AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-10-4845-2017Quality aspects of the Wegener Center multi-satellite GPS radio occultation
record OPSv5.6AngererBarbarabarbara.angerer@uni-graz.atLadstädterFlorianhttps://orcid.org/0000-0001-8369-0868Scherllin-PirscherBarbarahttps://orcid.org/0000-0003-4969-7462SchwärzMarcSteinerAndrea K.https://orcid.org/0000-0003-1201-3303FoelscheUlrichhttps://orcid.org/0000-0002-9899-6453KirchengastGottfriedhttps://orcid.org/0000-0001-9187-937XWegener Center for Climate and Global Change (WEGC), University
of Graz, Graz, AustriaInstitute for Geophysics, Astrophysics, and Meteorology/Institute
of Physics, University of Graz, Graz, AustriaZentralanstalt für Meteorologie und Geodynamik (ZAMG), Vienna, AustriaBarbara Angerer (barbara.angerer@uni-graz.at)13December20171012484548635July20177July20172November20176November2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://amt.copernicus.org/articles/10/4845/2017/amt-10-4845-2017.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/10/4845/2017/amt-10-4845-2017.pdf
The demand for high-quality atmospheric data records, which are applicable in
climate studies, is undisputed. Using such records requires knowledge of the
quality and the specific characteristics of all contained data sources. The
latest version of the Wegener Center (WEGC) multi-satellite Global
Positioning System (GPS) radio occultation (RO) record, OPSv5.6, provides
globally distributed upper-air satellite data of high quality, usable for
climate and other high-accuracy applications. The GPS RO technique has been
deployed in several satellite missions since 2001. Consistency among data
from these missions is essential to create a homogeneous long-term
multi-satellite climate record. In order to enable a qualified usage of the
WEGC OPSv5.6 data set we performed a detailed analysis of satellite-dependent
quality aspects from 2001 to 2017. We present the impact of the OPSv5.6
quality control on the processed data and reveal time-dependent and
satellite-specific quality characteristics. The highest quality data are
found for MetOp (Meteorological Operational satellite) and GRACE (Gravity
Recovery and Climate Experiment). Data from FORMOSAT-3/COSMIC (Formosa
Satellite mission-3/Constellation Observing System for Meteorology,
Ionosphere, and Climate) are also of high quality. However, comparatively
large day-to-day variations and satellite-dependent irregularities need to be
taken into account when using these data. We validate the consistency among
the various satellite missions by calculating monthly mean temperature
deviations from the multi-satellite mean, including a correction for the
different sampling characteristics. The results are highly consistent in the
altitude range from 8 to 25 km, with mean temperature deviations less than
0.1 K. At higher altitudes the OPSv5.6 RO temperature record is
increasingly influenced by the characteristics of the bending angle
initialization, with the amount of impact depending on the receiver quality.
Introduction
Detailed knowledge of the characteristics and the quality of data is
essential for their qualified usage in atmospheric research. This also
applies to the use of Global Positioning System (GPS) radio occultation
(RO) data, which play a major role in the characterization of the free
atmosphere . GPS RO is a limb-sounding satellite
technique, which has continuously provided atmospheric profiles since
2001. The technique uses the signal transmitted by Global Navigation
Satellite System (GNSS) satellites (in this work we only use RO data from
GPS satellites) and received by low Earth orbit (LEO) satellites to probe
the Earth's atmosphere.
The GPS signals are emitted at two radio frequencies in the L band
(wavelengths of about 20 cm) and refracted on their way through the
atmosphere. The LEO receiver measures the excess phase path due to the
Earth's refractivity field, which is proportional to density in regions where
humidity is negligible. Due to the relative motion of the satellites, the
atmosphere is scanned vertically, either from the top downwards for setting
occultations (satellites move away from each other) or from the bottom up for
rising occultations (satellites move towards each other).
An RO event lasts about 1 to 2 min. Since the basic measurement of RO
is the signal phase as function of time, external calibration is not needed
and only short-term measurement stability is required over an RO event, which
is ensured by the utilization of highly stable oscillators. The traceability
to the international time standard (SI second) ensures long-term stability,
which is an essential prerequisite for climate applications
.
The technique was first exploited in 1995 with the proof-of-concept mission
Global Positioning System/Meteorology (GPS/Met). The highest-quality data are
obtained in the upper troposphere to lower stratosphere (UTLS) region
. Global coverage, long-term stability, high
vertical resolution, and weather independence due to the GPS frequency in the
microwave domain are further advantages . With these
properties GPS RO has a significant impact on numerical weather prediction
e.g. and on our ability to monitor the
atmospheric climate system .
For climate applications, data consistency and quality are essential for
producing a homogeneous long-term multi-satellite record. Due to the
self-calibrating nature of GPS RO, data from
different RO missions and different sensor types can be combined into a
consistent multi-satellite climate record if the same processing system is
used .
showed that data from the CHAMP (Challenging Minisatellite
Payload) mission and SAC-C (Satélite de Aplicaciones Científicas-C)
are remarkably consistent. compared co-located profiles
from FORMOSAT-3/COSMIC (Formosa Satellite mission-3/Constellation Observing
System for Meteorology, Ionosphere, and Climate; F3C hereafter) satellites
and confirmed that the root mean square difference between 10 and 20 km
altitude is less than 0.2 % in refractivity. showed
that refractivity and temperature climate records from multiple RO missions
are consistent within 0.05 % if the same processing scheme is applied.
Differences in the processing methods lead to structural uncertainties. This
has been investigated in detail for RO data from CHAMP, processed by six
different RO data centres finding high intercentre
consistency, especially in the altitude range of about 8 to 25 km altitude
and within latitudes from 50∘ S and 50∘ N.
Besides the importance of consistent RO data, validating the quality of the
individual satellite data is essential to identify the atmospheric profiles
with reduced quality and to ensure the suitability of the data set for
climate applications.
In this paper we focus on the description of the Wegener Center (WEGC) RO
record Occultation Processing System version 5.6 (OPSv5.6) in terms of data
quality and multi-satellite consistency. An overview of the data used in
the OPSv5.6 retrieval is given in Sect. . In
Sect. we focus on the retrieval methods and the quality
control conducted during the retrieval. The data quality of individual
satellite missions is discussed in Sect. , and
Sect. describes the steps towards a
combined multi-satellite record. A summary and the main conclusions are given in
Sect. .
