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

Present day State of ArtPresent day State of Art

Andrei Mikhailov, Victor Depuev, Anna Depueva

IZMIRANIZMIRAN

Russian Academy of SciencesRussian Academy of Sciences

Disturbed F2-layer short-term Disturbed F2-layer short-term forecast is still unsolved and very forecast is still unsolved and very challenging problem despite long challenging problem despite long history and many attempts being history and many attempts being

undertaken. undertaken.

This is due to objective This is due to objective reasons reasons

Physical mechanisms forming both Physical mechanisms forming both negative and positive F2-layer negative and positive F2-layer

disturbances are well established by nowdisturbances are well established by now

Mid-latitude F2-layerMid-latitude F2-layer

Negative daytimeNegative daytime:: [O]/[N[O]/[N22] decrease, T] decrease, Teffeff increase increase

Negative nighttimeNegative nighttime:: [O]/[N [O]/[N22] decrease, wind (Vnx) ] decrease, wind (Vnx)

diurnal variations, plasmaspheric Odiurnal variations, plasmaspheric O++ flux decrease flux decrease

Positive daytimePositive daytime:: equatorward Vnx increase, absolute equatorward Vnx increase, absolute [O] increase [O] increase

Positive nighttimePositive nighttime:: wind (Vnx) diurnal variations, wind (Vnx) diurnal variations, plasmaspheric Oplasmaspheric O++ flux increase, absolute [O] increase flux increase, absolute [O] increase

High latitude and equatorial F2-layerHigh latitude and equatorial F2-layer

Auroral zoneAuroral zone

Negative disturbancesNegative disturbances:: mainlymainly [O]/[N[O]/[N22] decrease, ] decrease,

magnetospheric convection E field and Tmagnetospheric convection E field and Tnn increase increase

(T(Teffeff increase), upward plasma outflow increase), upward plasma outflow

Positive disturbancesPositive disturbances:: mainly due to particle mainly due to particle precipitation and horizontal plasma ExB transfer precipitation and horizontal plasma ExB transfer

Equatorial zoneEquatorial zone

Both Positive and Negative disturbancesBoth Positive and Negative disturbances are mainly are mainly due to zonal Edue to zonal Eyy electric field (E electric field (Eyyx B) drift + Vnx x B) drift + Vnx

variations (low geomagnetic latitudes)variations (low geomagnetic latitudes)

Main Approaches in the Ionosphere Main Approaches in the Ionosphere Forecast Practice Forecast Practice

TheoreticalTheoretical(First principle 1D-3D models)(First principle 1D-3D models)

EmpiricalEmpirical(Statistical, Neural networks)(Statistical, Neural networks)

Semi-EmpiricalSemi-Empirical(A combination of the two first)(A combination of the two first)

Upper Atmosphere is an Open System with Many Upper Atmosphere is an Open System with Many Uncontrolled InputsUncontrolled Inputs

Upper AtmosphereThermosphere Ionosphere

Solar EUV ParticlesPrecipitation

MagnetosphericElectric Fields

Dynamo and Tropospheric Electric Fields

Internal Gravity Waves

PlanetaryWaves

Depending on prehistory and current Depending on prehistory and current state of the magnetosphere and state of the magnetosphere and

thermosphere, the reaction will be thermosphere, the reaction will be differentdifferent to the sameto the same impact from above impact from above

howeverhoweverNo thermosphere and magnetosphere No thermosphere and magnetosphere

monitoring is made at present and is not monitoring is made at present and is not expected in an observable future expected in an observable future

