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2002 353DONLON ET AL.
q 2002 American Meteorological Society
Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements
for Climate Research
C. J. D
ONLON
,* P. J. M
INNETT
,
1
C. G
ENTEMANN
,
#
T. J . N
IGHTINGALE
,
@
I. J. B
ARTON
,
&
B. W
ARD
,**
AND
M. J. M
URRAY
11
*CEC-Joint Research Centre, Institute for Environment and Sustainability, Inland and Marine Waters Unit, Ispra, Italy
1
Meteorology and Physical Oceanography Division, Rosenstiel School of Marine and Atmospheric Science, University of Miami,
Miami, Florida
#
Remote Sensing Systems, Santa Rosa, California
@
Space Science and Technology Department, Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, United Kingdom
&
CSIRO Marine Research, Hobart, Tasmania, Australia
**Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School of Marine and Atmospheric Science, University of Miami,
Miami, Florida
11
Space Science and Technology Department, Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, United Kingdom
(Manuscript received 6 April 2001, in final form 27 July 2001)
ABSTRACT
A poor validation strategy will compromise the quality of satellite-derived sea surface temperature (SST)
products because confidence limits cannot be quantified. This paper addresses the question of how to provide
the best operational strategy to validate satellite-derived skin sea surface temperature (SST
skin
) measurements.
High quality in situ observations obtained using different state-of-the-art infrared radiometer systems are used
to characterize the relationship between the SST
skin
, the subsurface SST at depth (SST
depth
), and the surface wind
speed. Data are presented for different oceans and seasons. These data indicate that above a wind speed of
approximately6ms
21
the relationship between the SST
skin
and SST
depth
, is well characterized for both day- and
nighttime conditions by a cool bias of 20.17 6 0.07 K rms. At lower wind speeds, stratification of the upper-
ocean layers during the day may complicate the relationship, while at night a cooler skin is normally observed.
Based on these observations, a long-term global satellite SST
skin
validation strategy is proposed. Emphasis is
placed on the use of autonomous, ship-of-opportunity radiometer systems for areas characterized by prevailing
low–wind speed conditions. For areas characterized by higher wind speed regimes, well-calibrated, quality-
controlled, ship and buoy SST
depth
observations, corrected for a cool skin bias, should also be used. It is foreseen
that SST
depth
data will provide the majority of in situ validation data required for operational satellite SST
validation. We test the strategy using SST
skin
observations from the Along Track Scanning Radiometer, which
are shown to be accurate to approximately 0.2 K in the tropical Pacific Ocean, and using measurements from
the Advanced Very High Resolution Radiometer. We note that this strategy provides for robust retrospective
calibration and validation of satellite SST data and a means to compare and compile in a meaningful and
consistent fashion similar datasets. A better understanding of the spatial and temporal variability of thermal
stratification of the upper-ocean layers during low–wind speed conditions is fundamental to improvements in
SST validation and development of multisensor satellite SST products.
1. Introduction
It has long been realized that global ocean measure-
ments of sea surface temperature (SST) collected by
radiometers on satellites have made a major contribution
to climate research. In addition to its role as a ‘‘global
thermometer,’’ the SST is important in coupling the
ocean and atmosphere through exchanges of heat, mo-
mentum, moisture, and gases. SST products are required
by operational ocean analysis and prediction systems to
Corresponding author address: Dr. Craig Donlon, CEC-Joint Re-
search Centre, Institute for Environment and Sustainability, Inland
and Marine Waters Unit, Ispra, I-21020 TP272, Italy.
E-mail: craig.donlon@jrc.it
properly constrain the upper-ocean circulation and ther-
mal structure and SST is a key parameter in other ocean-
ographic fields such as coastal oceanography and mon-
itoring of biological resources. The high spatial and ra-
diometric resolution, the regular sampling, and the syn-
optic perspective of spaceborne sensors make them well
suited to improving SST measurement capability. Tech-
nological advances and innovative design have resulted
in new generations of satellite instruments [e.g., the
Along Track Scanning Radiometer (ATSR; Mutlow et
al., 1994)], the Tropical Rainfall Measuring Mission
(TRMM) Microwave Imager (Kummerow et al. 1998;
Wentz et al. 2000) and, the Moderate Resolution Im-
aging Spectroradiometer (MODIS; Esaias et al. 1998)
having higher accuracy and capability compared to their
354 V
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predecessors. This steady improvement has now reached
the stage where detailed investigation of the processes
and feedback mechanisms that govern the spatial and
temporal dynamics of the lower atmosphere and upper
ocean at scales from a few kilometers to the quasi-syn-
optic global is possible using satellite observations.
