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arXiv · 2102.11192

Variational Data Assimilation with a Learned Inverse Observation Operator

Thomas Frerix·Dmitrii Kochkov·Jamie A. Smith·Daniel Cremers·Michael P. Brenner·Stephan Hoyer
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Abstract

Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved into the future to make predictions. This principle is a cornerstone of large scale forecasting applications such as numerical weather prediction. As such, it is implemented in current operational systems of weather forecasting agencies across the globe. However, finding a good initial state poses a difficult optimization problem in part due to the non-invertible relationship between physical states and their corresponding observations. We learn a mapping from observational data to physical states and show how it can be used to improve optimizability. We employ this mapping in two ways: to better initialize the non-convex optimization problem, and to reformulate the objective function in better behaved physics space instead of observation space. Our experimental results for the Lorenz96 model and a two-dimensional turbulent fluid flow demonstrate that this procedure significantly improves forecast quality for chaotic systems.

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Thomas Frerix, Dmitrii Kochkov, Jamie A. Smith, Daniel Cremers, Michael P. Brenner, Stephan Hoyer. 2021-02-22. Variational Data Assimilation with a Learned Inverse Observation Operator. https://arxiv.org/abs/2102.11192

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