Predictability of ionospheric variations for quiet and disturbed conditions

Journal

ofArmspheric and Tmesbial

Pergamon 0021-9169(94)00144-g

Physics, Vol. 57, No. 12, pp. 1469-1481, 1995 Elsevier Science Ltd Printed in Great Britain 0021-9169/95 %9.50+0.00

Predictability of ionospheric variations for quiet and disturbed conditions P. J. Wilkinson IPS Radio and Space Services, P 0 Box 5606, West Chatswood, NSW 2057, Australia (Received infinalform

18 July

1994; accepted 4 August 1994)

Abstract-Empirical models can predict the average monthly behaviour of the ionosphere and allow for day-to-day variability. Significant departures from these predicted conditions can be forecast using a range of solar, magnetic and ionospheric observations. The main improvements needed in the prediction models is that they should be made more precise, by introducing variable prediction limits, and should be improved by harnessing the impressive power of the current physical models so that ionospheric data can be assimilated in near real time. This strategic objective will lead to ionospheric weather forecasting.

1. INTRODUCTION

The German physicist, Carl Friedrich Gauss, suggested as early as 1839 that an electrically conducting region of the atmosphere could account for variations in the Earth’s magnetic field. Balfour Stewart, in 1878, also made this observation and his article in the Encyclopedia Britannica (1892) is often regarded as the start of ionospheric science. Guglielmo Marconi, in 1901, conducted the first successful long distance radio communications experiment and in 1902 Kennelly and Heavyside independently postulated the existence of a conducting layer in the atmosphere capable of guiding radio waves. In 1924, Appleton and Barnett proved the existence of th.e ionosphere. From this point on, the development of ionospheric knowledge was rapid. Chapman (1931) modelled the ionosphere and explained the more obvious synoptic features. In fact, features that do not fit Chapman’s simple theory are still often referred to as anomalous. Towards the end of the 1930s ionospheric storms had been recognised and the first routine ionosonde stations were established by 1937. An important publication at this time, “Geomagnetism” (Chapman and Bartels, 1940) could be regarded as the start of solar terrestrial physics. During World War II HF communications highlighted the practical importance of the ionosphere and ionospheric disturbances. Many techniques for predicting and forecasting ionospheric conditions and managing HF propagation were developed, and possibly forgotten, during this time. Numerical prediction models of the ionosphere, developed to support the HF planning in the immediate post war period were precursors of the current

generation of computerised empirical models. All empirical prediction models rely on the strong relationship between the maximum F2 region ionisation, NmF2, and the smoothed sunspot number, R12. An empirical prediction model is a set of first order regression relationships between the maximum F2 region penetration frequency, foF2, and R12, constructed for a single location for each hour of the day and month of the year. Given a sunspot number, foF2 can be calculated for that location. This can be extended to encompass all known ionosonde sites and interpolation between these sites leads to a set of global maps of the F2 region. The resulting prediction models describe the ionosphere with varying degrees of success, and they still cannot give timing for an event such as an ionospheric storm. Forecasting adds this information, often in the form of a qualitative storm alert (Wilkinson et al., 1993). In Australia, as in other places, daily services are offered to support systems that depend on the ionosphere. Empirical ionospheric models are used to predict the synoptic ionosphere and daily variations are allowed for using the decile factors (Davis and Groome, 1964). There are two stages. First, some weeks in advance, a prediction is made using a likely sunspot -number for the required date and then, second, as the date draws near, a forecast is made to adjust the prediction, correcting for changes in solar activity or warning that there will be a significant departure. The purpose of these forecasts is to support system management by offering updates on the likely state of the ionosphere. The approach in Australia is based on updating

