Systematic evaluation of the predictability of different Mediterranean cyclone categories

Abstract. Cyclones are essential components of weather patterns in the densely populated Mediterranean region, providing necessary rainfall for both the environment and human activities. The most intense of them also lead to natural disasters because of their strong winds and heavy precipitation. Identifying sources of errors in the predictability of Mediterranean cyclones is therefore essential to better anticipate and prevent their impact. The aim of this work is to characterise the medium-range cyclone predictability in the Mediterranean. Here, it is investigated in a systematic framework using the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) and ensemble reforecasts in a homogeneous configuration over the 2001–2021 period. First, a reference dataset of 1960 cyclones is obtained for the period by applying a tracking algorithm to the ERA5 reanalysis. Then the predictability is systematically evaluated in the ensemble reforecasts. It is quantified using a new probabilistic score based on the error distribution of cyclone location and intensity (mean sea level pressure). The score is firstly computed for the complete dataset and then for different categories of cyclones based on their intensity, deepening rate, motion speed, and geographic area and season in which they occur. When crossing the location and intensity errors with the different categories, the conditions leading to poorer or better predictability are discriminated. The motion speed of cyclones appears to be crucial for the predictability of the location: the slower the cyclone, the better the forecast location. In particular, the location of stationary lows located in the Gulf of Genoa is remarkably well predicted. The intensity of deep and rapid-intensification cyclones, occurring mostly during winter, is for its part particularly poorly predicted. This study provides the first systematic evaluation of cyclone predictability in the Mediterranean and opens up possibilities to identify the key processes leading to forecast errors in the region.