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Poster
Variational Data Assimilation with a Learned Inverse Observation Operator
Thomas Frerix · Dmitrii Kochkov · Jamie Smith · Daniel Cremers · Michael Brenner · Stephan Hoyer

Tue Jul 20 09:00 AM -- 11:00 AM (PDT) @
in Poster Session 1 »

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.

Keywords: Sustainability and Environment

Author Information

Thomas Frerix (Technical University of Munich)
Dmitrii Kochkov (Google)
Jamie Smith (Google)
Daniel Cremers (TU Munich)
Michael Brenner (Google/Harvard)
Stephan Hoyer (Google Research)

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