RO data
WEGC OPSv5.6 uses amplitude and excess phase data as well as precise orbit
information (position and velocity vectors for both the GPS and the LEO
satellite) from the University Corporation of Atmospheric Research/COSMIC
Data Analysis and Archive Center (UCAR/CDAAC) as input data for the
retrieval of atmospheric variables. Data from CHAMP, SAC-C, GRACE (Gravity
Recovery and Climate Experiment), C/NOFS (Communications/Navigation Outage
Forecasting System), MetOp (Meteorological Operational satellite) and F3C
have been processed. Depending on the availability at the UCAR/CDAAC data
archive, reprocessed, or post-processed data are used.
Excess phase and orbit input data to the WEGC OPSv5.6 radio
occultation data processing. Regarding the differencing methods, SD denotes
single differencing and ZD denotes zero differencing.
MissionLaunchGPS receiverUCAR processor versionTime periodDifferencing methodCHAMP2000BlackJack2014.01402001-05-18 to 2008-10-05SDGRACE2002BlackJack2010.26402007-02-28 to 2014-03-30SD2014.27602014-03-31 to 2017-02-28ZDSAC-C2000BlackJack2005.30902001-08-13 to 2002-10-14SD2005.17202002-11-03 to 2002-11-152010.26402006-03-09 to 2011-08-03C/NOFS2008CORISS2010.26402010-03-01 to 2011-12-31SDMetOp-A2006GRAS2016.01202007-09-30 to 2017-02-28ZDMetOp-B2012GRAS2016.01202013-02-01 to 2017-02-28ZDF3C FM12006IGOR2013.35202006-04-23 to 2014-04-30SD2014.20502014-05-30 to 2014-06-292014.28602014-05-01 to 2017-02-28F3C FM22006IGOR2013.35202006-05-01 to 2014-04-30SD2014.28602014-05-01 to 2016-09-23F3C FM32006IGOR2013.35202006-04-24 to 2010-07-05SDF3C FM42006IGOR2013.35202006-04-22 to 2014-04-30SD2014.20502014-06-01 to 2014-06-292014.28602014-05-01 to 2015-07-07F3C FM52006IGOR2013.35202006-04-28 to 2014-04-30SD2014.20502014-06-01 to 2014-06-272014.28602014-05-01 to 2016-04-16F3C FM62006IGOR2013.35202006-04-22 to 2014-04-30SD2014.20502014-06-01 to 2014-06-292014.28602014-04-30 to 2017-02-28
The German mission CHAMP, operated by the Helmholtz-Zentrum Potsdam/German
Research Center for Geosciences (GFZ) and launched in 2000, was the first
mission to provide a continuous multi-year RO record (May 2001 to
October 2008) . The mission was equipped with the GPS
receiver BlackJack, also referred to as TRSR-2 (TurboRogue Space Receiver 2),
produced by the Jet Propulsion Laboratory (JPL). The receiver was a new-generation instrument of the GPS/Met receiver TRSR and was able to track
around 250 setting RO events per day.
The US/Argentinian mission SAC-C was in orbit from 2000 to
2013 and had the same BlackJack receiver mounted as CHAMP, although it was
named GPS occultation and passive reflection experiment (GOLPE). However,
SAC-C was the first mission where open-loop (OL) tracking was implemented,
which also enabled the acquisition of rising signals for the first time
. The ability to track the GPS signal during rising
occultations significantly increases the number of observations.
The two satellites of the joint US/German twin-satellite mission GRACE
were launched in 2002 into the same polar
orbit with the main focus of observing the Earth's time-variable gravity
field. Both satellites (GRACE-A and GRACE-B) carry a modified version of the
BlackJack receivers used on CHAMP and SAC-C. RO measurements have been taken by
GRACE-A since 2006, when the receiver was switched on permanently. GRACE-B
only provides measurements for some shorter time periods
(July–December 2014, June–October 2015, and April–September 2016) when
swapping manoeuvres took place, making GRACE-B the trailing satellite.
The major aim of the US C/NOFS mission was the
monitoring of ionospheric scintillation, but RO measurements were also
taken. The mission operated between 2008 and 2015; however RO data
at UCAR/CDAAC are only available for some months in 2010 and 2011. The orbit
design of the C/NOFS mission was chosen such that mostly the tropical regions
were covered (inclination of 13∘). The GPS receiver utilized on
C/NOFS, called CORISS (C/NOFS Occultation Receiver for Ionospheric Sensing
and Specification), is also of TRSR heritage.
The six identical spacecrafts of the Taiwanese/US F3C mission were
launched in April 2006, and since June 2006 UCAR/CDAAC has continuously
provided occultation measurements for F3C . Each
satellite is equipped with an IGOR (Integrated GPS Occultation
Receiver), which is also based on the design of the JPL
BlackJack receiver and is capable of tracking both setting and rising
occultations, yielding around 500 tracked RO events per day.
The MetOp series , operated by the European Organisation
for the Exploitation of Meteorological Satellites (EUMETSAT), consists of
three satellites, with two satellites (MetOp-A and MetOp-B) currently in
orbit (by mid-2017). MetOp-A has been providing RO data since the end of 2007
and MetOp-B since spring 2013. Both satellites are circulating in a
sun-synchronous, 98∘ inclined orbit and carry a GNSS receiver for
Atmospheric Sounding (GRAS), which was jointly developed by Saab Ericsson
Space (SES) of Sweden and Austrian Aerospace (AAE), now RUAG Space. The four
dual-frequency channels of GRAS allow two rising and two setting events to be
tracked simultaneously, yielding a comparatively high number of around 700
observed RO events per day.
Table lists the UCAR/CDAAC data used for the WEGC
OPSv5.6 record, the UCAR/CDAAC data versions and available time periods as
well as their launch dates, the mounted receiver and the differencing method
applied to remove clock errors. The latest WEGC-processed month is currently
February 2017; however the OPSv5.6 record will be extended based on new
UCAR/CDAAC data becoming available.
By mid-2017, only GRACE, both MetOp satellites and two out of six F3C
satellites (F3C FM1 and F3C FM6) are still providing data. With the impending
end of the F3C mission, new missions providing RO data are urgently needed.
