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Hydrological loading induced vertical displacements from GPS and GRACE

4.3 Datasets and their inconsistencies

filter), AG (anisotropic Gaussian filter), IGD (isotropic Gaussian filter combined with the de-striping filter),AGD(anisotropic Gaussian filter combined with the destriping filter). It should be noted that the combination of the destriping filter with the isotropic or anisotropic Gaussian filter is conducted by applying the destriping first.

4.3 Datasets and their inconsistencies 61

2003 2004 2005 2006 2007 2008 2009 2010 2011

-15 15 50 85 120 155 190 225 260 295 330 365 400 435

time/yr

mm

NYAL POLV GLSV ANKR SOFI UZHL JOZE PENC GRAZ WTZR ZIMM MORP POTS Original weekly time series Averaged monthly time series

Figure 4.2:ExemplaryGPSheight time series from Europe. In the figure, exceptNYAL,GPSheight time series of the rest stations are shifted for plotting purposes. Shaded areas are error bounds of each original weekly time series.

For the comparison over the Amazon area, theGPStime series fromSIRGAS GPSnetwork, which is processed byDGFI (German Geodetic Research Institute), are used. The whole SIRGAS GPS

network, see Fig.4.3, comprises 58IGSglobal stations and otherSIRGAS-CONregional stations, which makes up to around 300 stations in total up to now. The time series utilized in this comparison are the latest multi-year solutionSIR11P01 (Sánchez and Seitz,2011;Sánchez et al., 2013) and the detailed computation procedure is described in (Sánchez and Seitz, 2011). The final residual time series out of this multi-year solution are cleaned and detrended weekly solutions spanning from 2000 to 2011.29 when theIGS08 reference frame was introduced.

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30˚ SIRGAS 30˚

REFERENCE NETWORK

2014-08-07

IGS RNAAC SIRGAS Deutsches Geodaetisches Forschungsinstitut (DGFI)