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P. J. Wilkinson

synoptic models of the ionosphere using an equivalent sunspot number known locally as a T index. The T index, like the IF2 index (Minnis, 1955) is calculated using a set of calibration curves for solar activity effects on foF2 at selected locations. The calibration curves are usually similar to the regression models already discussed. Indices can be calculated for any month (Turner, 1968; Caruana 1990) or day (Wilkinson, 1986). Monthly indices are calculated using the observed median foF2 values converted to the R12 that would have been required, on average, to produce that level of ionisation. From these R12 estimates, a most likely value is calculated, typical of the population of all possible R12. When enough stations are used (at least 12 distributed globally) these indices will depend almost entirely on solar insolation. Minor differences between the selection of calibration curves, station locations and averaging methods lead to the different indices used internationally. The differences between these indices are small and usually not statistically significant (Wilkinson, 1982). Normally, the monthly ionospheric indices appear similar to R12. Daily T indices are calculated using hourly foF2 values and are used as a measure of the ionospheric response to changing solar activity--both electromagnetic and particle inputs. Again, the R 12 required to produce an hourly foF2 is calculated and similar estimates are then averaged. The resulting index gives a gross indication of daily ionospheric variability and, during disturbed periods will bear little resemblance to the smoothed sunspot number it has been scaled to represent. For instance, during a major ionospheric storm the method can give a negative equivalent sunspot number. A daily T index can be used with an empirical ionospheric model to forecast an foF2 value close to that which is observed. Solar, magnetic and ionospheric information, collected locally as well as obtained from similar groups around the world through the IUWDS data exchange (Thompson et al., 1993), are used in forecasts. Empirical prediction models and forecasts rely heavily on data from the Worldwide Ionosonde Network for their effectiveness. Ionosonde networks were elevated to international prominence by the International Geophysical Year (1957-1958) and many routine observatory practices established at that time form the core operational methods for measurements made today (Piggott and Rawer, 1978). Greater use could be made of the Worldwide Ionosonde Network to detect ionospheric storms in near real time and the United States Airforce Digital Ionospheric Sounder System (DISS) has been set up for this purpose (Buchau et al., 1994). In Australia, data from ionosonde stations in the

local region are used to calculate daily T indices for the regional ionosphere. Figure 1 shows the observed daily T index (filled line) for the first half of 1992 together with the forecast daily T index (dashed line). The forecasters have tracked the more significant changes in daily ionospheric activity reasonably well. An analysis of the forecasts in Fig. 1 shows that there is forecaster skill involved--the forecasts are consistently better than a persistence forecast. Yet the forecasts are based more on inspired interpretation than scientific methodology. Presumably even better forecasts will be made when forecasters have better information. Balfour Stewart's Encyclopedia Britannica paper is now over 100 years old and it could be claimed we know all we need to about predicting the ionosphere. The current empirical models describe most synoptic features of the ionosphere reasonably well. Although they are poor at coping with detail, such as the equatorial anomaly, limited in handling the traditional problem of ionospheric variability and have little physical basis, they are adequate for most practical ionospheric management problems. Ionospheric forecasting remains, after over 100 years, a limited, inspired capability. While it is possible to explain what has happened in the ionosphere where observations are available, it is still difficult to predict what is happening at locations remote from the observations. This is still the current challenge facing ionospheric forecasters. In the following sections, problems in predicting and forecasting the ionosphere are highlighted using examples when the ionosphere was clearly subject to solar disturbances. To place this in perspective, a basis for judging the state of the ionosphere precedes this discussion. The examples are then discussed and some conjectures are made about future prospects.

2. A DEFINITION FOR QUIET CONDITIONS

It is difficult to develop a universal definition of a quiet ionosphere. Joselyn (1988) reviewed a wide range of potential definitions for quiet magnetic conditions and demonstrated that the state will depend on the user and the situations under consideration. Using these ideas, a quiet ionosphere could be associated with low solar wind velocities, when solar induced effects are expected to be smallest, or when the convection electric field is small, resulting in less coupling between the ionosphere and the magnetosphere. Alternatively, a quiet ionosphere can be defined in terms of global magnetic field variations ; for instance, Ap < 10, or if a finer time measure is

