Processing of ground based GNSS data to produce near real time (NRT) tropospheric zenith

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Processing of groundbased GNSS

data to produce near realtime (NRT) tropospheric zenith

path delays (ZTD)

Jan Douša

(jan.dousa([email protected]@pecny.cz))

Geodetic Observatory Pecn

Geodetic Observatory Pecný,ý,

Research Institute of Geodesy, Topography and Cartography,

The

The Czech RepublicCzech Republic EGVAP Workshop

EGVAP Workshop

November 6, 2008 November 6, 2008

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Outline

• _{introduction to GNSS}_{introduction to GNSS} • _{the concept of GNSS contribution to meteorology}_{the concept of GNSS contribution to meteorology} • _{different GNSS processing approaches (PPP x Network)}_{different GNSS processing approaches (PPP x Network)} • _{general aspects of the network processing (in brief)}_{general aspects of the network processing (in brief)} • _{the requirements and features of near realtime (NRT) solution}_{the requirements and features of near realtime (NRT) solution} • _some_some_{results and comparisons}_{results and comparisons}

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GNSS Global Navigation Satellite Systems

• _GPS NAVSTAR_GPS NAVSTAR_–_–_N_N_AV_AV_igation_igation_S_S_ystem using_ystem using_T_T_iming_iming_A_A_nd_nd_R_R_anging_anging

The United States’ military service

The United States’ military service (

(1972, 1972, fully operationalfully operational since since 1994)1994)

• _GLONASS_GLONASS_–_–_GLO_GLO_balnaja_balnaja_NA_NA_vigacionnaja_vigacionnaja_S_S_putnikovaja_putnikovaja_S_S_istema_istema

Russian (the Soviet Union’) military service Russian (the Soviet Union’) military service (1978, (1978, scheduled for restoration by 2010scheduled for restoration by 2010))

• _GALILEO_GALILEO_{European Space Agency (ESA)}_{European Space Agency (ESA)}

European commercial service European commercial service (1999, (1999, scheduled to be fully operational by scheduled to be fully operational by 20201313)) DORIS (France), COMPASS or Beidou (China), QZSS (Japan), IRNSS (India) DORIS (France), COMPASS or Beidou (China), QZSS (Japan), IRNSS (India)

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Satellite tracks projected onto the surface

• _GPS_GPS_{– 31 (32) satellites / 6 orbital planes / 11h 58min}_{– 31 (32) satellites / 6 orbital planes / 11h 58min}

• _GLONASS_GLONASS_{– 21 (24) satellites / 3 orbital planes / 11h 15min}_{– 21 (24) satellites / 3 orbital planes / 11h 15min} • _Galileo_Galileo_{– 27 (30) satellites / 3 planes}_{– 27 (30) satellites / 3 planes}

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basic GNSS

observables

GPS oscillator with fundamental GPS oscillator with fundamental frequency 10.23 MHz multiplied by frequency 10.23 MHz multiplied by 154x > 1575.42 MHz (L1) 154x > 1575.42 MHz (L1) 120x > 1227.60 MHz (L2) 120x > 1227.60 MHz (L2) _{code pseudorange}_{code pseudorange} the measure of the transit time from satellite to receiver using autocorrelation of received and replicated signal (the the measure of the transit time from satellite to receiver using autocorrelation of received and replicated signal (the time is coded in signal) time is coded in signal)

observablesobservables: : C1 = L1 C1 = L1 CC/A/A, , P1 = L1 P(Y) P1 = L1 P(Y), , P2P2 = L2 P(Y) and many others in future = L2 P(Y) and many others in future ≈≈ 1m absolute positioning for civil usage1m absolute positioning for civil usage _{phase pseudorange}_{phase pseudorange} the measure of the phase difference btw. received and replicated carrier frequency the measure of the phase difference btw. received and replicated carrier frequency observables:

observables: L1, L2L1, L2 and others in future and others in future subcentimeterlevel relative positioning

subcentimeterlevel relative positioning

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Error sources for GNSS

Satellites

:

e

ph

emeri

s

,

clocks, differencial code biases

(AS

(ASantispoofingantispoofing, , S/AS/Aselective availabilityselective availability before 2000) before 2000)

