Investigation of short-term atmospheric mass variations and their effects on geoid height using meteorological data

Document Type : Research


1 Assistant Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

2 Associate Professor, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran

3 M.Sc. Student, Department of Surveying and Geomatics Engineering, Faculty of Engineering, University of Tehran, Iran


Modern missions such as CHAMP, GRACE and GOCE which derive the Earth’s static and time-variable gravity field with unprecedented accuracy with monthly or even sub-monthly resolution, are also sensitive to short-term (weekly or shorter) non-tidal mass variations due to mass transports and mass redistribution phenomena in the atmosphere, the oceans and the continental water storage. GRACE derived gravity solutions contain errors mostly due to instrument noise, anisotropic spatial sampling and temporal aliasing caused by incomplete reduction of short-term mass variations in models. Improving the quality of satellite gravimetry observations, in term of using more sensitive sensors and increasing the spatial isotropy, has been discussed in the context of the designed scenarios of GRACE-Follow On (GRACE-FO) mission. Temporal aliasing is still a factor that affects the quality of the gravity field. For GRACE data processing only the short-term variations are of importance, because with the monthly Grace gravity field solutions it is planned to provide data for determination of the seasonal variations. Short-term mass variations cannot be measured adequately by GRACE. Therefore they are removed from measurements beforehand using geophysical models (de-aliasing). This paper specifically focuses on the atmosphere of Earth and its mass variations using the ITG-3D method. In this paper, various type of data such as the atmospheric pressure parameter, the multilevel geopotential, temperature and humidity parameters from European Center for Medium-Range Weather Forecasts (ECMWF) have been used to perform three-dimensional integral solution. ERA-Interim and ERA5 reanalysis are considered as the datasets. In the procedure of calculations, the shape of earth is approximated as an ellipse. As a first step in calculation procedure, it is necessary to remove the effect of long-term variations. In order to eliminate this effect; the mean variations of atmospheric mass over a specific period should be subtracted from the mass variation. Atmospheric de-aliasing products can be illustrated as sets of spherical harmonic coefficients, which are estimated using atmospheric mass variations. Then, the effect of atmospheric mass changes on geoid height and vertical deformation were calculated. In the computation, the ECMWF data on 1 January 2015 at 00:00h were used, while the mean atmospheric mass variations were derived from the means of the years 2015 and 2016. The results of the comparison between two datasets demonstrated that the maximum differences in parameters are located in Asia and Antarctic. The results indicate that the mean of difference between atmospheric mass variations from ERA-Interim and ERA5 is 0.23 kgm-2. The results show that the difference between the coefficients is about one percent of their values. In addition, the geoid height from ERA5 changes on average of -0.16 cm whereas this parameter varies on average -0.17 cm using ERA-Interim data due to atmospheric mass variations. The difference of vertical deformation from two datasets is -0.002 cm on average. The atmospheric mass variations calculated by the two data sets (ERA-Interim and ERA5) is not significantly different. The validation results of the vertical deformation of the two data also show a high correlation with the GPS time series.


