Kinematic ZTD Estimation from Train-Borne Single-Frequency GNSS: Validation and Assimilation

Water vapour is one of the most important parameters utilized for description state and evolution Earth’s atmosphere. It effective greenhouse gas shows high variability, both in space time. Thus, detailed knowledge its distribution immense importance weather forecasting, therefore resolution observations are crucial accurate precipitation forecasts, especially short-term prediction severe weather. Although not intentionally built this purpose, Global Navigation Satellite Systems (GNSS) have proven to meet those requirements. The derivation water content from GNSS based on fact that electromagnetic signals delayed when travelling through prominent parameterization delay Zenith Total Delay (ZTD), which has been studied extensively as a major error term positioning. On other hand, ZTD also provide substantial benefits atmospheric research Numerical Weather Prediction (NWP) model performance. Based these facts, scientific area Meteorology emerged. present study goes beyond current status Meteorology, showing how reasonable estimates can be derived highly-kinematic, single-frequency (SF) data. This data was gathered trains Austrian Federal Railways (ÖBB) processed using Precise Point Positioning (PPP) technique. special nature yields number additional challenges, ranging appropriate pre-processing parameter settings PPP more sophisticated validation assimilation methodologies . treatment ionosphere SF-GNSS represents challenges study. Two test cases (train travels) were different strategies validated calculated ERA5 reanalysis results indicate good overall agreement between GNSS-ZTD solutions ERA5-derived ZTD, although variability still observed specific sections tracks. bias standard deviation values ranged 1 mm 8 cm, heavily depending processing strategy investigated train route. Finally, initial experiments into NWP conducted, showed observation acceptance rates 30–100% largely case strategy.