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.
Uncertainty Quantification for Atmospheric Motion Vectors with Machine Learning
