Interpolated Discrepancy Data Assimilation for PDEs with Sparse Observations

Abstract: Sparse sensor networks in weather and ocean modeling observe only a small fraction of the system state, which destabilizes standard nudging-based data assimilation. We introduce Interpolated Discrepancy Data Assimilation (IDDA), which modifies how discrepancies enter the governing equations. Rather than adding observations as a forcing term alone, IDDA also adjusts the nonlinear operator using interpolated observational information. This structural change suppresses error amplification when nonlinear effects dominate. We prove exponential convergence under explicit conditions linking error decay to observation spacing, nudging strength, and diffusion coefficient. The key requirement establishes bounds on nudging strength relative to observation spacing and diffusion, giving practitioners a clear operating window. When observations resolve the relevant scales, error decays at a user-specified rate. Critically, the error bound scales with the square of observation spacing rather than through hard-to-estimate nonlinear growth rates. We validate IDDA on Burgers flow, Kuramoto-Sivashinsky dynamics, and two-dimensional Navier-Stokes turbulence. Across these tests, IDDA reaches target accuracy faster than standard interpolated nudging, remains stable in chaotic regimes, avoids non-monotone transients, and requires minimal parameter tuning. Because IDDA uses standard explicit time integration, it fits readily into existing simulation pipelines without specialized solvers. These properties make IDDA a practical upgrade for operational systems constrained by sparse sensor coverage.