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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 16 minutes ago
, we are developing a 3-km resolution model for the Bering Sea-Chukchi Sea complex using the data-assimilative ECCO-Darwin ocean biogeochemistry state estimate to fully quantify and attribute abrupt
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, and assimilate the information into their project design/interpretation. Dissemination of studies: Interact on a regular basis with their faculty mentor and members of the laboratory to discuss
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. The work involves analysis of numerical dynamo simulations using quantitative metrics derived from the most recent global paleomagnetic reconstructions, and applying data assimilation to recover extreme core
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of ocean current (JCOPE) and wave (Wave Watch III) prediction models. Topic 5: Development of atmosphere–ocean data assimilation systems (one–three positions) Establishing an atmosphere–ocean data
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quantitative metrics derived from the most recent global paleomagnetic reconstructions, and applying data assimilation to recover extreme core dynamics. Your responsibilities: Assessing data-based models
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to account for uncertainties. Use data assimilation techniques to combine observational data with AI models, improving real time forecasting accuracy. Collaborate with hydrologists, climatologists, and data
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 7 hours ago
. Applications to consider extreme events such as floods, droughts, and wildfires are also encouraged. Research topics related to remote sensing data include surface data-assimilation, land surface model and
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substrate range and deeply rewired metabolism, enabling efficient substrate assimilation for sustainable production of chemicals from one-carbon (C1) substrates. You will combine adaptive laboratory evolution
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processes. Furthermore, combining radar-derived surface soil moisture maps with a land surface model using a data assimilation approach has the potential to produce daily root zone soil moisture estimates
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757