# An Optimization Framework to Improve 4D-Var Data Assimilation System Performance

arXiv: https://arxiv.org/abs/1308.1860 · HTML: https://mlsanity.com/paper/1308.1860/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2013.

## Built on by (most cited first, 2 of 2)

- [A Meta-learning Formulation of the Autoencoder Problem for Non-linear Dimensionality Reduction](https://mlsanity.com/paper/2207.06676/) (2022)
- [Weakly-Constrained 4D Var for Downscaling with Uncertainty using Data-Driven Surrogate Models](https://mlsanity.com/paper/2503.02665/) (2025)

## Abstract (arXiv)

This paper develops a computational framework for optimizing the parameters of data assimilation systems in order to improve their performance. The approach formulates a continuous meta-optimization problem for parameters; the meta-optimization is constrained by the original data assimilation problem. The numerical solution process employs adjoint models and iterative solvers. The proposed framework is applied to optimize observation values, data weighting coefficients, and the location of sensors for a test problem. The ability to optimize a distributed measurement network is crucial for cutting down operating costs and detecting malfunctions.