The succession mission of F3C, named FORMOSAT-7/COSMIC-2 is scheduled to be
launched in early 2018. For a better coverage of the tropics, the
FORMOSAT-7/COSMIC-2 mission will have a constellation of six satellites at
24∘ inclined orbits. In addition, six satellites are planned in a
near-polar orbit with a 72∘ inclination . The
designated end of lifetime of the GRACE mission is in late 2017. However, the
launch period for the GRACE-FO (GRACE Follow-On) mission will be between
December 2017 and February 2018, with first data available approximately
3 months after the launch, leaving the data gap comparatively small. The
launch of the third satellite of the MetOp series, MetOp-C, is currently
planned for October 2018.
Furthermore, the Chinese FengYun-3 (FY-3) meteorological satellite series and
commercial missions are expected to provide RO data to the international
scientific and operational community soon. FY-3C was launched in 2013 and
FY-3D is scheduled for launch in November 2017. Both missions carry the GNSS
radio occultation sounder GNOS . Together with the
planned exploitation of GNSS signals from the Russian GLONASS, the European
Galileo system, and the Chinese BeiDou system this will enhance the number of
RO observations in the future.
RO retrieval and quality control
In the following we describe the WEGC OPSv5.6 retrieval processing chain
and the approach to assess the quality of the retrieved atmospheric
parameters.
OPSv5.6 retrieval
OPSv5.6 includes a combined geometrics optics (GO)/wave optics (WO) retrieval
of bending angle profiles, with transition from GO to WO near or somewhat
below the tropopause. A combined GO/WO bending angle retrieval approach
yielding a vertical stratospheric resolution of 0.5 to 1 km and low noise
level is suitable for the targeted OPSv5.6 data usage purposes.
Input data are UCAR/CDAAC excess phase and amplitude profiles from
occulted GNSS signals as well as precise orbit data of the GPS and LEO
satellites. To reconstruct excess phases in the lower troposphere,
navigation message information is also used from UCAR/CDAAC. Detailed
information on the OPSv5.6 retrieval chain was recently given by
; here we summarize the steps and related key
aspects relevant for the quality analysis of this study.
In a first step, quality checks are performed, which comprise technical aspects and
consistency of the input data (see
Sect. for more details). Before entering the
bending angle retrieval, the excess phase is filtered using a regularization
filtering method, with identical filter settings for all RO missions.
Thereafter the bending angle is calculated separately for the two GPS signal
frequencies L1 (1575.42 MHz) and L2 (1227.60 MHz) after
calculating Doppler profiles from the excess phases
e.g.. Subsequently the ionospheric
influence on the excess phase measurement is removed by applying the
ionospheric correction where the L1 and L2 bending angle profiles are
linearly combined . This yields the ionosphere-corrected
bending angle profile. The tropospheric bending angle is obtained from a WO
retrieval following and . The
transition from a GO to WO bending angle is between 7 and 13 km.
To validate the quality of the retrieved bending angle in the upper
atmosphere where the contribution from the neutral atmosphere is small,
the bending angle bias and noise are determined between 65 and 80 km
. The bending angle bias is defined as the difference between
the mean ionosphere-corrected RO bending angle and the mean mass spectrometer
and incoherent scatter radar (MSIS) bending angle in this height layer
. The MSIS reference climatology is used with fixed
solar activity to avoid the influence of solar variations. The bending angle
bias (with respect to MSIS) is usually slightly negative, with typical values
of about -0.1µrad . Large systematic biases of
the bending angle can indicate systematic errors in the satellites' orbits,
velocities, clock bias estimates, or incomplete removal of the ionospheric
contribution to the measurement .
Bending angle noise between 65 and 80 km is defined as the standard
deviation of the difference between the ionosphere-corrected RO bending angle
and the MSIS bending angle shifted by the bias .
Typically, bending angle noise is smaller than 5 µrad. Larger
bending angle noise can result from measurement noise due to poor GPS
receiver quality on board the LEO satellite, large residual ionospheric noise
from ionospheric irregularities, or from the differencing method used to
remove clock errors e.g.. The latter is
already applied to raw measurement data at UCAR/CDAAC.
Table shows that single differencing (SD) has been
applied to most satellite data used in this study. SD involves another
(second) GNSS satellite as reference link , which adds
additional ionospheric noise. If ultra-stable oscillators are used on board
the LEO satellite (like on GRACE or MetOp), clock errors are so small that
high-quality data can be obtained with zero differencing (ZD), which avoids
additional ionospheric noise ; for more details and
comparisons of both methods see, e.g. and
.
The next retrieval step relates the bending angle to the refractivity by
using an Abel transform. Since this integral transform extends to infinity,
initialization at high altitudes is needed. We use co-located ECMWF
short-range forecasts, and MSIS above the uppermost ECMWF level for the
bending angle initialization. Using ECMWF forecasts (24 and 30 h) instead
of, for example, ECMWF analyses, prevents the direct impact of assimilated RO data
on the high-altitude initialization. The forecast range of at least a day is
sufficient to make the a priori information decorrelated from the analyses
information. These co-located ECMWF profiles are extracted from the model by
using the time layer closest to the mean event time and interpolated to the
mean RO event location. Hence the OPSv5.6 RO data are not completely
independent from ECMWF at high altitudes. The weight of the RO measurement
relative to the background information is determined based on the quality of
the retrieved ionosphere-corrected RO bending angle. The statistical
optimization uses an inverse covariance weighting technique
and is performed between 30 and 120 km impact
altitude.
The ratio of the retrieval error (influenced by both the observational and
background error) and the error of the background determines the amount of
background information contained in the statistically optimized profile. This
ratio is denoted by a retrieval to a priori error ratio (RAER), and the impact
altitude where this ratio reaches 50 % is called zRAER50. Since the
estimated observational error is a constant absolute value, the value of the
zRAER50 is driven by its magnitude and the background error, a constant
relative value . It indicates the transition altitude
between background- and observation-dominated height regions. In the case of
small observational errors the observation-dominated region will extend
higher up into the stratosphere. For more details see .