Main Subjects

Aghajany, S. H. and Amerian, Y., 2017a, Three dimensional ray tracing technique for tropospheric water vapor tomography using GPS measurements. Journal of Atmospheric and Solar-Terrestrial Physics, 164, 81-88.
Aghajany, S. H., Voosoghi, B. and Yazdian, A., 2017,b, Estimation of north Tabriz fault parameters using neural networks and 3D tropospherically corrected surface displacement field. Geomatics, Natural Hazards and Risk, 8(2), 918-932.
Benoit, A., Jolivet, R. and Pinel-Puysségur, B., 2019, Correction of tropospheric effects in SAR interferometry: a comparison of ERA-Interim, ERA-5 and HRES Global Atmospheric Models.
Boy, J. P. and Chao, B. F., 2005, Precise evaluation of atmospheric loading effects on Earth's time‐variable gravity field. Journal of Geophysical Research: Solid Earth, 110(B8).
Cong, X., Balss, U., Rodriguez Gonzalez, F. and Eineder, M., 2018, Mitigation of tropospheric delay in SAR and InSAR using NWP data: Its validation and application examples. Remote Sensing, 10(10), 1515.
Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M.A., Balsamo, G., Bauer, D.P. and Bechtold, P., 2011, The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society, 137(656), 553-597.
Dobslaw, H., Bergmann-Wolf, I., Dill, R., Poropat, L. and Flechtner, F., 2017, Product description document for AOD1B release 06. GFZ German Research Centre for Geosciences Department, 1.
Dong, D., Gross, R. and Dickey, J., 1996, Seasonal variations of the Earth's gravitational field: An analysis of atmospheric pressure, ocean tidal, and surface water excitation. Geophysical research letters, 23(7), 725-728.
Eicker, A., 2008, Gravity field refinement by radial basis functions from in-situ satellite data: Citeseer.
Fagiolini, E., Flechtner, F., Horwath, M. and Dobslaw, H., 2015, Correction of inconsistencies in ECMWF's operational analysis data during de-aliasing of GRACE gravity models. Geophysical Journal International, 202(3), 2150-2158.
Farrell, W., 1972, Deformation of the Earth by surface loads. Reviews of Geophysics, 10(3), 761-797.
Fernandes, M.J., Pires, N., Lázaro, C. and Nunes, A.L., 2013, Tropospheric delays from GNSS for application in coastal altimetry. Advances in Space Research, 51(8), 1352-1368.
Flechtner, F., 2007, AOD1B product description document for product releases 01 to 04 (Rev. 3.1, April 13, 2007). GRACE project document, 327-750.
Flechtner, F., Schmidt, R. and Meyer, U., 2006, De-aliasing of short-term atmospheric and oceanic mass variations for GRACE. In Observation of the earth system from space (pp. 83-97): Springer.
Flechtner, F., Thomas, M. and Dobslaw, H., 2010, Improved non-tidal atmospheric and oceanic de-aliasing for GRACE and SLR satellites. In System Earth via Geodetic-Geophysical Space Techniques (pp. 131-142): Springer.
Forootan, E., Didova, O., Kusche, J. and Löcher, A., 2013, Comparisons of atmospheric data and reduction methods for the analysis of satellite gravimetry observations. Journal of Geophysical Research: Solid Earth, 118(5), 2382-2396.
Forootan, E., Didova, O., Schumacher, M., Kusche, J. and Elsaka, B., 2014, Comparisons of atmospheric mass variations derived from ECMWF reanalysis and operational fields, over 2003–2011. Journal of Geodesy, 88(5), 503-514.
Gill, A. E., 2016, Atmosphere-ocean dynamics: Elsevier.
Gruber, T., Peters, T. and Zenner, L., 2009, The role of the atmosphere for satellite gravity field missions. In Observing our Changing Earth (pp. 105-112): Springer.
Hersbach, H. and Dee, D., 2016, ERA5 reanalysis is in production, ECMWF Newsletter 147, ECMWF. Reading, UK.
Hirt, C., Featherstone, W. and Claessens, S., 2011, On the accurate numerical evaluation of geodetic convolution integrals. Journal of Geodesy, 85(8), 519-538.
Hu, Z. and Mallorquí, J. J., 2019, An Accurate Method to Correct Atmospheric Phase Delay for InSAR with the ERA5 Global Atmospheric Model. Remote Sensing, 11(17), 1969.
