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

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.

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

“…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.…”

“…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.…”

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

“…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.…”

“…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.…”

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

“…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.…”