Recently the observation-to-background weighting ratio (rOBW) has been
introduced as a quantity that more directly reflects the fraction of
observational information . However, zRAER50 as
used in OPSv5.6 is still a valuable indicator of the fractionation of
information, in particular for inter-comparing different missions.
After the statistical optimization and the retrieval of refractivity
profiles, atmospheric variables are calculated. Neglecting the atmospheric
wet term, dry density is calculated from atmospheric refractivity by applying
the Smith–Weintraub formula . Dry pressure profiles are
then retrieved using the hydrostatic equation and dry temperature profiles
are subsequently obtained by the application of the ideal gas law
.
The retrieval of the wet (physical) atmospheric variables is done in a
simple version of the 1D-Var retrieval, where a priori knowledge of the
state of the atmosphere is required. Co-located ECMWF short-range forecast
profiles are again used as background data. A more detailed description of
the OPSv5.6 retrieval can be found in .
OPSv5.6 quality control
Quality assessment of the WEGC OPSv5.6 data is done in three major steps, as
illustrated in Fig. . First, the quality of the
UCAR/CDAAC input data is checked prior to the bending angle retrieval to
ensure that the retrieval can be performed. The input quality control (QC)
rejects measurements if the accuracy of the time vector is not within
0.002 s. Furthermore the signal duration must be greater than 15 s and the
straight-line tangent point of the occultation event has to be available
within 20 and 65 km impact altitude, otherwise the profile will be
discarded.
Schematic representation of the quality control approach of the WEGC
OPSv5.6 retrieval.
Quality flags (QFs) defined within the WEGC OPSv5.6 data
processing.
VariableFlagTypeMeaningBending angleQF = 0internal QCAll checks passed. Bending angle retrieval results are of high quality.QF = 2internal QCBending angle noise could not be calculated from bending angle profile. A large observational error (22 µrad) is used in the retrieval.QF = 5internal QCNegative bending angles below 50 km. A large observational error (22 µrad) is used in the retrieval. Only non-optimized bending angle profiles should be used.QF = 10external QCIf internal QF=0 but the relative difference between the retrieved non-optimized bending angle profile and the co-located ECMWF analysis bending angle profile is greater than 20 % somewhere between 10 and 35 km.QF = 12external QCIf internal QF=2 and relative difference to co-located ECMWF exceeds limit (see QF=10).QF = 15external QCIf internal QF=5 and relative difference to co-located ECMWF exceeds limit (see QF=10).QF = 20external QCRetrieved bending angle profile somewhere contains values outside of -1 mrad and 10 rad.QF = 22external QCIf internal QF=2 and bending angle profile contains values outside defined range (see QF=20).QF = 25external QCIf internal QF=5 and bending angle profile contains values outside defined range (see QF=20).RefractivityQF = 0, 1external QCIf the relative difference between retrieved RO refractivity profile and the co-located ECMWF analysis refractivity profile is greater than 10 % somewhere between 5 and 35 km, QF is set to 1. Else, QF is 0.Dry temperatureQF = 0, 1external QCIf the difference between the retrieved RO dry temperature profile and the co-located ECMWF analysis dry temperature profile is greater than 20 K somewhere between 8 and 25 km, QF is set to 1. Else, QF is 0.TemperatureQF = 0, 1external QCIf the difference between the retrieved RO physical temperature profile and the co-located ECMWF analysis physical temperature profile is greater than 20 K somewhere between 8 and 25 km, QF is set to 1. Else, QF is 0.ProfileQF = 0, 1external QC/internal QCProfile QF defines the high-quality profiles. If the QFs of each checked variable is 0, profile QF is set to 0, else to 1.
The second step of the quality assessment is the internal QC where the
quality of the retrieved ionosphere-corrected bending angle profile is
examined. If the quality of the bending angle is not sufficient (i.e. the
bending angle noise is greater than 22 µrad or the bending angle
bias is greater than 10 µrad) the profile is discarded. Each
profile that passes the internal QC gets marked with a bending angle quality
flag (QF) according to its quality level and is processed further to
atmospheric variables. The bending angle QF can be set to 0, 2, or 5 in the
internal QC where QF=0 marks high bending angle quality. A
detailed description of the meaning of the quality flags is given in
Table .
Apart from detecting and eliminating profiles with insufficient bending angle
quality, the bending angle QF also provides information on the weight of the
RO measurement in the statistically optimized bending angle, being directly
related to the observational error magnitude used in statistical
optimization. For profiles with bending angle QF=0, the
observational error is set to the value of the bending angle noise, or to a
constant value (4.5 µrad, empirically determined) for profiles with
degrading bending angle quality above 65 km. For profiles with worse bending angle quality (QF=2 or QF=5),
the use of a larger amount of background information is required.
The observational error is therefore set to a larger value
(22 µrad), leading to more background information and less
observational information in the retrieved variables.
External QC is the third step in the OPSv5.6 QC. It is conducted after
finishing the retrieval of all atmospheric parameters. In this step the
plausibility of the retrieved atmospheric profiles of bending angle,
refractivity, dry temperature, and temperature is examined by comparing them
to co-located ECMWF analysis profiles. In this stage of the QC, profiles are
not discarded but flagged with a QF that marks them as bad quality (QF=1)
if the deviations exceed a certain limit (see Table ). In the
case of the bending angle, a QF has already been set in the internal QC.
Temporal evolution of daily percentage of profiles (relative to
total number of input data) passing the various QC steps for
(a) CHAMP, (b) GRACE, (c) SAC-C,
(d) C/NOFS, (e) MetOp-A and (f) MetOp-B (upper
panels). Upper subpanels of (a)–(f) show the number of
profiles passing the input QC (blue dots); the number of bending angle
profiles passing internal QC, which equals the number of output profiles
(green dots); the number of profiles that are within the defined limits in
the external QC (bending angle QF is 0, 2, or 5, and all atmospheric QF are
0) (red dots); and the daily percentage of high-quality OPSv5.6 profiles
(orange dots). Daily percentage of flagged profiles relative to the number
of OPSv5.6 output profiles is shown separately for each bending angle QF in
the lower subpanels.
