1 / 6

Notes from the Data Assimilation Workshop

Discusses key challenges in magnetospheric data assimilation, differences from meteorological assimilation, sparse measurements, and specialized models. Explores combining forward models with inverses and integrating diverse data sources.

lighty
Download Presentation

Notes from the Data Assimilation Workshop

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

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

More Related