Application Of Research To Operations At Mesoscale Research Articles

A two-fold deep-learning strategy to correct and downscale winds over mountains

Abstract. Assessing wind fields at a local scale in mountainous terrain has long been a scientific challenge, partly because of the complex interaction between large-scale flows and local topography. Traditionally, the operational applications that require high-resolution wind forcings rely on downscaled outputs of numerical weather prediction systems. Downscaling models either proceed from a function that links large-scale wind fields to local observations (hence including a corrective step) or use operations that account for local-scale processes, through statistics or dynamical simulations and without prior knowledge of large-scale modeling errors. This work presents a strategy to first correct and then downscale the wind fields of the numerical weather prediction model AROME (Application of Research to Operations at Mesoscale) operating at 1300 m grid spacing by using a modular architecture composed of two artificial neural networks and the DEVINE downscaling model. We show that our method is able to first correct the wind direction and speed from the large-scale model (1300 m) and then accurately downscale it to a local scale (30 m) by using the DEVINE downscaling model. The innovative aspect of our method lies in its optimization scheme that accounts for the downscaling step in the computations of the corrections of the coarse-scale wind fields. This modular architecture yields competitive results without suppressing the versatility of the DEVINE downscaling model, which remains unbounded to any wind observations.

Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME

Inspecting the literature, much effort has been placed on the verification of irradiance forecasts from numerical weather prediction (NWP) models, as such forecasts are thought to have profound implications on the photovoltaic (PV) power forecasts, which in turn affects grid operators' confidence in integrating such power into the electricity grid. However, perhaps due to the proprietary nature of PV plants and lack of access to state-of-the-art NWP model output, only few have had the chance to conduct head-to-head comparisons of global mesoscale and regional downscaled NWP models, in terms of how their irradiance forecast inaccuracies propagate to PV power forecasts. In this regard, this work presents such a study, in which irradiance and PV power forecasts from the European Centre for Medium-Range Weather Forecasts' High-Resolution (HRES) and Météo-France's Application of Research to Operations at Mesoscale (AROME) models are thoroughly verified against the ground-based measurements from 32 research-grade radiometry stations and 94 actual PV plants in Hungary. A wide range of techniques and case studies concerning verification is herein considered, including variance ratio analysis, Murphy–Winkler decomposition, point-versus-areal verification, and seasonal verification. Despite that the results are too numerous to be summarized in a few sentences, the overarching observation from the verification exercise is that the performance of irradiance forecasts can only be used to infer that of PV power forecasts to a certain extent, which contrasts the conventional wisdom.

Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA

Observed by satellites for more than a decade, surface soil moisture (SSM) is an essential component of the Earth system. Today, with the Sentinel missions, SSM can be derived at a sub-kilometer spatial resolution. In this work, aggregated 1 km × 1 km SSM observations combining Sentinel-1 (S1) and Sentinel-2 (S2) data are assimilated for the first time into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land surface model using the global Land Data Assimilation System (LDAS-Monde) tool of Meteo-France. The ISBA simulations are driven by atmospheric variables from the Application of Research to Operations at Mesoscale (AROME) numerical weather prediction model for the period 2017–2019 for two regions in Southern France, Toulouse and Montpellier, and for the Salamanca region in Spain. The S1 SSM shows a good agreement with in situ SSM observations. The S1 SSM is assimilated either alone or together with leaf area index (LAI) observations from the PROBA-V satellite. The assimilation of S1 SSM alone has a small impact on the simulated root zone soil moisture. On the other hand, a marked impact of the assimilation is observed over agricultural areas when LAI is assimilated, and the impact is larger when S1 SSM and LAI are assimilated together.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

Sensitivity analysis of the convective‐scale AROME model to physical and dynamical parameters

AbstractA global sensitivity analysis of the convective‐scale Application of Research to Operations at Mesoscale (AROME) model is performed in order to determine the most influential parameters on the forecast of different near‐surface variables. For that purpose, the Morris method is applied to 21 parameters from six different physical and dynamical parametrization schemes, over different seasons. Results highlight a set of eight parameters with a noticeable influence on most variables, in particular 10 m wind speed and precipitation forecasts. The sensitivity of parameter uncertainties is also examined on different spatio‐temporal scales. A clear diurnal cycle of parameters influence is observed in summer, in close connection with the convective activity. In addition, the spatial distribution of parameters influence is mostly consistent with the underlying distribution of weather forecasts. A Sobol' sensitivity analysis, based on surrogate models, mostly confirms Morris conclusions and highlights some interactions between parameters.

