Space environment effects on geostationary spacecraft: Analysis and prediction

Adv. Space Res. Vol. 26, No. 1, pp. 31-36.2000 0 2000 COSPAR. Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0273-I 177/00 $20.00 + 0.00 PII: SO273-1177(99)01023-6

Pergamon www.elsevier.nl/locate/asr

SPACE ENVIRONMENT ANALYSIS J.-G.

EFFECTS

ON GEOST,4TIONARY

SPACECRAFT:

AND PR.EDICTION

Wu’>“. L. Eliasson2, H. Lundstedt”,

A. Hilgers4. L. Andersson2.

0. Norberg

I Now at Danrsh Meteorological Institute Lyngbyvej 100, DIG-2100 Coprnhagen. Denmark 2Swedish Institute of Space Physics. Box 812, S-981 28 liiruna. Sweden ,Sche&viigen 17. S7017. SE-%23 70 Lund. Sweden “Swedish Institute of Space Physics ” ESTEC. European Space Agcnwy. Noordwijk. !VI,-2,700 AG. Thhr Tethrrlands

ABSTRACT We investigate how the two geostationary spacecraft, Meteosnt-3 and Tele-X. are affected by t,hc space environment,, as characterized by Dst, Kp, and energetic electron flux (> 2 MeV, EEF). Neural network models are developed to predict spacecraft, anomalies 1 day ahead. It is found from superposed epoch analysis that the spacecraft anomalies frequently occurred during the recovery phase of geomagnetic storms and that the space environment during the last 4-6 days preceding an anomaly contributes statistically the most to the anomaly occurrence. We find from network prediction (1) that when training is only on Meteosat-3, Kp, Dst, and the EEF, respectively, give t,he total predict,ion rate of 79%, 73%, and 62% for Meteosat-3 (both anomalies and non-anomalies) and the total prediction rate of 640/o, 66%, and 67% for Tele-X; (2) that when training is only on Tele-X, Kp, Dst, and the EEF, respectively, give the total prediction rate of 57%, 58%, and 51% for Meteosat-3 and the total prediction rate of 70%, 71%, and 74% for Tele-X; (3) that Kp is the optimal environment parameter to correlate with the anomalies on both Meteosat-3 and Tele-X. It is revealed from the anomaly local time distribution, superposed epoch analysis, and network prediction that Meteosat-3 was more subject to surface charging than to int,ernal dielectric charging, and that Tele-X was more subject to internal dielectric charging than to surface charging. The developed neural network models can be used to predict times with higher risks for anomalies in real-time from ACE data. 0 2000 COSPAR. Published by Elsevier Science Ltd. INTRODUCTION The space environment (including atmosphere, plasma, radiat,ion, and micrometeoroid/orbital debris) can adversely affect spacecraft. The types of space environment effects depend on the altitude and inclination of spacecraft orbim. Interactions between the space environment and spacecraft vary with local t.ime. season, geomagnet,ic and solar activit.y. with magnitude varying from negligible to mission threatening. In the space plasma environment, due to the differences in surface conductivity, conductors and dielectrics will charge t,o different potentials. This differential charging may lead to arc discharging between surfaces if the potential difference is big enough. The arc discharging could cause permanent damage to spacecraft subsyst,ems and electromagnetic interference with sensitive electronics (Tribble, 1995). In addition to the ambient plasma, the currents of photoelectron emission and secondary electron emission are important sources of surface charging in geosynchronous orbit,, where plasma density is much lower and impact energy is higher than in low earth orbit. In the energetic radiation environment, the tot,al dose of radiation deposited in a mat,erial and the dose rate are the two most important factors responsible for radiation damage. Energetic electrons (2 100 KeV) can pen&rat,? the surface material into a spacecraft interior and deposit their energy. The penetrating electrons can become embedded within dielectrics on satellites building up electric potentials over time which can exceed the breakdown pot,ential of the dielectric, leading to internal dielectric (bulk, buried, or deep) charging (Vampola, 1987). The time scale of internal dielectric charging, for a hazardous electrostatic discharging t,o occur, may vary from many hours to several days (Vampola, 1994). Single event effects are triggered by cosmic rays and relativistic protons. With new sensitive electronic components and low-mass constraints (less shielding) the influence on spacecraft by the space environment is increasing. It therefore becomes increasingly important to predict times with higher risk for spacecraft anomalies. There has been much effort devoted towards understanding the space environmental origin of spacecraft anomalies (e.g., Baker et al., 1987; Vampola, 1987; Willcinsora, 1994; Wrenn, 1995; BalErr et al., 1996; Raker et al., 1998). In this study we investigate how the space environment, characterized by Dst, Kp, and energetic electron flux (> 2 MeV, EEF), affects the two geost,at,ionary satellit,es, the ESA Met,eosat-3 and the Swedish Tele-X. First, WP construct a space environment database for study of 900 anomalies on t,he two satellites. Second we make superposed epoch analysis. Third we develop neural nrt,work models to predict spacecraft) anomalies 1 day ahtad.

