An opportunity index for subseasonal prediction

The skill of subseasonal atmospheric forecasts has steadily improved in recent decades. Nevertheless, the operational use of such forecasts is still a major challenge for weather prediction centers and weather-dependent socio-economic sectors. A key reason for this challenge is that often only specifically trained forecasters understand and are able to keep track of the complex variety of so-called "windows of forecast opportunity" (WFOs) – periods during which subseasonal prediction skill is enhanced due to specific states of the atmosphere, the ocean, or the land surface acting as drivers of predictability. Here, we propose a novel method to combine the variety of WFOs into a single daily opportunity index, which can be used operationally like a traffic-light system to anticipate enhanced or reduced subseasonal prediction skill in advance. The opportunity index is a linear combination of the standardized anomalies of different known drivers of predictability at forecast initialization. The value of the index is constructed to increase as more WFOs are simultaneously active. Based on 20 years of subseasonal 2m-temperature anomaly hindcasts for Switzerland during summer, we demonstrate that large values of the opportunity index at forecast initialization are able to predict enhanced skill for weekly, two-weekly, and monthly mean anomalies up to four weeks ahead. Systematic sensitivity testing against overfitting indicates year-to-year variability in the performance of the opportunity index, which is something that might be overcome with training on larger hindcast datasets. Given that subseasonal prediction is particularly challenging for Central Europe and during summer, which is the focus of our study, the principle of such a regionally trained index could advance the operational usability of subseasonal predictions in other regions of Europe and the world throughout the year.