Expected future developments of
ground based GNSS meteorology
• Improvements of ”existing” system, e.g.:
–
–
–
–
widespread assimilation.
better geographical coverage
higher homogeneity of GNSS delay data
active quality control, withhold or flagging of questionable data.
• Use of IWV maps in now-casting.
• Introduction of real-time (in contrast to near real-time)
processing.
– Clearly useful in now-casting.
– Unclear if it is beneficial for NWP.
– Already done today, and data are available at E-GVAP server,
but only uploaded once per hour.
• Extraction and use of extra information from the GNSS
data processing, e.g.:
– GNSS slant total delays
– GNSS ZTD gradients
• Derivation of 3D humidity maps from GNSS delay data in
combination with other meteorological data via
tomography
Observing geometry
On slants
• The ZTD is an average property. Information about the
local variability of the atmosphere around a given GNSS
site is lost.
• The slant total delays (STD), or the residuals from the
estimation of the ZTD, contain this information.
• But besides ZTD, many other properties are estimated
(via least square fits to models) in the GNSS processing,
on different timescales.
• How much atmospheric information is in the slants and
how much noise?
On slants, 2
• Software for extraction of slants from the GNSS data
processing made at Techn. Univ. Delft, including
correction for multipath effects, and at GFZ.
• Data format in E-GVAP useful for slants, but no current
derivation and upload of slants in regi of E-GVAP.
• Software exists for assimilation of STDs in HIRLAM
– In a small assimilation experiment the impact was found to be
more or less like the impact of ZTDs.
– Clear that slants are noisy.
• Florian Zus work on slants (report at expert team
workshop tomorrow). Based on MM5?!
• Area for reseach, no near future operational use
expected.
On gradients
• Estimation of East-West and North-South gradients of ZTD provides
additional information to ZTD, but reduces somewhat the noise of
STDs.
• Inclusion of extra GNSS satellites in processing (GLONASS, future
Galileo) will clearly improve gradient estimates, less clear is
improvement of STDs.
• Some work has been done on the comparison of GNSS and NWP
ZTD gradients (e.g., Walpersdorf with ALADIN model).
• No (known) work on assimilation of gradients.
• In reseach, more work should be devoted to estimation of ZTD
gradients from GNSS data, and validation and assimilation of those
in NWP models. Could become useful on a relatively short
timescale.
GPS Tomography
Based largely on original material from
D. Leuenberger, MeteoSwiss
and
E. Troller, ETH Zürich
• Tomography is a method by which a large number of line of sight
integral observations in different directions and or different locations
are used to derive 3D images of a distribution.
• Tomography is widely used in certain types of medical scanning
techniques, as an elegant, low impact, non toxic way of deriving 3D
images of human bodies.
• For about a decade research has been done on ground based
GNSS tomography, in which line of sight delay, or IWV (integrated
water vapour), observations from a dense network are combined in
a tomographic reconstruction of the 3D delay or water varpour field.
• The 3D water vapour field can be assimilated into almost all types of
NWP models, including those based using a „nudging“ assimilation
system (such as MeteoSwiss and DWD).
• The 3D water vapour field can be used for now-casting (consider for
example potential in combining with wind profiler and orther radar
wind information).
• For 3 and 4DVar assimilation systems use of the original
observations is expected to be beneficial – always use observations
with as little preprocessing as possible.
Example from a project joint project of MeteoSwiss, ETH Zürich and
swisstopo, in which humidity profiles are derived at a number of
locations
– ETH
• GPS tomography
– MeteoSwiss
• Validation and assimilation of humidity profiles
– swisstopo
• Data provider
height [m]
Swiss Tomography system
GPS Station
Radiosonde
Wet refractivity [ppm]
M. Troller, ETH Zürich
GPS tomography
•
•
•
•
•
•
•
18 hourly profiles up to 6000m a.s.l, ∆x=50km
System of linear equations on N in each voxel
Largely under-determined
Surface humidity constraints
Spatial and temporal neighbour constraints
No use of radiosonde/model data
Least squares solver
Validation of humidity profiles
• Comparison of GPS humidity profiles with
– radiosonde of Payerne
– model analyses and forecasts
• More than one year of data
• Comparison of wet refractivity N
– Radiosonde:
– NWP COSMO model:
Yearly humidity distribution
GPS
2006
2006
2007
Radiosonde
2007
Yearly humidity distribution
GPS
2006
2007
BIAS
Jul/Aug 2006 STD
bold: GPS
thin: COSMO
bold: GPS
thin: COSMO
black: 00UTC
red: 12UTC
black: 00UTC
red: 12UTC
Jul/Aug 2006 (with model
BIAS
STD
constraints)
bold: GPS
thin: COSMO
bold: GPS
thin: COSMO
black: 00UTC
red: 12UTC
black: 00UTC
red: 12UTC
Dashed: + model constraint
Dashed: + model constraint
BIAS
Nov/Dec 2006 STD
bold: GPS
thin: COSMO
bold: GPS
thin: COSMO
black: 00UTC
red: 12UTC
black: 00UTC
red: 12UTC
Nov/Dec 2006 (with model
BIAS
STD
constraints)
bold: GPS
thin: COSMO
bold: GPS
thin: COSMO
black: 00UTC
red: 12UTC
black: 00UTC
red: 12UTC
Dashed: + model constraint
Dashed: + model constraint
Statistics
of July/August
2006
BIAS
STD
Statistics
with Bias Correction
BIAS
STD
Summary
• Winter:
– Large dry bias: -4ppm or -40% (max. at 2000m a.s.l.)
– Std dev larger than model forecasts
• Summer:
– Wet bias (15ppm or 30%) (max. at 1000m a.s.l.)
– Dry bias (-10ppm or -30%) (max. at 2000m a.s.l.)
– Std dev comparable to model forecasts
• Problem at ~2000m a.s.l.
• Model first guess does not seem to solve the problem
• Bias hard to handle
• -> Currently: quality not sufficient for assimilation