ANGEOAnnales GeophysicaeANGEOAnn. Geophys.1432-0576Copernicus GmbHGöttingen, Germany10.5194/angeo-33-55-2015Validation of GPS atmospheric water vapor with WVR data in satellite tracking modeShangguanM.sgming@gfz-potsdam.dehttps://orcid.org/0000-0003-3699-2163HeiseS.BenderM.DickG.RamatschiM.WickertJ.Department 1.1 GPS/Galileo Earth Observations, Helmholtz Centre
Potsdam, German Research Centre for Geosciences (GFZ), Potsdam, GermanyDeutscher Wetterdienst (DWD), Data Assimilation Unit, Frankfurter
Str. 135, 63067 Offenbach, Germanypresent address: GEOMAR Helmholz Centre for Ocean Research Kiel, Kiel, GermanyM. Shangguan (sgming@gfz-potsdam.de)13January201533155612July20147December20149December2014This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://angeo.copernicus.org/articles/33/55/2015/angeo-33-55-2015.htmlThe full text article is available as a PDF file from https://angeo.copernicus.org/articles/33/55/2015/angeo-33-55-2015.pdf
Slant-integrated water vapor (SIWV) data derived from GPS STDs (slant total delays), which provide
the spatial information on tropospheric water vapor, have a high potential
for assimilation to weather models or for nowcasting or reconstruction of the
3-D humidity field with tomographic techniques. Therefore, the accuracy of
GPS STD is important, and independent observations are needed to estimate the
quality of GPS STD. In 2012 the GFZ (German Research Centre for Geosciences)
started to operate a microwave radiometer in the vicinity of the Potsdam GPS
station. The water vapor content along the line of sight between a ground
station and a GPS satellite can be derived from GPS data and directly
measured by a water vapor radiometer (WVR) at the same time. In this study we
present the validation results of SIWV observed by a ground-based GPS
receiver and a WVR. The validation covers 184 days of data with dry and wet
humidity conditions. SIWV data from GPS and WVR generally show good agreement
with a mean bias of -0.4 kgm-2 and an rms (root mean square) of
3.15 kgm-2. The differences in SIWV show an elevation dependent
on an rms of 7.13 kgm-2 below 15∘ but of 1.76 kgm-2
above 15∘. Nevertheless, this elevation dependence is not observed
regarding relative deviations. The relation between the differences and
possible influencing factors (elevation angles, pressure, temperature and
relative humidity) are analyzed in this study. Besides the elevation,
dependencies between the atmospheric humidity conditions, temperature and the
differences in SIWV are found.
History of geophysics (geodesy) – meteorology and atmospheric dynamics (instruments and techniques) – radio science (remote sensing)Introduction
Atmospheric water vapor is the most important greenhouse gas, which
transports energy and affects physical processes in the troposphere. Thus, precise knowledge of the water vapor distribution in the atmosphere is
important for weather prediction and climate research. However, the temporal
and spatial water vapor distribution has a strong variability. The GFZ (German Research Centre for Geosciences) uses
observations from a ground-based GPS network to investigate variations in the
atmospheric water vapor over Germany, which are available with a high
temporal and spatial resolution under all weather conditions. Global
Positioning System (GPS) water vapor products (zenith total delay (ZTD) and
STD) in near real time are provided at the GFZ. GPS data at the GFZ are processed using the Earth
Parameter and Orbit determination System (EPOS) software
, which is based on the least squares adjustment of
undifferenced observations. The derived ZTDs have an
accuracy of 6–13 mm and are estimated every 15 min.
Many studies e.g.,
have shown that integrated water vapor (IWV) can be determined with an
accuracy better than 2 kgm-2 using GPS observations. Many
countries in different continents (e.g., the United States, countries in Europe, Japan) have
developed their own regional or national ground-based GPS networks to provide
IWV . Monitoring
IWV temporal and spatial variability on a local scale plays an important role
in operational weather forecasts, since the GPS IWV data have been included
in weather forecast models . However, IWV estimations do not provide any
information on the vertical distribution of water vapor, which can be derived
from slant-integrated water vapor (SIWV) . On the
other hand, the accuracy of GPS SIWVs is difficult to estimate due to the
complex processing. Important applications of the slant measurements are
the assimilation of weather models, nowcasting and reconstruction of 3-D humidity
fields by means of tomographic techniques
.
