The researchers turned their attention to a well-documented event from the Deep Propagating Gravity Wave Experiment, or DEEPWAVE, a field campaign conducted over New Zealand in 2014. On 12 July of that year, a research aircraft flown by the German Aerospace Center traced a rectangular path across the South Island, sampling the atmosphere at two stacked levels: one near 7.9 kilometers altitude and another near 10.9 kilometers, close to the tropopause. The meteorological setup was favorable for wave generation, with moderate northwesterly winds of roughly 10 to 15 meters per second at 5 kilometers altitude blowing perpendicular to the Southern Alps, and about 20 meters per second near the tropopause, with little change in wind direction with height. Those stacked flight legs provided rare, high-frequency in situ measurements of waves and turbulence signatures near the tropopause, exactly the kind of ground truth that model developers crave.

The simulation tool at the heart of the study is the ICON model, the ICOsahedral Nonhydrostatic modeling framework developed jointly by the German Weather Service and the Max Planck Institute for Meteorology. ICON uses an icosahedral-triangular grid built from geodesic Delaunay triangulation, a height-based vertical coordinate and C-grid staggering of its variables. Jagan and Schmidli ran ICON in numerical weather prediction mode at horizontal grid spacings of approximately 2 kilometers, 1 kilometer and 500 meters, all with 137 vertical levels and a model top at 30 kilometers. They then varied the maximum vertical grid spacing in the upper troposphere and lower stratosphere, the critical region spanning roughly 6 to 25 kilometers altitude, among 400, 200 and 100 meters. To anchor the finest end of the comparison, they added an online-nested large-eddy simulation that zoomed down to a remarkable 130 meters over the southern flight segment, using four nested domains stepping from 1 kilometer down to the 130-meter innermost nest.

The turbulence question was addressed by pitting two schemes against each other. The first is the operational turbulent kinetic energy scheme, which predicts a single TKE value and augments the classical shear-driven turbulence budget with empirical extra source terms, one for large-scale horizontal shear and one for breaking subgrid-scale mountains, both originally designed to support aviation turbulence products such as the eddy dissipation rate. The second is the newly developed two-energy scheme, which carries two prognostic turbulence energies, dynamically adjusts its mixing length, avoids prescribed minimum diffusion coefficients and employs an assumed probability density function method for buoyancy production, all intended to better handle the anisotropic, strongly stratified turbulence found aloft rather than the boundary-layer conditions for which most closures were originally tuned.

The reference simulation, run at 1 kilometer resolution with the two-energy scheme, painted a vivid picture of the event. Alternating bands of ascent and descent stretched downstream of the main ridge from the mid-troposphere into the lower stratosphere, strongest in the troposphere and weakening with height. A powerful wave with vertical velocities exceeding 3 meters per second rose above the Mount Cook region, New Zealand’s highest summit, while a weaker wave formed above the Two Thumb Range farther southeast. The simulated wave field proved quasi-stationary across the three-hour flight window, which made the comparison with the aircraft data meaningful. Against the observations, the model agreed reasonably well on the lower flight leg but underestimated wave amplitude on the upper leg, and the large-eddy simulation better captured the wavelength and phase of the waves even though the coarser NWP runs better reproduced their amplitude.

Resolution emerged as a decisive factor. At 2 kilometers, the simulated waves took on an artificially hydrostatic character, their horizontal wavelengths stretching as the model lost the ability to represent shorter scales. At 500 meters the waves became more non-hydrostatic and vertically aligned, and the number of resolved waves multiplied as shorter wavelengths entered the picture, an effect even more pronounced at 130 meters in the large-eddy simulation, where clear examples of trapped lee waves appeared in the stable layer below about 5 kilometers. Vertical resolution mattered just as much: simulations with 400-meter vertical spacing showed clear differences from the reference, particularly in the stratosphere, while 200-meter and 100-meter runs were nearly identical, indicating that near-convergence of the primary wave requires vertical grid spacing of 200 meters or finer in the upper troposphere and lower stratosphere.

The most striking finding concerned the turbulence schemes themselves. Both produced essentially the same resolved wave structures, yet the parameterized turbulent kinetic energy differed enormously between them. The operational TKE scheme generated substantially higher TKE throughout the wave field, with turbulence patches sprinkled across the troposphere and even reaching into the lower stratosphere, whereas the two-energy scheme sensibly confined turbulence to the atmospheric boundary layer and the strongest low-level wave regions. By systematically switching off the empirical source terms, the researchers traced the excess to the horizontal-shear term. This term is a two-dimensional Smagorinsky-type closure that assumes grid-scale deformation feeds an irreversible cascade into unresolved turbulence, but in the simulated mountain-wave case much of the deformation came from large, coherent, largely reversible gravity waves. Unable to tell the difference, the closure converted wave-related deformation into spurious TKE, which is why the excess energy appeared spatially correlated with the wave field rather than with genuinely turbulent regions.

