A Physics-Aware Neural Network Approach for Flow Data Reconstruction from Satellite Observations
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Date
2021-04-09
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
An accurate assessment of physical transport requires high-resolution and high-quality velocity information. In satellite-based wind retrievals, the accuracy is impaired due to noise while the maximal observable resolution is bounded by the sensors. The reconstruction of a continuous velocity field is important to assess transport characteristics and it is very challenging. A major difficulty is ambiguity, since the lack of visible clouds results in missing information and multiple velocity fields will explain the same sparse observations. It is, therefore, necessary to regularize the reconstruction, which would typically be done by hand-crafting priors on the smoothness of the signal or on the divergence of the resulting flow. However, the regularizers can smooth the solution excessively and will not guarantee that possible solutions are truly physically realizable. In this paper, we demonstrate that data recovery can be learned by a neural network from numerical simulations of physically realizable fluid flows, which can be seen as a data-driven regularization. We show that the learning-based reconstruction is especially powerful in handling large areas of missing or occluded data, outperforming traditional models for data recovery. We quantitatively evaluate our method on numerically-simulated flows, and additionally apply it to a Guadalupe Island case study—a real-world flow data set retrieved from satellite imagery of stratocumulus clouds.
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Publication status
published
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Book title
Journal / series
Volume
3
Pages / Article No.
656505
Publisher
Frontiers Media
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
deep learning–CNN; Karman vortex street; cloud motion winds; satellite wind data; wind velocity retrieval
Organisational unit
03420 - Gross, Markus (emeritus) / Gross, Markus (emeritus)
Notes
Funding
190296 - Flow Data Inpainting with Neural Networks (SNF)