Chapter07 LeTraon

CHAPTER 7
Satellites and Operational Oceanography

Pierre-Yves Le Traon1,2
1Mercator Océan, Parc Technologique du Canal, Ramonville-Saint-Agne, France; 2Ifremer, Plouzané,
France
The chapter starts with an overview of satellite oceanography, its role and use for operational oceanography.
Main principles of satellite oceanography techniques are summarized. We then describe key techniques of
radar altimetry, sea surface temperature, and ocean colour remote sensing. This includes measurement
principles, data processing issues, and the use of data for operational oceanography. Synthetic aperture
radar, scatterometry, sea ice and sea surface salinity measurements are also briefly described. Techniques
used to assess the impact of present and future satellite observations for ocean analysis and forecasting are
reviewed. We also discuss future requirements for satellite observations. Main prospects are given in the
conclusion.
Introduction
T
here are very strong links between satellite oceanography and operational oceanography.
The development of operational oceanography has been mainly driven by the development
of satellite oceanography capabilities. The ability to observe the global ocean in near real-
time at high space and time resolution is indeed a prerequisite for the development of global oper-
ational oceanography and its applications. The first ocean parameter to be globally monitored from
space was sea surface temperature by meteorological satellites in the late 1970s. It was, however,
the advent of satellite altimetry in the late 1980s that led to the development of ocean data assimi-
lation and global operational oceanography. In addition to providing all kinds of weather observa-
tions, sea level from satellite altimetry is an integral of the ocean interior and provides a strong
constraint on the 4D ocean state estimation. The satellite altimetry community was also keen to
develop further the use of altimetry, and this required an integrated approach merging satellite and
in situ observations with models. Thus, the GODAE demonstration was phased to coincide with the
Jason-1 and ENVISAT altimeter missions (Smith and Lefebvre, 1997). Satellite oceanography is
now a major component of operational oceanography. Data are usually assimilated in ocean models
but they can also be used directly for applications.
An overview of satellite oceanography will be provided here, focusing on the most relevant
issues for operational oceanography. The chapter is organized as follows. First is an overview of
satellite oceanography, its role and use for operational oceanography. Main operational oceanogra-
phy requirements are summarized. The complementary role of in situ observations is also empha-
sized. Next, main principles of satellite oceanography and general data processing issues are
described. We then detail key techniques of radar altimetry, sea surface temperature, and ocean
Le Traon, P.-Y., 2018: Satellites and operational oceanography. In "New Frontiers in Operational Oceanography", E.
Chassignet, A. Pascual, J. Tintoré, and J. Verron, Eds., GODAE OceanView, 161-190, doi:10.17125/gov2018.ch07.
161
162  PIERRE-YVES LE TRAON
colour remote sensing. This includes measurement principles, data processing issues, and the use
of these data for operational oceanography. Synthetic aperture radar (SAR), scatterometry, sea ice
and sea surface salinity measurements are briefly described next followed by a review of tools used
to quantify the impact of present and future satellite observations for ocean analysis and forecasting.
Finally, future challenges and requirements for satellite observations are discussed. Main prospects
are given in the chapter conclusion.
Role of Satellites in Operational Oceanography

The global ocean observing system and operational oceanography
Operational oceanography critically depends on the near real-time availability of high quality in
situ and remote sensing data with sufficiently dense space and time sampling. The quantity, quality,
and availability of data sets directly impact the quality of ocean analyses and forecasts and
associated services. Observations are required to constrain ocean models through data assimilation
and also to validate them. Products derived from the data themselves can also be directly used for
applications.
This requires an adequate and sustained global ocean observing system. Climate and operational
oceanography applications share the same backbone system (i.e., GOOS, GCOS, and JCOMM).
Operational oceanography has, however, specific requirements for availability of high space and
time resolution measurements and for near real-time measurements.
The unique contribution of satellite observation

Satellites provide long-term, continuous, global, high space and time resolution data for key ocean
parameters: sea level and ocean circulation, sea surface temperature, ocean colour, sea ice, waves,
and winds. These are the observational core variables required to constrain global, regional and
coastal ocean monitoring and forecasting systems. They are also needed to validate them. Only
satellite measurements can, in particular, provide observations at high space and time resolution to
partly resolve the mesoscale and coastal variability. Satellite data can also be directly used for
applications (e.g., SAR for sea ice and oil pollution monitoring, ocean colour for water quality
monitoring). Sea surface salinity is a new and important parameter that could be operationally
monitored from space; the feasibility has been demonstrated with the European Space Agency’s
Soil Moisture and Ocean Salinity (SMOS) mission and the NASA/Comisión Nacional de
Actividades Espaciales (CONAE) Aquarius mission.
SATELLITES AND OPERATIONAL OCEANOGRAPHY  163
Main requirements
Operational oceanography requirements have been presented in the GODAE strategic plan and by
Le Traon et al. (2001). They have been further detailed in Clark and Wilson (2009), Drinkwater et
al. (2010) and Le Traon (2011). Sea level, sea surface temperature (SST), surface geostrophic
currents, ocean colour, sea surface salinity (SSS), waves, sea ice, and winds form the core
operational satellite observations required for global, regional, and coastal ocean monitoring and
forecasting systems. To deliver sustained, high resolution observations while meeting operational
constraints such as near-real-time data distribution and redundancy in the event of satellite or
instrument failure requires international cooperation and the development of virtual constellations
as promoted by the Committee on Earth Observation Satellites (CEOS; e.g. Bonekamp et al., 2010).
Specific requirements for operational oceanography are as follows:
 In addition to meteorological satellites, a high precision (Advanced Along-Track Scanning

Radiometer - AATSR-class) SST satellite mission, is needed to give the highest absolute
SST accuracy. A microwave mission is also needed to provide an all-weather global
coverage.
 At least three or four altimeters are required to observe the mesoscale circulation. This
would also useful for significant wave height measurements. A long-term time series of a
high accuracy altimeter system (Jason satellites) is needed to serve as a reference for the
other missions and for the monitoring of climate signals.
 Ocean colour is increasingly more important, particularly in coastal areas. At least two
satellites are required.
 Two scatterometers are required to globally monitor the wind field and sea ice at high
spatial resolution.
 Two SAR satellites are required for waves, sea ice characteristics, and oil slick monitoring.
These minimum requirements have been only partly met over the past ten years (see Le Traon
et al., 2015, for a recent review). Long-term continuity and transition from research to operational
mode remains a major challenge.
Role of in situ data

Satellite observations need to be complemented by in situ observations. First, in situ data are needed
to calibrate satellite observations. Most algorithms used to transform satellite observations (e.g.,
brightness temperatures) into geophysical quantities are partly based on in situ/satellite match up
data bases. The in situ data are then used to validate satellite observations and to monitor the long-
term stability of satellite observations. The stability of the different altimeter missions is, for
example, commonly assessed by comparing the altimeter sea surface height measurements with
those from tide gauges (Mitchum, 2000). Other examples include the validation of altimeter velocity
products with drifter data (e.g., Bonjean and Lagerloef, 2002; Pascual et al., 2009), the systematic
validation of satellite SST with in-situ SST from drifting buoys and the use of dedicated ship
mounted radiometers to quantify the accuracy of satellite SST (Donlon et al, 2008). Comparison of
in situ and satellite data can also provide indications of the quality of in situ data (e.g., Guinehut et
al., 2009). The comparison of in situ and satellite data is also useful to check the consistency and
information content between the different data sets (e.g., satellite sea level versus in situ dynamic
height measurements) before they are assimilated in an ocean model (e.g., Guinehut et al., 2006).
Most importantly, in situ data are mandatory (and this is their main role) to complement satellite
observations and to provide measurements of the ocean interior. Only the joint use of high resolution
satellite data with precise (but sparsely available) in situ observations of the ocean interior has the
potential to provide a high resolution description and forecast of the ocean state. The development
of the Argo array of profiling floats and their integration with satellite altimetry and operational
oceanography is an outstanding example of the value of an integrated ocean observing system (see
discussion in Le Traon, 2013).
Data processing issues

