Rads4 Data Manual

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RADS Data Manual
Remko Scharroo

Version 4.3.2
7 November 2018

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NOAA

Laboratory for Satellite Altimetry
NOAA•NESDIS•STAR

This document was typeset with LATEX 2ε .
The layout was designed by Remko Scharroo c 1993–2015

Contents

1

Introduction

1

2

Time and location
2.1 Time . . . . . . . . . .
2.2 Latitude and longitude
2.3 Orbital altitude . . . .
2.4 Orbital altitude rate . .

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3
3
4
4
6

Sea level variables
3.1 Sea level anomaly . . . . . . . . . . . . . . .
3.2 Altimeter range . . . . . . . . . . . . . . . .
3.3 Altimeter range statistics . . . . . . . . . . .
3.4 Dry tropospheric correction and air tide . .
3.5 Wet tropospheric correction . . . . . . . . .
3.6 Ionospheric correction . . . . . . . . . . . .
3.7 Atmospheric (inverse barometer) correction
3.8 Solid earth and pole tide . . . . . . . . . . .
3.9 Ocean and load tide . . . . . . . . . . . . . .
3.10 Sea state bias . . . . . . . . . . . . . . . . . .
3.11 Mean sea surface and geoid . . . . . . . . .

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9
10
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13
14
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15
17
18

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20
20
21
22
23

5

Radiometer variables
5.1 Radiometer brightness temperatures . . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Water vapour content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5.3 Liquid water content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

24
24
24
24

6

Variables for data editing
6.1 Engineering and geophysical flags . . . . . . . . . . .
6.2 Bathymetry and topography . . . . . . . . . . . . . . .
6.3 Distance from coast and coastal proximity parameter
6.4 Basin codes . . . . . . . . . . . . . . . . . . . . . . . . .

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29
29

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Wind speed and wave height variables
4.1 Significant wave height . . . . . . .
4.2 Altimeter backscatter coefficient .
4.3 Wind speed . . . . . . . . . . . . .
4.4 Other wave model data . . . . . . .

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31

iii

iv

Index

Contents

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 Section 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 Table 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
GEOS 3
Seasat
Geosat
ERS-1
TOPEX
Poseidon
ERS-2
GFO
Jason-1
Envisat
Jason-2
CryoSat-2
SARAL
Jason-3
HY-2A
Sentinel-3A
Sentinel-3B
Table 1.1

Abbr.

Nr

Alternatives

References

g3
ss
gs
e1
tx
pn
e2
g1
j1
n1
j2
c2
sa
j3
2a
3a
3b

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

ge3 geos-3 geos3
sea seasat-a
geo geosat
er1 ers-1 ers1
top topex
pos poseidon
er2 ers-2 ers2
gfo gfo-1 gfo1
ja1 jason-1 jason1
en1 envisat
ja2 jason-2 jason2
cs2 cryosat-2 cryosat2
sa srl saral altika
ja3 jason-3 jason3
h2a hy-2a hy2a
s3a sentinel-3a sentinel3a sntnl-3a
s3b sentinel-3b sentinel3b sntnl-3b

(not included in RADS)
(not included in RADS)
[Francis, 1990; Francis et al., 1991]
[Fu et al., 1994]
[Francis et al., 1995]
[Ménard et al., 2003]
[Lambin et al., 2010]
[Wingham et al., 2006]

(not included in RADS)
(limited access in RADS)

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 tropospheric 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 (normally 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
time
time
time
time
time
time
time

1985
2000
rel eq
local solar
mjd
ymdhms

field
101
106
102
103
105
104
1

name

units

sat

time since 1985-01-01 00:00:00
time since 2000-01-01 00:00:00
time relative to equator crossing
local solar time
time since 1858-11-17 00:00:00
time formatted as ymdhms

s
s
s
s
days

all
all
all
all
all
all

1
2
3
4

all

5

alias of time 1985

range

note

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 command line.
6. A time tag bias (surplus) of 1.8 ms was removed from the time tags of ERS-1 as they occurred 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

