Abstract. This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.

This paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

An original one‐dimensional (1‐D) retrieval followed by a three‐dimensional variational (1D+3DVar) assimilation technique is being developed to assimilate volumes of radar reflectivity data in the high‐resolution numerical weather prediction Arome model. The good performance of the 1‐D retrieval is shown for an isolated storm over southwestern France through an observing system simulation experiment. The full method is applied with real data to a flash‐flood event, which occurred in a mountainous area. For this complex case, the assimilation of reflectivity data improves short‐term precipitation forecasts. The assimilation of reflectivity data has a positive impact on the convective system's dynamics by feeding the cold pool under the storm, which controls the intensity and location of the updrafts. A one‐hourly update cycle of 3 h further improves these results. A sensitivity study is also presented to evaluate the assimilation method for this flash‐flood event in different conditions. The smoothing coefficient involved in the 1‐D retrieval is shown to have a very small impact on analyses and quantitative precipitation forecasts. The assimilation of reflectivity data is found to be able to cause the creation of a cold pool, which modifies favourably the precipitation quantitative forecast. Finally, results from an 8‐d‐long assimilation cycle are presented.

[1] Estimations of zenith total delays (ZTD) were obtained during postprocessing of a high-resolution (2.4 km) nonhydrostatic atmospheric model (Méso-NH). These estimations were used to determine their sensitivity with respect to formulations of atmospheric refractivity, the approximation of zenith hydrostatic delays (ZHD) deduced from ground pressure, and the contributions of hydrometeors. The factor k for the conversion of zenith wet delay (ZWD) to integrated water vapor (IWV) was examined. Méso-NH is applied here to the extreme flash flood event of 8-9 September 2002 in southeastern France. The use of the hydrostatic formulation (to infer ZHD) leads to an overestimation of up to 18 mm with respect to the vertical integration of refractivity. Delay contributions of hydrometeors simulated by the high-resolution model reached more than 70 mm (%11 kg/m 2 IWV) in the heart of the convective cells in the case of the extreme flood event. The mean variations of IWV due to the use of different conversion factors (k used to transform ZWD to IWV) are evaluated to be less than 0.3 kg/m 2 . This is less than the mean underestimation of IWV by 0.6 kg/m 2 relative to the GPS-like evaluation of IWV using the hydrostatic formulation and the ground temperature. In this study we also use GPS ZTD observations to validate three different numerical simulations of this extreme flood event. The simulation with the best fit to the GPS observations is also in best agreement with the surface rainfall measurements.

International audienceA full radar simulator for high-resolution (1–5 km) nonhydrostatic models has been developed within the research nonhydrostatic mesoscale atmospheric (Meso-NH) model. This simulator is made up of building blocks, each of which describes a particular physical process (scattering, beam bending, etc.). For each of these blocks, several formulations have been implemented. For instance, the radar simulator offers the possibility to choose among Rayleigh, Rayleigh–Gans, Mie, or T-matrix scattering methods, and beam bending can be derived from an effective earth radius or can depend on the vertical gradient of the refractive index of air. Moreover, the radar simulator is fully consistent with the microphysical parameterizations used by the atmospheric numerical model. Sensitivity experiments were carried out using different configurations for the simulator. They permitted the specification of an observation operator for assimilation of radar reflectivities by high-resolution nonhydrostatic numerical weather prediction systems, as well as for their validation. A study of the flash flood of 8–9 September 2002 in southeastern France, which was well documented with volumetric data from an S-band radar, serves to illustrate the capabilities of the radar simulator as a validation tool for a mesoscale model

This article describes a forward observation operator for polarimetric radar variables, developed within the research mesoscale non-hydrostatic model Meso-NH. The forward operator enables direct comparisons of atmospheric simulations with polarimetric radar observations and constitutes a first step towards the assimilation of polarimetric radar data. The parameters of the polarimetric forward operator were defined in order to be consistent (whenever possible) with the model microphysical parametrization. Subjective and quantitative comparisons between observations and simulations are analyzed for S-, C-and X-band radars for two convective cases that were observed in autumn 2012 in southeastern France during the first observing period (SOP1) of the Hydrological cycle in the Mediterranean Experiment (HyMeX) campaign. The good consistency between the medians of the observed and simulated distributions of Z dr and K dp as a function of Z hh , in rain and in dry snow, enables the validation of the parametrization of the polarimetric forward operator. However, the higher spread in observed Z dr and K dp (due to noise and microphysical variability) reveals a need for careful data quality control and pre-processing for use in assimilation. Comparisons between observed and simulated vertical profiles of Z hh , K dp and Z dr in convective areas also show generally good agreement. Some differences between observations and simulations are attributed to model limitations, in particular its one-moment microphysics scheme and coarser resolution.

Abstract. In this paper, we study the impact of lightning and radar reflectivity factor data assimilation on the precipitation VSF (very short-term forecast, 3 h in this study) for two severe weather events that occurred in Italy. The first case refers to a moderate and localized rainfall over central Italy that occurred on 16 September 2017. The second case occurred on 9 and 10 September 2017 and was very intense and caused damages in several geographical areas, especially in Livorno (Tuscany) where nine people died. The first case study was missed by several operational forecasts, including that performed by the model used in this paper, while the Livorno case was partially predicted by operational models. We use the RAMS@ISAC model (Regional Atmospheric Modelling System at Institute for Atmospheric Sciences and Climate of the Italian National Research Council), whose 3D-Var extension to the assimilation of radar reflectivity factor is shown in this paper for the first time. Results for the two cases show that the assimilation of lightning and radar reflectivity factor, especially when used together, have a significant and positive impact on the precipitation forecast. For specific time intervals, the data assimilation is of practical importance for civil protection purposes because it changes a missed forecast of intense precipitation (≥40 mm in 3 h) to a correct one. While there is an improvement of the rainfall VSF thanks to the lightning and radar reflectivity factor data assimilation, its usefulness is partially reduced by the increase in false alarms, especially when both datasets are assimilated.

Abstract. This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes stateof-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.