Tropospheric wet tomography and PPP: Joint estimation from GNSS crowdsourcing data

Water vapor is the dominant greenhouse gas in the atmosphere accounting for to of the total effect, it plays a large role in determining the weather forecasts, and it accounts for up to one fifth of the atmospheric error for GNSS measurements. Hence, measuring the amount of water vapor in the atmosphere is important. Previously, data from GNSS base stations has been used to compute tomographic estimates for the water vapor distribution. Here, we present a technique that enables doing these tomographic estimates with receivers residing at roving positions instead of ones with a pre-determined position. Specifically, we show that combining probabilistic precise point positioning (PPP) with Bayesian tomography with 1 Hz posteriori update rate leads to the convergence of both the receiver position estimates and the wet residual estimates. Moreover, our findings include the fact that even low quality receiver data, such as the one crowdsourced from consumer-level equipment such as smartphones, may be used to improve the water vapor estimate, if the uncertainty models are not too optimistic nor too pessimistic. Simulated GNSS observations with realistic errors for up to one thousand GNSS receivers in Southern France, presented in the antecedent work (De Oliveira Marques et al., 2020), are employed for elaborate testing. We discuss why the proposed method is likely straightforwardly applicable on data coming from moving modern multi-sensor systems, although our testing data is limited to receivers that are not moving. Applications for the proposed technique include benefits in regions that are not covered by high-quality weather measurement instruments, e.g. islands, seas, and other remote areas such as the Arctic