Abstract
If left unmodeled, the delay suffered by electromagnetic waves while crossing the neutral atmosphere negatively affects Global Navigation Satellite System positioning. The modeling of the delay has been carried out by means of empirical models formulated based on climatological information or using information extracted from numerical weather prediction (NWP) models. This paper explores the potential use of meteorological information of several types that will become available with the increasing number of sensors (e.g. a cell phone, or the thermometer of a nearby smart home) in cyberspace. How can we make use of these potentially huge data-sets, which may help to provide the best possible representation of the neutral atmosphere at any given time, as readily and as accurately as possible? This situation falls in the realm of Big Data. A few potential scenarios, a sequential improvement of Marini mapping function coefficients, a self-feeding NWP, and near real-time empirical model updates, are discussed in this paper. The pros and cons of each approach are discussed in comparison with what is done today. Experiments indicate that they have potential for a positive contribution.
Highlights
The neutral-atmospheric delay is suffered by electromagnetic waves when they cross the neutral atmosphere
The modeling of the delay has been carried out by means of empirical models formulated based on climatological information or using information extracted from numerical weather prediction (NWP) models
The usual way to deal with the neutral-atmospheric delay is by modeling it. Models for this purpose can be constructed based on climatological information, or based on information derived from numerical weather prediction (NWP) models
Summary
The neutral-atmospheric delay is suffered by electromagnetic waves when they cross the neutral atmosphere (more popularly but imprecisely called the troposphere). Even though computationally more expensive, this approach has been shown to be more advantageous than modeling based on climatology because it provides the representation of the neutral atmosphere at the epoch, when the NWP is available This has led to the development of the Vienna Mapping Functions (VMF) (Boehm and Schuh 2003), which are based on several NWP models from the European Centre for Middle-Range Weather Forecast, National Oceanic and Atmospheric Administration, and Canadian Meteorological Center. This paper explores and discusses scenarios that show a tremendous potential of opening new trends in modeling the neutral-atmospheric delay It includes: (1) a sequential improvement of Marini mapping function coefficients (e.g. within a VMF), (2) a self-feeding NWP, and (3) near real-time empirical model updates. The discussion and simulations that will be shown cover the whole planet as an indication of their potential use under GGOS, but they can be tied to specific locations
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