Christian Natale Gencarelli

Methane () is a critical greenhouse gas, and accurate monitoring of its vertical distribution is essential for understanding its radiative impact and identifying emission sources. This paper introduces a novel methodology using a physics‐constrained neural network based on soft constraints to retrieve vertical profiles from hyperspectral Infrared Atmospheric Sounding Interferometer (IASI) data. The methodology combines the advantages of Optimal Estimation and neural networks, leveraging physical constraints to enhance retrieval accuracy while maintaining computational efficiency. The approach integrates dimensionality reduction techniques, such as Principal Component Analysis and Averaging Kernel optimization, to maximize information content while reducing computational complexity. Two models were developed: one for general profile retrieval and another optimized for detecting surface level enhancements. The novel approach was validated on synthetic data and applied to real IASI observations, including the Nord Stream pipeline sabotage event, and demonstrated that the algorithm can confidently detect enhancements as low as 50 ppbv near the surface and reconstruct profiles with average error of ppbv in the mid‐troposphere and >100 ppbv in the stratosphere on the considered test set. While effective in capturing strong enhancements, the technique exhibits slightly reduced sensitivity near the surface due to limited spectral information, which is mitigated by inflating covariance matrices in the cost function. This methodology offers a promising tool for global vertical monitoring with existing sensors and could be potentially extended to other compounds such as .

High-elevation observatories are crucial for monitoring atmospheric aerosols, which play a key role in the climate system due to their effects on radiation, cloud, and snow albedo. We present the first measurements of aerosol size distribution and absorption coefficient at a 1 h time resolution collected at the Testa Grigia Observatory (3480 m a.s.l.) in the Italian Alps. This dataset spans from September 2021 to May 2023. We identified three distinct aerosol population types reaching the observatory, reflecting distinct transport pathways. The coarse particle population is indicative of long-range transport of air masses from the Sahara. Conversely, the fine particle population is linked to mesoscale circulations and boundary layer dynamics (from the Po Valley and local alpine valleys), and broader continental flows. Finally, periods of generally low particle number correspond to the influence of clean air from the free troposphere and the Mediterranean basin. The upper bound of the frequency of boundary layer influence is equal to 28 %. Conversely, Sahara Dust Events (SDE), identified as periods characterized by coarse aerosol population transported from the Sahara region, are observed for 6 % of the time. These events are predominantly recorded during spring and early summer and show strong correspondence with reanalysis data provided by CAMS (Copernicus Atmosphere Monitoring Service) ensemble model. The seasonal variability of PM ₁₀ concentration associated to SDE is explained by the sensitivity to dust emission regions, dust mobilization over source region, and efficiency of dust transport mechanisms.

The prediction of complex dynamics remains an open problem across many domains of physics, where nonlinearities and multiscale interactions severely limit the reliability of conventional forecasting methods. Quantum Reservoir Computing has emerged as a promising paradigm for information processing by exploiting the nonlinear dynamical evolution of quantum systems to generate expressive feature representations of input signals. Here, we introduce a hybrid quantum-classical reservoir architecture capable of handling multivariate time series through quantum evolution combined with classical memory enhancement. Our model employs a five-qubit transverse-field Ising Hamiltonian with input-modulated dynamics and temporal multiplexing. We apply this framework to two paradigmatic models of chaotic behavior in fluid dynamics, where multiscale dynamics and nonlinearities play a dominant role: a low-dimensional truncation of the two-dimensional Navier–Stokes equations and the Lorenz-63 system. By systematically scanning the quantum system’s parameter space, we identify regions that maximize forecasting performance. The observed robustness and reliable performances for both dynamical systems suggest that this hybrid quantum approach offers a flexible platform for modeling complex nonlinear time series.

Mediterranean tropical-like cyclones (MTLCs), commonly referred to as Medicanes, are high-impact weather systems characterized by complex interactions between baroclinic forcing and tropical-like processes. Despite growing interest, their vertical structures and dynamical regimes remain incompletely understood. In this study, high-resolution Weather Research and Forecasting (WRF) simulations at 1 km resolution are used to investigate the structure and evolution of two dynamically contrasting MTLCs: Ianos (2020) and Qendresa (2014). The analysis focuses on the temporal evolution of kinetic energy and turbulent dissipation as well as on the three-dimensional organization of wind and temperature fields during representative phases of the cyclone life cycle. The results reveal pronounced differences between the two events, with Ianos exhibiting a compact, vertically coherent, convection-dominated structure and Qendresa showing a wider, more asymmetric, and less stationary organization influenced by baroclinic processes. A comparative framework with the ERA5 reanalysis is employed to contextualize cyclone intensity, with ERA5 used as a dynamically consistent large-scale reference rather than as an observational benchmark. Overall, the study highlights the importance of vertical structure and boundary-layer processes in shaping Mediterranean tropical-like cyclones and demonstrates the added value of high-resolution numerical simulations for distinguishing between different dynamical regimes.

