Joint Inversion of GPS site displacements, ocean bottom pressure models and GRACE gravimetry

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1 Joint Inversion of GPS site displacements, ocean bottom pressure models and GRACE gravimetry Status JIGOG project R. Rietbroek, J. Kusche, Ch. Dahle, F. Flechtner, R. Schmidt, J. Schröter, M.J.F. Jansen, B. Gunter GSTM+SPP1257 workshop, Potsdam GeoforschungsZentrum Potsdam Alfred Wegener institut Bremerhaven Delft University of Technology 29/11/2007 1

2 Overview Mass Transport and loading theory Data types GPS GRACE Finite Element Sea Ice Ocean Model (FESOM) Combination Approach Preliminary results Conclusions & Outlook Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 2

3 Mass Transport and Theory Proposed combination=> estimate of global mass load changes GPS data => low degrees, geocenter motion GRACE => Mid degrees Ocean Bottom pressure (OBP) => dynamics of the ocean Reference frame : center of figure of the earth CF Network/crust-fixed : Altimetry, SLR, GPS, Ocean models Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 3

4 Mass Transport and Theory Proposed combination=> estimate of global mass load changes GPS data => low degrees, geocenter motion GRACE => Mid degrees Ocean Bottom pressure (OBP) => dynamics of the ocean Reference frame : center of figure of the earth CF CM CM Network/crust-fixed : Altimetry, SLR, GPS, Ocean models Mass (re) distribution => Orbit perturbations: GRACE Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 4

5 Mass Transport and Theory Proposed combination=> estimate of global mass load changes GPS data => low degrees, geocenter motion GRACE => Mid degrees Ocean Bottom pressure (OBP) => dynamics of the ocean Reference frame : center of figure of the earth CF CM CM Network/crust-fixed : Altimetry, SLR, GPS, Ocean models Mass (re) distribution => Orbit perturbations: GRACE Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 5

6 Mass Transport and Theory (2) Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 6

7 Mass Transport and Theory (3) Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 7

Data types: GPS Used set: weekly IGS network solutions (surface deformations) 1998-present Advantages: Detection of degree 1 deformation (geocenter motion description) Estimation of low degree SH

8 Data types: GPS Used set: weekly IGS network solutions (surface deformations) 1998-present Advantages: Detection of degree 1 deformation (geocenter motion description) Estimation of low degree SH coefficients Disadvantages coverage => Ill posed for global solution contamination by (post) seismic effects, plate motion, PGR Systematic errors Source: cddis.nasa.gov/images Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 8

Data types: GPS Ill-posedness Bad coverage over ocean, Antarctica, Asia, Africa Causes normal matrix to be ill-posed for global solution Previous solution methods: Blewitt et al 2003: Impose global

9 Data types: GPS Ill-posedness Bad coverage over ocean, Antarctica, Asia, Africa Causes normal matrix to be ill-posed for global solution Previous solution methods: Blewitt et al 2003: Impose global mass consistency (add uniform layer over the ocean), low degree truncation Wu et al 2003: Solve normal systems with truncated SVD approach (combination GRACE, ECCO) Kusche Schrama 2005: Formal regularization over the ocean. Clarke et al 2007: Incorporate ocean constraints in the observation equation. Mendez et al 2006: Interpolated pseudo-data to fill ocean and void land regions. Formal standard deviations (mm eqv. Water height) left: GRACE-3000km smoothed, right: GPS+OBP(ECCO) lmax=7 Mass Transport Data types Combination Results Conclusions & Outlook 29/11/2007 9

Data types: Finite Element Sea Ice Ocean Model (FESOM, AWI) Bottom pressure model output as pseudo observations Key characteristics : Primitive equations Boussinessq approximation but with mass

10 Data types: Finite Element Sea Ice Ocean Model (FESOM, AWI) Bottom pressure model output as pseudo observations Key characteristics : Primitive equations Boussinessq approximation but with mass corrections terms (river run off, P-E) Free surface (no IB-assumption) Global ~ 1.5 degrees, 26 z- levels Sea-Ice interaction incorporated ( AWI ) FEOM/FENA, Kivman et al. Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

(Monte Carlo?) Impact of Unmodeled phenomena: eg. Eddies Marginal quality in shallow and semi enclosed seas.