A scientific priority for earth observation over the
next decade is to seek the best combination of tools
(satellite observations, in situ measurements, and nu-
merical models) to optimize the information content of
these data by promoting interdisciplinary and multisen-
sor research. While long-term regional and global sat-
ellite SST products do exist (e.g., Vasquez et al. 1998;
Kilpatrick et al. 2001), they are often limited in terms
of spatial resolution and consistency due to uneven sen-
sor performance. A new generation of satellite SST
products is required (e.g., Smith 2000) building on the
complementary aspects of different satellite instruments
by merging data to provide both increased fidelity, con-
sistency, and resolution. In fact the Global Ocean Data
Assimilation Experiment, (Le Traon et al. 1999) has
recently initiated a pilot project to develop high spatial
and temporal resolution SST data products (Donlon
2001). Against this background, it is important that a
robust and pragmatic approach to the continuing vali-
dation of global satellite-derived SST products is im-
plemented to assure accurate and dependable SST data
products.
In the past, accurate in situ radiometers mounted on
research vessels have obtained SST measurements con-
temporaneous with satellite measurements that are used
to validate the atmospheric correction strategies nec-
essary to derive SST from top of the atmosphere ra-
diance measurements. Collectively, these provide robust
and valuable validation data (e.g., Kearns et al. 2000)
but, due to the often limited geographic and temporal
coverage of individual campaigns, the cost and difficulty
of maintaining instrumentation, this approach falls short
of a continuing global satellite SST validation strategy.
If in situ radiometers remain the only SST validation
data source, it is clear that the SST validation dataset
will always be limited, both in spatial distribution and
quantity.
This paper specifically addresses the need for contin-
uous global validation of infrared satellite sea surface
temperature measurements. Section 2 presents state-of-
the-art in situ datasets that characterize the relationship
between the radiometrically measured SST and the sub-
surface temperature at depth. Section 3 outlines a strat-
egy for the operational use of existing oceanographic
infrastructure and highlights problems and the need for
new autonomous instrumentation in certain conditions.
Section 4 applies the methods discussed in sections 3
and 4 to satellite data. Finally, section 5 discusses a
global validation SST strategy and presents our final
conclusions and recommendations.
2. Measurements of sea surface temperature
The vertical temperature structure of the upper ocean
(;10 m) is both complex and variable depending on
the level of shear-driven ocean turbulence and the air–
sea fluxes of heat, moisture, and momentum. Thus, ev-
ery SST observation depends on the measurement tech-
nique and sensor that is used, the vertical position of
the measurement within the water column, the local his-
tory of all component heat flux conditions, and the time
of day the measurement was obtained. The vertical
structure of SST can be generally classified as follows.
R The interface SST, SST
int
, is the temperature of an
infinitely thin layer at the exact air–sea interface. It
represents the temperature at the top of the SST
skin
layer (and hence the top of the temperature gradient
in that layer) and cannot be measured using current
technology.
R The skin SST, SST
skin
, is a temperature measured by
a radiometer at depth within a thin layer (;500
m
m)
at the water side of the air–sea interface where con-
ductive and diffusive heat transfer processes domi-
nate. A strong temperature gradient is characteristi-
cally maintained in this thin layer sustained by the
magnitude and direction of the ocean–atmosphereheat
flux. Thus, SST
skin
varies according to depth within
the layer and because the penetration depth of the
emitted radiation is a function of the wavelength of
the radiation, the value of SST
skin
varies dependent on
the wavelength used for the measurement. This is the
basis of measuring the skin temperature gradient using
infrared interferometry at wavelengths shorter than 5
m
m (McKeown et al. 1995). Consequently, SST
skin
should always be quoted at a specific wavelength in
the water column, for example, SST
skin 10.5
m
m
. How-
ever, over the parts of the infrared spectrum used to
make the measurements of SST
skin
presented in this
study, the variation in the penetration depth is very
small, and the values of SST
skin
measured by ideal
infrared radiometers are expected to vary by less than
0.01 K, and the wavelength dependence of SST
skin
can
be ignored.
