Rads4 Data Manual

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RADS Data Manual
Remko Scharroo
Version 4.3.2
7 November 2018
Laboratory for Satellite Altimetry
NOAA•NESDIS•STAR
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Contents
1 Introduction 1
2 Time and location 3
2.1 Time ........................................... 3
2.2 Latitudeandlongitude................................. 4
2.3 Orbitalaltitude ..................................... 4
2.4 Orbitalaltituderate................................... 6
3 Sea level variables 7
3.1 Sealevelanomaly.................................... 7
3.2 Altimeterrange ..................................... 8
3.3 Altimeter range statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.4 Dry tropospheric correction and air tide . . . . . . . . . . . . . . . . . . . . . . . 10
3.5 Wet tropospheric correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.6 Ionosphericcorrection ................................. 13
3.7 Atmospheric (inverse barometer) correction . . . . . . . . . . . . . . . . . . . . . 14
3.8 Solidearthandpoletide................................ 15
3.9 Oceanandloadtide................................... 15
3.10Seastatebias....................................... 17
3.11 Mean sea surface and geoid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Wind speed and wave height variables 20
4.1 Signicantwaveheight................................. 20
4.2 Altimeter backscatter coefficient . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Windspeed ....................................... 22
4.4 Otherwavemodeldata................................. 23
5 Radiometer variables 24
5.1 Radiometer brightness temperatures . . . . . . . . . . . . . . . . . . . . . . . . . 24
5.2 Watervapourcontent.................................. 24
5.3 Liquidwatercontent .................................. 24
6 Variables for data editing 25
6.1 Engineering and geophysical flags . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2 Bathymetry and topography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
6.3 Distance from coast and coastal proximity parameter . . . . . . . . . . . . . . . 29
6.4 Basincodes........................................ 29
Bibliography 31
iii
iv Contents
Index 37
Chapter 1
Introduction
This manual is intended to explain the details about the many variables available in the RADS
data base. Many of the variables come in different ”flavours”, like one could choose to use
the wet tropospheric correction based on radiometer measurements (wet tropo rad) or one of
the atmospheric models (e.g., wet tropo ecmwf). RADS also provides the option to use more
generic variable names (like wet tropo) that will pick the best available correction depending
on the satellite mission (or period).
The variables are organised various chapters. Consult the table of contents to find the
variables you are looking for, or go to the index at the end of the manual, where all variables
are listed.
For each variable, a list of different flavours is presented. For an example, turn to Sec-
tion 3.5. This Section describes the various wet tropospheric corrections. The table in that
Section lists in the first column the various variable names, followed by the field number as
was used in RADS 3 (and can still be used in RADS 4), a short description, and the units of this
variable. The next column lists for which altimeter missions this variable is available (see Ta-
ble 1.1), where ”all” stands for all missions, and ”other” stands for all missions not mentioned
above it. The second to last column is the default range used for editing (NaN is returned when
Altimeter Abbr. Nr Alternatives References
GEOS 3 g3 1 ge3 geos-3 geos3 (not included in RADS)
Seasat ss 2 sea seasat-a (not included in RADS)
Geosat gs 3 geo geosat
ERS-1 e1 4 er1 ers-1 ers1 [Francis, 1990; Francis et al., 1991]
TOPEX tx 5 top topex [Fu et al., 1994]
Poseidon pn 6 pos poseidon
ERS-2 e2 7 er2 ers-2 ers2 [Francis et al., 1995]
GFO g1 8 gfo gfo-1 gfo1
Jason-1 j1 9 ja1 jason-1 jason1 [M´enard et al., 2003]
Envisat n1 10 en1 envisat
Jason-2 j2 11 ja2 jason-2 jason2 [Lambin et al., 2010]
CryoSat-2 c2 12 cs2 cryosat-2 cryosat2 [Wingham et al., 2006]
SARAL sa 13 sa srl saral altika
Jason-3 j3 14 ja3 jason-3 jason3
HY-2A 2a 15 h2a hy-2a hy2a (not included in RADS)
Sentinel-3A 3a 16 s3a sentinel-3a sentinel3a sntnl-3a
Sentinel-3B 3b 17 s3b sentinel-3b sentinel3b sntnl-3b (limited access in RADS)
Table 1.1 Abbreviation and numbers used for the various altimeter missions.
1
2 Introduction
the value exceeds this range). When this column is empty, no range limits are set. Finally, the
rightmost column relates to a list of notes provided in that Section.
Near the bottom of each variable table a number of ”aliases” are provided. These are
short-cuts to one (or more) of the flavours of variables. For example, the tables in Section 3.5
shows that for most altimeter missions the alias wet tropo means that radiometer wet tropo-
spheric correction is used (wet tropo), but, when not available, for example during extended
outage, the ECMWF model wet tropospheric correction (wet tropo ecmwf) is used instead.
These ”aliases” make it easier to get the preferred flavour of the variable which may differ
from mission to mission (for example, some do not have a radiometer). This largely simplifies
the construction of sea level anomalies, as described in Section 3.1.
Chapter 2
Time and location
2.1 Time
Time in the RADS data sets is stored as 8-byte floats in UTC seconds since a given epoch (nor-
mally 1 January 1985 or 1 January 2000). RADS will automatically convert these values into a
few common time scales, depending on which time variable is selected. However, irrespective
of the time scale, the clock references to UTC, rather than an atomic clock, meaning that leap
seconds may result in a duplication of measurement times. No provision has been made to
avoid confusion between measurements made before and after a leap second.
The time corresponds to the moment of reflection of the radar pulse on the sea surface and
is corrected for time tag biases.
Variable field name units sat range note
time 1985 101 time since 1985-01-01 00:00:00 s all
time 2000 106 time since 2000-01-01 00:00:00 s all
time rel eq 102 time relative to equator crossing s all 1
time local solar 103 local solar time s all 2
time mjd 105 time since 1858-11-17 00:00:00 days all 3
time ymdhms 104 time formatted as ymdhms all 4
time 1alias of time 1985 all 5
Notes:
1. Time is negative prior to equator crossing, positive thereafter.
2. The local solar time is expressed as seconds since the start of the day.
3. Time in Modified Julian Days.
4. The variable time ymdhms will produce a floating value of the type 20110908135001.536
for 8 Sep 2011 13:50:01.536 UTC.
5. No time limit is set by default. Can be controlled by the --t or --ymd flags on the com-
mand line.
6. A time tag bias (surplus) of 1.8 ms was removed from the time tags of ERS-1 as they oc-
curred on the ESA OPR (Ocean Product) data product. Likewise, all time tags of ERS-2
were decreased by 1.3 ms. The orbital altitude and location has been adjusted accordingly.
3
4 Time and location
2.2 Latitude and longitude
The position of the centre of the footprint of the measurement is given by its geographical
longitude and latitude relative the TOPEX reference ellipsoid. Longitude is in degrees relative
to the Greenwich meridian, positive measuring east. Latitude is in degrees relative to the
equator, positive measuring north.
Variable field name units sat range note
lat 2, 201 latitude degrees north all -90 90
lon 3, 301 longitude degrees east all -180 180 1
Note:
1. RADS will automatically adjust the values to be within the range specified. So, by default,
longitudes are kept within the -180 to +180 range.
2.3 Orbital altitude
The orbital altitude is the height of the centre-of-mass of the satellite above the TOPEX ref-
erence ellipsoid (semi-major axis = 6378136.3 m, inverse flattening = 298.257) as computed
by satellite orbit determination. Numerous solutions exist, based on varying combinations of
tracking data or gravity field solutions, or computed at shorter or longer latency. Some ”legacy
solutions” (those that were provided on the original data products) are included for reference
even when they have been long replaced by more accurate solutions.
The altitude is that of the centre-of-mass of the spacecraft, so corrections from the tracking
devices (DORIS, GPS, PRARE, SLR) to the centre-of-mass, as well as motion of the centre-of-
mass within the spacecraft are accounted for, and should also be accounted for when later
subtracting the altimeter range referenced to the same point.
If the time tags on the original GDR data include a bias, the orbit has either been
(re)interpolated at the corrected time tag, or a correction proportional to the orbital altitude
rate has been applied.
2.3 Orbital altitude 5
Variable field name units sat range note
alt jgm3 401 JGM-3 altitude m gs pn tx 1
alt dgme04 402 DGM-E04 altitude m e1 e2 2
alt cnes 404 CNES altitude m c2 n1 pn tx 3
alt pgs7777 410 PGS7777 altitude m g1 4
alt ggm02c itrf2000 411 GGM02c(ITRF2000) altitude m gs pn tx 5
alt ggm02c itrf2005 413 GGM02c(ITRF2005) altitude m pn tx 5
alt eiggl04s 414 EIGEN-GL04c altitude m j1 6
alt gdrcp 415 GDR-C’ altitude m g1 gs j1 j2 n1 pn tx 7
alt gps 416 GPS altitude m j1 j2 8
alt eig6c 417 EIGEN-6C altitude m c2 n1 9
alt eig6s2 417 EIGEN-6S2 altitude m j1 j2 9
alt gdrd 418 CNES GDR-D altitude m c2 j1 j2 n1 sa 10
alt std1204 419 GSFC/Std1204 altitude m j1 j2 pn tx 11
alt reaper 420 REAPER/COMBI altitude m e1 e2 12
alt reaper deos 421 REAPER/DEOS altitude m e1 e2 12
alt reaper gfz 422 REAPER/GFZ altitude m e1 e2 12
alt reaper esoc 423 REAPER/ESOC altitude m e1 e2 12
alt std1404 424 GSFC/Std1404 altitude m j2 13
alt gdre 425 CNES GDR-E altitude m c2 j2 j3 sa 14
alt slcci 426 GFZ/SLCCI altitude m e1 e2 j1 j2 n1 pn tx 15
alt 4alias of alt gdre alt cnes c2 16
alt 4alias of alt reaper alt gfz e1 e2 16
alt 4alias of alt pgs7777 g1 16
alt 4alias of alt gdrcp gs 16
alt 4alias of alt gdre j1 16
alt 4alias of alt gdre alt gdrd j2 16
alt 4alias of alt gdre j3 sa 16
alt 4alias of alt gdrd alt gdrcp n1 16
alt 4alias of alt gdrcp alt jgm3 pn tx 16
Notes:
1. JGM-3 [Tapley et al., 1996] was the original gravity field solutions used by NASA for the
orbit determination of Geosat and TOPEX/Poseidon that featured on the GDR products.