2.2

Time and location

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
lat
lon

field
2, 201
3, 301

name

units

sat

range

latitude
longitude

degrees north
degrees east

all
all

-90 90
-180 180

note
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 reference 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-ofmass 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

Variable
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt
alt

jgm3
dgme04
cnes
pgs7777
ggm02c itrf2000
ggm02c itrf2005
eiggl04s
gdrcp
gps
eig6c
eig6s2
gdrd
std1204
reaper
reaper deos
reaper gfz
reaper esoc
std1404
gdre
slcci

alt
alt
alt
alt
alt
alt
alt
alt
alt

5

field
401
402
404
410
411
413
414
415
416
417
417
418
419
420
421
422
423
424
425
426
4
4
4
4
4
4
4
4
4

name

units

sat

JGM-3 altitude
DGM-E04 altitude
CNES altitude
PGS7777 altitude
GGM02c(ITRF2000) altitude
GGM02c(ITRF2005) altitude
EIGEN-GL04c altitude
GDR-C’ altitude
GPS altitude
EIGEN-6C altitude
EIGEN-6S2 altitude
CNES GDR-D altitude
GSFC/Std1204 altitude
REAPER/COMBI altitude
REAPER/DEOS altitude
REAPER/GFZ altitude
REAPER/ESOC altitude
GSFC/Std1404 altitude
CNES GDR-E altitude
GFZ/SLCCI altitude

m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m

gs pn tx
e1 e2
c2 n1 pn tx
g1
gs pn tx
pn tx
j1
g1 gs j1 j2 n1 pn tx
j1 j2
c2 n1
j1 j2
c2 j1 j2 n1 sa
j1 j2 pn tx
e1 e2
e1 e2
e1 e2
e1 e2
j2
c2 j2 j3 sa
e1 e2 j1 j2 n1 pn tx

1
2
3
4
5
5
6
7
8
9
9
10
11
12
12
12
12
13
14
15

c2
e1 e2
g1
gs
j1
j2
j3 sa
n1
pn tx

16
16
16
16
16
16
16
16
16

alias of
alias of
alias of
alias of
alias of
alias of
alias of
alias of
alias of

alt
alt
alt
alt
alt
alt
alt
alt
alt

gdre alt cnes
reaper alt gfz
pgs7777
gdrcp
gdre
gdre alt gdrd
gdre
gdrd alt gdrcp
gdrcp alt jgm3

range

note

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 tailored to the ERS-1 and ERS-2 orbits and derived from the JGM-3 model. The model significantly 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 Service, 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

alt rate

5, 501

name

units

sat

orbital altitude rate

m/s

all

range

note

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:

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


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:

swh sig0 range_rms range_numval flags


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
0

sla
ssha

name

units

sat

range

sea level anomaly
sea level anomaly (precomputed)

m
m

all
all

-5 5

note
1
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 oscillator (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 Kuband) 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 Sband) which allows for the computation of the ionospheric path delay directly for altimeter
observations rather than models.
Variable
range
range
range
range
range
range
range

ka
ku
ku mle3
c
s

field
601
601
601
602
602
6
6

name

units

sat

altimeter range (Ka)
altimeter range (Ku)
altimeter range (MLE3)
altimeter range (C)
altimeter range (S)

m
m
m
m
m

sa
other
j2 j3
tx j1 j2 j3
n1

alias of range ka
alias of range ku

range

note

10

sa
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 Féménias, 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 correction 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 n − 2 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 standard 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

range rms ka
range rms ku

2002
2002

std dev of Ka-band range
std dev of Ku-band range

m
m

range rms c
range rms s

2004
2004

std dev of C-band range
std dev of S-band range

m
m

sa
g1 gs
j1
j2 j3
tx
other
j1 j2 j3
n1

0.0 0.17
0.0 0.25
0.0 0.2
0.0 0.17
0.0 0.15
0.0 0.4
0.0 0.4
0.0 0.4

range rms
range rms

20
20

alias of range rms ka
alias of range rms ku

sa
other

note

1

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

range numval ku
range numval ku

2101
2101

nr of valid Ka-band measurements
nr of valid Ku-band measurements

range numval c

2102

range numval
range numval

3.4

21
21

units

sat

range

nr of valid C-band measurements

sa
g1 gs tx
j1 j2 j3
other
j1 j2 j3

33 40
9 10
16 20
17 20
17 20

alias of range numval ka
alias of range numval ku

sa
other

note

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 interpolation (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 resolution 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 considered 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 removing 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
dry
dry
dry
dry

