Abstract
Knowledge on the contribution of observations to forecast accuracy is crucial for the refinement of observing and data assimilation systems. Several recent publications highlighted the benefits of efficiently approximating this observation impact using adjoint methods or ensembles. This study proposes a modification of an existing method for computing observation impact in an ensemble-based data assimilation and forecasting system and applies the method to a pre-operational, convective-scale regional modelling environment. Instead of the analysis, the modified approach uses observation-based verification metrics to mitigate the effect of correlation between the forecast and its verification norm. Furthermore, a peculiar property in the distribution of individual observation impact values is used to define a reliability indicator for the accuracy of the impact approximation. Applying this method to a 3-day test period shows that a well-defined observation impact value can be approximated for most observation types and the reliability indicator successfully depicts where results are not significant.
Highlights
Maintaining an operational observing network is an intricate and expensive task
It is essential to evaluate the contribution of various components of the network and potential new observing systems to forecast accuracy. This contribution, traditionally referred to as observation impact, can in principle be evaluated by parallel numerical data denial experiments, often named observing systems experiment (OSEs) (e.g. Harnisch et al, 2011; Weissmann et al, 2011)
The first approximation method emerged in the framework of developing adjoint models and four-dimensional variational data assimilation systems: Baker and Daley (2000) described a method of propagating the forecast sensitivity to the observations (FSO), building upon earlier research that developed the sensitivity of a forecast aspect to the analysis (Langland and Rohaly, 1996)
Summary
Maintaining an operational observing network is an intricate and expensive task. It is essential to evaluate the contribution of various components of the network and potential new observing systems to forecast accuracy. Langland and Baker (2004) linked this FSO to the impact of observations and by this performed the last step for the adjoint approximation of the forecast impact of observations (referred to as forecast sensitivity observation impact or FSOI) Building upon these developments, several studies calculated the FSOI to assess the contribution of components of the operational observing network (Langland, 2005; Cardinali, 2009; Gelaro et al, 2010; Baker et al, 2014) or special field campaign observations (Weissmann et al, 2012).
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