ISO 16698:2013

INTERNATIONAL ISO
STANDARD 16698
First edition
2013-05-01
Space environment (natural and
artificial) — Methods for estimation of
future geomagnetic activity
Environnement spatial (naturel et artificiel) — Méthodes
d’estimation de l’activité magnétique future
Reference number
©
ISO 2013
ISO 16698:2013(E)
COPYRIGHT PROTECTED DOCUMENT
© ISO 2013
All rights reserved. Unless otherwise specified, no part of this publication may be reproduced or utilized otherwise in any form
or by any means, electronic or mechanical, including photocopying, or posting on the internet or an intranet, without prior
written permission. Permission can be requested from either ISO at the address below or ISO’s member body in the country of
the requester.
ISO copyright office
Case postale 56 • CH-1211 Geneva 20
Tel. + 41 22 749 01 11
Fax + 41 22 749 09 47
E-mail copyright@iso.org
Web www.iso.org
Published in Switzerland
ii © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Contents  Page
Foreword .iv
Introduction .v
1 Scope . 1
2  Symbols and abbreviated terms . 1
3  General parameters . 1
3.1 Geomagnetic field variations . 1
3.2 Quiet level and disturbance fields . 2
3.3 K index (local 3 h range index). 2
3.4 Kp, ΣKp, ap, and Ap indices (planetary indices). 2
3.5 aa index (antipodal amplitude index) . 4
3.6 Dst index (storm time disturbance index) . 4
3.7 ASY and SYM indices (mid-latitude disturbance indices) . 5
3.8 AU, AL, AE, and AO indices (auroral electrojet indices) . 5
3.9 Time lag in the derivation and temporal resolution (sampling) . 6
4  Classification of prediction . 6
4.1 Short-term prediction . 6
4.2 Middle-term prediction . 8
4.3 Long-term prediction . 8
5  Methods of prediction . 9
5.1 Prediction based on statistical models. 9
5.2 Prediction based on physical principle . 9
6  Evaluation of prediction efficiency . 9
6.1 Definition of prediction error . 9
6.2 Methods of evaluation . 9
7 Compliance criteria .10
7.1 Rationale.10
7.2 Reporting .10
7.3 Documenting.10
7.4 Publishing .10
7.5 Archiving .10
Annex A (informative) Websites where geomagnetic indices are available .11
Annex B (informative) Websites where the space weather predictions and/or “now casting”
are presented .12
Annex C (informative) Definition of various skill scores .13
Bibliography .14
ISO 16698:2013(E)
Foreword
ISO (the International Organization for Standardization) is a worldwide federation of national standards
bodies (ISO member bodies). The work of preparing International Standards is normally carried out
through ISO technical committees. Each member body interested in a subject for which a technical
committee has been established has the right to be represented on that committee. International
organizations, governmental and non-governmental, in liaison with ISO, also take part in the work.
ISO collaborates closely with the International Electrotechnical Commission (IEC) on all matters of
electrotechnical standardization.
International Standards are drafted in accordance with the rules given in the ISO/IEC Directives, Part 2.
The main task of technical committees is to prepare International Standards. Draft International
Standards adopted by the technical committees are circulated to the member bodies for voting.
Publication as an International Standard requires approval by at least 75 % of the member bodies
casting a vote.
Attention is drawn to the possibility that some of the elements of this document may be the subject of
patent rights. ISO shall not be held responsible for identifying any or all such patent rights.
ISO 16698 was prepared by Technical Committee ISO/TC 20, Aircraft and space vehicles, Subcommittee
SC 14, Space systems and operations.
iv © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Introduction
This International Standard provides guidelines for specifying the process of estimating future
geomagnetic activity. Geomagnetic indices describe the variation of the geomagnetic field over a certain
time period and provide a measure of the disturbance of the magnetosphere.
The accuracy and method of predicting geomagnetic indices depends on the time scale of prediction.
This International Standard presents existing works based on three categories of time scale:
a) short-term prediction (1 h to a few days);
b) middle-term prediction (a few weeks to a few months);
c) long-term prediction (half year to one solar cycle).
These are required as input parameters for the magnetospheric magnetic field (ISO 22009), upper
atmosphere (ISO 14222), ionosphere, plasmasphere (ISO/TS 16457), magnetosphere charged particles,
and other models of the near-Earth space environment. They also serve as the input parameters for
orbital lifetime prediction and worst-case environment analysis of electrostatic charging.
Three International Standards deal with the Earth’s magnetic field, including ISO 16695 on the internal
magnetic field, ISO 22009 on the magnetospheric magnetic field, and this International Standard.
INTERNATIONAL STANDARD  ISO 16698:2013(E)
Space environment (natural and artificial) — Methods for
estimation of future geomagnetic activity
1 Scope
This International Standard specifies the methods used for estimating geomagnetic indices for time
intervals ranging from the short-term (hours to a few months) to the long-term (months to years).
Geomagnetic indices are used to describe the activity levels of the disturbance of the geomagnetic field.
