Trends In Modelling Neutral-Atmospheric Electromagnetic Delays in a ‘Big Data’ World

Abstract:

Modelling the delay suffered by electromagnetic waves while they cross the neutral-atmosphere is of fundamental importance for several applications that help enhancing our understanding of the Earth system. Initially, this modelling was based on climatological models derived from sparse data sets. An improvement in models followed as more observing techniques became available, and denser networks started to be developed. Somehow in parallel, and more recently, investigation efforts started to be concentrated on the use of numerical weather prediction (NWP) models, from where neutral-atmospheric delay parameters can derived through ray-tracing. There are a few limitations in both approaches. Models based on climatology are based on sparse data covering a certain period of time, whereas NWP models although based on more realistic data, are provided on intervals that range several hours. A third way is about to be engaged, and it can be seen as a natural development due to an increase in the number of sensors and an enhancement of their geographical distribution, generating a continuous flow of data, being them both satellite-based and ground-based. The question that is posed ahead of us is on how to make use of these huge data sets, which will provide the best possible representation of the neutral-atmosphere at any given time, as readily and as accurately as possible. This situation fits well within what today is known as big data. This paper will explore and discuss scenarios that have potential to open new trends in modeling the neutral-atmospheric delay. They include near real-time empirical model updates, sequential improvement of Marini mapping function coefficients (e.g., within a VMF) and a self-feeding. The discussion and simulations that will be shown cover the whole planet. The pros and cons of each approach will be discussed in comparison with what is done today. Simulations show potential improvement of up to 25% under certain circumstances.