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Daytime vs nighttime AVHRR sea surface temperature data: A report regarding Wellington et al. (2001)

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A recent study by Wellington et al. (2001) excluded daytime and selected only nighttime AVHRR sea surface temperature (SST) data for comparison with in situ observations, citing two studies to support this decision. One of these studies is incorrectly referenced since it does not discuss day-night satellite data issues, and the other supports the use of nighttime AVHRR data based on biases with a single buoy recording SST in a different ocean basin. This report discusses the diffculty in selecting only daytime or nighttime AVHRR data based on dissimilar studies owing to highly variable satellite SST biases and differences in satellite processing algorithms. Additionally, it presents a simple analysis based on co-located satellite and in situ data which highlights two important processes affecting the day-night satellite SST differences: diurnal warming and the skin effect.
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BULLETIN OF MARINE SCIENCE, 70(1): 169–175, 2002
169
DAYTIME VS NIGHTTIME AVHRR SEA SURFACE TEMPERATURE
DATA: A REPORT REGARDING WELLINGTON ET AL. (2001)
Kenneth S. Casey
ABSTRACT
A recent study by Wellington et al. (2001) excluded daytime and selected only night-
time AVHRR sea surface temperature (SST) data for comparison with in situ observa-
tions, citing two studies to support this decision. One of these studies is incorrectly refer-
enced since it does not discuss day-night satellite data issues, and the other supports the
use of nighttime AVHRR data based on biases with a single buoy recording SST in a
different ocean basin. This report discusses the diffculty in selecting only daytime or
nighttime AVHRR data based on dissimilar studies owing to highly variable satellite SST
biases and differences in satellite processing algorithms. Additionally, it presents a simple
analysis based on co-located satellite and in situ data which highlights two important
processes affecting the day-night satellite SST differences: diurnal warming and the skin
effect.
Recently, Wellington et al. (2001) compared nighttime-only Advanced Very High Reso-
lution Radiometer (AVHRR) sea surface temperature (SST) observations with in situ
measurements in the Galápagos Archipelago. That study found there were few overall
differences between the two techniques except at two of the six sites examined. At one
site the satellite-based SSTs were consistently warmer than the in situ data (a warm bias),
while at the one other site the satellite data were consistently cooler (a cold bias). These
differences were explained based on the local oceanographic conditions and specifc place-
ment of the in situ measurement devices. Wellington et al. (2001) referred to two papers
to support the decision to neglect satellite observations collected during the day and use
nighttime-only AVHRR SST data in the comparisons. The purpose of this note is to high-
light the problems and issues surrounding the selection of only nighttime AVHRR data by
examining these two studies and presenting a simple analysis of co-located in situ and
satellite data.
STUDIES CITED BY WELLINGTON ET AL. (2001)
One of the two papers cited by Wellington et al. (2001) to support the use of nighttime-
only AVHRR data was Montgomery and Strong (1994). That study compared weekly-
averaged AVHRR SSTs processed using the Multi-Channel Sea Surface Temperature
(MCSST) algorithm (McClain et al., 1985) at a resolution of 18 km with daily observa-
tions from a single buoy recording SST off Bermuda at a depth of 1 m. It concluded that
daytime satellite observations were biased too warm to include in their coral bleaching
study. They found daytime biases of 0.10C while nighttime biases were -0.04C for
1982–1991 data. Wellington et al. (2001) referred to this difference in the biases when
supporting the use of nighttime-only AVHRR data. Several issues regarding using only
nighttime AVHRR data in the Galápagos Archipelago based on Montgomery and Strong
(1994) are worth noting. First, that study was based on only a single buoy located in a
completely different ocean basin recording SST over a different period of years. Biases in
AVHRR data, however, are variable in space and time (e.g., Kilpatrick et al., 2001; Casey
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BULLETIN OF MARINE SCIENCE, VOL. 70, NO. 1, 2002
and Cornillon, 1999). This variability makes it diffcult to apply the results from one
location to another, which may be characterized by dramatically different sources of error
in the satellite data set, particularly cloudiness, water vapor, and aerosols. Other prob-
lems with applying the results of Montgomery and Strong (1994) may also arise. The
satellite algorithms used to convert the satellite-measured radiances into SST, the spatial
resolutions of the satellite data, and the temporal matchup requirements between satellite
and in situ data can all have significant efects on the biases. Discussing their impact here
is diffcult because these details are not provided by Wellington et al. (2001).