11 Data types: FESOM (2) Advantages: Global coverage Agreement with GRACE mass consistency Sea_ice interaction incorporated (better results near poles) ocean bottom pressure variance GRACE 5cm Disadvantages: Accuracy assessment? (Monte Carlo?) Impact of Unmodeled phenomena: eg. Eddies Marginal quality in shallow and semi enclosed seas. Forcing models (NCEP/NCAR daily reanalysis ) FESOM 0cm Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

Data types: GRACE Used set: 1) Submonthly GFZ solutions (see pres. Ch.

coverage Disadvantages: Correlated errors (requires filtering) Filtering

(Kusche 2007) GRACE Gaussian smoothed (rad 400km) Mass Transport Data types

12 Data types: GRACE Used set: 1) Submonthly GFZ solutions (see pres. Ch. Dahle), 2) GFZ RL04 Advantages: Direct measure of integrated mass Global coverage Disadvantages: Correlated errors (requires filtering) Filtering introduces bias No geocenter motion detectable Point Anisotropic filter (Kusche 2007) GRACE Gaussian smoothed (rad 400km) Mass Transport Data types GRACE anisotropically smoothed (alpha 1d13) Combination Results Conclusions & Outlook 29/11/

Maximum solution degree ~7-30 Combination of normal equations for separate datasets Relative contribution of datasets

13 Combination Approach Unknowns: Spherical harmonic coefficients, Helmert parameters (reference frame translation, rotation and scaling) GRACE global, GPS+OBP: complementary => global Normal equations based on Spherical harmonics Maximum solution degree ~7-30 Combination of normal equations for separate datasets Relative contribution of datasets depend on the geometry of the datapoints. Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

Combination Approach (2) What can GPS add to GRACE

14 Combination Approach (2) What can GPS add to GRACE (apart from deg-1)? In the IGS GPS world Correlated errors from SINEX GPS-contribution (%) Order m GRACE full covariance Degree l C20! GPS contribution Error (l=16) GRACE Scale 6mm Error GRACE+GPS Scale 6mm Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

15 Degree l Combination Approach (2) What can OBP add to GRACE? Global ocean error 3.0cm/ cell GRACE full covariance GPS-contribution (%) Order m OBP contribution Error (l=20) GRACE Scale 6mm Mass Transport Data types Combination Error (l=20) GRACE +OBP Results Conclusions & Outlook 29/11/

16 Preliminary results: Geocenter motion GPS-regularization: Kusche Schrama Annual fit: not well estimated, phase differences Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

noisier but realistic Mass Transport Data types

17 Preliminary results: Low Degree SHcoefficients Good agreement C20 and S21 Weekly solutions noisier but realistic Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

18 Conclusions Complementary data sources allow retrieval of mass transport and redistribution based on loading theory Addition of GPS enables retrieval of mass transport wrt center of figure of the earth (almost) GPS addition improves weakness of GRACE C20 Sub monthly GRACE data can improve the temporal resolution Output useful for a broad range of applications Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

19 Outlook Finish combination software Improve GPS Outlier detection Recompute earlier GPS-GRACE combinations (Jansen et al 06) for longer time span, with cleaner GPS series and with weekly GRACE solutions. Addition and Accuracy assessment OBP model data Study data contribution in combination approach Mass Transport Data types Combination Results Conclusions & Outlook 29/11/

Originally published as:

Originally published as: Rietbroek, R., Brunnabend, S. E., Dahle, C., Flechtner, F., Kusche, J., Schröter, J., Timmermann, R. (2009): Changes in total ocean mass derived from GRACE, GPS, and ocean modelling