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

Michel, Yann; Auligné, Thomas; Montmerle, Thibaut

doi:10.1175/2011mwr3632.1

Cited by 65 publications

(94 citation statements)

References 65 publications

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“…2, is quite consistent with the findings of Michel et al (2011) andBrousseau et al (2012). This temperature background error shape in AROME-France for the lowest levels is due to the estimation of the error covariances, which occurred during a period with more anticyclonic and stable meteorological conditions than that of the AROME-WMED.…”

Section: Background Error Statisticssupporting

confidence: 86%

“…Several studies have indeed demonstrated the strong dependence of the background error statistics on weather regimes. For instance, using the Weather Research and Forecasting (WRF) model at 3 km resolution, Michel et al (2011) showed that the specific humidity standard deviation of the model background error is larger in rain affected areas than in non-rainy conditions.…”

Section: Background Error Statisticsmentioning

confidence: 99%

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

et al. 2015

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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.

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Section: Background Error Statisticssupporting

confidence: 86%

Section: Background Error Statisticsmentioning

confidence: 99%

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

et al. 2015

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“…The extension of the control variable to hydrometeors and the possible direct assimilation of quantities provided by polarimetric radars need to be considered in the future. Michel et al (2011) have estimated the covariance errors for hydrometeors, which is a first step in that direction.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Michel et al (2011) points out such problems and suggests the use of an ensemble of shortrange forecasts at high resolution to estimate the background-error covariances (including hydrometeor variables). They propose heterogeneous covariances as a way to separate rainy and nonrainy areas.…”

Section: Introductionmentioning

confidence: 99%

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

Wattrelot

Caumont

Mahfouf

2014

Monthly Weather Review

128

139

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This 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.

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“…In order to get insights into the processes that may be involved in NG development, the diagnostics have been separately computed for cloudy and for clear sky areas, following a similar approach to that of Montmerle and Berre (2010) and Michel et al (2011), in which precipitating masks have been used. Grid points over the domain are separated in two bins: "cloudy" or "clear-sky" points.…”

Section: Time Evolution Of Non-gaussianitymentioning

confidence: 99%

Diagnosing non-Gaussianity of forecast and analysis errors in a convective-scale model

Legrand

Michel

Montmerle

2016

Nonlin. Processes Geophys.

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Abstract. In numerical weather prediction, the problem of estimating initial conditions with a variational approach is usually based on a Bayesian framework associated with a Gaussianity assumption of the probability density functions of both observations and background errors. In practice, Gaussianity of errors is tied to linearity, in the sense that a nonlinear model will yield non-Gaussian probability density functions. In this context, standard methods relying on Gaussian assumption may perform poorly.This study aims to describe some aspects of nonGaussianity of forecast and analysis errors in a convectivescale model using a Monte Carlo approach based on an ensemble of data assimilations. For this purpose, an ensemble of 90 members of cycled perturbed assimilations has been run over a highly precipitating case of interest. NonGaussianity is measured using the K 2 statistics from the D'Agostino test, which is related to the sum of the squares of univariate skewness and kurtosis.Results confirm that specific humidity is the least Gaussian variable according to that measure and also that nonGaussianity is generally more pronounced in the boundary layer and in cloudy areas. The dynamical control variables used in our data assimilation, namely vorticity and divergence, also show distinct non-Gaussian behaviour. It is shown that while non-Gaussianity increases with forecast lead time, it is efficiently reduced by the data assimilation step especially in areas well covered by observations. Our findings may have implication for the choice of the control variables.

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

Cited by 65 publications

References 65 publications

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

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

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

Diagnosing non-Gaussianity of forecast and analysis errors in a convective-scale model

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