Sensitivity to observations applied to FASTEX cases

Abstract

The concept of targeted observations was implemented during field experiments such as FASTEX, NORPEX or WSRP in order to cope with some predictability problems. The techniques of targeting used at that moment (adjoint-based or ensemble transform methods) lead to quite disappointing results: the efficiency of the additional observations deployed over sensitive areas did not turn out to remain consistent from one case to another. The influence of targeted observations on the forecasts could sometimes consist of strong improvements, or sometimes strong degradations. It turns out that the latter failure explains why the concept of optimal sampling arose. The efficiency of adaptive sampling appears to depend on the assimilation scheme that deals with the observations. It is then very useful to integrate the nature of the assimilation algorithm, as well as the deployment of the conventional network of observations (redundancy issues between targeted and conventional network) in the definition of the sensitive pattern to be sampled. Therefore, we chose the tool of the sensitivity to observations to allow us to test such an approach. The sensitivity to targeted observations (that utilizes the adjoint of the linearized NWP model and the adjoint of the assimilation operator) seems to be a suitable tool to obtain an insight into the tricky issue of the optimization of the sampling strategies. To understand better the intrinsic patterns and the influence of the 3D-Var assimilation scheme on the sensitive structures to be sampled, we present here some detailed results on a FASTEX targeting case. We focus on the dropsondes deployed by the Gulfstream IV (jet-aircraft) along its first flight during Intense Observing Period 17 that started on the 17 February 1997. The sensitivity to observation is used as a diagnostic tool for studing targeting from a critical point of view. It is shown that assimilation processes can have an important effect on the classical sensitivity fields, and particularly on their vertical extension. For example, in the studied case, the classical sensitivity fields remain at a lower level than 400 hPa, whereas the sensitivity to observations stretches up to 250 hPa. However, the maximum values can be found at approximately 700 hPa in both sensitivity fields. The studied case shows that the efficiency of observations depends not only on the sensitivity but also on the deviations between the observations and the background field. An example of the use of this diagnosis for comparing the relative efficiency of different kinds of observations is also presented. This work points out that it is very complicated to optimize the efficiency of adaptive observations, and that the assimilation of an entire set of observations (both conventional and adaptive network) needs to be considered.