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3DFIRELAB

3D Fire LABoratory
Funder: European CommissionProject code: 892463 Call for proposal: H2020-MSCA-IF-2019
Funded under: H2020 | MSCA-IF-EF-ST Overall Budget: 160,932 EURFunder Contribution: 160,932 EUR
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Description

This project develops a simulation strategy aiming to create a 3D virtual fire lab that can model radiative transfer in open landscape scale vegetation fire. The end objective is to help improving fire monitoring Earth Observation (EO) products. It builds on an initial system designed by the Experience Researcher (ER) during a previous European Space Agency project, and the coupling of models of fire spread, atmospherics dynamics and radiative transfer developed by the hosts. It takes opportunity of (a) the wide experience of the beneficiary host organization in fire monitoring and modeling, (b) recent efforts conducted by host partners to improve atmospheric representation in radiative transfer model and fire effects in atmospheric model, and (c) the ER’s experience in the fire remote sensing community. Fire disturbance is parameterized in large scale atmospheric modeling (e.g. forecast model) via emission inventory based on EO products. A well-established approach is to use the Fire Radiative Power product (FRP) to estimate total fire energy emission and infer the associated fuel mass consumption and trace gas emission. So far, the conversion from emissive radiative energy to mass consumption is based on a linear relationship that has only been demonstrated for small scale fire and little evidence are currently present to validate it in the context of large-scale fire scenario. The simulation strategy proposed here aims to setup a tool able to study energy transfer in large-scale fires that will help us understand the roles of the flames and the plume to eventually evaluate their sensitivity in the fire emission FRP retrievals. While project results have potential high application in the atmospheric community, the training organized with the host and the two partners will provide a wide range of expertise in atmospheric dynamics, radiative transfer, image processing, fire modeling, data science that will enrich ER's career prospective.

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