Temporal evolution of daily percentage of profiles (relative to
total number of input data) passing the various QC steps for the six F3C
satellites, (a) FM1, (b) FM2, (c) FM3,
(d) FM4, (e) FM5, and (f) FM6. Upper subpanels of
(a)–(f) show the number of profiles passing the input QC
(blue dots); the number of bending angle profiles passing internal QC, which
equals the number of output profiles (green dots); the number of profiles
that are within the defined limits in the external QC (bending angle QF is 0,
2, or 5, and all atmospheric QF are 0) (red dots); and the daily percentage of
high-quality OPSv5.6 profiles (orange dots). Daily percentage of flagged
profiles relative to the number of OPSv5.6 output profiles is shown
separately for each bending angle QF in the lower subpanels.
Spatial distribution of the RO events with bending angle QF = 0,
2, or 5 for the satellites (a) CHAMP, (b) GRACE,
(c) SAC-C, (d) C/NOFS, (e) MetOp-A, and
(f) F3C FM1, showing the geographical coverage for one exemplary
month (left subpanels) and the latitudinal distribution over the complete
available time range per satellite (right subpanels).
If the external QC fails for the bending angle, the bending angle QF is
updated and, depending on the result of the internal QC, the new QF can be
10, 12, 15, 20, 22, or 25 (see Table for details), where the
second digit indicates the internal QC and the first digit indicates the external QC. As
an example, QF=25 means that the internal QF=5
(negative bending angles below 50 km) and the external QF=20
(retrieved bending angle profile somewhere contains values outside of
-1 mrad and 10 rad). In addition to the individual QFs of the retrieved
atmospheric parameters, the so-called profile QF is defined. This QF is only
0 if all QFs are 0.
An OPSv5.6 output profile is hence always flagged with five quality flags:
bending angle QF, refractivity QF, dry temperature QF, temperature QF,
and an overall profile QF. Only if the profile QF=0, the
profile denotes an OPSv5.6 high-quality profile and is recommended to
be used for all general applications. However, depending on the user's
needs, profiles with other QFs can also be of particular interest.
Quality aspects of the individual satellites
Knowledge of the differences in quality of the various satellite data is
essential, especially if data from several missions are combined into a
multi-satellite record. Figures
and illustrate the quality of RO data
from different missions as identified by OPSv5.6.
Figure illustrates the spatial distribution of RO
event locations of all processed RO missions with their respective internal
bending angle QF for one specific month as well as the latitudinal
distribution of the internal bending angle QFs over the complete time range
per satellite. Figure comprises the information
about the number of high-quality profiles on a monthly timescale for all
satellites processed within the OPSv5.6 retrieval. In
Table an overview of the number of provided
UCAR/CDAAC phase delays, the OPSv5.6 output profiles, and the high-quality
profiles per satellite is given.
Daily number of high-quality OPSv5.6 profiles for different
satellites (different colours) as a function of time from 2001 to 2017.
In the following, we discuss commonalities and differences between
the satellites before going into the details of satellite-specific features.
General features
The upper panels of Figs.
and show the temporal evolution of the
relative number of profiles passing various QC steps. The daily percentage is
calculated relative to the total number of input files. In general, the input
QC and the internal QC have the strongest impact on the number of
high-quality profiles while the influence of the external QC is comparatively
low. The majority of all OPSv5.6 output profiles is flagged with QF=0 as found in the lower panels of Figs.
and . However, some irregularities and
satellite-specific characteristics can be detected. The percentage of
high-quality profiles differs considerably among the satellites, which
clearly points to the differences in individual satellite data quality.
Besides that, data quality can also vary significantly over time.
Overview of daily average number of available UCAR/CDAAC excess
phase files (one file per RO event) and the corresponding percentages of
retrieved OPSv5.6 output profiles and high-quality profiles.
The characteristics in the spatial event distribution, as shown in
Fig. , reflect the different orbit designs of the
receiving LEO satellites. The left subpanels show the horizontal
distribution of the internal QFs for one exemplary month (July 2008 and July
2011, respectively), indicating some seasonal effect at high latitudes. The
right subpanels depict the mean latitudinal distribution, revealing an equal
distribution for both the Northern and Southern hemispheres for all
satellites.
Figure reveals the vast increase in OPSv5.6
profiles in mid-2006, when RO data provision of the F3C mission was
started. A decline in the number of profiles can be observed in the last
years, as some of the used RO missions already exceeded their designated
lifetimes.
The capability to track setting as well as rising signals is reflected in the
number of provided measurements, as obtainable from
Table . The lowest number can be found for CHAMP
and GRACE, for which only setting measurements are available. The F3C
satellites and MetOp, which are capable of tracking both kind of signals,
provide two (F3C) or even three times (MetOp) as many measurements. The
number of output profiles varies between 57 % (C/NOFS) and a maximum of
94 % for GRACE. The majority of the missions obtain around 70 % of
high-quality data.
CHAMP
Of all CHAMP data, 18 % are rejected in the OPSv5.6 input QC and only
52 % of all UCAR/CDAAC input data yield high-quality output profiles
(Fig. a). Compared to the other satellites, this
number is quite low. The daily percentage of profiles passing the different
QC steps is constant over time, except at the beginning of the time series. A
firmware update in March 2002 slightly increased the number of high-quality
profiles. The difference between the total number of OPSv5.6 output profiles
and the OPSv5.6 high-quality profiles can mainly be attributed to the high
number of profiles flagged with QF=2, primarily induced by
negative bending angles between 50 and 55 km. A semi-annual cycle is
observable in the temporal evolution of high-quality profiles because of the
systematic rejection of very cold profiles .
The CHAMP orbit has an inclination of 87∘, which yields quite a
homogeneous global distribution, with slightly fewer measurements in the
tropics; see Fig. a. The number of profiles with
QF=2 and QF=5 is greatest between 60 and 90∘ S
in July 2008 (Antarctic winter). Averaged over the complete time period
(right panel of Fig. a) profiles with QF=2 and QF=5 are equally distributed above both poles. This can be
understood as follows: between 50 and 55 km the absolute atmospheric bending
angle is small. Due to the comparatively high noise of CHAMP data it can
happen that some values in the bending angle profile are negative. This
more likely occurs in very cold regions, where the absolute bending angle
values are particularly small. This effect is visible for all satellites;
however it is strongest for CHAMP.