Satellite data processing takes place in steps: level 0 and level 1 (from telemetry to calibrated sensor
measurements), level 2 (from sensor measurements to geophysical variables), level 3 (space/time
composites of level 2 data), and level 4 (merging of different sensors, data assimilation). Processing
from level 0 to level 2 is generally carried out as part of the satellite ground segments.
Assembly of level 2 data from different sensors, intercalibration of level 2 products, and higher
level data processing is usually done by specific data processing centers or thematic assembly
centers. The role of data processing centers is to provide modelling and data assimilation centers
with the real-time and delayed mode data sets required for validation and data assimilation. This
includes uncertainty estimates, which are critical to an effective use of data in modelling and data
assimilation systems. Links with ocean forecasting centers are needed, in particular, to organize
feedback on: the quality control performed at the level of ocean forecasting centers (e.g., comparing
an observation with a model forecast); the impact of data sets and data products in the assimilation
systems; and new or future requirements.
High level data products (level 3 and 4) are also needed for applications (e.g., a merged altimeter
surface current product for marine safety or offshore applications) and can be used to validate data
assimilation systems (e.g., statistical versus dynamical interpolation) and complement products
derived through modelling and data assimilation systems. It is important, however, to be fully aware
of the limitations (e.g., mapping errors, limited effective space/time resolution) of high level
satellite products (e.g., gridded sea surface temperature or sea level data sets) when using them.
Use of satellite data for assimilation into ocean models

The use of satellite data for assimilation into ocean models is discussed at length in other chapters
of this book. Three important issues are emphasized in this chapter:
1. There can be large differences in data quality between real-time and delayed mode
(reprocessed) data sets. Depending on applications, trade-offs between time delay and
accuracy often need to be considered.
2. Error characterisation is mandatory for data assimilation, and a proper characterisation of
error covariance can be quite complex for satellite observations. Data error variance (and
covariance, but this is much more challenging) should always be tested and checked as
part of the data assimilation systems.
In theory and for advanced assimilation schemes, it is much better to use raw data (level 2 or in
some cases level 1 when the model can provide data needed for level 1 processing). The data error
structure is generally more easily defined. The model and the assimilation scheme should also
deliver a better high level processing (e.g., a model forecast should provide a better background
than climatology or persistence). However, in practice this is not always true. Some high level data
processing is often needed (e.g., correcting biases or large-scale errors, intercalibration of satellite
missions) as it cannot be easily accomplished within complex data assimilation systems.
Overview of Satellite Oceanography Techniques

Passive/active techniques and choice of frequencies
There are two main types of satellite techniques to observe the ocean1. Passive techniques measure
the natural radiation emitted from the sea or from reflected solar radiation. Active or radar
techniques send a signal and measure the signal received from its reflection at the sea surface. In
both cases, the propagation of the signal through the atmosphere and the emission from the
atmosphere itself must be taken into account in order to extract the sea surface signal. The intensity
and frequency distribution of the radiation that is emitted or reflected from the ocean surface allows
the inference of its properties. The polarization of the radiation is also often used in microwave
remote sensing.
Satellite systems operate at different frequencies depending on the signal to be derived. Visible
(400 – 700 nm) and infrared (0.7 – 20 μm) frequencies are used for ocean colour and sea surface
temperature measurements. Passive (radiometry) microwave systems (1 cm-30 cm) are used for sea
surface temperature measurements in cloud situations, wind, sea ice and sea surface salinity
retrievals. Radars operate in the microwave bands and provide measurements of sea surface height,
wind speed and direction, wave spectra, sea ice cover, and types and surface roughness. Radar
pulses are emitted obliquely (15 to 60°) (SAR, scatterometer) or vertically (altimetry).
The choice of frequencies is limited by other usages (e.g., radio, cellular phones, military and
civilian radars, satellite communications). This is particularly important at microwave frequencies
1 Gravimetry satellites (e.g. GRACE, GOCE), which measure the earth gravity field and its variations, are not
included in these two categories.
in the range 1-10 GHz, which limits the frequencies used for earth remote sensing. The atmosphere
also greatly affects the transmission of radiation between the ocean surface and the satellite sensors.
The presence of fixed concentrations of atmospheric gases (e.g., O2, CO2, and O3) and of water
vapour means that only a limited number of windows exist in the visible, infrared and microwave
for ocean remote sensing. Even at these frequencies, the propagation effects through the atmosphere
(from the troposphere to the ionosphere) must be taken into account and corrected for. Clouds are
a strong limitation of visible and infrared measurements.
There are also technological constraints for the choice of frequencies. The resolution of a given
sensor is generally related to the ratio between the observed wavelength (λ) and the antenna
diameter (D). For antenna diameters of a few meters, typical resolution at around 1 GHz
(wavelength of 30 cm) is about 100 km, while at 30 GHz (wavelength of 1 cm) resolution is about
10 km. Radar altimeters use pulse limited techniques (which are much less sensitive to mispointing
errors). Their footprint size is related to the pulse duration and is much smaller than that of a beam
limited sensor. SAR uses the motion of the satellite to generate a very long antenna (e.g., 20 km for
the ENVISAT Advanced Synthetic Aperture Radar (ASAR)) and thus to provide very high
resolution measurements (up to a few meters).
Satellite orbits and measurement characteristics

Orbits for ocean satellites are geostationary, polar, or inclined. A geostationary orbit is one in which
the satellite is always in the same position with respect to the rotating Earth. The satellite orbits at
an elevation of approximately 36,000 km because that produces an orbital period equal to the period
of rotation of the Earth. By orbiting at the same rate, in the same direction as Earth, the satellite
appears stationary. Geostationary satellites provide a large field of view (up to 120°) at very high
frequency, enabling coverage of weather events. Because of the high altitude, spatial resolution is
on the order of a few kilometers, while it is 1 km or less for polar orbiting satellites. Because a
geostationary orbit must be in the same plane as the Earth's rotation (i.e., the equatorial plane), it
provides distorted images of the polar regions. Five or six geostationary meteorological satellites
can provide a global coverage of the earth (for latitudes below 60°) (e.g., Martin, 2004).
Polar-orbiting satellites provide a more global view of Earth by passing from pole to pole,
observing a different portion of the Earth with each orbit due to the Earth's own rotation. Orbiting
at an altitude of 700 to 800 km, these satellites have an orbital period of approximately 90 minutes.
They usually operate in a sun-synchronous orbit. At the same local solar time each day, the satellite
passes the equator and any given latitude. Inclined orbits have an inclination between 0 degrees
(equatorial orbit) and 90 degrees (polar orbit) and are used to observe tropical regions (e.g., Tropical
Rainfall Measuring Mission (TRMM) Microwave Imager). High accuracy altimeter satellites such
as TOPEX/Poseidon and Jason use higher altitude and non-synchronous orbits to reduce
atmospheric drag and (mainly) to avoid aliasing of the main tidal signals.
The sampling pattern of a given satellite will be different depending on instrument types (along-
track, imaging, or swath), frequencies, and antennas (see above). In addition, in the visible and
infrared frequencies, cloud cover can strongly reduce the effective sampling.
Radiation laws and emissivity

Radiation from a blackbody
Planck’s law describes the rate of energy emitted by a blackbody as a function of frequency or
wavelength. A blackbody absorbs all the radiation it receives and emits radiation at a maximum rate
for its given temperature. Planck’s law gives the intensity of radiation Lλ emitted by unit surface
area into a fixed direction (solid angle) from the blackbody as a function of wavelength (or
frequency). The law can be expressed through the following equation:
Lλ= 2hc2 / λ5 [exp (hc/λkT)-1]
where T is the temperature, c the speed of light (2.99 10-8 m s-1), h the Planck's constant (6.63 x 10-
34
J s), k the Boltzmann's constant (1.38 10-23 J °K-1), and Lλ the spectral radiance per unit of
wavelength and solid angle in W m-3 sr -1.
Planck’s law gives a distribution that peaks at a certain wavelength; the peak shifts to shorter
wavelengths for higher temperatures. Wien’s law gives the wavelength of the peak of the radiation
distribution (λmax= 3 107/T), while the Stefan-Boltzmann law gives the total energy E being emitted
at all wavelengths by the blackbody (E = σ T4). Thus, Wien’s law explains the shift of the peak to
shorter wavelengths as the temperature increases, while the Stefan-Boltzmann law explains the
growth in the height of the curve as the temperature increases. Notice that this growth is very abrupt,
since it varies as the fourth power of the temperature.
The Rayleigh-Jeans approximation (Lλ=2kcT/λ4) holds for wavelengths much greater than the
wavelength of the peak in the black body radiation form. This approximation is valid over the
microwave band.
Graybodies and emissivity

