Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery

Abstract

Abstract-In this paper, we address the problem of estimating mesoscale dynamics of atmospheric layers from satellite image sequences. Due to the great deal of spatial and temporal distortions of cloud patterns and because of the sparse 3-D nature of cloud observations, standard dense-motion field-estimation techniques used in computer vision are not well adapted to satellite images. Relying on a physically sound vertical decomposition of the atmosphere into layers, we propose a dense-motion estimator dedicated to the extraction of multilayer horizontal wind fields. This estimator is expressed as the minimization of a global function including data and spatio-temporal smoothness terms. A robust data term relying on the integrated-continuity equation massconservation model is proposed to fit sparse-transmittance observations related to each layer. A novel spatio-temporal smoother derived from large eddy prediction of a shallow-water momentumconservation model is used to build constraints for large-scale temporal coherence. These constraints are combined in a global smoothing framework with a robust second-order smoother, preserving divergent and vorticity structures of the flow. For optimization, a two-stage motion estimation scheme is proposed to overcome multiresolution limitations when capturing the dynamics of mesoscale structures. This alternative approach relies on the combination of correlation and optical-flow observations in a variational context. An exhaustive evaluation of the novel method is first performed on a scalar image sequence generated by direct numerical simulation of a turbulent 2-D flow. By qualitative comparisons, the method is then assessed on a METEOSAT image sequence.

Hybrid particle image velocimetry with the combination of cross-correlation and optical flow method

“…Liu et al (2015) characterized by systems with a range of motion greater than 10 km, therefore satellite imagery is particularly useful when coupled with computer vision programs in order to calculate cloud velocity. However, the techniques were not well adapted to the spatial displacement, thus a method incorporating a correlation method and the optical flow method were used to calculate the cloud flow more accurately (Hèas et al, 2007). Steps were taken further by utilizing the optical flow method with early storm warning systems such as the Short-range Warning Intense…”

“…By utilizing the generality of the cross-correlation method, particle movement can be found for relatively large movements as well as small displacement, low density, and larger particle size. through the use of a variational optical flow (Hèas et al, 2007). Hence, the need for a hybrid method is of high priority.…”

Variational fluid flow measurements from image sequences: synopsis and perspectives

“…This model provides a valid invariance condition for altimetric imagery of compressible flows (Héas et al, 2007a) or for transmittance imagery of compressible fluids (Fitzpatrick, 1988). In cases where the assumption I ∝ ρdz holds, the ICE data model provides a way to take into account mass changes observed in the image plan by associating two-dimensional divergence to brightness variations and reads like equation (2).…”

Sub-grid physical optical flow for remote sensing of sandstorm

“…Recently, [7] incorporates Navier-Stokes equations in the regularization constraint. But this method needs long temporal acquistion at regular time intervals.…”

“…Classical motion estimation algorithms become weak when applied to scalar quantities transported by turbulent fluid. Only [7] inserts turbulence effects through the regularization function. But the flow equation itself does not incorporate any notion of turbulence.…”