Probabilistic temperature forecasting based on an ensemble AR modification

TL;DR

This paper introduces a novel method combining ensemble model output statistics with an autoregressive adjustment to improve probabilistic temperature forecasts, demonstrating enhanced accuracy over traditional EMOS techniques.

Contribution

It proposes a new ensemble modification technique that integrates AR processes with EMOS, providing better calibration and sharpness in temperature forecast distributions.

Findings

Improved forecast accuracy over basic EMOS.

Enhanced calibration of probabilistic temperature predictions.

Effective adjustment method demonstrated on ECMWF ensemble data.

Abstract

To address the uncertainty in outputs of numerical weather prediction (NWP) models, ensembles of forecasts are used. To obtain such an ensemble of forecasts the NWP model is run multiple times, each time with different formulations and/or initial or boundary conditions. To correct for possible biases and dispersion errors in the ensemble, statistical postprocessing models are frequently employed. These statistical models yield full predictive probability distributions for a weather quantity of interest and thus allow for a more accurate assessment of forecast uncertainty. This paper proposes to combine the state of the art Ensemble Model Output Statistics (EMOS) with an ensemble that is adjusted by an AR process fitted to the respective error series by a spread-adjusted linear pool (SLP) in case of temperature forecasts. The basic ensemble modification technique we introduce may be used to simply adjust the ensemble itself as well as to obtain a full predictive distribution for the weather quantity. As demonstrated for temperature forecasts of the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble, the proposed procedure gives rise to improved results upon the basic (local) EMOS method.