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Join the M-WAVE team to develop a forward operator for land data assimilation. Job description Challenge: Understanding the climatic drivers of crop failure, monitoring crop growth and implementing
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
(GMAO), is a comprehensive Earth system model and data assimilation system. The GMAO assimilates aerosol optical depth (AOD) observations from space-borne (e.g. MODIS, VIIRS) and ground-based (AERONET
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 1 month ago
distribution on a global scale. This project will focus on developing a strategy to best utilize this data in a global atmospheric data assimilation framework. Activities that would be involved in this project
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in probabilistic and ensemble weather forecasting, deep learning for spatiotemporal data, uncertainty quantification, data assimilation, or related areas. Develop and rigorously evaluate AI-based
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, ocean data assimilation, and computational methods for understanding and predicting regional and coastal ocean circulation. This is a full-time position for 2 years, with the possibility of a 3rd year
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organization. About the position The Nansen Centre has long expertise in data assimilation (DA) and climate predictions, initially introducing the Ensemble Kalman Filter (EnKF) data assimilation method in
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of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric and hydrological observational datasets, data assimilation techniques
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as a member of the GHER contributing to the EU research project COMEDI in a consortium of 11 leading partners in the field of data assimilation and deep learning. A successful applicant will develop
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performance through validation and comparison with available measurements, assess uncertainty and limitations, and support data assimilation, initialization, forcing, downscaling, or model-selection activities
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modeling) and data assimilation and an interest in analyzing climate risks. We especially value applicants who are highly motivated to collaborate across more than one discipline and who have good