Improvement of numerical weather prediction model analysis during fog conditions through the assimilation of ground-based microwave radiometer observations: a 1D-Var study

Abstract. This paper investigates the potential benefit of ground-based microwave radiometers (MWRs) to improve the initial state (analysis) of current numerical weather prediction (NWP) systems during fog conditions. To this end, temperature, humidity and liquid water path (LWP) retrievals have been performed by directly assimilating brightness temperatures using a one-dimensional variational technique (1D-Var). This study focuses on a fog-dedicated field-experiment performed over winter 2016–2017 in France. In situ measurements from a 120 m tower and radiosoundings are used to assess the improvement brought by the 1D-Var analysis to the background. A sensitivity study demonstrates the importance of the cross-correlations between temperature and specific humidity in the background-error-covariance matrix as well as the bias correction applied on MWR raw measurements. With the optimal 1D-Var configuration, root-mean-square errors smaller than 1.5 K (respectively 0.8 K) for temperature and 1 g kg−1 (respectively 0.5 g kg−1) for humidity are obtained up to 6 km altitude (respectively within the fog layer up to 250 m). A thin radiative fog case study has shown that the assimilation of MWR observations was able to correct large temperature errors of the AROME (Application of Research to Operations at MEsoscale) model as well as vertical and temporal errors observed in the fog life cycle. A statistical evaluation through the whole period has demonstrated that the largest impact when assimilating MWR observations is obtained on the temperature and LWP fields, while it is neutral to slightly positive for the specific humidity. Most of the temperature improvement is observed during false alarms when the AROME forecasts tend to significantly overestimate the temperature cooling. During missed fog profiles, 1D-Var analyses were found to increase the atmospheric stability within the first 100 m above the surface compared to the initial background profile. Concerning the LWP, the RMSE with respect to MWR statistical regressions is decreased from 101 g m−2 in the background to 27 g m−2 in the 1D-Var analysis. These encouraging results led to the deployment of eight MWRs during the international SOFOG3D (SOuth FOGs 3D experiment for fog processes study) experiment conducted by Météo-France.

Air-Traffic Restrictions at the Madeira International Airport Due to Adverse Winds: Links to Synoptic-Scale Patterns and Orographic Effects

The Madeira International Airport (MIA) lies on the island’s south-eastern coast and it is known to be exposed to wind hazards. A link between these adverse winds at MIA and the synoptic-scale circulation is established using a weather type (WT) classification. From April to September (summer period), five WTs prevail, cumulatively representing nearly 70% of days. These WTs reflect the presence of well-established Azores high, with some variations on location and strength. Although with a low frequency of occurrence (<5%), this anticyclone occasionally strengthens and extends towards Iberia, inducing anomalously strong NNE/NE up to 3–5 km over Madeira. The most severe and longer-lasting wind conditions at the MIA, with a higher frequency of gusts above 35 kt, are driven by this synoptic-scale pattern and are more common in summer. An episode of adverse winds at the MIA is analysed, illustrating the occurrence of upstream stagnation, flow splitting, and lee wake formation. The upstream conditions include a low-level inversion, strong NNE/NE winds near and above the inversion and a Froude number less than 1. The AROME (Application of Research to Operations at Mesoscale) model predicted the occurrence of downslope winds, in association with a large-amplitude mountain wave. At this time, the strongest wind gusts were registered and one aircraft executed a missed approach. The wind regime in different places of the island suggests that these conditions are relatively frequent, mostly in summer. Finally, objective verification of AROME wind forecast, for a three-year period and from June to August, is discussed.