tropo
tropo
tropo
tropo

dry tropo
dry tropo

ecmwf
ncep
era
airtide

field

name

units

sat

range

701
702
709
4901

ECWMF dry tropo corr
NCEP dry tropo corr
ERA Interim dry tropo corr
air tide corr

m
m
m
m

all but g1 gs
all
all
c2 e1 j1 pn tx

-2.4 -2.1
-2.4 -2.1
-2.4 -2.1

7
7

alias of dry tropo era
alias of dry tropo ecmwf

note
1
2, 4
3, 4
5

g1 gs
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 spacing of 79 km [Berrisford et al., 2011].
4. NCEP and ERA sea level pressure grids are interpolated and, over land and lakes, the pressure 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 nondispersive, 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  IWVR w ( k3 /Tm + k 20 )
where IWV is integrated water vapour, Tm is the mean temperature in the atmospheric column,
and R w , k 20 and k 3 are constants [Bevis et al., 1994]. The mean temperature can further be
approximated from model data of near-surface air temperature, Ts , by
Tm  50.4 + 0.789Ts
[Mendes et al., 2000]. In this way, the 6-hourly models of integrated water vapour and nearsurface temperature are interpolated in space and time and then converted to a wet tropospheric 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 correction (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 Sections 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

n1
pn tx
e2
e1 g1 j1 j2 j3
all but g1 gs
all
gs
gs
gs
all

-0.6 0.05
-0.6 -0.001
-0.6 0.0
-0.6 0.0
-0.6 0.0
-0.6 0.0
-0.6 0.0
-0.6 0.0
-0.6 0.0
-0.6 0.0

wet tropo rad

801

radiometer wet tropo corr

m

wet
wet
wet
wet
wet
wet

tropo
tropo
tropo
tropo
tropo
tropo

802
803
804
805
807
809

ECMWF wet tropo corr
NCEP wet tropo corr
NASA NVAP wet tropo corr
TOVS/SSMI wet tropo corr
TOVS/NCEP wet tropo corr
ERA interim wet tropo corr

m
m
m
m
m
m

wet
wet
wet
wet

tropo
tropo
tropo
tropo

ecmwf
ncep
nvap
tovs ssmi
tovs ncep
era

8
8
8
8

alias of
alias of
alias of
alias of

wet
wet
wet
wet

tropo
tropo
tropo
tropo

ecmwf
era
rad wet tropo era
rad wet tropo ecmwf

c2
gs
g1
other

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 correction 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 atmosphere, 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 reference 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 spacing 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

note
1
2
3
4
5
6
7
8
9
10
10
10
10

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 single frequency altimeters we rely on TEC estimates from models based on other dual-frequency
equipment (GPS, DORIS) or climatologies.
Variable
iono
iono
iono
iono
iono
iono
iono
iono
iono

alt
alt smooth
bent
doris
gim
iri2007
nic09
alt mle3
alt smooth mle3

field
901
903
902
904
906
907
908
911
913

iono

9

iono
iono

9
9

name

units

sat

range

dual-frequency iono corr
smoothed dual-freq iono corr
Bent iono corr
DORIS iono corr
JPL GIM iono corr
IRI2007 iono corr
NIC09 iono corr
dual-freq iono corr (MLE3)
smoothed d-f iono corr (MLE3)

m
m
m
m
m
m
m
m
m

n1 j1 j2 j3 tx
n1 j1 j2 j3 tx
c2 e1 e2 n1 pn tx
n1 pn tx
all but gs e1 pn
all
all
j2 j3
j2 j3

