M. Nigussie

Validation of NeQuick TEC data ingestion technique against C/NOFS and EISCAT electron density measurements

This paper investigates a technique to estimate near-real-time electron density structure of the ionosphere. Ground-based GPS receiver total electron content (TEC) at low and high latitudes has been used to assist the NeQuick 2 model. First, we compute model input (effective ionization level) when the modeled slant TEC (sTEC) best fits the measured sTEC by single GPS receiver (reference station). Then we run the model at different locations nearby the reference station and produce the spatial distribution of the density profiles of the ionosphere in the East African region. We investigate the performance of the model, before and after data ingestion in estimating the topside ionosphere density profiles. This is carried out by extracting in situ density from the model at the corresponding location of C/NOFS (Communication/Navigation Outage Forecast System) satellite orbit and comparing the modeled ion density with the in situ ion density observed by Planar Langmuir Probe onboard C/NOFS. It is shown that the performance of the model after data ingestion reproduces the topside ionosphere better up to about 824 km away from the reference station than that before adaptation. Similarly, for high-latitude region, NeQuick 2 adapted to sTEC obtained from high-latitude (Tromso in Norway) GPS receiver and the model used to reproduce parameters measured by European Incoherent Scatter Scientific Association (EISCAT) VHF radar. It is shown that the model after adaptation shows considerable improvement in estimating EISCAT measurements of electron density profile, F2 peak density, and height.

Study of TEC fluctuation via stochastic models and Bayesian inversion

We propose stochastic processes to be used to model the total electron content (TEC) observation. Based on this, we model the rate of change of TEC (ROT) variation during ionospheric quiet conditions with stationary processes. During ionospheric disturbed conditions, for example, when irregularity in ionospheric electron density distribution occurs, stationarity assumption over long time periods is no longer valid. In these cases, we make the parameter estimation for short time scales, during which we can assume stationarity. We show the relationship between the new method and commonly used TEC characterization parameters ROT and the ROT Index (ROTI). We construct our parametric model within the framework of Bayesian statistical inverse problems and hence give the solution as an a posteriori probability distribution. Bayesian framework allows us to model measurement errors systematically. Similarly, we mitigate variation of TEC due to factors which are not of ionospheric origin, like due to the motion of satellites relative to the receiver, by incorporating a priori knowledge in the Bayesian model. In practical computations, we draw the so-called maximum a posteriori estimates, which are our ROT and ROTI estimates, from the posterior distribution. Because the algorithm allows to estimate ROTI at each observation time, the estimator does not depend on the period of time for ROTI computation. We verify the method by analyzing TEC data recorded by GPS receiver located in Ethiopia (11.6°N, 37.4°E). The results indicate that the TEC fluctuations caused by the ionospheric irregularity can be effectively detected and quantified from the estimated ROT and ROTI values.

Harmonic Analysis of Precipitation Time Series in Lake Tana Basin, Ethiopia

This study presents harmonic analysis of precipitation observations within the Lake Tana Basin for the periods of 1985–2015. The livelihood of several millions of people within the basin and outside the basin is governed by the precipitation conditions within this basin. Large spatial and temporal variabilities of precipitation can increase the incidence of extreme events such as floods and droughts. It is important to identify the characteristics of these variations, and this study aims at investigating the characteristics of the seasonal and annual cycles of precipitation within the Lake Tana Basin using harmonic analysis. Precipitation data of 31 years from four weather stations were used in the analysis. We then applied harmonic analysis to calculate the amplitude, phase shift, and variance of observation. Detailed characteristics of the first five harmonics are presented and discussed. We found the amplitude of the first harmonic to be and for Debre Tabor, Bahir Dar, Gondar, and Dangila, respectively. This shows that Dangila areas got more rainfall during this fundamental period than others increasing from Gondar to Dangila direction. Also, the variance in the first harmonic is smaller than the variances of other harmonics, and this means that the large variations of the precipitation originate from higher harmonics (short time periods). This shows that precipitation variations are governed mainly by monthly, seasonal, and semiannual variations. The analysis has shown that maximum precipitation for all stations occurred in July and August.

Temporal solar irradiance variability analysis using neural networks

A feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenbergmarquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.