Speaker
Description
Water vapor is a key driver of the Earth’s hydrological cycle and plays a critical role in both weather patterns and long-term climate processes. Ground-based GNSS TCWV observations have consistently proven to be accurate and reliable over extended periods. Their consistency with the most precise radiosonde and microwave products is currently within 0.5 – 0.7 kg m-2 in absolute value, with an RMS deviation of approximately 1 kg m-2. However, small biases and changes up to 1 kg m-2 have been observed, often linked to station instrumentation updates, changes in data processing methods, and potential alterations in the measurement environment. Fortunately, such discontinuities can be adjusted by a data homogenization procedure.
Building on this foundation, this paper introduces the data processing and post-processing steps, including homogenization, involved in creating a new global, long-term GNSS TCWV data record spanning from 1994 to 2023. The data set encompasses over 6,000 stations, each with more than a decade of observations. So far, more than 600 stations with over 20 years of data have been segmented and homogenized using a new statistical method proposed by Nguyen et al., 2024. This work will continue to include more stations, and the resulting data set will be made available to the scientific community for a range of applications, including climate change and atmospheric process monitoring, as well as the validation of reanalyses, satellite products, and climate models. The paper will also present some preliminary results of long-term linear trend analysis from GNSS and reanalyses, both at global and regional scales.
Nguyen, K. N., Bock, O., & Lebarbier, E. (2024). A statistical method for the attribution of change-points in segmented Integrated Water Vapor difference time series. International Journal of Climatology, 44(6), 2069–2086. https://doi.org/10.1002/joc.8441
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