Three results

1. Model constraintsand identifying nonlinear SW/M-I coupling effectsBob WeigelGeorge Mason University

2. Three results Conclusion: Detecting nonlinearities and coupling response effects is often complicated by model limitations. • Estimation of solar wind/magnetosphere coupling function • Seasonal dependence on responsiveness • Solar wind density and magnetosphere responsiveness

3. Result 1 G(t) is an averaged geomagnetic measurement centered on time t and S(t) is an average solar wind measurement centered on time t. • G(t) ~ S(t) • G(t) ~ S(t)*A(t) • G(t) = h0S(t)+h1S(t-1)+…+hTS(t-T) • If A(t) is correlated with S(t-1), S(t-2), … model (b) improvement over model (a) may be due to fact that (a) and (b) are poor approximations. • Boring result: as T is increased, “best-fit” S looks more like vBs

4. Result 2 Linear regression of 1-hour averages predicts only about 33% of actual semiannual variation. Model of M-I coupling is 3-hour average of geomagnetic index = 3-hour average of Bs. Is remainder explained by conductance effects? Change in reconnection efficiency?

5. Result 2 33% ~66% of variation explained when time history of Bs is included. ~75% when solar wind velocity is included In auroral zone, result is 50% of semiannual variation is explained by solar wind (up from 0%) [Weigel, 2007]

6. Result 3 • Many studies have looked at modifying input, S(t), in Burton equation dDst*/dt = -Dst*/t+ S(t) • Most recent finding is that modifying S(t) by Pdyn1/2 gives improvement • Others have looked at modifying t. • What if you don’t constrain to 1-D ODE?

7. Burton model is constrained to this response function Normalized Dst response Weigel 2010 vBs One finding is that Nsw modifies response efficiency, not Pdyn.