Denoising ozone concentration measurements with BAMS filtering

We propose a method for filtering self-similar geophysical signals infected by an autoregressive noise using a combination of non-decimated wavelet transform and a Bayesian model. In the application part, we consider separating the instrumentation noise from high frequency ozone concentration measurements sampled in the atmospheric boundary layer. The elicitation of priors needed to specify the statistical model in this application is guided by the well-known Kolmogorov K41-theory, which describes the statistical structure of turbulent high frequency scalar concentration fluctuations.