Abstract
AbstractWe quantify the value of sub‐seasonal forecasts for a real‐world prediction problem: the forecasting of French month‐ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than two weeks can have value for this application, both on their own and in conjunction with shorter‐range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that, while it captures the performance of the short‐range forecasts well, it misses the marginal value present in medium‐range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end‐users.
Highlights
Over the last 15 years, operational forecasting centres have increasingly extended forecasts into the 3–6 week time-scale, often called the monthly, extended or sub-seasonal range
Much of the literature focused on verifying sub-seasonal forecast skill uses either mid-tropospheric, large-scale fields (Buizza et al, 2005), or spatially localised station data (Monhart et al, 2018), both of which are somewhat removed from end-user application which in many cases is interested in national averages at high temporal resolution
In this work we focus on this question of skill scores, taking a specific case-study of French energy markets as an example, and we compare progressively more idealised metrics of value to highlight the different conclusions they might imply if used in isolation
Summary
Over the last 15 years, operational forecasting centres have increasingly extended forecasts into the 3–6 week time-scale, often called the monthly, extended or sub-seasonal range. Driven by the notion of seamless prediction (Hoskins 2013), the aim is to fill the gap between conventional two-week weather forecasts and longer-term seasonal projections. This can be done by harnessing the variability of slow drivers such as sea ice (Chevallier et al, 2018), the land surface (Dirmeyer et al., 2019), and atmospheric–oceanic processes such as the Madden–Julian Oscillation (Vitart 2017). End-users will get more value from forecasts if we can clearly understand the variables and time-scales that sub-seasonal forecasts can predict well, and where they. Much of the literature focused on verifying sub-seasonal forecast skill uses either mid-tropospheric, large-scale fields (Buizza et al, 2005), or spatially localised station data (Monhart et al, 2018), both of which are somewhat removed from end-user application which in many cases is interested in national averages at high temporal resolution
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