Sándor Baran

Bio: Sándor Baran is an academic researcher from University of Debrecen. The author has contributed to research in topics: Ensemble forecasting & Wind speed. The author has an hindex of 15, co-authored 78 publications receiving 911 citations. Previous affiliations of Sándor Baran include European Centre for Medium-Range Weather Forecasts & Heidelberg University.

Log-normal distribution based EMOS models for probabilistic wind speed forecasting

Abstract: Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles, they are usually under-dispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind speed forecasts based on the log-normal (LN) distribution, and we also show a regime-switching extension of the model which combines the previously studied truncated normal (TN) distribution with the LN. Both presented models are applied to wind speed forecasts of the eight-member University of Washington mesoscale ensemble, of the fifty-member ECMWF ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service, and their predictive performances are compared to those of the TN and general extreme value (GEV) distribution based EMOS methods and to the TN-GEV mixture model. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and to climatological forecasts. Further, the TN-LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with the models utilizing the GEV distribution without assigning mass to negative values.

Log-normal distribution based Ensemble Model Output Statistics models for probabilistic wind-speed forecasting

Abstract: Ensembles of forecasts are obtained from multiple runs of numerical weather forecasting models with different initial conditions and typically employed to account for forecast uncertainties. However, biases and dispersion errors often occur in forecast ensembles: they are usually underdispersive and uncalibrated and require statistical post-processing. We present an Ensemble Model Output Statistics (EMOS) method for calibration of wind-speed forecasts based on the log-normal (LN) distribution and we also show a regime-switching extension of the model, which combines the previously studied truncated normal (TN) distribution with the LN. Both models are applied to wind-speed forecasts of the eight-member University of Washington mesoscale ensemble, the 50 member European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble and the 11 member Aire Limitee Adaptation dynamique Developpement International-Hungary Ensemble Prediction System (ALADIN-HUNEPS) ensemble of the Hungarian Meteorological Service; their predictive performance is compared with that of the TN and general extreme value (GEV) distribution based EMOS methods and the TN–GEV mixture model. The results indicate improved calibration of probabilistic forecasts and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. Further, the TN–LN mixture model outperforms the traditional TN method and its predictive performance is able to keep up with models utilizing the GEV distribution without assigning mass to negative values.

Mixture EMOS model for calibrating ensemble forecasts of wind speed

Abstract: Ensemble model output statistics (EMOS) is a statistical tool for post-processing forecast ensembles of weather variables obtained from multiple runs of numerical weather prediction models in order to produce calibrated predictive probability density functions. The EMOS predictive probability density function is given by a parametric distribution with parameters depending on the ensemble forecasts. We propose an EMOS model for calibrating wind speed forecasts based on weighted mixtures of truncated normal (TN) and log-normal (LN) distributions where model parameters and component weights are estimated by optimizing the values of proper scoring rules over a rolling training period. The new model is tested on wind speed forecasts of the 50 member European Centre for Medium-range Weather Forecasts ensemble, the 11 member Aire Limitee Adaptation dynamique Developpement International-Hungary Ensemble Prediction System ensemble of the Hungarian Meteorological Service, and the eight-member University of Washington mesoscale ensemble, and its predictive performance is compared with that of various benchmark EMOS models based on single parametric families and combinations thereof. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison with the raw ensemble and climatological forecasts. The mixture EMOS model significantly outperforms the TN and LN EMOS methods; moreover, it provides better calibrated forecasts than the TN-LN combination model and offers an increased flexibility while avoiding covariate selection problems.

On Modelling Discrete Geological Structures as Markov Random Fields

Abstract: The purpose of this paper is to extend the locally based prediction methodology of BayMar to a global one by modelling discrete spatial structures as Markov random fields. BayMar uses one-dimensional Markov-properties for estimating spatial correlation and Bayesian updating for locally integrating prior and additional information. The methodology of this paper introduces a new estimator of the field parameters based on the maximum likelihood technique for one-dimensional Markov chains. This makes the estimator straightforward to calculate also when there is a large amount of missing observations, which often is the case in geological applications. We make simulations (both unconditional and conditional on the observed data) and maximum a posteriori predictions (restorations) of the non-observed data using Markov chain Monte Carlo methods, in the restoration case by employing simulated annealing. The described method gives satisfactory predictions, while more work is needed in order to simulate, since it appears to have a tendency to overestimate strong spatial dependence. It provides an important development compared to the BayMar-methodology by facilitating global predictions and improved use of sparse data.

Convergence of Probability Measures

Abstract: Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Gas response control through structural and chemical modification of metal oxide films: state of the art and approaches

Abstract: In this review the structural and physical–chemical properties of nanoscaled metal oxide films (SnO2 and In2O3), aimed for solid state chemical sensors were analyzed. It has been shown that structural factor even for nanoscaled materials is complicated conception. One has to consider not only size, but also such a parameters as crystallite shape; nanoscopic structure; crystallographic orientation of nanocrystallites planes, forming gas sensing surface; film agglomeration; phase composition; surface architecture. The methods suitable for control of these structural and physical–chemical parameters have been discussed. Results, mainly obtained during study of both SnO2 and In2O3 thin films deposited by spray pyrolysis have been used for showing an opportunity of structural engineering of metal oxides for optimization of gas sensing characteristics.

Metal oxide composites in conductometric gas sensors: Achievements and challenges

Abstract: The features of conductometric gas sensors based on metal oxide composites are considered. The methods of the composites forming and the advantages of their using in the development of gas sensors are discussed. It is given the analysis of the factors that reduce the effectiveness of the composite using in conductometric gas sensors and can restrict application of nanocomposites in these devices. Technology features of composite synthesis and device fabrication, which should be taken into account while designing and fabricating sensors based on metal oxide composites, are considered. The mechanisms explaining the operation of conductometric gas sensors based on metal oxide composites are also discussed.

Recent advances in electricity price forecasting: A review of probabilistic forecasting

Abstract: Since the inception of competitive power markets two decades ago, electricity price forecasting (EPF) has gradually become a fundamental process for energy companies’ decision making mechanisms. Over the years, the bulk of research has concerned point predictions. However, the recent introduction of smart grids and renewable integration requirements has had the effect of increasing the uncertainty of future supply, demand and prices. Academics and practitioners alike have come to understand that probabilistic electricity price (and load) forecasting is now more important for energy systems planning and operations than ever before. With this paper we offer a tutorial review of probabilistic EPF and present much needed guidelines for the rigorous use of methods, measures and tests, in line with the paradigm of ‘maximizing sharpness subject to reliability’. The paper can be treated as an update and a further extension of the otherwise comprehensive EPF review of Weron [1] or as a standalone treatment of a fascinating and underdeveloped topic, that has a much broader reach than EPF itself.