Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas

Möller, Annette; Lenkoski, Alex; Thorarinsdottir, Thordis L.

doi:10.1002/qj.2009

Cited by 114 publications

(140 citation statements)

References 51 publications

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“…However, it may be laeking in the observation reeord for the Sehaake shuffle approaeh owing to the likely laek of oeeurrenees of similar events historieally over the foreeast horizon. If the NWP model output is strongly struetured, parametrie eopula approaches might be used (as in Möller et al 2013) to eorreet for any systematie errors in the ensemble's representation of the eonditional dependenee strueture. However, sueh parametrie proeedures are very expensive Ü computationally and could be limited in practice by the output dimensionality.…”

Section: Meteorological Ensemble Fore-mentioning

confidence: 99%

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

Demargne¹,

Wu²,

Regonda³

et al. 2014

Bull. Amer. Meteor. Soc.

263

164

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Section: Meteorological Ensemble Fore-mentioning

confidence: 99%

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

Demargne¹,

Wu²,

Regonda³

et al. 2014

Bull. Amer. Meteor. Soc.

263

164

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“…However, there is still space for further improvements, e.g. by considering models including spatial dependence via state of the art approaches such as the Markovian EMOS (Möller et al ., ), ensemble copula coupling (Schefzik, , ) and the spatial extensions of BMA and EMOS suggested by Feldmann et al . (), which appear to be very suitable for the dataset at hand.…”

Section: Discussionmentioning

confidence: 99%

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

Díaz

Nicolis

Marı́n

et al. 2019

Meteorological Applications

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Modelling forecast uncertainty is a difficult task in any forecasting problem. In weather forecasting a possible solution is the use of forecast ensembles, which are obtained from multiple runs of numerical weather prediction models with various initial conditions and model parametrizations to provide information about the expected uncertainty. Currently all major meteorological centres issue forecasts using their operational ensemble prediction systems. However, it is a general problem that the spread of the ensemble is too small compared to observations at specific sites resulting in under‐dispersive forecasts, leading to a lack of calibration. In order to correct this problem, various statistical calibration techniques have been developed in the last two decades. In the present work different post‐processing techniques were tested for calibrating nine member ensemble forecasts of temperature for Santiago de Chile, obtained by the Weather Research and Forecasting model using different planetary boundary layer and land surface model parametrizations. In particular, the ensemble model output statistics and Bayesian model averaging techniques were implemented and, since the observations are characterized by large altitude differences, the estimation of model parameters was adapted to the actual conditions at hand. Compared to the raw ensemble, all tested post‐processing approaches significantly improve the calibration of probabilistic forecasts and the accuracy of point forecasts. The ensemble model output statistics method using parameter estimation based on expert clustering of stations (according to their altitudes) shows the best forecast skill.

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“…In geospatial statistics, it is common to specify the elements p ij of P through a parametric stationary, isotropic correlation function of the distance between location s i and location s j (Möller et al . ). However, attempts at fitting this type of function to the data set described in Section 2 produced unstable parameter estimates, suggesting that the assumptions of stationarity and isotropy do not hold in this case.…”

Section: Current Methods In Mme Postprocessingmentioning

confidence: 99%

New approaches to postprocessing of multi‐model ensemble forecasts

Barnes

Brierley

Chandler

2019

Quart J Royal Meteoro Soc

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Ensemble weather forecasts often under‐represent uncertainty, leading to overconfidence in their predictions. Multi‐model forecasts combining several individual ensembles have been shown to display greater skill than single‐ensemble forecasts in predicting temperatures, but tend to retain some bias in their joint predictions. Established postprocessing techniques are able to correct bias and calibration issues in univariate forecasts, but are generally not designed to handle multivariate forecasts (of several variables or at several locations, say). We propose a flexible multivariate Bayesian postprocessing framework, based on a directed acyclic graph representing the relationships between the ensembles and the observed weather. The posterior forecast is inferred from available ensemble forecasts and an estimate of the shared discrepancy, obtained from a collection of past forecast–observation pairs. We also propose a novel approach to selecting an appropriate training set for estimation of the required correction, using synoptic‐scale analogues to obtain a regime‐dependent estimate of the adjustment. The proposed technique is applied to forecasts of surface temperature over the UK during the winter period from 2007 to 2013. Although the resulting parametric multivariate‐normal probabilistic forecasts are marginally less sharp than those of the leading competitor, they capture the spatial structure of the observations better than a correlation structure based on either the ensembles or climatology alone, and are robust to changes in the variables and spatial domain of the forecast, at a greatly reduced computational cost.

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Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas

Cited by 114 publications

References 51 publications

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

The Science of NOAA's Operational Hydrologic Ensemble Forecast Service

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

New approaches to postprocessing of multi‐model ensemble forecasts

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