1/7
SPACE WEATHER
Supporting Information for
Statistical Analysis and Verification of 3-Hourly Geomagnetic Activity Probability
Predictions
–Jingjing Wang, Qiuzhen Zhong, Siqing Liu, Juan Miao, Fanghua Liu, Zhitao Li, Weiwei
Tang
Jingjing Wang, Qiuzhen Zhong, Siqing Liu, Juan Miao, Fanghua Liu, Zhitao Li, Weiwei Tang, National Space
Science Center, Chinese Academy of Science
Corresponding author: J. Wang, National Space Science Center, Chinese Academy of Sciences, NO.1
Nanertiao, Zhongguancun, Haidian district, Beijing, 100190, China. (wangjingjing@nssc.ac.cn)
Contents of this file
Figure S1 to S5
Additional Supporting Information (Files uploaded separately)
Captions for Tables S6 to S9
Introduction
This supporting information comprises the same figures as presented in the main article
but in different mode, and the same tables as presented in the main article but for all
models, and the tables of probability forecasts provided by the thee best models.
1
1/7
Figure S1. Same as Fig. 1 in the main paper, but only illustrating the distribution plots of
the relative occurrence frequency vs. the conditional parameters.
1
2/7
Figure S2. Same as Fig. 1 in the main paper, but only illustrating the distribution plots of
the sample number vs. the conditional parameters.
2
3/7
Figure S3. Same as Fig. 2 in the main paper, but drawn as colored line segments, which
simplifies the RPS values from smallest (red, with greatest accuracy and reliability) to
largest (black, with least accuracy and reliability) corresponding to specified 10 equally
divided intervals.
3
4/7
Figure S4. Same as Fig. 3 in the main paper, but drawn as colored line segments, which
simplifies the DISC values from largest (red, with greatest discrimination capability) to
smallest (black, with least discrimination capability) corresponding to specified 10
equally divided intervals.
4
5/7
Figure S5. Same as Fig. 4 in the main paper, but drawn as colored line segments, which
simplifies the ROCA values from largest (red, with greatest discrimination capability and
skill) to smallest (black, with least discrimination capability and skill) corresponding to
specified 10 equally divided intervals.
5
6/7
Table S6. Same as Tab. 1 in the main paper, but illustrating the verifications of all the
1268 models. The variations in the first and second lines were illustrated with the
explanation. It is different from the table 1 in the manuscript that, the conditional
parameters was divided into Kp parameter, 1st and 2nd solar wind parameters as
illustrated in Row C-H.
Table S7. Illustrating the probability forecasts of model 1121, accompany with the
observations of geomagnetic activity levels. The events respect to 10736 cases of the
test dataset from 2011-08-08 to 2015-04-10. The time respects to 3-hourly Kp index, and
for example, 03:00 respect to Kp over period from 00:00 to 03:00. The observations are
vectors for four geomagnetic activity levels: quiet to unsettled, active, minor to moderate
storm, major to severe storm levels. X=1 represents the geomagnetic activity reaching
one of the four levels. Note that, "storm level" will be X=1 when either minor to moderate
storm or major to severe storm level is X=1. The probability forecasts of geomagnetic
activity level in Row H-L are derived by model 1121, respectively for every geomagnetic
activity level in Row C-G.
Table S8. Illustrating the probability forecasts of model 1129, accompany with the
observations of geomagnetic activity levels. Same as table S7.
Table S9 Illustrating the probability forecasts of model 1181, accompany with the
observations of geomagnetic activity levels. Same as table S7.
6