What do large‐scale patterns teach us about extreme precipitation over the Mediterranean at medium‐ and extended‐range forecasts?

Abstract

Extreme precipitation events (EPEs) can have devastating consequences, such as floods and landslides, posing a great threat to society and economy. Predicting such events long in advance can support the mitigation of negative impacts. Here, we focus on EPEs over the Mediterranean, a region that is frequently affected by such hazards. Previous work identified strong connections between localized EPEs and large‐scale atmospheric flow patterns, affecting weather over the entire Mediterranean. We analyse the predictive skill of these patterns in the European Centre for Medium‐Range Weather Forecasts (ECMWF) extended range forecasts and assess if and where these patterns can be used for indirect predictions of EPEs, using the Brier skill score. The results show that the ECMWF model provides skilful predictions of the Mediterranean patterns up to 2 weeks in advance. Moreover, using the forecasted patterns for indirect predictability of EPEs outperforms the reference score up to ∼10 days lead time for many locations. For high orography locations or coastal areas in particular, like parts of western Turkey, western Balkans, Iberian Peninsula, and Morocco, this limit extends to 11–14 days lead time. This study demonstrates that the connections between localized EPEs and large‐scale patterns over the Mediterranean extend the forecasting horizon of the model by over 3 days in many locations, in comparison with forecasting based on the predicted precipitation. Thus, it is beneficial to use the predicted patterns rather than the predicted precipitation at longer lead times for EPE forecasting. The model's performance is also assessed from a user perspective, showing that the EPE forecasting based on the patterns increases the economic benefits at medium/extended range lead times. Such information could support higher confidence in the decision‐making of various users; for example, the agricultural sector and (re)insurance companies.

Forecasting extreme precipitation in the central Mediterranean: Changes in predictors' strength with prediction lead time

“…For short lead times, the BSS is over 0.8, while the forecasting horizon for most patterns exceeds 8-11 days, extending even to week 3 for the Biscay Low and the Mediterranean High. Predictability of the patterns is higher compared with the study of Mastrantonas et al (2022), as temporal aggregation in this analysis (3-day averages) boosts the scores. Figure S6 presents the same results as Figure 6a, but for the eight warning areas of Calabria.…”

“…Due to the selected datasets, spatiotemporal resolution and methodology, this study yields some key differences and innovations, as compared with Mastrantonas et al (2022) that address EPE forecasting over the Mediterranean including Calabria. Here, we investigated EPE forecasting at aggregated spatiotemporal resolution for winter‐half period instead of daily EPEs at 0.25° × 0.25° resolution for year‐round extremes.…”

“…Allocation of the 3-day weather synoptic variability to the Mediterranean patterns followed the methodology by Mastrantonas et al (2021Mastrantonas et al ( , 2022, using SLP and Z500 from the ERA5 dataset. We performed empirical orthogonal function followed by a K-means clustering of the initial modes (principal components) of 3-day SLP and Z500 anomalies, explaining at least 90% of the total variance of each variable (five and six respectively).…”

“…That study introduced a forecasting tool for indirect EPE predictions, based on the forecasted patterns and their climatological connections with EPEs. Particularly for Calabria, this method provides skilful forecasts up to 10 days ahead, compared with forecasts based on the predicted precipitation that have around 6 days forecasting horizon (Mastrantonas et al, 2022).…”

The application of predefined weather patterns over India within probabilistic medium‐range forecasting tools for high‐impact weather

“…The weather pattern approach explored here assumes that the predictability of the large‐scale circulation is better than the predictability of rainfall at a specific location within the medium range. This is an approach explored in previous studies for other parts of the world (Richardson, Neal, et al, 2020; Richardson, Fowler, et al, 2020 for the United Kingdom; Mastrantonas, Herrera‐Lormendez, et al, 2021; Mastrantonas, Magnusson, et al, 2021 for the Mediterranean and Howard et al, 2021 for Southeast Asia). This predictability assumption allows a high confidence forecast for a particular weather pattern to be used to infer rainfall, based on the climatological characteristics of that particular circulation type.…”