Error budget of GRACE gravity field recovery a simulation ...

u ifg.tugraz.at

(1) Institute of Geodesy, Graz University of Technology

(2) GFZ German Research Centre for Geosciences

S C I E N C E P A S S I O N T E C H N O L O G Y

Error budget of GRACE gravity field recovery –

a simulation study

Torsten Mayer-Guerr1, Henryk Dobslaw2, Andreas Kvas1, and Lea Poropat2

GRACE/GRACE-FO Science Team Meeting 2018

2018-10-10

2 Torsten Mayer-Gürr et. al.

Motivation

Real data analysis

Detecting anomalous effects, improving parametrization

Simulation study

Understanding underlying processes, separation


Simulation scenario

Simulated world

Gravity field (max. degree 120)

Static gravity field: GOCO05s

Time variable part: ESA ESM AOHIS

Tides: JPL DE421

Earth tides: IERS2010

Ocean tides: FES2014b

Non-conservative forces

Radiation pressure JPL421

Albedo CERES

Drag JB 2008

Instrument data

One month of data (2006-01)

Integrated orbit is fitted to real GRACE orbit

Attitude: Taken from GRACE star camera

Realistic instrument noise

KBR phases: white noise with 2.8 μm/s with 0.1 s sampling,

=> range rate: CNR low pass/derivation filter applied

ACC along, radial: 1 + 0.005𝑓 ∙ 10−10 m/𝑠2/ 𝐻𝑧

cross: 1 + 0.1𝑓 ∙ 10−9 m/𝑠2/ 𝐻𝑧

SCA white noise with 0.1 mrad

POD white noise with 3 cm per axis with 300 s sampling


Gravity field recovery

Instrument data Least squares adjustment

∆ 𝑥 = 𝐴𝑇𝑃𝐴 −1𝐴𝑇𝑃∆𝑙

Residuals (range rate)

Gravity field

Power spectral density (PSD)

ITSG-Grace2018 processing

Parameter:

Spherical harmonics

degree 2..120

Accelerometer bias:

6 hour cubic splines

Satellite states

Daily

Weight matrix P:

Estimated error covariance

3 hour blocks

Improved weight

matrix by analyizing

the residuals

Background models

Background models





Gravity field







Gravity field



Background models

ForcesSimulated world

(„True“ Earth)

Background models

in the recovery

Static gravity field GOCO05s EGM96

Time variable part ESA ESM AOHIS ESA ESM AOHIS

Direct tides JPL DE421 JPL DE421

Earth tides IERS2010 IERS2010

Ocean tides FES2014b FES2014b

Scenario: Instrument noise only

Perfect dealiasing/background models

Very old (inaccurate) static field





Gravity field



Background models


(„True“ Earth)

Background models

in the recovery






Scenario: Instrument noise only

Perfect dealiasing/background models

Very old (inaccurate) static field

EGM96 – GOCO05s


Gravity field recovery: Instrument noise only

degree variances Range rate residuals PSD

baseline

simulation

Differentiated

KBR noise

Integrated

ACC noise

Instrument noise only

Baseline can be reached

Even with EGM96 as background model

(Solution is independent of reference model)

Linearization is not an issue




Real data

ITSG2018





Gravity field



Background models


(„True“ Earth)

Background models

in the recovery










Gravity field



Background models


(„True“ Earth)

Background models

in the recovery


Time variable part ESA ESM AOHIS AO + ESM error



Ocean tides FES2014b EOT11a

Realistic scenario: Full aliasing

Only atmosphere and ocean signals are reduced

AOD model is not perfect

Ocean tide model is not perfect


Gravity field recovery: Full aliasing


Full aliasing

Recovered gravity field and residuals and field

show realistic error behavior

real residuals and solution accuracy can be explained by

this simulation




Improvement of the solution

Co-estimation of constrained daily gravity field solutions

(degree 40, background model uncertainties as constrained)

12:15: Andreas Kvas et. al.:

Incorporation of background model uncertainties into

the gravity field recovery process


Gravity field recovery: Full aliasing + Daily Kalman estimates


Full aliasing + Daily Kalman estimates

Daily estimates improves the monthly gravity field

and the post-fit residuals





Gravity field



Background models


(„True“ Earth)

Background models

in the recovery


Time variable part ESA ESM AOHIS AO + error








Gravity field



Background models


(„True“ Earth)

Background models

in the recovery






Scenario: AOD errors

Assumption:

ocean tide model can be improved in future

Here: ocean tide model is error free


Gravity field recovery: AOD errors



Gravity field recovery: AOD errors + Daily Kalman estimates


AOD errors + Daily Kalman estimates

Large improvement

but large subdaily signal remains in the residuals

Daily estimates cannot replace good dealiasing models

Torsten Mayer-Gürr et. al.22

Next generation missions:

GRACE + Second pair (Bender constellation)


Simulation scenario

Additional inclined satellite pair

Inclination: 70° Altitude: 429 km

Distance: ~120 km

Same forces and same noise models

Background models


(„True“ Earth)

Background models

in the recovery







GRACE: full aliasing


GRACE + Bender: full aliasing

Additional second pair

Solution is robust against aliasing errors


GRACE + Bender: full aliasing + daily Kalman estimates

Daily Kalman estimates

Slightly improvement in degree 30…60

Slightly worse in degree 2..20 (???)

Improved formal errors in the low degrees



Wiese approach

Wiese et. al. (2011): Estimating low resolution

gravity fields at short time intervals to reduce

temporal aliasing errors,

https://doi.org/10.1016/j.asr.2011.05.027

Daily normals

Daily parameter elimination of low degrees

Accumulation to monthly

=> solve high degrees

Accumulation of full normals to monthly

Subtract solution of high degrees

Solve normals (only part of low degrees)



GRACE + Bender: full aliasing + Wiese approach

Wiese approach





Daily normals







Daily

degree 30




Wiese approach





Daily normals







Daily

degree 20




Wiese approach





Daily normals







Daily

degree 10



GRACE + Bender: full aliasing + daily Kalman estimates


Are dealiasing models (AOD) still needed?


GRACE + Bender: full aliasing + daily Kalman estimates - AOD


Are dealiasing models (AOD) still needed?


Summary

GRACE

Real solution accuracy and real range rate residuals can be explained by simulation

Most of the error sources are understood

Taylorpoint for linearization is not an issue

Errors in the background models are major contributors to the total error budget

Co-estimation of daily gravity fields improve the solutions

Good dealiasing models are still needed

Bender constellation

Additional satellite pair improves the solution significantly

Daily co-estimation needs more fine tuning (Kalman and Wiese)

Good dealiasing models are still needed

u ifg.tugraz.at

(1) Institute of Geodesy, Graz University of Technology

(2) GFZ German Research Centre for Geosciences

S C I E N C E P A S S I O N T E C H N O L O G Y

Error budget of GRACE gravity field recovery –

a simulation study

Torsten Mayer-Guerr1, Henryk Dobslaw2, Andreas Kvas1, and Lea Poropat2

GRACE/GRACE-FO Science Team Meeting 2018

2018-10-10

Error budget of GRACE gravity field recovery a simulation ...

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