Abstract
Geostatistical Analyst is a set of advanced tools for analysing spatial data and generating surface models using statistical and deterministic methods available in ESRI ArcMap software. It enables interpolation models to be created on the basis of data measured at chosen points. The software also provides tools that enable analyses of the data variability, setting data limits and checking global trends, as well as creating forecast maps, estimating standard error and probability, making various surface visualisations, and analysing spatial autocorrelation and correlation between multiple data sets. The data can be interpolated using deterministic methods providing surface continuity, and also by stochastic techniques like kriging, based on a statistical model considering data autocorrelation and providing expected interpolation errors. These properties of Geostatistical Analyst make it a valuable tool for modelling and analysing the Earth’s ionosphere. Our research aims to test its applicability for studying the ionosphere, and ionospheric disturbances in particular. As raw source data, we use Global Navigation Satellite Systems (GNSS)-derived ionospheric total electron content. This paper compares ionosphere models (maps) developed using various interpolation methods available in Geostatistical Analyst. The comparison is based on several indicators that can provide the statistical characteristics of an interpolation error. In this contribution, we use our own method, the parametric assessment of the quality of estimation (MPQE). Here, we present analyses and a discussion of the modelling results for various states of the ionosphere: On the disturbed day of the St Patrick’s Day geomagnetic storm of 2015, one quiet day before the storm and one day after its occurrence, reflecting the ionosphere recovery phase. Finally, the optimal interpolation method is selected and presented.
Highlights
In recent decades, Global Navigation Satellite Systems (GNSS) have found several applications in a broad range of geosciences
There are a number of various modelling methods that differ in terms of accuracy and reliability [8]
There is a large number of available interpolation methods, and it is difficult to choose the most suitable one that would be appropriate to a range of geographical regions and ionospheric conditions. In this initial contribution, we aim to test different geostatistical methods offered by Geostatistical Analyst for modelling the ionosphere over the European region
Summary
Global Navigation Satellite Systems (GNSS) have found several applications in a broad range of geosciences. GNSS signals are primarily applied to provide the user’s position [1,2]. This technology is increasingly used to monitor deformations in the Earth’s crust [3]. Precise GNSS positioning and GNSS-based geodetic and geodynamic studies require accurate corrections of ionospheric delay [4,5]. In this respect, another field of studies based on GNSS data is atmosphere remote sensing [6,7]. A comprehensive review of the most popular GNSS-derived ionosphere models is provided in [9,10]
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