A Web-Based HF Near Real Time Tool based on a Neural Network based Bottomside Profile Model for the Ionosphere

Mohamed Osman Yousif and Sami Mohamed Sharif, University of Khartoum, Electrical and Electronic Engineering, Khartoum, Sudan

Contact First Author: Mohamed Osman Yousif; moy_smart@hotmail.com

Abstract ID#: 34063

 

English Abstract:
A web-based HF near real time tool based on a neural network based bottomside profile model for the Ionosphere is developed. The model gives near real time calculations for the Maximum Usable Frequency (MUF) and the Optimum Working Frequency (FOT) for distances shorter than 2000 km. The tool is named UKIS and it relies on critical frequencies and virtual heights. In this paper we run UKIS over Sudan using monthly median critical frequencies and virtual heights data from IRI and also we run UKIS over Three Australian cities using both near real time and archived critical frequencies and virtual heights obtained from digisonde stations in order to train the neural networks based ionospheric model. For validation we use SAO-Explorer which is a software developed by University of Massachusetts Lowell Centre of Atmospheric Research (UMLCAR) to get near real time Ionograms from different cities and then we scale MUF from the oblique ionograms between Digisonde stations using SAO-Explorer. Finally a comparison between predicted MUF with measured MUF is presented.
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