Assessing the predictability of Medicanes in ECMWF ensemble forecasts using an object‐based approach

Abstract

The predictability of eight southern European tropical‐like cyclones – seven Medicanes and the first‐ever documented case of such a storm in the Bay of Biscay – is studied evaluating European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ensemble forecasts against operational analysis data. Forecast cyclone trajectories are compared with the cyclone trajectory in the analysis by means of a dynamic time warping technique, which allows one to find a match in terms of their overall spatio‐temporal similarity. Each storm is treated as an object and its forecasts are analysed using parameters that describe intensity, symmetry, compactness and upper‐level thermal structure. This object‐based approach allows one to focus on specific storm features, while tolerating their shifts in time and space to some extent.The high compactness and symmetry of the storms are generally poorly predicted, especially at long lead times. However, forecast accuracy tends to improve strongly at short lead times, indicating that the ECMWF ensemble forecast model can adequately reproduce Medicanes, albeit only a few days in advance. In particular, late forecasts which have been initialised when the cyclone has already developed are distinctly more accurate than earlier forecasts in predicting its kinematic and thermal structure, confirming previous findings of high sensitivity of Medicane simulations to initial conditions.Findings reveal a markedly non‐gradual evolution of ensemble forecasts with lead time, which is often far from a progressive convergence towards the analysis value. Specifically, a rapid increase in the probability of cyclone occurrence (a “forecast jump”) is seen in most cases, generally with lead times between 5 and 7 days. Jumps are also found for the forecast distribution of storm thermal structure. This behaviour is consistent with the existence of predictability barriers. On the other hand, storm position forecasts often exhibit a consistent spatial distribution of storm position uncertainty and bias between consecutive forecasts.