Planetary atmosphere observation analysis

Abstract:

The loss of water from Mars to space is thought to result from the transport of water to the upper atmosphere, where it is dissociated to hydrogen and escapes the planet. Recent observations have suggested large, rapid seasonal intrusions of water into the upper atmosphere, boosting the hydrogen abundance. We use the Atmospheric Chemistry Suite on the ExoMars Trace Gas Orbiter to characterize the water distribution by altitude. Water profiles during the 2018–2019 southern spring and summer stormy seasons show that high-altitude water is preferentially supplied close to perihelion, and supersaturation occurs even when clouds are present. This implies that the potential for water to escape from Mars is higher than previously thought.

Abstract:

The origin of the red colouration in Jupiter's Great Red Spot (GRS) is a long-standing question in planetary science. While several candidate chromophores have been proposed, no clear conclusions have been reached regarding its nature, evolution, or relationship to atmospheric dynamics. In this work, we perform microphysical simulations of the reddish haze over the GRS and quantify the production rates and timescales required to sustain it. Matching the previously reported chromophore column mass and effective radius in the GRS requires column-integrated injection fluxes in the range 1×10⁻¹²–7×10⁻¹² kg m⁻² s⁻¹, under low upwelling velocities in the upper troposphere (v<inf>trop</inf>≲1.5×10⁻⁴ m s⁻¹) and particle charges of at least 20 electrons/μm. Such rates exceed the mass flux that standard photochemical models of Jupiter currently supply via NH<inf>3</inf>–C<inf>2</inf>H<inf>2</inf> photochemistry at 0.1–0.2 bar, the most popular chromophore pathway in recent literature. We find a lower limit of 7 years on the haze formation time. We also assess commonly used size and vertical distribution parameterisations for the chromophore haze, finding that eddy diffusion prevents the long-term confinement of a thin layer and that the extinction is dominated by particles that can be represented by a single log-normal size distribution.

Abstract:

The Lunar Trailblazer mission aimed to assess the presence of water on the lunar surface using imaging spectroscopy in visible shortwave infrared (VSWIR) coupled with high‐resolution multispectral imaging in thermal midwave‐infrared (MWIR), captured simultaneously over the same target from orbit around the Moon with two different instruments. Uncertainties in clock timing, instrument models, and instrument pointing knowledge manifest as geospatial offsets between the two data sets that must be corrected in post‐processing to enable co‐registration, tying the acquired images to their precise latitudes and longitudes on the Moon. This work describes an algorithmic approach to co‐registering and geolocalizing images after acquisition without high precision instrument and spacecraft pointing models, the Iterative Matching Pipeline for Post‐Acquisition Image Localization (IMPPAIL), utilizing previously acquired data for development. We use Lunar Orbiter Laser Altimetry (LOLA) and Kaguya data to make shaded relief maps as the basemap on which to project data. To test our processing pipeline prior to Lunar Trailblazer data collection, we use Moon Mineralogy Mapper (M3) data for VSWIR images and simulated MWIR images. When demonstrated on these data sets, IMPPAIL produces a 98% success rate registering VSWIR data to LOLA/Kaguya shaded relief maps and successfully co‐registered MWIR and VSWIR in all four simulation cases. We include a code package with software tools allowing this algorithm to be used for a variety of data sets across many other missions. The Lunar Trailblazer spacecraft was designed to map surface temperature and water content on the Moon via satellite imagery. Onboard are two imagers that measure thermal and visible light bands, which must be aligned to be used in conjunction with each other as well as located precisely relative to pre‐existing topography data. To do this, we develop an algorithm which iteratively matches key features between the images, allowing for the co‐registration of data points. We test the algorithm on topography and visible imagery data from a past satellite mission and simulated data for thermal images. We show 98% accuracy and success in both imagery wavelength bands. While this was originally created for use on the Lunar Trailblazer mission, the process is applicable to future missions requiring similar functionality, and we make the code available for other users. We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions