TY - JOUR
T1 - Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series
AU - Li, Zhao
AU - Chen, Wu
AU - van Dam, Tonie
AU - Rebischung, Paul
AU - Altamimi, Zuheir
N1 - Funding Information:
We thank Mike Bosilovich for helping us generate the MERRA integer land–sea mask. We thank the IGN for providing us the latest ITRF2014 residuals. We also thank Dr. Xavier Collilieux for helping us improve our paper. Figures in this paper are plotted with the GMT and MATLAB software. This research is supported by the National Key Research and Development Program of China (Project 2016YFB0502101), the European Commission/Research Grants Council (RGC) Collaboration Scheme sponsored by the Research Grants Council of Hong Kong Special Administrative Region, China (Project No. E-PolyU 501/16), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 41525014).
Funding Information:
We thank Mike Bosilovich for helping us generate the MERRA integer land?sea mask. We thank the IGN for providing us the latest ITRF2014 residuals. We also thank Dr. Xavier Collilieux for helping us improve our paper. Figures in this paper are plotted with the GMT and MATLAB software. This research is supported by the National Key Research and Development Program of China (Project 2016YFB0502101), the European Commission/Research Grants Council (RGC) Collaboration Scheme sponsored by the Research Grants Council of Hong Kong Special Administrative Region, China (Project No. E-PolyU 501/16), and the National Science Foundation for Distinguished Young Scholars of China (Grant No. 41525014).
Publisher Copyright:
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2020/4/1
Y1 - 2020/4/1
N2 - To remove atmospheric pressure loading (ATML) effect from GNSS coordinate time series, surface pressure (SP) models are required to predict the displacements. In this paper, we modeled the 3D ATML surface displacements using the latest MERRA-2 SP grids, together with four other products (NCEP-R-1, NCEP-R-2, ERA-Interim and MERRA) for 596 globally distributed GNSS stations, and compared them with ITRF2014 residual time series. The five sets of ATML displacements are highly consistent with each other, particularly for those stations far away from coasts, of which the lowest correlations in the Up component for all the four models w.r.t MERRA-2 become larger than 0.91. ERA-Interim-derived ATML displacement performs best in reducing scatter of the GNSS height for 90.3% of the stations (89.3% for NCEP-R-1, 89.1% for NCEP-R-2, 86.4% for MERRA and 85.1% for MERRA-2). We think that this may be possibly due to the 4D variational data assimilation method applied. Considering inland stations only, more than 96% exhibit WRMS reduction in the Up direction for all five models, with an average improvement of 3–4% compared with the original ITRF2014 residual time series before ATML correction. Most stations (> 67%) also exhibit horizontal WRMS reductions based on the five models, but of small magnitudes, with most improvements (> 76%) less than 5%. In particular, most stations in South America, South Africa, Oceania and the Southern Oceans show larger WRMS reductions with MERRA-2, while all other four SP datasets lead to larger WRMS reduction for the Up component than MERRA-2 in Europe. Through comparison of the daily pressure variation from the five SP models, we conclude that the bigger model differences in the SP-induced surface displacements and their impacts on the ITRF2014 residuals for coastal/island stations are mainly due to the IB correction based on the different land–sea masks. A unique high spatial resolution land–sea mask should be applied in the future, so that model differences would come from only SP grids. Further research is also required to compare the ATML effect in ice-covered and high mountainous regions, for example the Qinghai–Tibet Plateau in China, the Andes in South America, etc., where larger pressure differences between models tend to occur.
AB - To remove atmospheric pressure loading (ATML) effect from GNSS coordinate time series, surface pressure (SP) models are required to predict the displacements. In this paper, we modeled the 3D ATML surface displacements using the latest MERRA-2 SP grids, together with four other products (NCEP-R-1, NCEP-R-2, ERA-Interim and MERRA) for 596 globally distributed GNSS stations, and compared them with ITRF2014 residual time series. The five sets of ATML displacements are highly consistent with each other, particularly for those stations far away from coasts, of which the lowest correlations in the Up component for all the four models w.r.t MERRA-2 become larger than 0.91. ERA-Interim-derived ATML displacement performs best in reducing scatter of the GNSS height for 90.3% of the stations (89.3% for NCEP-R-1, 89.1% for NCEP-R-2, 86.4% for MERRA and 85.1% for MERRA-2). We think that this may be possibly due to the 4D variational data assimilation method applied. Considering inland stations only, more than 96% exhibit WRMS reduction in the Up direction for all five models, with an average improvement of 3–4% compared with the original ITRF2014 residual time series before ATML correction. Most stations (> 67%) also exhibit horizontal WRMS reductions based on the five models, but of small magnitudes, with most improvements (> 76%) less than 5%. In particular, most stations in South America, South Africa, Oceania and the Southern Oceans show larger WRMS reductions with MERRA-2, while all other four SP datasets lead to larger WRMS reduction for the Up component than MERRA-2 in Europe. Through comparison of the daily pressure variation from the five SP models, we conclude that the bigger model differences in the SP-induced surface displacements and their impacts on the ITRF2014 residuals for coastal/island stations are mainly due to the IB correction based on the different land–sea masks. A unique high spatial resolution land–sea mask should be applied in the future, so that model differences would come from only SP grids. Further research is also required to compare the ATML effect in ice-covered and high mountainous regions, for example the Qinghai–Tibet Plateau in China, the Andes in South America, etc., where larger pressure differences between models tend to occur.
KW - Atmospheric pressure loading (ATML)
KW - ERA-Interim
KW - GNSS position time series
KW - MERRA
KW - MERRA-2
KW - NCEP reanalysis
UR - http://www.scopus.com/inward/record.url?scp=85082430893&partnerID=8YFLogxK
U2 - 10.1007/s00190-020-01370-y
DO - 10.1007/s00190-020-01370-y
M3 - Journal article
AN - SCOPUS:85082430893
SN - 0949-7714
VL - 94
JO - Journal of Geodesy
JF - Journal of Geodesy
IS - 4
M1 - 42
ER -