Further confirmation came from bulk diagnostics averaged over a subdomain of the Southern Alps. Area-averaged vertical velocity variance at 500 meters matched the large-eddy simulation closely in the lower troposphere, while the 1 and 2 kilometer runs systematically underestimated it in the trapped-lee-wave layer. Above that layer, the 1 and 0.5 kilometer runs agreed well, signaling near-convergence for the dominant wave. Notably, the momentum flux profiles at 1 kilometer were nearly identical regardless of turbulence scheme, and the spurious horizontal-shear TKE left no corresponding signature in the wave drag, a decoupling the authors interpret as further evidence that the extra turbulence was artificial. The low-level gravity-wave momentum flux, a key target quantity for gravity-wave drag parameterizations, kept increasing as resolution improved from 1 kilometer to 500 meters, meaning the bulk momentum budget had not yet converged even at these very fine grids. Intriguingly, the comparison also showed that the large-eddy simulation is not automatically the more realistic reference: at 130 meters it resolved the shortest waves’ wavelength and phase but damped their amplitude, likely over-dissipating near-grid-scale wave energy, and it underestimated the vertical velocity fluctuations observed in the upper troposphere.

The practical implications reach well beyond academic curiosity. Turbulence in the upper troposphere and lower stratosphere is intermittent and localized, shaping stratosphere-troposphere exchange of greenhouse gases and pollutants and posing real hazards on long-haul routes that cross major mountain ranges. The study’s guidance is concrete: reliable simulation of small-scale mountain waves and turbulence at kilometer-scale resolution demands horizontal grid spacings of 1 kilometer or finer and vertical spacings of 200 meters or finer in the UTLS, while trapped lee waves and downstream wave structure remain stubbornly resolution-sensitive even then. Equally important, operational centers running kilometer-scale models over mountainous terrain should reconsider the empirical horizontal-shear and subgrid-orography terms of their TKE closures, which can inflate turbulence where none exists, or adopt alternatives like the two-energy scheme, which behaves stably in strongly stratified conditions without exhibiting the artifact. The authors caution that they do not claim the new scheme is more accurate everywhere, and genuine sub-kilometer turbulence downstream of the secondary ridge remained under-resolved in every configuration, including the large-eddy run. Future work will extend the analysis to additional DEEPWAVE cases and seek to couple the two-energy scheme with gravity-wave parameterizations, bridging the gap between explicitly resolved and parameterized wave-turbulence interactions in global and climate models, a step that could ultimately make turbulence forecasts that pilots and passengers depend on considerably more trustworthy.

Subject of Research: Simulation of mountain waves and turbulence in the upper troposphere and lower stratosphere using the ICON model at varying resolutions and turbulence schemes

Article Title: Impact of model resolution and turbulence scheme on the representation of mountain waves and turbulence

Article References: Impact of model resolution and turbulence scheme on the representation of mountain waves and turbulence. (n.d.). https://doi.org/10.5194/acp-26-13721-2026

Image Credits: AI Generated

DOI: 10.5194/acp-26-13721-2026

Keywords: mountain waves, gravity waves, turbulence, ICON model, DEEPWAVE, large-eddy simulation, turbulent kinetic energy, model resolution, upper troposphere, lower stratosphere, aviation safety, numerical weather prediction

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

The research team, comprising Badriya Al Mawali, Issam Ali and Alaa Ibrahim from the Physics Department at Sultan Qaboos University, together with Martin Köhler and Maike Ahlgrimm of Deutscher Wetterdienst, the German national weather service, and Sultan Al Yahyai of Code Academy in Muscat, built their investigation around controlled sensitivity experiments. Al Mawali also holds an affiliation with Oman’s Directorate General of Meteorology, a connection that underscores the practical motivation behind the work: understanding how regional weather models respond to environmental conditions is directly relevant to forecasting and hazard assessment in a country that sits at the edge of the cyclone-prone Arabian Sea.

At the heart of the study is the ICON numerical weather prediction model, a state-of-the-art forecasting system run here as a regional ensemble at a spatial resolution of 6.5 kilometers. Ensemble simulations are a cornerstone of modern cyclone research because they generate many slightly perturbed versions of the same forecast, allowing scientists to distinguish robust signals from noise. Rather than trying to predict Shaheen as it happened, the team ran a control simulation of the storm and then systematically modified individual ingredients of the environment: the temperature of the sea surface, the height of Oman’s terrain, and the roughness of the ocean surface as felt by the wind.

