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Center For Applied and Computational Mathematics

Advanced Assimilation in the Chesapeake Bay

Chesapeake Bay

Faculty: Matthew Hoffman

Summary:

I am implementing the Local Ensemble Transform Kalman Filter (LETKF) on a ROMS model of the Chesapeake Bay. The LETKF is an advanced method for data assimilation and was developed by the Weather and Chaos group at the University of Maryland, College Park. I am applying it to the ChesROMS model, which is being developed by scientists at NOAA, University of Maryland, CRC (Chesapeake Research Consortium) and MD DNR (Maryland Department of Natural Resources). Identical twin experiments have shown that the LETKF quickly reduces the analysis error, even with a very sparse data set. In addition, the ensemble spread has been utilized to explore the seasonal variability of the Chesapeake Bay and to advise new observation locations. The H-operator (observation operator) for the Bay was developed so that observation could be assimilated in real locations. We are currently exploring the impact of forcing errors on the assimilation and ways to correct these errors using the assimilation. We have also begun work with assimilating real satelite and in situ data. We are also working with other scientists to derive salinity information from satellite temperature and ocean color measurements and assimilate these as well.

Publications:

  1. An Advanced Data Assimilation System for the Chesapeake Bay: Performance Evaluation, Hoffman, M. J., T. Miyoshi, T. Haine, K. Ide, C. W. Brown, and R. Murtugudde, J. Atmos. Oceanic Tech. In press.

  2. Remotely sensed estimates of surface salinity in the Chesapeake Bay: A statistical approach, Urquhart, E, M.J. Hoffman, B.F. Zaitchik, S. Guikema, and E.F. Geiger, Remote Sensing of the Environment, 123, 522-531, (2012).

Collaborators:

Raghu Murtugudde (University of Maryland)
Christopher Brown (University of Maryland)
Tom Haine (Johns Hopkins)
Darryn Waugh (Johns Hopkins)