John Bosco Habarulema

Rhodes University

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Zama Katamzi

South African National Space Agency

Lee‐Anne McKinnell

Rhodes University

Tshimangadzo Merline Matamba

South African National Space Agency

Dalia Burešová

Czech Academy of Sciences, Institute of Atmospheric Physics

Daniel Okoh

National Space Research and Development Agency

Jean Uwamahoro

University of Rwanda

A. B. Rabiu

Obafemi Awolowo University

Mpho Tshisaphungo

South African National Space Agency

Pierre J. Cilliers

South African National Space Agency

Patrick Mungufeni

Muni University

Cooperative Institutions

South African National Space Agency

Goddard Space Flight Center

Universidade do Vale do Paraíba

Istituto Nazionale di Geofisica e Vulcanologia

National Institute for Space Research

Rhodes University

Centre National de la Recherche Scientifique

Johns Hopkins University Applied Physics Laboratory

University of Colorado Boulder

University of Michigan–Ann Arbor

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On Imaging South African Regional Ionosphere Using 4D‐var Technique

Space Weather (2019)

Nicholas Ssessanga Yong Ha Kim John Bosco Habarulema Young‐Sil Kwak

Abstract One of the major research areas in the space weather community is the ability to understand, characterize, and model a time‐space variant ionosphere through which transionospheric signals propagate. In this paper a strong constraint four‐dimensional variational data assimilation (4D‐var) technique was used to more accurately estimate the South African regional ionosphere (bound latitude 20–35°S, longitude 20–40°E, and altitude 100–1,336 km). The altitude was capped to the JASON‐1 satellite orbital altitude for the purpose of eliminating the plasmasphere contribution hence reducing the computation expense. Background densities were obtained from an empirical internationally recognized ionosphere model (IRI‐2016) and propagated in time using a Gauss‐Markov filter. Ingested data were STECs (slant total electron content) obtained from the South African Global Navigation Satellite System receiver network (TrigNet). The vertically integrated electron content was validated using Global ionosphere Maps and JASON‐3 data over the continent and ocean areas, respectively. Further, vertical profiles after assimilation were compared with data from a network of ground‐based regional ionosondes Hermanus (34.25°S, 19.13°E), Grahamstown (33.3°S, 26.5°E), Louisvale (21.2°S, 28.5°E), and Madimbo (22.4°S, 30.9°E). Results show that assimilation of STEC data has a profound improvement on the estimation of both the horizontal and vertical structures during quiet and storm periods. Accuracy of the horizontal structure decreases from the continent toward the ocean area where GPS receivers are less abundant. Superiority of assimilating STEC is best pronounced during daytime especially when estimating maximum electron density of the F 2 layer ( NmF 2), with a 60% root‐mean‐square error improvement over the background values.

International Reference Ionosphere

Longitude

Geographic coordinate system

10.1029/2019sw002321

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Citations (17)

Co-located Space Weather radar studies over Southern Africa

Lee‐Anne McKinnell R. Athieno John Bosco Habarulema Nicholas Ssessanga Chigomezyo M. Ngwira

Over the past decade considerable attention has been given to increasing the geophysical infrastructure within the African region with particular emphasis on data collection for the purpose of enhancing our knowledge of ionospheric events that result from adverse Space Weather. South Africa continues to contribute to this effort through the expansion of its own networks, the assistance with networks into other African countries, and the training of young researchers from Africa. Currently, the South African network includes four DPS-4D digisondes, approximately 50 Global Positioning System (GPS) receivers, 1 GPS Scintillation Receivers, 1 High Frequency (HF) Doppler Radar and 2 permanent Magnetic Observatory Sites. At least 4 sites in South Africa currently host, or will in the future host, co-located radar systems. This paper will demonstrate the ability to enhance scientific investigation of Space Weather events over South Africa using co-located radar systems. The examples will be given for the Hermanus, South Africa location, which was the first African site to host a DPS-4D digisonde, HF Doppler Radar and GPS Scintillation receiver at the same location. The benefits of this site in assisting with event analysis, particularly for Space Weather forecasts and predictions, will be shown.