Present Temperature, Precipitation, and Rain‐on‐Snow Climate in Svalbard

AbstractThe Svalbard Archipelago has undergone rapid warming in the recent decades leading to warmer and wetter winter conditions. This study relates the present (2013–2018) 2 m temperature, precipitation, and rain‐on‐snow (ROS) climate in Svalbard to different atmospheric circulation (AC) types utilizing the high‐resolution numerical weather prediction model Application of Research to Operations at Mesoscale (AROME)‐Arctic. We find that the 2 m median temperatures vary most across AC types in winter and spring and in summer they vary the least. In all seasons the 10th percentile 2 m temperatures are above 0°C with southwesterly AC types over Svalbard. In comparison, the relationship between AC type and precipitation varies more spatially, with most accumulated precipitation and highest median precipitation intensities with onshore flow over open water. Our results suggest that sea ice explains a large part of the local variability in both 2 m temperature and precipitation. In the studied period ROS is a frequent phenomenon up to 150 m above sea level (ASL) on land, with most events in the southwestern parts of the archipelago (57 cases during five winter seasons). ROS events in winter occur predominantly with AC types from the southerly sector or during a low‐pressure center/trough passage. The southwesterly cyclonic AC type, with a low‐pressure center west of Svalbard, is the most frequent AC type for ROS events. In addition to being the most frequent, the southwesterly AC has the largest spatial coverage of ROS.

Validation of the AROME, ALADIN and WRF Meteorological Models for Flood Forecasting in Morocco

Flash floods are common in small Mediterranean watersheds and the alerts provided by real-time monitoring systems provide too short anticipation times to warn the population. In this context, there is a strong need to develop flood forecasting systems in particular for developing countries such as Morocco where floods have severe socio-economic impacts. In this study, the AROME (Application of Research to Operations at Mesoscale), ALADIN (Aire Limited Dynamic Adaptation International Development) and WRF (Weather Research and Forecasting) meteorological models are evaluated to forecast flood events in the Rheraya and Ourika basin located in the High-Atlas Mountains of Morocco. The model evaluation is performed by comparing for a set of flood events the observed and simulated probabilities of exceedances for different precipitation thresholds. In addition, two different flood forecasting approaches are compared: the first one relies on the coupling of meteorological forecasts with a hydrological model and the second one is a based on a linear relationship between event rainfall, antecedent soil moisture and runoff. Three different soil moisture products (in-situ measurements, European Space Agency’s Climate Change Initiative ESA-CCI remote sensing data and ERA5 reanalysis) are compared to estimate the initial soil moisture conditions before flood events for both methods. Results showed that the WRF and AROME models better simulate precipitation amounts compared to ALADIN, indicating the added value of convection-permitting models. The regression-based flood forecasting method outperforms the hydrological model-based approach, and the maximum discharge is better reproduced when using the WRF forecasts in combination with ERA5. These results provide insights to implement robust flood forecasting approaches in the context of data scarcity that could be valuable for developing countries such as Morocco and other North African countries.

Observing System Experiments with an Arctic Mesoscale Numerical Weather Prediction Model

In the Arctic, weather forecasting is one element of risk mitigation, helping operators to have knowledge on weather-related risk in advance through forecasting capabilities at time ranges from a few hours to days ahead. The operational numerical weather prediction is an initial value problem where the forecast quality depends both on the quality of the forecast model itself and on the quality of the specified initial state. The initial states are regularly updated using environmental observations through data assimilation. This paper assesses the impact of observations, which are accessible through the global telecommunication and the EUMETCast dissemination systems on analyses and forecasts of an Arctic limited area AROME (Application of Research to Operations at Mesoscale) model (AROME-Arctic). An assessment through the computation of degrees of freedom for signals on the analysis, the utilization of an energy norm-based approach applied to the forecasts, verifications against observations, and a case study showed similar impacts of the studied observations on the AROME-Arctic analysis and forecast systems. The AROME-Arctic assimilation system showed a relatively high sensitivity to the humidity or humidity-sensitive observations. The more radiance data were assimilated, the lower was the estimated relative sensitivity of the assimilation system to different conventional observations. Data assimilation, at least for surface parameters, is needed to produce accurate forecasts from a few hours up to days ahead over the studied Arctic region. Upper-air conventional observations are not enough to improve the forecasting capability over the AROME-Arctic domain compared to those already produced by the ECMWF (European Centre for Medium-range Weather Forecast). Each added radiance data showed a relatively positive impact on the analyses and forecasts of the AROME-Arctic. The humidity-sensitive microwave (AMSU-B/MHS) radiances, assimilated together with the conventional observations and the Infrared Atmospheric Sounding Interferometer (IASI)-assimilated on top of conventional and microwave radiances produced enough accurate one-day-ahead forecasts of polar low.