To study the accuracy of GPS SIWV many studies were carried out in different
regions and at different times e.g.,. These investigations are mainly
restricted to a few days or months. Recently
used 3 months (October–December) of water vapor radiometer (WVR) data in the Black Forest for validation with GPS STDs, but the water vapor amounts were relatively low in
this time period. Furthermore, in all these studies temporal and spatial
interpolation had to be used because there were too few WVR observations at
the same time and in the same direction as the GPS observations. The water
vapor distribution varies with the latitude, the season and the
topographical features. Further studies with longer data sets, especially
during various weather conditions, are required to analyze all possible
influential factors for the accuracy of GPS SIWV.
For this purpose the GFZ will be starting to operate a WVR in the vicinity of the Potsdam
GPS station to make a long-term study under different atmospheric conditions. In
this study we use WVR data from the GFZ's microwave radiometer, which can be
operated in a dedicated satellite tracking mode providing SIWV observations
along the line of sight for all visible GPS satellites. The main focus is to
test dependences between the possible influential factors and SIWV
observations on seasonal and semiannual timescales.
In Sect. 2 we describe the WVR and GPS data used as well as the method to
derive SIWV from GPS STDs. The results of their comparison and the
relationship of these results to possible influential factors are discussed
in Sect. 3. In the final section the conclusions and future work are
outlined.
Data sources
The 6-channel microwave radiometer HATPRO (Humidity and Temperature Profiler)
from Radiometer Physics (www.radiometer-physics.de) is deployed in the
vicinity of the GPS station Potsdam (52.38∘ N, 13.07∘ E;
103.679 m above sea level) (∼ 10 m distance). The
accuracy of IWV measured by the HATPRO is about 0.7 kgm-2. For this study WVR
observations are compared with GPS SIWV observations covering the time period
from 1 January to 4 July 2013. Within this period rather dry
(∼ 1 kgm-2) and wet (∼ 45 kgm-2)
weather conditions can be studied. Both the GPS and WVR data used in this
study are described in the following sections.
GPS data processing
GPS STDs, i.e. the signal delays along each single signal path, have been
derived from the EPOS software using a time resolution
of 2.5 min and an elevation cutoff angle of 7∘. In EPOS the
precise point positioning (PPP) based on the
undifferenced phase and code observations with proper weights scaled
according to elevation angles is implemented. In PPP mode, the precise orbits
and clocks of all GPS satellites required by the PPP analysis are determined
by further refining the IGS (International GNSS Service) routine products . Each station
is processed independently by PPP and the processing of a large number of
stations can be easily parallelized in near real time. ZTDs are processed
using a 12 h sliding window shifted each hour, and STDs are reconstructed
every 150 s. Tropospheric delays are estimated using the Saastamoinen
model of the ZHD (zenith hydrostatic delay) and the global mapping function (GMF)
. The remaining tropospheric impact is parameterized in
zenith delays with 15 min resolution, and the gradients are estimated
every hour. Then STDs are retrieved as a combination of different estimates
:
STD=mh⋅ZHD+mw⋅(ZWD+D)+δD=cotϵ(GNcosα+GEsinα),
where ZHD and ZWD are the hydrostatic and the wet zenith delay,
mh and mw are the hydrostatic and the wet mapping
function, GN and GE are the delay gradient parameters
in the northern and eastern direction, ϵ is the elevation angle,
α is the azimuth angle and δ is the postfit phase residual.
As described above, STDs can be obtained by GPS data processing techniques,
but WVR provides IWV along a given line of sight. Therefore, GPS STDs are
converted into SIWV for the validation. The STD delay can be divided into a
hydrostatic and a wet component. The hydrostatic part can be calculated with
the Saastamoinen ZHD model using the surface pressure (P0) at the station
:
ZHD=0.0022768P01-0.0026cos(2ϕ)-0.00028h,
where h is the height above geoid in km, P0 is the surface
pressure in hPa and ϕ is the geodetic latitude of the observing
site. ZHD can be mapped onto the individual slant path by using the
hydrostatic global mapping function mh:
SHD=mh⋅ZHD.
