Abstract
ABSTRACTAn effort was made to objectively identify regions of parallel stripes in images recorded by the Spinning Enhanced Visible and InfraRed Imager instrument on board the Meteosat Second Generation geostationary satellites. Such a grating pattern can signal the presence of gravity waves which eventually may lead to atmospheric turbulence, a hazard to be dealt with in the daily routine work of aviation meteorologists. A pattern detection algorithm, based on Gabor filters plus subsequently applied grating cell operators, is implemented, adapted and tested. The method is shown to be capable of identifying grating patterns despite the unfavourable relation between the scale of the sought waves and the spatial resolution of the imagery. Some phenomena producing similar patterns (hence similar responses of the algorithm) have been identified: marine stratocumulus and regular arrangements of mountain ranges and valleys. If present in the image, they have the potential to considerably impact the analyses. The results for the 7.3 µm water vapour channel are particularly encouraging in this context as filtering the unwanted alarms is straightforward in this case.
Highlights
Gravity waves may occur in the atmosphere for various reasons and be spotted by several different sensors; see e.g. Ehard et al (2016) for comprehensive lists
Such a grating pattern can signal the presence of gravity waves which eventually may lead to atmospheric turbulence, a hazard to be dealt with in the daily routine work of aviation meteorologists
Experimentation with various sets of parameter combinations suggested the following one for application of the algorithm to Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) 0.6 μm imagery: γ=0.4, nmax 1⁄4 5, ρ 1⁄4 0:47; σ 1⁄4 0:4 Ã λ; the tests were run with six values of λ=2,. . .,7 image pixels, and eight values of Θ, namely π/16, 3π/16, . . ., 15π/16
Summary
Gravity waves may occur in the atmosphere for various reasons and be spotted by several different sensors; see e.g. Ehard et al (2016) for comprehensive lists.
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