Swarm as an Observing Platform for Large Surface

Swarm as an Observing Platform for Large Surface Mass Transport Processes J. Encarnacao1,5 D. Arnold2 A. Bezděk3 C. Dahle4,2 E. Doornbos5 J. van den IJssel5 A. Jäggi2 T. Mayer-Gürr6 J. Sebera7 C.K. Shum8 P. Visser5 N. Zehentner6 1Center

for Space Research, University of Texas at Austin Institute, University of Bern 3Astronomical Institute, Czech Academy of Sciences 4GFZ German Research Centre for Geosciences 5Delft University of Technology 6Institute of Geodesy, Graz University of Technology 7ESRIN, ESA 8School of Earth Science, Ohio State University 2Astronomical

Objectives • Report on 3 years of monthly Swarm solutions: – Update on estimated accuracy1 (wrt GRACE Kband solutions) – Parametric decomposition: • Mean, trend • Annual, semi-annual and 1.5-annual amplitudes

– Regional water storage estimates 1Encarnacao

et al. (2016), Gravity field models derived from Swarm GPS data. Earth Planets Space 68:127. http://dx.doi.org/10.1186/s40623-016-0499-9

Gravity field estimation approaches

Gravity field solutions • Individual Swarm solutions (aiub, asu, ifg): – kinematic orbits of all three Swarm satellites

• Combined Swarm solutions (combined): – (arithmetic) average of the individual solutions – 37 solutions: Dec 2013 to Dec 2016

• GRACE solutions: – GFZ RL05a – 26 solutions: Dec 2013 to Aug 2016 (less solutions due to gaps)

• All solutions: – – – –

truncation to degree 20 C20 replaced with value from “GRACE Technical Note 07” temporal variations relative to GGM05G low-pass smoothing with 750km radius (~ degree 13)

Agreement with GRACE Cumulative degree RMS

Geoid height

GRACE missing months


Geoid height


mean TEC


mean TEC

Geoid height


• Quality of Swarm gravity solutions generally depends on ionospheric activity • Updated tracking loop settings of the GPS receivers are benefical for Swarm gravity field recovery1 1Dahle

et al. (2017). Impact of tracking loop settings of the Swarm GPS receiver on gravity field recovery, Adv. Space Res.. http://dx.doi.org/10.1016/j.asr.2017.03.003

Parametric decomposition • For every time-variable SH coefficient of degree i and order j: n

 C ij ( t ) 

ns

p

 p0

Pijp t

p 1



 s 1

S ijs

 2 sin  t   ijs   Ts

model

 res    C ij ( t )  

residual

• Pijp and Sijs solved as linear system of equations (inner loop), with prescribed Φijs • Phase Φijs solved as an optimization problem (outer loop) • np=1 : mean and trend • ns=3 : Ts=[1 0.5 1.5] years

Modelled signal rnorm 

norm  p norm   Pijp t   p0 n

p 1

 C ns



 s 1

res ij

(t )



 2 S ijs sin  t   ijs  T  s

   

• rnorm=1: norm(res)= norm(signal)

• rnorm=0: norm(res)=0

Example: C7,-3

Mean field

Linear trend

Annual Amplitude

Semi-annual Amplitude

Signal RMS: selection of catchments

Amazon

E Antarctica

W Antarctica

Greenland

Hudson Bay

Severny Island

Siberia

W Africa

Conclusions • Combined monthly Swarm solutions: – – – –

Capable to detect large mass transport processes at ~ 1500km scale Long-term trends differ from GRACE by ~ factor of 2 Large scatter (in “smaller” catchments) distort water storage history Unreliable in the first months of the mission (due to high ionospheric activity and not yet updated GPS receiver settings)  situation has improved and will likely remain so during GRACE/GRACE-FO gap

• Outlook: – Consider a wider variety of kinematic orbits as well as gravity field solutions derived thereof – Ingest additional gravity field inversion methods, e.g. Energy Balance Approach (Ohio State Univ., planned), Classical Dynamic Approach (GFZ, planned) – Combination with other LEOs and SLR satellites (see next presentation by Weigelt et al. & Poster Nr. 11 by Arnold et al.) – More evolved combination strategies (EGSIEM)