A new method is presented to provide automatic sequencing of multiple hydrodynamic models and automated analysis of model forecast uncertainty. A Hydrodynamic and oil spill model Python (HyosPy) wrapper was developed to run the hydrodynamic model, link with the oil spill, and visualize results. The HyosPy wrapper completes the following steps automatically: (1) downloads wind and tide data (nowcast, forecast and historical); (2) converts data to hydrodynamic model input; (3) initializes a sequence of hydrodynamic models starting at pre-defined intervals on a multi-processor workstation. Each model starts from the latest observed data, so that the multiple models provide a range of forecast hydrodynamics with different initial and boundary conditions reflecting different forecast horizons. As a simple testbed for integration strategies and visualization on Google Earth, a Runge-Kutta 4th order (RK4) particle transport tracer routine is developed for oil spill transport. The model forecast uncertainty is estimated by the difference between forecasts in the sequenced model runs and quantified by using statistics measurements. The HyosPy integrated system with wind and tide force is demonstrated by introducing an imaginary oil spill in Corpus Christi Bay. The results show that challenges in operational oil spill modeling can be met by leveraging existing models and web-visualization methods to provide tools for emergency managers. / text

Discrete choice models (DCMs) are used to forecast demand in a variety of engineering, marketing, and policy contexts, and understanding the uncertainty associated with model forecasts is crucial to inform decision-making. This thesis evaluates the suitability of DCMs for forecasting automotive demand. The entire scope of this investigation is too broad to be covered here, but I explore several elements with a focus on three themes: defining how to measure forecast accuracy, comparing model specifications and forecasting methods in terms of prediction accuracy, and comparing the implications of model specifications and forecasting methods on vehicle design. Specifically I address several questions regarding the accuracy and uncertainty of market share predictions resulting from choice of utility function and structural specification, estimation method, and data structure assumptions. I1 compare more than 9,000 models based on those used in peer-reviewed literature and academic and government studies. Firstly, I find that including more model covariates generally improves predictive accuracy, but that the form those covariates take in the utility function is less important. Secondly, better model fit correlates well with better predictive accuracy; however, the models I construct— representative of those in extant literature— exhibit substantial prediction error stemming largely from limited model fit due to unobserved attributes. Lastly, accuracy of predictions in existing markets is neither a necessary nor sufficient condition for use in design. Much of the econometrics literature on vehicle market modeling has presumed that biased coefficients make for bad models. For purely predictive purposes, the drawbacks of potentially mitigating bias using generalized method of moments estimation coupled with instrumental variables outweigh the expected benefits in the experiments conducted in this dissertation. The risk of specifying invalid instruments is high, and my results suggest that the instruments frequently used in the automotive demand literature are likely invalid. Furthermore, biased coefficients are not necessarily bad for maximizing the predictive power of the model. Bias can even aid predictions by implicitly capturing persistent unobserved effects in some circumstances. Including alternative specific constants (ASCs) in DCM utility functions improves model fit but not necessarily forecast accuracy. For frequentist estimated models all tested methods of forecasting ASCs improved share predictions of the whole midsize sedan market over excluding ASC in predictions, but only one method results in improved long term new vehicle, or entrant, forecasts. As seen in a synthetic data study, assuming an incorrect relationship between observed attributes and the ASC for forecasting risks making worse forecasts than would be made by a model that excludes ASCs entirely. Treating the ASCs as model parameters with full distributions of uncertainty via Bayesian estimation is more robust to selection of ASC forecasting method and less reliant on persistent market structures, however it comes at increased computational cost. Additionally, the best long term forecasts are made by the frequentist model that treats ASCs as calibration constants fit to the model post estimation of other parameters.

