SOURCE-LINKED INTELLIGENCE

Compressed Sensing for Climate: A Novel Approach to Localize, Quantify and Characterize Urban Greenhouse Gas Emitters

CORDIS · observation · Publication date unknown

t with my fully automated differential column GHG network MUCCnet, the first of its kind. With my rich experience in applying computational fluid dynamics (CFD), solar-induced fluorescence (SIF), and machine learning (ML) for estimating GHG emissions, I will additionally create a high-resolution CFD-based atmospheric transport model, a satellite SIF-based urban CO2 biogenic flux model, and a ML method for source attribution based on ratios of GHG and air pollutant concentrations. CoSense4Climate will establish a new standard for GHG emission monitoring, and provide ground-breaking scientific methods to help solve one of todays most urgent problem: climate change. atmospheric science, urban greenhouse gas emissions, compressed sensing, computational fluid dynamics, solar-induced fluorescence, machine learning, sensor network, atmospheric measurements, atmospheric inverse modeling, atmospheric transport modeling, biogenic fluxes, particle dispersion modeling, ground based remote sensing, satellite remote sensing

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recordType

award

status

SIGNED

region

EU

value

1999848

unit

EUR