Influence of solar wind density on ring current response
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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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Influence of solar wind density on ring current response


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


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


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


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


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


(“Nsw”and “rsw” used interchangeably)


400 events split by average

rsw during event

Region shown

in next image


e


h/ho

hois efficiency at lowest rsw value


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.


Normalized impulse response functions (IRFs)

-Dst for

ht

t =


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 =


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 =


h/ho

hois efficiency at lowest rsw value


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


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.


Active projects

> D = get_data(‘Data set name’)

… Analysis …

> put_data(Dnew,‘Data set name’,

’version 2’,

‘Fixed baseline offsets’)


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


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


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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