Most bodies radiate less efficiently than a blackbody. The emissivity e is defined as the ratio of
graybody radiance to the blackbody. It has a non-dimensional unit and its value is comprised
between 0 and 1. The emissivity (e) generally depends on wavelength (λ) and polarization and has
a directional dependence; it can be considered as a physical surface property and is a key quantity
for ocean remote sensing. A graybody absorbs only part of the energy it receives and the remaining
part is reflected and/or transmitted. The absorptivity is equal to the emissivity, as a surface in
equilibrium must absorb and emit energy at the same rate (Kirchoff’s law). Similarly, the reflectivity
is equal to 1 – e.
Retrieval of geophysical parameters for microwave radiometers

The brightness temperature (BT) is defined as BT = eT where T is the (physical) temperature. In
the microwave band, it is proportional to the radiation Lλ. Brightness temperature is a measure of
the intensity of emitted radiation. It is the physical temperature a blackbody would have to yield the
same observed intensity of radiation emitted by a graybody.
The brightness temperature is an integrated measurement that includes all surface and
atmosphere emitted power. Depending on frequency, it is more sensitive to a given parameter.
Physical retrieval algorithms for geophysical parameters, such as the sea surface temperature, sea
surface wind speed, sea ice or sea surface salinity are derived from a radiative transfer model, which
computes the brightness temperatures that are measured by the satellite as a function of these
variables. The radiative transfer model is based on a model for the sea surface emissivity and a
model of microwave absorption in the Earth’s atmosphere. The ocean sea surface emissivity (or
reflectivity, see above) depends on the dielectric constant ε (which is a function of frequency, water
temperature, and salinity), small-scale sea surface roughness, foam, as well as viewing geometry
and polarization. The retrieval of a given parameter is possible through the inversion of a set of
brightness temperatures measured at different frequencies and/or at different incidence angles.
Inversion methods minimize the difference between measured and simulated (through a radiative
transfer model) brightness temperatures. And given uncertainties in radiative transfer models,
statistical or empirical inversions are also often used. These use a regression formalism (e.g.,
parametric, neural network) to find the best relation between brightness temperatures and the
geophysical parameter to be retrieved.
Altimetry
Overview
Satellite altimetry is the most essential observing systems required for global operational
oceanography (see Le Traon et al., 2017a for a recent review). It provides global, real-time, all-
weather sea level measurements (sea surface height or SSH) with high space and time resolution.
Sea level is directly related to ocean circulation through the geostrophic approximation. Sea level
is also an integral of the ocean interior (density) and a strong constraint for inferring the 4D ocean
circulation through data assimilation. Altimeters also measure significant wave height, which is
essential for operational wave forecasting. High resolution from multiple altimeters is required to
adequately represent ocean eddies and associated currents (aka the “ocean weather”) in models.
Only altimetry can constrain (through data assimilation) the 4D mesoscale circulation in ocean
models that is required for most operational oceanography applications.
Measurements principles
An altimeter is active radar that sends a microwave pulse towards the ocean surface. A precise clock
on board measures the return time of the pulse from which the distance or range (d) between the
satellite and the sea surface is derived (d=t/2c). The range precision is within a few centimeters for
a distance of 800 to 1300 km. The altimeter also measures the backscatter power related to surface
roughness and wind and significant wave height.
An altimeter mission generally includes a bifrequency altimeter radar (usually in Ku and C or S
Band) for ionospheric corrections, a microwave radiometer for water vapour correction, and a
tracking system for precise orbit determination (Laser, GPS, Doris) that provides the orbit altitude
relative to a given earth ellipsoid.
The main measurement for an altimeter radar is the SSH relative to a given earth ellipsoid. The
SSH is derived as the difference between the orbit altitude and the range measurement. SSH
precision depends on orbit and range errors. Altimeter range measurements are affected by a large
number of errors (propagation effects in the troposphere and ionosphere, electromagnetic bias,
errors due to inaccurate ocean and terrestrial tide models, inverse barometer effect, residual geoid
errors). Some of these errors can be corrected with dedicated instrumentation (e.g. dual frequency
altimeter, radiometer).
For a comprehensive description of altimeter measurement principles and measurement errors,
the reader is referred to Chelton et al. (2001) and Escudier et al. (2017).
Geoid and repeat‐track analysis

The altimeter missions provide along-track measurements every 7 km along repetitive tracks (e.g.,
every 10 days for the TOPEX/Poseidon and Jason series and 35 days for ERS, ENVISAT, and
SARAL/Alti-Ka in its repeat-track phase). The distance between tracks is inversely proportional to
the repeat time period (e.g., about 315 km at the equator for Jason and 90 km for
ERS/ENVISAT/SARAL).
The sea surface height SSH(x,t) measured by altimetry can be described by:
SSH(x,t) = N(x) + η(x,t) + ε(x,t)
where N is the geoid, η the dynamic topography and ε are measurement errors. The quantity of
interest for the oceanographer is the dynamic topography (see next subsection). Geoids are not
accurate enough to estimate globally the absolute dynamic topography η at all wavelengths.
The variable part of the dynamic topography η ' (η - < η >) (or sea level anomaly, SLA) is,
however, easily extracted using the so-called repeat track method. For a given track, η ' is obtained
by removing the mean profile over several cycles, which contains the geoid N and the mean
dynamic topography < η >:
SLA(x,t) = SSH(x,t) -<SSH(x)>t = η (x,t) - < η (x)>t + ε’(x,t)
To get the absolute signal, a climatology or existing geoids must be used together with altimeter
Mean Sea Surface (MSS), or both. A model mean can also be relied upon. Gravimetric missions
(GRACE, GOCE) are now providing much more accurate geoids. Even with GOCE, however,
repeat-track analysis is still needed because the small scales of geoid (below 50 to 100 km) will not
be precisely known. GOCE is used with an altimetric MSS to derive <η>t that can then be added to
η’ (see next subsection).
High‐level data processing issues and products

The SSALTO/DUACS system is the main multi-mission altimeter data center used today for
operational oceanography. It aims to provide directly usable, high quality near real-time and delayed
mode (for reanalyses and research users) altimeter products to the leading operational oceanography
and climate centers in Europe and worldwide. The main processing steps are product
homogenization, data editing, orbit error correction, reduction of long wavelength errors,
production of along-track data, and maps of sea level anomalies. Major progress has been made
with higher level processing issues such as orbit error reduction (e.g., Le Traon and Ogor, 1998),
intercalibration, and merging of altimeter missions (e.g., Le Traon et al., 1998; Ducet et al., 2000;
Pascual et al., 2006). The SSALTO/DUACS weekly production moved to daily production in 2007
to improve timeliness of data sets and products. A new real-time product was also developed for
specific real-time mesoscale applications. A review of the SSALTO/DUACS processing is given in
Dibarboure et al. (2011). Recent evolutions of the system are detailed on the DUACS website
(https://duacs.cls.fr/)
Accurate knowledge of the marine geoid is a fundamental element for the full exploitation of
altimetry for oceanographic applications and, in particular, for assimilation into operational ocean
forecasting systems. SSH measured by an altimeter is the sea level above the ellipsoid, which is the
sum of the Absolute Dynamic Topography (ADT or η) and the geoid height (N). The ADT is usually
obtained by estimating a Mean Dynamic Topography (MDT or <η>) and adding it to the altimetric
SLAs (η'). The MDT is obtained, at spatial scales where the geoid is known with sufficient accuracy,
as the difference between an altimeter Mean Sea Surface Height (MSSH=<SSH>) and a geoid
model.
Thanks to the recent dedicated space gravity missions of GRACE and GOCE, the knowledge of
the geoid at scales of around 100-150 km has greatly improved in the past years, so that the ocean
MDT is now resolved at those scales with centimetre accuracy. However, the true ocean MDT over
a given period (e.g., 10 – 20 years) contains scales shorter than 100-150 km, which are not resolved
in geoid models based on remote sensing. To compute higher resolution MDT, space gravity data
can be combined with altimetry and oceanographic in situ measurements such as hydrological
profiles from the Argo array and velocity measurements from drifting buoys. This approach was
used by Rio et al. (2014) to compute the CNES-CLS13 MDT, which is used in several global data
assimilation systems.
Operational oceanography requirements