Receivers

:

clocks

,

phase center offsets and variations, differencial code

biases

Environment

:

troposphere, ionosphere, multipath, Earth’s kinematics

Processing

:

cycleslips in phases, model errors

→

Elimination

by observable differences

by introducing precise models and products

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Parameters in GNSS mathematical model

thus we have to handle somehow these parameters in GNSS processing: thus we have to handle somehow these parameters in GNSS processing: • _{satellite and receiver position}_{satellite and receiver position} • _{satellite and receiver clock corrections}_{satellite and receiver clock corrections} • _{Earth orientation parameters and geocenter coordinates}_{Earth orientation parameters and geocenter coordinates} • _{satellite and receiver code differential bias}_{satellite and receiver code differential bias} • _{satellite and receiver phase center offsets and patterns}_{satellite and receiver phase center offsets and patterns} • _{troposphere effect}_{troposphere effect} • _{ionosphere effect}_{ionosphere effect} • _ambiguities_ambiguities

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Observable differences

to eliminate some of the errors in mathematical GPS

to eliminate some of the errors in mathematical GPS model model, we often create, we often create and use differences from the and use differences from the original observables

original observables::

_{singledifference}_{singledifference}_(SD)_(SD)_–_–_{difference between two stations}_{difference between two stations}₍₍_{baseline generation}_{baseline generation}₎₎_, which_, which_{eliminates the}_{eliminates the}

satellite clock errors observed at both stations

doubledifferencedoubledifference (DD) (DD) – – difference between two SDs difference between two SDs ( (measurement to two satellites from the single measurement to two satellites from the single baseline

baseline)), which eliminates, which eliminates reciever clock errorsreciever clock errors

_{trippledifference}_{trippledifference}_(TD)_(TD)_– diferen_– diferen_{ces between two}_{ces between two}_DD_DD_{in different epochs, which is useful to detect}_{in different epochs, which is useful to detect} the phase skips (e.g. when signal from satellite was discontinued)

the phase skips (e.g. when signal from satellite was discontinued)

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GPSmeteorology concept

GPS x NWP

we know

we know precise receiver and orbit positions, precise receiver and orbit positions, we we eliminate

eliminate i ionosonospherephere effect (receiver and satellite clock effect (receiver and satellite clock error),

error), we introduce (PCVs, OCTIDE, ...)we introduce (PCVs, OCTIDE, ...) we estimate:

we estimate: zenith path tropospheric delay (receiver zenith path tropospheric delay (receiver

and satellite clocks)

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GPS observation equation

Basic GPS carrier phase observable (scale to distance):

L

_rec_re_csatsat

=

σ

rec

recsatsat

+ c*

δ

satsat

+ c*

δ

recrec

+

λ

*n

recrecsatsat

+

∆

IONION

+

∆

TRP TRP

+

ε

rereccsatsat

σ

σ_rec_recsat sat _.._.._{receiversatellite distance in vacuum}_{receiversatellite distance in vacuum}

((receiverreceiver and and satellite coordinates satellite coordinates))

c

c .. speed of light.. speed of light

δ

δsatsat_,_,δδ rec

rec .. .. satellite and receiver clocksatellite and receiver clock errors errors

λ

λ .. wavelength of the carrier phase.. wavelength of the carrier phase

n

n_rec_recsat sat _{.. unknown initial phase}_{.. unknown initial phase}_ambiguities_ambiguities

∆

∆_ION_ION .. ionospheric (slant) delay.. ionospheric (slant) delay

∆

_TRP_TRP .. .. tropospheric (slant) path delaytropospheric (slant) path delay

∆

_TRP_TRP = = mmffhh(z) * ZHD + (z) * ZHD + mmffww(z) *(z) * ZWDZWD (z = zenith distance)(z = zenith distance) ZTD ZTD = ZHD + ZWD= ZHD + ZWD ZTD

ZTD [m][m] Zenith Total DelayZenith Total Delay (usually site & timedependent parameters)(usually site & timedependent parameters)

m

mff_w_w_/_/_h_h mapping function mapping function (wet / hydrostatic)(wet / hydrostatic)

L Lkl_kl ij ij = L = Lkl_kl i i – L – Lkl_kl j j = ( L = ( Lk_k i i – L – Ll_l i i ) – ( L ) – ( Lk_k j j – L – L _ll j j ) ) double differences in network sol. double differences in network sol.