Though the gravity field was tailored to the TOPEX orbit, it was generally regarded the
best at the time and was hence also used more widely than just for TOPEX/Poseidon. The
radial orbit accuracy of about 3 cm for TOPEX/Poseidon and 8 cm for Geosat has since
been superseded by more up-to-date orbit solutions.
2. DGM-E04 was a gravity field model developed at the Delft University of Technology tai-
lored to the ERS-1 and ERS-2 orbits and derived from the JGM-3 model. The model signifi-
cantly improved the radial orbit accuracy to about 3.5 cm, better than any general purpose
models available at the time [Scharroo and Visser, 1998].
3. CNES produces the operational and precise orbits for a number of satellites. Unfortunately,
in RADS the CNES orbits are based on mix of gravity field solutions. The CNES orbits for
the TOPEX/Poseidon mission feature on the GDRs and are based on the JGM-3 gravity
field model [Tapley et al., 1996]. The CNES orbits for CryoSat and Envisat are obtained
from their respective GDR products and are based on the EIGEN-GL04c gravity field model
[Ablain et al., 2008].
4. PGS7777 is a NASA preliminary gravity field solution tailored to the Geosat and GFO
satellite orbits. The NASA PGS7777 orbit solution for GFO [Lemoine et al., 2006] is based on
SLR data only as the GPS tracking system on GFO failed.
6 Time and location
5. Orbit solutions created at NASA using the GGM02c gravity field and station coordinates
in the ITRF2000 or ITRF2005 reference frame.
6. Orbits produced with the EIGEN-GL04c or EIGEN-GL04s gravity fields.
7. Orbits produced under strict Jason GDR-C’ standards.
8. Reduced-dynamic (fast-delivery) orbits based on GPS tracking data only.
9. Orbits provided by ESOC using the EIGEN-6C or EIGEN-6S2 gravity field. The orbits are
available for parts of the various altimeter missions only: CryoSat-2 cycles 4-58, Envisat all
cycles, Jason-1 cycles 1-260, Jason-2 cycles 1-220.
10. Orbits produced by CNES under strict Jason GDR-D standards [International DORIS Ser-
vice, 2011]. These orbits are kept on Jason-2 data (until April 2015) and SARAL data (until
June 2015) for comparison, although the GDR-E orbits are now default. Jason-1 data does
not have GDR-E orbits yet.
11. Orbits provided by GSFC using their standards ”Std1204”. They are ”GDR-D compatible”
and use the goce2s fit2 gravity field [Lemoine et al., 2013].
12. Orbits produced by the REAPER project. A combined solution and individual solutions
created by DEOS (TU Delft), GFZ and ESOC are available [Rudenko et al., 2011].
13. Orbits provided by GSFC using their standards ”Std1404”. They are ”GDR-E compatible”.
14. Orbits produced by CNES under strict Jason GDR-E standards (baseline for the production
of orbits since April 2015) [International DORIS Service, 2015].
15. Orbits produced by GFZ in the framework of the ESA Sea Level CCI project. RADS initially
included version VER06 of these orbits (based on the EIGEN-6S2A gravity field model)
[Rudenko et al., 2014] for ERS-1, ERS-2, Envisat and TOPEX/Poseidon. Any data produced
since 18 May 2016 (which includes Jason-1 and Jason-2) incorporated version VER11 of
these orbits and are based on the EIGEN-6S4 gravity field model [Rudenko et al., 2015, 2016].
16. The variable alt refers to the preferred (best) orbit solution for each satellite. If two variable
names are mentioned, RADS picks the first one by preference; it that is not available, the
second variable is used.
2.4 Orbital altitude rate
The rate of change of the orbital altitude is relevant for computing the Doppler correction and
for correcting the altitude as a result of a time tag bias. Furthermore, the orbital altitude rate
can be used to estimate time tag biases.
Variable field name units sat range note
alt rate 5, 501 orbital altitude rate m/s all
Chapter 3
Sea level variables
3.1 Sea level anomaly
The sea level anomaly (SLA) or sea surface height anomaly (SSHA) is the height for the sea
surface relative to a long term mean. It can be constructed by subtracting from the satellite
orbital altitude the altimeter range, propagation corrections, sea state bias, tides, and a mean
sea surface model.
The sea level anomaly in RADS is always computed on-the-fly. This means that the RADS
software will gather the required variables and their selected flavours from the netCDF data
files, edit those data based on the user-selected criteria, and then constructs the sea level
anomaly based on those. If any of the variables that make up the sea level anomaly is not
available, marked invalid, or is out of range, then the sea level anomaly is also marked invalid
(set to the NaN value). In addition, variables that do not strictly make up the sea level anomaly
(like wave height or wind speed) can be used as edit criteria, e.g. rejecting data with high sea
states.
The rads.xml configuration file spells out, in reverse polish notation (RPN), how the sea
level anomaly (sla) is computed. The ’sea level equation’ is written as:
<data>
alt range SUB dry_tropo SUB wet_tropo SUB iono SUB
dac SUB tide_solid SUB tide_ocean SUB tide_load SUB
tide_pole SUB ssb SUB mss SUB ref_frame_offset SUB
</data>
where SUB stands for ’subtract’ and the others are names or aliases of the various variables
that make up the sea level anomaly. If any of these variables is NaN, the result is NaN as well.
The use of aliases comes in handy here. We can, for example, switch out the orbit solution
between one flavour and another simply by directing the alias alt from say alt jgm3 to alt
dgme04. We do not have to change anything to the sea level equation.
The configuration file also spells out which variables are used as ’quality flag’. That is, if
any of these variables is set to NaN, the sea level anomaly is also set to NaN, even though
these variables are not added or subtracted as part of the ’sea level equation’. For example:
<quality_flag>
swh sig0 range_rms range_numval flags
</quality_flag>
While the sea level anomaly variable sla is computed on-the-fly by the RADS software,
there is a second variable ssha that is already stored on the RADS products. Users can use this
variable directly as well, but then give up the opportunity to edit the results themselves.
7
8 Sea level variables
Variable field name units sat range note
sla 0 sea level anomaly m all -5 5 1
ssha sea level anomaly (precomputed) m all 2
Notes:
1. The limits on the sea level anomaly can be changed in the configuration file, of by using
the --sla=MIN,MAX option on the command line.
2. The variable ssha is read directly from the RADS products and already is screened by
editing.
3.2 Altimeter range
The range between the satellite and the sea surface is based on the total travel time of the
radar pulse divided by twice the speed of light. This range is then corrected for internal paths
within the instrument (internal calibration), variations in the frequency of the ultra-stable os-
cillator (USO correction), and the distance between the antenna and the satellite centre of mass
(centre-of-mass offset). The range is further corrected for the Doppler effect. As such the range
measures the distance between the satellite centre of mass and the sea surface, except for path
delays in the atmosphere and sea surface interactions.
To compute the height of the sea surface, we subtract the primary range (generally Ku-
band) from the satellite orbital altitude and then further correct for path delays and other
corrections.
Dual-frequency altimeters measure the range also on a secondary frequency (C- and S-
band) which allows for the computation of the ionospheric path delay directly for altimeter
observations rather than models.
Variable field name units sat range note
range ka 601 altimeter range (Ka) m sa
range ku 601 altimeter range (Ku) m other
range ku mle3 601 altimeter range (MLE3) m j2 j3 10
range c 602 altimeter range (C) m tx j1 j2 j3
range s 602 altimeter range (S) m n1
range 6alias of range ka sa
range 6alias of range ku other
Notes:
1. A constant bias of -124 mm is added to the Geosat range. An additional internal calibration
and USO correction comes from an external file [Brian Beckley, priv. comm., 2002].
2. The range of the ERS-1 and ERS-2 altimeters has been corrected for SPTR bias jumps and
USO drift based on offline tables [Martini and F´em´enias, 2000].
3. A constant bias of 409.2 mm is added to the ERS-1 range [Francis et al., 1993; Stum et al.,
1998].
4. The ERS-2 USO correction appears to be low during the periods 1997-07-26 20:24:04 to
1998-01-07 03:58:09 and 1998-03-17 11:10:48 to 2000-01-08 06:04:13. During both periods
4.77 mm is added to the USO correction (and to range).
5. The USO correction for Envisat is based on external USO correction files (1-Hz data): http:
//earth.esa.int/pcs/envisat/ra2/auxdata/
3.3 Altimeter range statistics 9
6. Even though the Envisat Ku- and S-band ranges have biases of the order of 45 cm, this is
not corrected for in the range, but in the reference frame offset (ref frame offset). A cor-
rection of 150 mm is added to the S-band range to account for a bias in the dual-frequency
ionosphere correction [Scharroo and Smith, 2010]. During the short operation of the Side B
altimeter, an additional 9 mm was added to both Ku- and S-band ranges.
7. S-band range for Envisat is available only until the loss of the S-band signal.
8. TOPEX Ku- and C-band ranges are corrected for internal path delays and oscillator drifts
based on external correction files (one value per cycle: http://topex.wff.nasa.gov/) [Hayne
et al., 1994]. An additional -15.4 mm is added to the C-band range to account for a bias in
the dual-frequency ionosphere correction [Scharroo and Smith, 2010].
