Walter Meier

Senior Research Scientist, DAAC Scientist

About Walter

Walt Meier is a senior research scientist at NSIDC. His primary role is supporting the NASA Snow and Ice Distributed Active Archive Center (DAAC) at NSIDC as the DAAC scientist. In this role he provides scientific support to all NASA products at the NSIDC DAAC. His research focus is on sea ice remote sensing, particularly using passive microwave data, and tracking Arctic climate change.

He is the principal investigator for the NASA-funded Arctic Sea Ice News and Analysis (ASINA), and is a co-investigator on other NASA, NOAA, and NSF sea ice projects at NSIDC. He has participated in numerous collaborative activities, including National Academies committees, the International Arctic Science Committee (IASC) Cryosphere Working Group, the Interagency Arctic Research Policy Committee (IARPC), the World Climate Research Program (WCRP) Climate and Cryosphere (CliC) and Data Advisory Council (WDAC), the Global Cryosphere Watch, and the Submarine Arctic Science Program (SCICEX) Science Advisory Committee. Meier is lead author of the sea ice chapter of the annual NOAA Arctic Report Card. He also enjoys conveying information on Arctic sea ice and climate change to the public through media interviews, public lectures and seminars, and educational outreach.

Specialties

Sea ice, passive microwave remote sensing, climate data records of sea ice, Arctic climate change, climate change

Current Research

NSIDC DAAC Scientist: Working with other NSIDC DAAC on research-related activities to facilitate better use and understanding of DAAC data. This includes helping to develop new tools and services for data, providing science input to documentation and other data information, interacting with mission science teams, and assisting users on science issues regarding data.

Arctic Sea Ice News and Analysis (ASINA): The Arctic sea ice environment is rapidly changing. The NASA-funded ASINA tracks these changes in real-time with daily-update data, images, and statistics, as well as detailed analysis of conditions at least once per month. Antarctic sea ice is also regularly assessed. ASINA also manages the related Greenland Today website where Greenland seasonal melt and mass change are regularly tracked and analyzed.

Passive microwave sea ice climate data records: Passive microwave data provide one of the most valuable remotely-sensed climate indicators because of its long-term (since 1978), continuous, and consistent record of sea ice. These data include the heritage NASA sea ice concentration records from the NASA Team and Bootstrap algorithms. These have been adapted into the NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, which meets Climate Data Record criteria for documentation and reproducibility.

Enhancements to sea ice motion and age estimates, and application to Lagrangian tracking of properties: Sea ice is thinning and losing its older and thicker ice classes, as tracked by the NSIDC DAAC Sea Ice Age product. This project enhances and extends the current sea ice motion and sea ice age products through improved input data and implementation of new methods. The motion products are then used to track parcels of ice and atmospheric properties around the Arctic to investigate the evolution of those properties over time.

Advanced Microwave Scanning Radiometer 2 (AMSR2) sea ice products: The newest operating passive microwave sensor, AMSR2, on the JAXA Global Change Observation Mission - Water (GCOM-W) provides improved spatial resolution and advanced capabilities over the long-term heritage sensors. The potential for AMSR2 for new and improved sea ice concentration, motion, and age products is investigated through NOAA and NASA projects. Enhanced resolution techniques are investigated to further improve the utility of AMSR2 for sea ice.

Enabling analysis of heterogeneous, multi-source cryospheric data: The volume of cryospheric data is increasing exponentially. Two areas of rapid increase are synthetic aperture radar (SAR) and laser altimetry, such as observations from the NASA ICESat-2 mission. This project develops and applies machine learning techniques to autonomously interpret and classify different sea ice types. It takes advantage of new methodologies and new computing capabilities (e.g., cloud computing) to facilitate rapid analysis of the large data volumes.