Comparing probabilistic forecasts of the daily minimum and maximum temperature

Meng, Xiaochun and Taylor, James W (2022) Comparing probabilistic forecasts of the daily minimum and maximum temperature. International Journal of Forecasting, 38 (1). pp. 267-281. ISSN 0169-2070

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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.

Item Type: Article
Keywords: Probabilistic forecasting, Weather ensemble predictions, VARMA-MGARCH
Schools and Departments: University of Sussex Business School > Accounting and Finance
Depositing User: Xiaochun Meng
Date Deposited: 14 May 2021 07:49
Last Modified: 18 Mar 2022 14:00

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