The delay of satellite signals broadcasted by Global Navigation Satellite
System (GNSS) provides unique atmospheric observations which endorse
numerical weather prediction from global to limited-area models. Due to the
possibility of its frequent and near-real-time estimation, the zenith total
delays (ZTDs) are valuable information for any state-of-the-art data
assimilation system. This article introduces the data assimilation of ZTDs in
a Hungarian numerical weather prediction system, which was carried out by taking
into account observations from central European GNSS analysis and processing
centres. The importance of ZTD observations is described and shown by a
diagnostic tool in the 3-hourly updated 3D-Var assimilation
scheme. Furthermore, observing system experiments are done to evaluate the
impact of GNSS ZTDs on mesoscale limited-area forecasts. The results of the
use of GNSS ZTDs showed a clear added value to improve screen-level
temperature and humidity forecasts when the bias is accurately estimated and
corrected in the data assimilation scheme. The importance of variational, i.e.
adaptive bias correction, is highlighted by verification scores compared to
static bias correction. Moreover, this paper reviews the quality control of
GNSS ground-based stations inside the central European domain, the
calculation of optimal thinning distance and the preparation of the two
above-mentioned bias correction methods. Finally, conclusions are drawn on
different settings of the forecast and analysis experiments with a brief
future outlook.
Introduction
The interaction of satellite signals from Global Navigation
Satellite Systems (GNSS) with atmospheric constituents has been recognised as
valuable information for meteorological applications and numerical weather
predictions (NWPs). The GNSS signals were delayed along the emitted satellite ray's
path, which can be formulated as an excess length and are most generally
determined in zenithal path above the ground-based receiver station, providing
the zenith total delay (ZTD) . The total delay includes
a wet delay component, which is a function of the water vapour distribution of
the troposphere, bringing key humidity-related observations for meteorological
users. The high-resolution NWP and data assimilation are demanding more
frequent and denser observations ,
in particular by applying non-conventional data sources to a larger extent.
Consequently, state-of-the-art data assimilation systems rely significantly
on remote-sensing measurements like RADAR, satellite products including data
from navigation satellites as well. Therefore, the use of GNSS measurements
has been widely included in experimental and also operational data
assimilation systems since the second half of the 2000s. In a global 4D-Var
system, demonstrated the positive forecast impact of the
ZTD observations by correcting synoptic scales up to 4 days.
and published data assimilation
impacts and a case study, respectively, showing that the use of zenith tropospheric
delay observations over North America led to forecast improvements and error
reductions. At that time, the added value of ZTDs in European limited-area
DA (data assimilation) systems has been also justified by a number of authors such as
, , and
, focusing on local area and data set. After various
inter-European studies and projects, e.g. MAGIC (Meteorological Applications
of GPS Integrated Column Water Vapour Measurements in
the Western Mediterranean), COST Action 716, and TOUGH (Targeting Optimal Use
of GPS Humidity Measurements in Meteorology) the European Meteorological
Services Network (EUMETNET) organised the GNSS Water Vapour Programme
(E-GVAP). This EUMETNET observation programme shares ZTD estimates in near
real-time (NRT), primarily for use in operational meteorology. It aims to expand
the existing network with inclusion of new regions and helps its members
to use ground-based GNSS data in their operations. The programme was set up in
April 2005 through establishing timeliness and precision requirements of distributed
ZTD data. Given the efforts of the E-GVAP programme, and with a view of
increasing such observation usage, new actions and explorations of
meteorological applications were initiated during the last decade. Recently a
new European COST Action (ES1206), using advanced GNSS products for severe
weather events and climate , was also launched. In the
meantime, more recent studies have been carried out, for instance, by
, and , who pursued the objective of
improved GNSS ZTD assimilation and took into account one or more E-GVAP
networks. All these studies agreed that more accurate description of humidity
and precipitation forecast can be gained by the use of GNSS ZTD, although its
absolute contribution in terms of observation number is smaller compared to
other observation types. However, GNSS ZTD – like most of the observations
– include systematic errors which must be taken into account in the
assimilation procedure. Better characterisation and assessment of ZTDs were
proposed, e.g. by and recently and
, who demonstrated that the variational bias
correction approach is successful for eliminating GNSS ZTD bias and is advantageous
for controlling bias correction in an adaptive manner. The main objectives of this
paper are to assess the added value of GNSS ZTD observations in a
central European domain by taking into account all available E-GVAP ZTD networks
and summarising the work that has been done in the frame of COST ES1206. In
addition, the latest bias correction developments are studied and used in the
data assimilation system of AROME/Hungary. The paper is constructed as
follows. Section 2 introduces the operational AROME NWP model and data
assimilation system used in the current study. Section 3 gives an overview of
the applied data, the characteristics of E-GVAP networks and their ZTD
observations. In Sect. 4 the passive assimilation experiment, the
pre-processing of ZTD observations and the bias correction are described. In
Sect. 5 the results of active assimilation runs are discussed and, in the
last section, conclusions are drawn with a brief future outlook.