-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04
-0.4 0.04

alias of iono alt smooth
iono gim iono nic09
alias of iono gim iono nic09
alias of iono nic09

j1 j2 j3 n1 tx
c2 e2 g1 sa
e1 gs pn

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 altimeter 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◦ × 2.5◦ and 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.

note
1
2
4
5
6
7
8
3
3
9
9
9

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 ( p − p 0 )
where C is -9.948 mm/mbar, p is the sea level pressure and p0 is a reference pressure. Since
the global ocean as a whole is incompressible, p 0 here 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 includes wind and ocean dynamics and is hence called a dynamic atmospheric correction (DAC).
RADS, AVISO, and other altimeter datasets include the dynamic atmospheric correction produced by CLS Space Oceanography Division using the MOG2D model from Legos [Carrère and
Lyard, 2003; Roblou et al., 2008] and distributed by AVISO, with support from CNES. A more recent 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 [Carrère et al., 2016].
Over inland waters, this correction should not be applied [Crétaux and Birkett, 2006].
Variable

field

name

units

sat

range

inv
inv
inv
inv
inv
inv

1001
1002
1003
1003
1004
1005

static inverse baro corr
global mean inv baro corr
MOG2D dynamic atmospheric corr
MOG2D DAC from ERA-Int.
MOK2D dynamic atmospheric corr
local mean of MOG2D

m
m
m
m
m
m

all
all
all
all
all
all

-1 1
-1 1
-1 1
-1 1
-1 1
-1 1

bar
bar
bar
bar
bar
bar

inv bar
dac

static
global
mog2d
mog2d era
mok2d
mog2d mean

10

alias of inv bar mog2d era inv bar mog2d
inv bar mok2d

note

all

1
2
3
4
5
6
7
7

Notes:
1. This correction is the simplification explained above.
2. For reference the global mean pressure, converted to an IB correction, Cp 0 , 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 + k 2 ) = 1.302
for the combination of solid earth and oceans, and h 2 = 0.609 for the solid earth only (over land
and lakes) [Wahr, 1985].
Variable

field

name

units

sat

range

tide solid
tide pole

1101
1401

solid earth tide
pole tide

m
m

all
all

-1 1
-0.1 0.1

11
14

3.9

alias of tide solid
alias of tide pole

note

all
all

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

tide
tide
tide
tide
tide
tide
tide
tide
tide
tide
tide

1213
1313
1215
1219
1319
1222
1322
1224
1324
3901
3902

FES2004 ocean tide
FES2004 load tide
WebTide ocean tide
GOT4.8 ocean tide
GOT4.8 load tide
GOT4.10c ocean tide
GOT4.10c load tide
FES2014b ocean tide
FES2014a load tide
long-period equilib. tide
long-period non-equil. tide

m
m
m
m
m
m
m
m
m
m
m

all
all
all
all
all
all
all
all
all
all
all

-5 5
-0.5 0.5
-5 5
-5 5
-0.5 0.5
-5 5
-0.5 0.5
-5 5
-0.5 0.5
-1 1
-1 1

ocean fes04
load fes04
ocean webtide
ocean got48
load got48
ocean got410
load got410
ocean fes14
load fes14
equil
non equil

tide ocean
tide load

12
13
39

alias of tide ocean got410
alias of tide load got410
alias of tide equil

note
1
1
2
3
3
4
4
5
5
6
7

all
all
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 equilibrium 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 presented in tide ocean webtide, determined from (in order of decreasing preference) the regional 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 nonequilibrium).
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 admittance [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, improved modelling, and data assimilation techniques, and more accurate and higher resolution 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 assimilated 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-TaylerEdden 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 fraction, 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 (altered 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

ssb
ssb
ssb
ssb
ssb
ssb
ssb

1501
1502
1503
1504
1505
1512
1513

parametric sea state bias
CLS non-parametric SSB
CSR BM4 sea state bias
NOAA hybrid sea state bias
CLS non-parametric SSB (C-band)
CLS non-parametric SSB (MLE3)
Tran et al. 2012 non-parametric sea state bias

