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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

- 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

- 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

- 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?

- 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

h/ho

hois efficiency at lowest rsw value

- 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.

-Dst for

ht

t =

-Dst for

Same result if sorted by 4-hour rsw

Same result if Pdyn is used as sort variable

ht

t =

-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

- 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

- 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.

> D = get_data(‘Data set name’)

… Analysis …

> put_data(Dnew,‘Data set name’,

’version 2’,

‘Fixed baseline offsets’)

- 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.

- 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
- ?