Description of operational model and observations
At the Hungarian Meteorological Service (OMSZ), limited-area (LAM) NWP
activities were started in the 1990s as part of the ALADIN (Aire Limitée
Adaptation Dynamique Développement International) consortium, which led to
the implementation of the ALADIN model and later its
data assimilation system . For the purpose of having a
high (kilometric) spatial resolution of LAM, the non-hydrostatic dynamical
core of ALADIN and the physical parameterisation
package of the French research model, called Meso-NH , have been merged while setting up the AROME (Application of
Research to Operations at Mesoscale) model. After the successful operation of
AROME at Météo-France , OMSZ also began to implement
an AROME system running over a central European domain. The first Hungarian
AROME configuration (AROME/Hungary) has been performed with dynamical
adaptation of ALADIN/Hungary forecasts as the initial and boundary conditions.
Later, major upgrades brought significant improvements to operational AROME/Hungary
forecasts by the ECMWF (European Centre for Medium-Range Weather Forecasts)
IFS (Integrated Forecasting System) lateral boundary conditions and a local 3D-Var data assimilation system. The recent operational NWP model domain covers the
entire Carpathian Basin (highlighted by the black frame in
Fig. ) with a horizontal mesh size of 2.5 km and 60
vertical levels from the surface up to 0.6 hPa. The surface characteristics of
the AROME model are described by the surface scheme of Meso-NH called
Externalized Surface (SURFEX) and initialised by the optimal interpolation method
before every model integration.
The computational domain of AROME/Hungary (black rectangle) and all available GNSS stations
from SGO1 (red), GOP1 (green) and WUEL (blue) E-GVAP networks.
For the time being, the upper-air assimilation system of AROME/Hungary considers
only conventional observations, namely surface SYNOP, aircraft (AMDAR, ACARS
and Mode-S from Slovenia) and radiosonde reports. To use a larger number of
conventional observations, the 3 h assimilation cycle is set to produce eight
analyses per a day, which, for example, enable the utilisation of aircraft data
measured at asynoptic network times by the ±1.5 h assimilation window
in 3D-Var. The timeliness of conventional observations collected from GTS
(Global Telecommunication System) plus local sources in a 3-hourly rapid
update cycle (RUC) is still met with the time-critical applications of
operational AROME/Hungary. For forecasting needs at OMSZ, the short cut-off
AROME analysis and related forecast are scheduled to be performed no later
than 2 h after the actual time of the analysis, which includes the long cut-off
analyses and updated first guesses for the more accurate background
information. Regarding future perspectives of AROME/Hungary's upper-air DA,
the applied RUC approach favours observations which have a large temporal
frequency and small latency. For particular diagnostic purposes the AROME
3D-Var was experimentally run with all available non-conventional
observations. That is, the use of satellite radiances from Meteosat-10 SEVIRI (Spinning
Enhanced Visible and Infrared Imager), from NOAA-19 AMSU-A (Advanced
Microwave Sounding Unit-A) and MHS (Microwave Humidity Sounder), from Metop-A
and Metop-B AMSU-A, MHS, and IASI (Infrared Atmospheric Sounding
Interferometer) sensors. Additionally, this diagnostic consists of the assimilation
of satellite-derived winds MPEF (Meteorological Product Extraction Facility), called Geowind and
HRW (high-resolution winds) AMVs (atmospheric motion vectors), from Meteosat-10
satellite, the use of RADAR reflectivity and radial winds from Hungarian RADAR sites, and most importantly the use of GNSS ZTD observations. The non-conventional satellite
and RADAR observations were added to AROME experimental analyses solely for
a diagnostic study and they were not considered in the GNSS ZTD observing
system experiments. This experimental DA system performed 3D-Var analyses
with perturbed and unperturbed observation sets on a 10-day period (between
5 and 15 June 2017) in order to compute the degree of freedom for signal
(DFS) diagnostic as the following :
DFS≈Tr(HK)≈(y′-y)TR-1(H(x′a)-H(xa)),
where HK is the product of the linearised observation operator by
the Kalman gain, and the DFS scores can be approximated by its trace in
Eq. (). The y and y′ are the unperturbed and
perturbed observation sets, R is the observation-error covariance
matrix, and H(xa) and H(x′a) are
the unperturbed and perturbed analyses states in the observation space. DFS provides information on the observation's influence on
analyses with respect to the different observation types.