Ice loads on overhead lines due to freezing radiation fog events in plains

The paper discusses specific ice accretion events that may occur in plains, i.e. freezing radiation fog events, whose worst cases can damage transmission overhead lines. A simple model is proposed able to estimate ice loads due to such events, based on standard meteorological observational data. The simple model has been validated by comparing ice loads estimated by the model with historical input data and ice loads estimated based on historical damages to overhead lines. It has been shown that its most promising application can be obtained by replacing the observed meteorological input data with similar parameters from an operational numerical weather prediction model. Such icing forecasts can be used to generate alarms to anticipate potential damage to overhead lines. This application was successfully tested on a significant freezing radiation fog event that took place in the northern half of France in the very beginning of 2017, using data issued from the French convective-scale numerical weather prediction model AROME (Application of Research to Operations at MEsoscale).

A New Framework to Compare Mass-Flux Schemes Within the AROME Numerical Weather Prediction Model

In the Application of Research to Operations at Mesoscale (AROME) numerical weather forecast model used in operations at Meteo-France, five mass-flux schemes are available to parametrize shallow convection at kilometre resolution. All but one are based on the eddy-diffusivity–mass-flux approach, and differ in entrainment/detrainment, the updraft vertical velocity equation and the closure assumption. The fifth is based on a more classical mass-flux approach. Screen-level scores obtained with these schemes show few discrepancies and are not sufficient to highlight behaviour differences. Here, we describe and use a new experimental framework, able to compare and discriminate among different schemes. For a year, daily forecast experiments were conducted over small domains centred on the five French metropolitan radio-sounding locations. Cloud base, planetary boundary-layer height and normalized vertical profiles of specific humidity, potential temperature, wind speed and cloud condensate were compared with observations, and with each other. The framework allowed the behaviour of the different schemes in and above the boundary layer to be characterized. In particular, the impact of the entrainment/detrainment formulation, closure assumption and cloud scheme were clearly visible. Differences mainly concerned the transport intensity thus allowing schemes to be separated into two groups, with stronger or weaker updrafts. In the AROME model (with all interactions and the possible existence of compensating errors), evaluation diagnostics gave the advantage to the first group.

AROME-WMED, a real-time mesoscale model designed for the HyMeX special observation periods

Abstract. During autumn 2012 and winter 2013, two special observation periods (SOPs) of the HYdrological cycle in the Mediterranean EXperiment (HyMeX) took place. For the preparatory studies and to support the instrument deployment during the field campaign, a dedicated version of the operational convective-scale Application of Research to Operations at Mesoscale (AROME)-France model was developed: the AROME-WMED (West Mediterranean Sea) model. It covers the western Mediterranean basin with a 48 h forecast range. It provided real-time analyses and forecasts which were sent daily to the HyMeX operational centre to forecast high-precipitation events and to help decision makers on the deployment of meteorological instruments. This paper presents the main features of this numerical weather prediction system in terms of data assimilation and forecast. Some specific data of the HyMeX SOP were assimilated in real time. The forecast skill of AROME-WMED is then assessed with objective scores and compared to the operational AROME-France model, for both autumn 2012 (05 September to 06 November 2012) and winter 2013 (01 February to 15 March 2013) SOPs. The overall performance of AROME-WMED is good for the first HyMeX special observation period (SOP1) (i.e. mean 2 m temperature root mean square error (RMSE) of 1.7 °C and mean 2 m relative humidity RMSE of 10 % for the 0–30 h forecast ranges) and similar to those of AROME-France for the 0–30 h common forecast range (maximal absolute difference of 2 m temperature RMSE of 0.2 °C and 0.21 % for the 2 m relative humidity); conversely, for the 24–48 h forecast range it is less accurate (relative loss between 10 and 12 % in 2 m temperature and relative humidity RMSE, and equitable threat score (ETS) for 24 h accumulated rainfall), but it remains useful for scheduling observation deployment. The characteristics of parameters, such as precipitation, temperature or humidity, are illustrated by one heavy precipitation case study that occurred over the south of Spain.

Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

AbstractThis 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.

Impact of Stochastic Physics in a Convection-Permitting Ensemble

A stochastic physics scheme is tested in the Application of Research to Operations at Mesoscale (AROME) short-range convection-permitting ensemble prediction system. It is an adaptation of ECMWF’s stochastic perturbation of physics tendencies (SPPT) scheme. The probabilistic performance of the AROME model ensemble is found to be significantly improved, when verified against observations over two 2-week periods. The main improvement lies in the ensemble reliability and the spread–skill consistency. Probabilistic scores for several weather parameters are improved. The tendency perturbations have zero mean, but the stochastic perturbations have systematic effects on the model output, which explains much of the score improvement. Ensemble spread is an increasing function of the SPPT space and time correlations. A case study reveals that stochastic physics do not simply increase ensemble spread, they also tend to smooth out high-spread areas over wider geographical areas. Although the ensemble design lacks surface perturbations, there is a significant end impact of SPPT on low-level fields through physical interactions in the atmospheric model.

