{"id":276,"date":"2026-07-06T11:28:16","date_gmt":"2026-07-06T09:28:16","guid":{"rendered":"http:\/\/localhost:8888\/zenithal-blue\/?post_type=projects&p=276"},"modified":"2026-09-21T16:21:20","modified_gmt":"2026-09-21T14:21:20","slug":"dantex","status":"publish","type":"projects","link":"https:\/\/zenithalblue.com\/ca\/projects\/dantex\/","title":{"rendered":"DanteX"},"content":{"rendered":"\n

Summary<\/h2>\n\n\n\n

DANTEX is a project led by ECMWF and funded by ESA, aimed at unlocking the full scientific value of Copernicus Expansion Missions for numerical weather prediction and climate reanalyses. Copernicus features CIMR, CRISTAL and LSTM, and also wave spectra information from existing Sentinel-1. <\/p>\n\n\n\n

The project employs state-of-the-art methods, including machine learning elements that are particularly promising to represent complex processes involved in surface radiative transfer, with initial focus on the cryosphere, land, and ocean waves. <\/a><\/p>\n\n\n\n

A key challenge for the assimilation of microwave satellite observations is the presence of radio-frequency interference, which can degrade or invalidate measurements if not properly identified.<\/p>\n\n\n\n

Our contribution<\/h2>\n\n\n\n

Building on our work in RFI Scan, we contributed RFI analysis to support the project’s machine learning developments. During a focused engagement, we processed and delivered RFI detection datasets used to train AI models capable of identifying interference-affected observations within ECMWF’s coupled ocean-atmosphere data assimilation system.<\/p>\n\n\n\n

Our role was concentrated in time but specialized in nature, leveraging years of accumulated expertise in microwave RFI detection.<\/p>\n\n\n\n

Key activities:<\/p>\n\n\n\n