← satellite and receiver position need satellite and receiver position need to be accurately known

to be accurately known

← eliminated using double-differences eliminated using double-differences (estimated in PPP !!!)

(estimated in PPP !!!)

← need to be resolved integer or floatneed to be resolved integer or float

← first-order eliminated infirst-order eliminated in the ’ionospher

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Least Squares Adjustment

GPS „distance“ GPS „distance“ measurements measurements (code and/or phase) (code and/or phase) stochastic information stochastic information unknown unknown parameters parameters •_coordinates_coordinates •_ambiguities_ambiguities •_ztd‘s_ztd‘s • _{after linearization} • _{user usually knows the models for the orbits, tides, etc}_. Observations: Observations: residuals residuals

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Normal Equations (NEQs

)

normal

normal equationequation

parameter estimation

minimizing the residuals:

minimizing the residuals: e‘ P e e‘ P e min. min.

parameters of interest (coordinates, parameters of interest (coordinates, troposphere, ...) troposphere, ...) parameters to be eliminated (ambiguities) parameters to be eliminated (ambiguities)

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Sequential Adjustment: Idea

often applied in two ways:

often applied in two ways:

- time domain time domain

(sequential solutions)(sequential solutions)

- space domain space domain

(network clusters)(network clusters)

Processing of sequential

solutions :

identical

identical with processing all with processing all observations in a common

observations in a common

adjustment, if there are no

correlations

correlations of the original of the original observations

observations

tim

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Processing strategies & software

PPP approach: Epos - Epos - GFZGFZ Gipsy -

Gipsy - NGAANGAA

Network approach:

Gipsy -

Gipsy - ASI ASI

Bernese -

Bernese - BKG, GOP, KNMI, LTP, ROB, METO, SGNBKG, GOP, KNMI, LTP, ROB, METO, SGN

• Large NEQ

• Increasing CPU with incr.

number of sites/parameters

• Correlations btw stations are ignored • Use of external products (orbits, clocks)

Disadvantages

• Correlations between

parameters of all stations are taken into account

• Independence of external

products (except for small networks)

• Small NEQ

• Keeping CPU with increasing number of

sites / parameters (e.g. ZTD every 15 min, estimation of gradients)

• Investigations of site dependent effect

Advantages

Network using double differences Precise Point positioning (PPP)

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PPP processing strategy

(example GFZ)

Part 1 - Network orbit improvement:

Adjustment of precise orbits & clocks Global network : ~20 IGS+German sites Input orbits: GFZ 3h Ultra-rapid (pred.)

CPU (Linux PC): ~6 to 8 minutes

Part 2 - PPP Analysis:

Estimation of trop. parameters Large set of parameters possible (high sampling rate, trop. gradients)

NEW: ‚slant delays‘ estimation

GFZ EPOS Software

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General network processing steps

•

creating data batches (xhourly or sliding window)

•

data quality check

•

single point positioning for rough recei

ver

clock synchroniz

ation

•

network design by double differencing (clusters possible)

•

data screening for phase cycleslips, ambiguities set up

•

iterative site & satellite

quality check and outliers rejection

•

ionosphere product & ambiguity resolution

•

reference frame reali

z

ation & coordinate estimation

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Network processing strategy

(example GOP)

• _{preprocessing}_{preprocessing}_{is based on twohours data batches}_{is based on twohours data batches}

1 hour redundancy with the previous run easier ambiguity resolution, 1 hour redundancy with the previous run easier ambiguity resolution,

 coordinates also for regularly ‘late’ RINEX ( > 30min ) coordinates also for regularly ‘late’ RINEX ( > 30min )

• _{normal equations (NEQ)}_{normal equations (NEQ)}_{– 1h for ZTD and 2h for coordinates}_{– 1h for ZTD and 2h for coordinates} • _processing_processing_in clusters_in clusters_{of the network}_{of the network}