The second study cited by Wellington et al. (2001) was Casey and Cornillon (1999). It
was referenced to support the claim that daytime satellite SSTs are biased too warm be-
cause of excessive heating of the skin during periods of light winds. Casey and Cornillon
(1999), however, makes no reference to wind-related biases in satellite SSTs and instead
introduced a method for testing satellite and in situ based climatologies based on com-
parisons with large in situ SST collections. That study used both daytime and nighttime
satellite data produced by a more recent processing of the AVHRR data stream known as
the NOAA/NASA AVHRR Oceans Pathfinder Project (Kilpatrick et al., 2001). The refer-
ence as cited by Wellington et al. (2001) was possibly intended to be for a conference
presentation, Casey et al. (2000), which discussed biases related to air-sea temperature
difference, wind speed, and aerosols in the AVHRR Pathfinder data. The techniques of
Casey et al. (2000) are applied here to briefly examine the day and night biases in the
AVHRR Pathfinder data set and highlight two important issues affecting the selection of
daytime or nighttime satellite SST data.
DIURNAL WARMING AND THE SKIN EFFECT
Understanding the differences between daytime and nighttime satellite SST data re-
quires that two separate but closely related processes be addressed. The first of these is
the familiar diurnal warming effect in which solar insolation warms the surface waters,
especially in low wind speed conditions where mixing is reduced (e.g., Cornillon and
Stramma, 1985; Stramma et al., 1986). This process can be addressed by examining co-
located satellite and in situ data and relating their differences to wind speeds.
The second process affecting day-night biases is the less familiar skin effect (e.g.,
Schluessel et al., 1987; Wick et al., 1992). The skin of the ocean is always less than 1
mm thick (Grassl, 1976) and is generally a few tenths of a degree colder than the ocean
temperature just a few centimeters deeper (Schluessel et al., 1990; Donlon et al., 1999).
The ocean skin is the molecular boundary layer responsible for heat fluxes between
ocean and atmosphere. Because the net flux is generally to the atmosphere this skin is
generally cooler than the bulk SST. During night, the skin effect operates alone but
during the day the diurnal warming effect noted above is also important. At wind speeds
above 10 m s
-1
, the skin layer is disrupted but it is quickly reestablished at lower wind
speeds (Schluessel et al., 1990). This process can be addressed by examining co-located
satellite and in situ data and relating their differences to the air-sea temperature differ-
ence (ASTD).
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CASEY: DAYTIME VS NIGHTTIME AVHRR SST DATA
BIAS ANALYSIS
To examine the day-night biases, a set of co-located AVHRR Pathfinder and in situ
SSTs is created. The satellite data are resolved to 9 km in space and are processed to yield
twice-daily SST observations corresponding to one daytime and one nighttime field. Stan-
dard Pathfinder cloud masking is applied along with an additional erosion filter which
masks out any observations immediately adjacent to a cloud. This step reduces the likeli-
hood that any cloud-contaminated values are included in the co-located data set (Casey
and Cornillon, 1999, 2001).
The in situ observations are taken from the 1998 World Ocean Database (WOD98)
(Levitus et al., 1998) and consist of measurements collected from hydrographic sampling
bottles, conductivity-temperature-depth devices, and expendable, mechanical, and digi-
tal bathythermographs. Only the highest quality SST observations (those with a quality
flag of zero) were included in this analysis. In addition to SST, a valid wind speed and air
temperature associated with the in situ measurement are required and included in the co-
located set. The in situ and satellite measurements must fall within 6 h and approximately
9 km (the resolution of the satellite data) of one another. Roughly 5000 observations from
all ocean basins meet these conditions between 1985 and 1990.
To gauge the diurnal warming process, the Pathfinder biases are calculated, binned into 2
m s
-1
wind speed increments, and plotted separately for daytime and nighttime observations
in Figure 1. At wind speeds below 6 m s
-1
, the daytime satellite minus in situ SST biases are
positive with an average value of 0.12C. For higher wind speeds, these biases diminish,
approach zero, and become negative with an average value of -0.05C. On average, the
overall daytime bias is very low with a value of 0.02C. Nighttime biases are more consistent
regardless of wind speed, with overall values of -0.15C. Below 6 m s
-1
the nighttime bias is
-0.11C while above 6 m s
-1
it is -0.18C.
