Abstract Non-Tidal Atmospheric Loading (NTAL) plays a crucial role in the precision and reliability of GNSS-based positioning and geophysical interpretations, particularly in high-latitude regions, sensitive to atmospheric dynamics. This investigation examines the influence of non-tidal atmospheric loading on GNSS time series and velocities derived from them for high-latitude regions. With a dataset from 2020 to 2023, we process a GNSS network across northern Europe, focusing on the Finnish permanent GNSS network (FinnRef). Using GAMIT/GLOBK software, where corrections are applied at the observation level, we incorporate a new atmospheric grid model derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather data. This model provides higher spatial resolution compared to previously available models in GAMIT/GLOBK. Temporal variability of NTAL-corrected GNSS time series is reduced by 17% in the vertical component, and by 8% and 2% in the north and east components, respectively, across the FinnRef network. Additionally, our results highlight that NTAL correction lowers vertical trend uncertainty by an average of 33.5%. Besides evaluating metrics such as spectral power density (PSD) and annual amplitude variation, we observe that the spectral index of the vertical component drops from − 1.44 to − 0.9, indicating reduced long-term noise correlation. We also compare this observation-level approach with an alternative method that applies NTAL corrections at the raw-data level and find that the observation-level correction shows slightly better performance. These results demonstrate that significant improvements in the stability of GNSS time series can be expected after NTAL application, especially in the vertical component.
Fatemeh Khorrami (Fri,) studied this question.
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