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A severe weather event is analyzed in terms of cell development, dynamics, and thermodynamics. The Mediterranean region is sporadically affected by severe weather events causing damage and injuries. An unusual and severe weather event hit Italy on July 9–10, 2019 causing heavy damage. The meteorological structure was characterized by a trough entering the eastern Mediterranean area from northeastern Europe affecting Italy and the Balkans with cold-air advection over the Adriatic Sea. On the morning of 10 July 2019, a supercell developed along the coast north of Pescara (central Italy), producing intense rainfall (130 mm/3 h) and a heavy hailstorm with hailstones larger than 10 cm in diameter. In this work, the dynamics and thermodynamics for triggering and maintenance of the supercell are investigated using the numerical model WRF (Weather Research and Forecasting system), satellite, radar-data, soundings, and ground observations. In the companion paper, the role of SST and orography was investigated assessing that the SST spatial representation, its anomaly distribution and the topography play a key role in triggering the supercell (Ricchi et al., 2023). In this paper, high spatial and temporal-resolution model simulations and observations are used to investigate the characteristics of the supercell by analyzing both its horizontal and vertical structure. The hailstone characteristics are assessed by using the HAILCAST parameterization in which its performance and reliability are discussed. • Vertical velocity up to 50 m/s and overshooting top presence in development phase. • 10 min output, horizontal grid interval O(1 km), around 100 vertical levels needed. • Strong agreement with observed data and WRF simulated supercell signature. • WER and BWER are well simulated, proxies of strong vertical updraft structure. • The HAILCAST module adequately reproduces the size of the hail. [ABSTRACT FROM AUTHOR]

We show, for the first time over Italy and over part of the central Mediterranean Basin, the impact of lightning data assimilation (LDA) on the strokes forecast for a long period. We use the Weather Research and Forecasting (WRF) model coupled with the Dynamic Lightning Scheme (DLS) at convection allowing horizontal resolution (3 km). We carried out a two-seasons experiment (summer 2020 and fall 2021) providing the forecast of lightning and precipitation for the next 6 h (nowcasting), considering two sub-periods (0-3 h and 3-6 h) for verification. The LDA is done through a nudging scheme that increases the water vapor mass in the mixed-phase region based on observed flash density rates and simulated graupel mixing ratio. No changes are made to the model run if spurious convection is predicted or no flashes are observed. LDA can trigger convection missed by the control forecast, without LDA, and/or can redistribute the strokes predicted to be more consistent with observations. LDA has a positive impact on strokes forecast, improving correct forecasts and reducing false alarms. This improvement is however confined to the first three-hours of forecast with negligible to negative impact for longer time ranges, in line with other studies. The improvement pattern is different in summer and fall, depending on the convection development. The analysis of the Fraction Skill Score shows the usefulness of the forecast for practical purposes, considering the current areas used by the Civil Protection Department to issue meteorological alerts for intense convective events over Italy. Finally, it is shown that the forecast at the short-range (0−3 h) using LDA can improve the strokes forecast issued on the previous day, not using LDA, and the methodology of this paper can be applied to issue warnings and alerts as the storm is approaching. A brief examination of rainfall forecast shows positive impact of LDA at the short-range (0-3 h), with neutral impact for longer time ranges. The different impact of LDA on the strokes and precipitation forecasts is also highlighted. • Lightning data assimilation improves the lightning and precipitation forecast for the first three-hours in summer and fall. • The pattern of improvement is different in summer and fall. • The very short-term forecast refines the previous day forecast as the storm is approaching. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to road surface temperature (RST) forecasting by using satellite-derived cloud cover information as inputs for the FOrecast of Road TEmperature and condition (FORTE) road weather model (RWM). FORTE is a 1D physical based model used for solving energy balance and heat conduction equations. The new method uses the extrapolated cloud mask product calculated from Meteosat Second Generation satellite measurements to derive the forecasted cloud cover, which is then entered into the RWM. This method is based on the assumption that cloud cover extrapolated for the very near future (i.e., 1–3 h) from current conditions is able to provide more accurate input data (as compared to the currently used numerical weather prediction (NWP) model forecast) that can be used for calculating the radiation fluxes in the RWM. The resulting RSTs were verified over a 2-month period from December 2021 to January 2022 at road weather stations located on 4 selected Czech motorways. The obtained results showed that the innovated model run using satellite-derived cloud cover generated RSTs closer to the observed values, in contrast to the original model run, whose predictions have larger errors. The greatest improvement is evident during the day, when solar radiation dominates, and especially in the 2nd and 3rd forecasted hours, when extrapolated cloud cover helps to reduce the morning overestimation and afternoon underestimation of RSTs. If the NWP model incorrectly predicts clouds over the road surface on cloudless nights, the emitted downwards radiation from the clouds towards the Earth's surface is increased in the RWM, which leads to an overestimation of the RSTs. On the other hand, incorrectly predicting clear skies leads to a negative radiation balance in the RWM, which typically results in RSTs that are lower than the observations. • Satellite-derived cloud cover improves radiation fluxes in the road weather model. • Corrected radiation fluxes lead to more accurate road surface temperature forecast. • Improvement is more pronounced in the 2nd and 3rd forecasted hour during the daytime. [ABSTRACT FROM AUTHOR]