Le Traon et al. (2006) have defined the main priorities for altimeter missions in the context of the
European Copernicus Marine Service. Tables 7.1 and 7.2 from this paper give the requirements for
different applications of altimetry and characteristics of altimeter missions.
Application area Accuracy* Spatial resolution Revisit Time Priority

1 Climate 1 cm* 300-500 km 10-20 days High
applications and
reference mission
2 Ocean nowcasting/ 3 cm* 50-100 km 7-15 days High
forecasting for
mesoscale
applications
3 Coastal/local 3 cm* 10 km 1 day Low**
Table 7.1: User requirements for different applications of altimetry (*for the given resolution; **limited by
feasibility).
Class Orbit Mission characteristics Revisit interval Track separation at

the Equator
A Non-sun High accuracy for climate 10-20 days 150-300 km
synchronous applications and to
reference other missions
B Polar Medium-class accuracy 20 - 35 days 80 - 150 km
Table 7.2: Altimeter mission characteristics.
The main operational oceanography requirements for satellite altimetry can be summarized as
follows:
1. There is a strong need to maintain a long time series of a high accuracy altimeter system
(e.g., the Jason series) to serve as a reference mission and for climate applications. It
requires one class A altimeter with an overlap between successive missions of at least six
months.
2. The main requirement for medium to high resolution altimetry would be to fly three class
B altimeters in addition to the Jason series (class A). Most operational oceanography
applications (e.g., marine security, pollution monitoring) require high resolution surface
currents that cannot be adequately reproduced without a high resolution altimeter system.
Studies (e.g., Pascual et al., 2006) have shown that at least three, but preferably four,
altimeter missions are needed for monitoring the mesoscale circulation. This is particularly
desirable for real-time nowcasting and forecasting. Pascual et al. (2009) showed that four
altimeters in real time provide similar results as two altimeters in delayed mode. Such a
scenario would also provide improved operational reliability. Moreover, it would enhance
the spatial and temporal sampling for monitoring and forecasting significant wave height.
In parallel, there is a need to develop and test innovative instrumentation (e.g., wide swath
altimetry with the NASA/CNES SWOT mission) to better answer existing and future operational
oceanography requirements for high to very high spatio/temporal resolution (e.g.,
mesoscale/submesoscale and coastal dynamics). There is also a need to improve nadir altimetry
technology (e.g., increase resolution, reduce noise) and to develop smaller and cheaper instruments
that could be embarked on a constellation of small satellites. For instance, the use of the Ka band
(35 Ghz) allows for a major reduction in the size and weight of the altimeter and improved
performances (Verron et al., 2018). The new generation of nadir altimeters provide enhanced
capability thanks to a SAR mode (also known as Delay-Doppler processing mode) that allows
reducing significantly the measurement noise level compared to conventional pulse-limited
altimeters (Dibarboure et al., 2014; Boy et al., 2017). Noise level for 1 Hz sampling (about 7 km)
is thus about 1 cm RMS for SAR altimeters compared to about 3 cm RMS for conventional pulse
limited altimeters. More information is given in Chapter 8 (Morrow et al.).
Sea Surface Temperature

Sea surface temperature measurements and operational oceanography
Sea surface temperature (SST) is a key variable in operational oceanography and for assimilation
into ocean dynamical models. SST is strongly related to air-sea interaction processes and provides
a means to correct for errors in forcing fields such as heat fluxes and wind. It also characterizes the
mesoscale variability of the upper ocean, resolving eddies and frontal structures, at very high
resolution (a few km). SST data are often directly used for operational oceanography applications.
They provide useful indices (e.g., climate changes, upwelling, thresholds). SST data can also be
used to derive high resolution velocity fields (e.g., Bowen et al., 2002). Accurate, stable, well-
resolved maps of SST are essential for climate monitoring and climate change detection. They are
also central for numerical weather prediction, for which the role of high resolution SST
measurements has been clearly evidenced (e.g., Chelton, 2005).
Infrared radiometers (IR) operate at wavebands around 3.7, 10.5 and 11.5 μm where the atmosphere
is almost transparent. The brightness temperature measured from infrared radiometers differs from
the actual temperature of the observed surface because of non-unit emissivity and the effect of the
atmosphere. Emissivity at IR frequencies is between 0.98 and 0.99 (close to a blackbody).
Atmospheric correction is based on a multispectral approach, when the differences between
brightness temperatures measured at different wavelengths are used to estimate the contribution of
the atmosphere to the signal. At 10 μm, the solar irradiance reaching the top of the atmosphere is
about 1/300 of the sea surface emittance. At 3.7 μm, the incoming solar irradiance is on the same
order as the surface emittance. As a result, this wavelength can be used during nighttime only.
Different algorithms are thus used for nighttime and daytime.
There is no IR way of measuring SST below clouds. Thus, the first priority is to detect cloud
through a variety of methods. For cloud detection, the thermal and near-infrared waveband
thresholds are used, as well as different spatial coherency tests. Poor cloud detection biases the SST
low in climatic averages, and “false hits” of cloud can hide frontal and other dynamical structures.
Geostationary infrared sensors can see whenever the cloud breaks.
Microwave sensors operate at several frequencies. Retrieval of SST is done at 7 and/or 11 GHz.
Higher frequency channels (19 to 37 GHz) are used to precisely estimate the attenuation due to
oxygen, water vapour, and clouds. The polarization ratio (horizontal versus vertical) of the
measurements is used to correct for sea surface roughness effects. The great advantage of
microwave measurements compared to infrared measurements is that SST can be retrieved even
through non-precipitating clouds, which is very beneficial in terms of geographical coverage.
SST infrared and microwave sensors

Infrared radiometers, such as the Advanced Very High Resolution Radiometer (AVHRR), on board
operational meteorological polar orbiting satellites offer a good horizontal resolution (1 km) and
potentially global coverage, with the important exception of cloudy areas. However, their accuracy
(0.4 to 0.5°K derived from the difference between collocated satellite and buoy measurements) is
limited by the radiometric quality of the AVHRR instrument and the correction of atmospheric
effects. Geostationary satellites (e.g., the GOES and MSG series) are carrying radiometers with
infrared window channels similar to the ones on the AVHRR instrument. Their horizontal resolution
is coarser (3-5 km), but their high temporal resolution sampling provides an advantage. Advanced
measurements of SST suitable for climate studies include the Along Track Scanning Radiometer
(ATSR) series of instruments, which have improved on-board calibration and make use of dual
views at nadir and 55° incidence angle. The along track scanning measurement provides an
improved atmospheric correction leading to an accuracy better than 0.2°K (O’Carroll et al, 2008).
The main drawback of these instruments is their limited coverage due to a much narrower swath
than the AVHRR instruments. Several microwave radiometers have also been developed and flown
over the last 10 years (e.g., AMSR, TMI). The horizontal resolution of these products is around 25
km and their accuracy around 0.6 – 0.7°K.
Key developments in SST data processing

During the past ten years, there has been a concerted effort to understand satellite and in situ SST
observations, revolutionizing the way we approach the provision of SST data to the user community.
GODAE, recognizing the importance of high resolution SST data sets for ocean forecasting,
initiated the GODAE High Resolution SST Pilot Project (GHRSST-PP) to capitalize on these
developments and create a set of dedicated products and services. There have also been key
advances in the data processing of SST data sets over the last 10 years. As a result, new or improved
products are now available. A full description of the GHRSST-PP is provided by Donlon et al.
(2009). Data processing issues are summarized by Le Traon et al. (2009).
A satellite measures the so-called skin temperature, i.e., from a few tens of microns (infra-red)
up to only a few mm (microwave). Diurnal warming changes the SST over a layer of 1 to 10 meters.
The effect can be particularly large in regions of low wind speed and high solar radiation. GHRSST
has defined the foundation SST as the temperature of the water column free of diurnal temperature
variability. A key issue in SST data processing is to correct satellite SST measurements for skin and
diurnal warming effects to provide precise estimations of the foundation SST. Night and day SST
data from different satellites can then be merged through an optimal interpolation or a data
assimilation system.
Several new high resolution SST products have been produced specifically in the framework of
GHRSST-PP. These high resolution data sets are estimated by optimal interpolation methods
merging SST satellite measurements from both infrared and microwave sensors. The pre-processing
consists mainly of screening and quality control of the retrieved observations from each single data
set and then constructing a coherent merged multi-sensor set of the most relevant and accurate
observations (level 3). The merging of these observations requires a method for bias estimation and
correction (relative to a chosen reference, currently AATSR). Finally, the gap-free SST foundation
field is computed from the merged set of selected observations using an objective analysis method.
The guess is either climatology or a previous map.

