Dr. Elizabeth Satterfield- April 30th

Data assimilation (DA) for NWP combines a short forecast (“prior”) with current observations to create an analysis, which serves several purposes: accurate initial conditions for long forecasts, boundary conditions for limited area models, input into real-time tactical decision aids, and the initial conditions for the short forecasts that become the prior for the next DA cycle.  Observation and forecast error statistics, represented by error covariance matrices, control how much information is drawn from the observations as well as how that information is spread to observation sparse locations.  Improving the models of these covariance matrices is the subject of much current work.

Start

April 30, 2019 - 4:00 pm

End

April 30, 2019 - 5:00 pm

Address

120 David L Boren Blvd, Norman, OK 73072   View map

Dr. Elizabeth Satterfield

April 30th

4pm NWC 1313

3:30 pm Refreshments

 

Title: Statistical Estimation Methods for Parameters of Data Assimilation Systems

 

Abstract:

Data assimilation (DA) for NWP combines a short forecast (“prior”) with current observations to create an analysis, which serves several purposes: accurate initial conditions for long forecasts, boundary conditions for limited area models, input into real-time tactical decision aids, and the initial conditions for the short forecasts that become the prior for the next DA cycle.  Observation and forecast error statistics, represented by error covariance matrices, control how much information is drawn from the observations as well as how that information is spread to observation sparse locations.  Improving the models of these covariance matrices is the subject of much current work.

This talk will outline the NAVDAS Hybrid 4DVar data assimilation system and show how innovation (observation minus forecast) and residual (observation minus analysis) statistics from the data assimilation system can be used to improve the error covariance models for future use.  Some relevant current work being conducted at NRL includes 1) optimizing static versus flow-dependent contributions in a hybrid data assimilation system, and 2) accounting for non-zero observation error correlations

 

Bio:

Elizabeth Satterfield has been a meteorologist in the Data Assimilation Section, Marine Meteorology Division, NRL since 2012.  She holds a B.S. degree in Applied Mathematics from Georgia Tech, a M.S. degree in Atmospheric and Oceanic Sciences from the University of Maryland, and a Ph.D. in Atmospheric Science from Texas A&M University.  She was the recipient of a Karles fellowship (2012), an Allan Berman publication award (2014) and an Editor’s Award for Monthly Weather Review (2017).  She currently serves as an Editor for Monthly Weather Review and the chair of the American Meteorological Society Committee on Probability and Statistics.  Her areas of expertise are ensemble diagnostics and predictability and ensemble data assimilation.  Her current research interests include optimizing hybrid data assimilation systems, investigating model uncertainty and assessing ensemble predictability.

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