Assimilation of water‐vapour airborne lidar observations: impact study on the COPS precipitation forecasts

AbstractThe Convective and Orographically‐driven Precipitation Study (COPS) carried out in summer 2007 over northeastern France and southwestern Germany provided a fairly comprehensive description of the low‐troposphere water‐vapour field, thanks in particular to the deployment of two airborne differential absorption lidar systems. These lidar observations were assimilated using the 3D‐Var assimilation system of the Application of Research to Operations at MEsoscale (AROME) numerical weather prediction mesoscale model. The assimilation was carried out for the period 4 July–3 August by running a three‐hour forward intermittent assimilation cycle. First, the impact of the lidar observations was assessed by comparing the analyses with a set of more than 200 independent soundings. The lidar observations were found to have a positive impact on the analyses by reducing the dry bias in the first 500 m above ground level and by diminishing the root‐mean‐square error by roughly 15% in the first km. Then the impact of the lidar observations was assessed by comparing the precipitation forecasts (obtained with and without the lidar observations for the period 15 July–2 August) with the gridded precipitation observations provided by the Vienna Enhanced Resolution Analysis. In general, the impact was found to be positive but not significant for the 24 h precipitation and positive and significant for the 6 h precipitation, with an improvement lasting up to 24 h. Some selected case studies show that the improvement was obtained through a better depiction of convection initiation or through a more accurate positioning of the precipitation systems. Copyright © 2011 Royal Meteorological Society

Structure of a Warm Front: Helsinki Testbed Observations and Model Simulation

The purpose of this study is to document the structure of a warm front in northeast Europe, identify the effects that the Finnish coastline has on the evolution of the front, and investigate factors that influence the speed that the warm front moves at within, and above, the boundary layer. The warm front formed over Estonia, traveled northward across the Gulf of Finland, and then crossed the southern coastline of Finland. Surface-based measurements from the Helsinki Testbed are analyzed together with output from a high-resolution numerical weather prediction model, Application of Research to Operations at Mesoscale (AROME). During the early stages of development, the warm front interacted with a stationary baroclinic zone and, consequently, evolved into an S shape. As the front approached the southern coast of Finland, the temperature gradient at 1000 hPa increased, as it merged with a diabatically generated temperature gradient. At 1000 hPa, the front stalled at the coastline due to friction-enhanced convergence, while the front’s speed at 860 hPa was almost uniform and unaffected by the coastline. At both 860 and 1000 hPa, the front moved slower than the wind speed. Hence, the front’s movement had a propagating component that was directed in the opposite direction to that of the front’s movement. The distribution of the ageostrophic winds showed that the front’s propagation component was produced by the front’s secondary circulation and surface friction. These results highlight the importance of surface sensible heat fluxes and friction on the evolution and movement of warm fronts.

Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

Mesoscale Assimilation of Radial Velocities from Doppler Radars in a Preoperational Framework

Abstract This paper presents the results of a preoperational assimilation of radial velocities from Doppler radars of the French Application Radar la Météorologie InfraSynoptique (ARAMIS) network in the nonhydrostatic model, the Application of Research to Operations at Mesoscale (AROME). For this purpose, an observation operator, which allows the simulation of radial winds from the model variables, is included in the three-dimensional variational data assimilation (3DVAR) system. Several data preprocessing procedures are applied to avoid as much as possible erroneous measurements (e.g., due to dealiasing failures) from entering the minimization process. Quality checks and other screening procedures are discussed. Daily monitoring diagnostics are developed to check the status and the quality of the observations against their simulated counterparts. Innovation biases in amplitude and in direction are studied by comparing observed and simulated velocity–azimuth display (VAD) profiles. Experiments over 1 month are performed. Positive impacts on the analyses and on precipitation forecasts are found. Scores against conventional data show mostly neutral results because of the much-localized impact of radial velocities in space and in time. Significant improvements of low-level divergence analysis and on the resulting forecast are found when specific sampling conditions are met: the closeness of convective systems to radars and the orientation of the low-level horizontal wind gradient with respect to the radar beam. Focus on a frontal rainband case study is performed to illustrate this point.