9. A constant bias of -2.3 mm is added to the Jason-1 C-band range to account for a bias in the
dual-frequency ionosphere correction [Scharroo and Smith, 2010].
10. A separate range based on an MLE3 retracker is available for Jason-2 and -3.
3.3 Altimeter range statistics
The altimeter ranges are provided in RADS as (approximately) 1-second averages based on 10,
20, or 40 ’elementary’ measurements during that second. The altimeter range reported is, in
fact, not the statistical mean, but is generally determined as follows:
Interpolate the orbital altitude at the same time tags as the elementary measurements;
Subtract the orbital altitude from the range;
Fit a linear trend true ‘range minus orbit’;
Remove the linear trend;
Compute the standard deviation of the residuals (using n2 in the denominator) where n
is the number of elementary measurements during a 1-Hz period;
Evaluate the linear trend at the mid point and add the local orbital altitude back at this
point to obtain the average range.
Hence, technically, range rms is not the standard deviation of the altimeter range, but the stan-
dard deviation of the elementary ‘orbit minus range’ values with a trend fitted and removed
as well, and taking into account the extra degree of freedom. It should also not be confused
with an error estimate for the range measurement.
Variable field name units sat range note
range rms ka 2002 std dev of Ka-band range m sa 0.0 0.17
range rms ku 2002 std dev of Ku-band range m g1 gs 0.0 0.25
j1 0.0 0.2
j2 j3 0.0 0.17
tx 0.0 0.15
other 0.0 0.4
range rms c 2004 std dev of C-band range m j1 j2 j3 0.0 0.4
range rms s 2004 std dev of S-band range m n1 0.0 0.4 1
range rms 20 alias of range rms ka sa
range rms 20 alias of range rms ku other
10 Sea level variables
Note:
1. Standard deviation of S-band range for Envisat is available only until the loss of the S-band
signal.
Variable field name units sat range note
range numval ku 2101 nr of valid Ka-band measurements sa 33 40
range numval ku 2101 nr of valid Ku-band measurements g1 gs tx 9 10
j1 j2 j3 16 20
other 17 20
range numval c 2102 nr of valid C-band measurements j1 j2 j3 17 20
range numval 21 alias of range numval ka sa
range numval 21 alias of range numval ku other
3.4 Dry tropospheric correction and air tide
The dry tropospheric correction (the negative of the zenith hydrostatic delay, ZHD) accounts
for the delay of the radar signal in the atmosphere, not counting the effect of water vapour.
This effect is non-dispersive, i.e., it is the same on all frequencies, and is proportional to the
surface pressure [Saastamoinen, 1972]. The surface pressure, in turn, is determined by interpo-
lation (in space and time) of model grids of surface (or sea level) pressure (see Notes). Most
altimeter missions provide the ECMWF operational analysis models as baseline.
Generally, the temporal spacing of the model grids is 6 hours, while the spatial resolu-
tion varies. The 6-hourly interval between successive model grids hampers the capturing of
12-hourly and 24-hourly phenomena. Common 24-hourly traveling waves turn into standing
waves, while 12-hourly variations are insufficiently described. These phenomena are consid-
ered S1 and S2 air tides [Ponte and Ray, 2002].
To remedy this problem an air tide correction is applied to the sea level pressure, by remov-
ing the air tide from the 6-hourly grids before spatio-temporal interpolation and then adding
the air tide back for the time and location of the measurement. This correction is already
applied to the ECMWF dry tropospheric correction on the Jason-2 GDRs; for all others it is
corrected in the RADS processing.
Variable field name units sat range note
dry tropo ecmwf 701 ECWMF dry tropo corr m all but g1 gs -2.4 -2.1 1
dry tropo ncep 702 NCEP dry tropo corr m all -2.4 -2.1 2, 4
dry tropo era 709 ERA Interim dry tropo corr m all -2.4 -2.1 3, 4
dry tropo airtide 4901 air tide corr m c2 e1 j1 pn tx 5
dry tropo 7alias of dry tropo era g1 gs
dry tropo 7alias of dry tropo ecmwf other
Notes:
1. The pressure fields on which this correction is based are from the ECMWF operational
analysis runs. That means that numerous changes to the models and their resolution create
an unstable reference for long-term studies. Please verify any of the ECMWF model results
against the other models. Because of an unfortunate choice to use surface pressure fields,
instead of sea level pressure fields, for this correction, coastal areas may be affected by
“leaking” of the effect of higher terrain over land (thus lower pressure) into the sea and
ocean (where there should be no terrain effect).
3.5 Wet tropospheric correction 11
2. The NCEP reanalysis model used for this correction has not gone through any updates,
so from that point of view the correction should be consistent over time. However, this
older model also is less accurate and has a lower resolution (2.5×2.5) than the ECMWF
analysis fields.
3. The ERA (ECMWF ReAnalysis) Interim model provides an excellent long-term consistency.
The data is distributed by ECMWF as Gaussian grids with an approximately uniform spac-
ing of 79 km [Berrisford et al., 2011].
4. NCEP and ERA sea level pressure grids are interpolated and, over land and lakes, the pres-
sure is then corrected for altitude based on a high-resolution terrain model, thus avoiding
the coastal contamination seen in the ECMWF dry tropospheric correction.
5. The air tide correction to the dry tropospheric correction is provided for reference only. It
is already applied to all variations of the dry tropospheric correction (ECMWF, NCEP, ERA
interim).
3.5 Wet tropospheric correction
The wet tropospheric correction (the negative of zenith wet delay, ZWD) accounts for the delay
of the radar signal in the atmosphere due to the presence of water vapour. This effect is non-
dispersive, i.e., it is the same on Ku-, S- and C-band frequencies, and it can be determined by
integrating a function of water vapour density and temperature [e.g., Askne and Nordius, 1987],
which to good approximation simplifies to
ZWD IWVRw(k3/Tm+k0
2)
where IWV is integrated water vapour, Tmis the mean temperature in the atmospheric column,
and Rw,k0
2and k3are constants [Bevis et al., 1994]. The mean temperature can further be
approximated from model data of near-surface air temperature, Ts, by
Tm50.4+0.789Ts
[Mendes et al., 2000]. In this way, the 6-hourly models of integrated water vapour and near-
surface temperature are interpolated in space and time and then converted to a wet tropo-
spheric correction. Generally, the temporal spacing of the model grids is 6 hours, but since the
power of the water vapour content at 12 and 24 hours is low, an air tide correction like in case
of the pressure is not needed.
Because of the large spatial and temporal variability (much more so than pressure), most
altimeter missions are equipped with passive microwave radiometers, collecting brightness
temperatures of the ocean surface at two or three frequencies. Using the three frequencies, or
two frequencies plus the radar altimeter backscatter measurement, a wet tropospheric correc-
tion (and some related variables) can be derived. However, the radiometer measurements are
strongly affected by land in the measurement footprint (extending to a radius of 40 km in some
cases), limiting its use in coastal regions, hence the existence of a radiometer land flag.
The radiometer wet tropospheric correction for ERS-1, ERS-2, and Envisat is based
on the altimeter backscatter coefficient (before correction for atmospheric attenuation) and
the radiometer brightness temperatures at 23.8 GHz and 36.5 GHz, using all the same
neural network algorithm as was developed for Envisat [Labroue and Obligis, 2003]. For
TOPEX/Poseidon, Jason-1 and -2, the radiometer wet tropospheric correction is derived from
their respective 3-channel brightness temperatures using multi-layer parametric algorithms
[Keihm et al., 1995; Dumont et al., 2001]. For GFO, the the 2-channel brightness temperatures are
12 Sea level variables
combined with the altimeter wind speed using a log-linear model [Ruf et al., 1996]. See Sec-
tions 4.2 and 5.1 for information about corrections applied to the backscatter and brightness
temperatures prior to evaluating the models.
Variable field name units sat range note
wet tropo rad 801 radiometer wet tropo corr m n1 -0.6 0.05 1
pn tx -0.6 -0.001 2
e2 -0.6 0.0 3
e1 g1 j1 j2 j3 -0.6 0.0
wet tropo ecmwf 802 ECMWF wet tropo corr m all but g1 gs -0.6 0.0 4
wet tropo ncep 803 NCEP wet tropo corr m all -0.6 0.0 5
wet tropo nvap 804 NASA NVAP wet tropo corr m gs -0.6 0.0 6
wet tropo tovs ssmi 805 TOVS/SSMI wet tropo corr m gs -0.6 0.0 7
wet tropo tovs ncep 807 TOVS/NCEP wet tropo corr m gs -0.6 0.0 8
wet tropo era 809 ERA interim wet tropo corr m all -0.6 0.0 9
wet tropo 8alias of wet tropo ecmwf c2 10
wet tropo 8alias of wet tropo era gs 10
wet tropo 8alias of wet tropo rad wet tropo era g1 10
wet tropo 8alias of wet tropo rad wet tropo ecmwf other 10
Notes:
1. Because of model bias and noise, allow positive values.
2. The TOPEX Microwave Radiometer (TMR) measurements values come from the TMR
Replacement Product, version 1.0 (https://podaac.jpl.nasa.gov/dataset/TOPEX L2 OST
TMR Replacement). The upper limit of -0.001 m for the radiometer wet tropospheric cor-
rection is needed to exclude bogus zero values.
3. ERS-2 MWR is not available from 2009-05-04 to 2010-01-15.
4. The ECMWF correction is actually computed by integrating several layers of the atmo-
sphere, rather than using the simplified form discussed above. The meteorological fields
on which this correction is based are from the ECMWF operational analysis runs. That
means that numerous changes to the models and their resolution create an unstable ref-
erence for long-term studies. Please verify any of the ECMWF model results against the
other models.
