Surveys in Geophysics

, Volume 35, Issue 6, pp 1251–1266 | Cite as

Comparison of Daily GRACE Gravity Field and Numerical Water Storage Models for De-aliasing of Satellite Gravimetry Observations

  • L. Zenner
  • I. Bergmann-Wolf
  • H. Dobslaw
  • T. Gruber
  • A. Güntner
  • M. Wattenbach
  • S. Esselborn
  • R. Dill


Reducing aliasing effects of insufficiently modelled high-frequent, non-tidal mass variations of the atmosphere, the oceans and the hydrosphere in gravity field models derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission is the topic of this study. The signal content of the daily GRACE gravity field model series (ITG-Kalman) is compared to high-frequency bottom pressure variability and terrestrially stored water variations obtained from recent numerical simulations from an ocean circulation model (OMCT) and two hydrological models (WaterGAP Global Hydrology Model, Land Surface Discharge Model). Our results show that daily estimates of ocean bottom pressure from the most recent OMCT simulations and the daily ITG-Kalman solutions are able to explain up to 40 % of extra-tropical sea-level variability in the Southern Ocean. In contrast to this, the daily ITG-Kalman series and simulated continental total water storage variability largely disagree at periods below 30 days. Therefore, as long as no adequate hydrological model will become available, the daily ITG-Kalman series can be regarded as a good initial proxy for high-frequency mass variations at a global scale. As a second result of this study, based on monthly solutions as well as daily observation residuals, it is shown that applying this GRACE-derived de-aliasing model supports the determination of the time-variable gravity field from GRACE data and the subsequent geophysical interpretation. This leads us to the recommendation that future satellite concepts for determining mass variations in the Earth system should be capable of observing higher frequeny signals with sufficient spatial resolution.


GRACE De-aliasing Ocean circulation model (OMCT) WaterGAP Hydrology Model (WGHM) Land Surface Discharge Model (LSDM) Mass variations Atmosphere Ocean Hydrosphere 


