REVIEW 2 cited by
Data assimilation with model errors
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
Nudging is a data assimilation method amenable to both analysis and implementation. It also has the (reported) advantage of being insensitive to model errors compared to other assimilation methods. However, nudging behavior in the presence of model errors is little analyzed. This report gives an analysis of nudging to correct model errors. The analysis indicates that the error contribution due to the model error decays as the nudging parameter $\chi \to \infty$ like $\mathcal{O}(\chi^{-\frac{1}{2}})$, Theorem 3.2. Numerical tests verify the predicted convergence rates and validate the nudging correction to model errors.
Forward citations
Cited by 2 Pith papers
-
Data assimilation for slightly compressible flow
Nudging both velocity and pressure data into incompressible Navier-Stokes for slightly compressible flows produces exponential model error decay with O(H) residual and requires pressure nudging parameter scaling μ1 = O(1/H²).
-
Continuous Data Assimilation with Learned Surrogate Dynamics
Nudging with learned surrogate dynamics converges exponentially to an explicit error floor determined by surrogate error and observation noise, with training data requirements quantified for noise-free cases.
Discussion (0). Sign in to comment.