IAG 150 Years pp 355-365 | Cite as
Impact of Numerical Weather Models on Gravity Field Analysis
Abstract
Atmospheric pressure variations are one of the major sources of gravity perturbations. Due to the high variability of the atmospheric masses and the sparse sampling of these by GRACE the signals alias into the observations taken by the satellites. The determination of accurate atmospheric gravity field coefficients (AGC) is indispensable for the elimination of these signals. For the determination of AGC it is state of the art to use high resolution Numerical Weather Prediction (NWP) models which take into account the time-variable three-dimensional distribution of the atmospheric mass. By subtracting the gravity spherical harmonics of a long term atmospheric mean field from the ones of the instantaneous atmosphere, the residual gravity spherical harmonic series is obtained. It describes the deviation of the actual gravity field from the mean gravity field due to atmospheric mass variations. NWP models are not perfect as they can show significant differences to in situ measurements. Further these models evolve and change throughout time, which can lead to changes in the pressure data and therefore in the AGC. In this study several aspects of NWP models are investigated, and the influence they have on the determination of the AGC is discussed. We present a strategy that was developed for dealing with changes in the NWP models, and compare our products to those of the GRACE Atmosphere and Ocean Dealiasing level-1B products and those provided by the Groupe de Recherche de Géodésie Spatiale (GRGS).
Keywords
Atmospheric de-aliasing GRACE Gravity Numerical weather modelsNotes
Acknowledgements
We would like to thank the Austrian Science Fund (FWF) for supporting the project GGOS Atmosphere (P20902), and the ECMWF for providing the meteorological data. Special thanks go to Bruno Meurers from the University of Vienna for providing the in situ data at Conrad Observatory and to Pascal Gegout from the Géosciences Environnement Toulouse for providing the de-aliasing data. We also want to acknowledge the reviewers who helped to improve the manuscript.
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