Figure shows absolute and relative DFS scores computed on the
10-day period in the AROME/Hungary system. The relative DFS is normalised
by the number of observations for each observation subset, providing the
diagnostic information, regardless of the actual amount and geographical coverage
in the assimilation system. The GNSS ZTD has a limited absolute DFS due to
the small number of ZTD observations compared to other observation types.
However, it has a considerably high relative contribution, which can
significantly affect the AROME/Hungary analysis.
The absolute (a) and relative (b) DFS scores computed in AROME/Hungary 3D-Var
experimental analyses for the period 5–15 June 2017. The considered observations in DFS
computation are the following: SYNOP (parameter U, Q, T, Z) with blue, TEMP (parameter U, T, Q, Z)
with red, AMDAR (parameter U, T, AMDAR Q) with maroon, GEOW+HRW (parameter U) with cyan, RADAR
(parameter reflectivity, radial wind) with yellow, AMSU-A and AMSU-B (parameter Tb) with pink,
SEVIRI-WV and SEVIRI-SURF (parameter Tb) with orange, IASI (parameter Tb) with grey and GNSS
(parameter ZTD) with green.
GNSS ZTD observations
The first tests of ZTD retrievals using permanent GNSS stations in Hungary
started in 2009 . Due to the positive results of this
study, a near-real-time GNSS processing facility was set up by the
collaboration of the Satellite Geodetic Observatory Penc and the Budapest
University of Technology and Economics (BME). The applied computational
strategy can be found in . The processing centre (SGOB,
later renamed to SGO1) joined the EUMETNET's E-GVAP programme in 2013.
Since then, the ZTD estimates at the stations of the Hungarian GNSS Network
are available for meteorological applications. Hungary, with its representing
institutions, BME, OMSZ and SGO, participated in the COST ES1206. The network
processed by the SGO1 processing centre involves more than 80 ground-based
stations and provides accurate ZTD estimates using the Bernese software v5.2
. The estimates are computed from the network solution
with a +90 min latency. Due to its coverage, the SGO1 network provides most
of the ZTD estimates in the AROME/Hungary's NWP domain. To extend the
coverage of GNSS ZTD, other central European E-GVAP networks were included in
this study. For a long time the Geodetic Observatory Pecny (GOP) in the Czech Republic has
been preparing GNSS-based measurements for various users and also
contributing to E-GVAP with a large network (more than 120 stations), called
GOP1. Moreover, the GNSS network developed by Wroclaw University of
Environmental and Life Science (WUELS) serves additional ZTD estimates inside
our area of interest. The WUEL analysis centre provides ZTD estimates for a
network of 130 stations. Both of the latter centres use a network solution
provided by the Bernese software.
The computational domain (black rectangle) and preselected GNSS stations from SGO1 (red), GOP1 (green)
and WUEL (blue) E-GVAP networks.
Evaluation of the quality and use of GNSS ZTDs on a training periodPassive assimilation and preselection procedure set-up
The assimilation of ZTDs with very large observation errors has been
conducted in an experimental AROME/Hungary system for a training period. This
“passive” assimilation allows monitoring of ZTD observations inside the
variational assimilation scheme without influencing the analysis. Although
the quality-control procedure of the variational scheme contains the so-called
background check (which is dedicated to reject observations far from model
background state), one also needs to ensure that only observations with
Gaussian, zero mean and uncorrelated errors are selected in the assimilation
(i.e. reliable stations). For that purpose, a specific preselection
procedure has to be performed that checks passive observation minus
first-guess (OMF) departures over a training period. Due to the high analysis
cycle frequency, i.e. 8 AROME/Hungary analyses per a day, the training period
of 15 and 31 May 2017 is chosen, assuming a sufficient sample for every
GNSS station. The preselection of GNSS ZTDs means consecutive tests of time
availability, normality, maximum standard deviation and bias, and metadata
consistency together with domain and altitude difference examination of
GNSS stations. Considering that particular stations can be processed by a
several analysis centre (we can call it station multiplication), the
station–processing-centre pair is selected which has the smallest standard
deviation of OMF. Furthermore, the station thinning is also part of a
procedure to avoid observation error correlations. More details about the
preselection design are given in and
.
