Van Malderen, R. ; Pottiaux, E. ; Bock, O. ; Klos, A. ; Pacione, R

Invited talk presented at 7th International Colloquium on scientific and fundamental aspects of GNSS, Zurich, Switzerland on 2019-09-05

Abstract: In climate research, the role of water vapour can hardly be overestimated. Water vapour is the most important natural greenhouse gas and is responsible for the largest known feedback mechanism for amplifying climate change. Water vapour also strongly influences atmospheric dynamics and the hydrologic cycle through surface evaporation, latent heat transport and diabatic heating, and is, in particular, a source of clouds and precipitation. Atmospheric water vapour is highly variable, both in space and in time. Therefore, measuring it remains a demanding and challenging task. The Zenith Total Delay (ZTD) estimates of GNSS, provided at high temporal resolution and under all weather conditions, can be converted to Integrated Water Vapour (IWV) if additional meteorological variables are available. In the past years, several long-term (20+ years) reprocessed GNSS tropospheric delay and water vapor time series datasets have been produced and have become available for climate studies. These include worldwide datasets (e.g. the International GNSS Service ‘IGS troposphere repro 1’), pan-European datasets (e.g. the EUREF Permanent Network ‘EPN troposphere repro 2’), but also regional datasets. However, although homogeneously reprocessed, these time series might still suffer from remaining inhomogeneities due to e.g. instrumental changes, environmental changes, etc., impacting the interpretation of the trends and long-term variability of the total column of water vapour. In this contribution, we will describe the community activity on homogenization, in which we undertook an assessment of the performance of different break point identification algorithms on different synthetic datasets with inserted offsets. Furthermore, we will provide some examples of the use of GNSS retrieved IWV datasets for validation of (regional) climate models (e.g. EPN troposphere repro 2). Finally, we will show the results of different studies in which global GNSS IWV dataset are used, in complement to numerical weather prediction model reanalyses and satellite IWV retrievals, to understand and interpret the spatial and temporal variability of IWV. The presentation will hence provide a review of the progress made in and the status of using GNSS tropospheric datasets for climate research, highlighting the challenges, and outlining the major remaining steps ahead.

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