These results are consistent with the diurnal warming mechanism that results in warmer
SSTs during the day than at night. This warming on average does not penetrate to the
approximately 1 m depth of the ‘bulk’ in situ observations in the WOD98, but does warm
the waters closer to the surface as measured by the satellite, which observes only the top
‘skin’ of the ocean to a depth of around 10 mm (Emery et al., 2001). At low wind speeds
this warming results in the positive biases observed. At higher wind speeds, however,
mixing is enhanced thereby reducing or eliminating any differences between the deeper
and shallower SST observations. At night, lack of solar insolation does not lead to such
differences, and the biases observed remain more constant with respect to wind speeds.
To gauge the skin effect, the ASTD, defined as the air temperature minus the in situ
SST, is examined and related to the satellite and in situ temperatures. Positive ASTD
reflects situations where the air is warmer than the water while negative ASTD indicates
air that is colder than the water. The skin effect would predict that a positive ASTD would
result in a positive bias, because the satellite is measuring the skin, which is being warmed
by the air, while the in situ observation represents the bulk SST. Conversely, negative
ASTD would predict a negative bias, because the satellite-measured skin is losing heat to
the colder atmosphere. This effect is indeed observed for both daytime and nighttime data
in Figure 2, where the Pathfinder biases are binned into 1C ASTD increments.
At low wind speeds, stronger cold biases would be expected at night, similar to the
results of Murray et al. (2000). However, these colder biases are not observed in Figure 1
for the nighttime data. For low wind speed conditions during the day, stronger warm
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BULLETIN OF MARINE SCIENCE, VOL. 70, NO. 1, 2002
biases are predicted and observed in Figure 1 for the daytime data. What portion of these
elevated warm biases at low wind speeds during the day can be attributed to the diurnal
warming effect and what portion to the skin effect is impossible to say from this data set.
Direct in situ skin observations using ship-mounted radiometers are needed to more fully
investigate this process (e.g., Kearns et al., 2000; Murray et al., 2000; Emery et al., 2001).
Figure 1. AVHRR Pathfinder minus in situ SST differences binned versus wind speed in 2 m s
-1
increments with standard error bars for daytime (top panel) and nighttime (bottom panel) data. The
dashed line represents a linear fit to the data.
173
CASEY: DAYTIME VS NIGHTTIME AVHRR SST DATA
SUMMARY
The bias analysis presented here highlights the fundamental issues surrounding the
selection of daytime or nighttime AVHRR SST data for a given analysis. The overall
daytime bias for the approximately 5000 co-located observations is only 0.02C, much
smaller than the overall nighttime bias of -0.15C. At first glance, this result would indi-
cate the use of daytime-only Pathfinder data, but the high variability in space and time of
these biases must be considered. Also an important consideration is the particular algo-
Figure 2. AVHRR Pathfinder minus in situ SST differences binned versus ASTD in 1C increments
with standard error bars for daytime (top panel) and nighttime (bottom panel) data. The dashed line
represents a linear fit to the data.
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BULLETIN OF MARINE SCIENCE, VOL. 70, NO. 1, 2002
rithm used for the satellite data. The Pathfinder algorithm used here represents a repro-
cessing of the AVHRR data stream using improved techniques and a larger buoy matchup
database than is available at the time of operational processing of algorithms such as
MCSST. Its spatial resolution also differs from that of MCSST. Each of these issues
stresses the point that care must be taken in evaluating the relevance of previous studies
of AVHRR data to a new study involving the selection of daytime or nighttime AVHRR
data. To make accurate determinations regarding the use of daytime only or nighttime
only, it is important to examine similarly-processed satellite data sets from similar re-
gions and times.
Despite these considerations, it is important to note in closing that AVHRR data, whether
processed through the Pathfinder algorithm or an operational one like MCSST, do generally
correlate very well with surface in situ observations as illustrated in Wellington et al. (2001)
and many other studies as well (e.g., Casey and Cornillon, 1999; Kearns et al., 2000; Kilpatrick
et al., 2001). Accounting for the effects of both local atmospheric and oceanographic condi-
tions, however, is necessary for the optimal use of these SST data sets.
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Cornillon, P. and L. Stramma. 1985. The distribution of diurnal sea surface warming events in the
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ADDRESS: University Corporation for Atmospheric Research NOAA/NESDIS/NODC, E/OC5 1315
East-West Highway Silver Spring, Maryland 20910. E-mail: <Kenneth.Casey@noaa.gov>.
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