New perspectives and advancements in microwave-based analyses and characterization of Medicanes

Medicanes are Mediterranean cyclones with the potential to cause devastating floods, storm surges and windstorms, often leading to significant disruption and casualties. During their mature phase, they exhibit tropical-like cyclone features, such as a warm core (WC), a cloud free eye surrounded by spiraling rain bands around the center, a closed vortex associated with strong near-surface winds. Generally, these cyclones originate from extra-tropical cyclones showing a cut-off from the main flow allowing the intrusion of relatively warm stratospheric air resulting in a top-bottom WC development. More rarely, they undergo the so-called tropical transition during their mature phase, exhibiting at some point a deep axi-symmetric WC of diabatic origin. The term Medicane generally refers to both types of cyclones regardless of the processes originating them (https://glossary.ametsoc.org/wiki/medicane /).

The present work provides an overview of recent studies on the use of satellite-based microwave observations to monitor and characterize the WC, deep convection (DC), the presence of a closed eye surrounded by a ring-shaped band of intense winds during the cyclone evolution, and to identify the tropical transition also inferring the dynamics at the different stages of cyclone’s lifetime. Relevant advances on this topic are being accomplished within the ESA MEDICANES project (https://medicanes.isac.cnr.it/). The detection and characterization of WC and DC are based on satellite passive microwave (PMW) measurements from different radiometers onboard Low Earth Orbit (LEO) satellites. In particular, temperature sounding channels in the 50-60 GHz oxygen absorption band are used to identify the presence of the WC, while high frequency channels (90-190 GHz) are used to identify the presence of the closed eye and the DC areas. In addition, the 183.31 GHz water vapour channels can provide useful insight on the dynamic highlighting the potential vorticity (PV) anomaly resulting from warm dry stratospheric air intrusion. Surface wind structure and intensity are characterized using ocean surface wind products derived from scatterometer missions. Automated analysis through the Medicane Rotational Center Automated Detection (MeRCAD) algorithm identifies the radius of maximum wind, assesses wind-field symmetry, and detects nearly closed ring-shaped bands of intense surface winds typical of mature medicanes. This dynamical characterization complements the thermodynamic information derived from PMW observations, enabling objective identification of tropical-like surface circulation and its temporal evolution. An additional step forward in medicanes’ characterization and monitoring is being carried out exploiting different machine learning (ML) approaches for automated detection of the Medicanes’ features, including the identification of the WC. A semi-supervised deep learning anomaly-detection framework, based on convolutional autoencoders, is used to identify WC signatures as anomalies relative to the dominant non-WC atmospheric states. Training is performed primarily on unlabeled non-WC cases, with only a limited number of labeled WC events, enabling robust learning under strong data imbalance. The system is trained and evaluated on approximately 30,000 satellite overpasses covering nearly 900 Mediterranean cyclones (years 2000–2020), and demonstrates reliable WC discrimination using recall-oriented performance metrics. This work aims to show as a fully MW-based characterization of dynamics, thermodynamics and microphysical processes involved within a medicane is possible.