E-GVAP
The EUMETNET GPS Water
Vapour Programme
Presentor: Henrik Vedel
Danish Meteorological Institute
Coordinator of E-GVAP
email: egvap@dmi.dk.
E-GVAP
Space Weather Week, Brussels 2006-11-15
EUMETNET, the Network of European Meteorological Services,
is an association of European national met. offices
Memberships: E-GVAP / EUMETNET / none (by Jan 2006)
(Meteo-France will become a member of E-GVAP by April 2007)
E-GVAP
Space Weather Week, Brussels 2006-11-15
Why are GPS delay data interesting to
meteorology?
The zenith total delay, ZTD, which can be estimated
processing data from ground based GPS receivers, is
sensitive to properties of the atmosphere of importance to
meteorology.
E-GVAP
Space Weather Week, Brussels 2006-11-15
ZTD is the delay one would observe was there a GPS (GNSS)
satellite at zenith.
ZTD is estimated by fitting models including mapping
functions and the ZTD to observations of the visible GPS satellites
(We consider all ionospheric influence removed, by means of L1 L2
information)
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Space Weather Week, Brussels 2006-11-15
From geodetic side
• ZTD = ZHD + ZWD
(zenith hydrostatic and zenith wet delay)
• TD = f_H(θ) ZHD + f_W(θ) ZWD (f_ = ’known’ mapping functions
θ = zenith angle)
• ZHD = f(position) p_c
(f ’known’, p_c pressure from climatology)
ZWD is estimated by fitting a model including ZWD to observations
toward individual GPS satellites obtained from a ground based GPS
receiver. Corrections due to various time changing effects, such as
Earth tide displacement, ocean and atmospheric loading, and post
glacial rebound are applied.
Depending on the processing method a simultaneous fitting is done for
various other effect, such as clock and orbit errors. Some of those
may be provided as input (estimated apriori).
Different processing software packages and strategeies have been
found (e.g. in the TOUGH project, http://tough.dmi.dk) to produce
ZTD results of comparable quality when the necessary input data are
available. And that may vary, according to method and strategy.
E-GVAP
Space Weather Week, Brussels 2006-11-15
GPS Processing Strategy
Network Approach
simultaneous analysis of all the data
rnx1
rnx2
rnxi
Precise Point Positioning
each station is analyzed independently
rnxn
rnx1
rnx2
Orbits
ERP
Site Coord.
Data
reduction
Orbits+clk
ERP
Site Coord.
Data
reduction
rnxn
Data
reduction
Data
reduction
ZTD
ZTD
VAR/COV Matrix
• Computing time increases more than
proportionally with the number of stations
• Network has to be split in sub network
• VAR/COV Matrix
ZTD
• Computing time increases linearly with
the number of stations
ZTD
• Parallel processing
• No correlation between sites
IMPORTANT: orbits and site coordinates must be in the same RF
(This slide courtesy Olivia Lesne, ACRI-ST)
E-GVAP
Space Weather Week, Brussels 2006-11-15
From meteorological side
• ZTD = ZHD + ZWD
• ZHD = f(position) p_a
(f ’known’ p_a = pressure at GPS antenna)
• ZWD = f(T) IWV
(IWV = integrated water vapour,
f known, weakly depending on temperature profile)
Properties like pressure and IWV are important to numerical weather
prediction (NWP) models and to now-casting.
Currently water vapour measurements for meteorology are scarce.
However, to be of value to meteorology, the ZTD data must be:
• Available at the right time (1h 40min for NWP, faster for now-casting)
• Stable, both short and long term.
• Of consistent quality.
• From relevant region.
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Space Weather Week, Brussels 2006-11-15
Purpose of E-GVAP
1. The main purpose of EGVAP is to provide
quality checked ground based GPS delay and
integrated water vapour data (ZTDs and IWVs)
in near real time (NRT) for use in operational
numerical weather prediction (NWP) models
and in now-casting to the participating
EUMETNET members.
2. To improve on the data quality and enlarge
data coverage
3. To assist in utilising the data for weather
forecasting.
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Space Weather Week, Brussels 2006-11-15
Approach
• Build on results and collaboration established in
COST716, TOUGH, and other fora.
• Arrangements on the European level between EUREF
and E-GVAP
• Arrangements on national level between national met
office and national GPS site/data owners (to avoid
problems related to transfer of national data over
borders).
• For same reason no single central processing centre.
Processing to take place where it is most practical in a
given situation.
• Build mainly on exchange of data (GPS data versus
meteorological data) and on sharing of resources (e.g.
GPS stations being placed on meteorological sites.
• Funding for services are arranged on national level.
Mainly for extra expenses in setting up data transfer, for
transfer of processing expertise, in some cases for
processed data.
E-GVAP
Space Weather Week, Brussels 2006-11-15
NRT GPS Processed Data Flow
(As of Dec 2005. Since KNMI has started processing data, NKG and NKGS
have been replace be SMHI, and ACRI and ROB has stopped processing.
