Thomas Gastaldo

Accurate representation of atmospheric dynamics at convection scale remains a major challenge for numerical models and a critical aspect in operational weather predictions. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with the Local Ensemble Transform Kalman Filter (LETKF), following the operational configuration of Arpae Emilia-Romagna and ItaliaMeteo Agency. We focus on a poorly-predicted extreme convective storm in the Marche region, Italy, highlighting the crucial role of low-level moisture convergence in convection initiation and the significant undersampling of humidity in conventional data. To address this, we investigate the added value of humidity-sensitive microwave radiances from polar satellites. Assimilation of clear-sky observations from the Microwave Humidity Sounder (MHS) leads to notable improvements in precipitation forecasts compared to the current operational setup, based on conventional and radar observations only. Infrared all-sky radiances in water vapor channels from the geostationary Meteosat Second Generation SEVIRI instrument are further integrated, providing higher spatial and temporal resolution but limited cloud penetration capability. The joint assimilation of microwave and infrared satellite channels leads to improvements in both surface and upper-level variables, supporting the future operational assimilation of satellite radiances at convective-scale in the Arpae and ItaliaMeteo system. The relative contribution of each observation type is evaluated through an updated version of the Partial Analysis Increments algorithm (Diefenbach et al., 2023), which is corrected to account for posterior covariance inflation and localization.Building on the promising results of this work, we present ongoing developments towards ensemble-based data assimilation in a Machine Learning Limited Area Model (ML-LAM). Machine learning weather prediction models have demonstrated competitive forecast skill at coarse resolution, but convective-scale ML-LAMs remain much less explored. A recent study (Adamov et al., 2025) presents the development of a high-resolution ML-LAM, trained on a convective-scale analyses dataset over Switzerland. The potential reproducibility of ML-LAM across different regions makes it appealing for application in other LAM settings. We show here the implementation of ML-LAM over the Italian domain, evaluating the skill against the physics-based model in severe convection conditions. This serves as a first step towards the assimilation of satellite microwave radiances in a operational-like setting using an LETKF scheme. Diefenbach, T., Craig, G., Keil, C., Scheck, L. and Weissmann, M., QJRMS, 149(752), 740–756, (2023). Adamov, S., Oskarsson, J., Denby, L., Landelius, T., Hintz, K., Christiansen, S., Schicker, I., Osuna, C., Lindsten, F., Fuhrer, O. and Schemm,S., arXiv, (2025).

Extreme precipitation events represent a growing global challenge, particularly affecting the Mediterranean basin, where their frequency and intensity continue to rise. Accurate numerical weather prediction models are crucial to effectively forecast these events; however, operational models often struggle in capturing the precise magnitude and location of intense convective storms. This limitation mainly arises from coarse model resolution and reliance on convection, turbulence and microphysics parameterizations. This study explores the potential benefits of hectometric-scale numerical modelling (here approximately 500-meter resolution) by employing the ICON model to investigate the devastating flood that impacted Italy’s Marche region in September 2022. Through sensitivity tests and ensemble simulations initialized with ECMWF data, we demonstrate substantial improvements in the representation of extreme precipitation events. Our results show that hectometric-scale simulations, combined with explicit convection and the three-dimensional Smagorinsky turbulence scheme, significantly enhance the accuracy and realism of precipitation forecasts compared to traditional coarser-resolution operational setups. Importantly, the study highlights the crucial influence of initial and boundary conditions on forecast quality, with ensemble members providing different scenarios, which permit to have in some members a very accurate representation of critical atmospheric features, such as wind convergence and moisture transport. In this sense, the member “m2” that is used in the ensemble seems to have the better initial conditions for the correct forecasting of the event. These findings underline the necessity and added value of very high-resolution (hectometric-scale) ensemble modelling approaches to capture the fine-scale processes characterized by extremely high uncertainty in the development of convective storms, and, in this way, improve their inherently hard predictability. Also, the work demonstrates how advanced physical formulations, when coupled with explicit convection and careful ensemble design, can substantially play a critical role in the predictability of extreme rainfall. Indeed, the correct tuning of turbulence and microphysics must be balanced with such ensemble approach to catch the wide range of the phenomenology of extreme storms that may happen in the Mediterranean basin. Ultimately, such advancements are essential to strengthen our capacity for risk assessment and mitigation strategies in the face of increasingly severe climate-driven events.