5. NCEP reanalysis model used for this correction has not gone through any updates, so from
that point of view the correction should be consistent over time. However, this older model
also is less accurate and has a lower resolution (2.5×2.5) than the ECMWF analysis fields.
6. The NASA NVAP model is an old water vapour model used in the Geosat era.
7. TOVS and SSMI are separate radiometer constellations whose data were used to model the
water vapour content globally.
8. This the result of a merger of the TOVS radiometer data with the NCEP water vapour and
near-surface temperature models.
9. The ERA (ECMWF ReAnalysis) Interim model provides an excellent long-term consistency.
The data is distributed by ECMWF as Gaussian grids with an approximately uniform spac-
ing of 79 km [Berrisford et al., 2011].
10. CryoSat-2 and Geosat have no radiometer, so the wet tropospheric correction is always
based on models. For the other missions we may default to the model if the radiometer
correction is not at all available on the product. This would be the case when the radiometer
3.6 Ionospheric correction 13
is permanently switched off, or off for a long time. The model will not be used during short
outages.
3.6 Ionospheric correction
The radar signal is also delayed by ions and electrons in the upper layers of the atmosphere
(the ionosphere). The delay is inversely proportional to the altimeter radar frequency, and
otherwise proportional to the vertically integrated electron density, known as total electron
content or TEC. This dispersive nature allows dual-frequency altimeters (TOPEX, Envisat,
Jason-1 and -2) to directly determine the ionospheric path delay on either frequency. For sin-
gle frequency altimeters we rely on TEC estimates from models based on other dual-frequency
equipment (GPS, DORIS) or climatologies.
Variable field name units sat range note
iono alt 901 dual-frequency iono corr m n1 j1 j2 j3 tx -0.4 0.04 1
iono alt smooth 903 smoothed dual-freq iono corr m n1 j1 j2 j3 tx -0.4 0.04 2
iono bent 902 Bent iono corr m c2 e1 e2 n1 pn tx -0.4 0.04 4
iono doris 904 DORIS iono corr m n1 pn tx -0.4 0.04 5
iono gim 906 JPL GIM iono corr m all but gs e1 pn -0.4 0.04 6
iono iri2007 907 IRI2007 iono corr m all -0.4 0.04 7
iono nic09 908 NIC09 iono corr m all -0.4 0.04 8
iono alt mle3 911 dual-freq iono corr (MLE3) m j2 j3 -0.4 0.04 3
iono alt smooth mle3 913 smoothed d-f iono corr (MLE3) m j2 j3 -0.4 0.04 3
iono 9alias of iono alt smooth
iono gim iono nic09 j1 j2 j3 n1 tx 9
iono 9alias of iono gim iono nic09 c2 e2 g1 sa 9
iono 9alias of iono nic09 e1 gs pn 9
Notes:
1. Positive correction values are allowed to account for noise in the altimeter dual-frequency
ionospheric correction. The values for this correction have been adjusted from the original
GDR products to account for relative C-band and S-band biases (Section 3.2).
2. Because of the relatively large noise in the dual-frequency ionospheric correction, iono alt
is smoothed over 35 seconds along the altimeter track (appoximately 250 km), in contrast
to the 21 seconds suggested by Imel [1994].
3. For Jason-2 and -3, a separate dual-frequency correction based on the MLE3 retracker is
available.
4. The “ancient” Bent TEC climatology [Llewellyn and Bent, 1973] should no longer be used.
5. The TEC model based on DORIS featured for a while on TOPEX/Poseidon and Envisat al-
timeter products but was far behind the accuracy of the (similar in construct) GPS-derived
GIM models.
6. JPL produces, based on the global constellation of GPS satellites and IGS GPS receivers,
2-hourly maps of TEC, known as the JPL GIM model [Komjathy et al., 2000]. The models
have a spatial resolution of 5×2.5and start in August 1998.
7. The International Reference Ionosphere went through several versions, the latest of which
is IRI2007 [Bilitza and Reinisch, 2008]. Because of its rather coarse spatial and temporal
resolution, it cannot compete with the JPL GIM model, or even the NIC09 climatology.
14 Sea level variables
8. The NIC09 climatology is based on 12 years of JPL GIM maps and can be extended as
far back as the 1950s or extrapolated using predicted solar flux values [Scharroo and Smith,
2010]. This model is particularly useful for the period prior to August 1998 (before the
availability of iono gim).
9. The alias iono with use either the smoothed dual-frequency ionospheric correction or one
of the models in the order as given.
3.7 Atmospheric (inverse barometer) correction
The inverse barometer (IB) correction accounts for the suppression of sea level due to higher
sea level pressure, and its rise during lower sea level pressure. When we assume a linear
relation between pressure and suppression we talk about a ”static” IB correction, and because
the sea level goes down with increasing pressure, opposite to the way we think of a mercury
barometer, we use the term ”inverse”. The simplest form of this correction is:
IB C(pp0)
where Cis -9.948 mm/mbar, pis the sea level pressure and p0is a reference pressure. Since
the global ocean as a whole is incompressible, p0here stands for the global mean sea level
pressure over oceans.
However, there are also dynamics involved in this. For example, an ocean basin cannot
instantly be suppressed as a whole because of rapidly increasing regional pressure. Also wind
can play a role as well. Hence, the static correction is too simplistic. A more accurate model in-
cludes wind and ocean dynamics and is hence called a dynamic atmospheric correction (DAC).
RADS, AVISO, and other altimeter datasets include the dynamic atmospheric correction pro-
duced by CLS Space Oceanography Division using the MOG2D model from Legos [Carr`ere and
Lyard, 2003; Roblou et al., 2008] and distributed by AVISO, with support from CNES. A more re-
cent update of the MOG2D correction files is based on forcing by the ERA Interim atmospheric
models. Those have been made available for the period 1991 through 2015 [Carr`ere et al., 2016].
Over inland waters, this correction should not be applied [Cr´etaux and Birkett, 2006].
Variable field name units sat range note
inv bar static 1001 static inverse baro corr m all -1 1 1
inv bar global 1002 global mean inv baro corr m all -1 1 2
inv bar mog2d 1003 MOG2D dynamic atmospheric corr m all -1 1 3
inv bar mog2d era 1003 MOG2D DAC from ERA-Int. m all -1 1 4
inv bar mok2d 1004 MOK2D dynamic atmospheric corr m all -1 1 5
inv bar mog2d mean 1005 local mean of MOG2D m all -1 1 6
inv bar 10 alias of inv bar mog2d era inv bar mog2d all 7
dac inv bar mok2d 7
Notes:
1. This correction is the simplification explained above.
2. For reference the global mean pressure, converted to an IB correction, Cp0, is provided.
3. The dynamic atmospheric correction model MOG2D includes the ocean dynamic response
to wind and pressure forcing. It also accounts for the aliasing of the air tides as discussed
in Section 3.4. MOG2D also comes with two latencies: (a) a few days, and thus features on
the IGDR-derived products, and (b) a few weeks, and thus features on the GDR-derived
3.9 Ocean and load tide 15
products. For fast-delivery products there is generally no MOG2D correction (so MOK2D
will be used), but the MOG2D field will be filled in as soon as the MOG2D maps have been
distributed.
4. The DAC based on forcing by the ECMWF ERA Interim atmospheric reanalysis model is
available as inv bar mog2d era. When chosing the detail DAC (either dac or inv bar) this
model is used for the years 1991 through 2015. After that inv bar mog2d or inv bar mok2d.
5. Since the MOG2D only start in 1992, we have created a “mock-up” version of MOG2D
(a simple linear variant of inv bar static) to match the mean of MOG2D as to not disturb
continuity of the DAC prior to 1992..
6. For reference, the local long-term mean of the MOG2D correction is provided. This is the
interpolation of a static grid computed as the average of all MOG2D maps over the period
1993-2009.
7. When using the inv bar or dac alias, inv bar mog2d era is used when available (1991-2015),
otherwise inv bar mog2d is used, or automatically defaulting to inv bar mok2d.
3.8 Solid earth and pole tide
The solid earth tide is the variation of the elevation of the crust of the earth surface as a result
of the attraction by the sun and moon (other planets are generally ignored as their influence is
at least an order of magnitude smaller). As per geodetic convention the ”permanent tide” (the
mean tide resulting from the mere presence of the sun and moon) is excluded from the solid
earth tide but is included in the geoid.
The RADS implementation of the Cartwright-Taylor-Edden solid earth tide model includes
386 second order waves and 99 third order waves [Cartwright and Taylor, 1971; Cartwright and
Edden, 1973].
The pole tide is the vertical deformation of the earth crust as a result of polar motion.
We can visualise this as the ellipsoidal shape of the earth being moved as the rotation axis
of the earth moves away or closer to the mean pole [Munk and MacDonald, 1960]. We use
the IERS(EOP)05C04 earth orientation parameters and their predictions. The motion of the
mean pole, describing the effect of global isostatic adjustment (GIA) is represented by a linear
motion, as suggested by Wahr et al. [2015] following the work of Argus and Gross [2004]. The
implicit effect on pole tide is further discussed by Desai et al. [2015].
Two Love numbers multiply the results of this simple equilibrium model: (1 + k2)= 1.302
for the combination of solid earth and oceans, and h2= 0.609 for the solid earth only (over land
and lakes) [Wahr, 1985].
Variable field name units sat range note
tide solid 1101 solid earth tide m all -1 1
tide pole 1401 pole tide m all -0.1 0.1
11 alias of tide solid all
14 alias of tide pole all
3.9 Ocean and load tide
The (pure) ocean tide is the variation of the height of the water column as a result of luni-solar
attraction. Since this is measured relative to a fixed point on the solid earth (like the sea floor),
the ocean tide compares directly to tide gauge measurements. The load tide is the effect of
16 Sea level variables
the tides weighing on the elastic earth. In general, when the ocean tide goes up, the sea floor
is going down, hence reducing the sum of the two, the geocentric ocean tide. Consequently,
in order to detide the measurement of the water surface from altimetry, both the ocean tide
(tide ocean) and the load tide (tide load) need to be subtracted over ocean surfaces, while over
lakes only the load tide is subtracted.