Space Weather

10.1109/ursigass.2011.6050908

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Storm Time Total Electron Content Modeling Over African Low-Latitude and Midlatitude Regions

Journal of Geophysical Research Space Physics (2018)

Jean Uwamahoro John Bosco Habarulema Patrice Okouma

Abstract This paper presents storm time total electron content (TEC) modeling results based on artificial neural networks, for both low‐latitude and midlatitude African regions. The developed storm time TEC models were based on Global Positioning System (GPS) observations from GPS receiver stations selected in low latitude, Northern and Southern hemisphere midlatitude regions of the African sector. GPS data selection was based on a storm criterion of −50 nT and storm data sets used to develop the models were within the periods 2001–2015, 2000–2015, and 1998–2015, for African low latitude, Northern and Southern Hemisphere midlatitude regions, respectively. For the first time in storm time TEC modeling, the meridional wind velocity was introduced as an additional input to the well‐known TEC modeling inputs (diurnal variation, seasonal variation, solar activity, and geomagnetic activity representations) to take into account the effect of neutral winds in moving ionization within the ionosphere along the magnetic field lines. Results showed that the use of meridional wind as an additional input leads to overall percentage improvements of about 5%, 10%, and 5% for the low‐latitude, Northern and Southern Hemisphere midlatitude regions, respectively. High‐latitude storm‐induced winds and the interhemispheric blows of the meridional winds from summer to winter hemisphere may be associated with these improvements.

Middle latitudes

Storm track

10.1029/2018ja025455

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Citations (13)

Estimation of equivalent ground-based total electron content using CHAMP-based GPS observations

Advances in Space Research (2019)

Patrick Mungufeni Yenca Migoya‐Orué John Bosco Habarulema S. M. Radicella

10.1016/j.asr.2019.03.039

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Citations (1)

Longitudinal variability of occurrence of ionospheric irregularities over the American, African and Indian regions during geomagnetic storms

Advances in Space Research (2019)

Teshome Dugassa John Bosco Habarulema Melessew Nigussie

Local time

Midnight

10.1016/j.asr.2019.01.001

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Citations (30)

GNSS-based empirical models for inter-hemispheric comparison of the ionosphere-plasmasphere system at mid-latitude

Nicolas Bergeot Pierre J. Cilliers Jean-Marie Chevalier E. Pinat John Bosco Habarulema

Plasmasphere

Middle latitudes

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Estimation of Ionospheric Critical Plasma Frequencies From GNSS‐TEC Measurements Using Artificial Neural Networks

Space Weather (2019)

Vivian Otugo Daniel Okoh C. U. Okujagu Sylvester Onwuneme A. B. Rabiu

Abstract This paper describes a new neural network‐based approach to estimate ionospheric critical plasma frequencies ( f 0 F2) from Global Navigation Satellite Systems (GNSS)‐vertical total electron content (TEC) measurements. The motivation for this work is to provide a method that is realistic and accurate for using GNSS receivers (which are far more commonly available than ionosondes) to acquire f 0 F2 data. Neural networks were employed to train vertical TEC and corresponding f 0 F2 observations respectively obtained from closely located GNSS receivers and ionosondes in various parts of the globe. Available data from 52 pairs of ionosonde‐GNSS receiver stations for the 17‐year period from 2000 to 2016 were used. Results from this work indicate that the relationship between f 0 F2 and TEC is mostly affected by the seasons, followed by the level of solar activity, and then the local time. Geomagnetic activity was the least significant of the factors investigated. The relationship between f 0 F2 and TEC was also shown to exhibit spatial variation; the variation is less conspicuous for closely located stations. The results also show that there is a good correlation between the f 0 F2 and TEC parameters. The f 0 F2/TEC ratio was generally observed to be lower during enhanced ionospheric ionizations in the day time and higher during reduced ionospheric ionizations in the nights and early mornings. The analysis of errors shows that the model developed in this work (known as the NNT2F2 model) can be used to estimate the f 0 F2 from GNSS‐TEC measurements with accuracies of less than 1 MHz. The new approach described in this paper to obtain f 0 F2 based on GNSS‐TEC data represents an important contribution in space weather prediction.