Le Global Sourcing est aujourd'hui en pleine expansion car il offre aux entreprises une source potentielle de compétitivité dans un environnement de plus en plus concurrentiel. Néanmoins, il génère aussi une complexification des flux et une fragilisation de la Supply Chain Globale. La problématique du Global Sourcing est vaste et touche les différents niveaux de décision de l'entreprise. Pour cela nous nous sommes focalisés dans ce travail sur les aspects tactiques et opérationnels de ce domaine. Nous avons abordé ainsi diverses questions : Quels leviers d'action pour un pilotage efficace des flux en approvisionnement lointain? Comment sécuriser les approvisionnements lointains dans le contexte industriel actuel ? Les politiques classiques de pilotage de flux sont-elles suffisantes pour les approvisionnements lointains ? En collaboration avec les partenaires industriels de la Chaire Supply Chain de l'Ecole Centrale Paris, nous avons abordé différentes facettes de cette problématique. Nous nous sommes intéressés tout d'abord à la prévision comme élément nécessaire au pilotage des flux lointains et nous avons proposé une méthodologie de sélection et de mise à jour de méthodes de prévision. Les délais longs en approvisionnement lointain font que les erreurs de prévision s'amplifient, ce qui nous a amenés à étudier l'erreur prévisionnelle. Nous avons proposé dans ce sens une modélisation fine de cette erreur et de son évolution en fonction de l'horizon temporelle de la prévision. Dans la dernière étape de ce travail, nous avons utilisé cette modélisation de l'incertitude pour piloter efficacement les flux lointains. Nous avons montré sur des cas réels issus de l'entreprise PSA l'efficacité de la méthode proposée en termes de respect du niveau de service avec un niveau de stock largement inférieur aux méthodes classiques. / Global Sourcing is becoming a common practice in industrial activities since it offers companies opportunities to improve its competitiveness in an increasingly competitive business environment. At the same time, it makes the flows more complex and the supply chain more fragile. Global Sourcing thus gives rise to a wide range of issues and impacts different levels of decision making. To address such a problem, we focus on tactical and operational decision making. We attempt to answer a variety of questions: What are possible actions for flow management in global sourcing? How to secure the procurement in the current industrial context? Are classical flow management policies also efficient in global sourcing? In collaboration with the industrial partners of the Chaire Supply Chain at Ecole Centrale Paris, we consider different problems. Firstly, we are interested in demand forecasting, an essential element for flow management in global sourcing and proposed a methodology to select an appropriate forecasting method and to update it dynamically. The fact that the lead times are long in global sourcing makes the forecast less reliable and less and less reliable when the forecast horizon increases, which requires an evaluation of the forecast accuracy. We propose a detailed model of the forecast accuracy and its evolution with time horizon involved. As the last step of the work, this forecast accuracy model is applied to a real life flow management problem in global sourcing. The case study carried out based on real life data from PSA demonstrates a clear superiority of the proposed method over existing ones in terms of both service level and inventory level.

No description available.

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework.<p> Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

Electric power systems are experiencing dramatic changes with respect to structure, operation and regulation and are facing increasing pressure due to environmental and societal constraints. Bulk electric system reliability is an important consideration in power system planning, design and operation particularly in the new competitive environment. A wide range of methods have been developed to perform bulk electric system reliability evaluation. Theoretically, sequential Monte Carlo simulation can include all aspects and contingencies in a power system and can be used to produce an informative set of reliability indices. It has become a practical and viable tool for large system reliability assessment technique due to the development of computing power and is used in the studies described in this thesis. The well-being approach used in this research provides the opportunity to integrate an accepted deterministic criterion into a probabilistic framework. This research work includes the investigation of important factors that impact bulk electric system adequacy evaluation and security constrained adequacy assessment using the well-being analysis framework.<p> Load forecast uncertainty is an important consideration in an electrical power system. This research includes load forecast uncertainty considerations in bulk electric system reliability assessment and the effects on system, load point and well-being indices and reliability index probability distributions are examined. There has been increasing worldwide interest in the utilization of wind power as a renewable energy source over the last two decades due to enhanced public awareness of the environment. Increasing penetration of wind power has significant impacts on power system reliability, and security analyses become more uncertain due to the unpredictable nature of wind power. The effects of wind power additions in generating and bulk electric system reliability assessment considering site wind speed correlations and the interactive effects of wind power and load forecast uncertainty on system reliability are examined. The concept of the security cost associated with operating in the marginal state in the well-being framework is incorporated in the economic analyses associated with system expansion planning including wind power and load forecast uncertainty. Overall reliability cost/worth analyses including security cost concepts are applied to select an optimal wind power injection strategy in a bulk electric system. The effects of the various demand side management measures on system reliability are illustrated using the system, load point, and well-being indices, and the reliability index probability distributions. The reliability effects of demand side management procedures in a bulk electric system including wind power and load forecast uncertainty considerations are also investigated. The system reliability effects due to specific demand side management programs are quantified and examined in terms of their reliability benefits.