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sought, Kp < "some threshold" for several hours. An even more refined d,efinition of quiet conditions using low values of the A E and Dst indices was proposed by Campbell (1979). Although global magnetic indices are not ideal indicators, they are often used for convenience. All these definitions share a common feature--they are objective and different people will agree on which periods correspond to which thresholds. The possibilities refer to magnetic disturbances that have global extent. Ionospheric disturbances are regional and depend on the magnetic storm start time. Only large magnetic storms produce a global ionospheric storm. This is an important difference between ionospheric and magnetic disturbances. While magnetic disturbances can be inferred from a small number of sites, regional ionospheric disturbances must be observed. McNamara and Wilkinson (1986) foreshadowed an alternative description of the disturbed ionosphere by pointing out that there was a spectrum of disturbances, with no division between disturbed periods and undisturbed or quiet periods. Nevertheless, it is useful, for prediction purposes to give "disturbed" a meaning; e.g. disturbed periods are times when monthly empirical models are poor representations of the ionosphere and, equiwdently, periods that models cope

with are undisturbed, or quiet. This leads to a potential definition: all periods that are not disturbed are quiet. Once quiet periods are linked to the prediction model it is worthwhile identifying them because of the advantages they offer (Wilkinson, 1983 ; Reilly et al., 1991). This idea is now expanded. Daily variability is described by the decile range in ionospheric prediction models and is calculated from factors based on the work of Davis and Groome (1964) and given in the ITU H F prediction model. The decile range forms an envelope about the predicted foF2 values and accounts for the day-to-day variability in the ionosphere. It is generally accepted that the variability cannot be handled by conventional prediction programs. Therefore, observations that fall inside this envelope have, by definition, been predicted correctly by the model and those outside the range have not been correctly predicted. Assuming the model is correct, discrepancies occur because the ionosphere is disturbed from the quiet state to which the model refers. The envelope, or decile range, for correctly predicted values will be referred to in this paper as the prediction limits. A conservative monthly prediction of the ionosphere follows the line of lowest decile frequencies, the lower prediction limit, and is called the Optimum Working Frequency, or OWF, on an oblique circuit and 90% of the foF2 values

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P. J. Wilkinson

recorded in a month will exceed this. When the observed behaviour lies inside the prediction limits there is no need to predict detailed features. This is regarded as the "normal" or "quiet" ionosphere. Features inside the prediction limits can be thought of as under statistical control and the level of control is described by a mean (or median) and a range of variation (the decile range or prediction limits). The quiet ionosphere is now defined by the prediction limits. Currently, the prediction limits are defined in terms of the decile range which is a compromise between precision and customer's system needs. Structure seen inside the prediction limits will be downgraded in importance, yet if structure can be seen precision has been lost if it is not modelled. It is a normal scientific endeavour to seek the best possible precision and to therefore obtain the smallest possible error bounds. Alternatively, sometimes the prediction limits will be too small causing the model to be wrong. For instance, during an ionospheric storm there may be good consistency between individual observations, but all observations fall outside the prediction limits. Obviously, the predictions would be better if the prediction limits were increased ; but increased prediction limits would make other predictions less precise. Overall, more precise predictions can only be made if the prediction limits are variable, not fixed as they are in the current ionospheric prediction models. A forecast is a qualitative indication that the prediction limits are likely to be exceeded. The main forecasting rule is that excursions outside the prediction limits should be anticipated. Conventionally, forecasts are made for a region, such as Australia, with some allowance for latitudinal effects of ionospheric storms. Longitudinal differences are usually ignored. The narrower the prediction limits, the harder it is to make reliable forecasts of disturbed conditions. The following examples explore and illustrate these ideas further.