This paper describes the lessons learned from the implementation of a regional ensemble data assimilation and forecast system during the intensive observing period of the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign (central Argentina, November–December 2018). This system is based on the coupling of the Weather Research and Forecasting (WRF) model and the Local Ensemble Transform Kalman Filter (LETKF). It combines multiple data sources both global and locally available like high-resolution surface networks, AMDAR data from local aircraft flights, soundings, AIRS retrievals, high-resolution GOES-16 wind estimates, and local radar data. Hourly analyses with grid spacing of 10 km are generated along with warm-start 36-h ensemble-forecasts, which are initialized from the rapid refresh analyses every three hours. A preliminary evaluation shows that a forecast error reduction is achieved due to the assimilated observations. However, cold-start forecasts initialized from the Global Forecasting System Analysis slightly outperform the ones initialized from the regional assimilation system discussed in this paper. The system uses a multi-physics approach, focused on the use of different cumulus and planetary boundary layer schemes allowing us to conduct an evaluation of different model configurations over central Argentina. We found that the best combinations for forecasting surface variables differ from the best ones for forecasting precipitation, and that differences among the schemes tend to dominate the forecast ensemble spread for variables like precipitation. Lessons learned from this experimental system are part of the legacy of the RELAMPAGO field campaign for the development of advanced operational data assimilation systems in South America. • A LETKF-WRF system was run successfully in real-time to support RELAMPAGO operations. • A reduction in forecast error was shown due to data assimilation cycles. • 60-member RRR analyses and forecasts are available for the research community. [ABSTRACT FROM AUTHOR]

The regional and longer-duration dust weather in the Taklimakan Desert (TD) is mostly caused by the east-pathway dust events, which bring large amounts of dust aerosol to impact the local weather and air quality, but the dust-induced radiation feedback on meteorological processes and the dust cycle in TD is still unclear. Both dust absorption and scattering of shortwave (SW) and longwave (LW) radiation are important for dust radiation feedback (DRF), while most numerical simulations neglect the dust LW scattering effect. In this paper, we modify WRF-Chem v3.8.1 (Weather Research and Forecasting model coupled with chemistry) to include the full dust radiation effect in an only-dust simulation. Three east-pathway TD dust events were simulated by this modified WRF-Chem. For each dust event, four sensitive experiments were conducted: with SW + LW DRF, with only SW DRF, with only LW DRF, and without DRF. The modified WRF-Chem can reproduce the dust evolution and reasonably simulate the DRF. DRF enhances the mean 2-m air temperature (T2m) field by ∼1 °C in the TD and lowers the daily T2m range in the southern TD by 1.9–4.3 °C. The positive feedback of mean T2m owns mostly to two equally important contributors, i.e., the dust LW radiation impact and the synergistic impact between dust SW and LW radiation quantified by the factor separation technique. For these dust events, DRF enhances both dust loading (10–20%) and dust deposition in most TD areas, but with different impacts on the dust emission in the hinter (increasing) and the southern edge (decreasing) of the TD area. With a dynamic lift caused by mountain forcing and the DRF, the dust plume could be lifted higher in the southern TD. • Dust radiation effect of the east-pathway TD dust event is simulated by a modified WRF-Chem. • Dust radiation feedback enhances the mean T2m field by ∼1 °C. • Synergistic impact between dust SW and LW radiation contributes significantly to the mean T2m field. • Dust radiation feedback enhances TD dust loading by 10–20%. [ABSTRACT FROM AUTHOR]

