Feasibility Studies to Use Contemporary Satellite Geodetic Observations Towards the Improvement of Great Lakes Monitoring and Forecasting via Assimilative Modeling

Session: 36. - Improving Model Predictions through Coupled System and Data Assimilation

Yuanyuan Jia, Ohio State University, Division of Geodetic Science, School of Earth Sciences, [email protected]
Philip Chu, NOAA/GLERL, [email protected]
CK Shum, School of Earth Sciences, Ohio State University, [email protected]
Mike Bevis, Ohio State University, Division of Geodetic Science, School of Earth Sciences, [email protected]
Dana Caccamise, NOAA/NGS, [email protected]
Christopher Winslow, Ohio Sea Grant College Program, [email protected]

Abstract

A constellation of multiple-platforms, multi-band active remote sensing satellites
including all-weather sensors dedicated for scientific research are already operational or
to be launched by space agencies including NASA, ESA, JAXA and industries. Many of
these measurements are available at near real-time, and with different spatio-temporal
resolutions/accuracies. They could be exploited to complement existing CoastWatch
and OSAT data sets, to improve Great Lakes environmental monitoring, to refine the
Great Lakes height datum for safe navigation, and to potentially enhance Lake
forecasting skills via assimilative hydrodynamic modeling using available near real-time
data sets with the ultimate objectives to benefit citizens living in the Great Lakes region.
The sensors include multi-platform radar/laser altimetry, radiometry, NOAA-CO-
OPS/NGS and OSU GPS network around the Great Lakes, and GRACE/GRACE-
Followon satellite gravimetry. The observables include far-field water level, wave height,
wind speed, lake ice extents/thickness, lake storage, large-scale snow storm evolutions,
GNSS meteorology, vertical datum and land motion, water color, wetland and estuary
hydrologic data, and potentially meteosunamis signals. This presentation reports early
results of feasibility studies to use these satellite-based observations to improve Great
Lakes monitoring, and to conduct data assimilative modeling experiments to assess
their respective roles to potentially enhance the Great Lakes Operational Forecasting
System.