These indices can be used to estimate upper atmospheric and plasmaspheric densities and many other
space environment models. They are also used as the input parameters for orbital lifetime prediction
and worst-case environment analysis of electrostatic charging.
This International Standard is intended for use to predict future geomagnetic indices and space environment.
2  Symbols and abbreviated terms
Bs Southward component of the interplanetary field (Bs = 0 when Bz ≥ 0 and Bs = Bz when
Bz < 0)
Bz North-south component of the interplanetary field
F10.7 flux Measure of the solar radio flux at a wavelength of 10,7 cm at the earth’s orbit, given in
−22 −2
units of 10 W·m
GLat Geographic latitude
GLon Geographic longitude
IMF Interplanetary magnetic field
MLat Geomagnetic latitude
MLon Geomagnetic longitude
MHD Magnetohydrodynamics
Sq Daily geomagnetic field variations during quiet conditions (Solar quiet)
UT Universal time
3  General parameters
3.1  Geomagnetic field variations
The geomagnetic field consists of internal and external magnetic fields. The internal (main) magnetic
field is produced by source currents that are mostly inside the Earth’s core and by induced currents
present in the solid Earth and the ocean, caused by the temporal variation of external magnetic fields.
The external magnetic field is produced by magnetospheric and ionospheric currents.
The magnetosphere is highly dynamic with time scales ranging from minutes to days. Solar wind is
the ultimate source of magnetospheric dynamics. The role played by the IMF (interplanetary magnetic
field) north–south component, Bz, is particularly important, and its southward component, Bs, plays a
ISO 16698:2013(E)
fundamental role in substorm and magnetic storm activity through the process of magnetic field line
reconnection. Solar wind speed also plays an essential role in these dynamics.
3.2  Quiet level and disturbance fields
Five days of every month are selected as the Five International Quietest Days using the Kp index
(see 3.4.1). Note that the five quietest days are selected regardless of the absolute level of quietness.
Thus, in a disturbed month, the quietest days may not be very quiet.
Derivation: The quietest days (Q-days) of each month are selected using the Kp indices based on three
criteria for each day: (1) the sum of the eight Kp values, (2) the sum of squares of the eight Kp values,
and (3) the maximum of the eight Kp values. According to each of these criteria, a relative order number
is assigned to each day of the month; the three order numbers are then averaged and the days with the
first to fifth lowest mean order numbers are selected as the five international quietest days.
Reference: Website of the Deutsches GeoForschungsZentrum (http://www-app3.gfz-potsdam.
de/kp_index/qddescription.html).
Once the quiet level is determined using the Five International Quietest Days, disturbance fields can be
obtained as deviations from the quiet level of geomagnetic field.
3.3  K index (local 3 h range index)
The K index is a number in the range 0 (quiet) to 9 (disturbed) that provides a local classification of
the variations of the geomagnetic field observed after subtraction of the regular daily variation (Sq).
Each activity level relates almost logarithmically to the corresponding disturbance amplitude of the
horizontal field component during a 3 h UT interval. In a day, eight K indices are given in successive 3 h
UT (universal time) intervals (0 h to 3 h, 3 h to 6 h, ., 21 h to 24 h UT).
Derivation: The ranges R for the H and D (or X and Y) components are defined as the expected difference
between the highest and lowest deviation, within the three-hour interval, from a smooth curve (a regular
daily variation) for that element on a magnetically quiet day. Only the larger value of R, i.e. R for the most
disturbed element, is taken as the basis of K. To convert from R to K, a permanent scale prepared for each
observatory is used. Table 1 is an example of the permanent scale for the Niemegk observatory.
References: Bartels et al. [1939], Mayaud [1980], Menvielle et al. [2011]
Table 1 — Permanent conversion scale from R to K for Niemegk observatory
Range (nT) 0-5 5-10 10-20 20-40 40-70 70-120 120-200 200-330 330-500 ≥500
K value 0 1 2 3 4 5 6 7 8 9
3.4  Kp, ΣKp, ap, and Ap indices (planetary indices)
The planetary indices, Kp, ΣKp, ap, and Ap, are derived from 13 selected mid-latitude observatories (see
Table 2). The derivation scheme for each index is described in the corresponding subsection.
