290 likes | 530 Views
WSA Model and Forecasts. Nick Arge Space Vehicles Directorate Air Force Research Laboratory. WSA Coronal & Solar Wind Model. Schatten Current Sheet Model. PFSS Model. Solar Wind Model (e.g., 1D Kinematic model, ENLIL, HAF) (5-30 Rs to 1AU). 5-30 Rs. 2.5 Rs. Source Surface.
E N D
WSA Model and Forecasts Nick Arge Space Vehicles Directorate Air Force Research Laboratory
WSA Coronal & Solar Wind Model Schatten Current Sheet Model PFSS Model Solar Wind Model (e.g., 1D Kinematic model, ENLIL, HAF) (5-30Rs to 1AU) 5-30 Rs 2.5 Rs Source Surface Plot courtesy Sarah McGregor (BU/CISM)
PFSS+SCS MODEL (R = 5.0 R) Predicted Solar Wind Speed at 5.0 R (New Empirical Relationship ) WSA Model Coronal Output Coronal Holes km s-1 Coronal Field (5.0R) Where: fs = Magnetic field expansion factor. θb = Minimum angular distance that an open field footpoint lies from nearest coronal hole boundary (i.e., Angular depth inside a coronal hole)
IMF directed radially away from Sun. IMF directed radially toward from Sun. Solar Wind Speed and IMF Polarity in the Ecliptic Driven by Daily Updated Photospheric Field Maps
Predictions & Observations:Near Solar Maximum Solar Wind Speed Predictions & Observations IMF Polarity Predictions & Observations
Predictions & Observations Solar Wind Speed Predictions & Observations Solar Wind Speed Predictions & Observations
Missed Observed False Model Boston University Validation of WSA Event-Based Approach: (High Speed Events) Contingency Tables ( Owens et al., JGR 2005) • Validated 8 years of WSA predictions • Event-based approach: high speed enhancements (HSE): • Captures more than 72% of the observed HSE events • Most of the false HSEs are small • Missed HSEs: are small events or transients • Timing of HSEs shows no offset. Slight underestimation of magnitude of fastest events – probably due to transients
Solar Wind Model Driver: Photospheric Field Synoptic Maps Corrections that often need to be applied to photospheric field maps (depending on the observatory): • Line-of-sight fields need to be converted to radial orientation (including effects due to the Solar b angle). • Observational evidence suggests this is generally true except in strong active regions! • Monopole moment needs to be removed. • Polar fields need to be corrected and filled (when necessary). • Can use historical data for retrospective studies. • Field corrected (when necessary) for magnetic field saturation effects. • Flux transport processes (differential rotation, meridional flow, diffusion, etc.)
Modeling Results With & Without Polar Field Corrections Applied Polar Fields Not Corrected Polar Fields Corrected Derived Coronal Holes Derived Coronal Holes Solar Wind Speed Predictions (WSA Model) and Observations Poles NOT Corrected Poles Corrected
Monopole Moments in Synoptic Maps Split bi-polar Region Corresponding Negative polarity missing
Time Evolution of Photospheric & Coronal Features
NSO/SOLIS Coronal Field (5.0R) Photospheric Field & Coronal Hole Boundaries + /— = Outward/(Inward) Footpoint Field Polarity Coronal Holes Solar Wind Sources Near & Far From Active Regions WSA Model Predictions & Observations: CR2027 Observed & Predicted IMF Polarity Observed & Predicted Solar Wind Speed
NSO/SOLIS Coronal Field (5.0R) Photospheric Field & Coronal Hole Boundaries + /— = Outward/(Inward) Footpoint Field Polarity Coronal Holes Solar Wind Sources Near & Far From Active Regions WSA Model Predictions & Observations: CR2028 Observed & Predicted IMF Polarity Observed & Predicted Solar Wind Speed
NSO/SOLIS Coronal Field (5.0R) Photospheric Field & Coronal Hole Boundaries + /— = Outward/(Inward) Footpoint Field Polarity Coronal Holes Solar Wind Sources Near & Far From Active Regions WSA Model Predictions & Observations: CR2029 Observed & Predicted IMF Polarity Observed & Predicted Solar Wind Speed
Summary • The WSA model predicts ambient solar wind speed and IMF polarity 1-7 days in advance at L1. • Model validated using 8 years (~1 solar cycle) of predictions & the results are VERY encouraging. • 2) Careful handing of the input photospheric magnetic field data is essential for improving the predictive success of the model. In particular, • • Monopole moments. • • Polar fields. • • Radial field Assumption. • • Flux transport processes. • 3) The ability of the WSA model to successfully predict solar wind speed appears to be a function of the proximity of its source regions to strong active regions. That is • If the source region is close to (far from) a strong active region, then the model’s speed predictions are generally poor (good). • Possible reasons why the model performs less well when the solar wind source lies near an active region. • - Fields near active regions are not potential, as the WSA model assumes. • (MHD and/or Force Free coronal model could help here). • - The model assumes that the photospheric field is radial everywhere. • Observational evidence suggests this is generally true except in strong active regions! • (Direct measurement of radial fields needed in active regions). • - A different empirical solar wind speed relationship is required near active regions.