3. EXAMPLES OF THE DISTURBED IONOSPHERE

3.1. The presentation format for the figures In Fig. 2 and later figures, for six stations, hourly foF2 data (filled circles) are overlaid with the observed median foF2 (thin line) for the month in which the data were recorded and a monthly predicted foF2 (thick line) for the same period. The monthly prediction of the expected daily variability, the decile range or prediction limit, is shown as a vertical bar every 3 h. Almost any empirical ionospheric model available will give similar results and in the present examples the IPS prediction model is used.

The four examples are September 1989 (Figs 2 and 3), October 1989 (Fig. 4) and September 1986 (Fig. 5). Three of these examples occur during S U N D I A L campaigns and one, September 1986, has been analysed in detail already (e.g. Szuszczewicz et al., 1990, and other papers in the same issue). All of the examples are illustrated using data from six Australian stations (see Table 1 for further details). The station data are arranged in stack plots (Figs 2-5) with the highest latitude station (Hobart) placed at the bottom and the lowest latitude station (Vanimo) at the top giving a latitudinal chain 40 ° long (Hobart, Canberra, Townsville and Vanimo) and a longitudinal chain 50 ° wide (Mundaring to Norfolk Island). Magnetic activity has long been associated with ionospheric activity and the Kp index is therefore shown at the top of each figure as a needle plot. Beneath this plot, the daily Ap indices are shown when they are particularly large. 3.2. An isolated magnetic storm--24-30 September 1989 Before discussing the ionospheric storm effects, the average behaviour, described by the median foF2 is compared against predictions. While there is no detailed discussion on errors in the monthly prediction model in this paper, it is a non-trivial problem. The comments here clarify which features appear to be errors in the model so they will not become confused with the issue of ionospheric variability. The mid latitude stations Hobart, Canberra, Mundaring and Norfolk Island all agree with the predicted values; agreement falling well inside the prediction limits. For Townsville, midway between the maximum foF2, at 00 UT, or 10 LT, and the following predawn minimum, the observed average foF2 exceeds the prediction limits. This is a significant error in the model, for this month. At Vanimo, foF2 is consistently higher than the predicted level for all daylight hours, although it only exceeds the prediction limits for l h. Overall, the model agrees with the observations with some exceptions at lower latitudes. Magnetic activity during 24-30 September 1989 (Fig. 2) was initially quiet, followed by a brief isolated magnetic storm that reached a maximum Kp = 7 towards the end of the UT day 26 September. Ap for the same day was 54. The preceding two days had one Kp = 3 - and no other Kp values higher than 2 + . After the storm, magnetic activity returned to quiet levels by the middle of 27 September, although there was a brief increase in Kp to 3 - at the start of UT day 28 September. An Ap = 54 makes this a reasonably large, isolated magnetic storm.

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observed at 1027 UT on 26 September. This time roughly corresponds to increased predawn foF2 variability at Hobart. The predawn minimum was also deeper at Norfolk Island and Canberra, where it falls outside the prediction limits and corresponds to the start of the ionospheric effects associated with the magnetic storm. Daytime electron densities are depressed for nearly all the Australian stations on 27 September (after hour 72), a clear response to the magnetic storm, and in most cases the F2 region electron density drops below the F1 region electron density--a G condition (not shown in the figures). By the following day the ionosphere still has not returned to its pre-storm ionisation levels and a brief

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pulse in magnetic activity could possibly be related to sharp increases in ionisation at Townsville and Norfolk Island. Ionisation levels return gradually to prestorm levels over the next 36 h, except at Hobart where they remain depressed for a further 2 or more days. Summarising, following the start of the ionospheric storm on 26 September, Hobart falls close to the lower prediction limit, Canberra falls inside the prediction limits after 48 h and Mundaring takes a further 12 h to recover. Norfolk Island also recovers to pre-storm levels of peak ionisation during this time, while Townsville and Vanimo recover a day earlier. For most of this period the observed foF2 values fall inside the prediction limits. In some cases, particularly Canberra, the hourly foF2 values agree very well with the

predicted monthly foF2 and consequently the prediction limits appear too large. The ionospheric storm follows a well known pattern with a sharp onset, the ionosphere is depressed well below the lower prediction limit and is followed by a recovery that is more gradual at the higher latitude stations. These are the type of events that forecasters like to predict because the storm follows a clear pattern which is similar at all stations in the region. The forecasting method suggested in Section 2 will work well with storms like this one. 3.3. A disturbed period, 14-21 September 1989 Figure 3, is a more disturbed period recorded one week before the previous example. Most of the early

