Download
notes from the data assimilation workshop n.
Skip this Video
Loading SlideShow in 5 Seconds..
Notes from the Data Assimilation Workshop PowerPoint Presentation
Download Presentation
Notes from the Data Assimilation Workshop

Notes from the Data Assimilation Workshop

0 Views Download Presentation
Download Presentation

Notes from the Data Assimilation Workshop

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. D. Baker (CU/LASP) D. Vassiliadis (NASA/GSFC) A. Klimas (NASA/GSFC) R. McPherron (UCLA) G. Siscoe (BU) N. Crooker (BU) H. Spence (BU) H. Singer (SEC/NOAA) T. Onsager (SEC/NOAA) N. Arge (SEC/NOAA) Notes from the Data Assimilation Workshop R.S. Weigel (CU/LASP) Knowledge Transfer and Empirical Modeling Team Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003

  2. Why and What is Data Assimilation? What Data Assimilation is not Key Challenges in Data Assimilation Key Challenges with respect to magnetospheric DA How magnetospheric DA differs from meteorological DA Data Assimilation Workshop Notes • CU/LASP held a data assimilation workshop after Space Weather Week • Copies of the talks are available at http://lasp.colorado.edu/cism/Data_Assimilation Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003

  3. Purpose of data assimilation is to combine measurements and models to produce best estimate of current and future conditions. Kalman filter is most often used as a method for data assimilation. It became popular because it is a recursive solution to the optimal estimator problem. (Only last time step of information needs to be stored.) Full implementation of Kalman is usually not possible. There is a growing field in the study alternatives. Lessons LearnedWhy and What is DA? • Data assimilation does not require a “physics-based’’ model. • AD ≠ DA (The Assimilation of Data is not necessarily Data Assimilation) Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003

  4. Challenges in DA • Analyzed field does not match a realizable model state • Non-uniform and sparse measurements • Observed variables do not match variables predicted by the model • Observing systems are diverse and subject to error, sometimes poorly known. Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003

  5. Very sparse measurements Diverse set of both forward and inverse models that are highly specialized and are expert in different areas. Challenges For Magnetospheric DA • How to combine forward models (MHD, particle pushing) with inverse models (empirical, stochastic). • How to integrate data with these models Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003

  6. Differences between SW and Ordinary Weather • SW is usually more concerned with unlikely events • Magnetosphere is strongly forced Knowledge Transfer and Empirical Modeling at the University of Colorado—LASP GEM Workshop, June 24, 2003