Abstract
Understanding changes in the frequency, severity, and seasonality of daily temperature extremes is important for public policy decisions regarding heat waves and cold snaps. A heat wave is sometimes defined in terms of both the daily minimum and maximum temperature, which necessitates the generation of forecasts of their joint distribution. In this paper, we develop time series models with the aim of providing insight and producing forecasts of the joint distribution that can challenge the accuracy of forecasts based on ensemble predictions from a numerical weather prediction model. We use ensemble model output statistics to recalibrate the raw ensemble predictions for the marginal distributions, with ensemble copula coupling used to capture the dependency between the marginal distributions. In terms of time series modelling, we consider a bivariate VARMA-MGARCH model. We use daily Spanish data recorded over a 65-year period, and find that, for the 5-year out-of-sample period, the recalibrated ensemble predictions outperform the time series models in terms of forecast accuracy.
Original language | English |
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Pages (from-to) | 267-281 |
Number of pages | 15 |
Journal | International Journal of Forecasting |
Volume | 38 |
Issue number | 1 |
Early online date | 3 Jul 2021 |
DOIs | |
Publication status | Published - 1 Jan 2022 |
Funding
The authors are grateful to the European Centre for Medium-Range Weather Forecasts for providing the weather ensemble predictions used in this study. The authors are also very grateful to the three reviewers for providing comments that helped greatly to improve the paper.
Keywords
- Probabilistic forecasting
- VARMA-MGARCH
- Weather ensemble predictions
ASJC Scopus subject areas
- Business and International Management