4.6 Article

Homogenization of daily precipitable water vapor time series derived from GNSS observations over China

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ADVANCES IN SPACE RESEARCH
卷 72, 期 5, 页码 1751-1763

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ELSEVIER SCI LTD
DOI: 10.1016/j.asr.2023.04.052

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Precipitable water vapor time series; Changepoints; Homogenization; GNSS; ERA5

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This study proposes a new strategy to verify and correct the origins of non-climatic changepoints in GNSS-PWV. Results show that the new strategy effectively identifies undocumented NCCs and improves the homogeneity of GNSS-PWV.
Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008-2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75. & COPY; 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

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