Ionosonde

International Reference Ionosphere

10.1029/2019sw002257

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Citations (11)

A Hybrid Regression‐Neural Network (HR‐NN) Method for Forecasting the Solar Activity

Space Weather (2018)

Daniel Okoh Gopi K. Seemala A. B. Rabiu Jean Uwamahoro John Bosco Habarulema

Abstract The Sun is the major driver of space weather events, and as a result, most applications requiring modeling/forecasting of space weather phenomena depend largely on the activities of Sun. Accurate modeling of solar activity parameters like the sunspot number (SSN) is therefore considered significant for the quantitative modeling of space weather phenomena. Sunspot number forecasts are applied in ionospheric models like the International Reference Ionosphere model and in several other projects requiring prediction of space weather phenomena. A method called Hybrid Regression‐Neural Network that combines regression analysis and neural network learning is used for forecasting the SSN. Considering the geomagnetic Ap index during the end of the previous cycle (known as the precursor Ap index) as a reliable measurement, we predict the end of solar cycle 24 to be in March 2020 (±7 months), with monthly SSN 5.4 (±5.5). Using an estimated value of precursor Ap index as 5.6 nT for solar cycle 25, we predict the maximum SSN to be 122.1 (±18.2) in January 2025 (±6 months) and the minimum to be 6.0 (±5.5) in April 2031 (±5 months). We found from the model that on changing the assumed value of precursor Ap index (5.6 nT) by ±1 nT, the predicted peak of solar cycle 25 changes by about 11 sunspots for every 1‐nT change in the assumed precursor Ap index.

Space Weather

Sunspot number

Solar cycle 24

Sunspot

10.1029/2018sw001907

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Citations (52)

An attempt to validate HF propagation prediction conditions over Sub‐Saharan Africa

Space Weather (2011)

Mpho Tshisaphungo Lee‐Anne McKinnell Lindsay Magnus John Bosco Habarulema

Ionospheric behavior plays an important role in high‐frequency (HF) radio propagation, which subsequently provides an opportunity for studying ionospheric variability and space weather effects. The continuous change in ionospheric conditions caused by space weather strongly affects HF propagation. The use of HF communication is still very relevant over the African continent, as seen by the requirements for services provided by the Regional Warning Center for Space Weather in Africa, and this has necessitated an investigation into the prediction capabilities of the existing HF propagation models currently used over this region. This paper presents the validation of HF propagation conditions through the ionosphere by using the Ionospheric Communication Enhanced Profile Analysis and Circuit (ICEPAC) model with real‐time data from international beacons located in Africa. The HF propagation results presented are for the circuits Ruaraka (5Z4B), Kenya (1.24°S, 36.88°E), and Pretoria (ZS6DN), South Africa (25.45°S, 28.10°E) to Hermanus (ZS1HMO), South Africa (34.27°S, 19.12°E). The potential of this model as compared to real‐time data in terms of propagation condition predictions is illustrated. An attempt to draw conclusions for the future improvement of HF propagation models is also presented. Results show that ICEPAC performs better for the 5Z4B‐ZS1HMO than for the ZS6DN‐ZS1HMO circuit, although it does, in general, provide a low‐accuracy prediction compared to the real‐time data. Thus certain parameters need to be investigated further for future improvement in the performance of the ICEPAC model over Africa.

Beacon

Space Weather

High frequency

10.1029/2010sw000643

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Citations (5)

Challenges and Opportunities in Accurate Ionospheric Representation through Observations and Modelling.

AGU Fall Meeting Abstracts (2016)

John Bosco Habarulema

Representation

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