This paper analyses the dependency of regional air pollution in Europe on the atmospheric circulation as represented by a set of 8 weather regimes (WRs). For this purpose, daily average PM 2.5 concentrations and daily maximum concentrations of O 3 , NO 2 and CO obtained from an atmospheric composition reanalysis have been used. The results indicate that the pollutant concentrations change substantially with the occurrence of each WR according to the associated circulation patterns. In the extended winter, the responses of PM 2.5 , NO 2 and CO are quite similar, with zonal circulation regimes leading to better air quality than anticyclonic regimes linked to steady flows over Europe. On the other hand, the spatial patterns of O 3 anomalies under most WRs resemble those of temperature in the extended summer as temperature enhancements favour the photochemical production of O 3. The WR characterized by anticyclonic anomalies over Scandinavia leads to the highest concentrations at continental scale and is concurrent with an increased probability of exceeding the poor air quality limits of winter PM 2.5 and summer O 3 in most European countries. It is also found that a multiple linear regression model on the monthly frequency of WRs can explain a considerable fraction of the intermonthly variability of concentration anomalies over large parts of Europe regardless of the pollutant, with R2 values around 0.6–0.8 over large regions. Overall, the application of these models to sub-seasonal weather forecasts does not seem to bring major improvements to the prediction of month-ahead concentration anomalies as compared to the climatology. The poor skill of sub-seasonal forecasts in predicting the WRs for lead times beyond 15 days limits the forecast capability. However, these results open the possibility of extending current air quality forecast to scales of 5–10 days at a low cost in a near future. [Display omitted] • European-wide study of the modulation of air quality by weather regimes (WRs). • WRs explain 60–80% of the monthly variability in air pollution over large regions. • The poorest air quality in most of Europe is associated with Scandinavian blocking. • Evaluation of WRs as a tool to predict month-ahead air pollution anomalies. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most relevant fields in atmospheric modeling because of its environmental, social and economic implications. However, precipitation validation from weather model outputs presents substantial challenges, such as measurement uncertainties, use of gridded datasets vs. direct observations, and the selection of statistical goodness-of-fit measures. The main difficulty of working with precipitation is that it can be spatially irregular, especially in extreme events. High temporal aggregation smooths the field and reduces verification uncertainty. For this reason, validations are usually focused on a daily scale. However, many extreme events occur on shorter periods, for which a sub-daily precipitation assessment is required. In this paper, hourly precipitation verification of the Weather Research and Forecasting (WRF) model is explored for 45 extreme precipitation events (EPEs) recorded in northeastern Spain. For this, stations with recorded EPEs were classified according to the hourly distribution of precipitation. WRF simulations were established considering three microphysics and two planetary boundary layer (PBL) parameterizations. Finally, several statistical goodness-of-fit measures and spatial and temporal precipitation distributions were used for evaluating WRF performance. The results showed that microphysics were more important than PBL parameterizations. Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino PBL gave better results for most of the analyzed characteristics. However, an optimal combination of parameterizations was not obtained for all EPEs, because event characteristics had important effects on model performance. • Hourly precipitation verification of the WRF model has been explored. • We selected 965 stations where extreme precipitation was recorded. • Three microphysics and two planetary boundary layer parameterizations were tested. • Goddard and Thompson together with Mellor-Yamada-Nakanishi and Nino gave better results. [ABSTRACT FROM AUTHOR]

• Dryline formation is favoured by the advection of mid-level dry air from the Pacific. • Local circulations impact the dryline movement and strength in quiescent conditions. • Nocturnal LLJ and local circulations reinforce the moisture gradient at night. • In synoptically active periods, dry advection and diurnal mixing are more important. Drylines are a frequent feature of the atmospheric circulation in Argentina. Recent work described their climatological characteristics and synoptic-scale processes associated with their formation. However, little is known about the mesoscale processes that are relevant for their life cycle. In this paper, we investigate the influence of these mechanisms on the formation and movement using a case-study approach. Also, the relationship between these mechanisms and convective initiation near the dryline is investigated. A typical northern Patagonia dryline is simulated using the Weather Research and Forecasting (WRF) with an horizontal resolution of 3 km. The FLEXible PARTicle dispersion-WRF is also used to study some of the processes from a Lagrangian perspective. During the quiescent period corresponding to dryline formation and intensification, the dryline strengthens along the axis of a thermally-induced surface trough that promotes differential moisture advection. Also, convergence along the dryline is enhanced during night-time by the inertial oscillation ahead of the dryline and by the development of a thermally-driven circulation between the elevated terrain in northern Patagonia and the adjacent plains. These two mechanisms contribute to enhancing the intensity of the moisture gradient and to trigger deep moist convection. Under a period of stronger synoptic forcing, the dryline intensifies in response to the confluence induced by the deepening surface trough and moves away from the terrain elevations. Its movement during this period is driven by horizontal dry advection and the vertical mixing enhanced by the entrainment into the boundary layer of extremely dry air, whose origin can be traced back to the mid-levels over the Pacific Ocean. When the magnitude of the low-level westerly winds increase, moister air from lower levels cross the barrier, contributing to weakening the moisture gradient at the dryline. [ABSTRACT FROM AUTHOR]