R The subskin SST, SST
subskin
, is representative of the
SST at the bottom of the SST
skin
temperature gradient
(layer) where molecular and viscous heat transfer pro-
cesses begin to dominate. It varies on a timescale of
minutes and may be influenced by solar warming.
SST
subskin
may be approximated by measurements
made by a low-frequency (6–10 GHz) microwave ra-
diometer. In this region of the electromagnetic spec-
trum, the penetration depth in seawater is much great-
er, resulting in measurements depths of greater than
1 mm.
R The subsurface SST, SST
depth
, (traditionally referred to
as a ‘‘bulk’’ SST) considers any temperature within the
water column beneath the SST
subskin
where turbulent
heat transfer processes dominate. It may be signifi-
cantly influenced by local solar heating and has a time-
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IG
. 1. Idealized temperature profiles of the near-surface layer (;10-m depth) of the ocean during (a)
nighttime and daytime during strong wind conditions and (b) daytime low–wind speed conditions and high
insolation resulting thermal stratification of the surface layers.
scale of hours and typically varies with depth. Con-
sequently, SST
depth
should always be quoted at a specific
depth in the water column; for example, SST
5m
refers
to the SST at a depth of 5 m. SST
depth
is measured using
traditional temperature sensors mounted on buoys, pro-
filers, and ships at any depth beneath SST
subskin
.
There is considerable observational evidence con-
firming these relationships (e.g., Saunders 1967; Hasse
1971; Grassl 1976; Katsaros 1977; Paulson and Simpson
1981; Schlu¨ssel et al. 1990; Fairall et al. 1996; Kent et
al. 1996; Donlon and Robinson 1997; Minnett and Han-
afin 1998; Donlon et al. 1999a; Minnett and Ward 2000;
Ward and Minnett 2002). Figure 1 illustrates schemat-
ically the importance of referencing the mean depth or
wavelength at which an SST measurement is determined
when considering upper-ocean SST. Figure 1a shows
the characteristic thermal structure at night or during
moderate to strong winds during the day that homogenize
the temperature in the upper-water layers. SST
subskin
is
similar to SST
depth
at all depths but is characteristically
warmer than the cooler SST
skin
. Figure 1b depicts the
characteristic situation for late morning–early afternoon
following a period of light or absent wind and insolation.
Thermal stratification of the upper-ocean layers has oc-
curred resulting in significant temperature differences be-
tween SST
int
, SST
skin
, SST
subskin
, and SST
depth
. This un-
derscores the motivation to refine the traditional reference
to bulk SST, universally used in oceanography and me-
teorology, to the more exact parameter, SST
depth
. We note
that the cloud-free conditions described by Fig. 1b are
favorable for infrared remote sensing of SST from sat-
ellite instruments and in such cases, the significant ver-
tical variation of SST demands careful attention in terms
of SST data product conception, validation, and inter-
pretation. In the future, if microwave, infrared, and in
situ SST datasets are used together (e.g., Donlon 2001),
understanding and reconciling these differences is ex-
ceptionally important.
In order to quantify vertical thermal differences the
parameter DT is traditionally used, which is defined as
DT 5 SST 2 SST .
subskin skin subskin
(1a)
A negative value of DT computed using (1a) corre-
sponds to a cool skin temperature. (Note that some au-
thors use SST
subskin
2 SST
skin
that results in a positive
DT value for a cool skin temperature.) SST
subskin
is ex-
tremely difficult to measure requiring accurate multi-
spectral infrared instruments and has only been proven
in the laboratory (e.g., McKeown and Asher 1997;
McKeown et al. 1995). Measurements using microwave
radiometers provide SST
subskin
and may in the near future
attain the necessary accuracy for direct computation of
DT from satellite data alone (Wentz and Meissner 1999).