• _coordinates_coordinates_{are combined from last 28 days using 2hNEQs with ambiguity fixed, free}_{are combined from last 28 days using 2hNEQs with ambiguity fixed, free}

network solution, IGS05 reference frame

• _ZTD product_ZTD product_{based on last 12h stacking of 1hNEQs}_{based on last 12h stacking of 1hNEQs} • _{ionosphere product}_{ionosphere product}_{for ambiguity resolution}_{for ambiguity resolution}

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GOP processing scheme

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Ambiguity resolution in near realtime

•

initial phase ambiguities represent a huge number ( > 90% !) of necessarilly estimated

parameters in mathematical GPS model

•

in network solution, they can be resolved for integer numbers, which has strong impact

for the coordinate estimation in shorttime dataspan

•

ambituity resolution depends on timewindow and baseline lenght

•

in GOP solution, for example, the ambiguities are resolved for

70% in total

within

twohour data batch applying twostep approach (

widelane ambiguities

at

MelbourneWubbenna phase+code linear combination resolved in 8090% and

narrow

lane ambiguities

at ionospherefree phase linear combination resolved with 70%

success)

•

resolved ambiguities are introduced ‘as known’ at least for the official coordinate

estimation (

North/East/Up

coordinate repeatability improved from 10/10/25mm to

6/6/16mm

)

•

a positive bias of aprox. 1mm observed in ZTD solutions btw ambituity free and fix

solution !

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NRT coordinate solutions

The coordinates, which are ‘fixed’ or ‘tightly constrained’ in NRT ZTD solution should be as

good as possible (

good as possible ( ≈≈ 3:1 for CRD:ZTD) 3:1 for CRD:ZTD) example: GOP solution for the coordinates example: GOP solution for the coordinates • _{the coordinates are based on ambiguity fixed solution using last 28 days of twohourly}_{the coordinates are based on ambiguity fixed solution using last 28 days of twohourly} NEQs, the solution is updated every hour. NEQs, the solution is updated every hour. • _{the coordinates are expressed in local datum close to the last ITRF realization by IGS}_{the coordinates are expressed in local datum close to the last ITRF realization by IGS} (currently IGS05) by applying the Helmert transformation (fidutial stations are (currently IGS05) by applying the Helmert transformation (fidutial stations are iteratively checked) iteratively checked)

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Troposphere model – Bernese GPS software

Slant tropospheric path delays = wet + dry (hydrostatic) are mapped into zenith using a Slant tropospheric path delays = wet + dry (hydrostatic) are mapped into zenith using a mapping function (mf) mapping function (mf) S SPPD = D = mmff_H_H(z)(z) ZHD + ZHD + mmff_W_W(z)(z) ZWD [z = zenith distance]ZWD [z = zenith distance] where ZHD can be well a priori estimated if atmospheric pressure and station heiht+latitude are where ZHD can be well a priori estimated if atmospheric pressure and station heiht+latitude are known (e.g. Saastamoinen, 1972) known (e.g. Saastamoinen, 1972) Because its variability, ZWD should be estimated for baselines > 20km Because its variability, ZWD should be estimated for baselines > 20km Extended model could apply additionally the azimuthal dependency expressed as horizontal Extended model could apply additionally the azimuthal dependency expressed as horizontal tropospheric gradients (G

tropospheric gradients (G_N_N north, G north, G_E_E easth): easth): S

SPPD = D = mmff_H_H(z) ZHD + (z) ZHD + mmff_W_W(z)(z) ZWD +ZWD +

∂∂ mfmf_W_W//∂∂ z [ G z [ G_N_Ncos(A) + Gcos(A) + G_E_Esin(A)] [A = azimuth]sin(A)] [A = azimuth]

Constant or

Constant or piecewise linear functionpiecewise linear function is is applied for ZTDapplied for ZTD

Standard atmosphere (or insitu atm. pres. measurement) for a priori ZHD

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Troposphere model – impact study example

