arXiv subjects

Sándor Baran

Publications and source records attributed to Sándor Baran.

At least 37 records · Page 2Linked to original sources

arXiv2018

Statistical post-processing of dual-resolution ensemble forecasts

The computational cost as well as the probabilistic skill of ensemble forecasts depends on the spatial resolution of the numerical weather prediction model and the ensemble size. Periodically, e.g. when more computational resources become available, it is appropriate to reassess the balance between resolution and ensemble size. Recently, it has been proposed to investigate this balance in the context of dual-resolution ensembles, which use members with two different resolutions to make probabilistic forecasts. This study investigates whether statistical post-processing of such dual-resolution ensemble forecasts changes the conclusions regarding the optimal dual-resolution configuration. Medium-range dual-resolution ensemble forecasts of 2-metre temperature have been calibrated using ensemble model output statistics. The forecasts are produced with ECMWF's Integrated Forecast System and have horizontal resolutions between 18 km and 45 km. The ensemble sizes range from 8 to 254 members. The forecasts are verified with SYNOP station data. Results show that score differences between various single and dual-resolution configurations are strongly reduced by statistical post-processing. Therefore, the benefit of some dual-resolution configurations over single resolution configurations appears to be less pronounced than for raw forecasts. Moreover, the ranking of the ensemble configurations can be affected by the statistical post-processing.

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arXiv2018

Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile

Currently all major meteorological centres generate ensemble forecasts using their operational ensemble prediction systems; however, it is a general problem that the spread of the ensemble is too small, resulting in underdispersive forecasts, leading to a lack of calibration. In order to correct this problem, different statistical calibration techniques have been developed in the last two decades. In the present work different post-processing techniques are tested for calibrating 9 member ensemble forecast of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting (WRF) model using different planetary boundary layer and land surface model parametrization. In particular, the ensemble model output statistics (EMOS) and Bayesian model averaging techniques are implemented and since the observations are characterized by large altitude differences, the estimation of model parameters is adapted to the actual conditions at hand. Compared to the raw ensemble all tested post-processing approaches significantly improve the calibration of probabilistic and the accuracy of point forecasts. The EMOS method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill.

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arXiv2018

Statistical post-processing of hydrological forecasts using Bayesian model averaging

Accurate and reliable probabilistic forecasts of hydrological quantities like runoff or water level are beneficial to various areas of society. Probabilistic state-of-the-art hydrological ensemble prediction models are usually driven with meteorological ensemble forecasts. Hence, biases and dispersion errors of the meteorological forecasts cascade down to the hydrological predictions and add to the errors of the hydrological models. The systematic parts of these errors can be reduced by applying statistical post-processing. For a sound estimation of predictive uncertainty and an optimal correction of systematic errors, statistical post-processing methods should be tailored to the particular forecast variable at hand. Former studies have shown that it can make sense to treat hydrological quantities as bounded variables. In this paper, a doubly truncated Bayesian model averaging (BMA) method, which allows for flexible post-processing of (multi-model) ensemble forecasts of water level, is introduced. A case study based on water level for a gauge of river Rhine, reveals a good predictive skill of doubly truncated BMA compared both to the raw ensemble and the reference ensemble model output statistics approach.

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arXiv2018

On the optimal designs for the prediction of complex Ornstein-Uhlenbeck processes

Physics, chemistry, biology or finance are just some examples out of the many fields where complex Ornstein-Uhlenbeck (OU) processes have various applications in statistical modelling. They play role e.g. in the description of the motion of a charged test particle in a constant magnetic field or in the study of rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov used such a process to model the so-called Chandler wobble, the small deviation in the Earth's axis of rotation. A common problem in these applications is deciding how to choose a set of a sample locations in order to predict a random process in an optimal way. We study the optimal design problem for the prediction of a complex OU process on a compact interval with respect to integrated mean square prediction error (IMSPE) and entropy criteria. We derive the exact forms of both criteria, moreover, we show that optimal designs based on entropy criterion are equidistant, whereas the IMSPE based ones may differ from it. Finally, we present some numerical experiments to illustrate selected cases of optimal designs for small number of sampling locations.

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arXiv2017

Combining predictive distributions for statistical post-processing of ensemble forecasts

Statistical post-processing techniques are now widely used to correct systematic biases and errors in calibration of ensemble forecasts obtained from multiple runs of numerical weather prediction models. A standard approach is the ensemble model output statistics (EMOS) method, a distributional regression approach where the forecast distribution is given by a single parametric law with parameters depending on the ensemble members. Choosing an appropriate parametric family for the weather variable of interest is a critical, however, often non-trivial task, and has been the focus of much recent research. In this article, we assess the merits of combining predictive distributions from multiple EMOS models based on different parametric families. In four case studies with wind speed and precipitation forecasts from two ensemble prediction systems, we study whether state of the art forecast combination methods are able to improve forecast skill.

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arXiv2017

D-optimal designs for complex Ornstein-Uhlenbeck processes

Complex Ornstein-Uhlenbeck (OU) processes have various applications in statistical modelling. They play role e.g. in the description of the motion of a charged test particle in a constant magnetic field or in the study of rotating waves in time-dependent reaction diffusion systems, whereas Kolmogorov used such a process to model the so-called Chandler wobble, small deviation in the Earth's axis of rotation. In these applications parameter estimation and model fitting is based on discrete observations of the underlying stochastic process, however, the accuracy of the results strongly depend on the observation points. This paper studies the properties of D-optimal designs for estimating the parameters of a complex OU process with a trend. We show that in contrast with the case of the classical real OU process, a D-optimal design exists not only for the trend parameter, but also for joint estimation of the covariance parameters, moreover, these optimal designs are equidistant.

