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

Perrone

et al. 2013

The paper describes results of the studies devoted to the solar activity impact on the Earth's upper atmosphere and ionosphere, conducted within the frame of COST ES0803 Action. Aim: The aim of the paper is to represent results coming from different research groups in a unified form, aligning their specific topics into the general context of the subject. Methods: The methods used in the paper are based on data-driven analysis. Specific databases are used for spectrum analysis, empirical modeling, electron density profile reconstruction, and forecasting techniques. Results: Results are grouped in three sections: Medium-and long-term ionospheric response to the changes in solar and geomagnetic activity, storm-time ionospheric response to the solar and geomagnetic forcing, and modeling and forecasting techniques. Section 1 contains five subsections with results on 27-day response of low-latitude ionosphere to solar extreme-ultraviolet (EUV) radiation, response to the recurrent geomagnetic storms, long-term trends in the upper atmosphere, latitudinal dependence of total electron content on EUV changes, and statistical analysis of ionospheric behavior during prolonged period of solar activity. Section 2 contains a study of ionospheric variations induced by recurrent CIR-driven storm, a case-study of polar cap absorption due to an intense CME, and a statistical study of geographic distribution of so-called E-layer dominated ionosphere. Section 3 comprises empirical models for describing and forecasting TEC, the F-layer critical frequency foF2, and the height of maximum plasma density. A study evaluates the usefulness of effective sunspot number in specifying the ionosphere state. An original method is presented, which retrieves the basic thermospheric parameters from ionospheric sounding data.

Section: Modeling and Forecasting Techniquesmentioning

confidence: 99%

Solar activity impact on the Earth’s upper atmosphere

Kutiev

Perrone

et al. 2013

“…SWIF combines historical and real-time foF2 observations with IMF parameters obtained in real time at the L1 point from NASA/ ACE spacecraft. This is achieved through the cooperation of an autoregression forecasting algorithm, called Time Series AutoRegressive -TSAR (Koutroumbas et al 2008), with the empirical Storm-Time Ionospheric Model -STIM ) that formulates the ionospheric storm-time response based on IMF input. SWIF is able to provide ionospheric foF2 forecasts as well as alerts and warnings for upcoming ionospheric disturbances for the middle latitude ionosphere ).…”

Section: Solar Wind Driven Autoregression Model For Ionospheric Shortmentioning

confidence: 99%

Progress in space weather modeling in an operational environment

Belehaki

Bergeot

et al. 2013

This paper aims at providing an overview of latest advances in space weather modeling in an operational environment in Europe, including both the introduction of new models and improvements to existing codes and algorithms that address the broad range of space weather's prediction requirements from the Sun to the Earth. For each case, we consider the model's input data, the output parameters, products or services, its operational status, and whether it is supported by validation results, in order to build a solid basis for future developments. This work is the output of the Sub Group 1.3 ''Improvement of operational models'' of the European Cooperation in Science and Technology (COST) Action ES0803 ''Developing Space Weather Products and services in Europe'' and therefore this review focuses on the progress achieved by European research teams involved in the action.

“…In particular, TSAR is an autoregressive based (AR) model (Koutroumbas et al 2008) where the prediction of the foF2 value in a particular time is given as a linear combination of the most recent foF2 values. The number of the recent values taken into account depends on the order of the AR model.…”

Section: The Solar Wind Driven Autoregression Modelmentioning

confidence: 99%

“…Focusing in the prediction of the foF2, significant contribution for operational applications comes from data-driven empirical and semi-empirical models that exploit either time series forecasting techniques such as the standard autocorrelation as well as auto-covariance and neural networks (e.g. 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).…”

Section: Introductionmentioning

confidence: 99%

“…noa.gr. DIAS forecasting products and services are currently supported by the implementation of two ionospheric forecasting models that were designed following current trends in the field: (i) the Geomagnetically Correlated Autoregression Model (GCAM; Muhtarov et al 2002), which is a time series forecasting method driven by a synthetic geomagnetic index and (ii) the Solar Wind driven autoregression model for Ionospheric shortterm Forecast (SWIF; Tsagouri et al 2009), which combines an autoregression forecasting algorithm, called Time Series AutoRegressive (TSAR; Koutroumbas et al 2008), with the empirical Storm-Time Ionospheric Model (STIM; Tsagouri & Belehaki 2006 that formulates the ionospheric stormtime response based on IMF disturbances monitored at L1 point by NASA ACE spacecraft. Based on the implementation of the two models, DIAS system releases ionospheric forecasts up to 24 h ahead over single DIAS locations in a plot format and regional forecasts over Europe in a map format.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of the performance of DIAS ionospheric forecasting models

2011

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.