m
m
m
m
m
m
m

e1 e2 g1 gs pn tx
j1 j2 j3 n1 tx
tx
g1 gs n1 pn sa
j1 j2 j3
j2 j3
j2 j3

-1 1
-1 1
-1 1
-1 1
-1 1
-1 1
-1 1

bm3
cls
csr
hyb
cls c
cls mle3
tran2012

ssb
ssb
ssb

15
15
15

alias of ssb bm3
alias of ssb cls
alias of ssb hyb

e1 e2 pn
j1 j2 j3 n1 tx
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 altimeter (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

note
1
2
3
4
2
5
6

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

mss cnescls11
mss cnescls15
mss dtu13
mss dtu15
geoid egm2008
geoid eigen6

1614
1619
1616
1618
1611
1617

CNES-CLS11 mean sea surface
CNES-CLS15 mean sea surface
DTU13 mean sea surface
DTU15 mean sea surface
EGM2008 geoid
EIGEN-6C3stat geoid

m
m
m
m
m
m

all
all
all
c2 j2 j3 sa
all
all

-200 200
-200 200
-200 200
-200 200
-200 200
-200 200

mss
geoid

16

alias of mss dtu13
alias of geoid eigen6

note
1
2
3
4
5
6

all
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 series have been extended to 20 years from 17 years. Second, the DTU13MSS ingests Cryosat2 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). [Förste 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 parameters. There is one complexity in this, and that is that SWH is defined as follows:
SWH2  α2 ( σc2 − σp2 )
where σc is a measure of the waveform slope and σp is an instrument parameter, and α is a constant. Because of noise in the measurement of σc and a possible bias in σp , SWH2 could 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:
q
SWH  −α σp2 − σc2 when σc < σp

q

SWH  α σc2 − σp2 when σc ≥ σp
(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 measurements (denominator is ( n − 1) ), not an estimate of the error of SWH.

20

4.2

Altimeter backscatter coefficient

21

Variable

field

name

units

sat

range

swh ka
swh ku

1701
1701

Ka-band significant wave height
Ku-band significant wave height

m
m

swh c
swh s
swh ku mle3

1702
1702
1711

C-band significant wave height
S-band significant wave height
Ku-band significant wave height (MLE3)

m
m
m

sa
c2 n1
other
j1 j2 j3 tx
n1
j2 j3

08
-0.5 8
08
08
-0.5 8
08

swh ww3

1712

WAVEWATCH3 significant wave height

m

c2 j2 j3 sa

swh rms ka
swh rms ku

2802
2802

std dev of Ku-band SWH
std dev of Ku-band SWH

m
m

swh rms c
swh rms s

2804
2804

std dev of C-band SWH
std dev of S-band SWH

m
m

sa
g1
j1 j2 j3
tx
other
j1 j2 j3 tx
n1

swh
swh

17
17

alias of swh ka
alias of swh ku

sa
other

swh rms
swh rms

28
28

alias of swh rms ka
alias of swh rms ku

sa
other

note
1,2

1,3
4
5

0 2.1
0 0.5
0 1.5
0 0.9
0 2.1
0 2.1
0 2.1

3

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 WAVEWATCH III model [Tolman, 2009] and GFS analysis winds [Chawla et al., 2011]. The hindcasts 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 1◦ by
1◦ by 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 measurements. 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 backscatter 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 elementary measurements (denominator is ( n − 1) ), not an estimate of the error of the backscatter
coefficient.