This dissertation collects five independent essays. The first essay is An Alternative Explanation of the Price Puzzle. The most widely accepted explanation of the price puzzle points to an inadequate performance of the VAR in forecasting inflation. This essay suggests that the finding of a price puzzle is due to a seemingly innocent misspecification in taking the theoretical model to the data: a measure of output gap is not included in the VAR (output alone being used instead), while this variable is a crucial element in every equation of the theoretical models. When the VAR is correctly specified, the price puzzle disappears. Building on results contained in the first paper, the second-- Stronger Evidence of Long-Run Neutrality: A comment on Bernanke and Mihov---improves the empirical performance of standard models on the prediction that a monetary policy shock should have temporary effects on output. It turns out that the same misspecification causing the price puzzle is also responsible for overestimation of the time needed for the effects on output of a monetary policy shock to die out. The point can be proven in a theoretical economy, and is confirmed on US data. Monetary Policy Without Monetary Aggregates: Some (Surprising) Evidence , joint with Giovanni Favara) is the third essay. It points to what seems to be a falsified prediction of models in the New-Keynesian framework. In this framework monetary aggregates are reserved a pretty boring role, so boring that they can be safely excluded from the final lay out of the model. These models predict that a money demand shock should have no effect on output, inflation and interest rate. However, the prediction seems to be quite wrong Inflation Forecast Targeting, joint with Paul Söderlind, takes a step outside the representative-agent framework. In RE models, all agents typically have the same information set, and therefore make the same predictions. However, in the real even professional forecasters show substantial disagreement. This disagreement can have an impact on asset prices and transaction volumes, among other things. However, there is no unique way of aggregating forecasts (or forecast probability density functions) into a measure of disagreement. The paper deals with this problem, surveying some proposed methods. The most appropriate measure of disagreement turns out to depend on the intended use, that is, on the model. Moreover, forecasters underestimate uncertainty. Constitutions and Central-Bank Independence: An Objection to McCallum's Second Fallacy, joint with Giancarlo Spagnolo , is an excursion into the field of Political Economy. The essay provides some foundations for the assumption that renegotiating a delegation contract can be costly by illustrating how political institutions can generate inertia in re-contracting, reduce the gains from it or prevent it altogether. Once the nature of renegotiation costs has been clarified, it is easier to see why certain institutions can mitigate or solve dynamic inconsistencies better than others. / Diss. Stockholm : Handelshögsk., 2001

There is a need for a fuel-optimal required time of arrival (RTA) mode for aircraft flight management systems capable of enabling controlled time of arrival functionality in the presence of wind speed forecast uncertainty. A computationally tractable two-stage stochastic algorithm utilizing a data-driven, location-specific forecast uncertainty model to generate forecast uncertainty scenarios is proposed as a solution. Three years of Aircraft Communications Addressing and Reporting Systems (ACARS) wind speed reports are used in conjunction with corresponding wind speed forecasts from the Rapid Update Cycle (RUC) forecast product to construct an inhomogeneous Markov model quantifying forecast uncertainty characteristics along specific route through the national airspace system. The forecast uncertainty modeling methodology addresses previously unanswered questions regarding the regional uncertainty characteristics of the RUC model, and realizations of the model demonstrate a clear tendency of the RUC product to be positively biased along routes following the normal contours of the jet stream. A two-stage stochastic algorithm is then developed to calculate the fuel optimal stage one cruise speed given a required time of arrival at a destination waypoint and wind forecast uncertainty scenarios generated using the inhomogeneous Markov model. The algorithm utilizes a quadratic approximation of aircraft fuel flow rate as a function of cruising Mach number to quickly search for the fuel-minimum stage one cruise speed while keeping computational footprint small and ensuring RTA adherence. Compared to standard approaches to the problem utilizing large scale linear programming approximations, the algorithm performs significantly better from a computational complexity standpoint, providing solutions in fractional power time while maintaining computational tractability in on-board systems.

