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Machine learning based uncertainty quantification for wind-tracking algorithms (Conference Presentation)
Author(s): Joaquim Teixeira; Hai Nguyen; Hui Su; Derek Posselt

Paper Abstract

Wind-tracking algorithms produce Atmospheric Motion Vectors (AMVs) by tracking water vapor across spatial-temporal fields. Thorough error characterization of wind-track algorithms, otherwise known as uncertainty quantification, is critical in properly assimilating their produced AMVs into forecast models. Uncertainty quantification has two key quantities of interest: accuracy— the systematic difference between a measurement and the true value, and precision— a measure of variability of the measurement. Traditional techniques for uncertainty quantification through machine learning have focused on characterizing accuracy but often struggle when estimating precision. By pairing a random forest algorithm with unsupervised parametric clustering (using a Gaussian Mixture Model), we propose a machine learning based method of building uncertainty models characterizing both accuracy and precision using limited experimental data. In particular, we develop a Gaussian Mixture Model to cluster the principle quantities of interest in our training dataset— water vapor, measured AMVs, and true wind speed— into discrete regimes each with a distinct precision and accuracy. Concurrently, we train a random forest to predict true wind speed given the outputs of a wind-tracking algorithm, which works to model some of the extreme error in the algorithm. Combining these, we build a model which can place a retrieved AMV into a distinct regime with a characterized accuracy and precision.

Paper Details

Date Published: 12 September 2019
PDF
Proc. SPIE 11127, Earth Observing Systems XXIV, 1112705 (12 September 2019); doi: 10.1117/12.2529718
Show Author Affiliations
Joaquim Teixeira, Jet Propulsion Lab. (United States)
Hai Nguyen, Jet Propulsion Lab. (United States)
Hui Su, Jet Propulsion Lab. (United States)
Derek Posselt, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 11127:
Earth Observing Systems XXIV
James J. Butler; Xiaoxiong (Jack) Xiong; Xingfa Gu, Editor(s)

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