31

J. 4. Wu

Figure 1. Anomaly

occurrence

on Meteosat-3

etal.

and Tele-X in terms of (a) local time, (c) month,

and (d) year.

DATA DESCRIPTION Anomalies

on Meteosat-3

and Tele-X

During the operation of Meteosat-3, 724 anomalies were recorded from 21 June 1988 to 20 October 1995. The most anomalies on Meteosat-3 were from the radiometer, 70% of all anomalies. The radiometer is located in the middle of the satellite. On Tele-X (operating from April 1989 to January 1998), 192 anomalies were recorded from 2 April 1989 to 26 October 1996. 153 anomalies (80%) were from the command manager unit causing the command counter to reset to zero. On geosynchronous spacecraft the most probable causes of environmentally induced anomalies are surface charging, internal dielectric charging, and single upset events ( Vampola, 1994). Surface charging generally occurs in the satellite local time (SLT) sector from midnight to 9:00 am. Because electrons and ions subject to gradient and curvature drift are deflected by the geomagnetic field in different directions, spacecraft orbiting in this time sector will be exposed to an abundance of KeV electrons during isolated substorms or during geomagnetic storms. Spacecraft orbiting between 18:00 and midnight do not experience a similar effect as it is much easier for the ambient electrons to cancel the flux of storm ions. Geosynchronous satellites are also exposed to enhanced relativistic electrons with peak intensity in the early afternoon due to the interaction of the magnetosphere with high speed solar wind streams (Vampola, 1994). Therefore, the SLT is an important parameter which can be used to analyze anomalies probably caused by surface charging or by internal dielectric charging. The SLT dependence of the anomalies on Meteosat-3 is different from that on Telex, as seen from Figure l(a). It should be mentioned here that the most anomalies from the radiometer on Meteosat-3 or from the command manager unit on Tele-X have a similar SLT distribution to that of all of the anomalies on Meteosat-3 or Tele-X (not displayed in Figure l(a)). On Meteosat-3, 353 anomalies (48.8%) occurred in the SLT sector from 22:00 to 09:00, while in the same SLT sector 85 anomalies (44.3%) occurred on Tele-X. The anomalies occurred in this SLT sector are most probably caused by surface charging ( Vampola, 1994). Internal dielectric charging more probably occurs in the SLT lO:OO-18:00, in which 291 anomalies (40.2%) occurred on Meteosat-3 while 89 anomalies (46.4%) on Tele-X. For the remaining anomalies in other SLT on Meteosat-3 and Tele-X, it is more difficult to give a possible origin. Cosmic rays and proton events are not used here to identify single event effects in the anomalies. This issue will be pursued in a further study. The effect of the semiannual modulation of geomagnetic activity on the anomaly distribution of Meteosat-3 and Tele-X is seen in Figure l(b), with anomalies peaking around the equinoxes. The effect is more distinctive on Meteosat-3 than on Telex. 68.4% anomalies on Meteosat-3 and 66.1% anomalies on Tele-X occurred around equinoxes. In general, the anomalies sorted by seasonal peaks around equinoxes might be considered due to surface charging by KeV thermal electrons. However, the relativistic electrons are also enhanced due to major geomagnetic storms. It was shown (Nagai, 1988; KOOKS and Gorney, 1991) that the enhancement of relativistic electrons can extend from 1 to 5 days following the storm onset (as measured by Dst and Kp). Therefore, surface charging is not the unique origin of the anomalies around equinoxes. The solar activity effect on the spacecraft is shown in Figure l(c). Anomalies on both Meteosat and Tele-X are increasing towards the solar minimum (around 1996). This is primarily due to increasing number of solar coronal holes and consequently increasing fluxes of high speed solar wind streams during the declining phase of the solar cycle, although the effect of aging of spacecraft components might also have been involved. In this study we consider all the anomalies on Meteosat-3 and Tele-X equivalently as induced by space environmental factors without further classification of the anomalies in the following superposed epoch analysis and NN prediction. The choice of daily resolution is mainly because Meteosat-3, Tele-X and GOES satellites are located at different longitudes along the geostationary orbit. Thus, the SLT information can not be used as input for NN prediction. It will be very useful for prediction while using higher time resolution data as input.