IGS/IGS+ STATION SIRGAS REGIONAL STATION

/BOGA/ABCC/ABPD/ABPW

/QUEM/EPEC AACR/CRCP

AGCA

ALAR ALBE

ALEC

ALUM AMCO

AMHU ANDS

ANGO

APSA APTO

ARCA

AUCA

AUTF AZUE

AZUL

BABR

BAIL BAIR BARI

BATF BAVC

BCAR BECE

BEJA

BELE BERR

BLPZ/EMIB BNGA BOSC BQLA

CALI CASI CATR

CEEU/CEFT CESB CHEC

CHET CHIH

CHIS

CLEC CN30 COAT

COEC COL2

COTZ

CRAT CRCS

CRUZ

CSLO CUEC CULC

CUM3 DARI DAVI

DORA

EBYP ECEC

EESC ELEN

EREC ESMR

ESQU ETCG/RIDC

EXU0

FLOR FQNE GARA

GOGY GOJA GRE0 GTK0

GUAY

GVA1 GVRE

GYEC

GZEC HUEH

IACR IBAG

IBEC ICEP

IDGO

IGM1 IGN1

ILHA IMPZ ISCO

JBAL LIBE

LIMN

LJEC LREC

MA01

MABA MABB

MABS MAEC

MAGA

MAPA

MCL1/MGMC

MECO MEDE

MEXI

MGBH

MGIN MGRP

MGV1 MHEC

MOTE

MPL2 MRLS

MSCG

MSDR MTBA MTCN MTCO MTEC

MTSF MTSR

MTVB

MZAC MZAE MZAU

MZGA MZSR NARA

NEIL

NESA NEVA NICY

NJEC

OSOR

OURI PAAT PAIT PAMP

PAST

PBCG PBJP PDEC

PEAF

PEJO

PEPE PERA

PISR PITN PJEC

PMB1

POLI POPA

POPT

PRCV PREC

PRGU PRMA

PRNA PSTO

PTEC PUNT

QUIB

QVEC CXEC

RIOD/ONRJ RIOH

RJCG RNMO

RNNA

ROCD ROGM

ROJI

ROSA

RSAL RUBI SAGE SAYA

SCAQ SCCH SCEC

SCFL SCLA

SEAJ SEEC

SINC

SJRP

SJSP

SL01 SMAR SMRT

SNLR SNSN

SPAR

SPBO SPCA SPJA

SRLP SRNW

SRZN

SSA1 STEC

SVIC TAXI

TEG2

TERO TIKA

TINT

TNEC

TOGU TOL2

TUCU TUMA

TUNA

UBA1 UBE1/MGUB

UCOR UGTO

UNPA UNRO UNSJ USLP

UYDU UYLP UYMO UYNI

UYPA

UYRO UYSO

UYTA VALL

VICO VIL2

VIVI

YEMA YOPA ZARZ

ABMF

ANTC AREQ/AREV

BOAV BOGT

BOMJ

BRAZ BRFT BRMU

BUE2 BUEN

CALL CANO CART

CATA CBSB

CEFE

CHAC

CHPI

CHTI

CONZ COPO

CORD

COYQ CRO1

CUCU

CUIB

FALK GLPS

GOLD

GOUG GUAT

HER2

ICAM

IMBT INEG

IPAZ

IQQE IQUI

ISPA

KOUG KOUR

LHCL LPGS MANA

MARA

MAS1 MDO1

MERI

MGUE MKEA

MTY2

NAS0

NAUS

NEIA

NKLG OAX2

OHI2 PALM/PALV PARC

PDEL PIE1

POAL POVE

PPTE QUI1

RECF

RIO2 RIOB RIOP

RWSN SAGA

SALU SAMA

SANT

SAVO

SCRZ SCUB

SSIA

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Longitude

Figure 4.3:Map of theSIRGAS GPSnetwork (courtesy:www.sirgas.org) and distribution of the selected 46

GPSsites from this regionalGPSnetwork.

To compare with GRACE in this area, GPS stations with time series which overlap with the

GRACE time frame more than three years are selected. As this study focuses on hydrological signals, the stations which are not located inside or around the Amazon basin and its nearby basins are ruled out. Eventually 46 stations out of 228 stations are used in the comparison, see Fig.4.3. A few time series are shown in Fig.4.4as examples. Since theSIRGAS GPSnetwork is under development, time series length fromSIRGASvaries among theGPSsites.

Relative toGPSheight time series from Europe, seasonal signals are much stronger in theSIR

-GASnetwork because significant water mass variations are happening in and around the Ama-zon area. For example,NAUS(located in Manaus, Brazil) displays a peak-to-peak 40 mm annual oscillation with an exception in 2009 when a severe flood happened (Chen et al.,2010). In ad-dition to seasonal behavior, stations likeBOGA(located in Bogota, Columbia) also show a clear non-linear trend signal.

4.3.2 GRACE products

T

HE GRACE GSM RL05a product from GFZ(Dahle et al.,2014) is used for the following rea-sons. Firstly, as reported byTesmer et al.(2011), the GRACE datasets fromGFZ, CSRand

JPLdid not show significant systematic differences. Secondly, compared to theGRACE related

4.3 Datasets and their inconsistencies 63

2003 2004 2005 2006 2007 2008 2009 2010 2011

-25 25 75 140 180 215 265 300 330 360 400 430 460

time/yr

mm

BOGA POVE NAUS CUIB BOGT IMPZ MABA RIOB ROJI SAGA UBER VIVI

Original weekly time series Averaged monthly time series

Figure 4.4:Exemplary time series from SIRGAS. In the figure, exceptBOGA,GPS height time series of the rest stations are shifted for plotting purposes.

products from CSRandJPL, GFZprovides better metadata, e.g. the calibrated standard devia-tions of the Stokes coefficients, which are used in the regularization filtering.

