Comparative analysis of different atmospheric surface pressure models and their impacts on daily ITRF2014 GNSS residual time series

Abstract

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.

Appendix

To help users deal with the non-integer constants files, here we give a simple step of how to generate integer land/sea mask for MERRA and MERRA-2 datasets:

• 1. Download the MERRA constant file with short name as MAC0NXASM.

• 2. Create a local binary file that has integer masks for land and ocean in MERRA using gradsdap script. Then, generate a descriptor file that can be used to open the above binary integer mask file in GrADS.

• 3. Write the land and sea variables from the above descriptor file into separate files in netcdf format using GrADS command sdfwrite.¹⁴ Note that for using the command sdfwrite, users should install a fairly recent version of GrADS, e.g., version 2.0.a3 at least.

• 4. Combine the separate land and sea netcdf files to generate the MERRA integer land/sea mask.

• 5. Download the MERRA-2 constant file with short name as M2C0NXASM, and repeat the above procedures from (1) to (4) to generate the MERRA-2 integer land–sea mask

After implementing the above five steps, we can use the obtained integer land/sea mask for modeling the ATML effects using both MERRA and MERRA-2 PS grids. When creating the binary file from MERRA and MERRA-2 that containing integer masks for land and ocean, a fractional cutoff value is used to determine that whether a point is classified land or sea. Here, we choose the cutoff value as 0.25 for global study (from Mike Bosilovich’s cookbook). For regional studies where the land and ocean are intend to be discretized, more attempts should be examined to determine an appropriate cutoff value.