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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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Presentation Transcript
previous results
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
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
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
approach
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

slide8

400 events split by average

rsw during event

Region shown

in next image

slide10

h/ho

hois efficiency at lowest rsw value

conclusions
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 irfs1
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 irfs2
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 =

slide16

h/ho

hois efficiency at lowest rsw value

conclusions1
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
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
Active projects

> D = get_data(‘Data set name’)

… Analysis …

> put_data(Dnew,‘Data set name’,

’version 2’,

‘Fixed baseline offsets’)

active projects1
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
slide22
How will we simplify exchange. Need a data model and an API. PRBEM has partial model. Need to prepare for future.
active projects2
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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