Background‐error covariances for a convective‐scale data‐assimilation system: AROME–France 3D‐Var

Brousseau, Pierre; Berre, Loïk; Bouttier, François; Desroziers, Gérald

doi:10.1002/qj.750

Cited by 129 publications

(138 citation statements)

References 33 publications

(41 reference statements)

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“…The AROME data assimilation scheme (3D-Var/AROME) was derived from the 3D-Var/ALADIN system (Fischer et al, 2005;Montmerle et al, 2007) and is similar in terms of incremental formulation (Courtier et al, 1994), observation operators, minimization method and data flow. Background-error covariances have been adapted to the higher resolution of AROME (Brousseau et al, 2011a) and are estimated from an ensemble-based method (Berre et al, 2006), built from a six-member ensemble of AROME forecasts carried out over two distinct 15 day periods. The two components of the horizontal wind, temperature, specific humidity and surface pressure are analyzed on the 2.5 km grid.…”

Section: Arome Numerical Weather Prediction Systemmentioning

confidence: 99%

“…First, the water-vapour profiles were grouped into corresponding model analysis times within a 3 h window.Then each analysis group was scanned to find profiles within a 25 km range to perform the horizontal grouping. This distance of 25 km corresponds to the horizontal correlation length-scale of AROME background-error covariances for temperature and specific humidity (Brousseau et al, 2011a). It represents the horizontal range of the background modification provided by an observation.…”

Section: Airborne Lidar Water-vapour Observationsmentioning

confidence: 99%

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

Bielli

Grzeschik

Richard

et al. 2011

Quart J Royal Meteoro Soc

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The 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.

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Section: Arome Numerical Weather Prediction Systemmentioning

confidence: 99%

Section: Airborne Lidar Water-vapour Observationsmentioning

confidence: 99%

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

Bielli

Grzeschik

Richard

et al. 2011

Quart J Royal Meteoro Soc

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“…Derivation of a reasonable and accurate representation of background error covariances is a major challenge for variational data assimilation schemes (VAR) (Brousseau et al 2011). In the absence of knowing the true atmospheric state, some basic assumptions are made to estimate the background error statistics.…”

Section: Introductionmentioning

confidence: 99%

Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions

Chen

Rizvi

Huang

et al. 2013

Meteorol Atmos Phys

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For variational data assimilation, the background error covariance matrix plays a crucial role because it is strongly linked with the local meteorological features, and is especially dominated by error correlations between different analysis variables. Multivariate background error (MBE) statistics have been generated for two regions, namely the Tropics (covering Indonesia and its neighborhood) and the Arctic (covering high latitudes). Detailed investigation has been carried out for these MBE statistics to understand the physical processes leading to the balance (defined by the forecasts error correlations) characteristics between mass and wind fields for the low and high latitudes represented by these two regions. It is found that in tropical regions, the unbalanced (full balanced) part of the velocity potential (divergent part of wind) contributes more to the balanced part of the temperature, relative humidity, and surface pressure fields as compared with the stream function (rotational part of wind). However, the exact opposite happens in the Arctic. For both regions, the unbalanced part of the temperature field is the main contributor to the balanced part of the relative humidity field. Results of single observation tests and six-hourly data assimilation cycling experiments are consistent with the respective balance part contributions of different fields in the two regions. This study provides an understanding of the contrasting dynamical balance relationship that exists between the mass and wind fields in high-and low-latitude regions. The study also examines the impact of MBE on Weather Research and Forecasting model forecasts for the two regions.

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“…The time step is 60 s. The model is provided with lateral boundary conditions by the operational global model "ARPEGE" from Météo-France. The initial conditions are generated with a 3DVAR data assimilation (Fischer et al, 2005;Brousseau et al, 2011).…”

Section: Impact On Weather Forecastmentioning

confidence: 99%

Generalization and application of the flux-conservative thermodynamic equations in the AROME model of the ALADIN system

Degrauwe¹,

Seity²,

Bouyssel³

et al. 2016

Geosci. Model Dev.

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Abstract. General yet compact equations are presented to express the thermodynamic impact of physical parameterizations in a NWP or climate model. By expressing the equations in a flux-conservative formulation, the conservation of mass and energy by the physics parameterizations is a builtin feature of the system. Moreover, the centralization of all thermodynamic calculations guarantees a consistent thermodynamical treatment of the different processes. The generality of this physics-dynamics interface is illustrated by applying it in the AROME NWP model. The physics-dynamics interface of this model currently makes some approximations, which typically consist of neglecting some terms in the total energy budget, such as the transport of heat by falling precipitation, or the effect of diffusive moisture transport. Although these terms are usually quite small, omitting them from the energy budget breaks the constraint of energy conservation. The presented set of equations provides the opportunity to get rid of these approximations, in order to arrive at a consistent and energy-conservative model. A verification in an operational setting shows that the impact on monthlyaveraged, domain-wide meteorological scores is quite neutral. However, under specific circumstances, the supposedly small terms may turn out not to be entirely negligible. A detailed study of a case with heavy precipitation shows that the heat transport by precipitation contributes to the formation of a region of relatively cold air near the surface, the so-called cold pool. Given the importance of this cold pool mechanism in the life cycle of convective events, it is advisable not to neglect phenomena that may enhance it.

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Background‐error covariances for a convective‐scale data‐assimilation system: AROME–France 3D‐Var

Cited by 129 publications

References 33 publications

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

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

Balance characteristics of multivariate background error covariances and their impact on analyses and forecasts in tropical and Arctic regions

Generalization and application of the flux-conservative thermodynamic equations in the AROME model of the ALADIN system

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