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 – 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 “sw” used interchangeably)

Region shownin next image

400 events split by average sw during event

e

is efficiency at lowest sw 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)

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Normalized impulse response functions (IRFs)

-Dst forSame result if sorted by 4-hour sw

Same result if Pdyn is used as sort variable

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Normalized impulse response functions (IRFs)

-Dst forSame result if sorted by 4-hour sw

Same result if Pdyn is used as sort variable

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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– ?