Abstract. Wind-tracking algorithms produce atmospheric motion vectors (AMVs)
by tracking clouds or water vapor across spatial–temporal fields. Thorough
error characterization of wind-tracking algorithms is critical in properly
assimilating AMVs into weather forecast models and climate reanalysis
datasets. Uncertainty modeling 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 in inverse modeling is
often cursory and commonly consists of a mixture of model fidelity, expert
knowledge, and need for expediency. The method presented in this paper
supplements existing approaches to error specification by providing an
error characterization module that is purely data-driven. Our proposed
error characterization method 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 modeling 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 modeling 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 provide accurate and useful error features of
the tracked wind.
A path integral methodology for obtaining thermodynamic properties of nonadiabatic systems using Gaussian mixture distributions