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Fig. 4. The F2 region response to a major magnetic storm, 18-25 October 1989. (Further details on the figure are given in the text in Section 3.4.) part of the period is disturbed. Kp is greater than 4 for over twice as often as in the first example and the Ap index is as high or higher on two days. The hourly foF2 values give a visual impression of being less orderly. Right through this period, for all stations, there is greater variability in hourly foF2 values than in the fixst example. This irregularity leads to the occasional value lying outside the prediction limits suggesting they are the correct width. On 16 and 19 September, magnetic activity is high and at Hobart foF2 lies below the lower prediction limit for most of the day. Both these storms were preceded by sudden commencements, the larger being observed on 18 September. During the first magnetic storm, on 16 September, the ionospheric electron den-

sity at Mundaring was slightly depressed, but apart from Hobart there are no obvious effects at the other stations,. The second storm, on 19 September, has a major effect on Hobart, depressing foF2 more than on 16 September or in the previous example, on 27 September. The depression at Mundaring is comparable to that on 27 September (Fig. 2). Canberra is clearly depressed, but not as much as Mundaring and none of the lower latitude stations appear affected, except for a few hours around pre-dawn. In addition to the two depressions in ionisation, Hobart is just outside the prediction limits on other days. Canberra, by contrast, is just inside the prediction limit most of the time. Mundaring is more irregular and appears to be transitional between Hobart and Canberra. Nor-

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Fig. 5. The F2 region response to a magnetic storm near solar minimum, 22-29 September 1986. (Further details on the figure are given in the text in Section 3.5.) folk Island shows no obvious depressions, but there are brief periods when foF2 falls outside the prediction limits. Townsville, on six out of eight days, falls outside the prediction limits in the late evening and Vanimo is almost always higher than the predicted levels during the daytime. Both Vanimo and Norfolk Island show a low predawn minimum on 19 September. Summarising, the magnetic storms, although apparently larger than the isolated storm in the first example, have less impact on the regional ionosphere, represented by the six stations. At best, this suggests that Kp is not giving sufficient guidance. The forecasting method could work for this period, but it is more difficult because there are suggestions of longitudinal gradients. The prediction limits are just wide

enough for most of the stations and obviously the ionospheric predictions will appear better if the prediction limits are increased, although an increased prediction limit in the previous example would have made the predictions even less precise than they were. If the predictions are to be made more precise, the prediction limits must be variable, not fixed, as they are in the current ionospheric prediction models. 3.4. A major magnetic storm--18-25 October 1989 In the third example (Fig. 4), the monthly median observations agree well with the predictions for Hobart, Canberra, Mundaring and Norfolk Island. All foF2 values are well inside the prediction limits and the diurnal curve is followed well. Townsville

Predictability of ionospheric variations

1477

Table 1. Australian ionospheric stations used in examples Station

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141.3° E 146.9° E 168.0° E 116.2° E 149.0° E 147.3° E

