E Layer Dominated Ionosphere Occurrences as a Function of Geophysical and Space Weather Conditions

Kamal, Sumon; Jakowski, N.; Hoque, Mohammed Mainul; Wickert, Jens

doi:10.3390/rs12244109

Cited by 5 publications

(11 citation statements)

References 27 publications

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“…Similar to NmE, %ELDI also shows a maximum between 65 • and 70 • MLAT, which fits well with the findings of Kamal et al [8,9]. In the case of a reduced solar zenith angle, as for example around local noon or in local summer, %ELDI is decreased, which matches the observations of Cai et al [10] and Kamal et al [8,9]. A decrease in %ELDI is also visible for increased solar activity, which was likewise found previously by Kamal et al [8,9].…”

Section: Neem-p: Model For the Eldi Occurrence Probability %Eldisupporting

confidence: 91%

“…Considering the trends for both hemispheres, we see that the largest %ELDI value is about 12%. Similar to NmE, %ELDI also shows a maximum between 65 • and 70 • MLAT, which fits well with the findings of Kamal et al [8,9]. In the case of a reduced solar zenith angle, as for example around local noon or in local summer, %ELDI is decreased, which matches the observations of Cai et al [10] and Kamal et al [8,9].…”

Section: Neem-p: Model For the Eldi Occurrence Probability %Eldisupporting

confidence: 89%

“…Following other ionosphere models previously developed in Neustrelitz, as presented in Jakowski et al [19] and Hoque and Jakowski [20,21], we computed the geomagnetic latitude MLAT of the profile from its geographic latitude and longitude by means of the dipole magnetic field model based on a formula described in Davies [1]. Furthermore, we computed the convection electric field magnitude E Conv as discussed in Kamal et al [9] according to a formula given in Kelley [24] from observational solar wind speed data (v sw ) and interplanetary magnetic field data (B sw ) downloaded from the SPDF website. Overall, performing the aforementioned processing steps resulted in the following information for each profile: ELDI st , NmE, hmE, wvE, MLAT, LT, DOY, F10.7 and E Conv .…”

Section: Datamentioning

confidence: 99%

“…Such a depletion may arise, for example, inside the polar hole [4,5] or inside the mid-latitude trough region [6,7]. However, previous studies revealed a strong relationship of ELDI events recorded using ionospheric radio occultation (IRO) techniques with the auroral zone and not with the polar cap or the mid-latitude trough region [3,8,9]. Therefore, in this work, we interpret ELDI events as occurring due to enhanced particle precipitation, which is capable of producing electron densities in the order of more than 10 5 cm −3 .…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, they confirmed that more ELDI events occurred during winter and local night as well as during low solar activity and during geomagnetic storms. As a preliminary step for a possible ELDI model, Kamal et al [9] described the elliptical ELDI event distribution by three spatial parameters and one temporal parameter. They investigated the variations of these parameters as a function of season, solar activity, geomagnetic activity, interplanetary magnetic field, convection electric field and solar wind energy using COSMIC IRO data covering the period from 2006 to 2018.…”

Section: Introductionmentioning

confidence: 99%

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A High Latitude Model for the E Layer Dominated Ionosphere

et al. 2021

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Under certain conditions, the ionization of the E layer can dominate over that of the F2 layer. This phenomenon is called the E layer dominated ionosphere (ELDI) and occurs mainly in the auroral regions. In the present work, we model the variation of the ELDI for the Northern and Southern Hemispheres. Our proposed Neustrelitz ELDI Event Model (NEEM) is an empirical, climatological model that describes ELDI characteristics by means of four submodels for selected model observables, considering the dependencies on appropriate model drivers. The observables include the occurrence probability of ELDI events and typical E layer parameters that are important to describe the propagation medium for High Frequency (HF) radio waves. The model drivers are the geomagnetic latitude, local time, day of year, solar activity and the convection electric field. During our investigation, we found clear trends for the model observables depending on the drivers, which can be well represented by parametric functions. In this regard, the submodel NEEM-N characterizes the peak electron density NmE of the E layer, while the submodels NEEM-H and NEEM-W describe the corresponding peak height hmE and the vertical width whE of the E layer electron density profile, respectively. Furthermore, the submodel NEEM-P specifies the ELDI occurrence probability %ELDI. The dataset underlying our studies contains more than two million vertical electron density profiles covering a period of almost 13 years. These profiles were derived from ionospheric GPS radio occultation observations on board the six COSMIC/FORMOSAT-3 satellites (Constellation Observing System for Meteorology, Ionosphere and Climate/Formosa Satellite Mission 3). We divided the dataset into a modeling dataset for determining the model coefficients and a test dataset for subsequent model validation. The normalized root mean square deviation (NRMS) between the original and the predicted model observables yields similar values across both datasets and both hemispheres. For NEEM-N, we obtain an NRMS varying between 36.1 and 47.1% and for NEEM-H, between 6.1 and 6.3%. In the case of NEEM-W, the NRMS varies between 38.5 and 41.1%, while it varies between 56.5 and 60.3% for NEEM-P. In summary, the proposed NEEM utilizes primary relationships with geophysical and solar wind observables, which are useful for describing ELDI occurrences and the associated changes of the E layer properties. In this manner, the NEEM paves the way for future prediction of the ELDI and of its characteristics in technical applications, especially from the fields of telecommunications and navigation.

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Section: Neem-p: Model For the Eldi Occurrence Probability %Eldisupporting

confidence: 91%

Section: Neem-p: Model For the Eldi Occurrence Probability %Eldisupporting

confidence: 89%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A High Latitude Model for the E Layer Dominated Ionosphere

et al. 2021

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Ionosphere variability II: Advances in theory and modeling

Tsagouri

Themens

Belehaki

et al. 2023

Advances in Space Research

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Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

Adolfs,

Hoque,

Shprits

2024

Journal of Geophysical Research: Machine Learning and Computati

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An accurate prediction of the ionospheric state is important for correcting ionospheric propagation effects on Global Navigation Satellite Systems (GNSS) signals used in precise navigation and positioning applications. The main objective of the present work is to find a total electron content (TEC) model which gives a good estimate of ionospheric state not only during quiet but also during perturbed ionospheric conditions. For this, we implemented several long short‐term memory (LSTM)‐based models capable of predicting TEC up to 24 hr ahead. For the first time, we used the solar wind forcing parameters Wprot (a measure of the ionospheric disturbance during storm time) and Econv (measure of the solar wind parameters) as driver parameters. We found that using external drivers does not improve the accuracy of TEC predictions significantly. The final model is trained with data from the last two solar cycles using TEC from the rapid UQRG global ionosphere maps (GIMs). Data from the years 2015 and 2020 were excluded from the training data set and used for testing. The performance of the LSTM‐based TEC model is tested for near real‐time (RT) cases as well by using RT products (IRTG GIMs) as historical TEC inputs. We compared the performance of the LSTM‐based model to a quiet‐time feed forward neural network (FNN)‐based model and the Neustrelitz TEC model (NTCM). The results indicate that the LSTM‐based model proposed here is outperforming the FNN‐based model and NTCM in both cases, that is, using the UQRG or the IRTG GIMs as input for the historical TEC.

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E Layer Dominated Ionosphere Occurrences as a Function of Geophysical and Space Weather Conditions

Cited by 5 publications

References 27 publications

A High Latitude Model for the E Layer Dominated Ionosphere

A High Latitude Model for the E Layer Dominated Ionosphere

Ionosphere variability II: Advances in theory and modeling

Forecasting 24‐Hr Total Electron Content With Long Short‐Term Memory Neural Network

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