Likely data from Belgium will soon be processed again, at the met office)
IEEC
GOPE
GFZ
BKG
ASI
ACRI
LPT(R)
METO
THORN
FTP
server
NKG
BUFR
BUFR
Linux
Workstation
MetDB
BUFR
BUFR
NKGS
ROB
BUFR
SGN
NRT Users
& mirror sites
GTS Users
(Figure by Dave Offiler, UK Met Office)
E-GVAP
Space Weather Week, Brussels 2006-11-15
DATA COVERAGE
Status map from 20061113
from the E-GVAP validation
site. (See egvap.dmi.dk
under validation for current
situation).
Data available at ftp-server
at MetO: thorn.meto.gov.uk
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Timeliness monitoring
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Arrival time monitoring.
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Quality monitoring
•
•
•
•
•
Continuous quality monitoring is performed and shown
at the validation site. Monitoring is against NWP
HIRLAM data and against radiosonde data.
Will be updated with automatic flagging of deviating
data.
Will be updated with automatic feedback to processing
centres/site owners in case of detected problems.
Statistics is compiled for the NWP-GPS offsets and
presented at the KNMI validation site.
Periodic reports on performance of all stations/centres
against NWP and other data will be made. These can
be used to access the quality of various processing
methods.
E-GVAP
Space Weather Week, Brussels 2006-11-15
Quality monitoring (2)
•
•
•
Besides processing local stations all processing
centres are to process ZTDs for a common set of
about 10 super sites
Comparisons of NRT results between centres and to
post processed values will be made to access quality
of processing methods.
Many super sites will include additional observing
equipment, e.g. radiosondes and/or water vapour
radiometers.
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Space Weather Week, Brussels 2006-11-15
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Expert team on data processing
Expert team on data utilisation
Data liaison group.
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Space Weather Week, Brussels 2006-11-15
Examples of use
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Space Weather Week, Brussels 2006-11-15
IWV map/films for now-casting.
’Weather’ is visible in high density regions, example of few
station artefacts seen in some of the data sparse areas.
E-GVAP
Space Weather Week, Brussels 2006-11-15
Impact in NWP models
• The ZTD data are entered into the NWP models via data
assimilation. Typically 3 or 4 dimensional variational data
assimilation is used to assimilate ZTD. The GPS ZTD
data correspond only to a minute fraction of all the
observation assimilated.
• In general an improvement of rain forecasts is found. At
some centres only at mid and high precipitation levels.
• Some other fields (e.g., humidity/geopotential
height/2mT..) are improved, varying from met centre to
met centre.
• Importance of bias correction unclear – might be NWP
model dependent.
• Data quality is an issue. Sometimes a degradation of the
forecasts is seen. Sometimes due to poor GPS ZTDs,
also problems in the data assimilation and NWP systems
play a role.
E-GVAP
Space Weather Week, Brussels 2006-11-15
Impact in NWP models
12 hour precipitation. Observed (left) versus NWP without GPS (centre) and NWP
with GPS (right). Sattler and Vedel, DMI.
E-GVAP
Space Weather Week, Brussels 2006-11-15
Positive impact on precipitation from GPS data (and RH2m data).
INM study for Spain, covering mid April to mid May 2004,
by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya.
RE=no extra data
RH=RH2m data
GP=GPS data
RG=GP+RH2m data
Notice that combining
the data (RG) is less
good than just GPS,
suggesting problems
in the data assimilation software
E-GVAP
Space Weather Week, Brussels 2006-11-15
Positive impact on precipitation distribution from additional
GPS data in a data sparse region
INM study for Spain, covering mid April to mid May 2004,
by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya.
E-GVAP
Space Weather Week, Brussels 2006-11-15
Positive impact on relative humidity from additional GPS data
in a data sparse region
INM study for Spain, covering mid April to mid May 2004,
by Jana Sanchez Arriola, Beatriz Navascues, and Jose Garcia-Moya.
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Space Weather Week, Brussels 2006-11-15
Oct 2004: Height improved but temperature bias got worse.
Sattler and Vedel, DMI
E-GVAP
Space Weather Week, Brussels 2006-11-15
Example of problem to be solved, using GPS ZTDs in NWP The
errors of the GPS ZTDs are correlated, on both small and large
scales (Stoew, Johansson, and Elgered at Chalmers and Ridal at SMHI)
Black: Error correlation
of NWP HIRLAM
Red: Error correlation
of GPS ZTDs
Blue: Total spatial
correlation for GPSNWP offsets.
GPS ZTD errors
correlated on both small
and large scales!
E-GVAP
Space Weather Week, Brussels 2006-11-15
Thank you for your attention
End / Questions
Contact point: egvap@dmi.dk
Further info: http://egvap.dmi.dk
E-GVAP
Space Weather Week, Brussels 2006-11-15