GRACE
The data quality of the GRACE mission is very good. There is almost no loss
due to input QC and 80 % of all input data yield high-quality profiles
(Fig. b). Furthermore data quality is constant
over time: only a slight decline in high-quality profiles is visible, which
might be attributable to a degrading instrument performance after exceeding
its planned lifetime. There is no apparent change in data quality between the
twin satellites, as both are equipped with the same receiver (time periods
where RO measurements where taken by GRACE-B instead of GRACE-A are marked in
Fig. b).
The number of provided UCAR/CDAAC phase delay files is significantly lower
for GRACE than for other satellites (Table ):
Around 200 events per day, in contrast to, e.g. the average number of around
400 events per day for F3C FM5. Since the input QC rejects only very few of
these events, measurements of lower data quality are presumably rejected
already at a previous processing step.
Due to its polar orbit (89∘ inclined) the occultation event locations
are evenly distributed over the globe (Fig. b). The
same spatial pattern of QF distribution (i.e. high number of QF=2
and QF=5 profiles at high latitudes due to the rejection of very
cold profiles) can be observed for GRACE as for CHAMP but with reduced
strength.
SAC-C
The number of high-quality profiles varies strongly in the beginning of the
mission, but from 2006 onwards the quality remains constant with time with an
average number of 71 % high-quality profiles
(Fig. c). From late 2002 on, the newly developed
OL tracking mode was tested on SAC-C to enable the tracking of signals in
rising occultation. A stable version was then established in March 2006
. In the testing phase between 2003 and 2006, UCAR/CDAAC does
not provide any measurement data.
Data prior to the OL testing phase have been processed with an older
UCAR/CDAAC data version (Table ) and because of the
strongly varying data quality during this period we do not recommend using
SAC-C data before 2006. Again, the semi-annual cycle of the percentage of
high-quality measurements (Fig. c) as well as the
latitudinal dependency of the QFs (Fig. c) induced
by the rejection of very cold profiles is visible, similarly to CHAMP and
GRACE.
C/NOFS
The C/NOFS mission operated from 2008 to 2015; however UCAR/CDAAC only
provides post-processed C/NOFS measurements from 2010 to 2011. More than
40 % of all C/NOFS input data are discarded in the input QC
(Fig. d), because many events are too short and
do not cover the altitude range from 20 to 65 km. However, although there
seems to be a problem in the vertical availability of C/NOFS measurements,
the majority of the profiles that pass internal QC (48 % of all input
data) are of high quality (see lower panel of
Fig. d).
Because of the near-equatorial orbit of C/NOFS (inclination of 13∘),
RO measurements are only available at low latitudes up to 30∘
(Fig. d). Due to its focus on low latitudes, RO
data from the C/NOFS mission can be valuable for studies concerning the
tropical region, where the density of RO measurements is generally lower.
MetOp
Compared to the other satellite missions, the number of MetOp RO measurements
is significantly higher. Around 600 events per day can be detected, since
MetOp is able to take two rising and two setting occultation measurements
simultaneously. MetOp-A shows remarkably good and constant data quality,
especially until mid-2013. From mid-2013 onwards the number of data passing
the input and internal QC diminishes (from around 85 to 75 %), yielding a
total average of 80 % high-quality profiles. A tracking parameter update,
which took place in June 2013 on the MetOp-A GRAS receiver
(Christian Marquardt/EUMETSAT, personal communication, 2017), is reflected in
the statistics of the QC, showing a decline in the number of profiles passing
input QC as well as internal QC.
Compared to the other satellites, the external QC has the largest impact
for MetOp-A before mid-2013 (Fig. e). MetOp-B
also shows high data quality (Fig. f). A decline
in the number of data passing input and internal QC occurs in April 2013,
when the tracking parameter update has been applied to MetOp-B. Amongst
other things, this update introduced a change in the tracking of the L2
signal for rising occultations: it is now measured from a 15 km
straight-line-tangent altitude (about 20 km impact altitude) upwards only.
Since the OPSv5.6 uses the bending angles between 15 and 20 km to extend the
ionosphere correction in the troposphere this update implies that this
correction cannot be applied. If the lowermost ray is above 20 km impact
altitude the profile is rejected by the QC completely.
The temporal evolution of the percentage of high-quality profiles of both
MetOp satellites is constant with almost no outliers
(Fig. e and f).
Figure e illustrates the global coverage of MetOp-A
occultations, with a slightly increased coverage in the midlatitudes, which
is attributed to the orbit inclination of 98.7∘. Since MetOp-A and
MetOp-B are operating in the same orbit, we only show results from MetOp-A in
Fig. e as they are representative for both MetOp
satellites.
FORMOSAT-3/COSMIC
The six F3C satellites show similar characteristics in data quality
throughout their active time periods; see
Fig. . Distinct increases in input data
quality are induced by firmware updates of the GPS receivers, e.g. in
August 2006 and in January 2012.
The daily number of measurements varies strongly with time. There are also
significantly stronger variations in the temporal evolution of high-quality
profiles compared to the other satellites. These variations can mainly be
attributed to the internal QC. The semi-annual cycle of the percentage of
high-quality profiles mainly stems from the rejection of bending angle
profiles with a bending angle noise exceeding 22 µrad. All
satellites also display divergent behaviour in the very beginning of the
mission, between April and July 2006 (before the first firmware update took
place), where the number of profiles flagged with QF=2 is
significantly higher than in the subsequent time period. Strong variations in
data quality also appear in 2011, especially for F3C FM4. The time period is
much shorter for F3C FM3, as it has been out of operation since
August 2010.
Global coverage is also achieved for the F3C mission (see
Fig. f for FM1, which is representative for the
other F3C satellites), with slightly fewer measurements near the poles and in
the tropics.
Towards a combined multi-satellite record
The unique properties of RO, including high accuracy and long-term
stability, can be exploited to create a consistent multi-satellite climate
record. To ensure a high-quality and consistent multi-satellite OPSv5.6 RO
record, we first inspect the respective bending angle characteristics to
identify unusual behaviour that would lead to inconsistencies in the
combined data set. We then consider differences in the sampling
characteristics of the various missions and analyse deviations from the
multi-satellite mean in retrieved temperature time series.
