- Implementation of microphysical model in GCM
- Dynamical behaviour of dust
- Implementation of new radiative properties for dust
- Building climatologies
Lead / Participants
BIRA-IASB ( AU )
In the study of atmospheres, numerical models are an essential part of data retrieval, analysis, and interpretation. A General Circulation Model (GCM) simulates the meteorological conditions (temperature, pressure, and winds) everywhere in the planet's atmosphere, throughout all the seasons.
Global Circulation modelling
Due to the nature of any observation of the Mars atmosphere, which is typically limited either in spatial or temporal resolution, GCM simulations can fill in these gaps to present a more complete picture. There are still many unknowns and limitations in GCM simulations which cause inaccuracies in the modelled fields, and many of the uncertainties are related to aerosols in the Mars atmosphere. For example, the size distribution and radiative properties of dust and water ice particles are not well known. Through better characterisation of dust from laboratory measurements, we can improve the GCM simulations, and therefore improve the a priori required for data retrieval. As dust is the condensation nuclei for water ice clouds, we can also improve the microphysical representation of these clouds in the model, which in turn will provide more realistic temperatures and global circulation.
We will develop a new approach for combining the individual data sets from satellite- and ground-based records into a single best estimate with associated uncertainty estimates. The assessment of uncertainties is often the most important and most complicated task, since it tells us if the results are statistically significant. Historically, based on the experience developed for the terrestrial atmosphere, a variety of techniques have been used to merge the uncertainties. Different Machine Learning (ML) tools will be developed to answer the following issues: How to compare and then combine data from different instruments observing the same location and time (for example NOMAD SO, LNO, and UVIS on ExoMars TGO)? How to combine instruments’ data operated at different epochs to reconstruct climatologies to study long term and climate variations?
Expected results and Impact