Short-Term (1-24 h) foF2 Forecast:

1. Short-Term (1-24 h) foF2 Forecast: Present day State of Art Andrei Mikhailov, Victor Depuev, Anna Depueva IZMIRAN Russian Academy of Sciences

2. Disturbed F2-layer short-term forecast is still unsolved and very challenging problem despite long history and many attempts being undertaken. This is due to objective reasons

3. Physical mechanisms forming both negative and positive F2-layer disturbances are well established by now

4. Mid-latitude F2-layer Negative daytime:[O]/[N2] decrease, Teff increase Negative nighttime: [O]/[N2] decrease, wind (Vnx) diurnal variations, plasmaspheric O+ flux decrease Positive daytime: equatorward Vnx increase, absolute [O] increase Positive nighttime: wind (Vnx) diurnal variations, plasmaspheric O+ flux increase, absolute [O] increase

5. High latitude and equatorial F2-layer Auroral zone Negative disturbances:mainly[O]/[N2] decrease, magnetospheric convection E field and Tn increase (Teff increase), upward plasma outflow Positive disturbances: mainly due to particle precipitation and horizontal plasma ExB transfer Equatorial zone Both Positive and Negative disturbances are mainly due to zonal Ey electric field (Eyx B) drift + Vnx variations (low geomagnetic latitudes)

6. Main Approaches in the Ionosphere Forecast Practice Theoretical (First principle 1D-3D models) Empirical (Statistical, Neural networks) Semi-Empirical (A combination of the two first)

7. Upper Atmosphere is an Open System with Many Uncontrolled Inputs Particles Precipitation Magnetospheric Electric Fields Solar EUV Upper Atmosphere Thermosphere Ionosphere Internal Gravity Waves Planetary Waves Dynamo and Tropospheric Electric Fields

8. Depending on prehistory and current state of the magnetosphere and thermosphere, the reaction will be differentto the same impact from above however No thermosphere and magnetosphere monitoring is made at present and is not expected in an observable future

9. Thus the intensity of each particular process controlling the F2-region: magnetospheric electric fields, zones and characteristics of particle precipitation controlling Joule heating, global thermospheric circulation resulting in neutral composition and temperature variations, is known pretty poor for each particular geomagnetic storm

10. The impact from below:the intensity of gravity waves resulting in eddy diffusion in the 100-120 km height range which strongly controls thermospheric neutral composition, planetary waves, penetrating tropospheric electric fieldsis not controlled at all

11. So at present there is not much hope to obtain a deliberate short-term forecast of the F2-layer parameters

12. Theoretical Approach A comparison by Fuller-Rowell et al. (2000) for disturbed conditions has demonstrated more “visual” success of the model predictions than quantitative; correlation coefficients between 3D CTIM model and observations are typically 0.3-0.65,depending on how the data are selected and smoothed.Negative F2-layer storm effects which are the most crucial for HF radio-wave communication cannot be satisfactory modelled without special fitting of aeronomic parameters for each particular ionospheric storm (e.g. Richards, et al., 1989,1994; Buonsanto, 1999). Theoretical modelling may be considered as a tool for physical analyses rather than practical applications

13. Empirical Approach Based On Statistical methods for the foF2 short-term prediction (Zevakina, 1990; Wu and Wilkinson, 1995; Muhtarov et al., 1998; Kutiev et al., 1999; Muhtarov and Kutiev, 1999; Marin et al., 2000; Kutiev and Muhtarov, 2001; Araujo-Pradere et al., 2002, 2003; Tsagouri and Belehaki, 2005; Liu et al., 2005) Neural networks (Cander et al., 1998; Cander and Mihajlovic, 1998; Francis et al., 2000, 2001; Wintoft and Cander, 2000; Chan and Cannon, 2002 McKinnell and Poole, 2004;) In principle can provide an acceptable accuracy and so is widely used in practice