2 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Table 2 — Thirteen observatories that contributed to the Kp index
Observatory, country Code GLat (°N) GLon (°E) MLat (°) Notes
Meannook, Canada MEA 54.617 246.667 62.5
Sitka, USA SIT 57.058 224.675 60.0
Lerwick, Shetland Is.,UK LER 60.133 358.817 58.9
Ottawa, Canada OTT 45.400 284.450 58.9 Replaced Agincourt in 1969
Uppsala, Sweden UPS 59.903 17.353 58.5 Replaced Lovo in 2004
Eskdalemuir, UK ESK 55.317 356.800 54.3
Brorfelde, Denmark BJE 55.625 11.672 52.7 Replaced Rude Skov in 1984
Fredericksburg, USA FRD 38.205 282.627 51.8 Replaced Cheltenham in 1957
Wingst, Germany WNG 53.743 9.073 50.9
Niemegk, Germany NGK 52.072 12.675 48.8 Replaced Witteveen in 1988
Hartland, UK HAD 50.995 355.517 50.0 Replaced Abinger in 1957
Canberra, Australia CNB −35.317 149.367 −45.2 Replaced Toolangi in 1981
Eyrewell, New Zealand EYR −43.424 172.354 −50.2 Replaced Amberley in 1978
3.4.1  Kp index (planetary 3 h range index)
The Kp index is assigned to successive 3 h UT intervals (0 h to 3 h, 3 h to 6 h, ., 21 h to 24 h UT), giving
eight values per UT day, and ranges in 28 steps from 0 (quiet) to 9 (disturbed) with intermediate values
denoted by −, o, or +, resulting in 0o, 0+, 1−,1o, 1+, 2−, 2o, 2+, ., 8−, 8o, 8+, 9−, and 9o.
Derivation: The K indices at the 13 observatories given in Table 2 are standardized by means of
conversion tables that have been established through the rather complicated procedure introduced by
Bartels [1949]. The standardized K indices, called the Ks index, are averaged using weighting factors to
derive the Kp index.
References: Bartels [1949], Mayaud [1980], Menvielle et al. [2011]
3.4.2  ΣKp index (planetary daily range index)
ΣKp is the sum of the eight Kp values of the day.
3.4.3  ap index (planetary 3 h equivalent amplitude index)
The Kp index is not linearly related to the geomagnetic disturbances measured in the unit of nT. Instead,
the ap index is introduced as it is roughly proportional to the geomagnetic disturbances. One ap unit
corresponds to approximately 2 nT of geomagnetic variations.
Derivation: The ap index is derived directly from the Kp index by using the conversion table shown in Table 3.
References: Bartels and Veldkamp [1954], Mayaud [1980], Menvielle et al. [2011]
Table 3 — Conversion table from the Kp index to the ap index
Kp 0o 0+ 1− 1o 1+ 2− 2o 2+ 3− 3o 3+ 4− 4o 4+
ap 0 2 3 4 5 6 7 9 12 15 18 22 27 32
Kp 5− 5o 5+ 6− 6o 6+ 7− 7o 7+ 8− 8o 8+ 9− 9o
ap 39 48 56 67 80 94 111 132 154 179 207 236 300 400
ISO 16698:2013(E)
3.4.4  Ap index (planetary daily equivalent amplitude index)
The Ap index is the average of the eight values of the ap index in a UT day.
3.5  aa index (antipodal amplitude index)
The aa index is a simple measure of global geomagnetic activity, which can be traced back
continuously to 1868.
Derivation: The aa index is produced from the K indices of two nearly antipodal magnetic observatories
in England and Australia, which are listed in Table 4. The K indices at the two observatories are converted
back to amplitudes using Table 5. The aa index is computed as an average of the northern and southern
values of amplitude using the weighting factors, λ, shown in Table 4.
References: Mayaud [1971]
Table 4 — Observatories in England and Australia contributing to the aa index
Observatory, country Code Period GLat (°N) GLon (°E) MLat (°) λ
Greenwich, England 1868–1925 1,007
Ablinger, England ABN 1926–1956 51.18 359.62 53.4 0,934
Hartland, England HAD 1957– 50.97 355.52 54.0 1,059
Melbourne, Australia 1868–1919 0,967
Toolangi, Australia TOO 1920–1979 −37.53 145.47 −45.6 1,033
Canberra, Australia CNB 1979– −35.30 149.00 −42.9 1,084
Table 5 — Conversion table from the K index at the aa observatories to amplitudes
K index 0 1 2 3 4 5 6 7 8 9
Amplitude 2,3 7,3 15 30 55 95 160 265 415 667
3.6  Dst index (storm time disturbance index)
The Dst index is a measure of the axially symmetric part of the H component along the geomagnetic
equator on the ground, and the main physical source is a combination of the equatorial ring current, the
plasma sheet current and the magnetopause current.
Derivation: The Dst index is defined as the average of the disturbance variations of the H component,
D , at the four observatories (i = 1 to 4) listed in Table 6, divided by the average of the cosines of the
i
dipole latitudes at the observatories for normalization to the dipole equator. Dst is computed for each
UT hourly interval from the four observatories.
References: Sugiura [1964], Sugiura and Kamei [1991]
Table 6 — Four observatories contributing to the Dst index
Observatory, country Code GLat (°N) GLon (°E) Dipole Lat (°)
Kakioka, Japan KAK 36.230 140.190 26.0
San Juan, USA SJG 18.113 293.850 29.6
Honolulu, USA HON 21.320 201.998 21.1
Hermanus, South Africa HER −34.425 19.225 −33.3
4 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
3.7  ASY and SYM indices (mid-latitude disturbance indices)
The disturbance fields in mid- and low latitudes are generally not axially symmetric, in particular in the
developing phase of a magnetic storm. To describe the asymmetric and symmetric disturbance fields
in mid-latitudes with a high time resolution of 1 min, longitudinally asymmetric (ASY) and symmetric
(SYM) disturbance indices were introduced and derived for both the H and D components. The SYM-H
index is approximately the same as the Dst index, while its time resolution is 1 min.
