{"id":4396,"date":"2024-10-16T13:08:20","date_gmt":"2024-10-16T13:08:20","guid":{"rendered":"https:\/\/hygeos.com\/?p=4396"},"modified":"2024-10-16T13:10:07","modified_gmt":"2024-10-16T13:10:07","slug":"hygeos-at-the-aerocom-workshop","status":"publish","type":"post","link":"https:\/\/hygeos.com\/en\/hygeos-at-the-aerocom-workshop\/","title":{"rendered":"HYGEOS at the AEROCOM workshop"},"content":{"rendered":"
<\/span><\/div><\/div><\/div><\/div><\/div><\/div>

HYGEOS at the AEROCOM workshop<\/h1><\/div>

The AEROCOM workshop<\/a> in Lille (14th<\/sup> -18th<\/sup> October) is dedicated to the modelling and observations of aerosols. Many HYGEOS scientists participated presenting the latest results obtained in the operational CAMS2_35 project<\/a> and in the CAMAERA<\/a> project.<\/p>\n

Samuel Remy<\/a> presented first results (figure below) of long simulations aerosols with the global CAMS<\/a> system (IFS<\/a>-COMPO cycle 49R1) without data assimilation together with evaluation and trend analysis. Among other specificities, cycle 49R1 implements a coupling of stratospheric aerosols and chemistry. Such study is done in preparation of the next CAMS reanalysis whose production is expected to start in early 2025. His presentation is available here<\/a>.<\/p>\n<\/div><\/div><\/div><\/div>

\"Observed<\/span><\/div>

Observed (blue) and simulated PM2.5 over the US between 2003 and 2020. Simulations are from the CAMS reanalysis control run (without data assimilation) in green, and from preparatory simulations for next reanalysis, also without data assimilation, in red.<\/em><\/p>\n<\/div><\/div><\/div><\/div>

Nathan Capon<\/a> presented the use of neural network techniques, in the CAMAERA<\/a> project, to simulate whitecap fraction (figure below), a proxy for sea-salt aerosol emissions, in the global CAMS system (IFS-COMPO cycle 49R1). His presentation is here<\/a>.<\/p>\n<\/div>

\"Simulated<\/span><\/div>

Simulated whitecap fraction by IFS-COMPO on 1\/1\/2017 00:00UTC, using the operational A16 scheme (left) and with deep learning model enabled through the INFERO library (right).<\/i><\/p>\n<\/div><\/div><\/div><\/div>

<\/i>Go back<\/span><\/a><\/div><\/div><\/div><\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"

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