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Thibaut Montmerle

Abstract

This study focuses on the impact of using specific background error covariances in precipitating areas in the Application of Research to Operations at Mesoscale (AROME-France) numerical weather prediction (NWP) system that considers reflectivities and radial velocities in its assimilation system. Such error covariances are deduced from the application of geographical masks on forecast differences generated from an ensemble assimilation of various precipitating cases. The retrieved forecast error covariances are then applied in an incremental three-dimensional variational data assimilation (3D-Var) specifically in rainy areas, in addition to the operational climatological background error covariances that are used elsewhere. Such heterogeneous formulation gives better balanced and more realistic analysis increments, as retrieved from the assimilation of radar data. For instance, midlevel humidification allows for the reinforcement of the low-level cooling and convergence, the warming in clouds, and high-level divergence. Smaller forecast error horizontal lengths explain the smaller-scale structures of the increments and render possible the increase of data densities in rainy areas. Larger error variances for the dynamical variables give more weight to wind observations such as radial winds. A reduction of the spinup is also shown and is positively correlated to the size of the area where rainy forecast error covariances are applied. Positive forecast scores on cumulated rain and on low-level temperature and humidity are finally displayed.

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Thibaut Montmerle and Claudia Faccani

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.

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Thibaut Montmerle and Yvon Lemaître Lemaître

Abstract

The present study is devoted to a new analysis of wind measurements from dropsonding and/or radiosonding of Doppler information from multiple Doppler radar scanning and of other wind measurements (sodar, dynamical sensors on board aircraft, and instruments at ground) aimed at retrieving three-dimensional thermodynamical and dynamical fields both in clear air and in precipitating areas of mesoscale phenomena. This analysis, well suited to assimilate data from differing platforms specified at differing spatial/temporal resolutions, is based on the analytical and variational concept of the Multiple Analytical Doppler (MANDOP) analysis and thus is an extension of it. This new analysis presents many advantages, including the same as MANDOP and others well adapted for the verification or the initialization of a mesoscale cloud model. An application to simulated and to real data extracted from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment database is presented in the paper.

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Thibaut Montmerle, Alain Caya, and Isztar Zawadzki

Abstract

A new method based on four-dimensional variational radar data assimilation into a cloud-resolving model has been developed for nowcasting purposes. This method allows for the retrieval of the model prognostic variables that compose the initial state of the simulation. The echo-free regions are filled by a 3D wind analysis from single-Doppler data based on linearity of the horizontal wind components in a moving reference frame, which provides a realistic mesoscale flow that is in better agreement with the air circulation retrieved from dual-Doppler observations within the precipitating regions. Furthermore, the near-ground refractivity index of air derived from ground targets is used to diagnose a high-resolution and two-dimensional distribution of relative humidity in the mixed layer. Two experiments are performed: one uses multiple-Doppler information coming from McGill University's bistatic radar network and the second considers only single-Doppler observations. This updated algorithm has been applied to a shallow hailstorm and shows very encouraging skill in predicting the short-term evolution of this convective system. The time evolution of the storm is captured well, and a significant improvement is noticed when compared with the nowcasting method based on Lagrangian persistence. When compared with the results obtained with the bistatic network, results when a single-Doppler radar is used show weaker capability to forecast the radial velocity than the precipitation pattern but still give a better forecast than the Lagrangian persistence method does.

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Thibaut Montmerle, Alain Caya, and Isztar Zawadzki

Abstract

An analysis is developed to initialize a cloud-resolving model from an explicit structure of a precipitating convective system derived from multiple-Doppler radar observations. The different fields of the model prognostic variables that compose the initial state of the simulation are estimated or retrieved using a 4DVAR assimilation method in which the model is used as a weak constraint using two time level observations. This allows for the retrieval of physical fields consistent with the observations and the equations of the model.

This method is applied on a midlatitude summer storm sampled by the McGill bistatic Doppler radar network that occurred on 2 August 1997. During the 30-min-forward simulation, the model succeeds in representing the observed features of the three main cells that compose the storm in terms of precipitation distribution and evolution of the convective activity. After this period, the model produces less stratiform precipitations. Comparisons with a Lagrangian persistency prediction are performed and show a notable improvement in the short-term forecast.

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Yann Michel, Thomas Auligné, and Thibaut Montmerle

Abstract

Convective-scale models used in NWP nowadays include detailed realistic parameterization for the representation of cloud and precipitation processes. Yet they still lack advanced data assimilation schemes able to efficiently use observations to initialize hydrometeor fields. This challenging task may benefit from a better understanding of the statistical structure of background errors in precipitating areas for both traditional and hydrometeor variables, which is the goal of this study. A special binning has been devised to compute separate background error covariance matrices for precipitating and nonprecipitating areas. This binning is based on bidimensional geographical masks defined by the vertical averaged rain content of the background error perturbations. The sample for computing the covariances is taken from an ensemble of short range forecasts run at 3-km resolution for the prediction of two specific cases of convective storms over the United States. The covariance matrices and associated diagnostics are built on the control variable transform formulation typical of variational data assimilation. The comparison especially highlights the strong coupling of specific humidity, cloud, and rain content with divergence. Shorter horizontal correlations have been obtained in precipitating areas. Vertical correlations mostly reflect the cloud vertical extension due to the convective processes. The statistics for hydrometeor variables show physically meaningful autocovariances and statistical couplings with other variables. Issues for data assimilation of radar reflectivity or more generally of observations linked to cloud and rain content with this kind of background error matrix formulation are thereon briefly discussed.