12.6° S 28.4°S 34.8° S 43.5° S 44.0° S 51.6° S

21.8° 48.4° 56.5° 66.5° 66.1° 72.8°

again shows an error in the predawn period and Vanimo is again predicted too low. During this period, Kp/> 4 for most of the period. On three days Ap exceeded 50 and on two it exceeded 100. Two sudden commencements were observed; at the beginning of 18 October and on 20 October, but neither was large. This is a major storm period and the ionosphere is strongly affected up to Townsville. Hobart is outside 1:heprediction limits for much of the 192 h period and is unobservable, due to absorption caused by particle precipitation, for over 90 h - an indicator of a major magnetic storm. Canberra is also clearly disturbed for most of the period. Mundaring contrasts strongly with this, showing significant differences from Canberra, reflecting a large longitudinal gradient in foF2 in the region. Mundaring is depressed ,an 20 October, before Canberra was affected; but Canberra is depressed until sunset on 24 and 25 October, while Mundaring is unaffected. Norfolk Island and Townsville are affected in a similar fashion--both are silgnificantly depressed on 21 October and, to a lesser extent, on 22 October and both are enhanced, above the prediction limits, for the rest of the period. Thus there is also a steepened latitudinal electron density gradient across the Australian region during this disturbed period, electron densities being generally lower than predicted at high latitudes and higher at low latitudes. At the low latitude station of Vanimo, storm effects are not obvious, although equatorial spread F was seen only on the evening of 21 October--indicative of a storm effect. The predictions have met with significant problems during this period and it is difficult to imagine how the model could be improved in a simple fashion to meet all the demards placed on it during a major storm. To cater for the variability would also require something better titan daily forecasts and updates. The entire region has been altered from the average state and regional observations are necessary to track the changes. A forecaster could, correctly, regard the entire period as disturbed because the prediction

model is unsuccessful. However, the lack of detail would not be helpful for a customer. 3.5. A magnetic storm near solar minimum, 22-29 September 1986 The final example (Fig. 5) is for solar minimum. The average monthly behaviour at Mundaring falls at the edge of the prediction limits during the pre-dawn period, the observed levels being lower than predicted. The diurnal shape at Vanimo is incorrect, the predicted shape being broader than observed. In addition, the daily observations at Vanimo are rather variable and frequently fall outside the prediction limits. Because of this it is not possible to identify ionospheric storm effects unambiguously. Most of the other stations are predicted satisfactorily, The magnetic storm is minor and less impressive than the previous three examples. No sudden commencement was reported for the period, although a minor variation in the magnetic field late on the UT day of 22 September 1986 was seen globally. The magnetic field became active with Kp lying near 4 and higher for much of the 192 hour period. This is a typical recurrent storm caused by high speed solar wind streams associated with coronal holes on the sun (Szuszczewicz et al., 1990). At Mundaring, on 23 September, there is a marked positive phase storm that stands out clearly. Careful analysis shows the effects of the positive phase storm are propagating equatorward and can be linked to the double peak in foF2 for Norfolk Island. Globally, only a few stations showed as strong an effect as Mundaring. The subsequent magnetic storm resulted in only minor ionospheric effects. Hobart was depressed below the prediction limit on 25 September, as was Canberra, while Mundaring was depressed more on 24 September. All three stations showed moderate variability during the period. Norfolk and Townsville had more irregular foF2 values, but were generally inside the prediction limits. Even though this is a small magnetic disturbance,

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P. J. Wilkinson

many of the daily foF2 values are outside the prediction limits which may therefore be too narrow. Making forecasts is as important near solar minimum as it clearly is nearer solar maximum. 3.6. Summary These examples have explored the idea that ionospheric predictions could be split into two tasks : predicting the normal, quiet or synoptic ionosphere and forecasting disturbances that fall outside the prediction limits. The first example showed the current prediction model was not completely reliable and the prediction limits were too wide for much of the time. Nevertheless, forecasting the disturbed period would be straightforward. The second example, from a week earlier, showed that the prediction limits were possibly too narrow and therefore need to be chosen dynamically to cope with the variations. Forecasting this period would be more difficult. The third example, a major magnetic and ionospheric storm, reveals deficiencies in the synoptic model for major disturbances, and the last example emphasises the need for dynamic prediction limits for all stages of the solar cycle. Collectively, these examples demonstrate that for ionospheric predictions to be improved further the prediction model needs to be accompanied by variable prediction limits. This will lead to more precise knowledge of the ionospheric formation.