Table 7.3 from Le Traon et al. (2006) summarises weather, climate, and operational oceanography
requirements for SST. No single sensor is adequate meets the key requirements for SST. To remedy
this, GHRSST-PP has established an internationally accepted approach to blending SST data from
different sources that complement each other (refer to previous subsection). For this to work
effectively, there must be an assemblage of four distinct types of satellite SST missions in place at
any time, as defined in Table 7.4 (from Le Traon et al., 2006).
Application area Temperature Spatial resolution Revisit Time Priority

accuracy [K] [km]
1 Weather prediction 0.2 – 0.5 10 – 50 6 – 12 hrs High
2 Climate monitoring 0.1 20 – 50 8d High
3 Ocean forecasting 0.2 1 – 10 6 – 12 hrs High
Table 7.3: User requirements for SST provision.
The priority expressed by the international SST community, through GHRSST, is to continue to
provide a type B (ATSR class) sensor. Its on-board calibration system, especially its dual-view
methodology, allow AATSR to deliver the highest achievable absolute accuracy of SST, robustly
independent of factors that cause significant biases in other infrared sensors such as stratospheric
aerosols from major volcanic eruptions or tropospheric dust. Because its absolute calibration (for
dual view) is better than 0.2 K it is used for bias correction of the other data sources before
assimilation into models or analyses. A type C sensor (microwave) is also required beyond AMSR-
E on Aqua.
Ocean Colour
Ocean colour measurements and operational oceanography
Over the last decade, the applications of satellite-derived ocean colour data have made important
contributions to biogeochemistry, physical oceanography, ecosystem assessment, fisheries
oceanography, and coastal management (IOCCG, 2008). Ocean colour measurements provide a
global monitoring of chlorophyll (phytoplankton biomass) and associated primary production. They
can be used to calibrate and validate biogeochemical, carbon, and ecosystem models. Progress
towards assimilation of ocean colour data is less mature than for SST or SSH, but there are already
convincing examples of assimilation of chlorophyll-a (Chla) in ocean models.
Data products needed to support ocean analysis and forecasting models of open ocean
biogeochemical processes include the concentration of chlorophyll-a (Chla), total suspended
material (TSM), the optical diffuse attenuation coefficient (K), and the photosynthetically available
radiation (PAR). Use of K and PAR is needed to define the in-water light field that drives
photosynthesis in ocean ecosystem models and that is required to model and forecast the ocean
surface temperature. Ocean colour is a tracer of dynamical processes (mesoscale and submesoscale)
and this is of great value for model validation. It also plays a role in air-sea CO2 exchange
monitoring.
SST mission type Radiometer Nadir Swath Coverage /

wavebands resolution width revisit
A Two polar orbiting 3 thermal IR (3.7, 11, ~1 km ~2500 Day and
meteorological satellites 12 μm) 1 near-IR, 1 km night global
with infrared radiometers. Vis coverage by
Generates the basic global each satellite
coverage
B Polar orbiting dual-view 3 thermal IR (3.7, 11, ~1 km ~500 km Earth
radiometer. SST accuracy 12 μm) 1 near-IR, 1 coverage in
approaching 0.1K, used as vis, each with dual ~4 days
reference standard for other view
types.
C Polar orbiting microwave Requires channels at ~50 km ~1500 Earth
radiometer optimised for ~7 and ~11 GHz (25 km km coverage in 2
SST retrieval. Coarse pixels) days
resolution coverage of
cloudy regions
D Infrared radiometers on 3 thermal IR (3.7, 11, 2 - 4 km Earth Sample
geostationary platforms. 12 μm) 1 near-IR, 1 disk from interval < 30
Spaced around the Earth Vis 36000 min
km
altitude
Table 7.4: Minimum assemblage of missions required to meet the need for operational SST.
At regional and coastal scales, there are many applications that require ocean colour measurements:
monitoring of water quality, measurement of suspended sediment, sediment transport models,
measurement of dissolved organic material, validation of regional/coastal ecosystem models (and
assimilation), detection of plankton and harmful algal blooms, and monitoring of eutrophication.
Use of ocean colour data in coastal seas is, however, more challenging as explained below.
The sunlight is not merely reflected from the sea surface. The colour of water surface results from
sunlight that has entered the ocean, been selectively absorbed, scattered and reflected by
phytoplankton and other suspended material in the upper layers, and then backscattered through the
surface. The subsurface reflectance R(λ) (ratio of subsurface upwelled or water-leaving radiance on
incident irradiance) that is the ocean signal measured by a satellite is proportional to
b(λ)/[a(λ)+b(λ)] or b(λ)/a(λ) where b(λ) is the backscattering and a(λ) the absorption of the different
water constituents.
Sunlight backscattered by the atmosphere (aerosols and molecular/Rayleigh scattering)
contributes to more than 80% of the radiance measured by a satellite sensor at visible wavelengths.
Atmospheric correction is calculated from additional measurements in the red and near-infrared
spectral bands. Ocean water reflects very little radiation at these longer wavelengths (the ocean is
close to a blackbody in the infrared) and the radiance measured is thus due almost entirely to
scattering by the atmosphere.
Unlike observations in the infrared or microwave frequencies for which emission is from the
sea surface only, ocean colour signals in the blue-green can come from depths as great as 50 m.
Sources of ocean colour variations include:
 Phytoplankton and its pigments

 Dissolved organic material
o Coloured Dissolved Organic Material (CDOM or yellow matter) is derived from
decaying vegetable matter (land) and phytoplankton degraded by grazing or
photolysis.
 Suspended particulate matter (SPM)
o The organic particulates (detritus) consist of phytoplankton and zooplankton cell
fragments and zooplankton fecal pellets.
o The inorganic particulates consist of sand and dust created by erosion of land-
based rocks and soils (from river runoff, deposition of wind-blown dust, wave or
current suspension of bottom sediments).
Colour can tell us about relative and absolute concentrations of those water constituents that
interact with light. Hence we measure chlorophyll, yellow substance, and sediment load. It is
difficult to distinguish independently varying water constituents:
 Case 1 waters are where the phytoplankton population dominates the optical properties
(typically open sea). Only one component modulates the radiance spectrum backscattered
from the water (phytoplankton pigment). Concentration range is 0.03 – 30 mg m-3. Water
in the near IR is nearly black for blue water. Atmospheric correction that is based on IR
frequency measurements is thus relatively simple. Using green/blue ratio algorithms for
chlorophyll, of the form Chla = A(R550/R490), provides an accuracy for Chla of ~ ±30%
in open ocean.
 Case 2 waters are where other factors (CDOM, SPM) are also present. There are multiple
independent components in water, which have an influence on the backscattered radiance
spectrum. The retrieval procedure has to deal with these multiple components, even if only
one should be determined. At high total suspended matter concentrations, problems also
occur with atmospheric correction. Therefore, more complex algorithms (e.g., neural
network) and more frequencies are required. Although this remains a challenging task,
much progress has been made over the past five years. Useful estimations of Chla and SPM
can thus be obtained in the coastal zone (e.g., Gohin et al., 2005).
Ocean colour can also provide information on phytoplankton functional types as changes in
phytoplankton composition can lead to changes in absorption and backscattering coefficients
(IOCCG, 2014). This is an area of active research with important implications for the assimilation
of ocean colour data in ocean models.
An ocean colour satellite should have a minimum number of bands from 400-900nm. The role
of the various bands is:
 413 nm - Discrimination of CDOM in open sea blue water.

