SRF2—A Short‐Term (1–24)h f<sub>o</sub>F<sub>2</sub> Prediction Method

Perrone, Loredana; Mikhailov, A. V.

doi:10.1029/2022sw003047

Cited by 2 publications

(3 citation statements)

References 26 publications

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“…SSDMD is one among many recent attempts to improve short‐term forecasts of the foF2 and hmF2 parameters (cf., Mikhailov & Perrone, 2014; Perrone & Mikhailov, 2022; Tsagouri et al., 2018; Wang et al., 2020; Zhang et al., 2014). While other methods generally treat past foF2 or hmF2 measurements as inputs to the model, SSDMD instead uses the full EDP.…”

Section: Discussionmentioning

confidence: 99%

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

et al. 2023

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We present a method for forecasting the foF2 and hmF2 parameters using modal decompositions from measured ionospheric electron density profiles. Our method is based on Dynamic Mode Decomposition (DMD), which provides a means of determining spatiotemporal modes from measurements alone. Our proposed extensions to DMD use wavelet decompositions that provide separation of a wide range of high‐intensity, transient temporal scales in the measured data. This scale separation allows for DMD models to be fit on each scale individually, and we show that together they generate a more accurate forecast of the time‐evolution of the F‐layer peak. We call this method the Scale‐Separated Dynamic Mode Decomposition (SSDMD). The approach is shown to produce stable modes that can be used as a time‐stepping model to predict the state of foF2 and hmF2 at a high time resolution. We demonstrate the SSDMD method on data sets covering periods of high and low solar activity as well as low, mid, and high latitude locations.

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Section: Discussionmentioning

confidence: 99%

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

et al. 2023

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“…Both indices are unitless. Besides, the simultaneous investigation of dual-hemisphere stations during the same storm events can be also relevant as proposed by Mendillo and Narvaez [1], because it is important to get to know better the dependencies and differences between the two hemispheres if we want to predict the space weather effects with an empirical model in the future [48,49,52].…”

Section: Discussion and Concluding Remarksmentioning

confidence: 99%

“…There have already been studies that dealt with the correlation problem of geomagnetic indices and ionospheric parameters and also with foF2 forecasting during geomagnetic storms [43][44][45][46][47][48][49][50]. Linear regression methods are useful for studying the relationship between predictor and response variables; therefore, many studies have used them in the past to model the ionospheric response as a function of solar and geomagnetic activity [51][52][53][54][55][56][57].…”

Section: Introductionmentioning

confidence: 99%

Impact of ICME- and SIR/CIR-Driven Geomagnetic Storms on the Ionosphere over Hungary

Berényi,

Opitz,

Dálya

et al. 2023

Atmosphere

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We investigate the differences between the effects of geomagnetic storms due to Interplanetary Coronal Mass Ejections (ICME) and due to Stream Interaction Regions or Corotating Interaction Regions (SIR/CIR) on the ionospheric F2-layer during the maximum of solar cycle 24. We have created a unique list of the ICME- and SIR/CIR-driven geomagnetic storm events for the time interval between November 2012 and October 2014. Finally, 42 clear ICME and 34 clear SIR/CIR events were selected for this analysis. The individual geomagnetic storm periods were grouped by seasons, time of day, and local time of Dstmin and were analyzed using three different methods: linear correlation analysis using 4-hour averages of foF2 parameters and the geomagnetic indices (1st), daily variation of deltafoF2 (2nd), and 3D plotting: geomagnetic indices vs. time vs. deltafoF2 (3rd). The main phase day of the ICME- and SIR/CIR-induced geomagnetic storms was our main focus. We used manually evaluated ionospheric foF2 parameters measured at the Sopron ionosonde station and the geomagnetic indices (Kp, Dst, and AE) for this analysis. We have found that in most cases, the variation of the Dst index is the best indicator of the impact caused in the F2 layer. We conclude as well that the representation of the data by the third method gives a better description of the ICME and SIR/CIR-triggered storm behavior. In addition, our investigation shows that the SIR/CIR-related perturbations can be predicted with greater accuracy with the second method.

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SRF2—A Short‐Term (1–24)h f_oF₂ Prediction Method

Cited by 2 publications

References 26 publications

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

Impact of ICME- and SIR/CIR-Driven Geomagnetic Storms on the Ionosphere over Hungary

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SRF2—A Short‐Term (1–24)h foF2 Prediction Method

Cited by 2 publications

References 26 publications

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

Scale‐Separated Dynamic Mode Decomposition and Ionospheric Forecasting

Impact of ICME- and SIR/CIR-Driven Geomagnetic Storms on the Ionosphere over Hungary

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SRF2—A Short‐Term (1–24)h f_oF₂ Prediction Method