Although some altimeter products provide the geocentric ocean tide, we have chosen to
keep ocean and load tide separate, for two reasons: (1) it makes it easier to differentiate tides
over land/lakes and ocean, and (2) it makes it easier to add regional tides which are generally
expressed as pure ocean tides.
Tides are expressed as the sum of a (large) number of waves with different frequencies, each
combinations of the frequencies associated with the rotation and progression of the earth, sun
and moon. A select portion of those waves are expressed as grids of amplitude and phase,
which can be interpolated in space and evaluated at the time of the altimeter measurement.
Then, by a process called admittance, the amplitude and phase of a number of minor tides are
inferred.
Tide models generally only include the diurnal and semi-diurnal waves (those around
a period of 12 and 24 hours). On top of that there are secondary and tertiary waves with
periods of a week or longer. Most of these waves can be expressed by a simple latitude and
time dependent equilibrium model (the long-period equilibrium tide). The FES ocean tide
models also include the non-equilibrium component of the monthly, fortnightly, tri-monthly,
and weekly tides.
Although the FES and GOT models are global, they are limited in scope. Over land, the
ocean tide is not defined and will be set to the NaN value. In some coastal regions, where the
models may likely not be defined, the ocean tide value is set to NaN as well.
For a very elaborate and thorough accuracy assessment of most of these tide models, we
highly recommend reading the work by Stammer et al. [2014].
Variable field name units sat range note
tide ocean fes04 1213 FES2004 ocean tide m all -5 5 1
tide load fes04 1313 FES2004 load tide m all -0.5 0.5 1
tide ocean webtide 1215 WebTide ocean tide m all -5 5 2
tide ocean got48 1219 GOT4.8 ocean tide m all -5 5 3
tide load got48 1319 GOT4.8 load tide m all -0.5 0.5 3
tide ocean got410 1222 GOT4.10c ocean tide m all -5 5 4
tide load got410 1322 GOT4.10c load tide m all -0.5 0.5 4
tide ocean fes14 1224 FES2014b ocean tide m all -5 5 5
tide load fes14 1324 FES2014a load tide m all -0.5 0.5 5
tide equil 3901 long-period equilib. tide m all -1 1 6
tide non equil 3902 long-period non-equil. tide m all -1 1 7
tide ocean 12 alias of tide ocean got410 all
tide load 13 alias of tide load got410 all
39 alias of tide equil all
Notes:
1. The Finite Element Solution (FES2004) includes 9 short-period waves (Q1, O1, K1, P1, 2N2,
N2, M2, K2, and S2) plus 24 additional short-period waves determined by admittance, and
4 long-period waves (Mf, Mm, Mtm and MSqm) [Lyard et al., 2006]. Long-period equilib-
rium tides (not yet included in FES2004) are added to these.
2. WebTide is a collection of regional tide models along the Canadian coast, made
available on the web by the Bedford Institute of Oceanography http://www.bio.gc.ca/
3.10 Sea state bias 17
research-recherche/WebTide-MareeWeb/webtide-eng.htm. In RADS, a single value is pre-
sented in tide ocean webtide, determined from (in order of decreasing preference) the re-
gional models for: Scotia/Fundy/Maine, Northeast Pacific, Arctic, Hudson Bay. Each
of these models has a very limited amount of constituents, does not include admittance
to infer others, and also does not include any long-period tides (equilibrium or non-
equilibrium).
3. The Goddard Ocean Tide model GOT 4.8 includes 10 short-period waves (K1, O1, P1, Q1,
S1, K2, M2, N2, S2, and M4) plus 17 additional short-period waves determined by admit-
tance [Ray et al., 2011].
4. The Goddard Ocean Tide model GOT4.10c differs from GOT4.8 in two ways. First,
GOT4.10c is based only on Jason data, whereas 4.8 was from only TOPEX data. Second,
it includes an adjustment for the geocenter. (Hence the ’c’ in the name.) The processing
for 4.8 and 4.10 was largely identical; one exception involved an improvement to P1 alone.
GOT4.10c is an update of Ray [2013]. This model is now the default tide model.
5. FES2014 (Finite Element Solution 2014) takes advantage of longer altimeter time series, im-
proved modelling, and data assimilation techniques, and more accurate and higher reso-
lution ocean bathymetry. A new global finite element grid (with approx. 1.5 million nodes)
was used to create a ’free’ solution (independent of in situ and remote-sensing data) that
has more than twice the accuracy of the FES2004 version. The ’free’ solution was assim-
ilated with long-term altimetry data from TOPEX/POSEIDON, Jason-1, Jason-2, ERS-1,
ERS-2, and ENVISAT. The FES2014 solution shows particular improvement in coastal and
shelf regions, as well as in overall open ocean statistics, particularly due to a higher grid
resolution (1/16). The model is also extended into the coast to improve the data coverage.
As with other FES models tide ocean fes14 includes long-period non-equilibrium tides;
specifically: Mf, Mm, Mtm, MSf, MSqm, Sa, and Ssa.
6. The equilibrium ocean tide includes 15 tidal spectrum lines from the Cartwright-Tayler-
Edden tables [Cartwright and Taylor, 1971; Cartwright and Edden, 1973] plus an additional
123 second and third order waves [Tamura, 1987].
7. The four long-period non-equlibrium ocean tidal components (Mm, Mf, MSf, Mtm, and
MSqm) from the FES2004 model are represented in tide non equil. The equilibrium parts
of those have been removed to avoid double accounting.
3.10 Sea state bias
Sea state bias (SSB) is the term used for any altimetric range offset as a function of the sea state
(wave height, wind speed, wave age, swell). In fact, there are three components to the sea state
bias:
Electromagnetic (EM) bias is the tendency of a radar to measure towards the wave troughs
since they are better reflectors than the wave peaks.
Skewness bias comes from the fact that the sea surface has a skewed height distribution.
While the altimeter measures the median height of the surface in the footprint, what we
want to measure is the mean height, which is lower.
Tracker bias is the any error in the waveform tracker that is a function of the sea state, which
may be either instrumental or algorithmic.
Because of the instrumental part of sea state bias, every altimeter, in principle, requires
a different sea state bias correction model. But also when a new tracker algorithm is imple-
18 Sea level variables
mented, the sea state bias changes. This is one of the reasons for the wide proliferation of SSB
models.
In the earlier days of altimetry the sea state bias was generally considered as a simple frac-
tion, around –3.5%, of significant wave height [Chelton, 1994]. Then Gaspar et al. [1994] brought
a major improvement in SSB modelling by expressing SSB as a polynomial function of SWH
and wind speed, recognising that not only the wave height, but also the shape of the waves (al-
tered by wind) has influence on the altimeter range bias. But this approach still exhibits some
limitations, in that it imposes the type of variations that are allowed as a result of changes in
SWH or wind speed. Currently, SSB models tend to be non-parametric, generally expressed
in the form of a grid with SWH and wind speed as coordinates [e.g., Gaspar and Florens, 1998].
But several more complex multi-dimensional models are currently under development [e.g.,
Feng et al., 2010; Tran et al., 2010].
Variable field name units sat range note
ssb bm3 1501 parametric sea state bias m e1 e2 g1 gs pn tx -1 1 1
ssb cls 1502 CLS non-parametric SSB m j1 j2 j3 n1 tx -1 1 2
ssb csr 1503 CSR BM4 sea state bias m tx -1 1 3
ssb hyb 1504 NOAA hybrid sea state bias m g1 gs n1 pn sa -1 1 4
ssb cls c 1505 CLS non-parametric SSB (C-band) m j1 j2 j3 -1 1 2
ssb cls mle3 1512 CLS non-parametric SSB (MLE3) m j2 j3 -1 1 5
ssb tran2012 1513 Tran et al. 2012 non-parametric sea state bias m j2 j3 -1 1 6
ssb 15 alias of ssb bm3 e1 e2 pn
ssb 15 alias of ssb cls j1 j2 j3 n1 tx
ssb 15 alias of ssb hyb c2 g1 gs sa
Notes:
1. One-, three- or four-term polynomials of SWH and wind speed [Gaspar et al., 1994].
2. Non-parametric sea state bias models for Ku-band and C-band by CLS [Gaspar et al., 2002;
Labroue et al., 2004].
3. Four-term sea state bias model for TOPEX (different model for side A and side B altimeters)
[Chambers et al., 2003].
4. Hybrid (mix between parametric and non-parametric techniques) sea state bias models
produced at NOAA [Scharroo and Lillibridge, 2005].
5. For Jason-2 and -3 data retracked by MLE3, a separate non-parametric model is available.
6. The non-parametric SSB model developed by Tran et al. [2012] will be part of the upcoming
GDR-E standards.
3.11 Mean sea surface and geoid
The sea level anomaly (SLA) is expressed as the difference of the instantaneous tide-corrected
sea surface with respect to a well-established mean. Over the years several (more or less)
global mean sea surface models have been developed from the compilation of satellite altime-
ter (and sometimes gravity) data. Generally, the more altimeter data collected, the more precise
the model. But also the resolution of the model counts. Each model has been referenced to the
TOPEX reference ellipsoid, just as the satellite orbits (Section 2.3).
Another reference surface is the geoid (the theoretical mean sea surface in absence of ocean
currents, wind, etc.). Geoid models are generally made from satellite tracking data (for the
3.11 Mean sea surface and geoid 19
longer wave lengths), GRACE and/or GOCE (for the medium wave lengths), and altimetry
and in-situ gravimetry (for the shorter wave lengths).
