| 16 Apr 2026
Multi-model high-resolution analysis of Tropical-Like Cyclone Daniel with WRF and ICON: peculiarities and sensitivity to convection schemes
Abstract. Medicane Daniel (September 2023) featured a rapid transition from a baroclinic disturbance to a compact tropical-like vortex, challenging short-range prediction. This study delivers a side-by-side, high-resolution (∼2 km) assessment of Daniel using two state of the art weather forecasting models, WRF and ICON, configured to be as comparable as possible in terms of domain, forcing and vertical discretizations. Seven numerical simulations are compared assessing also sensitivity to the convection scheme: fully explicit, deep-cumulus parameterized and independent shallow-convection options (plus ICON's grayzone setting). Analysis methods include an objective cyclone tracker that combines mean sea-level pressure and lower tropospheric geopotential structure, intensity metrics (central pressure and 10 m wind) along the track, precipitation anomalies regridded against IMERG observations (Integrated Multi-satellitE Retrievals for GPM). Tropical characteristics are examined with Hart's Cyclone Phase Space and Temporal Annular Symmetric Mean (TASM) of equivalent potential temperature and wind to distill three-dimensional, time-mean storm structure during the peak warm-core phase.
Both models reproduce Daniel’s life cycle and produce realistic tracks. Intensity of the cyclone sharply varies from simulation to simulation, with different behavior of each model at changes in convection scheme.
The study emphasizes the different responses of the two models both in reproducing such an extreme meteorological phenomenon and in the variation of the convection scheme. Practical suggestions are established depending on the case study and the resolution used.
General comment:
The manuscript presents a high-resolution, multi-model analysis of Medicane Daniel, comparing WRF and ICON simulations at convection-permitting resolution and assessing the sensitivity of the simulated track, intensity, precipitation, and tropical-like structure to different treatments of convection. The main findings show that both models are able to reproduce the overall life cycle and track of Daniel, but with substantial differences in cyclone intensity, precipitation distribution, and internal storm structure depending on both the model and the convection scheme. In particular, the study highlights the relevance of shallow-convection parameterization at approximately 2 km resolution, showing that even in convection-permitting simulations not all convective processes are explicitly resolved, and that a parameterization can still be beneficial for representing convective processes.
I appreciated the work because it provides a robust and systematic comparison between two widely used numerical weather prediction models, WRF and ICON, and between different convection schemes at convection-permitting resolution. This is a very timely topic and is fundamental for improving the simulation of high-impact precipitation events, such as those associated with “medicanes”, where convective processes play a crucial role in storm intensification, precipitation and warm-core development.
I believe the manuscript fits well within the scope of the journal and deserves publication. However, a few specific comments should be addressed before it can be accepted for final publication.
Specific comments:
1) The objective of the present study is highly relevant for the community. In particular, understanding the role of convective parameterization at 2 km gray-zone resolution is important for high-impact Mediterranean systems such as medicanes. The comparison between WRF and ICON, and among the different convective configurations, is well structured, detailed, and clearly presented. However, I think the manuscript would benefit from a more explicit discussion of the physical mechanisms behind the differences among the simulations. This is particularly relevant in Section 4.2, where substantial differences are found in central sea-level pressure and 10 m wind speed (Fig. 5), as well as in precipitation and FSS scores (Figs. 6-7).
For example, the shallow-convection configurations appear to strongly affect the simulated medicane characteristics, but the analysis could better explain why this occurs physically. A clearer discussion of how the shallow-convection schemes in WRF and ICON influence boundary-layer processes, latent heat release, precipitation and cyclone characteristics would strengthen the interpretation of the results.
In addition, some differences between WRF and ICON may also arise from other model-dependent physical parameterizations, such as microphysics, turbulence/PBL schemes, surface fluxes, radiation, or gravity-wave drag. Although the effort to make the two configurations comparable is appreciated, these remaining differences in the physical setup could contribute to the different model responses. A short additional discussion, or where possible some supporting diagnostics, would help distinguish the role of convection schemes from the broader influence of the model physics and dynamical cores.
Overall, this would not require a major restructuring of the manuscript, but rather a more explicit physical interpretation of the differences already shown.
2) Regarding the precipitation analysis, I suggest adding a few additional diagnostics to better assess the differences between the simulations and the observations, both in terms of spatial distribution and precipitation intensity.
First, in Fig. 6, the accumulated precipitation fields are useful to compare the general patterns among the models and IMERG. However, the spatial differences would be easier to interpret if model-minus-observation difference maps were also provided. This would help identify more clearly where each configuration overestimates or underestimates precipitation, and whether the errors are mainly related to displacement, intensity, or spatial extent of the rainfall structures.
Second, the authors could consider computing the probability density function, or a similar distribution-based diagnostic, of precipitation during the event for each simulation and for the observations. This would provide a clearer comparison of the precipitation intensity distribution, especially for the highest percentiles and extreme values. Such an analysis would complement the FSS results and help clarify whether the models differ mainly in the localization of heavy precipitation or also in their ability to reproduce the intensity of extreme rainfall.
Minor comments:
1) Lines 73-75: “As a result, widespread thunderstorms developed, with cloud tops exceeding 13 km, producing extreme precipitation and severe flooding throughout central and eastern Greece, as well as parts of Bulgaria and Turkey. Surface stations recorded more than 750 mm of daily rainfall and up to 1235 mm in 4 days in the eastern parts of the Thessaly region.”
Please provide appropriate references for the description of the flooding impacts in Greece, Bulgaria, and Turkey, as well as for the reported precipitation records from surface stations.
2) Line 316: The statement “Fully parameterized configurations increase both the intensity and strength of the cyclone” should be clarified.
Based on Fig. 5, this seems to be clearly valid only for ICON, where ICON–CU produces a stronger cyclone than the other ICON configurations. However, for WRF, WRF–EXP appears to produce slightly stronger 10 m winds and lower central sea-level pressure than WRF–CU, while only WRF–SH simulates a weaker cyclone. I suggest revising this sentence to better distinguish the different responses of WRF and ICON to the fully parameterized convection configuration.
3) Line 322: Please clarify the meaning of “mean accumulated precipitation per grid cell” and briefly explain how this quantity was computed. For example, please specify whether this quantity represents the spatial average of the event-total accumulated precipitation over the full analysis domain, or whether it is computed over a specific area around the cyclone.