Derivation: The ASY/SYM indices are derived from six selected mid-latitude observatories (see Table 7)
in the following four steps: (1) subtraction of the geomagnetic main field and the Sq field to obtain the
disturbance field component, (2) coordinate transformation to a dipole coordinate system, (3) calculation
of the longitudinally symmetric indices, SYM-H and SYM-D, by taking averages of disturbance fields
of the six stations, and (4) calculation of the asymmetric disturbance indices, ASY-H and ASY-D, by
computing the range between the maximum and the minimum asymmetric fields.
References: Iyemori et al. [1992], Menvielle et al. [2011]
Table 7 — Six observatories contributing to the SYM/ASY indices
Observatory, country Code GLat (°N) GLon (°E) MLat (°) MLon (°E) Rotation angle (°)
Memambetsu, Japan MMB 43.9 144.2 34.6 210.2 −16.1
Honolulu, USA HON 21.3 202.0 21.5 268.6 0.5
Tuscon, USA TUC 32.3 249.2 40.4 314.6 2.7
Fredericksburg, USA FRD 38.2 282.6 49.1 352.2 0.4
Hermanus, South Aflica HER −34.4 19.2 −33.7 82.7 −10.1
Urmuqu, China WMQ 43.8 87.7 34.3 162.5 7.7
3.8  AU, AL, AE, and AO indices (auroral electrojet indices)
The auroral electrojet indices are measures of the intensity of the auroral electrojets and consist of
four indices, AU, AL, AE and AO. The AU and AL indices are intended to express the strongest current
intensity of the eastward and westward auroral electrojets, respectively. The AE index represents the
overall activity of the electrojets, and the AO index provides a measure of the equivalent zonal current.
Derivation: The auroral electrojet indices are derived from geomagnetic variations in the H component
observed at 12 selected observatories along the auroral zone in the northern hemisphere (see Table 8).
The AU and AL indices are respectively defined by the largest and the smallest values thus selected. The
symbols, AU and AL, derive from the fact that these values form the upper and lower envelopes of the
superposed plots of all the data from these stations as functions of UT. The difference, AU minus AL,
defines the AE index, and the mean value of the AU and AL, i.e. (AU+AL)/2, defines the AO index.
References: Davis and Sugiura [1966], Kamei and Maeda [1981]
ISO 16698:2013(E)
Table 8 — Twelve (and obsolete three) observatories contributing to the AE index
Observatory, country Code GLat (°N) GLon (°E) MLat (°) MLon (°E) Notes
Abisko, Sweden ABK 68.36 18.82 66.06 114.66
Dixon Island, Russia DIK 73.55 80.57 64.04 162.53
Cape Chelyuskin, Russia CCS 77.72 104.28 67.48 177.82
Tixie Bay, Russia TIK 71.58 129.00 61.76 193.71
Pebek, Russia PBK 70.09 170.93 63.82 223.31 Opened in 2001/04
Barrow, USA BRW 71.30 203.25 69.57 246.18
College, USA CMO 64.87 212.17 65.38 261.18
Yellowknife, Canada YKC 62.40 245.60 68.87 299.53
Fort Churchill, Canada FCC 58.80 265.90 67.98 328.36
Sanikiluaq, Canada SNK 56.5 280.8 66.6 349.7 Opened in 2007/12
Narssarssuaq, Denmark NAQ 61.20 314.16 69.96 37.95
Leirvogur, Iceland LRV 64.18 338.30 69.32 71.04
Cape Wellen, Russia CWE 66.17 190.17 62.88 241.36 Closed in 1996
Great Whale River, Russia GWR 55.27 282.22 65.45 351.77 Closed in 1984/07
Opened in 1984/09
Poste-de-la-Baleine, Canada PBQ 55.27 282.22 65.45 351.77
Closed in 2007/11
3.9  Time lag in the derivation and temporal resolution (sampling)
Some of the indices mentioned above have different classes (generations) for operational use. That is,
for quasi-real-time derivation, a different naming convention is used to distinguish from the original
definition with quality-controlled data. For example, in the case of the Dst index, there are Real-Time
(Quick-Look) Dst, Provisional Dst and Final Dst. There are also attempts to increase the temporal
resolution of the indices (e.g. Gannon and Love, 2011). (See Annex A.)
4  Classification of prediction
The accuracy and method of predicting geomagnetic indices depends on the time scale of prediction.
Subclauses 4.1 to 4.3 introduce some of the existing works which are based on a classification of three
time-scale categories: short-term (1 h to a few days), middle-term (a few weeks to a few months), and
long-term (half year to one solar cycle). Some of them are actually used and the results made available
online (see Annex B).