33

Analysis and Prediction of Space Environment Effects Daily-averagedEEF

,

102’ -9

-8

-7

-6

-5

-4

-3

-2

-1

Daily-summedKp

Daily-averagedDst (nT)

(~m%~‘sr-‘)

lo’,

,

-5

.

I

I

_401

0

1

-9

,

3.6,

I

J

*I

0

1

-9

. -6

-7

-6

-5

Days

-4

-3

-2

-1

(x 6) ,

-6

Days

-7

-6

-5

-4

-3

-2

-1

,

I

I

0

1

Days

Figure 2. Superposed epoch analysis on anomaly and non-anomaly intervals characterized by (a) Daily-averaged EEF (> 2 Me V): (b) Daily-averaged Dst; and (c) Daily-summed lip. The solid line represents the mean distribution of anomaly interval.3 over 691 anomalies on Meteosat-3 and the dashed line the mean distribution of non-anomaly intervals over 420 non-anomalies on Meteosat-3. The dotted line refers to the mean distribution of anomaly intervals over 167 anomalie.9 on Tele-S and the dashed-dotted line to the mean distribution of non-anomaly intervals over 140 non-anomalies on Tel+,Y.

Space Environment.

Data

Geomagnetic indices Iip and Dst and EEF (>2 MeV) are used here as environmental parameters to correlate with spacecraft, anomalies. Hourly Dst and 3-hourly Kp data are downloaded from the NSSDC OMNIWeb. Daily-averaged Dst and daily-summed Kp are actually used in t,his st,udy. The daily-averaged EEF are inferred from original 5-minute averaged data of NOAA GOES satellites. An anomaly interval is defined as a period of time preceding a day when an anomaly occurred on Meteosat-3 or on Tele-X. The maximal interval selected is 10 days. A non-anomaly interval is defined as a period of time preceding a day when anomalies did not occur on Meteosat-3 or on Tele-X and during the data interval anomalies did not occur on Meteosat-3 or on Tele-X. The maxima1 interval selected is also 10 days. Therefore, the definitions of anomaly and non-anomaly intervals are satellite-dependent. The purpose to define satellite-dependent anomaly and non-anomaly intervals is to see how the models trained on one satellite can generalize to the other. After data processing we obtain 613 anomaly intervals and 420 non-anomaly intervals for Meteosat-3, and 167 anomaly intervals and 140 non-anomaly int.ervals for Tele-X. SUPERPOSED