However, in situ microwave instruments have not been
deployed widely and consequently (1a) cannot be read-
ily applied. Instead, SST
subskin
is typically replaced by
SST
depth
, which can be measured using traditional con-
tact temperature sensors so that (1a) becomes
DT 5 SST 2 SST .
depth skin depth
(1b)
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F
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. 2. Daytime and nighttime vertical temperature profiles at the near-ocean surface in the Gulf of
California obtained using the SkinDeEP profiling instrument. In each plot, the circle is the skin temperature
measured by M-AERI, the plus signs the in situ temperature measured at a depth of about 5 cm, and the
star is the temperature measurement from a thermosalinograph on the ship at a nominal depth of 3 m. (a)
SkinDeEP profiles at 0655 LT, (b) SkinDeEP profile at 1309 LT, and (c) SkinDeEP profile at 1920 LT.
Clearly it is important to recognize that (1b) now includes
both the skin temperature deviation and the effect of ther-
mal stratification that may be present in the upper ocean
so that DT
depth
may be significantly different from DT.
a. Decoupling of SST
skin
and SST
depth
due to thermal
stratification
Figure 2 shows three vertical profiles taken by an
ascending instrument package called the Skin Depth Ex-
perimental Profiler (SkinDeEP; Ward and Minnett
2001). A complete description of SkinDeEP can be
found in Ward et al. (2001, manuscript submitted to J.
Atmos. Oceanic Technol.). Over 1000 profiles were
made by SkinDeEP on the MOCE-5 cruise in the Gulf
of California in October 1999, and Fig. 2 shows just
three profiles at three different times of day, on three
different days (a more complete description of the data
will appear elsewhere). Also shown as circles are SST
skin
observations measured by a Marine-Atmospheric Emit-
ted Radiance Interferometer (M-AERI, see section 3a(1)
below), the SST
0.1 m
measured by a thermistor (plus
sign), and the SST
3m
recorded by a thermosalinograph
(asterisk).
Figure 2a shows measurements taken just after sunrise
(0655 local time) demonstrating thermal structure nearly
identical to the schematic diagram shown in Fig. 1a. As
the surface layer becomes progressively warmed during
the day, strong stratification occurs, which is shown in
Fig. 2b taken at approximately 1309 local time. This is
nearly identical to Fig. 1b. Figure 2c shows profile mea-
surements made after sunset at approximately 1720 local
time that, although there was no solar heating, do not
reflect the homogeneous temperature structure shown in
Fig. 1a. Instead, a complex profile is observed probably
due to interleaving and overturning processes causing
gradients in both the horizontal and vertical directions.
In all cases, the absorption of shortwave radiation in the
molecular boundary skin layer is not enough to over-
come the heat loss due to the sensible and latent heat
fluxes, and the SST
skin
remains cooler than the water
beneath.
Considering Fig. 2, it is readily apparent the depth at
which SST
depth
is measured is of considerable impor-
tance in understanding how representative it may be of
subsurface temperature: DT
0.1 m
remains negative where-
as DT
3m
changes sign and has considerably more var-
iability depending on the dynamic structure of the strat-
15 F
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2002 357DONLON ET AL.
ification. In the context of satellite SST retrievals during
low wind speed, high-insolation conditions, the data
presented in Fig. 2 demonstrate that the SST
skin
and the
SST
depth
are related but quasi-independent variables that
are often different requiring knowledge of the time of
day and depth at which the SST
depth
is measured for a
correct interpretation. This poses significant implica-
tions for the validation, interpretation, and merging of
complimentary satellite SST datasets. Further research
characterizing and modeling the spatial and temporal
structure of thermal stratification is vital to understand-
ing both the accuracy of SST
skin
, derived from the mea-
surements of infrared radiometers on satellites, and how
best to merge these data with measurements of SST
subskin
from microwave radiometers on satellites.