4. CURRENT PREDICTION MODELS AND TACTICS So far, a simple two step prediction model has been proposed. First, monthly conditions are predicted using one of the many prediction models currently available. An important part of all prediction models is the representation of the F-region and there are at least two sets of F-region maps that have received international endorsement as reliable representations of the F-region of the ionosphere. The CCIR coefficient maps (CCIR, 1982) and the URSI coefficient maps (Fox and McNamara, 1988) form the basis of several methods for predicting the F region of the ionosphere and for modelling applications that are affected by the ionosphere. Many methods for upgrading these ionospheric maps have been tried and the most successful use an input from physical models of the ionosphere to improve the quality of the maps in data sparse regions. Rush et al. (1983) for instance, developed a neutral wind and atmospheric model at known locations, assumed this was valid at unsampled locations, and produced new foF2 maps. There are differences between the IPS ionospheric model, used in this paper, and the other recognised models, but

the differences are usually small compared with daily variations. All these prediction models use a single index to tune the model to the expected ionospheric conditions. This index may be a sunspot number, or an equivalent sunspot number; for example, the T index discussed earlier. The decile range, or prediction limits, take account of the daily variations in the ionosphere. Second, long term predictions are supported by short term forecasts of times when the ionosphere is expected to lie outside the prediction limits. The conventional forecasting model, just described, coped with the day-to-day variations in the ionosphere but lacked flexibility and may be imprecise. However, for many purposes it is adequate because it supplies sufficient information for systems that are either tolerant of errors or, often, cannot respond to frequent update information. Predictions and models of this type were developed shortly after World War II and have not evolved significantly since. Considerable effort has been devoted to improving magnetic forecasts. While this does not improve the prediction model, it makes better use of it. One of the principal problems is that neither the prediction limits nor ionospheric disturbances are directly linked to magnetic activity. While there is limited potential, it is worthwhile to improve both the monthly model of the ionosphere and the prediction limits. The monthly model errors shown in the examples are typical of errors seen in all the models currently available. Improving the prediction limits is a legitimate activity, although it may prove difficult to make significant advances without diverging somewhat from the current format that previous studies have preserved. Another approach is to minimise daily variability by adjusting the equivalent sunspot number based on current ionospheric observations. The potential effectiveness of this scheme was demonstrated in Fig. 1. It is not easy to develop a tactic to use this approach consistently since the recipient of the forecast must also be a party to the changes. To date, it has been sufficient to offer both a forecast of a storm coupled with an adjusted daily index for shifting the centre of the monthly prediction. Ideally, the daily prediction of the equivalent sunspot number should include a narrowing of the prediction limits. Such an approach was attempted, but it proved difficult to manage (Wilkinson, 1986). There is potential for more to be done in this area. However, the central problem remains: the ionospheric model is based on monthly F region maps that are not suitable for every situation. It is also possible to make better magnetic forecasts and tune these to represent the anticipated ionospheric effects. Wrenn (1987) and Wrenn and Rodger (1989)