The meteorological phenomenon that led to the Bayesian yacht sinking in Sicily at 0206 utc on 19 August 2024 is here investigated. A thunderstorm occurred during the sinking, but uncertainties exist whether a waterspout or a convective windstorm affected the ship. An in‐depth analysis of available observations highlighted that the ship was likely struck by a convective windstorm. The predictability of the event was also investigated. Operational simulations performed with the high‐resolution numerical model ICON initialised one day ahead pointed out that a convective wind gust hazard was predicted over the southern Tyrrhenian Sea during that night.

Accurate representation of atmospheric dynamics at convection scale still represents a major challenge for numerical models and a critical aspect in operational weather predictions. Reliable forecast initial conditions, generated by the data assimilation cycle using new observations coming from different platforms, are crucial to improve the forecast accuracy in deep convection environments. In this work, the ICOsahedral Non-hydrostatic (ICON) model is run at convection-permitting scale over the Italian domain, in combination with the Kilometre-scale Ensemble Data Assimilation (KENDA) system, to test the model performance on a poorly-predicted extreme convective storm in the Marche region, Italy, on 15 September 2022. We show here the positive impact of data assimilation at convection scale on the forecast of this event, which allows to improve the localization and the intensity of the storm although substantial underestimation of precipitation still persists. The relative impact of different observations datasets is evaluated, starting from conventional and radar data operationally assimilated for numerical weather predictions over Italy. After pointing out the importance of low-level moisture convergence in the process of convection initiation and the significant undersampling of humidity field in conventional data, the added value of humidity-sensitive microwave radiances from polar satellites is analyzed. Observations sensitive to mid-lower tropospheric humidity in clear-sky conditions are employed, taken from the Microwave Humidity Sounder instrument, still little investigated in limited-area models at many numerical weather prediction centers. In order to better exploit the information content of microwave satellite observations, the preliminary development towards all-sky assimilation is presented.

Extreme precipitation events are both changing in frequency of occurrence and intensity. Among them, we can identify the Mesoscale Convective Systems (MCSs), which have typical dimensions of the order of ~100 km and consist of large clusters of powerful thunderstorms that produce intense rainfall, large hailstones and, occasionally, dangerous tornadoes. Italy is being affected more and more frequently by such systems.Here, we carried out sensitivity tests using a limited-area setup at 2km horizontal resolution of the ICON numerical model, combined to a double nesting at 1km and 500m to simulate a strong quasi-linear convective system that triggered a huge flooding in central Italy on 15-16 September 2022, causing 13 deaths and 2 billion euros worth of damage. The convergence of warm, humid southwesterly winds (that produced an atmospheric river with peak values of 1000 kg*m-1*s-1) with northwestern atmospheric currents triggered intense convection over the central Apennine regions leading to a destructive V-shaped self-regenerating thunderstorm, despite the absence of very cold air at higher levels. The numerical models underestimated such event and did not reproduce well the spatial extent and intensity of the system. These discrepancies are related to model errors usually due to uncertainties in (i) estimation of moisture in the several layers of the atmosphere, (ii) parameterization schemes for cloud microphysics, (iii) orography representation and (iv) turbulence diffusion. It follows that convection-permitting models are crucial to better solve these phenomena. Thus, we did some sensitivity tests operating on the above-mentioned parameterizations to identify the main physical processes that are the most likely to impact the forecasts’ accuracy.Results showed that the activation of the Smagorinsky 3D turbulence scheme, compared to the COSMO 1D scheme, noticeably improves the forecast accuracy in terms of precipitation amounts and spatial distribution. Moreover, we employed an ensemble-based approach to enhance predictability, with a focus on extreme percentiles within the ensemble tail of specific variables such as total precipitation. This method offers more valuable insights compared to the traditional analysis of ensemble means, especially when it comes to rare and unusual weather patterns that lead to extreme precipitation events.