Secondly, the slant wet delay (SWD) or the zenith wet delay can be separated
from the STD/ZTD by the estimated hydrostatic delay (SHD/ZHD):
ZWD=ZTD-ZHDSWD=STD-SHD.
Then the ZWD/SWD is converted into the IWV/SIWV using a relationship based on the surface temperature :
IWV=Π⋅ZWDSIWV=Π⋅SWD,
where Π is a temperature dependent factor:
Π=106ρRv(C1Tm+C2),
where C1=373 900 K2hPa-1 and C2=22.1KhPa-1
are the refractivity coefficient, ρ is the mass density of liquid water,
Rv is the gas constant for water vapor, and Tm is a weighted
temperature of the atmosphere:
Tm=∫eTdh∫eT2dh≈70.2+0.72T0,
where T0 is the surface temperature in K and e is the water vapor
pressure. This approximation is accurate to 2 % for all weather conditions
.
WVR data
The GFZ HATPRO WVR exploits the microwave spectrum emitted by atmospheric
water molecules at different wavelengths to derive information on atmospheric
liquid water and IWV. It can clearly identify the spatial and temporal
distribution of clouds and IWV by measuring the absorption lines of
atmospheric water vapor at frequencies between 22.24 and
27.84 GHz and a window channel at 31.4 GHz for the liquid
water. The HATPRO can measure the SIWV directly in satellite direction by
applying the GPS tracking mode. For the tracking, GPS ephemeris data derived
via HATPRO's built-in GPS receiver are used to determine the satellite
positions. Then, the radiometer can periodically scan a number of visible GPS
satellites. This allows a comparison of the measured WVR SIWVs with the
GPS SIWVs without interpolation.
The rain-flagged data are excluded. In addition, only WVR data observations
with atmospheric liquid water (ALW) below 1 kgm-2 were used
for the comparison. If the ALW is too large, ALW can distort the measurement
of the brightness temperature. As a consequence, the measured WVR SIWVs can
be extremely high.
There were differences between elevation and azimuth angles of the GPS and
WVR data due to small time differences (∼ 1.25 min). The
derived GPS SIWVs were matched to the nearest WVR measurements where a
maximum deviation in the intersection angle of 2∘ was accepted. The
zenith-mapped SIWVs were calculated with the following formula:
IWVz=SIWV/mw.
Results and discussion
For the validation the differences in SIWV and zenith-mapped SIWV
(IWVz) were calculated:
ΔSIWV=SIWVGPS-SIWVWVRΔIWVz=IWVz,GPS-IWVz,WVR.
Statistical data of the comparison between GPS and WVR data.
ΔSIWV: differences in slant-integrated water vapor (SIWV)
GPS - WVR; ΔIWVz: differences in zenith-mapped SIWV; SD:
standard deviation; rms: root mean square; N: number of compared
observations.
Histogram of the SIWV observations (top) from 1 January to
4 July 2013; scatterplot of SIWV observations from GPS and WVR (bottom).
Hourly mean of SIWV observations derived from GPS (blue), WVR (red)
and their differences (cyan) from 1 January to 4 July 2013;
ΔSIWV is the difference in SIWV (GPS - WVR).
Table shows the statistical result of the validation. The mean
difference in SIWV is 0.4 kgm-2, with a standard deviation (SD)
of 3.12 kgm-2. Weather conditions during this time were
variable, with IWVs ranging from 1 to 45 kgm-2. This broad range was
captured in this data set of 446 934 observations over 6 months. These
characteristics indicate that this analysis is a much more robust comparison
than the data set reported in , in which the
maximal zenith-mapped water vapor amounts were smaller than
35 kgm-2. The root mean square (rms) of ΔSIWV
is 3.15 kgm-2, with a cutoff elevation angle of 7∘, in
which most large differences are located at a low elevation. With a cutoff
elevation angle of 15∘, the rms of ΔSIWV is only
1.80 kgm-2. In contrast, the rms of ΔIWVz
is almost same for different cutoff elevation angles. Regarding the
assumable measurement accuracy, GPS and WVR SIWVs are comparable to each
other.