Thus the intensity of each particular Thus the intensity of each particular process controlling the F2-region: process controlling the F2-region:

magnetospheric electric fields, zones and magnetospheric electric fields, zones and characteristics of particle precipitation characteristics of particle precipitation

controlling Joule heating, global controlling Joule heating, global thermospheric circulation resulting in thermospheric circulation resulting in neutral composition and temperature neutral composition and temperature

variations,variations, is known pretty poor for each particular is known pretty poor for each particular

geomagnetic stormgeomagnetic storm

The impact from below:The impact from below:the intensity of gravity waves resulting in the intensity of gravity waves resulting in eddy diffusion in the 100-120 km height eddy diffusion in the 100-120 km height

range which strongly controls range which strongly controls thermospheric neutral composition, thermospheric neutral composition,

planetary waves, penetrating tropospheric planetary waves, penetrating tropospheric electric fieldselectric fields

is not controlled at all is not controlled at all

So at present there is not much So at present there is not much hope to obtain a deliberatehope to obtain a deliberate

short-term forecast of short-term forecast of the F2-layer parameters the F2-layer parameters

Theoretical ApproachTheoretical Approach

A comparison by Fuller-Rowell et al. (2000) for disturbed A comparison by Fuller-Rowell et al. (2000) for disturbed conditions has demonstrated more conditions has demonstrated more “visual”“visual” success of the model success of the model predictions than quantitative; correlation coefficients between predictions than quantitative; correlation coefficients between

3D CTIM model and observations are typically 3D CTIM model and observations are typically 0.3-0.65,0.3-0.65, depending on how the data are selected and smoothed.depending on how the data are selected and smoothed.

Negative F2-layer storm effects which are the most crucial for Negative F2-layer storm effects which are the most crucial for HF radio-wave communication cannot be satisfactory modelled HF radio-wave communication cannot be satisfactory modelled without special fitting of aeronomic parameters for each without special fitting of aeronomic parameters for each particular ionospheric storm (e.g. Richards, et al., 1989,1994; particular ionospheric storm (e.g. Richards, et al., 1989,1994; Buonsanto, 1999).Buonsanto, 1999).

Theoretical modelling may be considered as a tool for Theoretical modelling may be considered as a tool for physical analyses rather than practical applications physical analyses rather than practical applications

Empirical Approach Based OnEmpirical Approach Based On

Statistical methods for the foF2 short-term prediction for the foF2 short-term prediction

(Zevakina, 1990; Wu and Wilkinson, 1995; Muhtarov et (Zevakina, 1990; Wu and Wilkinson, 1995; Muhtarov et al., 1998; Kutiev et al., 1999; Muhtarov and Kutiev, al., 1998; Kutiev et al., 1999; Muhtarov and Kutiev, 1999; Marin et al., 2000; Kutiev and Muhtarov, 2001; 1999; Marin et al., 2000; Kutiev and Muhtarov, 2001; Araujo-Pradere et al., 2002, 2003; Tsagouri and Araujo-Pradere et al., 2002, 2003; Tsagouri and Belehaki, 2005; Liu et al., 2005) Belehaki, 2005; Liu et al., 2005)

Neural networks

(Cander et al., 1998; Cander and Mihajlovic, 1998; (Cander et al., 1998; Cander and Mihajlovic, 1998; Francis et al., 2000, 2001; Wintoft and Cander, 2000; Francis et al., 2000, 2001; Wintoft and Cander, 2000; Chan and Cannon, 2002 McKinnell and Poole, 2004;) Chan and Cannon, 2002 McKinnell and Poole, 2004;)

In principle can provide an acceptable accuracy and so is widely used in practice

Problems on this wayProblems on this wayThere is no an effective geophysical index to predict There is no an effective geophysical index to predict the ionospheric storm onset, its magnitude and the ionospheric storm onset, its magnitude and duration. duration.

The correlation with currently available The correlation with currently available planetary planetary indicesindices is not very high. is not very high.