Results of the preselection procedure
The actual training period led to the availability of 197 GNSS stations
inside the NWP domain (from three different networks). The preselection
procedure excluded more than 30 % of them, resulting in 122 trusted
GNSS stations for active assimilation experiments. Due to time coverage (e.g.
data gaps or outages) and Gaussianity issues, 10 % of the data were
rejected. A further 2 %–3 % of the stations were denied, since the detected
bias and standard deviation of OMF were higher than the predefined limits. The
thresholds of bias, standard deviation and altitude difference limits were
set according to . Due to multiple station–analysis-centre pairs, 12 % of the stations are excluded from one or two
networks during the preselection. The selected GNSS stations are written
into a specific whitelist, which ensures the active assimilation of ZTDs. The
location of all available GNSS stations and trusted stations inside the NWP
domain can be seen on Figs. and respectively.
In order to determine the optimal thinning distance which is employed for
preselection, horizontal observation error correlations as a function of
various separation distances have been computed. The computation of error
correlations are based on the method proposed by :
EdbodaoT=R
where observation error covariances are estimated based on the expected value
of background (dbo) and analysis (dao) departures considering
various departure pairs for horizontal distances. The Desroziers method has
the advantage of providing error correlation structures in observation space,
i.e. at observation locations from the collected pairs of background and
analysis departures in a computationally efficient approach. For this
diagnostic purpose, a revised whitelist is generated with zero thinning in
order to execute very first active assimilation and to collect its OMF
departures. showed that horizontal thinning
distance is optimal, where the observation error correlations are less than
0.2–0.3. By the visualisation of these error correlations, which can be seen
in Fig. , a 20 km thinning distance is chosen for the final
GNSS preselection procedure.
Observation error correlations estimated by Desroziers method as a function of separating distances
for GNSS ZTDs inside AROME/Hungary's domain. Local polynomial regression method was used
to fit a smooth curve and the diagnostic was computed for the period of 15–31 May 2017.
Detected bias and static bias correction
During the preselection procedure, OMF departures are used to evaluate the
quality of ZTDs and also to identify systematic errors in measurements. The
bias might originate from the mapping function of ZTD processing, the
conversion of time delay to excess length, the contribution of the atmosphere
above the model top or, for instance, the altitude differences between the
model orography and the GNSS station elevation. The observation bias of a
GNSS station (station) is detected as the time average of the observation
(oi) minus model-background (bi) differences considering the number
of analyses (n) during the time period (Eq. ).
BIASstation=1n∑i=1noi-bi
Although, it assumes that the first-guess is an unbiased reference which is
not necessarily true, showed that this approach can be
efficiently applied for the initial bias estimation of GNSS ZTDs. The
distribution of OMF values taking into account all GNSS stations is plotted
in Fig. . Concerning the detected bias of each GNSS station
separately, one can see in Figs. and that
the observed bias strongly varies by station in SGO1, WUEL and
GOP1 networks respectively. Therefore, the bias correction should be done
individually for different GNSS stations.
Distribution of OMF values for all GNSS stations inside AROME/Hungary's domain. The period of 15–31 May 2017
was used for the calculation of OMF statistics.
The ZTD bias in millimetres for SGO1 (light blue) and WUEL (light orange) networks
calculated for the period of 15–31 May 2017.
The ZTD bias in millimetres for GOP1 (purple) network
calculated for the period of 15–31 May 2017.
After the preselection procedure, the bias and the standard deviation of
background departures are added to the whitelist for each station
independently. The standard deviation of OMF is assigned as the observation
error of trusted GNSS stations, ranging between 6 and 14 mm. The static bias
information of the whitelist can be applied before active assimilation by
removing the bias during the observation pre-processing. The impact of GNSS
ZTDs with the use of static bias correction (called ESGPS2 hereafter) is
investigated in observing system experiments presented in
Sect. .
Variational bias correction
Besides the choice of static bias correction, the AROME's variational
assimilation system offers the possibility of variational bias correction
(VARBC) as well. In this scheme, the bias parameters are a part of the
minimisation via the extension of control vectors and the cost function
. The GNSS ZTD is considered as a
type of surface observation in the data assimilation, therefore, VARBC
controls the bias separately for each station
using a bias offset predictor, similarly to static correction. This predictor in the current implementation
of the linear regression scheme is assumed to remove most of the
bias. Moreover, the introduction of additional predictors shown by
has a limited impact on the forecasting system. The
simplified background bias parameter error covariance matrix contains only
diagonal elements (σβb2), which are characterised by the proportion of observation
error variance (σo2) and the so-called stiffness parameter
(Nbg) (Eq. ).
σβb2=σo2Nbg,
In contrast to the static scheme, the VARBC adjusts bias information at every
analysis, making bias correction updates in an adaptive manner. The magnitude
of the adaptivity is decided by the stiffness parameter, which is
set to 60 by default and takes into consideration that AROME/Hungary has eight analyses in
a day, with the bias halving time corresponding to about 5 days .
