Two Weather Models, One Deadly Storm: Why Cloud Physics Decided Medicane Daniel’s Fury

When Storm Daniel swept across the Mediterranean in September 2023, it left behind a trail of destruction that few forecasters saw coming. The cyclone drowned central Greece under record-breaking rainfall, then transformed into a compact, hurricane-like vortex that made landfall near Benghazi in Libya, where collapsing dams killed thousands. Now, a new study published in Weather and Climate Dynamics has dissected this catastrophic event with unprecedented care, running seven high-resolution simulations on two of the world’s leading weather models to answer a deceptively simple question: how much does the way we represent clouds change what we predict?

The answer, according to Piero Serafini of the University of L’Aquila and colleagues, is: a great deal. The team compared the Weather Research and Forecasting model (WRF), developed by NCAR, NOAA and the US Air Force, with the ICON model (ICOsahedral Nonhydrostatic), created by the German Weather Service and the Max Planck Institute for Meteorology. Both were configured at roughly 2 kilometre grid spacing, the current operational standard for convection-permitting forecasts used by national weather services across Europe. The researchers matched the two models as closely as possible, aligning their domains, vertical level distributions, boundary conditions and even developing a custom Python algorithm to harmonise the 50 vertical levels, from a lowest layer just 20 metres above the surface to the upper atmosphere at 24,500 metres.

Even with this meticulous balancing, the results diverged dramatically. All seven simulations reproduced Daniel’s overall track, from its birth over the Aegean Sea on 4 September to its dissipation near Egypt on 12 September. But the storm’s intensity varied sharply from run to run. WRF consistently produced deeper central pressures and stronger winds than ICON, painting a more ferocious cyclone. The explicit WRF run generated a minimum central pressure of 982 hectopascals with winds near 36 metres per second, while the fully parameterized ICON run reached 980 hectopascals with winds of about 33 metres per second. At the other extreme, ICON’s special gray-zone configuration produced a feeble system with a central pressure of just 1001 hectopascals, essentially failing to capture the storm’s tropical transformation at all.

The stakes of these differences become clear when considering what Daniel actually did. The cyclone began as an extratropical disturbance embedded in a pronounced omega blocking pattern over Europe, with an intrusion of stratospheric potential vorticity triggering cyclogenesis near the Greek coast. On 5 and 6 September, persistent easterly winds funnelled moisture from the Aegean and Black Seas into thunderstorms with cloud tops exceeding 13 kilometres. Surface stations in the Thessaly region recorded more than 750 millimetres of rain in a single day and up to 1235 millimetres over four days. As the storm drifted south over sea surfaces approaching 28 degrees Celsius, an anomalously warm energy reservoir, it underwent a marked structural transformation, barotropically aligning into a warm-core vortex consistent with a medicane, the Mediterranean’s rare tropical-like cyclone.

To evaluate how faithfully the models captured this evolution, the team deployed an arsenal of diagnostic tools. An objective cyclone tracker, newly developed for the study, followed the storm by combining mean sea-level pressure with the geopotential structure of the lower troposphere, avoiding the trap of latching onto spurious pressure minima beneath individual thunderstorm cells. Precipitation was verified against the IMERG satellite product, using the Fractions Skill Score to measure how well each simulation captured both the amount and the spatial placement of rainfall. The storm’s tropical character was assessed with Hart’s Cyclone Phase Space, which tracks thermal asymmetry and warm-core structure, and with a novel technique the authors call the Temporal Annular Symmetric Mean, which averages equivalent potential temperature and wind in concentric rings around the storm’s core over eight consecutive time steps.

The track results revealed a nuanced picture. WRF’s explicit run achieved the best overall performance, with a mean error of 63 kilometres and a root-mean-squared error of 76 kilometres, while ICON’s runs clustered between 83 and 105 kilometres of mean error. Yet rigorous significance testing showed that this apparent WRF advantage was not statistically significant, being overshadowed by variability along the storm’s life cycle. Intriguingly, each model excelled at a different stage: WRF better reproduced the barotropic tropical-like phase, while ICON handled the early baroclinic dynamics more accurately, likely benefiting from its global-model heritage. For the critical landfall near Benghazi, the best simulation, WRF with a shallow convection scheme, placed the landfall within just 9 kilometres of the observed location, with a delay of only 5 hours.