EPOCH

ANALYSIS

The results of superposed epoch analysis on the database constructed above are shown in Figures 2(a)-2(c). As seen from the averaged anomaly interval pattern of Kp or Dst, the anomalies on both Meteosat-3 and Tele-X tend to occur during the period of time when geomagnetic activity starts decreasing after reaching the maximum. This indicates that, the spacecraft anomalies frequently occurred during the recovery phase of geomagnetic storms as measured by Dst or Kp. It is also seen that t,he space environment conditions during the last 4-6 days preceding an anomaly contribute statistically the most to the occurrence of anomalies on both Meteosat-3 and Tele-X. As seen pattern and Dst indicate electrons

from Figures 2(a)-2(c), of Kp or Dst are more would be better input that the anomalies on (> 2 MeV).

for Meteosat-3 the mean anomaly interval pattern and the mean non-anomaly interval clearly distinguished from each other than those of the EEF. This means that Kp parameters than the EEF in predicting anomalies on Meteosat-3. This might further Meteosat-3 were not mainly caused by internal dielectric charging due to energetic

In contrast, for Tele-X the mean anomaly interval and the averaged non-anomaly interval for the EEF are clearly distinguished from each other as those for Dst and Kp. This means that the EEF would be a parameter as good as Kp and Dst in predicting anomalies on Tele-X and might further indicate that internal dielectric charging is an important cause for the anomalies on Tele-X. In the mean non-anomaly interval, the energetic electron environment of Meteosat-3 is similar to that of Tele-X. However, in the mean anomaly interval, the EEF on Tele-X is considerably higher than on Meteosat-3 during the last 4-5 days preceding the occurrence of anomalies. This implies that there were more anomalies (in percentage) on Tele-X caused by internal dielectric charging than on Meteosat-3. These analyses are basically in line with the analyses in section 2.1 and will be further confirmed from NN predictions in the next section. NEURAL

NETWORK

Neural networks 1997a. 1997b).

PREDICTIONS

OF SPACECRAFT

ANOMALIES

(NNs) were applied in studies of solar wind-magnetosphere coupling With WIND data as input, t,he developed NN models were further

(e.g., Wu and Lundstedt, 1996, verified from their capability

J. 6. Wu et ul

34

i_::zy iil:~~~~~~ I:I:~~~ 1

2

TCw &sy~ine l&$(da$)

9

10

T&w &l$ne

9 l&th7(cla~)

10

2

&e &lay:m? I:“gth7(&)

Figure 3. The total prediction rate versus the length of the time delay line in the network Input layer, with the input parrrmetPr: (a) Daily-summed h’p; (b) Daily-averaged Dst; and (c) Daily-averaged EEF. The solid line stands for tht total predzction rate on Metcosat-3. the dashed line for the total pwdicfion rate on Telr-S. and the dotted line for the prediction rate ,for training on Mftfoscat-3.

of accurately predicting a major magnetic storm triggered by the 1997 January halo-CME ( iliu et al., 1998a). The resulting relativistic electron enhancement was suspected as t,he killer of ATkT Tclestar 401 satellite on January 11. 1997. NNs have also been used to make spacecraft anomaly predict,ions (~dl”o UU~/N~/~v.s. 1997; 1i.11ct d.. 1998h). In this section we develop NN models to predict spacecraft anomalies 1 day ahead. Training

and test data

Daily-averaged of Dst, Kp and and to 0 for a anomaly; If the

EEF, daily-averaged Dst and daily-summed Kp serve as the input parameter, respectively. The data the logarithm of EEF are linearly normalized to [-1, 11. The network output is set to 1 for an anomaly non-anomaly during training. For test, if the output is in the range (0.5,1.5). it is classified as an output is in the range (-0.5,0.5), it, is classified as a non-anomaly.