  1. Bergmann I, Dobslaw H (2012) Short-term transport variability of the Antarctic Circumpolar Current from satellite gravity observations. J Geophys Res 117(C5):1–12Google Scholar
  2. Bonin JA, Chambers DP (2011) Evaluation of high-frequency oceanographic signal in GRACE data: implications for de-aliasing. Geophys Res Lett 38:L17608Google Scholar
  3. Bruinsma S, Lemoine J, Biancale R, Valès N (2010) CNES/GRGS 10-day gravity field models (release 2) and their evaluation. Adv Space Res 45(4):587–601CrossRefGoogle Scholar
  4. Chambers D, Schröter J (2011) Measuring ocean mass variability from satellite gravimetry. J Geodyn 52(5):333–343CrossRefGoogle Scholar
  5. Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137(656):553–597. doi: 10.1002/qj.828 CrossRefGoogle Scholar
  6. Dill R (2009) Hydrological model LSDM for operational Earth rotation and gravity field variations. Science technical report, 08/09, Deutsches Geo-ForschungsZentrum, Potsdam, GermanyGoogle Scholar
  7. Dobslaw H, Flechtner F, Bergmann-Wolf I, Dahle C, Dill R, Esselborn S, Sasgen S, Thomas M (2013) Simulating high-frequency atmosphere–ocean mass variability for de-aliasing of satellite gravity observations: AOD1B RL05. J Geophys Res. doi: 10.1002/jgrc.20271 Google Scholar
  8. Döll P, Kaspar F, Lehner B (2003) A global hydrological model for deriving water availability indicators: model tuning and validation. J Hydrol 270:105–134. doi: 10.1016/S0022-1694(02)00283-4 CrossRefGoogle Scholar
  9. Döll P, Hoffmann-Dobrev H, Portmann FT, Siebert S, Eicker A, Rodell M, Strassberg G, Scanlon BR (2012) Impact of water withdrawals from groundwater and surface water on continental water storage variations. J Geodyn 59–60:143–156. doi: 10.1016/j.jog.2011.05.001
  10. Flechtner F, Dobslaw H (2013) GRACE AOD1B product description document for product release 05Google Scholar
  11. Frappart F, Papa F, Güntner A, Werth S, da Silva JS, Tomasella J, Seyler F, Prigent C, Rossow WB, Calmant S, Bonnet MP (2011) Satellite-based estimates of groundwater storage variations in large drainage basins with extensive floodplains. Remote Sens Environ 115:1588–1594. doi: 10.1016/j.rse.2011.02.003 CrossRefGoogle Scholar
  12. Geng S, Penning de Vries FWT, Supit I (1986) A simple method for generating daily rainfall data. Agric For Meteorol 36:363–376. doi: 10.1016/0168-1923(86)90014-6 CrossRefGoogle Scholar
  13. Gill A, Niiler P (1973) The theory of the seasonal variability in the ocean. Deep Sea Res Oceanogr Abstr 20(2):141–177CrossRefGoogle Scholar
  14. Gudmundsson L, Tallaksen LM, Stahl K, Clark DB, Dumont E, Hagemann S, Bertrand N, Gerten D, Heinke J, Hanasaki N, Voss F, Koirala AS (2012) Comparing large-scale hydrological model simulations to observed runoff percentiles in Europe. J Hydrometeorol 13:604–620CrossRefGoogle Scholar
  15. Güntner A, Stuck J, Werth S, Döll P, Verzano K, Merz B (2007) A global analysis of temporal and spatial variations in continental water storage. Water Resour Res 43:W05416. doi: 10.1029/2006wr005247 CrossRefGoogle Scholar
  16. Haddeland I, Coauthors (2011) Multimodel estimate of the global terrestrial water balance: Setup and first results. J Hydrometeor 12:869–884. doi: 10.1175/2011JHM1324.1
  17. Hagemann S (1998) Entwicklung einer Parametrisierung des lateralen Abflusses für Landflächen auf der globalen Skala, Examensarbeit 52. Max-Planck-Inst. für Meteorol, HamburgGoogle Scholar
  18. Hagemann S, Dümenil L (1998) A parametrization of the lateral waterflow for the global scale. Clim Dyn 14:17–31. doi: 10.1007/s003820050205 CrossRefGoogle Scholar
  19. Han S-C, Jekeli C, Shum CK (2004) Time-variable aliasing effects of ocean tides, atmosphere, and continental water mass on monthly mean GRACE gravity field. J Geophys Res 109:B04403. doi: 10.1029/2003JB002501 Google Scholar
  20. Harding R, Best M, Blyth E, Hagemann S, Kabat P, Tallaksen LM, Warnaars T, Wiberg D, Weedon GP, Van Lanen H, Ludwig F, Haddeland I (2011) Watch: current knowledge of the terrestrial global water cycle. J Hydrometeorol 12:1149–1156CrossRefGoogle Scholar
  21. Hughes CW et al (2012) Weighing the ocean: using a single mooring to measure changes in the mass of the ocean. Geophys Res Lett 39(17):L17602Google Scholar
  22. Hunger M, Döll P (2008) Value of river discharge data for global-scale hydrological modeling. Hydrol Earth Syst Sci 12:841–861CrossRefGoogle Scholar
  23. IOC (1985) Manual on sea level measurement and interpretation—volume 1: basic procedures. IOC manual and guides, 14Google Scholar
  24. Kurtenbach E, Mayer-Gürr T, Eicker A (2009) Deriving daily snapshots of the Earth’s gravity field from GRACE L1B data using Kalman filtering. Geophys Res Lett 36:L17102CrossRefGoogle Scholar
  25. Kurtenbach E, Eicker A, Mayer-Gürr T, Holschneider M, Hayn M, Furhmann M, Kusche J (2012) Improved daily GRACE gravity field solutions using a Kalman smoother. J Geodyn 59–60:39–48CrossRefGoogle Scholar
