Passive microwave observations are highly sensitive to many variables (temperature, humidity, hydrometeors, etc.) that are useful for numerical weather prediction (NWP), especially in an all-sky context. Several studies have demonstrated the positive impact of all-sky passive microwave assimilation in cloudy and rainy areas on global NWP model forecasts, but the impact is less documented on regional-scale NWP models. The aim of this study is to investigate the impact of the assimilation of passive microwave observations in cloudy and precipitating areas in the operational kilometre-scale NWP model AROME of M & eacute;t & eacute;o-France. In particular, this study takes advantage of the recent works performed at M & eacute;t & eacute;o-France, allowing the direct initialization of hydrometeors in the three-dimensional ensemble-variational (3DEnVar) assimilation system of AROME, and the all-sky assimilation method of European Center for Medium-Range Weather Forecasts for microwave radiances. In this context, we examine the impact of the all-sky assimilation of four passive microwave sensors (microwave humidity sounder, microwave humidity sounder-2, GPM microwave imager, and advanced microwave scanning radiometer 2) in the 3DEnVar kilometre-scale AROME NWP on forecasts up to 48 hr. To this end, cycled experiments were carried out over two 1-month periods. For both periods, there is a reference experiment assimilating the four sensors in clear-sky conditions only and an all-sky experiment, assimilating them in all-sky conditions. Statistical studies were carried out to assess the impact on precipitation, clouds, temperature, relative humidity, geopotential height, and wind forecasts in order to evaluate the benefits of the all-sky assimilation of the four sensors. Significant improvements are demonstrated in 1-hr forecasts, especially regarding rainfall. For longer range forecasts, the results indicate both positive and negative effects, although there is a general trend toward a slight significant improvement.

Because of their high sensitivity to hydrometeors and high vertical resolutions, spaceborne radar observations are emerging as an undeniable asset for numerical weather prediction (NWP) applications. The EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) NWP SAF (Satellite Application Facility for Numerical Weather Prediction) released an active sensor module within version 13 of the RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) software with the goal of simulating both active and passive microwave instruments within a single framework using the same radiative transfer assumptions. This study provides an in-depth description of this radar simulator. In addition, this study proposes a revised version of the existing melting-layer parameterization scheme of Bauer (2001) within the RTTOV-SCATT v13.1 model to provide a better fit to observations below the freezing level. Simulations are performed with the revised and default schemes for the Dual-frequency Precipitation Radar (DPR) instrument on board the Global Precipitation Measurement (GPM) mission using the ARPEGE (Action de Recherche Petite Echelle Grande Echelle) global NWP model, operational at Meteo-France for two different 1-month periods (June 2020 and January 2021). Results for a case study over the Atlantic Ocean show that the revised melting scheme produces more realistic simulations much closer to observations compared to the default scheme both at Ku (13.5 GHz) and Ka (35.5 GHz) frequencies. A statistical assessment covering several cases shows significant improvement of the first-guess departure statistics. As a step further, this study showcases the use of melting-layer simulations for the classification of precipitation (stratiform, convective, and transition) using the dual-frequency ratio (DFR) algorithm. The classification results reveal a significant overestimation of the rain reflectivities in both hemispheres, which could indicate either an overproduction of convective precipitation in ARPEGE or a misrepresentation of the convective precipitation fraction within the forward operator.

