The ARPEGE/ALADIN limited area ensemble prediction system: sensitivity experiments using global singular vectors 

E. Hagel1*
1Hungarian Meteorological Service, Hungary
hagel.e@met.hu

By perturbing the initial state of a numerical weather prediction (NWP) model it is possible to take into account the impact of the errors in the initial conditions (the fully exact description of the initial state is not achievable due to observation errors, errors in the data assimilation techniques etc). Then the model is integrated from these different initial conditions forming an ensemble of numerical weather predictions. The spread of this ensemble provides useful information on the predictability of the atmospheric state and on the probability of different weather events. One possible way to create such an ensemble is to use the singular vector method [1] to perturb the initial conditions of the model. The aim is to find perturbations for a given initial state which grow most rapidly according to the chosen norm (e.g. total energy norm) focusing on a specific area (the optimization area) during a given time interval (optimization time). In this study sensitivity experiments were carried out in order to explore whether or not it is possible to optimize an existing global ensemble system (based on the French global NWP model ARPEGE) for Central Europe by changing only the optimization area and optimization time used for the global singular vector computations. With this purpose several optimization areas and times were defined and tested through case studies and longer test periods. Global ensemble forecasts were downscaled with a limited area NWP model (called ALADIN). Verification results show that the proper choice of the singular vector optimization domain and optimization time can increase the spread of the ensemble and (on average) improves the skill for the Central European area. This conclusion was found to be valid for the global forecasts and the limited area predictions (i.e. the simple downscaling of the global model) as well. 

References
 
[1] Buizza R, Tribbia J, Molteni F, Palmer T: Computation of optimal unstable structures for a numerical weather prediction model. Tellus 45A, 388-407(1993)