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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

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Influence of solar wind density on ring current response

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  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 • ?

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