To fill the gap of in‐cloud wind observations in the global observing system, the European Space Agency selected the wind velocity radar nephoscope (WIVERN) mission as one of the Earth Explorer 11 candidate missions to enter Phase A in 2023. WIVERN, with its dual‐polarisation Doppler conically scanning W‐band radar, will be the first space‐based mission to provide in‐cloud horizontal line‐of‐sight (HLOS) winds at a fine vertical resolution of 650 m sampling and a broad swath of width 800 km. We report on the impact of WIVERN simulated HLOS winds to improve global numerical weather prediction (NWP) model forecasts, using an ensemble of data assimilation (EDA) approach. In this methodology, the benefits of adding WIVERN simulated HLOS wind observations to the current observing system are measured by their ability to reduce the EDA spread at a given forecast lead time. The operational EDA system of the global NWP model ARPEGE (Action de Recherche Petite Echelle Grande Echelle) is used for a 1‐month period in 2021. Results indicate that WIVERN HLOS will not only significantly improve the uncertainty of the wind forecasts throughout the entire troposphere, but also of the temperature and humidity fields. This positive impact is particularly seen in the midlatitudes. Results of this study also highlight the strong vertical complementarity between WIVERN, Aeolus (Doppler wind lidar), and atmospheric motion vectors observations. Finally, the impact of WIVERN is also studied in synergy with the EUMETSAT follow‐on EPS‐Aeolus mission, and results demonstrate that the two active wind satellite missions would vertically complement each other as they provide wind observations at different altitudes, and in different meteorological areas.

This study investigates mixed-phase cloud (MPC) processes along the warm conveyor belts (WCBs) of two extratropical cyclones observed during the North Atlantic Waveguide and Downstream Impact Experiment (NAWDEX). The aim is to investigate the effect of two radically distinct parameterizations for MPCs on the WCB and the ridge building downstream: the first one (REF) drastically limits the formation of liquid clouds, while the second one (T40) forces the liq-uid clouds to exist. REF exhibits a stronger heating below 6-km height and a more important cooling above 6-km height than T40. The stronger heating at lower levels is due to more important water vapor depositional processes while the larger cooling at upper levels is due to differences in radiative cooling. The consequence is a more efficient potential vorticity destruction in the WCB outflow region and a more rapid ridge building in REF than T40. A comparison with airborne remote sensing measurements is performed. REF does not form any MPCs whereas T40 does, in particular in regions detected by the radar-lidar platform like below the dry intrusion. Comparison of both ice water content and reflectivity shows there may be too much pristine ice and not enough snow in REF and not enough cold hydrometeors in general in T40. The lower ice-to-snow ratio in T40 likely explains its better distribution of hydrometeors with respect to height com-pared to REF. These results underline the influence of MPC processes on the upper-tropospheric circulation and the need for more MPC observations in midlatitudes.

The development of ground-based cloud radars offers a new capability to continuously monitor fog structure. Retrievals of fog microphysics are key for future process studies, data assimilation, or model evaluation and can be performed using a variational method. Both the one-dimensional variational retrieval method (1D-Var) or direct 3D/4D-Var data assimilation techniques rely on the combination of cloud radar measurements and a background profile weighted by their corresponding uncertainties to obtain the optimal solution for the atmospheric state. In order to prepare for the use of ground-based cloud radar measurements for future applications based on variational approaches, the different sources of uncertainty due to instrumental, background, and forward operator errors need to be properly treated and accounted for. This paper aims at preparing 1D-Var retrievals by analysing the errors associated with a background profile and a forward operator during fog conditions. For this, the background was provided by a high-resolution numerical weather prediction model and the forward operator by a radar simulator. Firstly, an instrumental dataset was taken from the SIRTA observatory near Paris, France, for winter 2018–2019 during which 31 fog events were observed. Statistics were calculated comparing cloud radar observations to those simulated. It was found that the accuracy of simulations could be drastically improved by correcting for significant spatio-temporal background errors. This was achieved by implementing a most resembling profile method in which an optimal model background profile is selected from a domain and time window around the observation location and time. After selecting the background profiles with the best agreement with the observations, the standard deviation of innovations (observations–simulations) was found to decrease significantly. Moreover, innovation statistics were found to satisfy the conditions needed for future 1D-Var retrievals (un-biased and normally distributed).

