The Role of Operational Constraints in Selecting

Adaptive observation strategies in numerical weather prediction aim to improve forecasts by exploiting additional observations, at locations which are themselves determined by the current state of the atmosphere. The role played by an inexact estimate of the current state of the atmosphere (i.e. error in the \analysis") in restricting adaptive observation strategies is investigated; necessary conditions valid across a broad class of modeling strategies are identiied for strategies based on linearised model dynamics to be productive. It is demonstrated that the assimilation scheme, or more precisely, the magnitude of the analysis error, is crucial in limiting the scope of application of dynamically based strategies. In short, strategies based on linearised dynamics require that analysis error is suuciently small that the model linearisation about the analysis is relevant to linearised dynamics of the full system about the true system state. Inasmuch as the analysis error depends on the assimilation scheme, the level of observational error, the spatial distribution of observations and model imperfection, so too will the preferred adaptive observation strategy. For analysis errors of suuciently small magnitude, dynamically based selection schemes will outperform those based only upon uncertainty estimates; it is in this limit that singular vector based adaptive observation strategies will be productive. A test to evaluate the relevance of this limit is demonstrated. 3