Bending angle consistency
To validate the quality and consistency of OPSv5.6 data, we analyse bending
angle bias, standard deviation of the bending angle noise (for brevity just
termed “noise” hereafter), and the altitude at which the retrieval to a priori
error ratio equals 50 % (zRAER50). All these parameters are defined
within the OPSv5.6 retrieval chain (see Sect. for how
they are estimated) and characterize the quality of the retrieved bending
angle. Therefore, they are suitable quantities for validating data
consistency, as already shown by ,
, and .
In Fig. a we show the temporal evolution
of the daily median bias for all satellites processed within the OPSv5.6
retrieval. For climate applications it is important that the bending angle
bias is similar for all satellites and close to zero, which is true for all
satellites. The slightly negative values are mainly attributed to residual
ionospheric effects . No distinct inhomogeneities are
visible over time and the mean values vary between -0.09µrad for
SAC-C and -0.20µrad for C/NOFS. These slightly different mean
values might result from data being available from different time periods.
C/NOFS data, for example, are only available in 2010 and 2011, when solar
activity was high, and only cover the lower latitudes, where the total electron
content (TEC) is comparatively high. High solar activity causes a higher
level of ionization in the Earth's upper atmosphere, which again affects
the quality of RO measurements (i.e. a larger ionospheric residual and
therefore a larger bending angle bias). This has been empirically shown by
and and underpinned by end-to-end
simulations including atmospheric and ionospheric models by
.
Temporal evolution of daily median bending angle bias
(a) and bending angle noise standard deviation (b) for all
RO missions used in OPSv5.6. Total mean of bending angle bias and noise is
shown as a value in parentheses in the respective legend. Only high-quality
profiles are used in these statistics.
Temporal evolution of daily mean bending angle zRAER50 for all RO
missions processed in OPSv5.6. The total mean of zRAER50 is shown as a value in
parentheses in the legend. Only high-quality profiles are used in these
statistics.
Monthly averaged differences between non-optimized (NonOptBA) and
statistically optimized OPSv5.6 bending angle (BA) (violet dots) as well as
between statistically optimized OPSv5.6 bending angle and co-located ECMWF
bending angle (orange dots) shown for F3C FM2 (a) and for
MetOp-A (b) at an impact altitude of 45 km.
The temporal evolution of the daily median bending angle noise is depicted in
Fig. b. The bending angle noise, which
mainly reflects the quality of the GPS receiver and the residual clock
errors, is largest for CHAMP with 4.00 µrad. The BlackJack receiver
mounted on CHAMP is a receiver of the first generation
(Sect. ), which explains the comparatively high noise for
CHAMP. Bending angle noise is significantly lower (2.47 µrad) for
GRACE, which also utilizes a BlackJack receiver but an advanced version. In
addition, zero differencing can be applied to account for the receiver clock
error due to ultra-stable oscillators used in the GRACE mission, which also
leads to less noisy data . We assume that zero
differencing has been first applied to GRACE in the UCAR/CDAAC 2014.2760 data
version, as the noise value becomes smaller in the time period after April
2014.
The smallest bending angle noise is found for the two MetOp satellites, with
0.90 µrad for MetOp-A and 0.95 µrad for MetOp-B, which
reflects the excellent quality of the MetOp/GRAS receiver. SAC-C and all six
F3C satellites reveal large temporal variability of noise at the beginning
of their mission lifetimes. Due to these large fluctuations, which match the
results shown in Sect. , data from these time
periods (August 2001 to November 2002 for SAC-C and April 2006 to July 2006
for F3C) are not included in OPSv5.6 multi-satellite climatologies; in the
subsequent time periods the noise amounts to about 2.5 µrad
including single differencing for F3C and around 3 µrad for SAC-C.
Figure shows that zRAER50 for MetOp is at a
considerably higher impact altitude than for all other satellites. The mean
zRAER50 is similar for F3C and GRACE, whereas CHAMP shows zRAER50 values at
the lowest impact altitude. The assumed change to zero differencing for GRACE
in the newer data version (2014.2760) is reflected in the increased zRAER50
value in April 2014. The zRAER50 parameter (Sect. ) is
strongly influenced by the quality of the satellite's receiver. Because of
the high quality of the MetOp/GRAS receiver, the influence of the ECMWF
background field on the MetOp observations is far smaller than for any other
satellite at the same altitude level, confirming early theoretical/simulation
studies such as by and .
Figure shows the impact of the statistical optimization on
the monthly mean bending angle profiles of F3C FM2 and MetOp-A at 45 km
impact altitude, where the RAER is already at around 50 % for F3C FM2
but still significantly lower for MetOp. Differences between the
non-optimized bending angle and the statistically optimized bending angle for
both satellites fluctuate around zero (violet dots). However F3C FM2 shows
larger variations than MetOp. The influence of background information on
statistically optimized F3C FM2 bending angles can best be seen during
significant changes in the ECMWF system (e.g. mid-2012 and mid-2013). Since
ECMWF data are used as background information in the statistical optimization
(see Sect. ), the difference between non-optimized and
statistically optimized bending angle shows some (small) jumps during these
time periods.
In comparison, such changes in ECMWF are less visible in the MetOp time
series. From this we can conclude that due to the high quality of the
Metop/GRAS receiver, the high-altitude initialization (and with that, ECMWF
model system changes) has less of an impact on retrieved MetOp profiles compared
to other missions. This has to be kept in mind when generating a combined
multi-satellite record from all RO missions.
Deviations of individual satellites from the multi-satellite mean
for monthly mean dry temperature in altitude layer 8–25 km, without
sampling error (SE) correction (a) and with SE correction
applied (b). The satellite mean is calculated from all missions
available for the respective month (note that before May 2006 only CHAMP and
SAC-C delivered data).
Monthly mean multi-satellite climatologies
When combining data from different satellite missions to a global
multi-satellite record, not only do the quality and consistency of the retrieved
atmospheric profiles have to be taken into account, but so do the differences in spatial and temporal
sampling. The error due to discrete sampling
(sampling error) can be estimated from the difference between the averaged
co-located ECMWF analysis profiles and the averaged full ECMWF field
. In order to account for the sampling
error, it is subtracted from the climatology
e.g., leaving a small residual sampling
error (for detailed information see ).