For the active assimilation trial, instead of cold-start initialisation of the
bias, VARBC coefficients were spun up on the preselection training period
and stored to prepare a warm-start initialisation. As the observation bias
does not significantly vary during a day (not shown), the 3-hourly cycled VARBC
strategy was chosen, which supports faster adaptivity compared to a daily
cycled bias correction. The use of GNSS ZTDs and
variational bias correction are called EVGPS2 hereafter.
Active assimilation and the observing system experiment
An observing system experiment (OSE) has been carried out for a summer
period, estimating the impact of GNSS ZTDs and the performance of static and
variational bias corrections. The first AROME/Hungary configuration using the
operational set-up (without ZTD observations) is considered as a reference
(EEGPS2 in verification). The one (ESGPS2) with ZTD observations on the top
of the operational observation set and a static bias correction is compared
to the reference. Furthermore, the second experiment is similar to ESGPS2 but
employs a variational bias correction (EVGPS2), which is analysed together
with ESGPS2. The experiment and the basic set-up are summarised in
Table .
Summary of the observing system experiment with the data
assimilation system of AROME/Hungary and the use of GNSS ZTDs. Dates are in dd/mm/yy format.
The RMSE and bias of screen-level temperature (∘C), relative humidity (%),
and dew-point temperature (∘C) as a function of forecast range. Scores are plotted for
EEGPS2 (red), ESGPS2 (green) and EVGPS2 (blue). Verification period between 5 and
30 June 2017. Data selection: Hungary, 30 stations.
The normalised RMSE difference of screen-level temperature (∘C), relative humidity (%),
and dew-point temperature (∘C) as a function of forecast range. Scores are comparing EEGPS2 and
EVGPS2 experiment. Verification period between 5 and 30 June 2017. Data selection: Hungary, 30 stations.
Verification of AROME/Hungary forecasts
The examined summer period is basically the continuation of the training
period excluding 5 days from the verification and covering 25 days until the
end of June 2017. This means that statistical verification was computed for
00:00 and 12:00 UTC AROME +24 h forecasts between 5 and 30 June 2017.
The verification was performed against quality-controlled conventional
observations for the measurement of all scores. For the same reason that GNSS ZTDs are
used as surface observations in the variational assimilation method, the
added value of ZTD observations is expected to reflect more on near-surface
verification scores. More importantly, temperature and humidity parameters
are the most influenced because the model equivalent of wet delay is closely
related to the temperature and humidity fields of the model via the observation
operator. Figure shows RMSE and bias scores for screen-level
temperature, relative humidity, and dew-point temperature forecasts, while in
Fig. , the related normalised RMSE differences of EEGPS2 and
EVGPS2 can be seen. For these surface parameters the error reduction with
respect to the reference during the first 6 h in the AROME forecast is
apparent by the use of ZTD observations, with both static and variational bias
corrections. Nevertheless, the temperature bias is slightly overestimated, but
dew-point temperature and relative humidity bias remain more or less
the same for short forecast ranges. The AROME/Hungary forecasts usually have
warm and dry biases at night-time. However, the assimilation of GNSS ZTD
cannot mitigate this issue. The most important result is that the error reduction is
statistically significant for the short and very short ranges (see
Fig. ), when variational bias correction is used. Furthermore,
similar results are obtained with the static bias correction, but they are
not statistically significant (not shown).
The ETS and SEDI scores of +12 h precipitation (12 h accumulation) as a function of
precipitation thresholds. Scores are visualised for experiments EEGPS2 (red), ESGPS2 (green) and EVGPS2
(blue). Verification period between 5 and 30 June 2017. Data selection: Hungary, 30 stations.
The AROME's precipitation forecasts are verified in Fig. , in
terms of equitable threat score (ETS) and a symmetric external dependency index
(SEDI) for a +12 h forecast range. Overall,
for the small (less or equal than 1 mm) precipitation thresholds, both ESGPS2
and EVGPS2 can improve the precipitation forecasts, but for 3 or 10 mm
thresholds only the experiment with ZTD and VARBC (EVGPS2) has a positive
impact compared to the reference. Due to the limited number of high
precipitation cases, the verification of larger precipitation thresholds
(above 10 mm) is not taken into account. These results suggest that the
update of bias information during the (active) assimilation cycles is
important for better precipitation forecasts. Other surface variables and
also upper-air scores show a mostly neutral impact, i.e. slightly better or
worse scores at various levels without statistically significant differences
(not shown). It is also important to note that significant differences can
only be seen in surface verification scores against 30 Hungarian SYNOP
stations (see the header of verification figures). Taking into account all
available SYNOP stations inside the NWP domain would indicate a smaller
impact given the relatively small amount of assimilated ZTD observations.