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Thibaut Montmerle, Jean-Philippe Lafore, and Jean-Luc Redelsperger

Abstract

Results from a three-dimensional cloud model are extensively compared with airborne Doppler radar data in the case of a tropical oceanic squall line observed during the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The comparison is based on the precipitation patterns, the dynamical and thermodynamical distributions, and the vertical transport of horizontal momentum.

The model simulates the evolution of the mesoscale convective system (MCS) frontal convective line from a quasi-linear to a broken pattern. The area located south of the “break,” which designates the region where the MCS leading edge reorientates from the N–S to the E–W direction, is composed of a pronounced bow-shaped structure with two vortices located on both sides of a strong rear inflow.

The vertical circulation is characterized by a jump updraft and an overturning downdraft. Both structures exhibit a vertical, intense updraft in the break zone, whereas the jump updraft is more sloped and less intense in the bow region. Front-to-rear momentum is injected mainly by the jump updraft. Both observations and simulation indicate the major role played by convective eddies in the vertical transport of cross-line and parallel-line horizontal momentum.

A synthesis summarizes the complex three-dimensional structure of the simulated system, based on three salient features and their relative locations: the deep convection region, the leading edge of the cold pool, and the melting area. The relative positions between the two last mentioned explains the observed asymmetric structure and the existence of more upright and narrow updrafts in the northern part of the system. Numerical experiments suggest that the wind profile at midlevel is mainly responsible for the location of the melting area relative to the cold pool. The system tends to generate new convective elements organized along the direction that reduces the angle between the convective line and the midlevel shear vector.

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Benjamin Ménétrier, Thibaut Montmerle, Yann Michel, and Loïk Berre

Abstract

In data assimilation (DA) schemes for numerical weather prediction (NWP) systems, the estimation of forecast error covariances is a key point to get some flow dependency. As shown in previous studies, ensemble data assimilation methods are the most accurate for this task. However, their huge computational cost raises a strong limitation to the ensemble size. Consequently, covariances estimated with small ensembles are affected by random sampling errors. The aim of this study is to develop a theory of covariance filtering in order to remove most of the sampling noise while keeping the signal of interest and then to use it in the DA scheme of a real NWP system. This first part of a two-part study presents the theoretical aspects of such criteria for optimal filtering based on the merging of the theories of optimal linear filtering and of sample centered moments estimation. Its strength relies on the use of sample estimated quantities and filter output only. These criteria pave the way for new algorithms and interesting applications for NWP. Two of them are detailed here: spatial filtering of variances and covariance localization. Results obtained in an idealized 1D analytical framework are shown for illustration. Applications on real forecast error covariances deduced from ensembles at convective scale are discussed in a companion paper.

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Alain Caya, Stéphane Laroche, Isztar Zawadzki, and Thibaut Montmerle

Abstract

A 3D wind analysis based on single-Doppler data is proposed using mass conservation and assuming a linear horizontal wind field, which is constant in a moving reference frame. Data over an assimilation period that includes several volume scans are employed, allowing the retrieval of the full linear wind field, including vorticity. The method proposed here can be considered an extension of the volume velocity processing (VVP) procedure. The robustness of the method is examined in detail and a criterion on the condition number is obtained. The method is tested in the context of synthetic data, which respect the simplified model assumptions. Simulated data from a high-resolution numerical weather prediction model are used to assess the impact of errors in the simplified model. The results indicate that 1) the analysis improves as the assimilation period is lengthened up to 1 h, 2) the best results are obtained when the radar is surrounded by precipitation and is in the middle of the analysis domain, and 3) vorticity is the most sensitive parameter. The addition of a vertical smoothing constraint is shown to be beneficial for the minimization and improves the results.

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Jean-François Caron, Yann Michel, Thibaut Montmerle, and Étienne Arbogast

Abstract

Following the recent development of a three-dimensional ensemble–variational (3DEnVar) data assimilation algorithm for the AROME-France NWP system, this paper examines different approaches to reduce the sampling noise in the ensemble-derived background error covariances in this new scheme without modifying the background ensemble generation strategy. We first examine two variants of scale-dependent localization: one method consists of applying different amounts of localization to different ranges of background error covariance spatial scales, while simultaneously assimilating all of the available observations. Another separate approach uses time-lagged forecasts in order to increase the effective ensemble size, up to a factor of 3 here. This approach of time-lagged forecasts is considered both on its own and together with scale-dependent localization. When the background error covariances are derived from the most recent 25-member ensemble forecasts, the results from data assimilation cycles over a 33-day winter period show that avoiding cross covariances between scales in the scale-dependent localization formulation first proposed by Buehner performs better than the more recent formulation of Buehner and Shlyaeva. However, when increasing the effective ensemble size to 75 members with time-lagged forecasts, the two scale-dependent formulations provide similar forecast improvements overall. It is also found that the lagged-members approach outperforms scale-dependent localization on its own. The largest forecast improvements are obtained when combining the two approaches.

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