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Dive into the research topics where Sándor Baran is active.

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Featured researches published by Sándor Baran.


Mathematical Geosciences | 2002

On Modelling Discrete Geological Structures as Markov Random Fields

Tommy Norberg; Lars Rosén; Ágnes Baran; Sándor Baran

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.


Computational Statistics & Data Analysis | 2014

Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components

Sándor Baran

Bayesian model averaging (BMA) is a statistical method for post-processing forecast ensembles of atmospheric variables, obtained from multiple runs of numerical weather prediction models, in order to create calibrated predictive probability density functions (PDFs). The BMA predictive PDF of the future weather quantity is the mixture of the individual PDFs corresponding to the ensemble members and the weights and model parameters are estimated using forecast ensembles and validating observations from a given training period. A BMA model for calibrating wind speed forecasts is introduced using truncated normal distributions as conditional PDFs and the method is applied to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and to the University of Washington Mesoscale Ensemble. Three parameter estimation methods are proposed and each of the corresponding models outperforms the traditional gamma BMA model both in calibration and in accuracy of predictions.


Quarterly Journal of the Royal Meteorological Society | 2015

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

Sándor Baran; Sebastian Lerch

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.


arXiv: Methodology | 2014

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

Sándor Baran; Sebastian Lerch

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.


Computers & Mathematics With Applications | 2003

Estimation of the mean of stationary and nonstationary Ornstein-Uhlenbeck processes and sheets

Sándor Baran; Gyula Pap; M.C.A. van Zuijlen

Abstract We consider the problem of estimating an unknown parameter m in case one observes in an interval (rectangle) stationary and nonstationary Ornstein-Uhlenbeck processes (sheets), which are shifted by m times a known deterministic function on the interval (rectangle). It turns out that the maximum likelihood estimator (MLE) has a normal distribution and, for instance, in case of the sheet this MLE is a weighted linear combination of the values at the vertices, integrals on the edges, and the integral on the whole rectangle of the weighted observed process. We do not use partial stochastic differential equations; we apply direct discrete time approach instead. To make the transition from the discrete time to the continuous time, a tool is developed, which might be of independent interest.


Environmetrics | 2015

Joint probabilistic forecasting of wind speed and temperature using Bayesian model averaging

Sándor Baran; Annette Möller

Ensembles of forecasts are typically employed to account for the forecast uncertainties inherent in predictions of future weather states. However, biases and dispersion errors often present in forecast ensembles require statistical post-processing. Univariate post-processing models such as Bayesian model averaging (BMA) have been successfully applied for various weather quantities. Nonetheless, BMA and many other standard post-processing procedures are designed for a single weather variable, thus ignoring possible dependencies among weather quantities. In line with recently upcoming research to develop multivariate post-processing procedures, for example, BMA for bivariate wind vectors, or flexible procedures applicable for multiple weather quantities of different types, a bivariate BMA model for joint calibration of wind speed and temperature forecasts is proposed on the basis of the bivariate truncated normal distribution. It extends the univariate truncated normal BMA model designed for post-processing ensemble forecast of wind speed by adding a normally distributed temperature component with a covariance structure representing the dependency among the two weather quantities. The method is applied to wind speed and temperature forecasts of the eight-member University of Washington mesoscale ensemble and of the 11-member Aire Limitee Adaptation dynamique Developpement International-Hungary Ensemble Prediction System (ALADIN-HUNEPS) ensemble of the Hungarian Meteorological Service, and its predictive performance is compared to that of the independent BMA calibration of these weather quantities and the general Gaussian copula method. The results indicate improved calibration of probability and accuracy of point forecasts in comparison to the raw ensemble and the independent BMA approach, and the overall performance of this bivariate model is able to keep up with that of the Gaussian copula method. Copyright


Meteorologische Zeitschrift | 2013

Statistical post-processing of probabilistic wind speed forecasting in Hungary

Sándor Baran; András Horányi; Dóra Nemoda

Prediction of various weather quantities is mostly based on deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result ensembles of forecasts which are applied for estimating the distribution of future weather quantities. However, the ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work Bayesian Model Averaging (BMA) is applied for calibrating ensembles of wind speed forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for wind speed data of the HMS and show that BMA post-processing significantly improves the calibration and precision of forecasts.


Communications in Statistics-theory and Methods | 2005

A Consistent Estimator for Linear Models with Dependent Observations

Sándor Baran

Abstract In this article, an estimator for the classical linear model and its generalization for the linear measurement error model are studied in the case of dependent (strong mixing) observations. These estimators are based on the Fourier transform of a certain weight function. Consistency of both estimators is established and asymptotic normality is proved for the estimator in the classical model. Simulation results are also presented for both models.


Environmetrics | 2016

Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting

Sándor Baran; Dóra Nemoda

Recently, all major weather prediction centers provide forecast ensembles of different weather quantities, which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations. However, ensemble forecasts often show an underdispersive character and may also be biased, so that some post-processing is needed to account for these deficiencies. Probably the most popular modern post-processing techniques are the ensemble model output statistics (EMOS) and the Bayesian model averaging (BMA), which provide estimates of the density of the predictable weather quantity. In the present work, an EMOS method for calibrating ensemble forecasts of precipitation accumulation is proposed, where the predictive distribution follows a censored and shifted gamma (CSG) law with parameters depending on the ensemble members. The CSG EMOS model is tested on ensemble forecasts of 24-h precipitation accumulation of the eight-member University of Washington mesoscale ensemble and on the 11-member ensemble produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service. The predictive performance of the new EMOS approach is compared with the fit of the raw ensemble, the generalized extreme value (GEV) distribution-based EMOS model, and the gamma BMA method. According to the results, the proposed CSG EMOS model slightly outperforms the GEV EMOS approach in terms of calibration of probabilistic and accuracy of point forecasts and shows significantly better predictive skill than the raw ensemble and the BMA model. Copyright


Environmetrics | 2016

Mixture EMOS model for calibrating ensemble forecasts of wind speed

Sándor Baran; Sebastian Lerch

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 Limitée Adaptation dynamique Développement 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.

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Gyula Pap

University of Debrecen

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Sebastian Lerch

Heidelberg Institute for Theoretical Studies

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Milan Stehlík

University of Valparaíso

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M.C.A. van Zuijlen

Radboud University Nijmegen

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