Work

Data Assimilation with Sparse Observations

Year: 2026

Type: preprint

Abstract: Data assimilation by nudging (also called CDA) yields exponentially decaying errors and an infinite predictability horizon if the method parameter is large enough and the observations are frequent enough in time and dense enough in space. We consider the complementary case of moderate parameters and sparse and infrequent observations. We prove that assimilation with any data and any(positive) parameter strictly decreases errors and strictly increases the (now finite) predictability horizon.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.06901

Authors: Jiang, Nan, Layton, William, Raghunathan, Nanda Nechingal, Yang, Troy

Institutions: University of Florida, University of Pittsburgh

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Unsteady Reynolds averaged Navier-Stokes models and computational fluid dynamics, CAREER: New Algorithms and Models for Turbulence in Incompressible Fluids

OpenAlex ID: W7212023481

Cites: 1

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Related to: 10