Comparing probabilistic forecasts of the daily minimum and maximum temperature

Xiaochun Meng, James W. Taylor

Research output: Contribution to journalArticlepeer-review

5 Citations (SciVal)
2 Downloads (Pure)

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 languageEnglish
Pages (from-to)267-281
Number of pages15
JournalInternational Journal of Forecasting
Volume38
Issue number1
Early online date3 Jul 2021
DOIs
Publication statusPublished - 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

Fingerprint

Dive into the research topics of 'Comparing probabilistic forecasts of the daily minimum and maximum temperature'. Together they form a unique fingerprint.

Cite this