4.1  Short-term prediction
Stimulated by the space weather programmes, there are many proposed methods and related research
papers for predicting geomagnetic indices in a time scale of 1 h to a few days. These fall into four categories:
(1) linear prediction technique, (2) neural network model, (3) probabilistic prediction with solar wind
data, and (4) MHD (magnetohydrodynamics) simulation. Most of the recent techniques need real-time
solar wind parameters and near-real-time geomagnetic observations as the input. Predicting solar wind
disturbance from solar surface observation may be a key to improving geomagnetic index predictions.
6 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Examples of prediction:
Kp, ap, and Ap indices
McPherron R.L., Predicting the Ap index from past behavior and solar wind velocity, Phys. Chem.
Earth (C), 24, pp. 45-56, 1999. (Type 1)
Boberg F. et al., Real time Kp predictions from solar wind data using neural networks, Phys. Chem.
Earth (C), 25, pp. 275-280, 2000. (Type 2)
Costello K.A., Moving the Rice MSFM into a real-time forecast mode using solar wind driven
forecast models, Ph.D. dissertation, Rice Univ., Houston, Texas, 1998 (http://hdl.handle.
net/1911/19251). (Type 2)
Thomson A.W.P., Nonlinear predictions of Ap by activity class and numerical value, Pure Appl.
Geophys., 146, pp. 163-193, 1996. (Type 2)
Wing S. et al., Kp forecast models, J. Geophys. Res., 110, A04203, doi:10.1029/2004JA010500,
2005. (Type 2)
Detman T. and Joselyn J., Real-time Kp predictions from ACE real time solar wind, Solar Wind Nine,
edited by Habbal et al., AIP Conf. Proc., 271, pp. 729-732, 1999. (Type 2)
McPherron R.L. et al., Probabilistic forecasting of the 3-h ap index, IEEE Trans. Plasma Sci., 32,
pp. 1425-1438, 2004. (Type 3)
Dst index
Balikhin M.A. et al., Terrestrial magnetosphere as a nonlinear resonator, Geophys. Res. Lett., 28,
pp. 1123-1126, 2001. (Type 1)
Boaghe O.M. et al., Identification of nonlinear processes in the magnetospheric dynamics and
forecasting of Dst index, J. Geophys. Res., 106, pp. 30047-30066, 2001. (Type 1)
Iyemori, T. and Maeda H., Prediction of geomagnetic activities from solar wind parameters based on
the linear prediction theory, in Solar-Terrestrial Predictions Proceedings, Vol. IV, ed. by R.F. Donnelly,
Apr.23-27, 1979, Boulder, 1980. (Type 1)
Lundstedt H., Solar origin of geomagnetic storms and prediction of storms with the use of neural
networks, Surv. Geophys., 17, pp. 561-573, 1996. (Type 2)
Stepanova M.L. et al., Prediction of Dst variations from polar cap indices using time-delay neural
network, J. Atmos. Sol.-Terr. Phys., 67, pp. 1658-1664, 2005. (Type 2)
Burton R.K. et al., Empirical relationship between interplanetary conditions and Dst, J. Geophys.
Res., 80, pp. 4204-4214, 1975. (Type 3)
O’Brien T.P. and McPherron R.L., Forecasting the ring current Dst in real time, J. Atmos. Sol.-Terr.
Phys., 62, pp. 1295-1299, 2000. (Type 3)
Temerin M. and Li X., A new model for the prediction of Dst on the basis of the solar wind, J. Geophys.
Res., 107, p. 1472, doi:10.1029/2001JA007532, 2002. (Type 3)
Fok M.-C. et al., Comprehensive computational model of the Earth’s ring current, J. Geophys. Res.,
106, pp. 8417-8424, 2001. (Type 4)
ISO 16698:2013(E)
AE indices
Iyemori, T. and Maeda H., Prediction of geomagnetic activities from solar wind parameters based on
the linear prediction theory, in Solar-Terrestrial Predictions Proceedings, Vol. IV, ed. by R.F. Donnelly,
Apr.23-27, 1979, Boulder, 1980. (Type 1)
Pallocchia G. et al., AE index forecast at different time scales through an ANN algorithm based on L1
IMF and plasma measurements, J. Atmos. Sol.-Terr. Phys., 70, pp. 663-668, 2008. (Type 2)
Takalo J. and Timonen J., Neural network prediction of the AE index from the PC index, Phys. Chem.
Earth (C), 24, pp. 89-92, 1999. (Type 2)
Li X. et al., Prediction of the AL index using solar wind parameters, J. Geophys. Res., 112, A06224,
doi:10.1029/2006JA011918, 2007. (Type 3)
Kitamura K. et al., Properties of AE indices derived from real-time global simulation and their implications
for solar wind-magnetosphere coupling, J. Geophys. Res., 113, A03S10, doi:10.1029/2007JA012514,
2008. (Type 4)
4.2  Middle-term prediction
There are only a few research papers that use recurrences of geomagnetic disturbances in a time scale
of a few weeks to a few months.