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arXiv2016

K-optimal designs for parameters of shifted Ornstein-Uhlenbeck processes and sheets

Continuous random processes and fields are regularly applied to model temporal or spatial phenomena in many different fields of science, and model fitting is usually done with the help of data obtained by observing the given process at various time points or spatial locations. In these practical applications sampling designs which are optimal in some sense are of great importance. We investigate the properties of the recently introduced K-optimal design for temporal and spatial linear regression models driven by Ornstein-Uhlenbeck processes and sheets, respectively, and highlight the differences compared with the classical D-optimal sampling. A simulation study displays the superiority of the K-optimal design for large parameter values of the driving random process.

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arXiv2016

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

Recently all major weather prediction centres 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 that the raw ensemble and the BMA model.

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arXiv2015

Mixture EMOS model for calibrating ensemble forecasts of wind speed

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 (PDFs). The EMOS predictive PDF 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 to 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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arXiv2015

Bivariate ensemble model output statistics approach for joint forecasting of wind speed and temperature

Forecast ensembles are typically employed to account for prediction uncertainties in numerical weather prediction models. However, ensembles often exhibit biases and dispersion errors, thus they require statistical post-processing to improve their predictive performance. Two popular univariate post-processing models are the Bayesian model averaging (BMA) and the ensemble model output statistics (EMOS). In the last few years increased interest has emerged in developing multivariate post-processing models, incorporating dependencies between weather quantities, such as for example a bivariate distribution for wind vectors or even a more general setting allowing to combine any types of weather variables. In line with a recently proposed approach to model temperature and wind speed jointly by a bivariate BMA model, this paper introduces a bivariate EMOS model for these weather quantities based on a truncated normal distribution. The bivariate EMOS model is applied to temperature and wind speed forecasts of the eight-member University of Washington mesoscale ensemble and of the eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to the performance of the bivariate BMA model and a multivariate Gaussian copula approach, post-processing the margins with univariate EMOS. While the predictive skills of the compared methods are similar, the bivariate EMOS model requires considerably lower computation times than the bivariate BMA method.

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arXiv2015

Optimal designs for the methane flux in troposphere

The understanding of methane emission and methane absorption plays a central role both in the atmosphere and on the surface of the Earth. Several important ecological processes, e.g., ebullition of methane and its natural microergodicity request better designs for observations in order to decrease variability in parameter estimation. Thus, a crucial fact, before the measurements are taken, is to give an optimal design of the sites where observations should be collected in order to stabilize the variability of estimators. In this paper we introduce a realistic parametric model of covariance and provide theoretical and numerical results on optimal designs. For parameter estimation D-optimality, while for prediction integrated mean square error and entropy criteria are used. We illustrate applicability of obtained benchmark designs for increasing/measuring the efficiency of the engineering designs for estimation of methane rate in various temperature ranges and under different correlation parameters. We show that in most situations these benchmark designs have higher efficiency.

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arXiv2014

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

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.

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arXiv2014

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

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, e.g., 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 based on 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 eleven-member ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service and its predictive performance is compared to that of the general Gaussian copula method. The results indicate improved calibration of probabilistic and accuracy of point forecasts in comparison to the raw ensemble and the overall performance of this model is able to keep up with that of the Gaussian copula method.

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arXiv2013

Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction

The evolution of the weather can be described by deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions and/or model physics result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required. In the present work we compare different versions of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) post-processing methods in order to calibrate 2m temperature and 10m wind speed forecasts of the operational ALADIN Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service. We show that compared to the raw ensemble both post-processing methods improve the calibration of probabilistic and accuracy of point forecasts and that the best BMA method slightly outperforms the EMOS technique.

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arXiv2013

Optimal designs for parameters of shifted Ornstein-Uhlenbeck sheets measured on monotonic sets

Measurement on sets with a specific geometric shape can be of interest for many important applications (e.g. measurement along the isotherms in structural engineering). In the present paper the properties of optimal designs for estimating the parameters of shifted Ornstein-Uhlenbeck sheets, that is Gaussian two-variable random fields with exponential correlation structures, are investigated when the processes are observed on monotonic sets. Substantial differences are demonstrated between the cases when one is interested only in trend parameters and when the whole parameter set is of interest. The theoretical results are illustrated by computer experiments and simulated examples from the field of structure engineering. From the design point of view the most interesting finding of the paper is the loss of efficiency of the regular grid design compared to the optimal monotonic design.

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arXiv2013

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

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 ensemble members and validating observation from a given training period. In the present paper we introduce a BMA model for calibrating wind speed forecasts, where the components PDFs follow truncated normal distribution with cut-off at zero, and apply it to the ALADIN-HUNEPS ensemble of the Hungarian Meteorological Service. 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. Moreover, since here the maximum likelihood estimation of the parameters does not require numerical optimization, modelling can be performed much faster than in case of gamma mixtures.

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arXiv2013

Probabilistic temperature forecasting with statistical calibration in Hungary

Weather forecasting is mostly based on the outputs of deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions result in forecast ensembles which is are used for estimating the distribution of future atmospheric variables. However, these 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 temperature forecasts produced by the operational Limited Area Model Ensemble Prediction System of the Hungarian Meteorological Service (HMS). We describe two possible BMA models for temperature data of the HMS and show that BMA post-processing significantly improves calibration and probabilistic forecasts although the accuracy of point forecasts is rather unchanged.

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arXiv2013

Probabilistic wind speed forecasting in Hungary

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.

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