Variable field name units sat range note
mss cnescls11 1614 CNES-CLS11 mean sea surface m all -200 200 1
mss cnescls15 1619 CNES-CLS15 mean sea surface m all -200 200 2
mss dtu13 1616 DTU13 mean sea surface m all -200 200 3
mss dtu15 1618 DTU15 mean sea surface m c2 j2 j3 sa -200 200 4
geoid egm2008 1611 EGM2008 geoid m all -200 200 5
geoid eigen6 1617 EIGEN-6C3stat geoid m all -200 200 6
mss 16 alias of mss dtu13 all
geoid alias of geoid eigen6 all
Notes:
1. Iteration of mean sea surface models at CNES/CLS from 2011, based on altimeter data
from 1993 to 1999 [Schaeffer et al., 2012].
2. Latest iteration of mean sea surface models at CNES/CLS from 2015, based on altimeter
data from 1993 to 2012 [Schaeffer et al., 2012].
3. The DTU13MSS is the penultimate release of the global high-resolution mean sea surface
from DTU Space, which includes two major advances over DTU10MSS. First, the time se-
ries have been extended to 20 years from 17 years. Second, the DTU13MSS ingests Cryosat-
2 SAR lead data in order to map the high latitude parts of the Arctic Ocean. In high-latitude
regions a combination of joint ERS-1/ERS-2/ENVISAT and Cryosat-2 altimetry have been
used. Also, the Jason-1 geodetic mission has been used for the DTU13MSS [Andersen et al.,
2013]. This is the default mean sea surface model and is used as reference for the sea level
anomaly variable (sla).
4. The DTU15MSS is the latest state-of-the-art of the global high-resolution mean sea surface
derived by DTU Space from satellite altimetry. The main improvement over DTU13 is
the inclusion of four years of CryoSat-2 data, with a new treatment of orbit errors and ice
classification [Stenseng et al., 2015]. This model is intended to replace in the near future
DTU13 as the default mean sea surface.
5. Combined geoid and mean sea surface solution from the US Defence Mapping
Agency[Pavlis et al., 2012].
6. The EIGEN-6C3stat geoid model has been generated in preparation for the final release of
EIGEN-6C4. It was computed from a combination of LAGEOS, GRACE, and GOCE data,
augmented with DTU13 surface gravity data to degree and order 1949 (corresponding to
approximately 10 km spatial resolution). [F¨orste et al., 2013; Shako et al., 2014]. This is the
default geoid model.
Chapter 4
Wind speed and wave height variables
4.1 Significant wave height
The significant wave height (SWH) is generally defined as the mean wave height (peak to
trough) of the highest one-third of the ocean waves. Another commonly used definition is
four times the standard deviation of the elevation of the sea surface in the radar footprint.
SWH is determined from the rate of increase of returned power of the radar altimeter pulse
(the waveform slope) and requires no further correction other than some instrument parame-
ters. There is one complexity in this, and that is that SWH is defined as follows:
SWH2α2(σ2
cσ2
p)
where σcis a measure of the waveform slope and σpis an instrument parameter, and αis a con-
stant. Because of noise in the measurement of σcand a possible bias in σp, SWH2could become
negative. In most GDR products, SWH is then set to zero, which creates a wrongly truncated
measurement, and makes it difficult to correct for any biases in SWH the measurement (which
would raise the zero SWH above zero). Where we can, however, in RADS, this case is tackled
by writing out the negative of the square root of the absolute value of the argument instead.
Hence:
SWH αqσ2
pσ2
cwhen σc< σp
SWH αqσ2
cσ2
pwhen σcσp
(See Note 1 below).
The 1-Hz standard deviation of SWH is determined from the individual 10-, 20-, or 40-Hz
elementary measurements. Note that this is the standard deviation of the elementary mea-
surements (denominator is (n1)), not an estimate of the error of SWH.
20
4.2 Altimeter backscatter coefficient 21
Variable field name units sat range note
swh ka 1701 Ka-band significant wave height m sa 0 8
swh ku 1701 Ku-band significant wave height m c2 n1 -0.5 8 1,2
other 0 8
swh c 1702 C-band significant wave height m j1 j2 j3 tx 0 8
swh s 1702 S-band significant wave height m n1 -0.5 8 1,3
swh ku mle3 1711 Ku-band significant wave height (MLE3) m j2 j3 0 8 4
swh ww3 1712 WAVEWATCH3 significant wave height m c2 j2 j3 sa 5
swh rms ka 2802 std dev of Ku-band SWH m sa 0 2.1
swh rms ku 2802 std dev of Ku-band SWH m g1 0 0.5
j1 j2 j3 0 1.5
tx 0 0.9
other 0 2.1
swh rms c 2804 std dev of C-band SWH m j1 j2 j3 tx 0 2.1
swh rms s 2804 std dev of S-band SWH m n1 0 2.1 3
swh 17 alias of swh ka sa
swh 17 alias of swh ku other
swh rms 28 alias of swh rms ka sa
swh rms 28 alias of swh rms ku other
Notes:
1. The SWH of Envisat and CryoSat will be set to a negative value when σc< σp. For other
missions, the value is set to 0.
2. During the degradation of the TOPEX SWH measurements (cycles 98-235) the SWH values
are corrected according to Queffeulou [2004]. Thereafter 32 mm was added.
3. S-band SWH for Envisat is only until the loss of the S-band signal.
4. For Jason-2 and -3, significant wave heights from the MLE3 retracker are available as well.
5. The SWH in this field is based on wave hindcasts done by NOAA using the WAVE-
WATCH III model [Tolman, 2009] and GFS analysis winds [Chawla et al., 2011]. The hind-
casts cover the entire globe and are carried out in monthly installments, so they are only
available on delay-time data in RADS. The original resolution of these model grids is 1by
1by 6 hours. For other WAVEWATCH III fields see Section 4.4.
4.2 Altimeter backscatter coefficient
The backscatter coefficient is derived from the total returned power of the radar altimeter
pulse. After correction for losses due to water vapour in the atmosphere, it identifies the small
scale ripples on the sea surfaces, and hence becomes a measure for wind speed (Section 4.3).
The correction for atmospheric losses is generally determined from the radiometer mea-
surements. In case the brightness temperatures were corrected with respect to the GDR values
(Section 5.1), so is the wet tropospheric correction, the atmospheric correction to the backscat-
ter and the backscatter coefficient itself.
The 1-Hz standard deviation of backscatter coefficient is determined from the individual
10-, 20- or 40-Hz elementary measurements. Note that this is the standard deviation of the ele-
mentary measurements (denominator is (n1)), not an estimate of the error of the backscatter
coefficient.
22 Wind speed and wave height variables
Variable field name units sat range note
sig0 ka 1801 Ka-band backscatter coefficient dB sa 6 27
sig0 ku 1801 Ku-band backscatter coefficient dB other 6 27 1-6
sig0 c 1802 C-band backscatter coefficient dB j1 j2 j3 tx 6 27 3-6
sig0 s 1802 S-band backscatter coefficient dB n1 6 27 7
sig0 ku mle3 1811 Ku-band backscatter coefficient (MLE3) dB j2 j3 6 27 8
sig0 rms ka 2902 std dev of Ka-band SWH dB sa 0 1
sig0 rms ku 2902 std dev of Ku-band SWH dB all 0 1
sig0 rms c 2904 std dev of C-band SWH dB j1 j2 j3 tx 9
sig0 rms s 2904 std dev of S-band SWH dB n1 7
sig0 18 alias of sig0 ka sa
sig0 18 alias of sig0 ku other
sig0 rms 29 alias of sig0 rms ka sa
sig0 rms 29 alias of sig0 rms ku other
Notes:
1. The ERS-1 backscatter coefficient is corrected for varying biases due to the attitude control.
Between 0 and 0.35 dB was added.
2. The GFO backscatter is corrected for a few deficiencies in lookup tables, adding 0.37 dB
before 6 Dec 2000 and between 7 and 9 March 2001.
3. The Jason-1 Ku- and C-band backscatter are aligned with TOPEX values by subtracting
2.40 and 0.725 dB, respectively. For the time being, the same biases are applied to Jason-2.
However, wind speed values are not adjusted.
4. The Jason-2 Ku- and C-band backscatter are reduced in noise based on a correlation with
off-nadir angle [Quartly, 2009]. In addition, biases of 2.40 and 0.725 dB have been removed
from the Ku- and C-band backscatter, respectively.
5. The Jason-3 Ku- and C-band backscatter are adjusted the same way as Jason-2.
6. TOPEX backscatter is corrected following the off-line Wallops correction tables.
7. S-band backscatter for Envisat is only until the loss of the S-band signal.
8. For Jason-2 and -3, the backscatter coefficient from the MLE3 retracker is available as well.
9. For TOPEX, the standard deviation is that of the automatic gain control, not of the backscat-
ter coefficient. That means that the variation of the total volume of the waveform in not
included, only the variation of the gain setting of the instrument.
4.3 Wind speed
Wind speed can be derived from the altimeter backscatter coefficient (Section 4.2). The larger
the backscatter, the lower the wind speed. Several models have been developed to map this
relationship, some depending merely on backscatter, some also taking into account significant
wave height. Which models are applied to which satellites is shown in the notes below.
Three-channel radiometers provide the opportunity to estimate wind speed. In essences
this is the reverse side of the fact that one of those channels can be replaced with the altimeter
wind speed to obtain the wet tropospheric correction. This variable is available for all missions
with 3-channel radiometers.
Atmospheric models, like those at ECMWF also provide wind speed and wind directions,
or their vectorial components pointing north and east. Those model values are provided for
some missions as well.