b. Measurements of SST from space
Accurate and dependable instrument prelaunch char-
acterization and postlaunch self-calibration underpin the
integrity of all satellite-derived SST estimates.However,
the quality and credibility of derived data products de-
pends on accounting comprehensively for the uncer-
tainties associated with the measurement itself, the ac-
curacy of atmospheric correction algorithm and, the
changes within satellite sensors throughout their life-
time. Thus SST product validation is distinct from sen-
sor calibration (e.g., Mutlow et al. 1994). The funda-
mental measurement made by a self-calibrating satellite
infrared radiometer is the upwelling radiance at the
height of the satellite sensor specified for a number of
spectral intervals and atmospheric pathlength. For views
of the sea surface, the calibrated radiance is composed
of sea surface, direct atmospheric and reflected surface
radiance (due to the nonunity emissivity of the ocean
surface). Atmospheric absorption and emission along
the atmospheric path between the sea surface and sat-
ellite sensor (ignoring aerosol contributions) is princi-
pally by water vapor, carbon dioxide, and ozone (e.g.,
Saunders and Edwards 1989; Zavody et al. 1995). SST
measurements are derived by compensating for un-
wanted radiance components reflected at the sea surface
and the atmospheric attenuation of the emitted oceanic
radiance. This is achieved using a weighted combination
of spectral and view-specific radiant temperatures or
‘‘brightness temperatures’’ (i.e., the temperature of a
blackbody emitter that produces that radiance, weighted
by the radiometer’s relative spectral response function,
for a particular spectral interval).
Considerable effort has been invested in developing
semiempirical infrared atmospheric correction algo-
rithms by regression of satellite brightness temperatures
and quality-controlled in situ, subsurface SST
depth
ob-
servations; these lead to an estimate of the subsurface
temperature, referred to here as ‘‘pseudo-SST
depth
’’
(pSST
depth
) algorithms (for a review see Barton 1995).
An alternative, geophysically based, atmospheric cor-
rection strategy uses detailed radiative transfer calcu-
lations to predict a set of theoretical brightness tem-
peratures for a satellite radiometer, given a set of at-
mospheric conditions and associated SST
skin
values(e.g.,
Llewellyn-Jones et al. 1984; Minnett 1986, 1990; Za-
vody et al. 1995; Merchant et al. 1999) thereby speci-
fying explicitly the effect of the atmosphere. The model-
derived brightness temperatures are then used in a sim-
ilar regression procedure as for pSST
depth
(i.e., matching
top of the atmosphere brightness temperatures with the
surface temperatures), but in this case providing a re-
trieval of SST
skin
. It is important to recognize that this
approach generates algorithms that assume no relation
between the subsurface SST
depth
and the SST
skin
.
In contrast to SST
skin
algorithms, pSST
depth
algorithms
implicitly assume that the difference between the SST
skin
and the SST
depth
is constant or correlated with the effects
of the overlying atmosphere and well specified by the
in situ observations that are used to define the algorithm
(typically comprising of several thousand contempora-
neous satellite and in situ data points). Advanced
pSST
depth
algorithms generate monthly regression co-
efficients after in situ data quality control procedures
have been completed (e.g., Kilpatrick et al. 2001). The
pSST
depth
approach may accommodate the effects of di-
urnal stratification by simply providing a separate day-
time pSST
depth
algorithm. For example, differences be-
tween SST
skin
and SST
depth
during the day are significant
for the Advanced Very High Resolution Radiometer
(AVHRR) sensor considering the variable local overpass
times, caused by orbit drift, and the range of local time
across its wide swath. Such differences highlight the
reason why daytime pSST
depth
algorithms may be prob-
lematic to derive but, perhaps more importantly,difficult
to validate using in situ SST
depth
observations (see Bar-
ton 1998). Nevertheless, in the following sections, we
will show why the pSST
depth
approach has performed
surprisingly well during the last 20 years.
3. Reconciling SST
skin
and SST
depth
Donlon et al. (1999a) use extensive in situ observa-
tions obtained during independent experiments in the
Atlantic Ocean to demonstrate the relationship between
the surface wind speed and DT
5m
. SST
skin
was measured
using a scanning infrared sea surface temperature ra-
diometer (SISTeR). The Scanning Infrared Sea Surface
Temperature Radiometer (SISTeR) is a compact and ro-
bust chopped self-calibrating filter radiometer. The in-
strument is divided into three compartments containing
the fore optics, scan mirror, and reference blackbodies,
and a small-format PC with signal processing and con-
trol electronics. The fore optics compartment contains
a pyroelectric detector and preamplifier, mounted onto
an assembly containing filter wheel with three narrow-
band filters centered at 3.7, 10.8, and 12.0
m
m, and a
black rotating chopper. The entire optical system is re-
ferred to two highly accurate reference blackbodies, one
floating near to ambient temperature and the other at
