1 / 23

Influence of solar wind density on ring current response

Influence of solar wind density on ring current response. Previous Results. Chen et al. 1994, Jordanova et al., 1998 and others – N ps contributes to the RC Borovsky 1998 – N sw pulses lead to response at geosynchronous. Thomson 1998 – N ps , D st * correlation

eliza
Download Presentation

Influence of solar wind density on ring current response

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. Influence of solar wind density on ring current response

  2. Previous Results • Chen et al. 1994, Jordanova et al., 1998 and others – Nps contributes to the RC • Borovsky 1998 – Nsw pulses lead to response at geosynchronous. • Thomson 1998 – Nps, Dst* correlation • Smith et al., 1999 – Dst has Nsw dependence that is independent of Esw at 3 hour time lag • O’Brien et al., 2000 – With more storms, no independent Dst dependence on Nsw • Lopez et al., 2004 – High compression ratio leads to higher reconnection rate • Boudouridis et al., 2005 – Dynamic pressure and geoefficiency • Lavraud 2006 – CME and CIR storms had larger response when CME or CIR was preceded by Bz>0

  3. Related Results • Including Nsw in neural network filter improves predictions a small amount • Adding Pdyn to coupling function in various ways leads to small improvements in average prediction efficiency • Pdyn, which depends on Nsw, may modify dayside reconnection rate. Event studies support this

  4. Problems • Conflicting or ambiguous results in statistical studies • use multiple statistical approaches and use as much data as possible • There is evidence of an effect, primarily in event studies • Identify location of events in distribution of events (not addressed here) • Uniqueness problem in driver– different processes have different input drivers, but give about the same improvement in statistics • use very simple driver and test hypothesis that other drivers give statistically different result • Uniqueness problem mode - same as above • look at perturbations of simple linear model • Bias problem – most storms have large solar wind density • use geoefficiency

  5. Not addressed: is change in geoeff due to energy showing up somewhere else?

  6. Approach • Look for changes in geoefficiency – how much output you get for a given input • Define geoefficiency in a number of ways: • Integral analysis – compare integrated input to integrated output for many events. Efficiency is slope of integrated output to integrated input. • Epoch averages – compute epoch averages first and then perform integral analysis on these curves. Efficiency is ratio of integrated epoch average of input to integrated epoch average output. • Linear filter model – derive a linear filter (impulse response) model under different Nsw conditions. Efficiency is area under impulse response curve. Using OMNI2 data set (1-hr) and AMIE reanalysis data set (1-min) not shown here

  7. (“Nsw”and “rsw” used interchangeably)

  8. 400 events split by average rsw during event Region shown in next image

  9. e

  10. h/ho hois efficiency at lowest rsw value

  11. Conclusions • If one studies storm event lists (< 80 events), Nsw effect is not large/significant – most events are in high category already. • Results from epoch analysis are very noisy.

  12. Normalized impulse response functions (IRFs) -Dst for ht t =

  13. Normalized impulse response functions (IRFs) -Dst for Same result if sorted by 4-hour rsw Same result if Pdyn is used as sort variable ht t =

  14. Normalized impulse response functions (IRFs) -Dst for Same result if sorted by 4-hour rsw Same result if Pdyn is used as sort variable ht t =

  15. h/ho hois efficiency at lowest rsw value

  16. Conclusions • If one studies storm event lists (~ 100 events), Nsw effect is marginally significant. • Results consistent with integral and epoch efficiencies • No difference in Nsw effect to Pdyn or pre-Nsw effect • No significant (> 3% difference in RMSE) if more complex drivers are used

  17. ViRBO Update • Senior review underway • Future • More VO activities – implement services on top of data we have collected and made available • RBSP participation • More data for climatology studies • More participation with broader community • How to participate: ask! • We have a list of active projects at http://virbo.org/#Active_Projects • If you want something, talk to us. We may know someone who has already done it, or we may be interested in doing it as a project.

  18. Active projects > D = get_data(‘Data set name’) … Analysis … > put_data(Dnew,‘Data set name’, ’version 2’, ‘Fixed baseline offsets’)

  19. Active Projects • Requires developing data model for typical data types (time series, spectrograms, L-sort, channel sweep). Build on PRBEM standard • Metadata model is also needed that can accurately describe the many complex radiation belt data types. Build on SPASE standard

  20. How will we simplify exchange. Need a data model and an API. PRBEM has partial model. Need to prepare for future.

  21. Active projects • Finish and validate metadata • Add visualizations to all data sets • Implement subsetting and filtering server • Event lists • Implement new services • L and L* data base • Fly-throughs of AP-8/AE-8 and AP-9/AE-9 • L-sort plots • ?

More Related