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Operational implementation of the SWIF model in DIAS system

Operational implementation of the SWIF model in DIAS system. Tsagouri Ioanna Koutroumbas Konstantinos Belehaki Anna National Observatory of Athens. DIAS system: Operational since August 2006 (http://dias.space.noa.gr) Developed under the eContent Programme of the European Commission ).

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Operational implementation of the SWIF model in DIAS system

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  1. Operational implementation of the SWIF model in DIAS system Tsagouri Ioanna Koutroumbas Konstantinos Belehaki Anna National Observatory of Athens

  2. DIAS system:Operational since August 2006 (http://dias.space.noa.gr)Developed under the eContent Programme of the European Commission ) To develop and provide ionospheric products and services in real-time, and thus to fully characterise the ionospheric conditions over European middle latitudes.

  3. F-plot: nowcasting foF2 F-plot: forecasting foF2 SSN daily plot foF2 Map Nowcasting MUF Map Nowcasting Ionograms MUF Point to Point M(3000)F2 Map Nowcasting Ne Map Nowcasting

  4. DIAS users’ requirements survey reveals strong interest in receiving: • Short term forecasts for prediction step more than 1h ahead • Alerts and warnings DIAS users: more than 100 registered users

  5. TSAR:Time Series AutoRegressive technique for short term prediction of the foF2 (Koutroumbas et al., 2008) GCAM: Geomagnetically Correlated Autoregression Model for short term prediction of foF2 (Muhtarov et al., 2002) DIAS forecastingproducts

  6. Athens 15min ahead Pruhonice 1h ahead 3h ahead 6h ahead Further improvements • Issuing alerts and warnings for forthcoming ionospheric disturbances over Europe • Upgrade of TSAR for storm conditions

  7. SWIF: Solar Wind driven autoregression model for Ionospheric short term ForecastIonospheric forecasts up to 24 h ahead as well as alerts and warnings TSAR:Time Series AutoRegressive technique that provides ionospheric forecasts up to 24 h ahead under all possible geophysical conditions (Koutroumbas et al., 2008) STIM: Empirical Storm Time Ionospheric Modelthat formulates the ionospheric storm time response in respect to IMF disturbances (Tsagouri and Belehaki, 2008) foF2 current and recent past values Reference foF2 values and IMF parameters from ACE

  8. STIM (Tsagouri and Belehaki, JASTP, 2008) The idea: Use as “driver” the solar wind magnetic field at L1 contributing to the forecast of the high latitude Joule heating at least one hour in advance. STIM is triggered by an alert signalfor upcoming ionospheric storm-time disturbances obtained by the analysis of IMF observations from ACE.

  9. Bmag dB/dt Bz Dst foF2obs/foF2median Determination of alert conditions based on IMF observations • Crucial parameters: • B • dB/dt • IMF-Bz orientation • The alert provides the IMF disturbance onset at L1 point some hours in advance.

  10. i) The IMF-B should record either a rapid increase (3.8 nT/h) or absolute values greater than 13 nT. ii) The IMF-Bz component should be southward directed (Bz < - 10 nT for at least three hours) either simultaneously or a few hours later (maximum 5 hours later). iii) Each event ends when Bz is turned northward (Bz > -1 nT).

  11. Comparative evaluation of TSAR’S and STIM’s predictions • TSAR provides successful predictions 1h ahead • for prediction steps greater than 1h ahead, STIM’s performance is systematically more successful than TSAR’s

  12. SWIF model Alert Detection Algorithm (ADA) ACE Real-time data No Alert (Quiet Conditions) Alert (Forthcoming storm conditions) TSAR algorithm Short Term Predictions issued by: TSAR Short Term Predictions issued by: TSAR (1 hour after the ADA) STIM (more than 1 hour after the ADA until 24 hr after the end of storm disturbance) STIM algorithm Historical and real-time data from Ionospheric Station Local Time in the station location SWIF Short Term Predictions

  13. Evaluation tests Over 10 storm events

  14. Comparison of SWIF’s – TSAR’s –GCAM’s (Muhtarov et al., 2002) performance

  15. This along with the additional advantage of SWIF in providing alerts and warnings for forthcoming ionospheric disturbances make SWIF algorithm a powerful tool in the development of a full set of ionospheric forecasting services. Conclusions We believe that the results presented here support SWIF’s potential efficiency in providing reliable ionospheric forecasts during all possible ionospheric conditions for operational applications.

  16. Implementation of the SWIF algorithm to the DIAS related products Short term predictions for each station, in time plots and ASCII for 24 hours ahead; European maps of foF2 for the next 24 hours based on SWIF predictions, calculated with the SIRMUP method; Upgraded of the European map of Ionospheric Disturbances currently provided by DIAS with the accurate SWIF predictions; Implementation of the SWIF alert detection algorithm in DIAS system to issue storm alerts for Europe. The upgrade is expected to be accomplished until March, 2009. Acknowledgements: This work is part of the EOARD grant FA 8655-07-M-4008

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