COSMO-LEPS; in particular the spread/skill relation of the two 10-member ensembles in terms of total precipitation is evaluated. Finally, the performance of an experimental 20-member ensemble system (which has 10 members run with the Bechtold plus 10 members run with the Tiedtke scheme) is compared to that of operational COSMO-LEPS over the 2-month period. The new system turned out to have higher skill in terms of precipitation forecast with respect to COSMO-LEPS over the period. In this approach the use of the Bechtold scheme is proposed as a perturbation for the COSMO-LEPS ensemble, relatively to how uncertainties in the model representation of the cumulus convection can be described and quantified.
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Mohammadipour, Gishani Azadeh. "An Introduction to Application of Statistical Methods in Modeling the Climate Change." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175770.
Full textAbstract:
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.