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 Composite InfraRed Spectrometer (CIRS) instrument onboard the Cassini spacecraft performed 8.4 million spectral observations of Titan at resolutions between 0.5–15.5 cm-1. More than 3 million of these were acquired at a low spectral resolution (SR) (13.5–15.5 cm-1), which have excellent spatial and temporal coverage in addition to the highest spatial resolution and lowest noise per spectrum of any of the CIRS observations. Despite this, the CIRS low-SR dataset is currently underused for atmospheric composition analysis, as spectral features are often blended and subtle compared to those in higher SR observations. The vast size of the dataset also poses a challenge as an efficient forward model is required to fully exploit these observations. Here, we show that the CIRS FP3/4 nadir low-SR observations of Titan can be accurately forward modelled using a computationally efficient correlated-k method. We quantify wavenumber-dependent forward modelling errors, with mean 0.723 nW cm-2sr-1/cm-1 (FP3: 600–890 cm-1) and 0.248 nW cm-2sr-1/ cm-1 (FP4: 1240–1360 cm-1), that can be used to improve the rigour of future retrievals. Alternatively, in cases where more accuracy is required, we show observations can be forward modelled using an optimised line-by-line method, significantly reducing computation time.