We carry out NN predictions in the following two case studies. In the Case I, t,raining is only on Meteosat-3 (454 anomalies and 279 non-anomalies); Trained NNs are generalized from the remaining events on Meteosat-3 (159 anomalies and 141 non-anomalies) and all the events on Tele-X (167 anomalies and 140 non-anomalies). In the Case II, training is only on Tele-X (95 anomalies and 80 non-anomalies); Trained NNs are generalized from t,he remaining events on Tele-X (72 anomalies and 60 non-anomalies) and all the events on Meteosat-3 (613 anomalies and 420 non-anomalies). Anomaly

predictions

The prediction rate for anomalies is defined as the number of correct predictions for anomalies divided by the number of anomalies in a dataset. The prediction rate for non-anomalies is defined as the number of correct predictions for non-anomalies divided by the number of non-anomalies in the dataset. The total prediction rate is defined as the total number of correct predictions for both anomalies and non-anomalies divided by t,he tot,al number of anomalies and non-anomalies in the dataset. In the Case I study, as the length of the time delay line in the NN input layer, rY varies from 1 day to 10 days in terms of different number of network hidden neurons, the best prediction results are shown in Figures 3(a)-3(c). The best prediction rates are summarized in Table 1, where R, is the total prediction rate for anomalies and non-anomalies rate for non-anomalies on the prediction rate for anomalies on Meteosat-3, R,z the prediction on Meteosat-3, R,I Meteosat-3, Rt the total prediction rate for Tele-X, Rtl the prediction rate for anomalies on Tel+X, and Rtz the prediction rate for non-anomalies on Tele-X. As seen from Table 1, with the training on Meteosat-3 the best tot,al prediction rat.es from Kp, Dst,, and the EEF are 79%, 73%, and 62% for Meteosat-3, respectively. The corresponding total prediction rates from Kp. Dst, and t,he EEF are, respectively, 64%, 66%, and 67% for Telex. In the Case II study, as r varies from 1 day to 10 days in terms of different number of net,work hidden neurons, the best prediction results are illustrated in Figures 4(a)-4(c). It is interesting to note t.hat, the best predict,ion is produced at r = 4 (days). This might be associated with the time scale of accumulation effect for anomalies t,o occur on Telex. The best prediction rates are also summarized in Table 1. With the training on Tele-X, t,he best t,otal prediction rates from Kp, Dst, and the EEF are 70%, 71%, and 74% for Telex, respectively. The corresponding total prediction rates from Kp, Dst, and the EEF are, respectively, 57%, 58%, and 51% for Meteosat-3. In the Case I, the trained NN models are based only on the events (anomalies and non-anomalies) on Meteosat-3. As analyzed in section 2.1, at least about 50% of the anomalies on Meteosat-3 were most probably caused by surface charging. The EEF can be used to predict the anomalies caused by internal dielectric charging. For the anomalies caused by surface charging, the EEF can not accurately predict them since thermal energy electrons are simultaneously not strongly correlated with relativistic electrons. This might explain why the EEF produces the total prediction rate about 10% less than Kp and Dst.

Analysis and Prediction of Space Environment Effects

35

Figure 4. The total prediction rate versus (a) Daily-summed Kp; (b) Daily-averaged on Tele-X, Tele-S.

the dashed

line for

the length of the time delay line in the network input layer, with the input parameter: Dst; and (c) Daily-averaged EEF The solid line stands jot the total prediction rate the total prediction rate on Meteosat-3, and the dotted line for the prediction rate for training on

Table

I.

