Thibaut Montmerle

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 Meteorologie 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 mont...

Heterogeneous Convective-Scale Background Error Covariances with the Inclusion of Hydrometeor Variables

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.

Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part I: Optimality Criteria and Application to Variance Filtering and Covariance Localization

AbstractIn 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 algori...

Toward a new cloud analysis and prediction system

What: Reviewing current and recent cloud analysis efforts and evaluating the state of the science, synergies, and collaborations in modeling clouds, 40 experts in cloud modeling, observations, and data assimilation met to move decisively toward a realization of cloud analysis systems for operational use. When: 1–3 September 2009 Where: Boulder, Colorado TOWARD A NEW CLOUD ANALYSIS AND PREDICTION SYSTEM

Linear Filtering of Sample Covariances for Ensemble-Based Data Assimilation. Part II: Application to a Convective-Scale NWP Model

AbstractIn Part I of this two-part study, a new theory for optimal linear filtering of covariances sampled from an ensemble of forecasts was detailed. This method, especially designed for data assimilation (DA) schemes in numerical weather prediction (NWP) systems, has the advantage of using optimality criteria that involve sample estimated quantities and filter output only. In this second part, the theory is tested with real background error covariances computed using a large ensemble data assimilation (EDA) at the convective scale coupled with a large EDA at the global scale, based respectively on the Applications of Research to Operations at Mesoscale (AROME) and ARPEGE operational NWP systems. Background error variances estimated with a subset of this ensemble are filtered and evaluated against values obtained with the remaining members, which are considered as an independent reference. Algorithms presented in Part I show relevant results, with the homogeneous filtering being quasi optimal. Heterogene...

A Tropical Squall Line Observed during TOGA COARE: Extended Comparisons between Simulations and Doppler Radar Data and the Role of Midlevel Wind Shear

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 in...

Optimization of the Assimilation of Radar Data at the Convective Scale Using Specific Background Error Covariances in Precipitation

AbstractThis 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...