Furthermore, another reason might be that AROME/Hungary's background errors
are derived from AROME EDA statistics (ensemble data assimilation), which
provide more localised increments and sharp background error correlations.
Conclusions
The use of GNSS ZTDs from three central European E-GVAP networks in
AROME/Hungary was presented and discussed in detail. The potential and the
importance of this observation type was shown through DFS diagnostics. This
is particularly relevant in data assimilation system with a high frequency
analysis cycle. It was also discussed that GNSS products including ZTD can
bring extra humidity-related observations for the initial conditions of NWP
models and have the potential to improve precipitation forecasts. A
preselection of reliable GNSS ground-based stations has been done carefully,
this was described in Sect. . The studied E-GVAP networks
cover sufficiently a wide area of Hungary, although there is still room for
further extension and the system is still lacking such observations from the
southern and eastern parts of the NWP domain. Furthermore, the optimal thinning
distance was determined to maximise the number of ZTDs from neighbouring
networks and to avoid observation error correlations. It was also shown that
the detected bias varies by station; therefore, a specific
correction for each station makes sense during the assimilation of GNSS ZTDs.
In addition, using only the bias-offset predictor in the VARBC scheme satisfies
its functionality for removing the bias of GNSS ZTD observations in the
variational analysis. During the active assimilation experiment, it was
demonstrated that GNSS data have a positive impact on short-range
screen-level temperature and humidity forecasts. This positive impact on
forecast scores during the summer period was held for 6 h, which is
considerable given the small number of GNSS observations. Additionally, the
precipitation forecasts clearly became better in AROME forecasts when using the
variational bias correction, whereas with the static bias correction the
impact of ZTDs was rather mixed. It can be concluded that the use of GNSS
ZTD,
together with VARBC, has a positive impact on AROME/Hungary forecasts, which
correspond to other impact studies. In this paper the use of
central European E-GVAP networks and their ZTD estimations were highlighted
in an operational AROME mesoscale data assimilation system. It became evident
that a small amount of GNSS ZTD observations can still provide valuable
atmospheric information for a well-characterised and -parameterised NWP system
and its data assimilation. For future perspectives and to understand the importance
of bias correction, an improved stiffness parameter might be investigated in
order to allow more flexibility into the system. Moreover, a better description
and use of observation errors should be studied as well.
Data availability
Underlying research data are available upon request to matem@met.no.
Author contributions
SR prepared the GNSS data in an appropriate format and helped
to establish data dissemination between SGO and OMSZ. PB
provided a the diagnostic tool to determine optimal thinning distance and
contributed to the evaluation of bias correction for GNSS ZTDs. MM carried out the observation pre-processing, the passive assimilation,
and observing system experiments. MM prepared the manuscript
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Advanced Global Navigation Satellite Systems
tropospheric products for monitoring severe weather events and climate (GNSS4SWEC) (AMT/ACP/ANGEO inter-journal SI)”. It is not associated with a
conference.
Acknowledgements
The authors kindly acknowledge the support of the Satellite Geodetic
Observatory, Penc for providing the ZTD estimates for this study. Support of
grant BME FIKP-VÍZ by EMMI is kindly acknowledged. The comments of three
anonymous reviewers are also appreciated and acknowledged, and they helped to
improve the manuscript significantly.
Edited by: Jonathan Jones
Reviewed by: three anonymous referees
ReferencesAuligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for
satellite data in numerical weather prediction system, Q. J. Roy. Meteor. Soc., 133, 631–642, 10.1002/qj.56, 2007.
Benjamin, S. G., Dévényi, D., Weygandt, S., Brundage, K., Brown, J.,
Grell, G., Kim, D., Schwartz, B., Smirnova, T., Smith, T., and Manikin, G.: An
hourly assimilation/forecast cycle: The RUC, Mon. Weather Rev., 132,
495–518, 2004.
Benjamin, S. G., Jamison, B. D., Moninger, W. R., Sahm, S. R., Schwartz, B.
E., and Schlatter, T. W.: Relative short-range forecast impact from aircraft,
profiler, radiosonde, VAD, GPS-PW, METAR and mesonet observations via the RUC
hourly assimilation cycle, Mon. Weather Rev., 138, 1319–1343, 2010.
