New applications and opportunities of GNSS meteorology

Water vapor content of the atmospheric low layer, up to about 18 km, known as troposphere or neutral atmosphere, affects GNSS (Global Navigation Satellite System) signals by lowering their propagation velocities with respect to vacuum. A diminished speed results in a time delay in the signal propagation along the satellite-receiver path, that multiplied by the vacuum speed of light adds an extra-distance to the satellite-receiver geometrical one. This delay defines a parameter which takes the name of Tropospheric Delay (TD) and consists of an Hydrostatic (HD) and aWet (WD) part. Anyway, if from the positioning point of view this delay is just a systematic error to be removed, it puts forward GNSS as a tool for the remote sensing of the troposphere water vapor content. The role of GNSS in meteorology is rapidly increasing; water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology [16] and GNSS data can be extremely useful for the purpose of a multi-instrumental investigation; indeed it was used to calibrate and validate other instruments [26] or methodologies [87], but probably one of the most impactful application is related to the assimilation of GNSS data into the meteorological models [8] [42] [19]. In this study, a global analysis of the role of the GPS data in the field of meteorology was carried out. A focus was kept on the assimilation of data in NWP Models, also according to the cooperation with the Institute of Atmospheric Sciences and Climate of National Research Council of Italy, which included this work in the frame of a collaboration with the Department of Italian Civil Protection. The main aim of this thesis is to find parameters able to support the analysis and forecast of intense meteorological events. To do this, a comparative analysis was carried out between GPS outputs and other Precipitable Water Vapor (PWV) measurement instruments; results show great consistency between the data (St. Dev.1cm). Another test was performed on the assimilation in NWP Models, in particular RAMS Model; in this case it has been found a noticeable impact (20-30% improvement) on ZTD and IWV for short term forecast. As for the Near Real Time (NRT) processing, results obtained are encouraging with a St. Dev.< 1cm with respect to post-processing (PP). To sum up, results provide an overall assessment of the data quality obtained through GPS post-processing and a milestone for NRT processing, also in view of early warning systems.