A Methodology for Estimation of Hourly‐Monthly Stochastic Trend Characteristics of Midlatitude Ionosphere. Issue 11 (27th October 2020) - Arikan, F.; Koroglu, O.
Abstract: Spatiotemporal variability of ionosphere poses a major challenge in characterization of observable parameters, critical frequency of F2 layer (foF2), maximum ionization height (hmF2), total electron content (TEC), and slab thickness. The nature of the multiscale space‐time variability of ionosphere can be modeled as a random field. In this study, as a first step in random field modeling of the trend, hourly‐monthly parametric probability density functions (PPDF) of foF2, hmF2, slab thickness, and TEC are obtained using IONOLAB‐PDF method for three midlatitude stations (namely, Juliusruh, Pruhonice, and Rome) for low, moderate, and high solar activity years of 2009, 2012, and 2014, respectively. foF2 and hmF2 are obtained from ionosondes. TEC is estimated from dual‐frequency Global Positioning System (GPS) receivers. It is observed that hourly‐monthly PPDFs of all ionospheric parameters at the three midlatitude stations can best be represented by lognormal and Weibull distributions. The estimates of mean and standard deviations of hourly‐monthly PPDFs indicate that the spatiotemporal stochastic trend variation of the midlatitude ionosphere differs in terms of foF2 and GPS‐TEC. The hourly‐monthly PPDF of foF2 represents all major anomalies as well as seasonal and hourly trends, whereas those of GPS‐TEC is highly dependent on solar activity level and latitude. The general statistical trend of hmF2 is anticorrelated with foF2 as expected. The mean of hourly‐monthly PPDF of slab thickness is around 400 km in 2012 and 2014 for all stations between 0200 UT and 1600 UT. Key Points: Hourly‐monthly stochastic trend structure of ionosphere is investigated for three midlatitude locations IONOLAB‐PDF estimates the probability density functions of ionospheric parameters for high, moderate, and low solar activity years The stochastic trend nature of ionospheric parameters can be defined with lognormal and Weibull probability distributions