Combining spatio-temporal weighting with reanalysis data for filling in GNSS PWV time series

doi:10.11947/j.AGCS.2023.20220241

Abstract

Abstract: Precipitable water vapor (PWV) is one of the most critical parameters in the troposphere, and the long time series of continuous PWV has an essential impact on long-term climate change studies. However, due to the influence of external factors, the current long time series of PWV acquired by GNSS have missing data or poor resolution, which cannot meet the application needs of long time series analysis. This paper proposes a spatio-temporal weighted (STW) method to fill in the PWV long time series to address this problem. This method considers both the spatio-temporal variability of water vapor over GNSS stations and assigns different weights to the spatio-temporal variability of PWV according to the station locations. An experiment tested the STW using the Crustal Movement Observation Network of China (CMONOC) and the ECMWF 5th generation global atmospheric reanalysis dataset ERA5. The results show that the proposed STW method outperforms the traditional linear interpolation and period model methods in terms of missing data and PWV long time series resampling at different time scale resolutions and can obtain more reliable, accurate, and complete PWV long time series.

Key words: GNSS, spatio-temporal weighting, PWV, time-series filling

CLC Number:

P228

ZHAO Qingzhi, DU Zheng, YAO Yibin, YAO Wanqiang. Combining spatio-temporal weighting with reanalysis data for filling in GNSS PWV time series[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(10): 1661-1668.

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