The impact of the sampling error correction is clearly visible in
Fig. . These monthly mean global mean dry
temperature differences are calculated for each satellite relative to the
multi-satellite mean between 8 and 25 km height following
and . If no sampling error
correction is applied (Fig. a), deviations mainly
vary between 0.5 K with large outliers occurring in mid-2011 (out of
plot scale). These outliers stem from large deviations of C/NOFS, which only
provides data for the tropics. Not considering these special spatial sampling
characteristics leads to a bias in the global mean. With the sampling error
correction applied (Fig. b), deviations are below
0.1 K for all satellites in the 8 to 25 km height range where RO is of
the highest quality.
At higher altitudes, between 25 and 35 km, MetOp-A reveals a distinctively
different behaviour from the other satellites before mid-2013 but a
consistent one after that (Fig. ). This change in
the characteristics of MetOp dry temperature deviations coincides precisely
with an ECMWF model system change (cycle 38r2), where, among other changes,
the number of vertical levels was increased in the model.
Deviations of individual satellites from the multi-satellite
mean (a) and from the ECMWF analysis (b) for monthly mean
dry temperature in the altitude layer 25–35 km. Points in time (vertical
dashed line) mark important ECMWF model system changes (b).
Several other ECMWF model system changes can be identified in the deviations
from each satellite to the ECMWF analysis field in this altitude range; see
Fig. . We find that the improvements of the model
are reflected in decreasing deviations of the RO data from ECMWF after 2013.
This is specifically evident for MetOp, which is generally less
ECMWF-affected in this altitude range than the other satellites and which
shows the largest decrease in deviation from ECMWF. With the increase in
quality of the ECMWF model system, the analysis approaches the high-quality
atmospheric information provided by MetOp, resulting in better quality of the
other, more ECMWF-affected, satellites.
At lower altitudes up to 25 km the impact of the high-altitude
initialization on the RO temperature is small. Consequently different RO
missions can be readily combined into a multi-satellite data set (see
Fig. and ).
Above 25 km, the RO temperature record is increasingly influenced by the
characteristics of the initialization, with the amount of impact depending on
the receiver quality. This effect is most pronounced for temperature, since
the hydrostatic integration in the retrieval step from refractivity (density)
to pressure leads to a downward propagation of high-altitude initalization
errors, propagating further into temperature. High consistency in
refractivity can be achieved up to altitudes of about 30 km
.
Summary and conclusions
In this study, we performed a quality analysis of the individual satellite
data sets comprising the latest version of the Wegener Center multi-satellite
GPS RO record WEGC OPSv5.6. We described the QC procedure applied in the
OPSv5.6 retrieval and included a detailed analysis of the impact of the
various QC steps on each individual satellite data set for the missions CHAMP,
GRACE, SAC-C, C/NOFS, MetOp, and FORMOSAT-3/COSMIC. A rigorous QC is key for
establishing a combined multi-satellite record with known properties and
shall facilitate proper application of the WEGC OPSv5.6 record.
From this analysis, we conclude that for CHAMP our bending angle quality control
passes a less than average number of events as high-quality
profiles compared to the other satellites. The six F3C satellites have a
comparable, high bending angle quality with quite large day-to-day
variations and some satellite-specific irregularities. GRACE and especially
the two MetOp satellites show the highest-quality profiles; however the MetOp time
series reflects relevant changes in the receiver software. The improvement
of the receiver quality with time, from the older BlackJack receivers to the
modified BlackJack receiver on GRACE and the GRAS receiver on MetOp, is
evident in our analysis.
As the signal-to-noise ratio in the RO observations gets worse with
increasing altitude, the observations are merged with background information
at high altitudes in the bending angle retrieval. The impact of the
background field is strongly dependent on the quality of the receiver and
increases with decreasing receiver quality. To establish a homogeneous
long-term RO climate record it is thus essential to track the quality and
influence of the background field to understand the height-dependent
characteristics.
In the OPSv5.6 retrieval ECMWF short-term forecasts are used as background.
At higher-altitude levels the influence of the background field increases,
and with that certain ECMWF model changes are reflected in the OPSv5.6
retrieval results. In the RO retrieval chain this impact propagates further
downwards for each retrieved parameter. We conclude that the OPSv5.6
multi-satellite refractivity/temperature record is only marginally influenced
by the ECMWF model up to about 30/25 km and higher for MetOp due to its
excellent receiver quality.
The WEGC OPSv5.6 record provides a valuable observational record for
atmospheric analysis and climate monitoring. Based on the knowledge from our
careful quality control we find that high-quality temperature data from different
satellites are highly consistent between 8 and 25 km, with deviations from the
multi-satellite mean of less than 0.1 K. Above, the quality of individual
satellite records depends on receiver quality and the amount of background
information that is entered. The MetOp record provides high-quality
observational information to higher altitudes which is reflected in a larger
divergence from the multi-satellite mean between 25 and 35 km. Improvements
in the quality of the background field in 2013 led to much smaller deviations
of less than 0.2 K. These findings have to be taken into account when
using the WEGC OPSv5.6 RO record above about 25 to 35 km for climate trend
applications.
This work aids the maturation of the RO record with respect to
knowledge of data quality and its description and helps to
meet the stringent requirements as defined by the Global Climate Observing
System for the generation of a climate
data record of essential climate variables.
The data set is currently available on request from the
authors and will be made publicly available soon via an online resource.
The RO excess phase and orbit data from UCAR/CDAAC are available at
http://cdaac-www.cosmic.ucar.edu/. The analysis and
forecast data from ECMWF are available at
http://www.ecmwf.int/en/forecasts/data sets.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Observing Atmosphere
and Climate with Occultation Techniques – Results from the OPAC-IROWG 2016
Workshop”. It is a result of the International Workshop on Occultations for
Probing Atmosphere and Climate, Leibnitz, Austria, 8–14 September 2016.
Acknowledgements
We are grateful to UCAR/CDAAC (Boulder, CO, USA) for the provision of its RO
excess phase and orbit data and ECMWF (Reading, UK) for providing access to
analysis and forecast data. This work was funded by the Austrian Science Fund
(FWF) under grant P27724-NBL (VERTICLIM) as well as by the FFG-ALR projects
OPSCLIMTRACE (ASAP-9 844395) and OPSCLIMVALUE (ASAP-10
848013). Edited by: Axel von
Engeln Reviewed by: two anonymous referees
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