Ashley Payne

By |

I am interested in using observations, reanalysis, and numerical modeling to investigate the impacts, characteristics and climatology of extreme precipitation events. I use process-based studies for model evaluation and employ simple experiments to develop a mechanistic understanding of weather extremes and their interaction with climate.

A snapshot of an atmospheric river using MERRA-2 reanalysis. The shading shows total precipitable water (cm) and the contours show the magnitude of integrated vapor transport. Atmospheric rivers (ARs) are narrow and elongated pathways of anomalously strong horizontal water vapor transport in the lower portions of the atmosphere. They are found over most major ocean basins and are generally visible in integrated water vapor imagery poleward of the tropics. Their characteristics, such as landfall location, intensity, and duration, vary on intraseasonal and interannual timescales. This variability directly affects populations through impacts to water resources, hydrological extremes, and potential to contribute to compound hazards, such as the incidence of mudslides in regions recently impacted by wildfire.

Andrea Thomer

By |

Andrea Thomer is an assistant professor of information at the University of Michigan School of Information. She conducts research in the areas of data curation, museum informatics, earth science and biodiversity informatics, information organization, and computer supported cooperative work. She is especially interested in how people use and create data and metadata; the impact of information organization on information use; issues of data provenance, reproducibility, and integration; and long-term data curation and infrastructure sustainability. She is studying a number of these issues through the “Migrating Research Data Collections” project – a recently awarded Laura Bush 21st Century Librarianship Early Career Research Grant from the Institute of Museum and Library Services. Dr. Thomer received her doctorate in Library and Information Science from the School of Information Sciences at the University of Illinois at Urbana‚ÄźChampaign in 2017.

Matthew Johnson-Roberson

By |

Matthew Johnson-Roberson, PhD, is Assistant Professor of Naval Architecture and Marine Engineering and Assistant Professor of Electrical Engineering and Computer Science, College of Engineering, the University of Michigan, Ann Arbor.

The increasing economic and environmental pressures facing the planet require cost-effective technological solutions to monitor and predict the health of the earth. Increasing volumes of data and the geographic dispersion of researchers and data gathering sites has created new challenges for computer science. Remote collaboration and data abstraction offer the promise of aiding science for great social benefit. Prof. Johnson-Roberson’s research in this field has been focused on developing novel methods for the visualization and interpretation of massive environments from multiple sensing modalities and creating abstractions and reconstructions that allow natural scientists to predict and monitor the earth through remote collaboration. Through the promotion of these economically efficient solutions, his work aims to increase access to hundreds of scientific sites instantly without traveling. In undertaking this challenge he is constantly aiming to engage in research that will benefit society.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.

Traditional marine science surveys will capture large amounts of data regardless of the contents or the potential value of the data. In an exploratory context, scientists are typically interested in reviewing and mining data for unique geological or benthic features. This can be a difficult and time consuming task when confronted with thousands or tens of thousands of images. The technique shown here uses information theoretic methods to identify unusual images within large data sets.