HMI Team Workshop:
Local Helioseismology Pipelines Stanford University March 7-9, 2007 |
INPUTS expect calibrated Dopplergrams, photograms, line-depth?, of Uniform quality (whatever that means), mapped with a uniform plate scale to something close to the observed pixel resolution, with sufficient ancillary data to remap the photosphere and possibly detrend the data: image center location, plate scale, effective observing time, observer location and velocity inversion kernels solar model for fluid dynamic diagnostics (Model S) and for sound-speed inversions OUTPUT maps of inverted V,c as data segments of common records (along with errors, inversion coefficients for multiple data series with primary keys of Carrington Time (center of tracking time interval, keyed to equal intervals in central meridian longitude (geocentric or at SDO?) and Carrington longitude and latitude Each series characterized by different surface grid spacing, cadence, and depths fit parameters with errors for each set used for above (i.e. each tracked cube) Grid spacings (= 0.5 track cube diameter) selected from range (1deg, 2deg, 4º, 8deg, 16deg) approx - total of 3—5 series coverage to be complete within region such that outermost edge of tracked data within 80deg of disc center; grid locations appropriate integral multiples of spacing, with longitudes characterized by center of tracking interval Cadence TBD in range ~3hr - 15hr, fixed to simple fractions of Carrington rotation, e.g. 15deg of rotation, 5 deg of rotation... oberving interval twice cadence PROCEDURES tracking at rate for fixed model - either agreed-upon in advance, or one that minimzes variance in mean zonal velocity profile at surface (3 param in cos(lat)) over say first 3 rotations of HMI data (go back and redo); use Postel's projection, cubic convolution interpolation for now, but revisit. power spectra apodized with circular symmetry in k-space to 0 power at edge of region ring fits by two procedures (Haber, Basu), both subject to future refinement and modifications inversions by RLS and OLA for both fitting procedures, as above RESEARCH TASKS 1. Try using larger rings for getting down to tachocline (LONG-TERM) 2. Global fitting of power spectrum rather than individual ridges (") 3. Characterize results from both methods for fits to MDI Hi-Res data at grid sizes of 2-16deg, in time ranges 4-15 hrs in order to optimize selection of output tilings (SHORT-TERM 9/1) 4. Calculate 3-d inversion kernels and develop 3-d inversions (LONG-TERM?) 5. Forward calculations (LONG-TERM) 6. Inversions from artifical data of Stein et al., Hanasoge (SHORT-TERM?) TESTING/VALIDATION Run existing and pipeline code on MDI Hi-Res data in traditional environment and in DRMS TASKS/RESPONSIBLE PEOPLE port fastrack and powrspec - Bogart port ringfit11 (Basu style) - Bogart, Soares port ringfitaz (Haber style), include consolidation into single module - Haber, González provide FORTRAN binding to API for above - Suarez port SSW inversions - Haber port Basu inversions - Basu, Bogart Analyze Stein artificial data - ? Analyze Hanasoge artificial data - González Run characterization (research 3) - Bogart, Haber, Basu Invert large-rings data (reearch 1) - Hill SCHEDULE 1. FORTRAN binding 15 May (Suarez) 2. port MDI code 15 June (Bogart, Soares) 3. port GONG/JILA code 15 June (González, Haber) 4. port Yale code 1 July (Basu) 4.5 import MDI spectra to DRMS 15 May (Bogart) 5. verification 15 Aug (tutti) a. specification of tracking input, output 1 Apr (Bogart, Schou) b. specification and import of 1D kernels 1 Apr (Haber, Basu) c. specification and import of 3D kernels 1 Sep (Haber) d. specify & import model 1 May (Hill) e. strawman data product specification 1 Apr (Haber, Bogart) f. refine data product specification 1 Sep (") g. output keyword specifications 1 May (Bogart) h. artificial data analysis (Hanasoge) 1 Jun (González) i. artificial data analysis (Stein et al.) ? j. determine appropriate tracking rate suitable 15 Aug (Scherrer) for both rings and time-distance (and other?)