4.4 Other wave model data 23
Variable field name units sat range note
wind speed alt 1901 altimeter wind speed m/s all -1 30 1-5
wind speed rad 1902 radiometer wind speed m/s j1 j2 j3 pn tx 0 30
wind speed ecmwf u 1903 ECMWF model wind speed (U) m/s e2 j1 j2 j3 n1 sa 6
wind speed ecmwf v 1904 ECMWF model wind speed (V) m/s e2 j1 j2 j3 n1 sa 6
wind speed ecmwf ECMWF model wind speed m/s e2 j1 j2 j3 n1 sa 6
wind speed gfs u 1903 NOAA/GFS model wind speed (U) m/s sa 7
wind speed gfs v 1904 NOAA/GFS model wind speed (V) m/s sa 7
wind speed gfs NOAA/GFS model wind speed m/s sa 7
wind speed 19 alias of wind speed alt all
Notes:
1. For Geosat, GFO, ERS-1 and ERS-2, the wind speed is based on the Modified Chelton-
Wentz (MCW) algorithm [Witter and Chelton, 1991]. In case of GFO 0.63 dB was subtracted
from the backscatter coefficient before feeding it into the MCW algorithm.
2. The CryoSat and Envisat wind speed is based on the ECMWF 1-parameter algorithm tai-
lored to Envisat [Abdalla, 2007].
3. The Ka-band altimeter of SARAL required a new 1-parameter algorithm, similar to En-
visat’s, again matching ECWMF wind fields [Lillibridge et al., 2014].
4. The TOPEX/Poseidon wind speed is based on the 2-parameter model by Gourrion et al.
[2002].
5. For Jason-1 and Jason-2, a variant of the 2-parameter model by Gourrion et al. [2002] tailored
to Jason-1 is used to derive wind speed [Collard, 2005].
6. The U (towards east) and V (towards north) components of the wind speed according to
ECMWF model data. The absolute magnitude can be computed on-the-fly.
7. The U (towards east) and V (towards north) components of the wind speed according to
0.5×0.5×6h model grids from NOAA’s Global Forecast System. The absolute magnitude
can be computed on-the-fly. The inclusion of these fields is experimental, and may be removed in
the future.
4.4 Other wave model data
For calibration and validation purposes, and to support further studies that include wind and
wave processes (like swell and their effect on sea state bias) a number of variables from the
WAVEWATCH III model (version 3.14) [Tolman, 2009] as run by the University of New Hamp-
shire. These variables are currently only available during the year 2000 to 2012 (inclusive).
The original resolution of the model grids is 1by 1by 6 hours, and are restricted to latitudes
lower than 77.5.
In addition, SWH from the WAVEWATCH III model is made available as swh ww3 (see
Section 4.1).
Variable field name units sat range note
wave m0 4001 WaveWatch3 wave height variance m2all
wave m1 4002 WaveWatch3 first moment of wave height m2/s all
wave m2 4003 WaveWatch3 wave velocity variance m2/s2all
wave m4 4004 WaveWatch3 wave slope variance rad2all
wave shs 4005 WaveWatch3 wave swell m all
Chapter 5
Radiometer variables
5.1 Radiometer brightness temperatures
5.2 Water vapour content
5.3 Liquid water content
24
Chapter 6
Variables for data editing
6.1 Engineering and geophysical flags
The engineering and geophysical flags are historically a number of bits in a 2-byte integer
number that describe either instrument settings, type of surface overflow, or warnings about
the quality of the data. In RADS4 there are a number of aliases to help pick out single bits
from this word. The editing, however, is currently still determined by the limits set on the flag
word, where the lower limits indicates which bits of the flag word should not be set, and the
upper limits indicates the bits of the flag word that should be set. In other words, a record will
be rejected if either:
flags AND flags low is not equal to 0.
flags AND flags high is not equal to flags high.
where flags low and flags high are the lower and higher limit of flags specified, and AND is the
logical bitwise AND operator.
In a future version of the data base the flag word flags will be phased out and only the
more elementary flag variables that are now defined as aliases will be available.
Variable field name units sat range note
flags 2601 flag word 3a 3b 65448 0 1
c2 32 0
e1 e2 j1 j2 65512 0
j3 65384 0 1
g1 65384 0
gs 51176 0
n1 36712 0
pn 480 0
tx 49640 0
26 alias of flags
Notes:
1. This is a temporary value, where the radiometer land flag is not considered as an edit
criterion.
The individual bits of the flag word flags are described in the following table. Note that the
limits on these alias are not actually set at this time, but they are a transposition of the editing
25
26 Variables for data editing
ranges for each individual altimeter as indicated in the table above. In general, 0 means no or
OK, 1 means yes or bad.
6.1 Engineering and geophysical flags 27
Variable field name values sat note
flag alt oper mode 2516 bit 0: hardware/software status 0 = nominal, 1 = bad pn tx
2516 bit 0: altimeter operating mode 0 = Side A, 1 = Side B j1 j2 j3 n1
2516 bit 0: altimeter operating mode 0 = LRM, 1 = SAR 3a 3b c2
qual sptr 2516 bit 0: SPTR availability e1
qual attitude 2501 bit 1: quality of attitude 0 = ok, 1 = bad 3a 3b e2 j1 j2 j3 n1 tx sa
bit 1: quality of attitude 0 = ok, 1 = suspect g1 gs pn
qual dh 2502 bit 2: dH status 1 = suspect gs
flag rad oper mode 2502 bit 2: TMP 21 GHz Channel status 0 = A, 1 = B pn tx
flag continental ice 2502 bit 2: continental ice flag 0 = no, 1 = yes 3a 3b c2 j1 j2 j3 n1 sa
qual iono alt 2503 bit 3: quality of dual-frequency iono corr 0 = ok, 1 = bad 3a 3b j1 j2 j3 n1 tx
flag water 2504 bit 4: water/dry flag 0 = open ocean or enclosed sea or lake, 1 = land all
flag ocean 2505 bit 5: ocean/land flag 0 = open ocean, 1 = land or enclosed sea or lake all
surface type rad 2506 bit 6: radiometer land flag 0 = water, 1 = land all but c2 gs
qual alt rain ice 2507 bit 7: altimeter rain/ice flag 0 = no rain/ice, 1 = rain/ice 3a 3b g1 j1 j2 j3 n1 pn sa tx
qual rad rain ice 2508 bit 8: radiometer rain/ice flag 0 = no rain/ice, 1 = rain/ice 3a 3b e1 e2 j1 j2 j3 n1 pn tx
qual rad tb 2509 bit 9/10: radiometer quality flag 0 = ok, 1 = interp. near land, 2 = extrap., 3 = interp. failed pn tx
0 = ok, 1 = bad tb238, 2 = bad tb365, 3 = both bad 3a 3b e1 e2 n1
0 = ok, 1 = bad tb220, 2 = bad tb370, 3 = both bad g1
0 = ok, 1 = bad tb187/tb238, 2 = bad tb340, 3 = both bad j1 j2 j3
0 = ok, 1 = bad tb238, 2 = bad tb370, 3 = both bad sa
qual range 2511 bit 11: quality of range 0 = ok, 1 = some 10Hz invalid gs
0 = ok, 1 = suspect other
qual swh 2512 bit 12: quality of SSB 0 = ok, 1 = suspect gs
bit 12: quality of SWH 0 = ok, 1 = suspect other
qual sig0 2513 bit 13: quality of wind speed 0 = ok, 1 = suspect gs
bit 13: quality of sigma0 0 = ok, 1 = suspect other
flag alt track mode 2514 bit 14: altimeter tracking mode 0 = nominal, 1 = preset e1 e2
0 = nominal, 1 = coarse or acquisition gs tx
0 = nominal, 1 = C-band coarse j1
0 = nominal, 1 = acquisition pn
qual orbit 2515 bit 15: orbital quality flag 0 = ok, 1 = suspect all
surface type surface type 0 = open ocean, 2 = enclosed sea or lake, 3 = land, all 1
4 = continental ice
surface class surface class 0 = open ocean, 1 = land, 2 = continental water, all 2
3 = aquatic vegetation, 4 = continental ice or snow,
5 = floating ice, 6 = salted basin
28 Variables for data editing
Notes:
1. A new variable surface type has been introduced to combine the original flag bits 2, 4,
and 5 into a single variable. Generally, the flag mask determined by flags low as discussed
above will be set to only allow data over open ocean. Effort is made to get rid of this quirky
method of screening the data and use the individual flags instead.
The values of surface type are based on the GSHHG coastline dataset [Wessel and Smith,
1996] that is distributed with the Generic Mapping Tools (GMT) plotting package [Wessel
et al., 2013]. Version 2.3.4 of this data set was used to create land mask of ocean/land/lake
indicators at 1 arcminute resolution. This grid was than queried to determine whether
the satellite nadir point was over ocean, land, or lakes or enclosed seas. The additional
information about continental ice came for the original GDR data. If this indicator was set
in the GDR, surface type was set to the value 4, and bits 4 and 5 of flags were both set to 1,
irrespective of the aforementioned land mask.
Experience has shown that for Antarctica only the grounded ice is marked as ”continental
ice” (4). The ice sheets are marked ”land” (2) as the GSHHG coastline datasets marks the
(minimum) extent of the ice sheets.
The value of 1 of surface type has been reserved for later use.
2. A more elaborate version of surface type is available as surface class. It fas 7 different
states and is based on a high-resolution mask built from MERIS and GlobCover data.
6.2 Bathymetry and topography
Bathymetry is the depth of the oceans (and seas). It is given as a negative number, and thus
constitutes the elevation of the sea bottom with respect to the geoid. The bathymetry is gener-
ally predicted from altimeter data, by inverting altimeter-derived gravity anomalies into ocean
depth [Smith and Sandwell, 1994, e.g.].
Topography is the elevation of the land (and lakes). It is represented generally by a positive
value and is measured relative to the geoid. Occasionally the values can be negative, like in
large parts of The Netherlands, and around the Dead Sea. By convention, the elevation of the
lake surfaces (not the lake bottom) is stored, except for the Caspian Sea for which generally
the bottom topography is given. The topography models are based on a number of different
sources: altimetry, the SRTM mission, and local leveling.