Example of prediction:
Zhou X.-Y. and Wei F.-S., Prediction of recurrent geomagnetic disturbances by using adaptive filtering,
Earth Planets Space, 50, pp. 839-845, 1998. (prediction of the Kp index)
4.3  Long-term prediction
There are very few proposed techniques and/or research papers on predicting geomagnetic indices in
a time scale of half a year to one solar cycle, as compared with those on solar activities such as sun spot
numbers or F10.7 flux. However, the sun spot number or F10.7 flux indicates quite different behaviour
from geomagnetic indices such as the aa index during some solar cycles. Therefore, the long-term
prediction method of geomagnetic indices is necessary.
Examples of prediction:
Niehuss K.O. et al., Statistical technique for intermediate and long-range estimation of 13-month
smoothed solar flux and geomagnetic index, NASA Technical Memorandum 4759, 1996. (prediction
of the Ap index)
Cliver E.W. et al., A prediction of geomagnetic activity for solar cycle 23, J. Geophys. Res., 104,
pp. 6871-6876, 1999. (prediction of the aa index).
Long-term prediction of solar activities (sun spot number and F10.7 flux) is presented by NOAA/Space
Weather Prediction Center (see Annex B). The possibility of combining the technique of solar activity
prediction with the solar-geomagnetic disturbance relationship has been examined in a number of studies.
Examples of solar-geomagnetic disturbance relationship:
Clilverd M.A. et al., Increased magnetic storm activity from 1868 to 1995, J. Atom. Sol.-Terr. Phys., 60,
pp. 1047-1056, 1998.
Stamper R. et al., Solar causes of the long-term increase in geomagnetic activity, J. Geophys. Res., 104,
pp. 28325-28342, 1999.
8 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
5  Methods of prediction
The prediction methods can be split into two broad categories: (1) those based on a statistical model,
and (2) those based on a physical principle.
5.1  Prediction based on statistical models
5.1.1  Prediction filter
This method of prediction uses data from a preceding interval of similar (or longer) length to that of the
period to be predicted. Precision of prediction generally depends on the temporal distance between the
most recent data and the period to be predicted. There are two types of prediction: one uses the index
of the preceding interval as the input data (see Zhou and Wei, 1998) and the other uses the solar wind
parameters (see Iyemori and Maeda, 1980; McPherron et al., 2004; Li et al., 2007).
5.1.2  Neural network model
There are several neural-network models. This method is applicable for time scales of several days to
one sunspot cycle. It has been concluded that the interplanetary magnetic field and solar wind plasma
data are significant components for any of the models (see Thomson, 1996; Wing et al., 2005).
5.1.3  Regression analysis
This method is based on the periodicity of geomagnetic disturbances such as the sun spot cycle, annual
or semi-annual variation (see Joselyn, 1995). Predictions made over long time scales (one to ten years)
require the prediction of a sunspot number (see Feynman and Gu, 1986). Similar techniques used to
predict the F10.7 flux and Ap index (e.g. Niehuss et al.,1996) are also available.
5.2  Prediction based on physical principle
This type of prediction is based on numerical MHD simulation of the magnetospheric process or energy
principle. These methods need the solar wind parameters as the input. See, for example, Burton et al.
(1975) and Kitamura et al. (2008).
6  Evaluation of prediction efficiency
6.1  Definition of prediction error
For a simple time series, the most popular definition of prediction error is as the average of the square
of the differences between the predicted values and the observed values. This provides a reasonable
measure of prediction error.
6.2  Methods of evaluation
It has been reported that the accuracy of prediction is different for the sunspot maximum and minimum
period. It has also been reported that the accuracy is different for different solar cycles (see Feynman
and Gu, 1986). Accuracy is also different depending on the time scale of prediction. The prediction
efficiency should therefore be given together with the conditions applied for its evaluation.
A prediction can be evaluated using a skill score. In the case of a dichotomous forecast, the true skill
statistics, the Gilbert skill score, the Heidke skill score, and others can be used (see Detman and Joselyn,
1999). If predicting continuous-variables, the mean square skill score can be used (see Murphy, 1988).
These skill scores are detailed in Annex C.
ISO 16698:2013(E)
7 Compliance criteria
7.1 Rationale
The prediction principle and scheme should be described concisely and clearly. They should be published
as scientific articles in refereed/peer-review international journals and their references should be
available to the public. Otherwise, journal-style documents suitable for publication in international
journals should be accessible to the public.
7.2  Reporting
Prediction results of geomagnetic indices should be made public for evaluation and application by
third parties (e.g. individuals or institutes who are interested in the prediction results). As a minimum,
digital values of the prediction results should be given in the same data format as the corresponding
geomagnetic indices, such as the WDC exchange format.
7.3  Documenting
The following information relating to prediction should be clearly documented or displayed.
a) Input:
1) types of data;
2) source of data;
3) time resolution of data;
4) number of data points;
5) time of data acquisition.
b) Output:
1) types of predicting data;
2) time of predicting data;
3) time at which prediction was performed.
c) Miscellaneous:
1) type of prediction method (choose from the four types listed in Clause 4, otherwise describe briefly);
2) point of contact.