 443, 490, 510, 560 nm - Chlorophyll retrieval from blue-green ratio algorithms.
 560, 620, 665 nm and others - Potential to retrieve water content in turbid Case 2 waters
using new red-green algorithms.
 665, 681, 709 nm and others - Use of fluorescence peak for chlorophyll retrieval.
 779, 870 nm for atmospheric correction plus another above 1000 nm to improve correction
over turbid water.
Processing issues
The processing transforms the level 1 data, normalized radiances observed by the ocean colour
radiometer, into geophysical properties corrected from atmospheric effects. Level 2 products
include water leaving radiances at different wavelengths, chlorophyll-a concentration of the surface
water (usually with case 1 and case 2 algorithms), total suspended matter (TSM), coloured dissolved
and detrital organic materials (CDOM), diffuse attenuation coefficient (K) and photosynthetically
available radiation (PAR).
Merging of several ocean colour satellites is needed to improve the daily ocean coverage. This
requires combining data from individual sensors with different viewing geometries, resolution, and
radiometric characteristics (e.g. IOCCG 2007; Le Traon et al., 2015). The availability of merged
datasets allows the users to exploit a unique, quality-consistent, time series of ocean colour
observations, without being concerned with the performance of individual instruments.
Category Category of Optical Minimum set of Accuracy Spatial Revisit

use class of satellite-derived [%] resolution Time
water variables needed [km]
1 Assimilation Case 1 Chlor 30% 2-4 1 – 3 days
into K 5%
operational PAR 5%
open ocean Lw(λ) 5%
models
2 Ingestion in Case 2 K- 5% 0.5 - 2 1 day
operational PAR 5% desired,
shelf sea & Lw(λ) 5% but 3-5
local models Chlor 30% days
TSM 30% useful
CDOM 30%
3 Data products Case 2 K 5% 0.25 - 1 1 day
used directly PAR 5% desired,
by marine Lw(λ) 5% but 3-5
managers in Chlor 30% days
shelf seas TSM 30% useful
CDOM 30%
4 Global ocean Case 1 Chlor 10 – 30% 5 - 10 8d
climate K 5% average
monitoring PAR 5%
5 Coastal ocean Case 2 Chlor 10 – 30% 5 8 day
climate TSM 10 – 30% average
monitoring CDOM K 10 – 30%
PAR 5%
K 5%
6 Coastal and Case 2 Lw(λ) 5% 0.1 - 0.5 0.5 – 2 hrs
estuarine
water quality
monitoring
Table 7.5: User requirements for ocean colour data products

The needs and the broad classes of colour sensor are summarised in Tables 7.5 and 7.6 from Le
Traon et al. (2006). They distinguish categories of use between the needs of the open ocean
forecasting models, the finer scale shelf sea and local models, and those operational end users who
analyse the data directly rather than through assimilation into a model system. There is a variety of
additional products desired in coastal waters depending on the local water character. These include
the CDOM and the discrimination of different functional groups of phytoplankton.
Some operational users prefer to use directly the atmospherically corrected water leaving
radiance, Lw(λ) (defined over the spectrum of given wavebands), applying their own approach for
deriving water quality information or for confronting a model. Climate applications (categories 4
and 5) are envisaged to be derived from the operational categories 1 and 2, respectively, trading
spatial and temporal resolution for improved accuracy. Category 6 is included in Table 7.5 to
represent those users needing to monitor estuarine processes in fine spatial detail and to resolve the
variations within the tidal cycle. This is a much more demanding category than the others.
A Class A simple SeaWiFS-like instrument with a resolution of 1 km and a set of 5 or 6
wavebands would be adequate for user Categories 1 and 4, to monitor global chlorophyll for
assimilation into open ocean ecosystem models and for monitoring global primary production. It
would fail to meet the main requirement to monitor water quality in coastal and shelf seas
represented by user categories 2 and 3. These require a Class B imaging spectrometer sensor.
In order to satisfy the ocean colour measurement requirements for operational oceanography,
the minimum requirement is for one Class B sensor and at least one other sensor (Class A, B or C).
The Class C sensor corresponds to an imaging spectrometer on a geostationary platform. As well
as uniquely serving the user Category 6 by resolving variability within the tidal cycle, it also serves
other user categories in cloudy conditions by exploiting any available cloud windows that occur
during the day.
Class Orbit Sensor type Revisit Spatial Priority

Time resolution
A Polar SeaWiFS type multispectral 3 days 1 km High
scanner, 5-8 Vis-NIR
wavebands
B Polar Imaging spectrometer 3 days 0.25 – 1 km High
(MERIS/MODIS type)
C Geostationary Radiometer or spectrometer - 30 min 100 m – 2 km Medium
feasibility to be determined
Table 7.6: Classes of ocean colour sensor
Other Techniques
Synthetic aperture radar
SAR is an active instrument that transmits and receives electromagnetic radiation. It operates at
microwave (or radar) frequencies. Wavelengths are in the range of 2 cm to 30 cm corresponding to
frequencies in the range of 15 GHz – 1 GHz. It works in the presence of clouds, day and night.
Synthetic aperture principle is to generate a very long antenna through the motion of the platform.
For ASAR, the length of the synthetic antenna is approximately 20 km. This leads to very high
resolution.
The surface roughness is the source for the backscatter of the SAR signal. The signal that arrives
at the antenna is registered both in amplitude and phase. Although the SAR sees only the Bragg
waves (λB = λ/2 sin θ, where θ is the incidence angle, λ the radar wavelength and λB the resonant
Bragg wavelength), these waves are modulated by a large number of upper ocean and atmospheric
boundary layer phenomena. This is the reason why SAR images express wave field, wind field,
currents, fronts, internal waves, and spilled oil. They also provide high resolution images of sea ice
(see next sub section).
Sea ice
Low resolution passive microwave sensors (Special Sensor Microwave/Imager (SSM/I) series of
the US Defense Meteorological Satellite Program) have provided essential sea ice extent and
concentration data from 1979 to present. These data can be extended and combined with
scatterometer data. Moreover, sea ice drift estimates from scatterometers and radiometers are widely
used for model validations and contribute importantly to the long-term sea ice monitoring. Sea ice
thickness observations are needed to enable accurate estimations of the total sea ice volume. Great
expectations were given to the CryoSat-2 launched in April 2010 with an altimeter designed for sea
ice freeboard measurements. Thanks to the careful inter-comparison to the ICESAT laser altimeter
mission data the retrieval accuracy has been reliably assessed (e.g. Laxon et al., 2013). Thickness
of thin sea ice (<30 cm) for the Arctic freeze-up period can also be derived from the SMOS mission,
which make these data highly complementary to the Cryosat-2 estimates of the thicker sea ice. From
2016, the Sentinel-1 A/B satellites have delivered high resolution (better than 30 m) SAR data in
wide swath mode and simultaneous co- and cross-polarisation. This allows estimations of sea ice
drift with a daily temporal resolution. Studies are now focusing on sea ice thickness, drift, and
deformation analysis combining satellite data (like SMOS, CryoSat-2) with new data from the
Sentinel-1 SAR missions.
Winds and waves