According to “Short-Term Prediction Manual” by According to “Short-Term Prediction Manual” by Zevakina et.al.(1990) depending on latitude the Zevakina et.al.(1990) depending on latitude the correlation coefficient for correlation coefficient for foF2 are:foF2 are:

(0.86-0.52) with AE; (0.71-0.46) with Dst;(0.86-0.52) with AE; (0.71-0.46) with Dst;

(0.86-0.69) with Bz; (0.77-0.33) with Kp(0.86-0.69) with Bz; (0.77-0.33) with Kp

Problems on this wayProblems on this way

Time weighed accumulation indices such as ap(Time weighed accumulation indices such as ap() ) proposed by Wrenn, 1987, Wrenn et al., 1987 seem to proposed by Wrenn, 1987, Wrenn et al., 1987 seem to increase the correlation with increase the correlation with foF2, but the foF2, but the improvement is not significantly larger than for improvement is not significantly larger than for instantaneous indices (aa, ap, Kp, Dst). Correlation instantaneous indices (aa, ap, Kp, Dst). Correlation coefficients r < 0.7.coefficients r < 0.7.

So time-weighted accumulation indices may have So time-weighted accumulation indices may have limited use in a forecasting environment (Wu and limited use in a forecasting environment (Wu and Wilkinson, 1995)Wilkinson, 1995)

Problems on this wayProblems on this wayNext step was made by Araujo-Pradere, Fuller-Rowell Next step was made by Araujo-Pradere, Fuller-Rowell and Codrescu who proposed a correction model and Codrescu who proposed a correction model STORM (2002) based on a new index - the integral of STORM (2002) based on a new index - the integral of 3-hour ap index over the previous 33 hours weighted 3-hour ap index over the previous 33 hours weighted by a filter obtained by the method of singular value by a filter obtained by the method of singular value decomposition.decomposition.

foF2={afoF2={a00+a+a11X(tX(t00)+a)+a22XX22(t(t00)+a)+a33XX33(t(t00)} )}

wherewhere

X(tX(t00)=)=F(F()P(t)P(t00--)d)d, and F(, and F() is the filter weighting ) is the filter weighting

function of the ap index over the 33 previous hours.function of the ap index over the 33 previous hours.

STORM model is a part of IRI2000 nowSTORM model is a part of IRI2000 now

Correlation Correlation foF2 with the IRI2000 index forfoF2 with the IRI2000 index for severe storms severe storms

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 00 35 0IR I In d ex /1 0

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4A p ril S ev ere S torm sS lou gh (1 9 49 -1 996 )

foF

2

/foF

2ob

sm

ed

r = - 0 .35 9

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 00 35 0IR I In dex /10

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4S ep tem b er S evere S torm sS lou gh (19 49 -1 996)

foF

2

/foF

2ob

sm

ed

r = - 0 .15 8

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 3 0 0 3 5 0 40 0IR I In dex /10

0 .0

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4Ju ly S evere S torm sS lou gh (19 49-1 996)

foF

2

/foF

2ob

sm

ed

r = - 0 .43 3

0 5 0 1 0 0 1 5 0 2 0 0 2 5 0 30 0IR I In dex /10

0 .2

0 .4

0 .6

0 .8

1 .0

1 .2

1 .4N o vem b er S evere S torm sS lou gh (19 49 -1 996 )

foF

2

/foF

2ob

sm

ed

r = - 0 .17 0

So no miracle with geomagnetic activity So no miracle with geomagnetic activity indices either direct or transformed!indices either direct or transformed!

But there is no much choice as:But there is no much choice as:

1. Only geomagnetic indices (aa, ap, kp) are available 1. Only geomagnetic indices (aa, ap, kp) are available for the whole period of ionospheric observations - this for the whole period of ionospheric observations - this is important for forecast methods development.is important for forecast methods development.

2. Only daily Ap is predicted currently 1-3 days in 2. Only daily Ap is predicted currently 1-3 days in advance. Prediction of a controlling index is necessary advance. Prediction of a controlling index is necessary for any forecast method functioning. for any forecast method functioning.

Additional problems with global Additional problems with global geomagnetic indicesgeomagnetic 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.

