The impact of ASCAT surface winds data assimilation on medicane prediction: results for two cases.

Mediterranean cyclones can be a threat for human lives and can also have important economical impacts, since are often associated with heavy rainfall and intense winds. 

Among mediterranean cyclone, a particular class, known as Medicanes (Mediterranean Hurricanes) has attracted great attention is the last years. Medicanes are interesting from the scientific point of view because of their tropical-like characteristics: a symmetric structure, spiraling clouds, a calm cloud-free eye and a warm core. 

Numerical Weather Prediction (NWP) models can be employed to predict Medicane trajectories and impacts. The accuracy of a NWP forecast strictly depends on the representation of the initial state of the atmosphere, which can be improved by data assimilation.

In this work, we focus on the assimilation of the Advanced SCATterometer (ASCAT) radar data into the Weather Research and Forecasting (WRF) model and we consider the impact of ASCAT assimilation for two medicanes: Ianos, which occurred between 15 and 21 September 2020 in the central Mediterranean and made landfall on the west coast of Greece, and Jolina, which occurred between 14 and 19 March 2026 over the central Mediterranean, impacting parts of northern Africa, southern Italy, and Libya. 

For both medicanes, simulations are performed using an En3DVar approach with the initial and boundary conditions derived from the European Centre for Medium range Weather Forecast – Ensemble Prediction System (ECMWF-EPS). Using this method the background error covariance matrix is computed from the ensemble and is aware of the meteorological conditions of the day. Two kind of simulations are considered: without ASCAT data assimilation (named CTRL) and with ASCAT data assimilation (named ASCAT).

The forecast trajectories are compared  with the best a-posteriori estimate of the trajectory. Results show that ASCAT assimilation into the WRF model positively impacts the prediction of the Medicane trajectory for both cases, and ASCAT trajectories are improved for most members and for all forecasting times compared to CTRL.