14. Problems on this way There is no an effective geophysical index to predict the ionospheric storm onset, its magnitude and duration. The correlation with currently available planetary indices is not very high. According to “Short-Term Prediction Manual” by Zevakina et.al.(1990) depending on latitude the correlation coefficient for foF2 are: (0.86-0.52) with AE; (0.71-0.46) with Dst; (0.86-0.69) with Bz; (0.77-0.33) with Kp

15. Problems on this way Time weighed accumulation indices such as ap() proposed by Wrenn, 1987, Wrenn et al., 1987 seem to increase the correlation with foF2, but the improvement is not significantly larger than for instantaneous indices (aa, ap, Kp, Dst). Correlation coefficients r < 0.7. So time-weighted accumulation indices may have limited use in a forecasting environment (Wu and Wilkinson, 1995)

16. Problems on this way Next step was made by Araujo-Pradere, Fuller-Rowell and Codrescu who proposed a correction model STORM (2002) based on a new index - the integral of 3-hour ap index over the previous 33 hours weighted by a filter obtained by the method of singular value decomposition. foF2={a0+a1X(t0)+a2X2(t0)+a3X3(t0)} where X(t0)=F()P(t0-)d, and F() is the filter weighting function of the ap index over the 33 previous hours. STORM model is a part of IRI2000 now

17. Correlation foF2 with the IRI2000 index for severe storms

18. So no miracle with geomagnetic activity indices either direct or transformed!But there is no much choice as: 1. Only geomagnetic indices (aa, ap, kp) are available for the whole period of ionospheric observations - this is important for forecast methods development. 2. Only daily Ap is predicted currently 1-3 days in advance. Prediction of a controlling index is necessary for any forecast method functioning.

19. Additional problems with global geomagnetic indices 1. During severe geomagnetic storms magnetometric stations are out of the auroral zone underestimating index values. 2. High latitude energy deposition (heating) is not uniform in longitude while global indices do not reflect this. 3. Ionospheric storm onset depends on LT, season and prehistory (state of the magnetosphere and thermosphere). Items 2,3 result in large scatter for delays between geomagnetic and ionospheric storm onsets.

20. Estimates of the time delay between geomagnetic and ionospheric storm onsets 1. 0-6 h for positive disturbances (Zevakina and Kiseleva, 1978) 2. 12 h (Wrenn et al., 1987) 3. 15 h (Wu and Wilkinson, 1995) 4. 6-12 h (Forbes et al., 2000) 5. 16-18 h (Kutiev and Muhtarov, 2001) 6. 8-20 h depending on season (Pant and Sridharan, 2001) 7. 3-20 h depending on LT sector (Tsagouri and Belehaki, 2005) 8. No time delay is considered in IRI2000 (Araujo-Pradere at al., 2002) No global geomagnetic index can provide an efficient F2-layer forecast under such conditions

21. Despite all the problems the majority of the ionosphere forecast methods are based on the geomagnetic indices

22. Some estimates of an improvement achieved over median prediction for storm conditions(a statistical approach) 1. A 34% in the Northern and 20% in the Southern Hemispheres. The best results are for Summer (up to 50%) and no improvement in Winter (STORM model, Araujo-Pradere et al., 2003) 2. A 29% gain over climatology (Kutiev and Muhtarov, 2001) 3. A 44% gain obtained over 15 impulse storm events (Tsagouri and Belehaki, 2005)

23. Some estimates of an improvement achieved over median prediction using a neural networks approach(no special data selection) 1. An up to 50% improvement for 1-hour ahead foF2 forecast (Chan and Cannon, 2002) 2. About 40-45% gain in foF2 RMS for noonday (Fransis et al., 2000) Severe storm condition cases study was made by Wintoft and Cander, 2000 and problems on this way were discussed.

24. After analysis in the whole the situation with the empirical approach to the foF2 short-term (1-24 h ahead) forecast, a method for practical use has been developed and implemented at IZMIRAN

25. Main features of the Method The Method is designed to predict foF2 for various geophysical conditions. Negative storm effect as the most important for HF radio communication is the main concern. Input: a) hourly foF2 for previous 28 (one solar rotation) days and current hourly foF2 observations; b) 3-hour ap index for previous 30 days + current data + daily Ap forecast for the next day. Output: 24 foF2 forecasts per day with 1-24 h lead times (00-23 UT) for a given station (ionosonde location), so the forecast is renovated each hour.

26. Main features of the Method The Method is not designed to predict Positive and Quiet time F2-layer disturbances with lead time > 2-6 hours as no reliable precursors are known. The forecast is completely automatic

27. The idea of the Method The regression is used: foF2(UT+n)=C0+C1foF2(UT)+C2 AI(UT+n) where: foF2=foF2/foF2med, foF2med - running median over the 28-day training period; AI - aeronomic index for the (UT+n) moment; n - lead time (1-24 h);

28. The Idea of the Method (Aeronomic Index AI)

29. The idea of the Method Unlike global direct solar and geomagnetic indices which exhibit only UT dependence, the proposed index AI, in principle, should demonstrate (via thermospheric parameters variations) the dependence on UT, LT, latitude and longitude, season, level of solar activity etc.

30. The idea of the Method The above mentioned method is use for quiet and moderately disturbed conditions. An approach is different for severe storm periods. Specially selected foF2 strong disturbances observed at a given station were used for foF2 versus AI regressions for each month of the year. The thresholds for the ionospheric storm onset were specified for each months as well. When the threshold is exeeded, the method switches from usual mode to a corresponding regression.

31. Training and Testing the Method The Method was tested using all severe storms observed at Slough (Chilton) during 1949-2004. A comparison was also made with: a) median forecast; b) IRI2000 storm corrections c) empirical model by Shubin and Anakuliev (1995)

32. Summer (Chilton, 22 storm events)

33. Equinox (Chilton, 21 storm events)

34. Winter (Chilton, 21 storm events)

35. Results of Testing for Storm Events 1. The prediction accuracy (MRD) decreases and scatter (SDR) increases from Summer to Equinox and Winter. 2. MRD ranges from 6 to 24% depending on lead time and season. For quiet time and moderately disturbed conditions typical MRD10-15% for all lead times. 3. Median forecast is the worst under all conditions.

36. Results of Testing for Storm Events 4. The IRI2000 and Shubin’s models provide less accurate forecast, but both models are not linked to any current foF2 observations and, in principle, can be used globally and this is a great merit of the two models. 5. Both models provide close results in summer and equinox, but the Shubin’s model is more efficient in winter. This is a very important result keeping in mind the IRI2000 problems for winter season when no improvement over median forecast can be demonstrated (Araujo-Pradere et al., 2002).

37. Visual comparisons for some storms events

38. June 4-6, 1991 Storm Event (Chilton)

39. April 6-8, 1973 Positive Q-disturbance Event (St.Petersburg)

40. April 21-23, 1980 Negative Q-disturbance Event (Moscow)

41. Conclusions 1. A deliberate high accuracy foF2 forecast is impossible at present due to objective reasons. 2. A statistical approach can provide an acceptable (MRD = 6 -24%) short-term (1-24 h) foF2 forecast for various geophysical conditions (including severe storm periods). 3. The IRI2000 storm time correction of median foF2 may be recommended for foF2 forecast where current ionospheric observations are absent. IRI2000 and Shubin’s models provide close prediction accuracy during summer and equinoxes while in winter the Shubin’s model is more efficient. Both models can be used globally as they are based on easy-accessible solar and geomagnetic indices.

42. Some Unsolved Problems (Empirical Approach) 1. Absence of an efficient geophysical index(es) for ionospheric F2-layer storms forecast. 2. Prediction of the ionospheric storm onset moment as well as the storm duration. 3. Positive F2-layer storm effect prediction (its magnitude and duration) for a particular storm event (however this is not crucial for HF communication as the working band becomes broader under such conditions). 4. Absence a precursor to predict quiet time both positive and negative F2-layer disturbances (Q-disturbances).

43. T H A NY o u K

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