The assimilation of radar reflectivity data is of great interest for numerical weather prediction (NWP) models at the convective scale since these observations are very dense both in space and time and they are related to microphysical prognostic variables. So far, the assimilation of reflectivity in operational frameworks has been limited to derived products, like precipitation rates and humidity profiles. This approach is also adopted at ARPAE‐SIMC, the Hydro‐Meteo‐Climate Structure of the Regional Agency for Prevention, Environment and Energy of Emilia‐Romagna region in Italy, where latent heat nudging (LHN) is employed to assimilate precipitation rates in the COSMO‐2I model, the convection‐permitting version of the regional model developed by the COnsortium for Small‐scale MOdelling (COSMO) which is run operationally to provide high‐resolution forecasts over Italy. However, to fully exploit the information contained in these observations, the whole reflectivity volume should be directly assimilated. Nevertheless, despite several promising studies, its implementation in an operational framework has not yet been achieved. In this study, a set‐up designed to assimilate reflectivities operationally in the COSMO‐2I model through a local ensemble transform Kalman filter (LETKF) has been evaluated over 37 days in 2018 and performed 303 forecasts. The comparison with the current operational set‐up based on LHN reveals an average improvement in quantitative precipitation forecasts (QPFs) up to 7 hr, even if the impact on convective cases is much stronger than that observed in conditions of stratiform precipitation. Moreover, a small but positive impact is noticed for the RMSE of upper‐air variables, while the impact on bias is mixed. Mixed results are also obtained when considering surface variables. In the light of the results of this study, a pre‐operational parallel system in which COSMO‐2I analyses are generated replacing LHN by the direct assimilation of reflectivity volumes was implemented in April 2020.

The major flood that affected the Piedmont region in Italy in November 1994 is re-forecast after 25 years in ensemble mode at the convection-permitting resolution of 2.2 km using the regional model COSMO. The performance of the probabilistic forecast of precipitation is assessed against rain-gauge observations, also in comparison with the driver system, i.e., the probabilistic re-forecast produced by ECMWF based on the operational IFS (Cycle 46r1) at grid spacings of 18 km. The convection-allowing system dynamically downscales the ECMWF ensemble and includes an explicit treatment of deep convection. Results indicate that both systems can predict up to 4 days in advance the timing and the spatial patterns of the precipitation, although with higher confidence for the convection-resolving system. The benefit of high resolution is shown mainly in the prediction of intense precipitation and in terms of correct amounts and locations, and confidence of occurrence (at day 3, the estimated probability of exceedance of 200 mm was higher than 90% over areas actually hit by such rainfall amounts). Additionally, convection-permitting resolution improves the representation of orographic precipitation, reducing the upwind precipitation displacement typical of coarser models and including the possible development of strong convection episodes embedded in the large-scale-forced orographic rise. For the high-resolution ensemble, the spread indicates large uncertainty at the local scale, mainly in defining the flow tendency to flank or flow over each mountain.

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure ( S ), amplitude ( A ) and location ( L ). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error ( roe ). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, very fewattempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and to several open issues, like the arise of imbalances in the analyses and the estimation of the observational error. In this work, it is evaluated the impact of the assimilation of radar reflectivity volumes employing a Local Ensemble Transform Kalman Filter (LETKF), implemented for the convection permitting model of the COnsortium for Small-scale Modelling (COSMO). A 4 days test case on February 2017 is considered and QPF is evaluated in terms of the SAL technique, an object-based method which allows to evaluate structure, amplitude and location of precipitation fields Results obtained assimilating radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna region (Arpae-SIMC), in which only conventional data are employed. Furthermore, some sensitivity tests are performed to evaluate the impact of the additive inflation, of the lenght of assimilation windows and of the reflectivity observational error. Finally, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields.