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IMITATE – Introducing Machine-learning Into Targeted Analysis for Terrestrial Ecosystems

UNIVERSITY OF LEICESTER (GB)

Summary

The proposed IMITATE project aims to address the following questions:

 

  • How well can machine learning methods emulate physical process-based land surface models, focused over Europe?
  • Can explainable AI techniques provide new insights into process understanding when combining land surface models and Earth Observation data
  • Are the learnt relationships between the modeled inputs and outputs consistent with those from Earth Observation data?

 

 

To do this the project will:

 

  • produce land surface model simulations from the JULES Earth System Model over Europe focusing on the carbon cycle
  • develop, train and evaluate machine learning models (emulators) against the simulated land surface parameters
  • use these emulators to investigate the complex emergent relationships and feedback to gain an increased understanding of the underlying Earth System processes and to test whether data from satellite-based essential climate variables (e.g. ESA-CCI) are consistent with the relationships learnt from the land surface models.
  • produce an Emulated-GPP (gross primary productivity) data product based on EO data, using the relationships learnt from the land surface model.


Information

Domain
Science
Prime contractor
UNIVERSITY OF LEICESTER (GB)
Subcontractors
  • UNIVERSITY OF READING (GB)