Estimates of the time delay between Estimates of the time delay between geomagnetic and ionospheric storm onsetsgeomagnetic and ionospheric storm onsets

1. 0-6 h for positive disturbances (Zevakina and Kiseleva, 1978)1. 0-6 h for positive disturbances (Zevakina and Kiseleva, 1978)

2. 12 h (Wrenn et al., 1987)2. 12 h (Wrenn et al., 1987)

3. 15 h (Wu and Wilkinson, 1995)3. 15 h (Wu and Wilkinson, 1995)

4. 6-12 h (Forbes et al., 2000)4. 6-12 h (Forbes et al., 2000)

5. 16-18 h (Kutiev and Muhtarov, 2001)5. 16-18 h (Kutiev and Muhtarov, 2001)

6. 8-20 h depending on season (Pant and Sridharan, 2001)6. 8-20 h depending on season (Pant and Sridharan, 2001)

7. 3-20 h depending on LT sector (Tsagouri and Belehaki, 2005)7. 3-20 h depending on LT sector (Tsagouri and Belehaki, 2005)

8. No time delay is considered in IRI2000 (Araujo-Pradere at al.,8. No time delay is considered in IRI2000 (Araujo-Pradere at al.,

2002) 2002)

No global geomagnetic index can provide an efficientNo global geomagnetic index can provide an efficient

F2-layer forecast under such conditionsF2-layer forecast under such conditions

Despite all the problemsDespite all the problems the majority of the ionosphere the majority of the ionosphere forecast methods are based on forecast methods are based on

the geomagnetic indices the geomagnetic indices

Some estimates of an improvement achieved Some estimates of an improvement achieved over median prediction for storm conditionsover median prediction for storm conditions

(a statistical approach)(a statistical approach)

1. A 34% in the Northern and 20% in the Southern 1. A 34% in the Northern and 20% in the Southern Hemispheres. The best results are for SummerHemispheres. The best results are for Summer (up to 50%) and no improvement in Winter (up to 50%) and no improvement in Winter (STORM model, Araujo-Pradere et al., 2003)(STORM model, Araujo-Pradere et al., 2003)2. A 29% gain over climatology2. A 29% gain over climatology (Kutiev and Muhtarov, 2001)(Kutiev and Muhtarov, 2001)3. A 44% gain obtained over 15 impulse storm events3. A 44% gain obtained over 15 impulse storm events (Tsagouri and Belehaki, 2005)(Tsagouri and Belehaki, 2005)

Some estimates of an improvement achieved Some estimates of an improvement achieved over median prediction using a neural networks over median prediction using a neural networks

approachapproach(no special data selection)(no special data selection)

1. An up to 50% improvement for 1-hour ahead1. An up to 50% improvement for 1-hour ahead foF2 forecast (Chan and Cannon, 2002) foF2 forecast (Chan and Cannon, 2002) 2. About 40-45% gain in foF2 RMS for noonday2. About 40-45% gain in foF2 RMS for noonday (Fransis et al., 2000)(Fransis et al., 2000)

Severe storm condition cases study was made by Severe storm condition cases study was made by Wintoft and Cander, 2000 and problems on this way Wintoft and Cander, 2000 and problems on this way were discussed.were discussed.

After analysis in the whole the situation After analysis in the whole the situation with the empirical approach to the foF2 with the empirical approach to the foF2

short-term (1-24 h ahead) forecast, a short-term (1-24 h ahead) forecast, a method for practical use has been method for practical use has been

developed and implemented at developed and implemented at IZMIRAN IZMIRAN

Main features of the MethodMain features of the Method

The Method is designed to predict foF2 for various The Method is designed to predict foF2 for various geophysical conditions. geophysical conditions. Negative storm effectNegative storm effect as the as the most important for HF radio communication most important for HF radio communication is the main is the main concern.concern.InputInput::a) hourly foF2 for previous 28 (one solar rotation) days a) hourly foF2 for previous 28 (one solar rotation) days and current hourly foF2 observations;and current hourly foF2 observations;b) 3-hour ap index for previous 30 days + current data + b) 3-hour ap index for previous 30 days + current data + daily Ap forecast for the next day.daily Ap forecast for the next day.OutputOutput: : 24 foF2 forecasts per day with 1-24 h lead times24 foF2 forecasts per day with 1-24 h lead times(00-23 UT) for a given station (ionosonde location), so (00-23 UT) for a given station (ionosonde location), so the forecast is renovated each hour. the forecast is renovated each hour.

