NWM/GNSS tightly coupled tropospheric delay estimation and application on an unmanned aerial vehicle (UAV) platform

Water vapor is of great importance to the atmosphere and weather research. Airborne GNSS-based tropospheric delay estimation can reveal the atmosphere profile information, which is of more importance than site-based products and acts as an independent observation for meteorological application. On the other hand, progress in the meteorological community such as numerical weather models (NWMs) and forecast operations have the potential to augment GNSS. Many studies have investigated methods for applying NWMs in GNSS, mainly considering NWMs as a priori information. However, most methods implemented are merely suitable for static ground stations. They may not be optimal for dynamic platforms like unmanned aerial vehicles (UAVs) since the troposphere condition changes dramatically with vertical velocity and height. Under this background, we propose an NWM/GNSS tightly coupled method to take the best advantage of NWMs into GNSS data processing. The technique utilizes NWMs as a priori and considers the vertical distribution of the atmosphere to adaptively adjust the stochastic model for tropospheric delay estimation depending on the actual circumstance. The proposed method has been evaluated by an experiment using UAV and Global Forecast System (GFS) and found an improvement of precision and stability of tropospheric delay estimates.