Figure (bottom) shows the scatterplot of all observations. It
indicates the good agreement between GPS and WVR data. The distribution of
observations can be seen in Fig. (top). Most of the compared SIWV
observations are smaller than 50 kgm-2. Figure shows
time series of the hourly mean SIWV from GPS and WVR and their differences.
There are some gaps (up to 36 h) in the WVR data due to rain events and high
ALW during the 184 days. For each hour the mean values of SIWVs were
calculated to get a good overview of the change in the SIWVs. The overall
impression of the validation is that GPS SIWVs are consistent with WVR
observations on average. However, the SIWV differences vary significantly
with the time. The SIWV bias between GPS and WVR varies from positive to
negative values. To study these variations, attempts are made to correlate
the SIWV differences with atmospheric parameters, which usually vary with
periods of hours, days or weeks. The relation between the SIWV differences
and possible influential factors (elevation angles and meteorological
conditions) are studied in the following sections.
Dependency of the bias on the elevation angle
Relation between ΔSIWV and elevation angles. Blue
points are all validation values. Black points are means of every 500
observations along the x axis, and the red line is their corresponding SD.
Relation between ΔIWVz and elevation angles.
Blue points are all validation values. Black points are means of every 500
values along the x axis, and the red line is their corresponding SD.
The elevation angle plays an important role in data processing. The
mapping function error is large on the low elevation angle
, and the scattering and multipath effects on the
GPS signal are typical problems at low elevation angles in the GPS data
processing . Also, WVR data at low elevation angles can
be influenced by ground radiation sources. Therefore, it is necessary to
study the dependency of the GPS - WVR variations on the elevation. The SD of
ΔSIWV strongly increases with decreasing elevation angles
(Fig. ). The data with elevation angles below 15∘ have
obviously larger biases with a larger SD, but, above about 30∘, the bias
and SD of ΔSIWV show no significant dependence on elevation.
In contrast, the variations in ΔIWVz and also relative
ΔSIWV (not shown here) have a low dependence on elevation for
the whole elevation range (Fig. ). ΔIWVz are
mainly in the range of [-4 kgm-2, 4 kgm-2], while
ΔSIWV show a strong variability at low elevation angles.
Dependency of validation results on atmospheric humidity conditions
Relation between ΔSIWV and WVR IWVs measured in
zenith direction (top); relation between relative ΔSIWV and
WVR IWVs measured in zenith direction (bottom). Blue points are all
validation values. Black points are means of every 150 values along the axis, and the red line is their corresponding SD.
Relation between ΔIWVz and WVR IWVs measured in
zenith direction. Blue points are all validation values. Black points are
means of every 150 values, and the red line is their corresponding SD.
Furthermore, the relation between ΔSIWV and humidity
conditions is studied. Typically, the amount of IWV shows strong variations
with time. To classify the humidity conditions, within this study we used
WVR IWVs measured within time periods close to each other. About 203 202 observations
have corresponding WVR IWVs within a time interval of ± 3 min.
It was checked whether the bias and SD of ΔSIWV depend on atmospheric humidity.
As shown in Fig. (top), the SD of ΔSIWV increases
slightly with increasing WVR IWVs. For WVR IWVs above 25 kgm-2, the biases of ΔSIWV drift slightly towards negative values, while
rather positive values are observed below 10 kgm-2. The
variations in ΔSIWV generally increase at higher WVR IWVs,
and GPS SIWVs are increasingly smaller than WVR SIWVs. Regarding the bias
tendency, a similar behavior can be seen for relative ΔSIWV
(Fig. , bottom), while the SD shows the opposite behavior to the absolute
comparisons as it decreases with increasing WVR IWVs. Due to observation
values near the expected measurement accuracy, relative bias and SD reach
large values at very dry conditions (WVR IWV below 3 kgm-2).