22

Wind speed and wave height variables

Variable

field

name

units

sat

range

sig0
sig0
sig0
sig0
sig0

ka
ku
c
s
ku mle3

1801
1801
1802
1802
1811

Ka-band backscatter coefficient
Ku-band backscatter coefficient
C-band backscatter coefficient
S-band backscatter coefficient
Ku-band backscatter coefficient (MLE3)

dB
dB
dB
dB
dB

sa
other
j1 j2 j3 tx
n1
j2 j3

6 27
6 27
6 27
6 27
6 27

sig0
sig0
sig0
sig0

rms
rms
rms
rms

2902
2902
2904
2904

std dev of Ka-band SWH
std dev of Ku-band SWH
std dev of C-band SWH
std dev of S-band SWH

dB
dB
dB
dB

sa
all
j1 j2 j3 tx
n1

01
01

ka
ku
c
s

sig0
sig0

18
18

alias of sig0 ka
alias of sig0 ku

sa
other

sig0 rms
sig0 rms

29
29

alias of sig0 rms ka
alias of sig0 rms ku

sa
other

note
1-6
3-6
7
8

9
7

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 backscatter 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

Variable
wind
wind
wind
wind
wind
wind
wind
wind

speed
speed
speed
speed
speed
speed
speed
speed

alt
rad
ecmwf u
ecmwf v
ecmwf
gfs u
gfs v
gfs

field

name

units

sat

range

note

1901
1902
1903
1904

altimeter wind speed
radiometer wind speed
ECMWF model wind speed (U)
ECMWF model wind speed (V)
ECMWF model wind speed
NOAA/GFS model wind speed (U)
NOAA/GFS model wind speed (V)
NOAA/GFS model wind speed

m/s
m/s
m/s
m/s
m/s
m/s
m/s
m/s

all
j1 j2 j3 pn tx
e2 j1 j2 j3 n1 sa
e2 j1 j2 j3 n1 sa
e2 j1 j2 j3 n1 sa
sa
sa
sa

-1 30
0 30

1-5

1903
1904
19

wind speed

23

alias of wind speed alt

all

Notes:
1. For Geosat, GFO, ERS-1 and ERS-2, the wind speed is based on the Modified CheltonWentz (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 tailored to Envisat [Abdalla, 2007].
3. The Ka-band altimeter of SARAL required a new 1-parameter algorithm, similar to Envisat’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 Hampshire. These variables are currently only available during the year 2000 to 2012 (inclusive).
The original resolution of the model grids is 1◦ by 1◦ by 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
wave
wave
wave
wave
wave

m0
m1
m2
m4
shs

field
4001
4002
4003
4004
4005

name

units

sat

WaveWatch3 wave height variance
WaveWatch3 first moment of wave height
WaveWatch3 wave velocity variance
WaveWatch3 wave slope variance
WaveWatch3 wave swell

m2

all
all
all
all
all

m2 /s
m2 /s2
rad2
m

range

note

6
6
6
7
7
7

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

flags

2601

flag word

26

units

sat

range

3a 3b
c2
e1 e2 j1 j2
j3
g1
gs
n1
pn
tx

65448 0
32 0
65512 0
65384 0
65384 0
51176 0
36712 0
480 0
49640 0

note
1

1

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.

bit 11: quality of range

bit 12:
bit 12:
bit 13:
bit 13:
bit 14:

bit 15: orbital quality flag

2502
2502
2502
2503
2504
2505
2506
2507
2508
2509

2511

2512

2513

2514

2515

qual dh
flag rad oper mode
flag continental ice
qual iono alt
flag water
flag ocean
surface type rad
qual alt rain ice
qual rad rain ice
qual rad tb

qual range

qual swh

qual sig0

flag alt track mode

qual orbit

surface type

surface class

surface type

surface class

quality of SSB
quality of SWH
quality of wind speed
quality of sigma0
altimeter tracking mode

bit 0: hardware/software status
bit 0: altimeter operating mode
bit 0: altimeter operating mode
bit 0: SPTR availability
bit 1: quality of attitude
bit 1: quality of attitude
bit 2: dH status
bit 2: TMP 21 GHz Channel status
bit 2: continental ice flag
bit 3: quality of dual-frequency iono corr
bit 4: water/dry flag
bit 5: ocean/land flag
bit 6: radiometer land flag
bit 7: altimeter rain/ice flag
bit 8: radiometer rain/ice flag
bit 9/10: radiometer quality flag

