Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020
Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020
EPSC Abstracts
Vol.14, EPSC2020-920, 2020, updated on 08 Oct 2020
Europlanet Science Congress 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Inclusion of scientific algorithms in MATISSE

Edoardo Rognini1,2, Angelo Zinzi1,3, Davide Grassi4, Alberto Adriani4, Alessandro Mura4, Maria Teresa Capria4, and the JIRAM team*
Edoardo Rognini et al.
  • 1ASI Space Science Data Center (SSDC), Via del Politecnico snc, 00133 Rome, Italy
  • 2INAF-OAR Osservatorio Astronomico di Roma, Via Frascati 33, 00078, Monte Porzio Catone (RM), Italy
  • 3Agenzia Spaziale Italiana (ASI), Via del Politecnico snc, 00133, Rome, Italy
  • 4INAF-IAPS Istituto di Astrofisica e Planetologia Spaziali, Via del Fosso del Cavaliere 100, 00133 Rome, Italy
  • *A full list of authors appears at the end of the abstract

MATISSE (Multi-purpose Advanced Tool for the Solar System Exploration) [1] is a tool that allows the visualization of observations from space missions and datasets derived from these observations on  a  three-dimensional  model  of  the  selected  target  body.  The  second  version  of  the  tool  (named MATISSE  2.0 –  will,  among  other  things,  include  algorithms developed  by  partner  research  teams;  in  this  work  we  focalize  our  attention  on  the  MATISSE inclusion of two codes developed for atmospheric retrieval and thermophysical modeling. The retrieval code is used for the analysis of the spectra provided by the JIRAM instrument (Jovian Infrared Auroral Mapper [2]) onboard the NASA’s Juno mission, whose main purpose is the study of the upper regions of Jupiter’s atmosphere in the 2-5 μm wavelength range and pressure up to 5-7 bar. The spectra provided by the instrument are processed with the retrieval code that calculates, for each pixel of a hyperspectral image, the chemical and physical parameters in the corresponding points of the  atmosphere  [3].  The  code  processes  all  pixels  of  a  hyperspectral  image,  so  parallelization  is convenient  in  order  to  reduce  the  computation  time;  this  is  possible  by  using  the  Python  language tools, which allow the execution of a code written in its own language (FORTRAN in this case) by providing  the  required  parallelization. As a further optimization step,  the  code has been converted into a Docker image to make it portable and easy to run on heterogeneous architectures. The second  code  included  in  MATISSE  is  a  thermophysical  model  that  calculates  the  surface temperature of airless bodies as function of thermal conductivity [4,5] and other physical properties; the calculated temperature can be compared with the measured ones, if any, in order to retrieve the thermal properties of the soil, or can be used to compute other temperature-dependent quantities. At the present time this code is going to be used for Mercury and Ceres and is almost ready to be included in MATISSE 2.0.

[1] Zinzi, A., et al. (2016), Astronomy & Computing, 15, 16-28
[2] Adriani, A., et al. (2017), Space Science Reviews, 213, 393-446
[3] Grassi et al. (2010), Planetary and Space Science, 58, 1265-1278
[4] Capria, M. T. et al (2014), Geophysical Research Letters, 41, 1438-1443
[5] Rognini et al. (2019), Journal of Geophysical Research,

JIRAM team:

Christina Plainaki, Giuseppe Sindoni et al.

How to cite: Rognini, E., Zinzi, A., Grassi, D., Adriani, A., Mura, A., and Capria, M. T. and the JIRAM team: Inclusion of scientific algorithms in MATISSE, Europlanet Science Congress 2020, online, 21 September–9 Oct 2020, EPSC2020-920,, 2020