7.4  Publishing
When the geomagnetic index becomes available, comparison should be made with the prediction results.
Comparison includes calculating the prediction error, skill score, correlation coefficients, and so on, as
listed in Clause 5.
7.5  Archiving
The results of prediction should be archived and available to the public for evaluation.
10 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Annex A
(informative)
Websites where geomagnetic indices are available
(1) GFZ-Potsdam
http://www-app3.gfz-potsdam.de/kp_index/ (Kp)
(2) Service International des Indices Géomagnetiques (ISGI)
http://isgi.latmos.ipsl.fr/lesdonne.htm (aa, am, Kp, AE, Dst, PC)
(3) WDC for Geomagnetism, Kyoto
http://wdc.kugi.kyoto-u.ac.jp/wdc/Sec3.html (AE, Dst, ASY/SYM, RT-AE, RT-Dst)
(4) Arctic and Antarctic Research Institute
http://www.aari.nw.ru/index_en.html (PCS)
(5) WDC for Geomagnetism, Copenhagen
ftp://ftp.space.dtu.dk/WDC/indices/pcn/ (PCN)
(6) US Geological Survey
http://geomag.usgs.gov/dst/ (RT-USGS-Dst)
ISO 16698:2013(E)
Annex B
(informative)
Websites where the space weather predictions and/or “now
casting” are presented
(1) NOAA Space Environment Center
http://www.swpc.noaa.gov/
(2) Magnetospheric Specification and Forecast model (MSFM)
http://space.rice.edu/ISTP/dials.html
(3) International Space Weather Service
http://www.ises-spaceweather.org/
(4) NiCT Space Environment Information Service
http://www2.nict.go.jp/y/y223/sw_portal/sw_portal-e.html
(5) Belgium SIDC
http://sidc.oma.be/
(6) The Australian Space Weather Agency
http://www.ips.gov.au/Space_Weather
(7) WINDMI model
http://orion.ph.utexas.edu/~windmi/
(8) Lund space weather model
http://www.lund.irf.se/rwc/
(9) CISM forecast model
http://www.bu.edu/cism/
http://lasp.colorado.edu/cism/
(10) Solar Cycle Progression, NOAA/Space Weather Prediction Center
http://www.swpc.noaa.gov/SolarCycle/
12 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
Annex C
(informative)
Definition of various skill scores
C.1  Dichotomous forecast
In the following contingency table:
Forecast
Yes No
Observed Yes x (hits) y (misses)
No z (false alarm) w (correct negatives)
The true skill score (TSS) is defined as:
xw− yz
TSS=
()xy++()zw
The Gilbert skill score (GSS) is defined as:
xc−
GSS=
xy++zc−
()xy++()xz
c =
xy++zw+
The Heidke skill score (HSS) is defined as:
xw+−c
HSS=
xy++zw+−c
()xy++()xz ++()wy ()wz+
c =
xy++zw+
C.2  Continuous variables
The mean-square skill score (SS) is defined as:
MSEf(,x)
SS=−1
MSEx(,x)
n
MSE fx,(=−fx )
()
∑ ii
n
i=1
where MSE represents “mean square error”; f and x denote the ith forecast and ith observation,
i i
respectively; x is the mean value of x over i = 1 − n.
ISO 16698:2013(E)
Bibliography
[1] ISO 22009, Space systems — Space environment (natural and artificial) — Model of the earth’s
magnetospheric magnetic field
1)
[2] ISO 14222 , Space environment (natural and artificial) — Earth upper atmosphere
[3] ISO/TS 16457, Space systems — Space environment (natural and artificial) — The Earth’s ionosphere
model: international reference ionosphere (IRI) model and extensions to the plasmasphere
2)
[4] ISO 16695 , Space environment (natural and artificial) — Earth’s internal magnetic reference
field models
[5] Akasofu S.I., & Fry C.F. A 1st Generation Numerical Geomagnetic Storm Prediction Scheme.
Planet. Space Sci. 1986, 34 (1) pp. 77–92
[6] Baker D.N., Bargatze L.F., Zwickl R.D. Magnetospheric Response to the Imf - Substorms. J.
Geomag. Geoelectr. 1986, 38 (11) pp. 1047–1073
[7] Baker D.N., Weigel R.S., Rigler F., McPherron R.L., Vassilladis D., Arge C.N. et al. Sun-to-
magnetosphere modeling: CISM forecast model development using linked empirical methods.
J. Atmos. Sol.-Terr. Phys. 2004, 66 pp. 15–16, pp. 1491–1497
[8] Bargatze L.F., Baker D.N., Mcpherron R.L., Hones E.W. Magnetospheric Impulse-Response for
Many Levels of Geomagnetic-Activity. J. Geophys. Res. 1985, 90 (NA7) pp. 6387–6394
[9] Bartels J. The standardized index, Ks, and the planetary index, Kp. IATME Bulletin. 1949, 12b p. 97
[10] Bartels J., Heck N.H., Johnston H.F. The three-hour-range index measuring geomagnetic
activity. Terr. Magn. Atmos. Electr. 1939, 44 (4) pp. 411–454. DOI: doi:10.1029/TE044i004p00411
[11] Bartels J., & Veldkamp J. International data on magnetic disturbances, first quarter, 1954. J.
