Transcript Document

Kp Forecast Models
S. Wing1, Y. Zhang1, and J. R. Johnson2
1 Applied Physics Laboratory, The Johns Hopkins University
2 Princeton Plasma Physics Laboratory, Princeton University
Questions: [email protected]
Outline
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2.
3.
4.
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Background and motivations
Data Set
Method
APL Kp models
Magnetospheric predictability as a function of solar cycle
Comparison with previous Kp models
Summary
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Background and Motivations for
developing Kp forecast models
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Moderate and high activities are notoriously difficult to predict
[Joselyn, 1995].
Real-time magnetometer data can be used to calculate nowcast
Kps, which could improve the accuracy of the forecast Kps.
Why Kp?
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Kp is one of the most popular global indices.
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Kp has been playing significant roles in space weather, e.g.,
satellite drags etc.
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Many magnetospheric and ionospheric models require Kp as an
input parameter, e.g., T89 magnetic field model, Fok ring
current-radiation belt model, MSFM, OVATION, etc.
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The long uninterrupted Kp record since 1932 makes it ideal for
studying solar-wind magnetosphere interactions, e.g., the solar
cycle effects, etc.
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Data Set
1. Solar wind and IMF data spanning over 2 solar cycles : IMP8 (19751999), Wind (1994-2000), and ACE (2000-2001). Data are publicly
available from NASA CDAWeb. IMP-8 plasma data are publicly
available from MIT IMP-8 website.
2. Historical/official Kps (1975-2001) are available at GFZ Postdam
website.
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Method
1. The time granularity of the input and output of the model is 15 min.
Solar wind/IMF were propagated to Earth and hourly averaged every
15 min. Kps are interpolated to 15 min resolution.
2. The solar wind, IMF, and Kp data set were randomly selected into 2
equal subsets: (1) training set and (2) testing set.
3. Neural Network (NN) has been used in space physics/weather,
particularly as predictors or classifiers [e.g., Koons and Gorney, 1991;
Vandegriff et al., 2004; Wu and Lundstedt, 1997; Newell et al., 1991;
Wing et al., 2003].
4. In this study, NN:
• Input: solar wind, IMF and nowcast Kp (in some models)
• Output: forecast Kp.
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APL Kp forecast models
In order to satisfy different needs and operational constraints, three
different models were developed:
1. APL model 1
• Input: ACE solar wind/IMF parameters and nowcast Kp
• Output: ~1-hr ahead Kp forecast
2. APL model 2
• Input: same as model 1
• Output: ~4-hr ahead Kp forecast
3. APL model 3
• Input: ACE solar wind/IMF parameters
• Output: ~1-hr ahead Kp forecast
Wing et al., Radio Sci., 2003
Wing et al., JGR, 2005
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APL Kp forecast
models
1. APL model 1
• Input: ACE solar wind
Vx, n, IMF |B|, Bz, and
nowcast Kp
• Output: ~1-hr ahead Kp
forecast
• Note: nowcast Kp
algorithm has recently
improved, achieving r >
0.9 [Takahashi et al.,
2001].
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APL Kp forecast models
APL model 1
predicts low
and high Kps
very well
Evaluation
based on data
spanning over
2 solar cycles
The higher
performance can
be attributed to:
1. Larger data
set.
2. Input nowcast
Kp.
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APL Kp forecast models
The
performance
is worse
(larger
scatter) than
APL model 1.
It is still better
than the
previous
models that
provide 1 hr
ahead
forecast.
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APL Kp forecast models
The
performance
is better than
the previous
models.
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Kp (magnetospheric) predictability as a function
of solar cycle
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Papitashvilli et al. [2000] report that there is solar cycle variation in the
average Kp.
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It would be interesting to determine if the accuracy of Kp (a proxy for
the magnetospheric state) forecast based partly or entirely on solar
wind/IMF has solar cycle dependence.
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Calculate the skill scores (TSS and GS [Detman and Joselyn, 1999])
for Kp forecast models for 2 solar cycles, 1975-2000.
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TSS: True Skill Statistics
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GS: Gilbert Skill
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Kp predictability as a function of solar cycle
0 = random
forecast
1 = perfect forecast
Costello NN Kp
model predicts
Kp more
accurately near
solar maximum
than minimum.
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Kp predictability as a function of solar cycle
The solar cycle dependence
of APL model 3
While the scores are higher
than those for Costello
model, they still exhibit solar
cycle variation, albeit with
smaller amplitude.
Training with a larger
data set cannot
eliminate the solar cycle
effect completely.
APL model 3 performs
better during solar max
than solar min.
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Kp predictability as a function of solar cycle
The nonlinear
response anticorrelates with
sunspot number
in every solar
cycle since Kp
record is kept.
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Comparison between Kp models
Costello NN Kp model [Costello, 1997]
Input: IMF Bz, |B|, and |V|
1-hour Kp forecast
APL model 1
(1 hour forecast)
NARMAX [Balikhin et al., GRL]
Bz, |B|, |V|, N, 1-hour forecast
APL model 2
(4 hour forecast)
Boberg et al. [2000]
Operational at Lund Obs.
APL model 3, 1-hour forecast
(purely driven by solar wind)
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The Kp models were developed at JHU/APL. They have been
transitioned to USA NOAA SWPC and other space weather centers
for operational use.
For example, See http://www.swpc.noaa.gov/wingkp/
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Summary and Conclusion
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Extensive model evaluations based on data spanning over 2 solar
cycles show that
1. Kp is more predictable during solar max than solar min.
2. Cumulant based information dynamics analysis of Kp shows that
Kp (the magnetosphere state) is more strongly nonlinearly coupled
with the past Kps during solar min than solar max [Johnson and
Wing, 2004].
3. (1) and (2) suggest that the magnetosphere is more externally
driven during solar max (declining phase of solar max) than solar
min, when internal dynamics play a more significant role.
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