Scatterometers (e.g., Seawinds/QuikSCAT, ASCAT/MetOp) are radars operating at C or Ku bands.
The main ocean parameters measured are the wind speed and direction. They also provide useful
information on sea ice roughness. The principle is based on the resonant Bragg scattering. For a
smooth surface, oblique viewing of the surface with active radar yields virtually no return. When
wind increases, so does surface roughness and the reflected signals towards the satellite sensor. The
wind direction can be derived because of the azimuthal dependence of the reflected signal with
respect to the wind direction.
Over the past five years, there have been significant advances to develop and make accessible a
harmonized set of altimeter and SAR wave products. These have been invaluable for numerical
wave modelling and for applications. In particular, multiple altimeter missions have continued to
provide precise significant wave height observations with a global coverage, which are essential to
calibrate and validate numerical wave models and improve their forecasting skills through data
assimilation. The sequence of ESA SAR C- and X-band instruments continuously operated on the
ERS-1, ERS-2, ENVISAT RADARSAT, and TerraSAR-X satellites from 1991–2015 has also had
a valuable impact on ocean wave observation and modelling, especially with regards to adequate
determination of the swell attenuation over large distances. The future CFOSAT mission (to be
launched in 2018) is expected to provide significant advances for winds and waves monitoring from
space.
A new challenge: estimate sea surface salinity from space

At L-band (1.4 GHz), brightness temperature (BT) is mainly affected by ocean surface emission
(atmosphere is almost transparent):
BT = e SST = (1-R) SST
where BT is brightness temperature and e is the sea surface emissivity. R (θ, SSS, SST, U…) is the
reflection coefficient (see section 3.3). R depends on sea water permittivity and thus on sea surface
salinity. Sensitivity is maximum at L-band, however it is very low (0.2 - 0.8 °K/psu) and increases
with sea surface temperature.
The SMOS satellite was launched in November 2009. It is an L-band radiometer that measures
BT at different incidence angles (0-60°). SMOS is a synthetic aperture radiometer which provides
a high spatial resolution (∼40 km, precision 1 PSU). SSS accuracy of 0.1-0.2 PSU over 10-day 200
km x 200 km areas is achieved through averaging of individual measurements. The Aquarius
satellite was launched in 2010. It is a conventional L-band radiometer operating at three incident
angles. Aquarius includes an L-band scatterometer to correct for sea surface roughness effects.
These missions provide global SSS at spatial resolution varying from 50 km (SMOS) to 100 km
(Aquarius) on weekly to monthly time scales. Many scientific studies have revealed the high
potential of these new data sets (see Lagerloef et al., 2014 and Reul et al., 2014 for a review). Efforts
to demonstrate the impact of satellite SSS data assimilation for ocean analysis and forecasting is an
ongoing activity (see chapter by T. Lee). Measurement errors remain an issue, but more work should
be carried out in the coming years thanks to improved data sets and products.
Assessing the Impact of Satellite Observations in GOV models

GOV (GODAE OceanView) systems have been used to investigate the impact of satellite data
within their data assimilation and forecasting framework in a number of global and regional studies
summarized in Oke et al. (2015a) and Oke et al. (2015b). This is also the focus of the GOV OSEval
(Observing System Evaluation) Task Team. Most of these studies demonstrate the impact of the
satellite data in the context of the other observing systems using Observing System Experiments
(OSEs) whereby an experiment is run assimilating all available data, and a parallel run of the system
is carried out assimilating all the data except for the data type to be investigated. The difference
between the two runs shows the impact of the withheld data in the context of all the other data, and
the two runs can be assessed by comparing the outputs with assimilated and independent data. Le
Traon et al. (2017) provided a review on the use of OSEs to assess the impact of the altimeter
constellation on GOV systems. A complementary approach for estimating the influence of the ob-
servations on the analysis is the computation of the so-called “degrees of freedom for signal” (DFS),
which represents the equivalent number of independent observations that constrain the model anal-
ysis at the observation point (Cardinali et al., 2004). DFS quantifies the influence of observations
on analyses without having to run dedicated experiments withholding some data.
Impact of future observations can be assessed using OSSEs (Observing System Simulation Ex-
periments). OSSEs typically use two different model runs (from two different models or from the
same model with different resolution, parametrization, or forcing). One model is used to perform a
“truth” run and it is treated as if it is the real ocean. The truth run is sampled in a manner that mimics
either an existing or future observing system, yielding synthetic observations. A measurement error
(and often a representativity error) is then added to the synthetic observations that are then assimi-
lated into the second model. The model performance is evaluated by comparing it against the truth
run. OSSEs need a careful design so that results are representative of the actual ocean. Calibration
of OSSEs with OSEs (i.e., verifying that, for an existing observing system, an OSSE yields the same
result as an OSE) should be systematically applied. For example, Mercator Ocean has performed
first OSSEs of SWOT observations using a 1/12° regional model of the Iberian Biscay region that
includes tidal forcing (Benkiran et al., 2017). The truth run was derived from a 1/36° model run
over the same region. SWOT errors were derived using the JPL SWOT Simulator. This first study
quantified the highly significant improvement of SWOT data with respect to existing conventional
nadir altimeters to constrain ocean models.
Future Satellite Requirements

Challenges for the next decade
A complete overview of the evolution (past, present, future) of the satellite ocean mission details is
maintained within the CEOS Earth Observation handbook (http://www.eohandbook.com/) and/or
the World Meteorological Organization’s Observing Systems Capability Analysis and Review (OS-
CAR) tool (http://www. wmo-sat.info/oscar/).
The satellite-based data record lengths now exceed 25 years (altimetry, SAR, scatterometry) and
35 years (radiometry). In the 2020-2030 timeframe, sustained satellite observations will be common
for almost all the variables addressed in the previous section, except for the sea surface salinity.
Altogether, this ensures that operational oceanography will be supplied with a rich amount of highly
important satellite observations. In addition to securing continuity in the observations, the retrieval
accuracies have also gradually improved thanks to advancing sensor technology and retrieval algo-
rithm performances.
Still, many key ocean phenomena are undersampled and require much higher space and time
resolution. Model resolutions are regularly increasing, but our observation capabilities are not.
Coastal regions, which are of paramount importance for operational oceanography, are character-
ized by small spatial and temporal scales. There is also growing evidence that we need to better
observe the submesoscale ocean dynamics. Hence, a major challenge for satellite oceanography is
to address the ongoing need for improved resolution. This challenge is partly addressed through the
development of virtual constellations, but it also requires developing new observing capabilities
(e.g., swath altimetry with SWOT, geostationary ocean colour missions). New satellite observing
capabilities (e.g., for surface currents) also need to be developed (e.g., SKIM, see Ardhuin et al.,
2017). Fusing of different types of observations to extract better information is another complemen-
tary approach.
Future requirements: The Copernicus Marin Service perspective