One day ahead prediction

of spacecraft

anomalies

Case I Input

7

Case II

R,

R*1

Rmz

0.78 0.77 0.90

0.80 0.68 0.38

KP

8

0.79

Dst EEF

10 2

0.73 0.66

Rt

0.64 0.66 0.66

Rt1

Rt2

Input

7

0.78 0.83 0.90

0.46 0.45 0.36

KP Dst EEF

4 4 4

Rt

0.70 0.71 O.i4

Rt1

Rtz

Rm

R,l

Rm2

0.71 0.76 0.74

0.68 0.63 0.75

0.57 0.58 0.51

0.46 0.49 0.42

0.73 0.70 0.64

In the Case I for Meteosat-3 Kp produces better prediction than Dst. It is known that Kp includes contributions both from substorms currents and the ring current, giving a fairly global characterisation of the energy input into the magnetosphere (Bartels, 1949; Menvielle and Berthelier, 1991). Dst has contributions mainly from the equatorial ring current, with some uncertainties due to magnetic contributions from other sources, e.g, magnetopause currents and asymmetric ring current (Sugiura, 1964; Rostoker, 1972). Therefore, Kp can produce better prediction than Dst if more anomalies on Meteosat-3 are associated with surface charging than with internal dielectric charging. For Tele-X, the EEF, Kp and Dst produce nearly the same total prediction rate. This implies that internal dielectric charging due to the EEF is an important source for the anomalies on Tele-X. In the Case II of training NNs only on Tele-X, for Tele-X Kp and Dst give nearly the same total prediction rate, but give a bit lower prediction rate than does the EEF. This again proposes that internal dielectric charging is an important source for anomalies on Tele-X. For Meteosat-3, the total prediction rates from Kp and Dst are almost the same, but better than that from the EEF. In addition, the EEF can produce 74% total prediction rate for Tele-X but only 51% for Meteosat-3. These results support the analysis that there were less anomalies on Meteosat-3 due to internal dielectric charging than on Tele-X (in percentage) and that surface charging is an important cause for the anomalies on Meteosat-3. In view of prediction accuracy in the two case studies, Kp and Dst produce either similar prediction rates to those from the EFF or produce better predictions than the EEF for Meteosat-3 and Tele-X. Therefore, Kp and Dst are generally more useful environment parameters than the EFF to correlate suspected surface charging and internal dielectric charging anomalies. Also, Kp containing the contribution from substorms and storms is slightly a better environment parameter than the storm index Dst. Thus, Kp is the optimal environment parameter as compared tfo Dst and the EEF. Therefore, the difference of prediction accuracy in the two case studies can be explained in terms of the type of anomalies and optimal environment parameters. Also, the analysis here is primarily in agreement with the analyses in section 2.1 and section 3 in terms of the type of anomalies on the two satellites and the resultant prediction accuracy. While generalising the trained NN models from one spacecraft to the other, the prediction rate declines as seen in Table 1. This could be due to the following factors: (1) Definition of anomaly and non-anomaly is satellite-dependent; 2) The local conditions (e.g. spacecraft design, susceptibility) are different; (3) The training was only on one satellite; of anomalies caused by surface charging and by internal dielectric charging is different between the I 4) t.he proportion two spacecraft (i.e., different SLT distribution). Therefore, training on one spacecraft and testing on the other can provide the information about the difference of the types of anomalies between the two spacecraft. It should be noted that the SLT environment along the geostationary orbit has been averaged out due to the use of daily data. The NN models developed here can be used to predict times with higher risks for anomalies in real-time from ACE data because the NN models which can accurately predict Dst from solar wind data have been available ( WU and Lundatedt, 1996, 1997a, 1997b; Wu et al., 1998a).

J. 4. Wu et al.

36

SUMMARY

AND CONCLUSIONS

In this paper we investigate predict spacecraft anomalies

how the space environment affects geostationary spacecraft 1 day ahead. The main results are summarised below.

and develop

NN models

t,o

We find from the superposed epoch analysis that the spacecraft anomalies frequently occurred during the recovery phase of geomagnetic storm and that the space environment conditions during the last 4-6 days preceding an anomaly contribute statistically the most to the anomaly occurrence. We find from network predictions (1 that in the case give the total prediction rate of 79 dO, 73%, and 62% and 67% for Tele-X; 2) that in the case of training prediction rate of 57 d0, 58%, and 51% for Meteosat-3 Tele-X; and (3) that Kp is the optimal environment and Tele-X.

of training on Meteosat-3, Kp, Dst, and the EEF, respectively, for Meteosat-3 and give the total prediction rate of 64%, 66%, on Tele-X, Kp, Dst, and the EEF, respectively, give the total and give the total prediction rate of 70010, 71%, and 74% for parameter to correlate with the anomalies on both Meteosat-3