Main features of the MethodMain features of the Method

The Method is not designed to predict Positive and The Method is not designed to predict Positive and Quiet time F2-layer disturbances with lead time > 2-6 Quiet time F2-layer disturbances with lead time > 2-6

hours as no reliable precursors are known.hours as no reliable precursors are known.

The forecast is completely automaticThe forecast is completely automatic

The idea of the MethodThe idea of the Method

The regression is used:The regression is used:

foF2(UT+n)=CfoF2(UT+n)=C00+C+C11 foF2(UT)+CfoF2(UT)+C2 2 AI(UT+n)AI(UT+n)

where:where:

foF2=foF2/foF2med, foF2med - running median over foF2=foF2/foF2med, foF2med - running median over the 28-day training period;the 28-day training period;

AI - aeronomic index for the (UT+n) moment; AI - aeronomic index for the (UT+n) moment;

n - lead time (1-24 h); n - lead time (1-24 h);

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

4/13/1

1

1

3/2

1

1

][

][

med

med

med T

T

O

OAI

TT hh ee rr ee gg rr ee ss ss ii oo nn ii ss bb aa ss ee dd oo nn aa nn ee ww AA ee rr oo nn oo mm ii cc II nn dd ee xx ,, AA IIrr ee ss uu ll tt ee dd ff rr oo mm aa ss oo ll uu tt ii oo nn oo ff cc oo nn tt ii nn uu ii tt yy ee qq uu aa tt ii oo nn ff oo rr ee ll ee cc tt rr oo nncc oo nn cc ee nn tt rr aa tt ii oo nn ii nn tt hh ee FF 22 -- rr ee gg ii oo nn ..AA ll ll pp aa rr aa mm ee tt ee rr ss aa rr ee gg ii vv ee nn aa tt 33 00 00 kk mm hh ee ii gg hh tt ::[[ OO ]] -- aa tt oo mm ii cc oo xx yy gg ee nn ,, -- ll ii nn ee aa rr ll oo ss ss cc oo ee ff ff ii cc ii ee nn tt ==

11 [[ NN 22 ]] ++ 22 [[ OO 22 ]]

TT –– nn ee uu tt rr aa ll tt ee mm pp ee rr aa tt uu rr eeff oo rr tt hh ee cc oo nn dd ii tt ii oo nn ss ii nn qq uu ee ss tt ii oo nn aa nn dd ff oo rr tt hh ee mm ee dd ii aa nn dd aa yyTT hh ee rr mm oo ss pp hh ee rr ii cc mm oo dd ee ll NN RR LL MM SS II SS EE -- 00 00 (( PP ii cc oo nn ee aa tt aa ll ,, 22 00 00 22 ii ss uu ss ee dd ..

The idea of the MethodThe idea of the Method

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

The idea of the MethodThe idea of the Method

The above mentioned method is use for quiet and The above mentioned method is use for quiet and moderately disturbed conditions.moderately disturbed conditions.

An approach is different for severe storm periods.An approach is different for severe storm periods.

Specially selected foF2 strong disturbances observed at Specially selected foF2 strong disturbances observed at a given station were used for a given station were used for foF2 versus AI foF2 versus AI regressions for each month of the year. regressions for each month of the year.

The thresholds for the ionospheric storm onset were The thresholds for the ionospheric storm onset were specified for each months as well. When the threshold specified for each months as well. When the threshold is exeeded, the method switches from usual mode to a is exeeded, the method switches from usual mode to a corresponding regression. corresponding regression.