The most dramatic response came from the ocean. When the researchers raised sea surface temperature by 2 degrees Celsius, the simulated cyclone intensified and rainfall along the coast increased. This result follows directly from the physics of tropical cyclones, which are heat engines fueled by the flux of energy and moisture from warm water into the atmosphere. A warmer sea surface raises the enthalpy available to the storm, encouraging stronger convection, lower surface pressure and higher wind speeds, and in this case the model translated that extra energy into heavier precipitation concentrated along the coastal zone where the storm interacted with land.

Terrain, by contrast, acted less as an energy source and more as a steering and organizing influence. When the team reduced the height of Oman’s mountains, the simulated cyclone’s track shifted, and the terrain-enhanced rainfall that typically develops when moist winds are forced up over topography weakened over the mountainous interior. The changes rippled through the storm’s vertical structure as well: the low-level flow, the convergence of moisture flux that feeds convective bands, the mid-level ascent that drives cloud formation, and the upper-level divergence that vents air out of the storm’s core were all reorganized when the mountains were lowered. In other words, topography did not simply block the storm; it reshaped the three-dimensional circulation that determines how a cyclone behaves as it approaches land.

The third variable, sea-surface roughness, produced a comparatively muted response. Roughness describes the frictional drag that the ocean surface exerts on the wind, and physically based changes to this parameter altered the simulated cyclone’s intensity and precipitation structure only modestly. This hierarchy of effects is one of the study’s clearest takeaways: of the surface properties tested, temperature dominated the storm’s energy budget and the magnitude of coastal rainfall, while terrain height dominated the storm’s path, its vertical circulation and the geographical distribution of where rain fell. Roughness, though physically important, sat in the background of this particular case.

For Oman and the surrounding region, the distinction between these mechanisms carries real weight. Coastal rainfall magnitude and storm intensity are the quantities that matter most for wind damage and flash flooding along the shore, and those responded most strongly to sea surface temperature. Track and rainfall distribution, which determine which valleys, cities and wadis are actually hit, responded most strongly to terrain. A forecasting system that misrepresents either ingredient could therefore err in different directions: too-warm sea surfaces in a model might inflate the amount of rain a storm delivers, while poorly resolved mountains might misplace the heaviest precipitation or shift the landfall zone. The study suggests that evaluating both of these surface conditions is essential for trustworthy regional cyclone simulation.

The work also speaks to a broader question that looms over the Arabian Sea: what happens as ocean waters warm? The researchers are careful on this point. The 2-degree increase was used as a controlled test of the model’s sensitivity, not as a prediction of how future cyclones will behave. Because the experiments involve a single storm and idealized modifications of the environment, the results should be read as a diagnostic of how the ICON model responds to changed conditions, not as a forecast of a warmer future. That caveat matters, because the relationship between sea surface temperature and cyclone behavior in the real climate involves many competing factors, from atmospheric stability to wind shear, that a single-storm sensitivity study cannot capture.

Even so, the methodology offers a template for future work. By isolating one environmental variable at a time within an ensemble framework, the researchers could attribute specific changes in the simulated storm to specific causes, something that is nearly impossible to do with observations alone. The approach can be extended to other cyclones, other regions and other model configurations, building a picture of which surface conditions regional prediction systems handle well and where they need improvement. The authors suggest the findings may support further evaluation and refinement of regional cyclone simulations and contribute to assessments of heavy-rainfall and flood hazards, capabilities that are increasingly urgent as coastal populations in the Middle East grow and extreme weather draws closer scrutiny.

Tropical Cyclone Shaheen itself remains a benchmark event for the region. Its direct impact on northern Oman in October 2021 was unusual enough that it provides a valuable, well-observed case for testing how models represent cyclones in a basin where such storms are historically infrequent but potentially devastating. By dissecting that storm in the laboratory of a 6.5-kilometer ensemble model, the Sultan Qaboos University team and their collaborators have shown that the ocean’s warmth and the mountains’ height are not interchangeable pieces of the forecast puzzle. One sets the storm’s power and the volume of rain it can deliver to the coast; the other sets its course and the map of where that rain will fall. For forecasters, modelers and hazard planners alike, keeping both in sharp focus may be the key to anticipating the next time warm Arabian Sea waters send a cyclone toward Oman’s shores.

Subject of Research: Sensitivity of regional ensemble simulations of Tropical Cyclone Shaheen to sea surface temperature and terrain-height changes in Oman

Article Title: Could warmer waters bring heavier coastal rain? Insights from Cyclone Shaheen simulations

Article References: Could warmer waters bring heavier coastal rain? Insights from Cyclone Shaheen simulations. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Tropical Cyclone Shaheen, Oman, sea surface temperature, terrain height, ICON model, numerical weather prediction, ensemble simulation, coastal rainfall, Arabian Sea, Scientific Reports, Sultan Qaboos University, flood hazard