2516
2516
2516
2516
2501

flag alt oper mode

qual sptr
qual attitude

name

field

Variable

0 = open ocean, 2 = enclosed sea or lake, 3 = land,
4 = continental ice
0 = open ocean, 1 = land, 2 = continental water,
3 = aquatic vegetation, 4 = continental ice or snow,
5 = floating ice, 6 = salted basin

0 = ok, 1 = bad
0 = ok, 1 = suspect
1 = suspect
0 = A, 1 = B
0 = no, 1 = yes
0 = ok, 1 = bad
0 = open ocean or enclosed sea or lake, 1 = land
0 = open ocean, 1 = land or enclosed sea or lake
0 = water, 1 = land
0 = no rain/ice, 1 = rain/ice
0 = no rain/ice, 1 = rain/ice
0 = ok, 1 = interp. near land, 2 = extrap., 3 = interp. failed
0 = ok, 1 = bad tb238, 2 = bad tb365, 3 = both bad
0 = ok, 1 = bad tb220, 2 = bad tb370, 3 = both bad
0 = ok, 1 = bad tb187/tb238, 2 = bad tb340, 3 = both bad
0 = ok, 1 = bad tb238, 2 = bad tb370, 3 = both bad
0 = ok, 1 = some 10Hz invalid
0 = ok, 1 = suspect
0 = ok, 1 = suspect
0 = ok, 1 = suspect
0 = ok, 1 = suspect
0 = ok, 1 = suspect
0 = nominal, 1 = preset
0 = nominal, 1 = coarse or acquisition
0 = nominal, 1 = C-band coarse
0 = nominal, 1 = acquisition
0 = ok, 1 = suspect

0 = nominal, 1 = bad
0 = Side A, 1 = Side B
0 = LRM, 1 = SAR

values

all

all

pn tx
j1 j2 j3 n1
3a 3b c2
e1
3a 3b e2 j1 j2 j3 n1 tx sa
g1 gs pn
gs
pn tx
3a 3b c2 j1 j2 j3 n1 sa
3a 3b j1 j2 j3 n1 tx
all
all
all but c2 gs
3a 3b g1 j1 j2 j3 n1 pn sa tx
3a 3b e1 e2 j1 j2 j3 n1 pn tx
pn tx
3a 3b e1 e2 n1
g1
j1 j2 j3
sa
gs
other
gs
other
gs
other
e1 e2
gs tx
j1
pn
all

sat

2

1

note

6.1
Engineering and geophysical flags
27

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 generally 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

topo dtm2000
topo srtm30plus
topo dtu10

2202
2204
2205

DTM2000 topography
SRTM30PLUS topography
DTU10 topography

m
m
m

j1 j2 j3 n1 sa
all
all

topo

22

alias of topo srtm30plus

range

note
1
2
3

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 latitudes 81◦ S and 81◦ N 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 International 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 topographic (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 altimetric 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
dist coast
prox coast

6.4

field
45, 4501
4502

name

units

sat

distance to coast
coastal proximity parameter

km

all
all

range

note

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 represented 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

20 Hudson Bay

41 Great Slave

60 Mediterranean

2 Atlantic Ocean

21 Gulf of Mexico

42 Lake Winipeg

61 Adriatic Sea

3 Indian Ocean

22 Caribbean Sea

43 Lake Superior

70 Black Sea

4 Arctic Ocean

23 North Sea

44 Lake Michigan

71 Caspian Sea

24 Baltic Sea

45 Lake Huron

72 Aral Sea

10 Bering Sea

31 Arabian Sea

46 Lake Ontario

73 Lake Baikal

11 Sea of Okhotsk

32 Bay of Bengal

47 Lake Erie

74 Lake Balkhash

12 Sea of Japan

33 Andaman Sea

50 Lake Titicaca

80 Lake Chad

13 Yellow Sea

34 Persian Gulf

81 Lake Malawi

14 South China Sea

35 Red Sea

82 Lake Tanganyika

15 Indonesian

Figure 6.1

83 Lake Victoria

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
basin code

field
36, 3601

name

units

sat

basin code

-

all

range

note

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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

wind speed gfs u, 23
wind speed gfs v, 23
wind speed rad, 23

39



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