Before deriving displacements from theGRACEdata, theC20term is replaced in theGRACE GSM

data using the product fromCheng et al.(2011). The resultant monthlyGRACE GSMStokes co-efficients are then filtered using the deterministic filters and the regularization filter tabulated in Table4.2. TheDDKfiltered datasets are downloaded directly from theICGEMwebsite. Note that theC20term in theDDKfiltered datasets are replaced as well before deriving the displace-ments for comparison.

4.3.3 Inconsistencies between GPS and GRACE

A

Sdiscussed in the previous chapters,GPSandGRACEobserve two fundamentally different quantities and they both experience totally different data processing procedures. Several issues exist in reality affecting the agreement betweenGPSandGRACE. For example,van Dam et al.(2007) andTesmer et al.(2011) mentioned possible error sources in bothGPSandGRACE

which could probably influence the consistencies. To be more precise, van Dam et al.(2007) pointed out possible error sources from the GPSpart, e.g. atmospheric mismodelling, bedrock thermal expansion, monument thermal expansion, phase center modeling and common orbital errors. Tesmer et al. (2011) also attributed disagreements in part to GRACE, e.g. externalC20

term used in GRACE, GRACE data filtering and atmospheric and oceanic dealiasing models.

Apart from these possible errors, however, two fundamental issues should be resolved before comparingGPSandGRACE: 1) the reference frame issue; 2) the atmospheric loading and non-tidal oceanic loading issue. These two issues are not dealt consistently in GPS and GRACE

products and they should be corrected in those solutions before comparison.

Reference frame issue Several reference frames exist in use in geodesy and they have been discussed in Section2.3. From there we know that the resultantGPSdata stay in theCFreference frame while GRACE GSM products lie in the CM frame. Besides, translation of the reference frame from one to another is essentially linked to the translation of the degree-1 terms. As

GRACE does not sense the geocenter motion, the degree-1 terms in the GRACE GSM gravity monthly solutions are set to zero. To keep GPS andGRACE consistent in the reference frame, several ways were adopted in the literature. However, in essence, the rule is to change from one into the other, that is to say, we either change GPSfrom theCFframe into theCM frame or the other way around.

Davis et al. (2004) firstly started to compare the displacements observed by GPS and derived from GRACE. They added the l = 1 contribution to the deformation inferred from GRACE, which was followed by Nahmani et al.(2012). Whilstvan Dam et al.(2007) corrected the ref-erence frame issues by removing the displacements due to the degree-1 effects from GPS (see Fig. 2.8) and this approach was also applied by Tesmer et al.(2011). It should be noted that the displacements computed byvan Dam et al.(2007) andTesmer et al.(2011) were simply an average of theX,Y,Zcomponents from a globalGPSnetwork. Apparently, this way is not suit-able for a regionalGPSnetwork study, e.g. theGPSnetwork in Europe and theSIRGASnetwork used in this chapter. Alternatively,Tregoning et al.(2009) directly restored the degree-1 Stokes coefficients from Munekane(2007). Fu et al.(2012) followedTregoning et al. (2009) but with the degree-1 Stokes coefficients obtained fromSwenson et al. (2008). In theory, all the above-mentioned approaches should be able to maintain the consistency in the reference frame issues.

However, different approaches using different datasets will certainly cause differences.

Here we conduct one experiment regarding the frame issue. Two geocenter motion datasets, which are provided in coordinates sensed by Satellite Laser Ranging (SLR) (Cheng et al.,2013) and in the degree-1 Stokes coefficients delivered bySwenson et al.(2008), are applied. One is to follow van Dam et al.(2007) but using the degree-1 displacements computed fromCheng et al.(2013) and the other way is to followFu et al.(2012). We find that the latter way provides slightly better but negligible consistencies than the former way in both two study areas. Thus, the degree-1 coefficients fromSwenson et al.(2008) are restored back to theGRACE GSM data