Sampled Together: Assessing the Value of Simultaneous Collocated Measurements for Optimal Satellite Configurations

Abstract

This work applies a quantitative metric in order to capture the relative representativeness of non-simultaneous or non-co-located observations and quantify how these observations decorrelate in both space and time. This methodology allows for the effective determination of thresholding decisions for representative matchup conditions, and is especially useful for informing future network designs and architectures. Future weather and climate satellite missions must consider a range of architectural trades to meet observing requirements. Frequently, fundamental decisions such as the number of observatories, the instruments manifested, and orbit parameters are determined based upon assumptions about the characteristic temporal and spatial scales of variability of the target observation. With the introduced methodology, representativity errors due to separations in space and time can be quantified without prior knowledge of instrument performance, and errors driven by constellation design can be estimated without model ingest or analysis.

An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry

“…Further, the fact that the single-differenced observations generally drive the doubledifferenced autocorrelation behavior suggests that Powell et al's metric of simultaneity and collocation [42] generally applies for CYGNSS when satellites are in a near overlap condition. This interesting result suggests that double-differencing may not be required for statistical estimations of GNSS-R errors given a sufficiently large dataset of observations that nearly overlap.…”