  26. Li B, Rodell M, Zaitchik BF, Reichle RH, Koster RD, van Dam TM (2012) Assimilation of grace terrestrial water storage into a land surface model: evaluation and potential value for drought monitoring in western and central Europe. J Hydrol 446–447:103–115. doi: 10.1016/j.jhydrol.2012.04.035 CrossRefGoogle Scholar
  27. Lyard F et al (2006) Modelling the global ocean tides: modern insights from FES2004. Ocean Dyn 56(5):394–415CrossRefGoogle Scholar
  28. Macrander A et al (2010) Validation of GRACE gravity fields by in situ data of ocean bottom pressure. In: Flechtner F (ed) System Earth via geodetic–geophysical space techniques. Springer, Berlin, pp 169–185CrossRefGoogle Scholar
  29. Mayer-Gürr T, Savcenko R, Bosch W, Daras I, Flechtner F, Dahle C (2012) Ocean tides from satellite altimetry and GRACE. J Geodyn 59–60:28–38CrossRefGoogle Scholar
  30. Mitchell TD, Jones PD (2005) An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int J Climatol 25:693–712CrossRefGoogle Scholar
  31. Riegger J, Tourian MJ, Devaraju B, Sneeuw N (2012) Analysis of grace uncertainties by hydrological and hydro-meteorological observations. J Geodyn 59–60:16–27. doi: 10.1016/j.jog.2012.02.001 CrossRefGoogle Scholar
  32. Rietbroek R, Brunnabend S, Dahle C, Kusche J, Flechtner F, Schröter J, Timmermann R (2009) Changes in total ocean mass derived from GRACE, GPS, and ocean modeling with weekly resolution. J Geophys Res 114(C11):C11004CrossRefGoogle Scholar
  33. Rodell M, Velicogna I, Famiglietti JS (2009) Satellite-based estimates of groundwater depletion in India. Nature 460:999CrossRefGoogle Scholar
  34. Sasgen I, Klemann V, Martinec Z (2012) Towards the inversion of GRACE gravity fields for present-day ice-mass change and glacial-isostatic adjustment in North America and Greenland. J Geodyn 59–60:49–63CrossRefGoogle Scholar
  35. Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2013) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol. doi: 10.1007/s00704-013-0860-x
  36. Seitz F, Schmidt M, Shum CK (2008) Signals of extreme weather conditions in central Europe in grace 4-d hydrological mass variations. Earth Planet Sci Lett 268:165–170. doi: 10.1016/j.epsl.2008.01.001 CrossRefGoogle Scholar
  37. Smith AB, Walker JP, Western AW (2011) Variational gravity data assimilation to improve soil moisture prediction in a land surface model. In Chan F, Marinova D, Anderssen RS (eds) MODSIM2011, 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, December 2011, pp 3391–3397. ISBN: 978-0-9872143-1-7.
  38. Tapley BD et al (2004) GRACE measurements of mass variability in the Earth system. Science 305(5683):503–505CrossRefGoogle Scholar
  39. Thomas M, Sündermann J, Maier-Reimer E (2001) Consideration of ocean tides in an OGCM and impacts on subseasonal to decadal polar motion excitation. Geophys Res Lett 28(12):2457–2460CrossRefGoogle Scholar
  40. Weedon GP, Coauthors (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth cenrury. J Hydrometeor 12:823–848. doi: 10.1175/2011JHM1369.1
  41. Werth S, Güntner A (2010) Calibration analysis for water storage variability of the global hydrological model WGHM. Hydrol Earth Syst Sci 14:59–78. doi: 10.5194/hess-14-59-2010 CrossRefGoogle Scholar
  42. Werth S, Güntner A, Petrovic S, Schmidt R (2009) Integration of grace mass variations into a global hydrological model. Earth Planet Sci Lett 277:166–173. doi: 10.1016/j.epsl.2008.10.021 CrossRefGoogle Scholar
  43. Willebrand J, Philander SGH, Pacanowski RC (1980) The oceanic response to large-scale atmospheric disturbances. J Phys Oceanogr 10:411–429CrossRefGoogle Scholar
  44. Zaitchik BF, Rodell M, Reichle RH (2008) Assimilation of grace terrestrial water storage data into a land surface model: results for the Mississippi river basin. J Hydrometeorol 9:535–548. doi: 10.1175/2007JHM951.1 CrossRefGoogle Scholar
  45. Zenner L (2013) Atmospheric and oceanic mass variations and their role for gravity field determination; DGK, Reihe C, Heft 699, Verlag der Bayerischen Akademie der Wissenschaften. ISBN:978-3-7696-5111-9, ISSN:0065-5325Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • L. Zenner
    • 1
  • I. Bergmann-Wolf
    • 2
  • H. Dobslaw
    • 2
  • T. Gruber
    • 1
  • A. Güntner
    • 3
  • M. Wattenbach
    • 3
  • S. Esselborn
    • 4
  • R. Dill
    • 2
  1. 1.Institute for Astronomical and Physical GeodesyTechnische Universität MünchenMunichGermany
  2. 2.Section 1.3: Earth System ModellingHelmholtz Centre Potsdam, GFZ German Research Centre for GeosciencesPotsdamGermany
  3. 3.Section 5.4: HydrologyHelmholtz Centre Potsdam, GFZ German Research Centre for GeosciencesPotsdamGermany
  4. 4.Section 1.2: Global Geomonitoring and Gravity FieldHelmholtz Centre Potsdam, GFZ German Research Centre for GeosciencesPotsdamGermany

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