In RADS the bathymetry and topography are combined into a single field. Please use the
surface type variable to distinguish between ocean, land, and lakes.
Variable field name units sat range note
topo dtm2000 2202 DTM2000 topography m j1 j2 j3 n1 sa 1
topo srtm30plus 2204 SRTM30PLUS topography m all 2
topo dtu10 2205 DTU10 topography m all 3
topo 22 alias of topo srtm30plus all
Notes:
1. On some of the GDR products, the topography/bathymetry is determined from the
DTM2000.1 model (N. Pavlis and J. Saleh, GSFC) and is copied into the RADS data base.
2. Ocean data are based on the Smith and Sandwell global 1-minute grid between the lat-
itudes 81S and 81N degrees [Sandwell et al., 2014]. Higher resolution grids have been
added from the LDEO Ridge Multibeam Synthesis Project, the JAMSTEC Data Site for
6.4 Basin codes 29
Research Cruises, and the NGDC Coastal Relief Model. Arctic bathymetry is from the In-
ternational Bathymetric Chart of the Oceans (IBCAO) [Jakobsson et al., 2012].
Land data are based on the 1-km averages of topography derived from the USGS SRTM30
gridded DEM data product created with data from the NASA Shuttle Radar Topography
Mission. GTOPO30 data are used for high latitudes where SRTM data are not available.
V10 of SRTM30 PLUS was released in May 2014. For more information about
SRTM30 PLUS, please see: http://topex.ucsd.edu/WWW html/srtm30 plus.html
3. The DTU10 topography/bathymetry model was derived from altimeter data together with
the DTU10 mean sea surface model [Andersen and Knudsen, 2010] and is an update of the
DNSC08 bathymetry model [Andersen and Knudsen, 2009]. It is not clear where the topo-
graphic (land) data stem from. The model is interpolated to the altimeter ground track
location.
6.3 Distance from coast and coastal proximity parameter
Because the altimeter and radiometer measurements are affected by land in their respective
footprints, it is worthwhile to know what the distance from the satellite nadir to any coastline
is, since it would facilitate editing out of possibly corrupted measurements. RADS contains
two parameters for this purpose, to be used by the user at leisure: the distance from the coast
and the coastal proximity parameter. Both are based on the proximity of the altimeter footprint
to land, but potentially suit different purposes.
The distance to (of from) the coast is measured from the centre of the altimeter footprint
(i.e. the satellite nadir point) to the nearest ocean or lake shoreline. The values in the RADS
data base have been interpolated in a grid with a resolution of 1 arcminute. Positive values are
offshore distances to the nearest shoreline, negative values are inland distances to the nearest
ocean or lake shore. The grid is based on Version 2.3.0 of the GSHHG shoreline dataset [Wessel
and Smith, 1996] that is distributed with the Generic Mapping Tools (GMT) plotting package
[Wessel et al., 2013]. Any islets or lakes of less than 1 square kilometer have been excluded.
The coastal proximity parameter is a dimensionless measure of the effect of land over al-
timetric waveforms, and has values in the range from -1 to +1, where -1 means unaffected by
land (normally offshore, open-ocean points) and 1 means totally affected by land (for instance
points a few km inland). Therefore this parameter can be used for screening purposes in place
of distance from coast. The grid for this parameter was developed by NOC Southampton in
the framework of the ESA Sea Level CCI project and has a resolution of 0.01×0.01[Cipollini,
2011].
Variable field name units sat range note
dist coast 45, 4501 distance to coast km all
prox coast 4502 coastal proximity parameter all
6.4 Basin codes
Eric Leuliette (NOAA) divided the world’s larger water bodies into 39 different ocean basins,
enclosed seas and lakes, giving each of them a separate numerical code. This has been rep-
resented in a grid with a 5’×5’ resolution as shown in Figure 6.1. This grid is queried to the
nearest grid point when creating the RADS data and stored as the variable basin code. For
land areas a default value of NaN is stored.
30 Variables for data editing
1 Pacific Ocean
2 Atlantic Ocean
3 Indian Ocean
4 Arctic Ocean
10 Bering Sea
11 Sea of Okhotsk
12 Sea of Japan
13 Yellow Sea
14 South China Sea
15 Indonesian
20 Hudson Bay
21 Gulf of Mexico
22 Caribbean Sea
23 North Sea
24 Baltic Sea
31 Arabian Sea
32 Bay of Bengal
33 Andaman Sea
34 Persian Gulf
35 Red Sea
41 Great Slave
42 Lake Winipeg
43 Lake Superior
44 Lake Michigan
45 Lake Huron
46 Lake Ontario
47 Lake Erie
50 Lake Titicaca
60 Mediterranean
61 Adriatic Sea
70 Black Sea
71 Caspian Sea
72 Aral Sea
73 Lake Baikal
74 Lake Balkhash
80 Lake Chad
81 Lake Malawi
82 Lake Tanganyika
83 Lake Victoria
Figure 6.1 Basin codes. The different colours relate to the various numerical identifiers used for
each ocean basin, enclosed sea or lake.
This field allows users to separate the selected data by region, or select data from just a
single region. Normally data from all regions is selected.
Variable field name units sat range note
basin code 36, 3601 basin code - all
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Index
files
rads.xml, 7
variables
alt, 5–7
alt cnes, 5
alt dgme04, 5, 7
alt eig6c, 5
alt eig6s2, 5
alt eiggl04s, 5
alt gdrcp, 5
alt gdrd, 5
alt gdre, 5
alt gfz, 5
alt ggm02c itrf2000, 5
alt ggm02c itrf2005, 5
alt gps, 5
alt jgm3, 5, 7
alt pgs7777, 5
alt rate, 6
alt reaper, 5
alt reaper deos, 5
alt reaper esoc, 5
alt reaper gfz, 5
alt slcci, 5
alt std1204, 5
alt std1404, 5
basin code, 29, 30
dac, 14, 15
dist coast, 29
dry tropo, 10
dry tropo airtide, 10
dry tropo ecmwf, 10
dry tropo era, 10
dry tropo ncep, 10
flag alt oper mode, 27
flag alt track mode, 27
flag continental ice, 27
flag ocean, 27
flag rad oper mode, 27
flag water, 27
flags, 25, 28
flags high, 25
flags low, 25, 28
geoid, 19
geoid egm2008, 19
geoid eigen6, 19
inv bar, 14, 15
inv bar global, 14
inv bar mog2d, 14, 15
inv bar mog2d era, 14, 15
inv bar mog2d mean, 14
inv bar mok2d, 14, 15
inv bar static, 14, 15
iono, 13, 14
iono alt, 13
iono alt mle3, 13
iono alt smooth, 13
iono alt smooth mle3, 13
iono bent, 13
iono doris, 13
iono gim, 13, 14
iono iri2007, 13
iono nic09, 13
lat, 4
lon, 4
mss, 19
mss cnescls11, 19
mss cnescls15, 19
mss dtu13, 19
mss dtu15, 19
prox coast, 29
qual alt rain ice, 27
qual attitude, 27
qual dh, 27
qual iono alt, 27
qual orbit, 27
qual rad rain ice, 27
qual rad tb, 27
qual range, 27
37
38 Index
qual sig0, 27
qual sptr, 27
qual swh, 27
range, 8
range c, 8
range ka, 8
range ku, 8
range ku mle3, 8
range numval, 10
range numval c, 10
range numval ka, 10
range numval ku, 10
range rms, 9
range rms c, 9
range rms ka, 9
range rms ku, 9
range rms s, 9
range s, 8
ref frame offset, 9
sig0, 22
sig0 c, 22
sig0 ka, 22
sig0 ku, 22
sig0 ku mle3, 22
sig0 rms, 22
sig0 rms c, 22
sig0 rms ka, 22
sig0 rms ku, 22
sig0 rms s, 22
sig0 s, 22
sla, 7, 8, 19
ssb, 18
ssb bm3, 18
ssb cls, 18
ssb cls c, 18
ssb cls mle3, 18
ssb csr, 18
ssb hyb, 18
ssb tran2012, 18
ssha, 7, 8
surface class, 27, 28
surface type, 27, 28
surface type rad, 27
swh, 21
swh c, 21
swh ka, 21
swh ku, 21
swh ku mle3, 21
swh rms, 21
swh rms c, 21
swh rms ka, 21
swh rms ku, 21
swh rms s, 21
swh s, 21
swh ww3, 21, 23
tide equil, 16
tide load, 16
tide load fes04, 16
tide load fes14, 16
tide load got410, 16
tide load got48, 16
tide non equil, 16, 17
tide ocean, 16
tide ocean fes04, 16
tide ocean fes14, 16, 17
tide ocean got410, 16
tide ocean got48, 16
tide ocean webtide, 16, 17
tide pole, 15
tide solid, 15
time, 3
time 1985, 3
time 2000, 3
time local solar, 3
time mjd, 3
time rel eq, 3
time ymdhms, 3
topo, 28
topo dtm2000, 28
topo dtu10, 28
topo srtm30plus, 28
wave m0, 23
wave m1, 23
wave m2, 23
wave m4, 23
wave shs, 23
wet tropo, 1, 2, 12
wet tropo ecmwf, 1, 2, 12
wet tropo era, 12
wet tropo ncep, 12
wet tropo nvap, 12
wet tropo rad, 1, 12
wet tropo tovs ncep, 12
wet tropo tovs ssmi, 12
wind speed, 23
wind speed alt, 23
wind speed ecmwf, 23
wind speed ecmwf u, 23
wind speed ecmwf v, 23
wind speed gfs, 23
Index 39
wind speed gfs u, 23
wind speed gfs v, 23
wind speed rad, 23

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