Abstract
Abstract. Ideally, a validation and assimilation scheme should maintain the physical principles embodied in the model and be able to evaluate and assimilate lower dimensional features (e.g., discontinuities) contained within a bulk simulation, even when these features are not directly observed or represented by model variables. We present such a scheme and suggest its potential to resolve or alleviate some outstanding problems that stem from making and applying required, yet often non-physical, assumptions and procedures in common operational data assimilation. As proof of concept, we use a sea-ice model with remotely sensed observations of leads in a one-step assimilation cycle. Using the new scheme in a sixteen day simulation experiment introduces model skill (against persistence) several days earlier than in the control run, improves the overall model skill and delays its drop off at later stages of the simulation. The potential and requirements to extend this scheme to different applications, and to both empirical and statistical multivariate and full cycle data assimilation schemes, are discussed.
Highlights
Data assimilation deals with the optimal combination of observations and a model forecast, or background field, into an analysis field that forms the basis for the forecast (Daley, 1992)
Similar to four-dimensional (4-D) data assimilation, this procedure produces results that are consistent with internal model physics and dynamics and avoids forcing unrealizable model states
The impact of the one step assimilation is evident throughout the entire 16day simulation, it is most dramatic during the first week
Summary
The observed data that are assimilated do not match the variables predicted by the model, requiring parameterizations that are not consistent with the data or the physics These issues arise when the validation and assimilation schemes do not maintain the physical principles embodied in the model and are unable to evaluate and assimilate lower dimensional features (e.g., discontinuities) contained within a bulk simulation that are not directly observed or represented by model variables. The resultant assimilation and validation scheme is compatible with state of the science methods and is capable of handling lower dimensional features in a bulk simulation This scheme addresses issues (i), (ii), and (iii) above. We conclude with some thoughts about extending this method to a full assimilation cycle and to statisticalestimation data assimilation systems
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