Probabilistic temperature forecasting with a heteroscedastic autoregressive ensemble postprocessing model. (25th November 2019)

• Record Type: Journal Article
• Authors: Möller, Annette; Groß, Jürgen
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 146:Number 726(2020)
• Page Start: 211
• Page End: 224
• Publication Date: 2019-11-25
• Subjects: autoregressive process -- ensemble postprocessing -- heteroscedastic model -- high‐resolution forecast -- predictive probability distribution -- spread‐adjusted linear pool -- spread‐error correlation; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3667
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: Weather prediction today is performed with numerical weather prediction (NWP) models. These are deterministic simulation models describing the dynamics of the atmosphere, and evolving the current conditions forward in time to obtain a prediction for future atmospheric states. To account for uncertainty in NWP models it has become common practice to employ ensembles of NWP forecasts. However, NWP ensembles often exhibit forecast biases and dispersion errors, thus require statistical postprocessing to improve reliability of the ensemble forecasts. This work proposes an extension of a recently developed postprocessing model utilizing autoregressive information present in the forecast error of the raw ensemble members. The original approach is modified to let the variance parameter depend on the ensemble spread, yielding a two‐fold heteroscedastic model. Furthermore, an additional high‐resolution forecast is included into the postprocessing model, yielding improved predictive performance. Finally, it is outlined how the autoregressive model can be utilized to postprocess ensemble forecasts with higher forecast horizons, without the necessity of making fundamental changes to the original model. We accompany the new methodology by an implementation within the R package ensAR to make our method available for other researchers working in this area. To illustrate the performance of the heteroscedastic extension of the autoregressive model, and its use for higher forecast horizons we present a case‐study for a dataset containing 12 years of temperature forecasts and observations over Germany. The case‐study indicates that the autoregressive model yields particularly strong improvements for forecast horizons beyond 24 h. Abstract : Individual members of a forecast ensemble can exhibit substantial autoregressive behaviour, which is not accounted for by standard statistical postprocessing models such as EMOS. A heteroscedastic modification of the EMOS temperature model is presented, which corrects the original ensemble forecasts for the autoregressive structure present in the forecast error and fits a predictive distribution based on the corrected ensemble and its respective ensemble spread. Especially for higher forecast horizons beyond 24 h ahead, the heteroscedastic autoregressive model yields significantly better predictive performance than the standard EMOS.