WSA Coronal - ENLIL MHD Solar Wind Model Coupling (A Joint AFRL-CISM Effort) Output of WSA MODEL (R = 21.5 R) ENLIL 3D MHD Solar Wind Model Coronal Field Strength Solar Wind Speed Output of ENLIL MODEL at 1AU
Coupled Model: PFSS+SCS Schatten Current Sheet Model (SCS): 2.5 – 21.5 R Solar Wind Model (e.g., 1D Kinematic model, ENLIL, HAF) 21.5 R Potential Field Source Surface Model (PFSS): 1.0 – 2.5 R 2.5 R Schatten, 1971; Wang and Sheeley 1995
Model Input Magnetic Field Measurements at the Photosphere LOS Disk Image: Magnetograms LOSB Field Remapped to Heliographic Coordinates LOSB Field Remapped to Heliographic Coordinates & Converted to Radial Courtesy Mount Wilson Solar Observatory
Boston University Validation of WSA ( Owens et al., JGR 2005) • Validated 8 years of WSA predictions • Mean Squared Error (MSE) • 3 day old magnetograms give optimal prediction • No systematic time lag • Skill scores low on average (<10%) Hypothetical Example MSE(A)< MSE(B) (Same for correlation coefficients) Courtesy Matt Owens (BU/CISM)
Validating Coronal Models Using Coronal Holes Short After Solar Maximum Solar Maximum Solar Minimum de Toma, Arge, and Riley (2005) MAS/SAIC
Predictions & Observations:Near Solar Minimum Solar Wind Speed Predictions & Observations IMF Polarity Predictions & Observations
Weighted mean of boundary values used to fill the poles. The weighting is function of inverse distance raised to some power. Boundary Values used to Fill Poles • Pole • Pole Equator A Technique For Filling Missing Polar Regions
Daily-Updated Synoptic Map With Poles Filled Pole filled using a “noisy” boundary.* Pole filled using a “trimmed” boundary.* *Note, the synoptic maps shown here are NOTfrom CR1921 or 1922 but illustrate well why filling the poles needs to be done very carefully!
Weighting Functions ~13º New Magnetogram (a) Latitude Longitude DAILY UPDATED MAP (b) Zhao Frame Method +90º (c) -90º 0º 347º 347º 347º 360º Merged Field Data Unmerged Field Data From Latest Magnetogram Latitude +90º Longitude DAILY UPDATED FRAME MAP -90º 360º 0º 347º FULL CARRINGTON MAP +90º Latitude -90º Longitude 360º 0º 250º 250º Cut from Previous Map DAILY UPDATED MAP Synoptic Map Types
Solar Wind Predictions Using Photospheric Field Maps With Different Grid Resolutions 5 Degree 2.5 Degree Solar Wind Speed Predictions & Observations Solar Wind Speed Predictions & Observations ICME ICME IMF Polarity Predictions & Observations IMF Polarity Predictions & Observations Arge et al. 2005 Arge et al. 2004
NSO/SOLIS + /— = Outward/(Inward) Footpoint Field Polarity WSA Model Predictions & Observations: CR2018 Observed & Predicted IMF Polarity Coronal Field (5.0R) Photospheric Field & Coronal Hole Boundaries Observed & Predicted Solar Wind Speed Coronal Holes