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Curriculum Vitae (pdf) Updated: October 2008
Research Statement (pdf)
Publications:
Hoffman, Matthew J., Raghu Murtugudde, Christopher W. Brown, and Steve Penny, An Advanced Data Assimilation System for the Chesapeake Bay. In preparation.
Hoffman, Matthew J., Eugenia Kalnay, James A. Carton, and Shu-Chih Yang, Use of Breeding to Detect and Explain Instabilities in the Global Ocean. (Submitted to GRL). pdf
Gibbons, Kathleen S., Matthew J. Hoffman, and William K. Wootters, Discrete Phase Space Based On Finite Fields. Phys. Rev. A 70, 062101 (2004). Abstract, pdf, ps
Research Projects:
Presentations:
Effects of Observational Data on an Advanced Data Assimilation System Chesapeake Modeling Symposium—May 13, 2008—Annapolis, MD
Ocean Data Assimilation Using an Ensemble Kalman Filter University of São Paulo—April 25, 2008—São Paulo, Brasil Center for Weather Forecasts and Climate Studies (CPTEC)—April 3, 2008—Cachoeira Paulista, Brasil National Institute of Space Research (INPE)—March 25, 2008—São Jose do Campos, Brasil
Ocean Instabilities Captured by Breeding on a Global Ocean Model International Union of Geodesy and Geophysics Meeting 2007—July 12, 2007—Perugia, Italy
Posters:
Ocean Instabilities Captured by Breeding on a Global Ocean Model American Meteorological Society Meeting—January 18, 2007—San Antonio, TX
Ocean Instabilities Captured by Breeding on a Global Ocean Model AGU Fall Meeting—December 11, 2006—San Francisco, CA
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I am currently working on 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 in NOAA, University of Maryland, CRC (Chesapeake Research Consortium) and MD DNR (Maryland Department of Natural Resources). This research is being advised directly by Raghu Murtugudde and Christopher Brown.
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. We are beginning work on developing the H-operator (observation operator) for the Bay so that we can transition to assimilating real observations. |
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In addition, I am part of a collaborative effort between CPTEC/INPE in Brazil and the University of Maryland to develop an operational global oceanic data assimilation using the LETKF and the MOM4 ocean model. The first phase of the project was completed in May 2008 and involved interfacing the LETKF with the MOM4 model. The MOM4 setup used has 1°x1° horizontal resolution with 50 vertical levels and is coupled to an ice model. Assimilating temperature, salinity, and current observations in identical twin experiments have shown a quick and asymptotic reduction on both analysis errors and the errors of the subsequent forecasts. Results from the first phase of this project were presented at the Brazilian Meteorological Congress and a short paper appeared in the conference proceedings. |
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My research has also included using the Modular Ocean Model (MOM), originally created by the Geophysical Fluid Dynamics Laboratory (GFDL) and modifying it to run breeding experiments. Breeding allows us to identify instabilities in the ocean and the shapes of prediction errors, among other things. In addition, we are running energetics on the bred vectors in order to diagnose the dynamic causes of these instabilities. My advisors for this project are James Carton and Eugenia Kalnay. Results have been presented at a number of conferences (see below) and a publication on the research has been submitted to GRL. |