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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/amt-2020-95
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2020-95
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 23 Apr 2020

Submitted as: research article | 23 Apr 2020

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This preprint is currently under review for the journal AMT.

Uncertainty Quantification for Atmospheric Motion Vectors with Machine Learning

Joaquim V. Teixeira, Hai Nguyen, Derek J. Posselt, Hui Su, and Longtao Wu Joaquim V. Teixeira et al.
  • Jet Propulsion Laboratory, California Institute of Technology, USA

Abstract. Wind-tracking algorithms produce Atmospheric Motion Vectors (AMVs) by tracking clouds or water vapor across spatial-temporal fields. Thorough error characterization (also known as uncertainty quantification) of wind-tracking algorithms is critical in properly assimilating AMVs into weather forecast models and climate reanalysis datasets. Uncertainty quantification should yield estimates of two key quantities of interest: bias, the systematic difference between a measurement and the true value, and standard error, a measure of variability of the measurement. The current process of specification of the errors input into inverse modelling is often cursory and commonly consists of a mixture of model fidelity, expert knowledge, and need for expediency. The methods presented in this paper supplement existing approaches to error specification by providing an error-characterization module that is purely data-driven and requires few tuning parameters. This paper proposes an error-characterization method that combines the flexibility of machine learning (random forest) with the robust error estimates of unsupervised parametric clustering (using a Gaussian Mixture Model). Traditional techniques for uncertainty quantification through machine learning have focused on characterizing bias, but often struggle when estimating standard error. In contrast, model-based approaches such as k-means or Gaussian mixture modelling can provide reasonable estimates of both bias and standard error, but they are often limited in complexity due to reliance on linear or Gaussian assumptions. In this paper, a methodology is developed and applied to characterize error in tracked-wind using a high-resolution global model simulation, and it is shown to adequately capture the error features of the tracked wind.

Joaquim V. Teixeira et al.

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Joaquim V. Teixeira et al.

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Latest update: 03 Jun 2020
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Short summary
Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite observations. Accurately characterizing the uncertainties in AMVs is essential in assimilating them into data assimilation models. We develop a machine learning based approach for error characterization which involves gaussian mixture model clustering and random forest using a simulation dataset of water vapor, AMVs, and true winds. We show that our method improves on existing AMV error characterizations.
Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite...
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