TemperatureMinMax_S5.pdf (715.32 kB)
Comparing probabilistic forecasts of the daily minimum and maximum temperature
journal contribution
posted on 2023-06-09, 23:52 authored by Xiaochun MengXiaochun Meng, James W TaylorUnderstanding 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.
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Publication status
- Published
File Version
- Accepted version
Journal
International Journal of ForecastingISSN
0169-2070Publisher
ElsevierExternal DOI
Issue
1Volume
38Page range
267-281Department affiliated with
- Accounting and Finance Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2021-05-14First Compliant Deposit (FCD) Date
2021-05-14Usage metrics
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