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Resumo(s)
This study focuses on the dynamics of the Martian atmosphere, by studying mainly its winds and atmospheric waves. Using images from the Colour and Stereo Surface Imaging System (CaSSIS), we detected hundreds of images with clouds, first manually and, then, via a cloud detector trained on the manually detected cloud images. In the end, 473 cloud images were detected in the CaSSIS dataset. With these cloud images we built a CaSSIS cloud and wave catalogue and measured wind velocities, cloud altitudes and characterised atmospheric wave properties, with recourse to geographic information systems, like ArcGIS Pro. Clouds are predominantly detected in the northern hemisphere, likely due to a higher humidity, and during the northern and southern Spring equinoxes, possibly due to a higher efficiency in the ice sublimation cycle. The wind speed magnitudes measured were of the order of V ≈ 6.9±2.5 m/s, be it for average speeds along an image, be it for individual tracer pairs. The horizontal wavelengths, packet widths and packet lengths measured for detected atmospheric waves were of the order of the kilometer, and the wave orientations were, generally, lower than 45◦, with it not varying much between neighbouring wave packets, suggesting low wind direction variability for the scale of a CaSSIS image (≈ 10 km). Meanwhile, a dynamical characterisation of these waves yielded low intrinsic phase speeds and vertical wavelengths of the order of 100 m to more than 1 km. Cloud altitudes were also determined, with values between 10 and 30 km, consistent with H2O ice clouds, as established by previous studies. Next steps for this research include further cloud tracking and altitude measurements with greater seasonal and diurnal variability, as such results would be important in constraining current climate models better.
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Palavras-chave
Mars Clouds Gravity Waves CaSSIS Machine learning