Mountain lee waves usually involve aircraft icing and turbulence events. These weather phenomena, in turn, are a threat to aviation safety. For this reason, mountain lee waves are an interesting subject of study for the scientific community. This paper analyses several mountain lee waves events in the south-east of the Guadarrama mountain range, near the Adolfo Suarez Madrid-Barajas airport (Spain), using the Weather Research and Forecasting (WRF) and the HARMONIE-AROME high-resolution numerical models. For this work, simulated brightness temperature from the optimum WRF parametrization schemes and from the HARMONIE are validated using satellite observations to evaluate the performance of the models in reproducing the lenticular clouds associated to mountain lee waves. The brightness temperature probability density shows interesting differences between both models. Following, a mountain wave characterization is performed simulating some atmospheric variables (wind direction, wind speed, atmospheric stability, liquid water content and temperature) in several grid points located in the leeward, windward and over the summit of the mountains. The characterization results are compared for both numerical models and a decision tree is developed for each to forecast and warn the mountain lee waves, lenticular clouds and icing events with a 24 to 48 h lead time. These warnings are validated using several skill scores, revealing similar results for both models. • HARMONIE-AROME and WRF models can be used to simulate mountain wave episodes. • Mountain waves are characterized analysing several atmospheric variables in the windward, leeward and over the summit. • A decision tree is developed to provide a warn methodology for mountain waves, wave clouds and icing. [ABSTRACT FROM AUTHOR]

Subtropical cyclones (STCs) are low-pressure systems characterized by having a thermal hybrid structure and sharing tropical and extratropical characteristics. These cyclones are widely studied due to their harmful impacts, in some cases, similar to those caused by hurricanes or tropical storms. From a numerical modeling point of view, they are considered a challenge on account of their rapid intensification. That is the reason why this paper analyzes the simulations of the STC that occurred in October 2014 near the Canary Islands through two high-resolution numerical models: Weather Research and Forecasting (WRF) and HARMONIE-AROME. In this study, the simulations obtained with both models of this STC are analyzed versus different observational data. METAR data are used to validate some surface simulated variables throughout the STC life while soundings are chosen to study the tropospheric behavior. Finally, MSG-SEVIRI satellite brightness temperature is used to be compared to those brightness temperatures simulated by both models to give information of the cloud top spatial structure of this atmospheric system. The 2 m temperature, 2 m dew-point temperature, and 10 m wind speed variables do not show significant deviations when carrying out the validation of both models against the available METAR data. It is outstanding the good results found for the HARMONIE-AROME model when analyzing the temperature sounding for both analyzed dates. Additionally, regarding the wind speed sounding, better results are presented in general by the HARMONIE-AROME model, being the WRF model slightly better during the pre-STC stage. Moreover, the skillfulness of the HARMONIE-AROME model is highlighted when simulating the infrared brightness temperature and cloud distribution compared to the WRF model. • A subtropical cyclone is analyzed using two high-resolution models (WRF and HARMONIE-AROME). • Simulations are assessed against several observational data. • MSG-SEVIRI brightness temperature is compared to the simulations. • Both models show similar results but HARMONIE-AROME performs better in terms of realism and cloud distribution. [ABSTRACT FROM AUTHOR]

Radar data assimilation (DA) can improve short-term precipitation forecasts. However, assimilating radar observations into a numerical prediction model may produce spurious precipitation and large errors in the position and magnitude of precipitation, and high-frequency cyclic assimilation may exacerbate this problem. In this paper, a severe convection case that occurred on 27 June 2018 over eastern China was used to investigate the impacts of the radar DA frequency and large-scale constraint on precipitation forecasts. The Weather Research and Forecasting (WRF) three-dimensional variational (3D-Var) DA system was used to assimilate radial velocity and reflectivity data. The sensitivity of the DA frequency using 15-min and 1-h assimilation intervals was examined first, revealing that the 15-min DA interval produced a greater overprediction bias of precipitation. A diagnosis of the analysis fields showed that higher-frequency DA cycling produced excessive wind speed and water vapor convergence at low levels and exaggerated upward movement. Then, the large-scale constraint was imposed on the regional model by the spectral nudging (SN) technique, which assimilated Global Forecast System (GFS) forecast field data during the forecast periods. The results demonstrated that the application of SN could significantly reduce the positional deviation of the precipitation forecast and the magnitude of overpredicted precipitation. SN could effectively adjust large-scale circulation fields, thereby improving the conditions of water vapor convergence. Moreover, SN also improved the forecasts of surface variables such as wind, temperature, and humidity. • The impacts of the radar data assimilation frequency and large-scale constraint on precipitation forecasts are investigated. • A higher frequency of radardata cyclic assimilation leads to a larger overprediction bias of precipitation. • Using the spectral nudging to assimilate GFS forecast data in the forecast periods can improve precipitation forecasting. [ABSTRACT FROM AUTHOR]