Data assimilation of GNSS zenith total delays from a Nordic processing centre

• Published in: Atmospheric chemistry and physics Vol. 17; no. 22; pp. 13983 - 13998
• Main Authors: Lindskog, Magnus, Ridal, Martin, Thorsteinsson, Sigurdur, Ning, Tong
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 24.11.2017; Copernicus Publications
• Subjects: Algorithms; Atmospheric models; Atmospheric moisture; Bias; Data; Data analysis; Data assimilation; Data collection; Data processing; Global navigation satellite system; Ground stations; Humidity; Information processing; Investigations; Mathematical models; Methods; Modelling; Moisture; Navigation; Navigation satellites; Navigation systems; Navigational satellites; Numerical weather forecasting; Numerical weather prediction; Satellite observation; Satellites; Sensitivity; Technology application; Thinning; Weather forecasting; Zenith; Sweden; Norway; Europe; Finland
• ISSN: 1680-7324; 1680-7316; 1680-7324
• DOI: 10.5194/acp-17-13983-2017

Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.