Global navigation satellite system (GNSS) microwave signals are nearly unaffected by clouds but are delayed as they travel the troposphere. The hydrostatic delay accounts for approximately 90 % of the total delay and can be modelled well as a function of temperature, pressure, and humidity. On the other hand, the wet delay is highly variable in space and time, making it difficult to model accurately. A zenith wet delay (ZWD) can be estimated as part of the GNSS positioning adjustment and is proportional to the specific humidity in the atmospheric boundary layer (ABL). While its average term can describe mesoscale events, its small-scale component is associated with turbulent processes in the ABL and is the focus of the present contribution. We introduce a new filtering and estimation strategy to analyse small-scale ZWD variations, addressing questions related to daily or periodic variations in some turbulent parameters and to the dependence of these parameters on climate zones. Five GNSS stations were selected for case studies, revealing promising specific daily and seasonal patterns depending on the estimated turbulence at the GNSS station (buoyancy or shear). This research lays the groundwork for more accurate models and prediction strategies for integrated water vapour, WV (and potentially liquid water clouds), turbulence. It has far-reaching applications, from nowcasting uncertainty assessments to the stochastic modelling for very large baseline interferometry or GNSS.
