Assessing the influence of the model trajectory in the adaptive observation Kalman Filter Sensitivity method

The purpose of adaptive observation strategies is to design optimal observation networks in a prognostic way. The implementation of such strategies is based on adaptive observation numerical techniques that provide guidance on where to deploy future additional observations. Most advanced techniques account for the dynamical aspects of the atmosphere and the data assimilation system (DAS). This study aims to assess the influence of the model trajectory on the Kalman Filter Sensitivity (KFS) method used at Meteo-France. KFS is an adjoint-based method identifying sensitive areas by means of a forecast score variance. Targeted observations are designed to reduce this score variance. In its first version, KFS was not able to deal with trajectory uncertainties. An ensemble-based approach is undertaken to investigate if it is possible to account for these uncertainties. We assess the robustness of the method regarding trajectory errors and propose a practical solution. To avoid high computational costs, a simplified framework is used. We perform experiments with a two-layer quasi-geostrophic model in an incremental 4D-Var system. KFS is computed to target a potential vorticity anomaly interacting with a simplified jet stream. Numerical experiments show that the impact of additional observations depends equally on the suboptimal formulation of the DAS components and the trajectory errors. When the KFS sensitivity fields are constrained by the DAS components, the sensitivity values are determined by the selection of the reference trajectory. A targeting strategy based on the ensemble mean sensitivity field is proposed. Experiments show this to be more efficient than a random targeting strategy. Copyright (c) 2011 Royal Meteorological Society