SMHI:s hydrologiska prognos- och varningstjänst använder sig av meteorologiska ensembleprognoser som indata i hydrologiska modeller. De hydrologiskaensembleprognoserna tar därmed hänsyn till framtida osäkerhet i temperatur och nederbördoch används som underlag vid utfärdandet av risker och varningar för höga flöden. För närvarande beaktas dock inte osäkerheten i modellens starttillstånd, vilket består av de tillståndsvariabler i modellen som beskriver bland annat markvattenhalt och snötäcke. I dennastudie undersöktes hur starttillståndet i den hydrologiska modellen HYPE inverkar på prognoser i syfte att kvantifiera osäkerheten och på sikt möjliggöra säkrare prognoser.Studien hade tre mål: 1) Ta fram ett förslag på hur starttillståndet kan varieras för att ge en god uppskattning av prognososäkerheten relaterat till det hydrologiska starttillståndet. 2) Undersöka sambandet mellan starttillståndens spridning och det hydrologiska prognosfelet. 3) Analysera hur årstider, avrinningsområdens area, sjöprocent, skogsprocent och höjd över havet inverkar på prognososäkerheten. En central hypotes var att mindre skillnad mellan starttillståndets vattenföring och den observerade vattenföringen vid prognosstart resulterar i mer träffsäkra prognoser. Studien begränsades av att starttillstånden endast genererades med hjälp av störningar i drivdata.Indata till HYPE-modellen var femton temperatur- och nederbördsserier som manipulerats i syfte att skapa en ensemble av olika starttillstånd. Denna ensemble användes sedan för att göra vattenföringsprognoser med observerad temperatur och nederbörd som drivdata. Studien omfattade 76 avrinningsområden från hela Sverige med data för perioden 1999-2008. Prognoser utfördes varje dygn och ensemblespridningen utvärderades 2, 4 och 10 dygn in i prognosen. Samma utvärderingar utfördes även på autoregressiva prognoser, vilket innebär att modellerad rättas utefter observerad vattenföring.Resultaten indikerade ett samband mellan ensemblespridning och prognosfel, vilket innebär att spridning kan användas som ett mått på starttillståndets osäkerhet. Prognosfelet korrelerade positivt med skogsprocent och negativt med avrinningsområdenas area, sjöprocent och höjd över havet. Samma samband uppvisades mellan dessa områdesvariableroch spridning. Spridningen var störst på vintern och våren då normalisering skett med medelvattenföring över tio år, och under vår och sommar då normalisering skett med medelvattenföring per månad. Hypotesen att mindre skillnad mellan starttillståndets vattenföring och den observerade vattenföringen vid prognosstart resulterar i mer träffsäkraprognoser bekräftades av resultaten. Implementering av en ensemble av olika starttillstånd i operationella prognoser vid SMHIs hydrologiska prognos- och varningstjänst föreslås i syfte att kvantifiera osäkerheten och därigenom utöka bedömningsunderlaget vid utfärdande av risker och varningar. / The Hydrological Forecast and Warning Service of The Swedish Meteorological and Hydrological Institute (SMHI) use meteorological ensemble forecasts as input in hydrological models. The hydrological ensemble forecasts take the uncertainty of future temperature and precipitation into account and serve as the basis of issued risks and warnings of high flows. Currently not considered is the uncertainty of the initial state, which consists of state variables in the model describing for instance soil water content and snow pack. This study assessed the impact of the initial state on forecasts in the hydrological model HYPE aiming to quantify the uncertainty and eventually enable more accurate forecasts.There were three aims of this study : 1) Evaluate a suggestion about how the initial state can be varied to give a good estimation of forecast uncertainty related to the hydrological initial state. 2) Examine the relationship between the spread of initial states and the hydrological forecast error. 3) Analyze the impact of seasons, catchment area, lake percentage, forest percentage and elevation on forecast uncertainty. A central hypothesis was that a smaller difference between the discharge of the initial state and the observed discharge results in more accurate forecasts. A restriction of the study was that the initial states only could be generated by disturbances of forcing data in before the forecast.Input data to the HYPE model were fifteen temperature and precipitation series, manipulated to generate an ensemble of different initial states. This ensemble was then used to make discharge forecasts with observed temperature and precipitation as forcing data. The study was performed on 76 catchments all over Sweden with data from the time period 1999-2008. Forecasts were made every day and the ensemble spread was evaluated 2, 4 and 10 days into the forecast. Autoregressive forecasts where the modelled discharge is corrected after the observed discharge were executed and evaluated as well. The results indicated a relationship between ensemble spread and forecast error, which implies that the spread can be used as a measure of the uncertainty of the initial state. The forecast error and ensemble spread correlated positively to forest percentage and negatively to catchment area, lake percentage and elevation. The same trend was detected between spread and catchment characteristics. The spread was biggest in winter and spring when normalization was made with mean discharge for the ten-year period and in spring and summer when normalization was done with mean discharge per month. The hypothesis that a smaller difference between the discharge of the initial state and the observed discharge results in more accurate forecasts was confirmed by the results. An implementation of an ensemble of different initial states in operational forecasts at SMHI’s Hydrological Forecast and Warning Service is suggested in order to further quantify the uncertainty of hydrological forecasts, and thereby improve the basis of judgment when issuing risks and warnings.