Instead of the relative ΔSIWV, the relation between
ΔIWVz and WVR IWV is shown in Fig. . The same
tendency is observed with the bias and SD of ΔIWVz. A
linear tendency of the differences from positive values to negative values is
observed in the figures.
Dependency of the bias on ground weather conditions
Relation between the mean (green) and SD (red) of
ΔSIWV (left column), ΔIWVz (middle
column) and ΔIWV (right column) and relative humidity (RH) (top
row), pressure (P) (middle row) and temperature (T) (bottom row).
Pressure (P), temperature (T) and relative humidity (RH) were measured by
the meteo sensor of the type Vaisala PTU200 near the POTS station
with high accuracy. Pressure and temperature were used to calculate the
GPS SIWV and have an effect on the estimated SIWV. Therefore, pressure and
temperature may also influence the comparison results.
The comparisons between RH, T, P and the differences are shown in
Fig. . ΔIWV is the difference between the WVR-measured IWV in zenith direction and the GPS-ZTD-derived IWV. The GPS IWV has
a temporal resolution of 15 min. Due to the WVR measurements
periodically switching between GPS satellite tracking and zenith IWV
observation mode every 5 min, only about 14 132 GPS IWVs have the
corresponding WVR IWVs within the time interval of ±3 min. In
Fig. (middle), the biases of ΔSIWV,
ΔIWVz and ΔIWV change considerably with
the variation in pressure. It is difficult to find the dependency between
pressure and the differences because of the strong variability. Similarly
to pressure, no clear relation between the relative humidity and
differences is found (Fig. , top). In contrast, the biases of
ΔSIWV, ΔIWVz and ΔIWV
decrease with increasing temperature, and the corresponding SDs increase
at the same time (Fig. , bottom). A similar tendency is observed
between the differences and WVR IWVs (see Figs. , ). The relation
between the IWV and temperature is shown in Fig. . The amount of
water vapor increases with temperature, showing the temperature effect on
the atmospheric water-holding capacity. In Fig. , the water vapor
amount of GPS observations at high temperatures is smaller than the measured
WVR IWVs.
Relation between the measured IWV and temperature: scatterplots of
GPS and WVR IWV observations.
Conclusions
The validation of the GPS slant delay data with WVR provides evidence of good
agreement between the compared data. The bias between GPS and WVR data is
-0.40 kgm-2, with an rms of 3.15 kgm-2, which
is dominated by the large differences at low elevation angles. With the cutoff
elevation of 15∘, the rms of difference in SIWV is only
1.80 kgm-2. The GPS SIWV has an accuracy comparable to that of the WVR
data.
In this study the relations between ΔSIWV,
ΔIWVz and five possible influential factors are tested.
It indicates a relative dependency between ΔSIWV and
elevation angles. However, ΔIWVz are almost as good
at different elevation angles. It indicates that the relative error of
GPS SIWV is almost constant at different elevation angles.
Both biases of ΔSIWV and ΔIWVz show a weak
dependence on atmospheric humidity, changing from slightly positive values at
low IWV conditions to slightly negative values at high IWV. Similar effects are observed with the relation to temperature, which can be explained by the temperature
dependence of the atmospheric water-holding capacity. GPS estimates are
smaller than WVR SIWVs in most cases at high temperature or when there are large water vapor
amounts. The study shows that the ground weather condition has some influence
on the difference in SIWV, especially the temperature or humidity condition.
These differences (negative biases at high temperatures or very humid
conditions) can be caused by the errors in both GPS and WVR data. Further studies are planned
with GPS data and WVR data in satellite tracking mode. Furthermore, the
adjustments to GPS data processing parameters with the help of WVR data would
be investigated, which may contribute to improvements of GPS product
quality.
Acknowledgements
The authors thank the Helmholtz Centre Potsdam, German Research Centre for Geosciences (GFZ), for supporting this work.
The service charges for this open access publication have been
covered by a Research Centre of the Helmholtz
Association.
Topical Editor V. Kotroni thanks
G. Guerova and one anonymous referee for their help in evaluating this
paper.
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