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A global sensitivity analysis of the convective-scale Application of Research to Operations at Mesoscale (AROME)model is performed in order to determine the most influential parameters on the forecast of different near-surface variables. For that purpose, the Morris method is applied to 21 parameters from six different physical and dynamical parametrization schemes, over different seasons. Results highlight a set of eight parameters with a noticeable influence on most variables, in particular 10m wind speed and precipitation forecasts. The sensitivity of parameter uncertainties is also examined on different spatio-temporal scales. A clear diurnal cycle of parameters influence is observed in summer, in close connectionwith the convective activity. In addition, the spatial distribution of parameters influence is mostly consistent with the underlying distribution of weather forecasts. A Sobol’ sensitivity analysis, based on surrogate models, mostly confirms Morris conclusions and highlights some interactions between parameters. [ABSTRACT FROM AUTHOR]

The AROME-EPS convection-permitting ensemble prediction system has been evaluated over the HyMeX-SOP1 period. Objective verification scores are computed using dense observing networks prepared for the HyMeX experiment. In probabilistic terms, the AROME-EPS ensemble performs better than the AROME-France deterministic prediction system, and a state-of-the-art ensemble at a lower resolution. The strengths and weaknesses of AROME-EPS are discussed. Here, impact experiments are used to study perturbation schemes for the initial conditions and the model surface. Both have a significant effect on the ensemble performance. The interactions between the perturbations of lateral boundaries, initial conditions and surface perturbations are studied. The consistency between initial and lateral perturbations is found to be unimportant from a meteorological point of view. Ensemble data assimilation is not as effective as a simpler surface perturbation scheme, and it is noted that both approaches could be usefully combined. [ABSTRACT FROM AUTHOR]

Convective-scale ensemble prediction systems (EPSs) are often initialized with downscaled initial condition perturbations (ICPs) from a global coarser EPS. Although downscaled ICPs have been shown to have a positive impact at short ranges, they cannot represent the uncertainty at small scales. Hence, there is a spin-up of around 9-12 h until the forecast perturbations develop realistic small-scale structures. On the other hand, ensemble data assimilation (EDA) is a common approach to obtain initial perturbations at all scales resolved by the numerical model. However, the high computational cost of EDA systems severely limits their size and their resolution. An alternative cheaper method to derive small-scale ICPs is considered here, based on a random sampling of themodel backgrounderror covariances. This article provides an evaluation of random and EDA-based IC perturbation methods against the baseline downscaling approach, in the framework of the pre-operational convective-scale EPS developed at Météo-France with the AROME-France model at a 2.5 km horizontal resolution. Small-scale IC perturbation methods are shown to significantly improve the short-range EPS performance for surface weather variables. For 2m temperature and 10m wind speed, random ICPs give as good results as the EDA, owing to the very short spin-up of random perturbations. Precipitation forecasts are also strongly improved during the first six forecast hours, especially when initial humidity perturbations are included. The sensitivity of the EPS performance to the EDA size, horizontal resolution and representation of model errors is discussed. It is found that a large fraction of the initial uncertainty can be properly described with an EDA of reasonable size and at a slightly coarser resolution than the EPS. [ABSTRACT FROM AUTHOR]

Abstract: Background‐error variances estimated from a small‐size ensemble of data assimilations need to be filtered because of the associated sampling noise. Previous studies showed that objective spectral filtering is efficient in reducing this noise, while preserving relevant features to a large extent. However, since such filters are homogeneous, they tend to smooth small‐scale structures of interest. In many applications, nonlinear thresholding of wavelet coefficients has proved to be an efficient technique for denoising signals. This algorithm proceeds by thresholding the wavelet coefficients of the noisy signal using an estimated threshold. This is equivalent to applying an adaptive local spatial filtering. A quasi‐optimal value for the threshold can be computed from the noise variance. We show that the statistical properties of the sampling noise associated with the estimation of background‐error variances can be used to calculate the noise level and the appropriate threshold value. This method is first applied to 1D academic examples, with emphasis on correlated and heterogeneous noises. This approach is shown to outperform the commonly used homogeneous filters, since it automatically adapts to the local structure of the signal. We also show that this technique compares favourably to a heterogeneous diffusion‐based filter, with the advantage of requiring less trial‐and‐error tuning. These results are next confirmed in a more realistic 2D problem, using the Arome‐France convective‐scale model. [ABSTRACT FROM AUTHOR]

Background-error length-scales are key components of the correlation model since they give an idea of the distance over which the influence of an observation extends. The length-scales can be estimated from an ensemble of perturbed forecasts, however the finite size of the ensembles in operational applications introduces a relatively large sampling noise on the estimated length-scales. We first give some insight into the statistical properties of the noise. On the one hand, the noise variance can be analytically expressed as a simple function of the length-scale and the ensemble size. On the other hand, it is shown that the spatial structure of the noise is relatively small-scale and is directly related to the heterogeneity of background error. Such spatial properties tend to support the use of local averaging techniques to remove the noise. This is explored with the application of homogeneous and heterogeneous averagings based on the integration of the diffusion equation. It is observed that both filters improve the accuracy of the length-scale estimates, with the heterogeneous filter being more efficient in the regions where the length-scales are small and rapidly varying. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Ensemble prediction systems at the convective scale are often under-dispersive. In order to alleviate this problem, a time-lagged ensemble can be created from ensemble forecasts initialized at different production times. While an equal-weight combination of lagged forecasts generally provides competitive results, this article introduces and discusses the efficiency of an objective weighting. The proposed approach is based on nonlinear Bayesian filtering, and the weights are determined online for each member according to the observation likelihood. The method is illustrated with short-range ensemble forecasts provided by the cloud-resolving AROME-France model. A time-lagged ensemble is then constructed from the current ensemble forecasts combined with older ensemble forecasts started 6 and 12 h earlier. It is first shown that the weighting scheme provides reasonable results, in particular it is able to detect differences in forecast quality due to different production times. The question whether these unequal flow-dependent weights can be successfully applied to the members of the time-lagged ensemble is then examined. Results indicate that the weighting does not lead to a noticeable gain in forecast quality. Possible reasons for this limited impact are discussed. [ABSTRACT FROM AUTHOR]