Abstract
Adjoint models calculate the first order sensitivity of a scalar output parameter to an input vector. Adjoint numerical weather prediction models have been used for a variety of sensitivity and data assimilation studies to provide a gradient for a measure of error with respect to the model’s analysis variables. Recent work has shown that the adjoint of the data assimilation system can map the gradient information in analysis space onto individual observations to provide a quantitative estimate of an observation’s influence on short-term forecast error. This chapter will review the framework of an adjoint observation impact system and some reported applications. Aspects of the framework particular to limited area atmospheric models will be the main focus of this chapter and results from a specific system will be presented. Issues discussed include: the effect of horizontal grid spacing on observation impact, the influence of lateral boundaries on forecast error, the relative importance of observations for different physical locations, and appropriate error metrics for limited area forecast models.
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Notes
- 1.
COAMPS®;  is a registered trademark of NRL.
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Acknowledgements
This work was supported by the US Office of Naval Research’s program element 0601153N. Computational resources of the Department of Defense High Performance Computing Modernization Program were vital to this work.
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Amerault, C., Sashegyi, K., Pauley, P., Doyle, J. (2013). Quantifying Observation Impact for a Limited Area Atmospheric Forecast Model. In: Park, S., Xu, L. (eds) Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. II). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35088-7_6
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