Jiang, C., Xu, T., Wang, S., Nie, W. and Sun, Z., 2020, Evaluation of Zenith Tropospheric Delay Derived from ERA5 Data over China Using GNSS Observations. Remote Sensing, 12(4), 663.
Jungclaus, J., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei, D., Von Storch, J., 2013, Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI‐Earth system model. Journal of Advances in Modeling Earth Systems, 5(2), 422-446.
Karbon, M., Wijaya, D., Schindelegger, M., Böhm, J. and Schuh, H., 2011, Atmospheric effects on the Earth gravity field featured by TU Vienna. Österreichische Z Vermessung Geoinform, 99(2), 122-130.
Krylov, V. I. and Stroud, A. H., 2006, Approximate calculation of integrals: Courier Corporation.
Kusche, J. and Schrama, E., 2005, Surface mass redistribution inversion from global GPS deformation and Gravity Recovery and Climate Experiment (GRACE) gravity data. Journal of Geophysical Research: Solid Earth, 110(B9).
Kvas, A., Behzadpour, S., Ellmer, M., Klinger, B., Strasser, S., Zehentner, N. and Mayer‐Gürr, T., 2019, ITSG‐Grace2018: Overview and evaluation of a new GRACE‐only gravity field time series. Journal of Geophysical Research: Solid Earth, 124(8), 9332-9344.
Mayer-Gürr, T., Eicker, A., Kurtenbach, E. and Ilk, K.-H., 2010, ITG-GRACE: global static and temporal gravity field models from GRACE data. In System Earth via geodetic-geophysical space techniques (pp. 159-168): Springer.
Mayer-Gürr, T., Behzadpour, S., Kvas, A., Ellmer, M., Klinger, B., Strasser, S. and Zehentner, N., 2018, ITSG-Grace2018: Monthly, Daily and Static Gravity Field Solutions from GRACE.
Organization, W. M., 1983, Guide to meteorological instruments and methods of observation: Secretariat of the World Meteorological Organization.
Peters, T., 2007, Modellierung zeitlicher Schwerevariationen und ihre Erfassung mit Methoden der Satellitengravimetrie. Retrieved from.
Pichler, H., 1986, Dynamik der Atmosphäre: Bibliographisches Institut.
Salstein, D.A., Ponte, R.M. and Cady‐Pereira, K., 2008, Uncertainties in atmospheric surface pressure fields from global analyses. Journal of Geophysical Research: Atmospheres, 113(D14).
Simmons, A.J. and Burridge, D.M., 1981, An energy and angular-momentum conserving vertical finite-difference scheme and hybrid vertical coordinates. Monthly Weather Review, 109(4), 758-766.
Stockdale, T. N., Anderson, D. L., Alves, J. O. S. and Balmaseda, M. A., 1998, Global seasonal rainfall forecasts using a coupled ocean–atmosphere model. Nature, 392(6674), 370-373.
Swarztrauber, P. N., 2003, On computing the points and weights for Gauss--Legendre quadrature. SIAM Journal on Scientific Computing, 24(3), 945-954.
Swenson, S. and Wahr, J., 2002, Estimated effects of the vertical structure of atmospheric mass on the time‐variable geoid. Journal of Geophysical Research: Solid Earth, 107(B9), ETG 4-1-ETG 4-11.
Tesmer, V., Steigenberger, P., van Dam, T. and Mayer-Gürr, T., 2011, Vertical deformations from homogeneously processed GRACE and global GPS long-term series. Journal of Geodesy, 85(5), 291-310.
Thompson, P., Bettadpur, S. and Tapley, B., 2004, Impact of short period, non‐tidal, temporal mass variability on GRACE gravity estimates. Geophysical research letters, 31(6).
Wahr, J., Molenaar, M. and Bryan, F., 1998, Time variability of the Earth's gravity field: Hydrological and oceanic effects and their possible detection using GRACE. Journal of Geophysical Research: Solid Earth, 103(B12), 30205-30229.
White, P. W., 2000, IFS documentation: Part III: Dynamics and numerical procedures (CY21r4): European Centre for Medium-Range Weather Forecasts.
Zenner, L., Gruber, T., Jäggi, A. and Beutler, G., 2010, Propagation of atmospheric model errors to gravity potential harmonics—impact on GRACE de-aliasing. Geophysical Journal International, 182(2), 797-807.
Zenner, L., Fagiolini, E., Daras, I., Flechtner, F., Gruber, T., Schmidt, T. and Schwarz, G., 2012, Non-tidal atmospheric and oceanic mass variations and their impact on GRACE data analysis. Journal of Geodynamics, 59, 9-15.