Predictability of ionospheric variations offered an improved magnetic index for forecasting. This was explored fer the Australian region (Wu and Wilkinson, 1993) and found to offer a marginal improvement in for,~casting magnetic effects on the ionosphere. Howeve.r, any reduction in the variance is an advantage. Who will use these forecasts if they are successful? Most forecasters expect to offer a useful forecast with a 24 h lead and attempts are now made to make three to five day forecasts of global magnetic activity. While it is worthwhile trying to make these forecasts, there is little forecaster skill evident in 2 day and greater forecasts. This lack of success is well known and conditions customers' attitudes against relying on the forecasts since certainty about the environmental conditions is often of paramount importance. Alternatively, many services cannot respond particularly rapidly to changes so moderately better forecasts would have real value for them. Most educated customers for forecast:Lng services have an unquantified, but nevertheless a clear understanding of the balance between system response time and the required accuracy of the forecast. As it is not clear that forecasting, or ionospheric predictions for that matter, have become appreciably more accurate over the last thirty years, it is not possible to look at examples where more accurate forecasts have changed customers' habits. On the other hand, examples do exist where groups were unaware of ionospi~eric predictions, for instance, and when they started using them for H F frequency management, they rapidly changed their old habits. Another example is the rapid penetration of PC based prediction programs in service management. This paper assumes thai if forecasts are more accurate, they will be used. In the near term, there will be an advantage in improving the monthly synoptic models of the ionosphere and methods should be sought for making variable prediction limits, in keeping with the examples discussed above. These should be the major short term tactics for improving ionospheric predictions. Finally, some method for forecasting ionospheric storm effects and duration would be invaluable for improving the fiJrecasting of disturbances--those times when the ionosphere falls outside the prediction limits. While it may always be difficult to pick the start time of an ionospheric storm, it seems likely that once a storm has started, then physical models should be able to give good guidance on the storm recovery phase. A model of this type would have immediate applications in forecasting. An essential tactic for making better forecasts is to give the forecaster much greater support.

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5. STRATEGY FOR THE FUTURE

Significantly better ionospheric predictions are possible provided full use is made of our integrated knowledge of the sun, the magnetosphere and its coupling to the ionosphere and, most important, the ionosphere itself. Empirical models have been improved and fine tuned, but they still have significant limitations. In Section 2, quiet ionospheric conditions were defined as "not disturbed". This definition was chosen to emphasise the qualitative nature of the terms and to highlight that, for ionospheric prediction and forecasting to progress, a general approach to these terms must be used. The forecaster must take account of the spectrum of possible disturbances and also forecast changes in the prediction limits. The ionosphere has a wide range of responses to energy inputs, as the examples have shown. In some cases, such as the isolated storm in Fig. 2, one or two locations will give a good indication of the state of the ionosphere over a large region. However, it is not usually this simple, as the later examples showed. Larger, real time data sets will alleviate the forecasting problem for a well sampled region, but will not be a help for the data sparse region. There are two problems to solve. First, during a disturbed period, given an observation at one location, what is the likely behaviour at a distant location? Second, what is the likely evolution of the disturbance? Forecasters may not be able to answer these questions, but by using solar and global magnetic field observations they can make useful ionospheric forecasts, as Fig. 1 confirms. The forecasters task would be easier, and confidence in their forecasts would increase, if forecasting took place in a more ordered environment. Physical models of the ionosphere offer that environment and there are now several good physical models available (Daniell et a/., 1994; Anderson, 1993; Fuller-Rowell et al., 1994; Schunk, 1988 ; Schunk and Sojka, 1993 ; Rees, 1995) that should be used to improve predictions of the ionosphere and also assist in forecasting departures from the predicted behaviour. These models already make use of a range of inputs. To this range should be added current observations of the ionosphere and possibly other readily available information on energy inputs. For instance, in the longer term, Joule heating might be deduced from ground based magnetometer arrays using algorithms such as A M I E (Assimilative Mapping of Ionospheric Electrodynamics) (e.g. Emery et aL, 1990). The ideal approach for predicting the ionosphere is to start with a physical model that can be updated

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P. J. Wilkinson

with observations of the ionosphere. The model outp u t would have some uncertainty range, but this range should be more responsive, a n d p r o b a b l y smaller, than the current prediction limits. There is always going to be some limit b e y o n d which forecasts are not possible; this is the geophysical noise limit a n d will p r o b a b l y be set by travelling ionospheric disturbances (TID). All excursions outside this limit should be pre-

dictable, a n d possibly the likelihood of increased T I D activity could be used to increase the prediction limits. G r e a t e r reliance on physical models is the strategic objective for i m p r o v i n g ionospheric weather forecasting. Acknowledgements--I would like to thank Garth Patterson, the leader of the IPS Forecasting team, for preparing Fig. 1. and John Caruana for reading an early version of the paper.

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