1 C. “Nick” Arge Space Vehicles Directorate/Air Force Research Laboratory Kirtland AFB Magnetogram Mini-Workshop UCLA, CA April 2-3, 2009 The ADAPT Model ( A ir Force D ata A ssimilation for P hotospheric Flux T ransport)

2 Main Objective: Obtain high quality, reliable, and as “instantaneous” snapshots of the photospheric field distribution as possible. Incorporation of a Generalized Data Assimilation Module within a Global Photospheric Flux Transport Model Result: Improved, high quality “snapshots” of the photospheric field Improved modeling and forecasts of the Corona, Heliosphere, & Global Sun-Earth system. Motivation: The photospheric magnetic field is the sole large-scale observable and serves as a key driver to virtually all coronal and solar wind models. Accurate knowledge of the global photospheric field distribution is critical for obtaining the most reliable description of the state of the corona, heliosphere and by extension, the solar wind-magnetosphere interaction. Approach: rigorous data assimilation Improve the NSO time-dependant solar photospheric magnetic field flux transport model by incorporating rigorous data assimilation methods into it. Project team: Joseph Koller Jack Harvey Alex Fay AFRL Nick Arge (PI) Carl Henney Rich Compeau Shawn Young David Mackenzie LANL NSO

3 ADAPT Model A ir Force D ata A ssimilation for P hotospheric Flux T ransport Model Intelligent Front End (IFE)  Automatically imports best data from available GONG sites  Selects highest quality data based on variance or other criteria  Constructs datacube for assimilator Data Assimilator  Kalman Filter for optimal data Assimilation  Model variance (~fixed) required by Kalman Filter  NSO/Worden-Harvey flux transport model for flux evolution IMPACT: Improved modeling of the corona & heliosphere Purpose: To provide high quality and “instantaneous” snapshots of the global photospheric field distribution, which serve as critical input to coronal and solar wind models. GONG Observatories Modeling / Forecasting Applications California Hawaii Australia India Canary Islands Chile Intelligent Front End (IFE) Improved Solar Synoptic Maps Flare Models Data Assimilator Coronal & Solar Wind Models Global Magnetic FieldSolar Wind Import & Select Data Construct Datacube for Assimilator Magnetogram Variance Magnetic Field Initial Conditions Analysis Step: (combine observations with forecast) Forecast Step: (using model to calculate forecast) Observa tions WH flux transport model Assimilation Results

4 IFE Highlights: 1)Near real-time monitoring of GONG magnetic data  detect and download candidate observations 2)Select best available observations per desired cadence – GONG’s 10-minute averages comprised of one to ten 1-minute frames Prefer higher number for noise reduction – Up to 4 GONG observatories can simultaneously record magnetograms Some 10-minute averages may have more observations than others Signal variance may differ between the observatories Signal variance may differ in the frame Observations with low zenith angles may be preferred over those with high ones – Set flexible variance thresholds Generally, exclude observations with unacceptably high variance – Some with high variance may need to be included, e.g., polar fields 3)Perform data integrity checks – Detect when change between consecutive 10-minute averages exceeds a threshold – Ensure images are not streaked, smeared, etc 4)Prepare the datacube to be used as input to the data assimilator Intelligent Front End (IFE) importselectprepare The function of the Intelligent Front End is to import, select, and prepare the best available observations for data assimilation.

5 flux transport modelmagnetogram observations uncertainties. Function: Find best estimate of true global photospheric field state using flux transport model & magnetogram observations considering both of their uncertainties. Ensemble method Computes error statistics of non-linear models. Estimates poorly known model parameters. Parameter and state estimation simultaneously. Recursive steps of ensemble Kalman filter Calculate Kalman gain using observational and model uncertainties. Compute “assimilated state” by applying Kalman gain (nowcast) to innovation vector. Compute a forecast using the model. Repeat the cycle. State and covariance are updated leading to a best estimate of Photospheric flux. Model parameters. Uncertainties in both. Data Assimilator LANL Ensemble Kalman Filter

6 Current Effort  Integrating the LANL Kalman ensemble filter into the WH Harvey model.  GONG working on adding variance information as part of their 10 minute magnetogram data product.  Intelligent data grabber being developed will 1. Efficiently retrieve pertinent magnetograms from large GONG data archive. 2. Perform data integrity checks. 3. Prepare the datacube to be used as input to the data assimilator.  Model uncertainties being determined.  Adding metric parameter data to FITS output maps. Used to help validate the model. Used to help model keep track of key (cycle-dependent?) model parameters (e.g., meridional flow rates).

7Goals  Establish and quantify the improvement in the predicted solar magnetic field distribution using the ADAPT model over that obtained using more traditional approaches.  Establish how well the ADAPT model helps us model and predict the polar fields.  Attempt to better quantify model parameters (e.g., meridional flow and diffusion rates) and their variations in time. Use the new data assimilated maps in coronal and solar wind models and compare the results obtained (e.g., coronal holes, solar wind) using them with those obtained using older, more traditional maps.

8 Top: Traditional Magnetic flux synoptic map for Carrington rotation Bottom: The result of evolving the corrected synoptic map for Carrington rotation 1928 if we use all the different transport processes, add a small-scale background magnetic flux, and include daily magnetogram observations. Figures from Worden & Harvey, Solar Physics, 195, 247, The boxes labeled 1, 2, and 3 are referenced in the paper. Traditional NSO Carrington Map for CR1929 CR1928 Evolved and Updated to CR1929 Using the WH Model Worden & Harvey Flux Transport Model