Bennitt, G. V. and Jupp, A.: Operational assimilation of GPS Zenith Total
Delay Observations into the Met Office numerical weather prediction models,
Mon. Weather Rev., 140, 2706–2719, 2012.
Bevis, M., Businger S., Herring, T., Rocken, C., Anthes, R., and Ware, R.:
GPS meteorology: Remote sensing of atmospheric water vapor using the Global
Positioning System, J. Geophys. Res., 97, 15787–15801, 1992.Boniface, K., Ducrocq, V., Jaubert, G., Yan, X., Brousseau, P., Masson, F., Champollion, C., Chéry, J., and
Doerflinger, E.: Impact of high-resolution data assimilation of GPS zenith delay on Mediterranean heavy
rainfall forecasting, Ann. Geophys., 27, 2739–2753, 10.5194/angeo-27-2739-2009, 2009.
Bölöni, G.: Development of a variational data assimilation system for a
limited area model at the Hungarian Meteorological Service, Időjárás, 110,
309–327, 2006.
Bubnová, R., Hello, G., Bénard, P., and Geleyn, J.-F.: Integration of the
fully elastic equations cast in the hydrostatic pressure terrain-following in
the framework of the ARPEGE/ALADIN NWP system, Mon. Weather Rev., 123,
515–535, 1995.Cameron, J. and Bell, W.: The testing and planned implementation of
variational bias correction (VarBC) at the Met Office, available at:
https://cimss.ssec.wisc.edu/itwg/itsc/itsc20/papers/11_01_cameron_paper.pdf (last access: 4 July 2017),
2016.
Chapnik, B., Desroziers, G., Rabier, F., and Talagrand, O.: Diagnosis and
tuning of observational error in a quasi-operational data assimilation
setting, Q. J. Roy. Meteor. Soc., 132,
543–565, 2006.
Cucurull, L., Vandenberghe, F., Barker, D., Vilaclara, E., and Rius, A.:
Three-Dimensional Variational Data Assimilation of Ground-Based GPS ZTD and
Meteorological Observations during the 14 December 2001 Storm Event over the
Western Mediterranean Sea, Mon. Weather Rev., 132, 749–763, 2004.Dach, R., Lutz, S., Walser, P., and Fridez, P.: Bernese GNSS Software Version 5.2.
User manual, Astronomical Institute, University of Bern, Bern Open
Publishing, ISBN 978-3-906813-05-9, 10.7892/boris.72297, 2015.
Dee, D. P.: Variational bias correction of radiance data in the ECMWF
system, Proceedings of the ECMWF workshop on assimilation of high spectral
resolution sounders in NWP, Reading, UK, 28, 97–112, 2004.De Haan, S.: Assimilation of GNSS ZTD and radar radial velocity for the
benefit of very-short-range regional weather forecasts, Q. J. Roy. Meteor.
Soc., 139, 2097–2107, 10.1002/qj.2087, 2013.
De Pondeca, M. S. F. V. and Zou, X.: A case study of the variational
assimilation of GPS Zenith Delay observations into a mesoscale model, J.
Clim. Appl. Meteorol., 40, 1559–1576, 2001.
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation,
background and analysis error statistics in observation space, Q.
J. Roy. Meteor. Soc., 131, 3385–3396, 2005.
Faccani, C., Ferretia, R., Pacione, R., Paolucci, T., Vespe, F., and
Cucurull, L.: Impact of a high density GPS network on the operational
forecast, Adv. Geosci., 2, 73–76, 2005.Ferro, C. A. T. and Stephenson, D. B.: Extremal Dependence Indices:
Improved Verification Measures for Deterministic Forecasts of Rare Binary
Events, Weather Forecast., 26, 699–713,
10.1175/WAF-D-10-05030.1, 2011.
Girard, D.: A fast Monte-Carlo cross-validation procedure for large
least-squares problems with noisy data, Technical Report 687-M, IMAG,
Grenoble, France, 1987.Guerova, G., Jones, J., Douša, J., Dick, G., de Haan, S., Pottiaux, E., Bock, O., Pacione, R., Elgered, G.,
Vedel, H., and Bender, M.: Review of the state of the art and future prospects of the ground-based GNSS
meteorology in Europe, Atmos. Meas. Tech., 9, 5385–5406, 10.5194/amt-9-5385-2016, 2016.
Horányi, A., Ihász, I., and Radnóti, G.: ARPEGE/ALADIN: a numerical
weather prediction model for Central-Europe with the participation of the
Hungarian Meteorological Service, Időjárás, 100, 277–301, 1996.Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C.,
Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F.,
Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P.,
Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F.,
Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R.,
Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.:
Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969, 10.5194/gmd-11-1929-2018, 2018.Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P.,
Redelsperger, J. L., Richard, E., and Vilà-Guerau de Arellano, J.: The Meso-NH Atmospheric Simulation System.
