Climate Research, Earth Science, Environmental Sciences, Physics
Deep Learning, Dynamical Models, Geographic Information Systems, Machine Learning
Relevant Projects:

NOAA, UM Graham Institute

Ayumi Fujisaki-Manome

Assistant Research Scientist


Fujisaki-Manome’s research program aims to improve predictability of hazardous weather, ice, and lake/ocean events in cold regions in order to support preparedness and resilience in coastal communities, as well as improve the usability of their forecast products by working with stakeholders. The main question Fujisaki-Manome’s research aims to address is: what are the impacts of interactions between ice and oceans / ice and lakes on larger scale phenomena, such as climate, weather, storm surges, and sea/lake ice melting? Fujisaki-Manome primarily uses numerical geophysical modeling and machine learning to address the research question; and scientific findings from the research feed back into the models and improve their predictability. Her work has focused on applications to the Great Lakes, the Alaska’s coasts, Arctic Ocean, and the Sea of Okhotsk.

Areal fraction of ice cover in the Great Lakes in January 2018 modeled by the unstructured grid ice-hydrodynamic numerical model.