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CoSense4Climate

Financiado

Compressed Sensing for Climate: A Novel Approach to Localize, Quantify and Chara...

Compressed Sensing for Climate: A Novel Approach to Localize, Quantify and Characterize Urban Greenhouse Gas Emitters Climate change is a defining issue of our time. Without significant, rapid greenhouse gas (GHG) emission reductions, we will face unpredictable consequences for climate and life. To effectively reduce GHGs, the emissions must be a... ver más

30/06/2028

TUM

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-04-11

ERC-2022-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

0% 100% 100%

1 Participantes

TUM

2.00M€ | Lider

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Duración del proyecto: 62 meses Fecha Inicio: 2023-04-11
Fecha Fin: 2028-06-30

Líder del proyecto

TECHNISCHE UNIVERSITAET MUENCHEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Climate change is a defining issue of our time. Without significant, rapid greenhouse gas (GHG) emission reductions, we will face unpredictable consequences for climate and life. To effectively reduce GHGs, the emissions must be accurately quantified and unknown emitters found. However, measuring global GHG emissions is very challenging, and hence current emission inventories rely mostly on bottom-up calculations, which lack accuracy and the ability to detect unknown sources. In CoSense4Climate, I will use the powerful mathematical theory of compressed sensing (CS) to revolutionize atmospheric inversion. My goal is to develop a method to locate, quantify, and attribute GHG emitters with unmatched spatial resolution and accuracy. CS has been used with great success in signal and image processing by taking advantage of the fact that most signals contain redundancies. Using CS in combination with domain transformations, I will generate accurate high resolution emission fields and reveal unknown sources, yet require less data than conventional methods. I will develop a CS inversion framework not only for local sensor data, but also for satellite data, which, upon success, will lead to a breakthrough in monitoring urban GHG emissions globally. I am best suited to reach this goal. I have gathered a unique dataset 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 today’s most urgent problem: climate change.

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