Training and Testing the MethodTraining and Testing the Method

The Method was tested using all severe storms The Method was tested using all severe storms observed at Slough (Chilton) during 1949-2004. observed at Slough (Chilton) during 1949-2004.

A comparison was also made with:A comparison was also made with:

a) median forecast;a) median forecast;

b) IRI2000 storm correctionsb) IRI2000 storm corrections

c) empirical model by Shubin and Anakuliev (1995)c) empirical model by Shubin and Anakuliev (1995)

Summer (Chilton, 22 storm events)Summer (Chilton, 22 storm events)

Lead Time(hours)

MRD(%)

SD(MHz)

1 6.1 0.37

3 13.4 0.67

6 16.6 0.66

12 16.7 0.63

24 16.6 0.62

28-day median 42.6 0.79

Shubin’s model 19.4 0.70

IRI-2000 correction 20.4 0.78

Equinox (Chilton, 21 storm events)Equinox (Chilton, 21 storm events)

Lead Time(hours)

MRD(%)

SD(MHz)

1 7.8 0.49

3 17.0 0.96

6 23.5 0.99

12 23.8 0.98

24 21.8 0.92

28-day median 49.1 1.08

Shubin’s model 29.0 1.10

IRI-2000 correction 30.5 1.07

Winter (Chilton, 21 storm events)Winter (Chilton, 21 storm events)

Lead Time(hours)

MRD(%)

SD(MHz)

1 8.6 0.49

3 19.7 1.02

6 22.3 1.05

12 22.4 1.05

24 21.6 1.02

28-day median 39.2 1.32

Shubin’s model 22.5 1.00

IRI-2000 correction 35.2 1.23

Results of Testing for Storm EventsResults of Testing for Storm Events

1. The prediction accuracy (MRD) decreases and scatter 1. The prediction accuracy (MRD) decreases and scatter (SDR) increases from Summer to Equinox and Winter.(SDR) increases from Summer to Equinox and Winter.

2. MRD ranges from 2. MRD ranges from 6 to 24%6 to 24% depending on lead time depending on lead time and season. For quiet time and moderately disturbed and season. For quiet time and moderately disturbed conditions typical MRDconditions typical MRD10-15%10-15% for all lead times. for all lead times.

3. Median forecast is the worst under all conditions.3. Median forecast is the worst under all conditions.

Results of Testing for Storm EventsResults of Testing for Storm Events

4. The IRI2000 and Shubin’s models provide less 4. The IRI2000 and Shubin’s models provide less accurate forecast, but both models are not linked to any accurate forecast, but both models are not linked to any current foF2 observations and, in principle, can be used current foF2 observations and, in principle, can be used globally and this is a great merit of the two models.globally and this is a great merit of the two models.

5. Both models provide close results in summer and 5. Both models provide close results in summer and equinox, but the Shubin’s model is more efficient in equinox, but the Shubin’s model is more efficient in winter. This is a very important result keeping in mind winter. This is a very important result keeping in mind the IRI2000 problems for winter season when no the IRI2000 problems for winter season when no improvement over median forecast can be demonstrated improvement over median forecast can be demonstrated (Araujo-Pradere et al., 2002). (Araujo-Pradere et al., 2002).

Visual comparisons for some Visual comparisons for some storms events storms events

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

2

4

6

8

1 0

foF

2, M

Hz

J u n 4 J u n 5 J u n 6

O b serv edP red ic ted

2 8 -d a y m ed ia nL ea d tim e = 1 h.

W in to ft a n d C a n d er

2

4

6

8

1 0

foF

2, M

Hz

J u n 4 J u n 5 J u n 6

L ea d tim e = 3 h.

2

4

6

8

1 0

foF

2, M

Hz

J u n 4 J u n 5 J u n 6

L ea d tim e = 1 2 h.

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

0

1 0

2 0

3 0

3-ho

ur a

p

A p r 6 A p r 7 A p r 8.