Part I: adiabatic formulation and control simulations, Ann. Geophys., 16, 90–109, 10.1007/s00585-997-0090-6, 1998.Lindskog, M., Ridal, M., Thorsteinsson, S., and Ning, T.: Data assimilation of GNSS zenith total delays from a
Nordic processing centre, Atmos. Chem. Phys., 17, 13983–13998, 10.5194/acp-17-13983-2017, 2017.
Liu, Z. Q. and Rabier, F.: The potential of high-density observations for
numerical weather prediction: A study with simulated observations, Q.
J. Roy. Meteor. Soc., 129, 3013–3035, 2003
Macpherson, S. R., Deblonde, G., Aparicio, J. M., and Casati, B.: Impact of
NOAA ground-based GPS observations on the Canadian Regional Analysis and
Forecast System, Mon. Weather Rev., 136, 2727–2746, 2008
Mahfouf, J.-F.: Analysis of soil moisture from near-surface parameters: A
feasibility study, J. Appl. Meteorol., 30, 506–526, 1991.Mahfouf, J.-F., Ahmed, F., Moll, P., and Teferle, F. N.: Assimilation of
zenith total delays in the AROME France convective scale model: A recent
assessment, Tellus, 67A, 26106, 10.3402/tellusa.v67.26106, 2015.Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F.,
Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L.,
Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A.,
Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P.,
Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation
of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, 10.5194/gmd-6-929-2013, 2013.
Mile, M., Bölöni, G., Randriamampianina, R., Steib, R., and Kucukkaraca, E.:
Overview of mesoscale data assimilation developments at the Hungarian
Meteorological Service, Q. J. Hungar. Meteorol. Serv., 119, 213–237, 2015.Poli, P., Moll, P., Rabier, F., Desroziers, G., Chapnik, B., Berre, L.,
Healy, S. B., Andersson, E., and Guelai, F. Z. E.: Forecast impact studies of
zenith total delay data from European near real-time GPS stations in Météo
France 4DVAR, J. Geophys. Res., 112, D06114, 10.1029/2006JD007430, 2007.
Rózsa, S., Dombai, F., Németh, P., and Ablonczy, D.: Integrált
vízgőztartalom becslése GPS adatok alapján, Geomatikai Közlemények, XII, 187–196, 2009 (in Hungarian).
Rózsa, S., Kenyeres, A., Weidinger, T., and Gyöngyösi, A. Z.: Near real-time
estimation of integrated water vapour from GNSS observations in Hungary,
International Association of Geodesy Symposia, 139, 31–39, 2014.Sánchez-Arriola, J., Lindskog, M., Thorsteinsson, S., and Bojarova, J.:
Variational Bias Correction of GNSS ZTD in the HARMONIE Modeling System, J. Appl. Meteorol. Clim., 55, 1259–1276,
10.1175/JAMC-D-15-0137.1, 2016.Seity, Y., Brousseau, P., Malardel, S., Hello, G., Benard, P., Bouttier, F.,
Lac, C., and Masson, V.: The AROME-France convective-scale operational model,
Mon. Weather Rev., 139, 976–991, 10.1175/2010MWR3425.1,
2011.
Storto, A. and Randriamampianina, R.: A New Bias Correction Scheme for
Assimilating GPS Zenith Tropospheric Delay Estimates, Idojaras, 114,
237–250, 2010.
Vedel, H. and Huang, X.-Y.: Impact of ground based GPS data on numerical
weather prediction, J. Meteorol. Soc. Jpn., 82, 459–472, 2004
Yan, X., Ducrocq, V., Poli, P., Jaubert, G., and Walpersdorf, A.: Mesoscale
GPS Zenith delay assimilation during Mediterranean heavy precipitation event,
Adv. Geosci., 17, 71–77, 2008.
Yan, X., Ducrocq, V., Jaubert, G., Brousseau, P., Poli, P., Champollion, C.,
Flamant, C., and Boniface, K.: The benefit of GPS zenith delay assimilation
on high-resolution quantitative precipitation forecast of the COPS cases IOP
9, Q. J. Roy. Meteor. Soc., 135, 1788–1800, 2009.Yan, X., Ducrocq, V., Poli, P., Hakam, M., Jaubert, G., and Walpersdorf, A.:
Impact of GPS zenith delay assimilation on convective-scale prediction of
Mediterranean heavy rainfall, J. Geophys. Res., 114, D03104,
10.1029/2008JD011036, 2009.