Geophys. Res. 1954, 59 (3) pp. 423–427
[12] Blanchard G.T., & McPherron R.L. Analysis of the Linear-Response Function Relating Al to Vbs
for Individual Substorms. J. Geophys. Res. 1995, 100 (A10) pp. 19155–19165
[13] Boaghe O.M., Balikhin M.A., Billings S.A., Alleyne H. Identification of nonlinear processes in
the magnetospheric dynamics and forecasting of Dst index. J. Geophys. Res. 2001, 106 (A12)
pp. 30047–30066
[14] Boberg F., Wintoft P., Lundstedt H. Real time Kp predictions from solar wind data using neural
networks. Phys. Chem. Earth (C). 2000, 25 (4) pp. 275–280
[15] Burton R.K., McPherron R.L., Russell C.T. Empirical Relationship between Interplanetary
Conditions and Dst. J. Geophys. Res. 1975, 80 (31) pp. 4204–4214
[16] Clauer C.R.The technique of linear prediction filters applied to studies of solar wind-
magnetosphere coupling. In: Solar Wind-Magnetosphere Coupling, (Kamide Y., & Slavin J . A . e d s .) .
Terra Scientific Publishing Co, 1986, pp. 39–57.
[17] Clauer C.R., McPherron R.L., Searls C., Kivelson M.G. Solar-Wind Control of Auroral-Zone
Geomagnetic-Activity. Geophys. Res. Lett. 1981, 8 (8) pp. 915–918
[18] Detman T., & Joselyn J.A.Real-time Kp predictions from ACE real time solar wind. In: Solar Wind
Nine, (Habbal S.R. et al.eds.). AIP Conf. Proc., Vol. 471, 1999, pp. 729–32.
1) To be published.
2) To be published.
14 © ISO 2013 – All rights reserved

ISO 16698:2013(E)
[19] Detman T., & Vassiliadis D.Review of techniques for magnetic storm forecasting. In: Magnetic
Storms, (Tsurutani B.T., Gonzalez A.L.C., Kamide Y., Arballo J.K.eds.). American Geophysical
Union, Washington, DC, 1997, pp. 253.
[20] Doggett K.A. (1993), An operational forecast verification at the Space Environment Laboratory,
in Solar-Terrestrial predictions, IV, Proceedings of a Workshop, edited by J. Hruska, Ottawa, Canada
[21] Doggett K.A. (1994), Verification of NOAA Space Environment Laboratory Forecasts:
1 January-31 December 1994, in NOAA Tech. Memo, edited, p. 64, Boulder, CO
[22] Doxas I., & Horton W. Magnetospheric dynamics from a low-dimensional nonlinear dynamics
model. Phys. Plasmas. 1999, 6 (5) pp. 2198–2204
[23] Doxas I., Horton W., Smith J.P. A physics based nonlinear dynamical model for the solar wind
driven magnetosphere-ionosphere system. Phys. Chem. Earth (C). 1999, 24 (1-3) pp. 67–71
[24] Doxas I., Horton W., Smith J.P. A physics based nonlinear dynamical model for the solar wind
driven magnetosphere-ionosphere system. Phys. Chem. Earth (C). 1999, 24 (1-3) pp. 67–71
[25] Dryer M., Akasofu S.I., Kroehl H.W., Sagalyn R., Wu S.T., Tascione T.F. et al. The
solar/interplanetary/magnetosphere/ionosphere connection: a strategy for prediction of
geomagnetic storms. Advances Astro. Sci, 1986, pp. 58.
[26] Echer E., Alves M.V., Gonzalez W.D. Geoeffectiveness of interplanetary shocks during solar
minimum (1995-1996) and solar maximum (2000). Sol. Phys. 2004, 221 (2) pp. 361–380
[27] Fay R.A., Garrity C.R., McPherron R.L., Bargatze L.F.Prediction filters for the Dst index and
polar cap potential. In: Solar Wind-Magnetosphere Coupling, (Kamide Y., & Slavin J.A.eds.). Terra
Scientific Publishing Co, 1986, pp. 111–7.
[28] Foti M.A., & Arizmendi C.M. Randomness in geomagnetic storms: Chaos or noise? Fractals. An
Interdisciplinary Journal on the Complex Geometry of Nature. 1997, 5 (1) pp. 169–173
[29] Francq C., & Menvielle M. A model for the am (Km) planetary geomagnetic activity index and
application to prediction. Geophys. J. Int. 1996, 125 (3) pp. 729–746
[30] Freeman J., Nagai A., Reiff P., Denig W., Gussenhoven-Shea S., Heinemann M. et al. (1993), The use
of neural networks to predict magnetospheric parameters for
...