Some impacts in past using older models: Some impacts in past using older models: 2. 2. no a priori model (zero value) and dry Niell mapping function used for the total zenith delay estimated (used no a priori model (zero value) and dry Niell mapping function used for the total zenith delay estimated (used until May 2005). until May 2005). 3. 3. a priori ZHD based on standard atmosphere and wetNiell mapping function estimating ZTD (hopefully a priori ZHD based on standard atmosphere and wetNiell mapping function estimating ZTD (hopefully most of the ZWD). most of the ZWD). → _{bias variable}_{bias variable} in time and space in time and space Another sitedependent Another sitedependent bias was introduced bias was introduced in 2006 due to changing in 2006 due to changing relative

relative →→ absolute absolute Phase Center Variations Phase Center Variations and Offsets model used and Offsets model used (upto 5mm) (upto 5mm)

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Tropospheric

product

(GOP example)

•

ZTDs for every hour (HH:00 + HH:59)

•

a linear trend is considered between the values

•

coordinates are heavily constrained to our estimated values realizing the IGb00

reference frame and written to the COST 716 format.

•

ZTD product filtering:

– _{Sites with less than 4 hours of data in ZTD solution are excluded from the product}_{Sites with less than 4 hours of data in ZTD solution are excluded from the product} – _{Sites with less than 2 days of data in coordinate solution are excluded.}_{Sites with less than 2 days of data in coordinate solution are excluded.} •

ambiguityfree (AF) and ambiguityfixed (AX) ZTD solutions are provided

(officially AF), both using the same a priori coordinates values (ambiguityfixed).

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Requirements: Requirements: hourly GNSS data (IGS, EPN, national,...) precise orbits (IGS ultrarapids, ...) precise orbits (IGS ultrarapids, ...) for PPP: precise satellite clocks, DCB bias Features: Features: processing started every hour usually ZTD at the edge of the processing window correlation with respect to previous estimates (physical, via processing, possible constraints – depends on timeresolution) Other important models: Other important models: ocean and Earth tides (station coordinate, geocentr, satellite orbits) receiver and satellite phase center offsets and variations troposphere mapping function 2nd, 3rd order ionosphere many others especially in precise orbit determination

N

ear realtime

aspects of

ZTD

estimation

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GNSS hourly data availability

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predominantly IGS ultrarapid orbits used predominantly IGS ultrarapid orbits used

Requirements on predicted orbits for ZTD

errors in ZTD 20 01 2008 Synthetic error in orbit position Synthetic error in orbit position  1m in alongtrack  1m in crosstrack

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ZTD results PPP vs Network

ZIMM and GOPE – one of the 12 ‘supersites’

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Some ZTD/PWV comparison at GOP

20012003 comparison 20012003 comparison NRT x postprocessing NRT x postprocessing StdDev : StdDev : 47mm 47mm B Biasias : : 13mm13mm

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 weeklyweekly Sdev and Bias Sdev and Bias  GPS ZTD from GOP near realtime GPS ZTD from GOP near realtime  NWM Hirlam from DMI  NWM Hirlam from DMI StdDev: 816mm (28mm) Bias: upto 16mm (25mm)

ZTD comparison

NRT GOP

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AC’s NRT

ZTD

x postprocessing @ GOP

ztd differences freqency & distribution functions (2004/2005) ztd differences freqency & distribution functions (2004/2005) ACRI ASI BKG GFZ GOP IEEC LPT NKG NKGS B O R 1 G O P E H E R S P O T S W T Z R O N S A M A R 6 C A G L M A

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Hour x day plots (ztd differences)

NRT

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Groundbased GPSmeteorology

(Europe)

COST-716 Action (1998-2003):

"Exploitation of Ground-Based GPS for

Operational Numerical Weather Prediction and Climate Applications“

15 Institutions 7 ACs

> 200 GPS sites

TOUGH (2003-2006):

„Targeting Optimal Use of GPS Humidity Measurements in Meteorology“

15 Institutions (Coordinator DMI) 12 ACs

> 400 GPS sites

E-GVAP (2006 - 2009):

„The EUMETNET GPS Water Vapor Programme“

13 Institutions 10 ACs

Processing of ground based GNSS data to produce near real time (NRT) tropospheric zenith