The article reports on the impact of the assimilation of wind vertical profile data in a kilometre-scale NWP system on predicting heavy precipitation events in the northwestern Mediterranean area. The data collected in diverse conditions by the airborne W-band radar RASTA (Radar Airborne System Tool for Atmosphere) during a 45-day period are assimilated in the 3 h 3DVAR assimilation system of AROME. The impact of the length of the assimilation window is investigated. The data assimilation experiments are performed for a heavy rainfall event, which occurred over south-eastern France on 26 September 2012 (IOP7a) and over a 45-day cycled period. Results indicate that the quality of the rainfall accumulation forecasts increases with the length of the assimilation window, which recommends using observations with a large period centred on the assimilation time. The positive impact of the assimilation of RASTA wind data is particularly evidenced for the IOP7a case since results indicate an improvement in the predicted wind at short-term ranges (2 and 3 h) and in the 11 h precipitation forecasts. However, in the 45-day cycled period, the comparison against other assimilated observations shows an overall neutral impact. Results are still encouraging since a slight positive improvement in the 5, 8 and 11 h precipitation forecasts was demonstrated.

This article investigates the potential of W-band radar reflectivity to improve the quality of analyses and forecasts of heavy precipitation events in the Mediterranean area. The “1D+3DVar” assimilation method, operationally employed to assimilate ground-based precipitation radar data in the Météo-France kilometre-scale numerical weather prediction (NWP) model AROME, has been adapted to assimilate the W-band reflectivity measured by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2-month period over the Mediterranean area. After applying a bias correction, vertical profiles of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as relative humidity pseudo-observations in the 3DVar assimilation system of AROME. The efficiency of the 1-D Bayesian method in retrieving humidity fields is assessed using independent in-flight humidity measurements. To complement this study, the benefit brought by consistent thermodynamic and dynamic cloud conditions has been investigated by separately and jointly assimilating the W-band reflectivity and horizontal wind measurements collected by RASTA in the 3 h 3DVar assimilation system of AROME. The data assimilation experiments are conducted for a single heavy precipitation event and then also for 32 cases. Results indicate that the W-band reflectivity has a larger impact on the humidity, temperature and pressure fields in the analyses compared to the assimilation of RASTA wind data alone. Besides, the analyses get closer to independent humidity observations if the W-band reflectivity is assimilated alone or jointly with RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more rapidly as the forecast range increases when compared to the assimilation of RASTA wind data alone. Generally, the joint assimilation of the W-band reflectivity with wind data results in the best improvement in the rainfall precipitation forecasts. Consequently, results of this study indicate that consistent thermodynamic and dynamic cloud conditions in the analysis leads to an improvement of both model initial conditions and forecasts. Even though to a lesser extent, the assimilation of the W-band reflectivity alone also results in a slight improvement of the rainfall precipitation forecasts.

This article describes a reflectivity forward operator developed for the validation and assimilation of W‐band radar data into the regional AROME class of numerical weather prediction models. The forward operator is consistent with the AROME ICE3 one‐moment microphysical scheme and is devised for vertically pointing radars. A new neighbourhood validation method, the Most Resembling Column (MRC) method, is designed to disentangle spatial location model errors from errors in the forward operator. This novel method is used to validate the forward operator using data collected in diverse conditions by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere) during a 2 month period over a region of the Mediterranean. The MRC method is then applied to retrieve the optimal effective shapes (i.e. the mean axis ratios) of the predicted graupel, snow and pristine ice, by minimizing the standard deviation between observations and simulations. The optimal mean axis ratio is approximately 0.7 for snow and 0.8 for graupel. It is shown that treating snow and graupel particles as oblate spheroids with axis ratios close to their optimal values leads to good agreement between the observations and simulations of the ice levels. Conversely, there is a large bias if snow and graupel particles are considered to be either spherical or overly flattened. The results also indicate that pristine ice can be approximated by a sphere, but this conclusion should be taken cautiously since the amount of pristine ice particles is probably overestimated in the ICE3 microphysical scheme.