A standalone prediction model for atomic oxygen and coronal mass ejections

Astrophysics and Space Science (2023) 368:20 https://doi.org/10.1007/s10509-023-04170-w RESEARCH A standalone prediction model for atomic oxygen and coronal mass ejections W.M. Mahmoud1 · D. Elfiky2 · S.M. Robaa3 · M.S. Elnawawy3 · S.M. Yousef3 Received: 7 August 2022 / Accepted: 3 March 2023 © The Author(s) 2023 Abstract This paper presents a standalone predictive model for Atomic Oxygen (AO), Coronal Mass Ejections (CMEs) and other space-environment parameters. The prediction is based on the numerical method of Holt–Winter’s triple smooth exponential forecasting of atmospheric constituents. Solar cycle 25 is likely to show about the same activity as cycle 23. The corresponding AO-flux–solar-activity correlation coefficients for altitudes 100, 200, and 300 km are: 0.62, 0.53, and 0.48, respectively, while the correlation coefficients for higher altitudes are lower than 0.48, an advantage that makes them more favorable for LEOs due to the harmful corrosive effects. Keywords Atomic Oxygen (AO) · Standalone prediction model · Low-Earth Orbits · Coronal Mass Ejections 1 Introduction Satellite protection depends greatly on accurate predictions of the surrounding space-environment components and hazards. The space environment of the Low-Earth Orbits (LEOs) is highly affected by solar activity. Solar activity is the key factor affecting the space environment. Assessment of the space environment depends strictly on three subprocesses or consecutive steps. The first step is the study of all components of the environment of the space mission by ad-  D. Elfiky W.M. Mahmoud S.M. Robaa M.S. Elnawawy S.M. Yousef dressing all questions about that mission (orbit, start time, duration, solar activity, type of mission, etc.). Secondly, detailed investigations of all the mission components should be done. The final step it to use all the analysis done to protect the mission in order to fulfill its target. Satellite protection requires full prediction of all the constraints and worst-case scenarios related to the mission. The prediction may be developed using deep learning, machine learning or statistical analyses. During solar-activity cycles particle, radiation, and magnetic fluxes in the heliosphere change, which cause different space-weather effects at Earth. Prediction of the solaractivity cycle is therefore the main step in protecting space missions and satellite technology (Bhowmik and Nandy 2018). Space weather influences the electromagnetic environment around Earth and human life. Many space-weather events that are caused by solar eruptions, are potential risks to the social infrastructure such as aviation, communications, artificial satellites, electric power, and positioning systems (Kusano et al. 2021). Solar cycles 23 and 24 were weak cycles at the bottom of the 80–120 years longterm Wolf–Gleissberg solar cycle (Yousef 2006), (Mawad 2017), (Yousef et al. 2018), and (Mawad and Abdel-Sattar 2019). 1 Assembly, Integration and Testing, AIT Center at Egyptian Space Agency, EgSA, Cairo, Egypt 2 Thermal, structure and space environment dep. at National Authority of Remote Sensing and Space Sciences, NARSS, Cairo, Egypt 1.1 Atomic Oxygen (AO) Meteorology, Astronomy and Space department faculty of Science Cairo University, Giza, Egypt Previously, it was shown through various studies that solar activity has a strong influence on the ionosphere (Mawad 3 20 Page 2 of 8 W.M. Mahmoud et al. to the sample as used in Aknil (Hudaningsih et al. 2020) and (Khodairy et al. 2020). The two methods were used to compare the most accurate forecasting methods close to their actual values. Previous studies used empirical methods for prediction of the travel time of interplanetary Coronal Mass Ejection Shocks (ICME) such as (Youssef et al. 2011). Also, many studies used the empirical methods to investigate the CME occurrence in accordance with the solar flare. Other studies used artificial-intelligence networks to detect the arrival time of interplanetary coronal mass ejection shocks during solar cycles, (Mawad et al. 2016). For this study, to present numerical predictions for CMEs characteristics empirical methods were used. The main objective of this study is to develop a standalone predictive model for the space-environment parameters and compare the results with real data. Fig. 1 Relation between AO and solar activity [11] 2 Methodology and data sources 2015) and (Farid et al. 2020). Solar activity has the greatest impact on AO density in the ionospheric layers leading to the enhancement of the erosion depths for material surfaces directly exposed to AO. The density and flux values of AO are higher during maximum solar activity than at minimum. The average fluence of AO per year and the corresponding erosion depth varies in response to solar-activity variations within the solar cycle (Samwell 2014) and (Farid et al. 2015) as shown in Fig. 1. In general, AO, hence O+, are the dominant species in the LEO environment. It is anticipated that as solar-flare events reach Earth, they enhance O and O+. Oxygen atoms have high corrosive power during and after combining with the material. According to the satellite’s orbital velocity of 7.8 km/s in LEO, the satellite is exposed to very strong streams of AO at energies in the range of 5 eV (Dooling and Finckenor 1999) and (Mahmoud et al. 2021). 1.2 Coronal Mass Ejections Coronal Mass Ejections (CMEs) are powerful eruptions of magnetic flux and plasma from the Sun into interplanetary space (Liu et al. 2020). The most powerful CMEs and their associated flares have very strong impacts on the nearEarth environment, affecting the lifetime of space technology, (Baker et al. 2004) and (Mawad et al. 2014). Many efforts should be made to develop new models for forecasting CMEs, (Bobra and Ilonidis 2016) and (Inceoglu et al. 2018). Predicting the lifetime of the LEO satellites is of great concern to the satellite industry and technology. All LEO satellites suffer orbital decay due to their interactions with the Earth’s atmosphere. The single-exponential smoothing method and the single moving-average method were applied Statistical analyses and forecasting techniques depend on gathering historical data and finding the pattern (trend or seasonal variation) and determining the forecasting method. Following forecasting calculations, a verification step is required to assure the accuracy and precision of the prediction model. The Holt–Winters method is used by many companies to produce short-term forecasts as their sales data contain a trend or seasonal pattern. This method is simple, easily automated, and has low data-storage requirements. The Holt–Winters method was applied to the air-transportation industry. The time series was decomposed into three additive components: trend, seasonal, and remainder. Multiple regression may be recognized as a better forecasting method for daily and weekly short-term forecasting, whereas Holt–Winters methods p (...truncated)