0

2

4

6

8

10

12

foF

2, M

Hz

A p r 6 A p r 7 A p r 8

O b serv edP red ic tedR u n n in g m ed ia n

L ea d tim e = 1 hm rd = 3 .9 %

.

m rd (IR I)= 3 0 .9 %

0

2

4

6

8

10

foF

2, M

Hz

A p r 6 A p r 7 A p r 8

L ea d tim e = 3 hm rd = 9 .0 %

.

IR I2 0 0 0 co rrec tio n

m rd (IR I)= 3 0 .9 %

T im e , h o u rs0

2

4

6

8

1 0

foF

2, M

Hz

A p r 6 A p r 7 A p r 8

L ea d tim e = 1 2 hm rd = 1 7 .2 %

.

m rd (IR I)= 3 0 .9 %

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

0

1 0

2 0

3 0

3-ho

ur a

p

A p r 2 1 A p r 2 2 A p r 2 3.

4

6

8

10

12

14

foF

2, M

Hz

A p r 2 1 A p r 2 2 A p r 2 3

O b serv edP red ic tedR u n n in g m ed ia n

L ea d tim e = 1 hm rd = 8 .1 %

.

m rd (IR I)= 2 5 .8 %

4

6

8

10

12

14

foF

2, M

Hz

A p r 2 1 A p r 2 2 A p r 2 3

L ea d tim e = 3 hm rd = 1 6 %

.

IR I2 0 0 0 co rrec tio n

m rd (IR I)= 2 5 .8 %

T im e , h o u rs4

6

8

10

12

1 4

foF

2, M

Hz

A p r 2 1 A p r 2 2 A p r 2 3

L ea d tim e = 1 2 hm rd = 2 6 .5 %

.

m rd (IR I)= 2 5 .8 %

ConclusionsConclusions1. A deliberate high accuracy foF2 forecast is impossible at 1. A deliberate high accuracy foF2 forecast is impossible at present due to objective reasons.present due to objective reasons.

2. A statistical approach can provide an acceptable (MRD = 6 -2. A statistical approach can provide an acceptable (MRD = 6 -24%) short-term (1-24 h) foF2 forecast for various geophysical 24%) short-term (1-24 h) foF2 forecast for various geophysical conditions (including severe storm periods).conditions (including severe storm periods).

3. The IRI2000 storm time correction of median foF2 may be 3. The IRI2000 storm time correction of median foF2 may be recommended for foF2 forecast where current ionospheric recommended for foF2 forecast where current ionospheric observations are absent. IRI2000 and Shubin’s models provide observations are absent. IRI2000 and Shubin’s models provide close prediction accuracy during summer and equinoxes while close prediction accuracy during summer and equinoxes while in winter the Shubin’s model is more efficient. Both models can in winter the Shubin’s model is more efficient. Both models can be used globally as they are based on easy-accessible solar and be used globally as they are based on easy-accessible solar and geomagnetic indices.geomagnetic indices.

Some Unsolved ProblemsSome Unsolved Problems (Empirical Approach) (Empirical Approach)

1. Absence of an efficient geophysical index(es) for ionospheric 1. Absence of an efficient geophysical index(es) for ionospheric F2-layer storms forecast.F2-layer storms forecast.

2. Prediction of the ionospheric storm onset moment as well as 2. Prediction of the ionospheric storm onset moment as well as the storm duration. the storm duration.

3. Positive F2-layer storm effect prediction (its magnitude and 3. Positive F2-layer storm effect prediction (its magnitude and duration) for a particular storm event (however this is not duration) for a particular storm event (however this is not crucial for HF communication as the working band becomes crucial for HF communication as the working band becomes broader under such conditions). broader under such conditions).

4. Absence a precursor to predict quiet time both positive and 4. Absence a precursor to predict quiet time both positive and negative F2-layer disturbances (Q-disturbances). negative F2-layer disturbances (Q-disturbances).

TT H H A A N N Y o u Y o u KK

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