??Creating high resolution atmospheric data-based greenhouse gas budgets by supercomputing and machine learning (CHARM)?

Description of the granted funding

Developments in HPC will provide timely policy-relevant information on the use of atmospheric data for greenhouse gas (GHG) budgeting to support national inventories. LUMI resources will be applied to the use of satellite-based GHG observations in atmospheric inverse modelling of emissions and removals, with the aim of improving the spatial resolution of these models while reducing the computational time required, to increase the readiness for operational GHG systems and future high-intensity satellite observations. GHG source identification will be improved using novel satellite data and machine learning together with high-dimensional data cubes. Collaboration with NASA JPL, CSU and JAXA on the use of advanced mathematical tools and satellite data will improve GHG modelling and source categorisation in LUMI environment. Current GHG (CO2 and CH4) emissions and removals and their uncertainties will be estimated globally and at scales relevant for national decision making.
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Starting year

2025

End year

2027

Granted funding

Tuula Aalto Orcid -palvelun logo
469 008 €

Funder

Research Council of Finland

Funding instrument

Targeted Academy projects

Decision maker

Suomen akatemian muu päättäjä
18.12.2024

Other information

Funding decision number

364975

Fields of science

Geosciences

Research fields

Geotieteet