Colloquium: Henno Frericks (Space Flight)
26 augustus 2020 10:00 - Locatie: Meeting Room 1, Faculty of Aerospace Engineering, Kluyverweg 1, Delft
Atmospheric Characterisation of Jupiter using Polarimetric Data
Jupiter is the most visited outer solar system planet, but the exact variation in atmospheric properties along its disk remains largely a mystery. This is where polarimetry fits into the picture. Its added value to spectrometry by additionally measuring the polarisation degree and direction of light makes it a suitable remote sensing tool for the characterisation of planetary atmospheres. It can potentially be used to detect and characterise exoplanets as starlight is originally unpolarised while light reflecting from an object is not. The degree of polarisation is sensitive to the atmospheric properties and its coupling with the wavelength, phase angle and absorption are used to derive the approximate upper atmospheric structure of Jupiter. For this purpose, polarimetric observations of the Torino Polarimeter are compared to the results of a numerical model coded in Fortran. This numerical model uses a doubling-adding radiative transfer algorithm to simulate the polarisation properties of the designated atmospheric profile. The atmospheric profile consists of gas and aerosols, the latter modelled by spherical particles using Mie scattering theory. The numerical model results are processed and compared to the observations using a Matlab script. The particle properties are constrained by the observations using the wavelength filters, the implemented methane absorption and by using a variable cloud pressure and haze optical thickness.The numerical model results best matching the observations show higher altitude clouds in the higher polarisation degree regions known as the zones, and lower altitude clouds in the belts. The optical thickness of the haze layer turns out to be low or zero in the zones and higher in the belts. To better characterise Jupiter's atmospheric structure, several aspects relating to the observations and the numerical model have to be investigated in more detail in order to improve the matching of the two.