The most striking finding concerned the shallow convection schemes, which represent small, non-precipitating cumulus clouds that remain unresolved even at 2 kilometre grid spacing. Including these schemes proved important for both models, significantly improving landfall accuracy compared with fully explicit or fully parameterized configurations. The researchers propose a physical explanation: shallow cumuli vent boundary-layer moisture into the lower free troposphere, drying the sub-cloud layer and delaying the onset of resolved deep convection, so that latent heating is released over a wider area rather than in a few grid-point updrafts. This produces a weaker but better organised cyclone, consistent with the smoother eyewall structures and improved extreme-rainfall localisation seen in the shallow runs. WRF’s shallow configuration was the only setup to achieve skill at the native grid scale for the most extreme rainfall thresholds.

The internal anatomy of the simulated storms told an equally compelling story. WRF runs generated a vertically coherent warm core extending up to about 300 hectopascals, with a distinct eyewall-like structure and winds packed tightly around the centre. ICON’s explicit run, by contrast, produced a shallow warm anomaly confined below 550 hectopascals, limiting the depth-integrated thickness anomaly and hence the surface pressure deepening. The fully parameterized runs of both models tended to over-intensify the cyclone, apparently by creating deep convection cells that rotate in phase around the storm axis, feeding an unrealistic positive feedback between latent heat release and pressure fall. And ICON’s gray-zone configuration represented a critical failure mode, over-damping convective development so severely that the storm never transitioned to a tropical-like maintenance mechanism, remaining cold-core and disorganised throughout.

The authors are careful to note the limits of their conclusions. Validation relied heavily on satellite-derived tracks and sparse surface observations, since Daniel evolved mostly over the open sea, and Libya has few weather stations near the affected area. The scatterometer measurements that were available tend to underestimate wind speeds in deep convection, and the observed minimum pressure of 996 hectopascals may itself be an overestimation given the lack of data near the storm’s peak intensity. As a single-case study, the findings may be specific to Daniel’s unusual, multi-stage evolution, which stretched model physics across heterogeneous regimes within a single integration. Differences between the full WRF and ICON configurations also reflect unmatched physics suites, such as WRF’s single-moment ice microphysics versus ICON’s fully two-moment scheme, that cannot be fully disentangled from the convection treatment itself.

Nevertheless, the practical message for forecasters is clear. At kilometre-scale resolutions, the same physical option may not behave equivalently across different models, because discretization, diffusion and vertical coordinates all shape the outcome. Scheme portability cannot be assumed, and each model requires dedicated calibration. For long-lived, high-impact systems like Daniel, no single optimal setup is robust across all phases of a storm’s life, so case-by-case sensitivity analysis remains essential. Early-warning systems, the authors argue, should not rely exclusively on nominal horizontal resolution but on demonstrated model skill for the specific class of events being targeted. As the Mediterranean warms and rare tropical-like cyclones threaten an increasingly vulnerable coastline, knowing exactly how the clouds are represented in the forecast could mean the difference between a warning issued in time and a catastrophe that no one predicted.

Subject of Research: High-resolution sensitivity of Mediterranean tropical-like cyclone Daniel simulations to convection scheme choices in the WRF and ICON weather models

Article Title: Multi-model high-resolution analysis of Tropical-Like Cyclone Daniel with WRF and ICON: peculiarities and sensitivity to convection schemes

Article References: Serafini, P., Ricchi, A., Marsigli, C., D'Amico, C., Nastasi, M., Pelosini, R., & Ferretti, R. (2026). Multi-model high-resolution analysis of Tropical-Like Cyclone Daniel with WRF and ICON: peculiarities and sensitivity to convection schemes. Weather and Climate Dynamics, 7(3), 1525-1546. https://doi.org/10.5194/wcd-7-1525-2026

Image Credits: AI Generated

DOI: 10.5194/wcd-7-1525-2026

Keywords: Medicane Daniel, Mediterranean cyclone, WRF, ICON model, convection parameterization, tropical-like cyclone, numerical weather prediction, cyclone tracking, Fractions Skill Score, shallow convection, Libya flooding, extreme precipitation