The Copernicus Marine Environment Monitoring Service (CMEMS) is one of the six pillar services
of the Copernicus program (see Le Traon et al., 2017 and chapter by Drevillon et al.). CMEMS
includes most of the European contributions to GOV. It provides regular and systematic reference
information on the physical state, variability, and dynamics of the ocean, ice, and marine ecosys-
tems for the global ocean and the European regional seas. Copernicus Marine Service perspectives
with respect to the long-term evolution of satellite observing systems are given hereafter.
First, continuity of the present Copernicus satellite observing system should be guaranteed as this
is mandatory for maintaining the CMEMS service. This is particularly relevant to the Sentinel 6
altimeter reference mission (follow-on of Jason-3) and the two satellite constellation of Sentinel 3
(altimetry, sea surface temperature, and ocean colour) and Sentinel 1 (SAR).
In the post 2025 time period, Copernicus Marine Service model resolutions will be increased by
a factor of at least 3 (e.g. global 1/36°, regional 1/108°) compared to the present and more advanced
data assimilation methods will be available. The objective will be to describe at fine scale the upper
ocean dynamics to improve our capabilities to describe and forecast the ocean currents and provide
better boundary conditions for very high resolution coastal models (up to a few hundred meters).
This is essential for key applications such as maritime safety, maritime transport, search and rescue,
fish egg and larvae drift modelling, riverine influence in the coastal environments, pollution moni-
toring, and offshore operations. When moving to higher resolution, it will be necessary to constrain
CMEMS models with new observations. The most important satellite-based observation is SSH
from altimetry. As explained in previous sections, the SSH is an integral of the ocean interior prop-
erties and is a strong constraint for inferring the 4D ocean circulation through data assimilation.
Multiple nadir altimeters (at least 4 altimeters) are required to adequately represent ocean eddies
and associated currents in models. Much higher space/time resolution (e.g., 50 km / 5 days) will be
needed in the post-2025 time period. This can be achieved through a combination of swath altimetry
(to be demonstrated during the SWOT mission to be launched in 2021) with nadir SAR altimetry.
Monitoring the ecosystems in European seas is also a fundamental need, both for European pol-
icies (Marine Strategy) to monitor the health of the seas and coastal waters and to support sustain-
able fishery and aquaculture industries. Specifically, much more frequent observations of the highly
variable biological parameters of the European regional seas are urgently needed. This is required
to monitor the ocean ecosystem functioning at the diurnal scale and to monitor rapidly evolving
phenomena (e.g. river outflows, phytoplankton and harmful algae blooms, sub-mesoscale features)
and to constrain coupled biological-physical 3D models in regional seas and coastal zones. An
ocean colour geostationary satellite would provide unique capabilities to provide such a monitoring.
It should be complemented with new in situ biogeochemical observations (e.g., BGC Argo, Gliders,
FerryBoxes). The development of hyperspectral sensor capabilities would also be relevant to
CMEMS (e.g., to improve the quality of Ocean Colour products in coastal zones and to differentiate
between the types of phytoplankton in the ocean) (e.g., the NASA PACE and ASI PRISMA mis-
sions).
Sustainable passive microwave SST and sea ice observations are also very important in the
global ocean and in polar regions. Such observations are available in all weather conditions, while
infrared SST observations are available in cloud-free conditions only. Passive microwave SST and
sea ice are a crucial contribution to weather forecasting and CMEMS ocean and analysis and fore-
casting models. CMEMS also requires specific observations for polar regions. In addition to passive
microwave observations, one of the most important short term priorities is the continuation (includ-
ing a few enhancements such as additional use of Ka band, more optimized orbit configuration,
real-time capabilities) of the Cryosat-2 mission to monitor sea ice thickness, continental ice shelves
elevation changes and contribute to the observation of the ocean surface topography in ice free
regions.
Other requirements (e.g., surface current, SSS, wave, improved geoid, wind) should also be
considered. Today, they do not have today the same (due to maturity of satellite technology or
foreseen impact on the service). Surface current and SSS are two very important variables required
for CMEMS. The potential impact is high but this requires developments or improvements in sat-
ellite technology. Thus, research and development should be done to further advance our capabili-
ties to observe SSS from space building on SMOS achievements. There is also a strong need to test
new mission concepts allowing the direct measure of surface currents at high resolution and to
develop capabilities to fully use these observations to constrain ocean analysis and forecasting sys-
tems. Meanwhile, use of imaging SAR-based range Doppler retrievals (S1) should be reinforced.
Waves are observed today through altimeter (S3, S6) and SAR (S1) missions. New satellite
concepts (CFOSAT) will soon allow for a better retrieval of directional wave spectra. This could
lead to an improved design of future Sentinel missions. Finally, new gravity missions could be
required to improve the geoid at small scales (and derived mean dynamic topography) and to mon-
itor large-scale mass changes in the ocean. However, the main priority at present is to further de-
velop the exploitation of GOCE data and to derive new mean dynamic topographies from the
merging of GOCE, altimetry, and in situ observations. The Ocean Surface Vector Wind (OSVW)
measurements through scatterometers are also important to improve NWP forcing fields. Europe,
through the Eumetsat MetOP series, provides a unique contribution to the international CEOS
OSVW virtual constellation. This should be pursued and coordination of the CEOS OSVW should
be reinforced to optimize the existing and future scatterometer constellation.
Concluding Remarks
The chapter provides a brief introduction to ocean remote sensing measurement principles and the
use of satellite observations for operational oceanography. The different techniques will be detailed
further in the other chapters. More information can also be found in Fu and Cazenave (2001), Rob-
inson (2004), Martin (2004), Emery and Camps (2017), and Stammer and Cazenave (2017).
Satellite data plays a fundamental role for operational oceanography. There have been important
achievements over the past 10 years to ensure real-time availability of high quality satellite data and
to develop the use of satellite observations for operational oceanography. Multiple mission high
resolution altimeter products are now readily available. There have been many improvements (time-
liness, new products) and major efforts were undertaken to include new missions in the operational
data stream in a very limited time. New MDTs from GRACE and GOCE have a major impact on
data assimilation systems and further improvements are expected. Thanks to GHRSST, major im-
provements in SST data processing techniques and use of different types of sensors have occurred.
The use of ocean colour data in operational oceanography has become a reality and there has been
continuous progress in data access, data processing, and data assembly systems. SMOS and Aquar-
ius have demonstrated the feasibility and utility of measuring SSS from space. The ocean commu-
nity and GOV now need to fully invest in the critical assessment and application of the data.
In situ data are mandatory to calibrate and validate and to complement satellite observations.
Although the consolidation of the Argo in situ observing system and its integration with satellite
altimetry and operational oceanography (e.g., Le Traon, 2013) was an outstanding advancement,
the evolution of the in situ observing system remains a strong concern. The potential of satellite
observations is not and will not be fully realized without a sustained in situ observing system.
Improvements in models and data assimilation techniques have resulted in a better use of satel-
lite observing capabilities. However, the information content of satellite observations is not being
fully exploited. Use of new theoretical frameworks to better exploit high resolution information
from satellite data is required and further improvements in data assimilation schemes are needed to
better take into account observations (e.g., towards L1B and L2 assimilation, correlated errors, bi-
ases, representativity errors). The potential of ocean colour data to calibrate or improve biogeo-
chemical models is considerable. But this is a complex issue and, as such, development lags behind
other remote sensing techniques. This is a challenging and high priority research topic for opera-
tional oceanography.
There is a need to develop further OSE/OSSE activities (GOV Task Team). This is essential to
define needs, quantify impacts, and to improve data assimilation systems.
Finally, new satellite missions such as high resolution altimetry (SWOT) missions will likely
have a major impact on operational oceanography. There is also great potential for satellite missions
to improve the monitoring of waves and winds (e.g., CFOSAT, see Hauser et al., 2016) or for di-
rectly measuring surface currents from space (e.g., SKIM, see Ardhuin et al., 2017).
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CHAPTER 7

Satellites and Operational Oceanography

Role of Satellites in Operational Oceanography

The unique contribution of satellite observation

 In addition to meteorological satellites, a high precision (Advanced Along-Track Scanning

Role of in situ data

Data processing issues

Use of satellite data for assimilation into ocean models

Overview of Satellite Oceanography Techniques

Satellite orbits and measurement characteristics

Radiation laws and emissivity

Lλ= 2hc2 / λ5 [exp (hc/λkT)-1]

Graybodies and emissivity

Retrieval of geophysical parameters for microwave radiometers

Geoid and repeat‐track analysis

SSH(x,t) = N(x) + η(x,t) + ε(x,t)

SLA(x,t) = SSH(x,t) -<SSH(x)>t = η (x,t) - < η (x)>t + ε’(x,t)

High‐level data processing issues and products

Operational oceanography requirements

Application area Accuracy* Spatial resolution Revisit Time Priority

Class Orbit Mission characteristics Revisit interval Track separation at

Table 7.2: Altimeter mission characteristics.

Sea Surface Temperature

SST infrared and microwave sensors

Key developments in SST data processing

Operational oceanography requirements

Application area Temperature Spatial resolution Revisit Time Priority

Table 7.3: User requirements for SST provision.

SST mission type Radiometer Nadir Swath Coverage /

 Phytoplankton and its pigments

 413 nm - Discrimination of CDOM in open sea blue water.

Category Category of Optical Minimum set of Accuracy Spatial Revisit

Table 7.5: User requirements for ocean colour data products

Operational oceanography requirements

Class Orbit Sensor type Revisit Spatial Priority

Table 7.6: Classes of ocean colour sensor

Winds and waves

A new challenge: estimate sea surface salinity from space

BT = e SST = (1-R) SST

Assessing the Impact of Satellite Observations in GOV models

Future Satellite Requirements

Future requirements: The Copernicus Marin Service perspective

Le Traon, P.Y., G. Larnicol, S. Guinehut, S. Pouliquen, A. Bentamy, D. Roemmich, C. Donlon, H. Roquet, G.

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