It is revealed from the anomaly local time distribution, superposed epoch analysis, and network prediction that Meteosat-3 was more subject to surface charging than to internal dielectric charging, and that Tele-X was more subject to internal dielectric charging than to surface charging. The developed NN models can be used to predict t,imes with higher risks for anomalies in real-time from ACE data. Finally we have the following suggestions for the future work: (1) Analyze and model on higher time resolution, the SLT information can therefore be encoded to NN; (2) Use other global geomagnetic indices like ap or am and substorm indices like AE; and (3) Use low energy electron flux parameters to capture the anomalies caused by surface charging, in combination with EEF. REFERENCES Baker, D.N., R.D. Belian, P.R. Higbie, R.W. Klebesadel, and J.B. Blake, Deep Dielectric Charging Effects Due to High Energy Electrons in Earth’s Outer Magnetosphere, J. Electrost., 20, 3 (1987) Baker, D.N. et al., An Assessment of Space Environmental Conditions During the Recent Anik El Spacecraft Failure, ISTP Neural., 6, 8-29, June (1996) Baker, D.N., J.H. Allen, S.G. Kanekal, and G.D. Reeves, Disturbed space environment may have been related to pager satellite failure, EOS, 79, 477 (1998) Bartels, J., The standardised Index, KS, and the Planetary Index, Kp, IATME Bull. 12b, 97, IUGG Pub. Office, Paris (1949) Hertz, J., A. Krogh and R. G. Palmer, Introduction to the Theory of Neural Computation, ch. 6, Addison-Wesley (1991) Koons, H.C., and D.J. Gorney, A Neural Network Model of the Relativistic Electron Flux at Geosynchronous Orbit, 1. Geoghya. Res., 96, 5549 (1991) L6pez Honrubia, F.J., A. Hilgers, Some Correlation Techniques for Environmentally Induced Spacecraft Anomalies Analysis, J. Spacecraft and Rockets, 34, 670 (1997) Menvielle, M., and A. Berthelier, The K-derived Planetary Indices: Description and Availability, Rev. Geophys., 29, 415 (1991) Nagai, T., Space Weather Forecast: Prediction of Relativistic Electron Intensity at Geosynchronous Orbit, Geophys. Res. Lett., 15, 425 (1988) Rostoker, G., Geomagnetic Indices, Rev. Geophys. Space Phys., 10,935 (1972) Sugiura, M., Hourly Values of Equatorial Dst for the IGY, Ann. ht. Geophys. Year, 35 (1964) nibble, A.C., The Space Environment: Implications for Spacecraft Design, Princeton University Press (1995) Vampola, A.L., Thick Dielectric Charging on High-Altitude Spacecraft, J. Electrost., 20, 21 (1987) Vampola, A.L., Analysis of Environmentally Induced Spacecraft Anomalies, J. Spacecraft and Rockets, 31, 154 (1994) Wilkinson, D.C., NOAA’s Spacecraft Anomaly Data Base and Examples of Solar Activity Affecting Spacecraft, J. Spacecraft and Rockets, 31, 160 (1994) Wrenn, G.L., Conclusive Evidence for Internal Dielectric Charging Anomalies on Geosynchronous Communications Spacecraft, J. Spacecraft and Rockets, 32, 514 (1995) Wu, J.-G., and H. Lundstedt, Prediction of Geomagnetic Storms from Solar Wind Data using Elman Recurrent Neural Networks, Geophys. Res. Lett., 23, 319 (1996) Wu, J.-G., and H. Lundstedt; Geomagnetic Storm Predictions from Solar Wind Data with the Use of Dynamic Neural Networks, J. Geophys. Res., 102, 14255 (1997a) Wu, J.-G., and H. Lundstedt, Neural Network Modeling of Solar Wind-Magnetosphere Interaction, J. Geophys. Res., 102, 14457 (1997b) Wu, J.-G., H. Lundstedt, P. Wintoft, and T.R. Detman, Neural Network Models Predicting the Magnetospheric Response to the 1997 January Halo-CME Event, Geophys. Res. Lett, 25, 3031 (1998a) Wu, J.-G. et al., Study of Plasma and Energetic Electron Environment and Effects, Technical Report, Work Package 220, ESTEC/Contract No. 11974/96/NL/JG(SC) (1998b)