The evolution of phenomena at the interface between the terrestrial surface and the atmospheric boundary layer, like the coupling between litospheric and atmospheric phenomena during an earthquake, can be described as a nonlinear process, affected by mechanically induced disturbances originating from the solid Earth. In this work, a two-dimensional Direct Numerical Simulation (DNS) model is designed to investigate the nonlinear atmospheric response to seismic-wave-induced forcing within the lowest ~100 m of the atmosphere.The model is based on the incompressible Navier–Stokes equations under the Boussinesq approximation. Stratification is imposed via a prescribed, constant, background buoyancy gradient representative of near-surface stable atmospheric conditions. Seismic forcing is introduced as a dynamical perturbation on the gravity acceleration field.A set of numerical experiments has been performed to explore the nonlinear dynamics arising from the interaction between stratification, gravity forcing, and intrinsic flow instabilities. Simulations span a range of Reynolds numbers representative of the atmospheric boundary layer, enabling the investigation of transitions from linear wave dynamics to strongly nonlinear flow regimes. These first simulations demonstrate the feasibility of using a reduced-dimensional DNS framework to investigate atmosphere–solid Earth coupling processes at small scales. The results show the excitation and upward propagation of internal gravity waves, their nonlinear interaction with background shear, and the development of localized instabilities and turbulence. Energy diagnostics highlight anisotropic and scale-dependent transfers, as well as the conversion between kinetic and potential energy driven by the imposed seismic perturbation.The model provides a controlled platform for studying nonlinear wave–boundary layer interactions and for assessing the potential role of seismic forcing in modulating near-surface atmospheric dynamics.This study was carried out within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0 - CUP n. I53D24000060005.

High-elevation observatories are crucial for monitoring atmospheric aerosols, which play a key role in the climate system due to their effects on radiation, cloud, and snow albedo. We present the first measurements of aerosol size distribution and absorption coefficient at a 1-hour time resolution collected at the Testa Grigia Observatory (3,480 m asl) in the Italian Alps. This dataset spans from September 2021 to May 2023. We identified three distinct aerosol population types reaching the observatory, reflecting distinct transport pathways. The coarse particle population is indicative of long-range transport air masses from Sahara. Conversely, the fine particle population is linked to mesoscale circulations and boundary layer dynamics (from the Po Valley and local alpine valleys), and broader continental flows. Finally, periods of generally low particle number correspond to the influence of clean air from the free troposphere and the Mediterranean basin. The upper bound of the frequency of boundary layer influence is equal to 28 %. Conversely, Sahara Dust Events (SDE), identified as periods characterized by coarse aerosol population transported from the Sahara region, are observed for 6 % of the time. These events are predominantly recorded during spring and early summer and show strong correspondence with reanalysis data provided by CAMS (Copernicus Atmosphere Monitoring Service) ensemble model. The seasonal variability of PM10 concentration associated to SDE is explained by the variability to dust emission regions, dust mobilization over source region, and efficiency of dust transport mechanisms.

The tropical cyclones are one of the biggest hazards to life and socio-economic activities even in the formative stages of their development. Some of their main features, especially in the shape evolution and in the dynamics, are common with events that in recent years are increasingly affecting the Mediterranean basin, defined as Mediterranean Tropical-Like Cyclones (MTLCs), or Medicanes.In this study, we investigate the spatial and temporal properties of two Medicanes through high spatial resolution (1 km) reanalysis-based numerical simulations, generated using the Weather Research and Forecasting (WRF) model. The events examined are Qendresa (occurred in November 2014) and Ianos (occurred in September 2020), both developing over the Mediterranean Sea.The WRF reanalyses were also used to conduct sensitivity studies on the parameterizations of atmospheric physics, focusing on PBL, radiation and microphysics schemes.In order to address the problem in the classical fluid turbulence picture, the results were also analyzed using the Proper Orthogonal Decomposition (POD) method. With the aim of providing a first approach to understand how different contributions, operating on distinct spatio-temporal scales, can influence the local dynamics and the evolution of the medicanes. In addition the analysis will uncover the formation of coherent structures in the extreme event maturation.