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The Euro-Mediterranean Center on Climate Change (CMCC) is seeking a motivated scientist to join the development of the Scalable PaRallelised EArth Data Assimilation System (SPREADS), an ensemble Kalman filter built on the open-source DART framework. SPREADS assimilates millions of observations, including satellite radiances, and is evolving into a weakly coupled system for atmosphere, land, and ocean components. The position will initially focus on the assimilation of hyperspectral infrared radiances from new instruments such as IASI-NG, MTG-IRS, and CrIS, using RTTOV. Novel algorithmic approaches and the application of machine learning methods are strongly encouraged. The goal is to improve the representation of the atmospheric state and enhance forecast skill from the sub-seasonal to seasonal timescales. We are looking for a dynamic researcher with strong problem-solving skills, the ability to work independently as well as collaboratively, and a proactive attitude toward adapting to evolving priorities.
Job Responsibility:
Assimilate radiance measurements from new hyperspectral infrared instruments (IASING, MTG-IRS, CrIS) using the RTTOV forward operator
develop and test novel algorithms to enhance radiance assimilation
apply machine learning methods to improve the representation of atmospheric states
extend assimilation to additional satellite instruments beyond infrared, including microwave radiances, GNSS radio occultation, and imager data
Requirements:
PhD and University degree in Physics, Maths, Meteorology or related fields
At least 10-years relevant work experience
Good knowledge of satellite observations and data assimilation
Good computing skills are required including a good knowledge of the Unix/Linux environment, the Fortran and possibly the C++ language, Python and IDL
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