Short-Term fo F2 Forecast: Present Day State of Art

Михайлов, А. А.; Depuev, V.; Depueva, A. Kh.

doi:10.1007/1-4020-5446-7_16

Cited by 19 publications

(27 citation statements)

References 40 publications

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“…Although theoretical models (e.g., Crowley et al, 1996;Daniell et al, 1995) could be considered as powerful tools for physical analyses providing real input in the understanding of the mechanisms that govern the ionospheric formation under various geophysical conditions, they hardly offer real contribution in operational applications (Mikhailov et al, 2007). On the other hand, the empirical approach based on the correlation between the ionospheric disturbances and the level of the geomagnetic activity as it is described by various geomagnetic activity indices (Fuller-Rowell et al, 2000b;Fuller-Rowell et al, 2002;Muhtarov, 2001 a, b, 2003;Muhtarov and Kutiev, 1999;Muhtarov et al, 2002;Tsagouri and Belehaki, 2006), is widely used in practice.…”

Section: Introductionmentioning

confidence: 99%

“…observed variations (Kutiev and Muhtarov, 2003). Moreover the utilization of geomagnetic indices to ionospheric prediction models may cause a number of complications that arise from the following two facts a) geomagnetic indices do not provide high enough correlation with the relative f oF 2 deviation from monthly medians (Mikhailov et al, 2007); b) the only geomagnetic index which is available for real-time use is the predicted daily Ap index. The accuracy of the geomagnetic index predictability is an issue of major consideration.…”

Section: Introductionmentioning

confidence: 99%

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Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

2008

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Abstract.A new method for ionospheric predictions based on time series autoregressive models (AR) that was recently developed to serve the needs of the European Digital Upper Atmosphere Server (DIAS) for short term forecast of the f oF 2 parameter over Europe (up to the next 24 h) is described. Its performance for various steps ahead is compared with the outcome of neural network predictors for both storm and quiet periods in two DIAS locations, Athens and Pruhonice. The results indicate that the proposed method provides robust short term forecasts of the f oF 2 for the middle latitude ionosphere.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

2008

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“…Here it is worthy to note that the dependence of ionospheric forecasting capabilities on the prediction step is a common feature in many similar studies (e.g. Mikhailov et al 2007) and may be attributed to the profound diurnal variation of the foF2 parameter.…”

Section: Summary and Discussionmentioning

confidence: 66%

“…Similar results reported in the literature include accuracy gain of 29% over climatology obtained by Kutiev & Muhtarov (2001) for their prediction model and gain of 33% for STORM model . In a recent review, Mikhailov et al (2007) tested their model's performance in terms of MRE estimates. For quiet conditions typical MRE up to 10-15% were received, while during severe storm events the tests were performed in comparison to the performance of IRI2000.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Koutroumbas et al 2008;Koutroumbas & Belehaki 2005;Tulunay et al 2004aTulunay et al , 2004bCander 2003;Stanislawska & Zbyszynski 2001McKinnell & Poole 2001;Wintoft & Cander 2000a, 2000b or space weather indices and proxies as drivers of the ionospheric response during disturbed conditions (e.g. Mikhailov et al 2007;Muhtarov et al 2002;Kutiev & Muhtarov 2001, 2003Muhtarov & Kutiev 1999;Pietrella & Perrone 2008;Tsagouri et al 2009). In addition, the existing modeling background can take significant advantage from the use of real-time networks of ionospheric ground-based sounding systems (Galkin et al 2006;Belehaki et al 2007) that are currently available to sufficiently drive the development of operational ionospheric forecasting services.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the performance of DIAS ionospheric forecasting models

Tsagouri

2011

J. Space Weather Space Clim.

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Nowcasting and forecasting ionospheric products and services for the European region are regularly provided since August 2006 through the European Digital upper Atmosphere Server (DIAS, http://dias.space.noa.gr). Currently, DIAS ionospheric forecasts are based on the online implementation of two models: (i) the Solar Wind driven autoregression model for Ionospheric short-term Forecast (SWIF), which combines historical and real-time ionospheric observations with solar-wind parameters obtained in real time at the L1 point from NASA ACE spacecraft, and (ii) the Geomagnetically Correlated Autoregression Model (GCAM), which is a time series forecasting method driven by a synthetic geomagnetic index. In this paper we investigate the operational ability and the accuracy of both DIAS models carrying out a metrics-based evaluation of their performance under all possible conditions. The analysis was established on the systematic comparison between models' predictions with actual observations obtained over almost one solar cycle (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007) at four European ionospheric locations (Athens, Chilton, Juliusruh and Rome) and on the comparison of the models' performance against two simple prediction strategies, the median-and the persistence-based predictions during storm conditions. The results verify operational validity for both models and quantify their prediction accuracy under all possible conditions in support of operational applications but also of comparative studies in assessing or expanding the current ionospheric forecasting capabilities.

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A method forf_oF₂short-term (1-24 h) forecast using both historical and real-timef_oF₂observations over European stations: EUROMAP model

Михайлов

Perrone

2014

Radio Sci.

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A method for f o F 2 short-term forecast over Europe has been developed and implemented in the EUROMAP model. The input-driving parameters are 3 h ap indices (converted to ap(τ)), effective ionospheric T index, and real-time f o F 2 observations. The method includes local (for each station) regression storm models to describe strong negative disturbances under ap(τ) > 30 and training models to describe f o F 2 variations under ap(τ) ≤ 30. The derived model was tested in two regimes: descriptive when observed 3 h ap indices were used and real forecast when predicted daily Ap were used instead of 3 h ap indices-. In the case of strong negative disturbances the EUROMAP model demonstrates on average the improvement over the lnternational Reference Ionosphere STORM-time correction model (IRI(STORM)) model: 40% in winter, 24% in summer, and 39% in equinox. The average improvement over climatology is 41% in winter, 59% in summer, and 55% in equinox. In the majority of cases this difference is statistically significant. In the case of strong positive disturbances, higher-latitude stations also manifest a significant difference between the two models but this difference is insignificant at lower latitude stations. The substitution of 3 h ap input indices for the predicted daily Ap ones decreases the f o F 2 prediction accuracy in the case of negative disturbances but practically has no effect with positive disturbances. In both cases the proposed method manifests better accuracy than the IRI(STORM) model provides. The obtained results show a real opportunity to provide f o F 2 forecast with the (1-24 h) lead time on the basis of predicted Ap indices.

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Short-Term fo F2 Forecast: Present Day State of Art

Cited by 19 publications

References 40 publications

Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

Evaluation of the performance of DIAS ionospheric forecasting models

A method forf_oF₂short-term (1-24 h) forecast using both historical and real-timef_oF₂observations over European stations: EUROMAP model

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Short-Term fo F2 Forecast: Present Day State of Art

Cited by 19 publications

References 40 publications

Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

Time series autoregression technique implemented on-line in DIAS system for ionospheric forecast over Europe

Evaluation of the performance of DIAS ionospheric forecasting models

A method forfoF2short-term (1-24 h) forecast using both historical and real-timefoF2observations over European stations: EUROMAP model

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A method forf_oF₂short-term (1-24 h) forecast using both historical and real-timef_oF₂observations over European stations: EUROMAP model