Now open for application
Closed for application
C42.CU-Alpha.12

Lunar IR/FIR Observations of Earth: Added Value for Climate and Weather

  • Reference person
    Ortolani
    Alberto
    (alberto.ortolani@cnr.it)
  • Host University/Institute
    Scuola Universitaria Superiore IUSS Pavia
  • Internship
    N
  • Research Keywords
    Earth Observation from the Moon
    Atmospheric modelling
    Data Assimilation
  • Reference ERCs
    PE10_2 Meteorology, atmospheric physics and dynamics
    PE10_14 Earth observations from space/remote sensing
    PE6_12 Scientific computing, simulation and modelling tools
  • Reference SDGs
    GOAL 13: Climate Action
  • Studente
  • Supervisor
  • Co-Supervisor

Description

The proposed PhD research project focuses on innovative approaches to Earth observation (EO) from a lunar platform and their exploitation in weather modelling. Its goal is to develop advanced methodologies to assess the scientific and operational impact of Medium and Far Infrared (MIR and FIR) observations of the Earth acquired from the Moon. The reference mission concept is the Lunar Earth Temperature Observatory (LETO), developed under the "Earth-Moon-Mars" Italian NRRP project. The concept is based on the ESA FORUM mission spectrometer (planned for launch in 2028), adapted to observe from the Moon, lacking the capability to spatially resolve Earth emission, but retaining full spectral resolution and enabling continuous observation of an entire Earth hemisphere.

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The project integrates atmospheric modelling, satellite observations, and advanced Data Assimilation (DA) techniques. It includes the development of observation operators based on fast radiative transfer models to simulate LETO data and assimilate them into global atmospheric models, testing different approaches. The aim is to quantify the contribution of FIR and MIR observations from the Moon to improving atmospheric state estimation and weather forecasting, also exploring synergies with existing and future EO platforms. Computational challenges related to DA will be addressed, including the use of artificial intelligence in atmospheric and radiative transfer models.

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Suggested skills:

Suggested Skills for this Research Topic

• Master’s degree in one of the following disciplines: physics, engineering, mathematics, computer  science

• Basic experience in developing and running numerical codes

• Basic knowledge of atmospheric physics and radiative transfer processes

• Knowledge of at least one programming languages among Fortran, C++, Python, Matlab

• Knowledge of the operating systems Linux and Windows or MacOS 

• Ability to work within a team

• Attention to detail and organizational skills

• Excellent interpersonal and communication skills

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Research team and environment

The PhD candidate will be based in Florence. The team consists of researchers from CNR (IFAC, INO, IBE), supported by colleagues of the Universities of Bologna and Basilicata, already cooperating in the context of projects and academic programs.

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