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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
Kenneth S. Casey
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
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.
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
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.
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
, 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).
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
wind speed increments, and plotted separately for daytime and nighttime observations
in Figure 1. At wind speeds below 6 m s
, 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
the nighttime bias is
-0.11C while above 6 m s
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
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
increments with standard error bars for daytime (top panel) and nighttime (bottom panel) data. The
dashed line represents a linear fit to the data.
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.
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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ADDRESS: University Corporation for Atmospheric Research NOAA/NESDIS/NODC, E/OC5 1315
East-West Highway Silver Spring, Maryland 20910. E-mail: <>.
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Using 1.1 km resolution imagery from NOAA-12, -14, -16, and -17 recorded from April 2001 to May 2003 by “HAZO” HRTP mid-Atlantic satellite receiving station, 8-day average image are calculated to investigate AVHRR-derived SST distributions and associated dominant space and time scales around the Azores archipelago (34o to 42o N, 33o to 23o W). Eight-day average images together with zonal and meridional averages show a distinct seasonal cycle and typical gradi- ents, which emphasise the dual influence of the Gulf Stream and the Azores Current in this region. In late spring, iso- therms start moving to the north and retreat in early autumn. Low horizontal gradients are found during summertime, with warmer waters located to the south and west. Orientation of SST patterns changes with time from SW-NE (e.g. July 2001) to NNW-SSE (e.g. July 2002, August 2001 and 2002). The later orientation involves the sudden warming of the waters surrounding the northwestern group of islands of the Azores archipelago. This warming persists during 3 to 6 weeks with mean temperature differences of the order of 0.8 oC. At a more local scale (2o x 2o in size) SST variability is also observed. In some cases, it is found that wind-driven coastal upwelling, a few km wide, occurs to the south of the islands during spring and summer months. Field data demonstrate that upwelling events increase local biomass. This result highlights the relevance of SST data to improve stock assessment and fishery management studies.
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Individual sea surface temperature (SST) anomalies are calculated using a satellite-based climatology and observations from the World Ocean Atlas 1994 (WOA94) and the Comprehensive Ocean-Atmosphere Data Set (COADS) to characterize global and regional changes in ocean surface temperature since 1942. For each of these datasets, anomaly trends are computed using a new method that groups individual anomalies into climatological temperature classes. These temperature class anomaly trends are compared with trends estimated using a technique representative of previous studies based on 5° latitude-longitude bins. Global linear trends in the data-rich period between 1960 and 1990 calculated from the WOA94 data are found to be 0.14° ± 0.04°C decade-1 for the temperature class approach and 0.13° ± 0.04°C decade-1 for the 5° bin approach. The corresponding results for the COADS data are 0.10° ± 0.03°C and 0.09° ± 0.03°C decade-1. These trends are not statistically different at the 95% confidence level. Additionally, they agree closely with both SST and land-air temperature trends estimated from results reported by the Intergovernmental Panel on Climate Change. The similarity between the COADS trends and the trends calculated from the WOA94 dataset provides confirmation of previous SST trend studies, which are based almost exclusively on volunteer observing ship datasets like COADS. Regional linear trends reveal a nonuniformity in the SST rates between 1945-70 and 1970-95. Intensified warming during the later period is observed in the eastern equatorial Pacific, the North Atlantic subtropical convergence, and in the vicinity of the Kuroshio extension. Also, despite close agreement globally, localized differences between COADS and WOA94 trends are observed.
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The purpose of this study is to present a satellite-derived sea surface temperature (SST) climatology based on Pathfinder Advanced Very High Resolution Radiometer (AVHRR) data and to evaluate it and several other climatologies for their usefulness in the determination of SST trends. The method of evaluation uses two long-term observational collections of in situ SST measurements: the 1994 World Ocean Atlas (WOA94) and the Comprehensive Ocean-Atmosphere Data Set (COADS). Each of the SST climatologies being evaluated is subtracted from each raw SST observation in WOA94 and COADS to produce several separate long-term anomaly datasets. The anomaly dataset with the smallest standard deviation is assumed to identify the climatology best able to represent the spatial and seasonal SST variability and therefore be most capable of reducing the uncertainty in SST trend determinations.The satellite SST climatology was created at a resolution of 9.28 km using both day and night satellite fields generated with the version 4 AVHRR Pathfinder algorithm and cloud-masking procedures, plus an erosion filter that provides additional cloud masking in the vicinity of cloud edges. Using the statistical comparison method, the performance of this `Pathfinder + erosion' climatology is compared with the performances of the WOA94 1° in situ climatology, the Reynolds satellite and in situ blended 1° analysis, version 2.2 of the blended 1° Global Sea-Ice and Sea Surface Temperature (GISST) climatology, and the in situ 5° Global Ocean Surface Temperature Atlas (GOSTA).The standard deviation of the anomalies produced using the raw WOA94 in situ observations and the reference SST climatologies indicate that the 9.28-km Pathfinder + erosion climatology is more representative of spatial and seasonal SST variability than the traditional in situ and blended SST climatologies. For the anomalies created from the raw COADS observations, the Pathfinder + erosion climatology is also found to minimize variance more than the other climatologies. In both cases, the 5° GOSTA climatology exhibits the largest anomaly standard deviations.Regional characteristics of the climatologies are also examined by binning the anomalies by climatological temperature classes and latitudinal bands. Generally, the Pathfinder + erosion climatology yields lower anomaly variances in the mid- and high latitudes and the Southern Hemisphere, but larger variances than the 1° climatologies in the warm, Northern Hemisphere low-latitude regions.
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The World Ocean Database (WOD) is the most comprehensive global ocean profile-plankton database available internationally without restriction. All data are in one well-documented format and are available both on DVDs for a minimal charge and on-line without charge. The latest DVD version of the WOD is the World Ocean Database 2009 (WOD09). All data in the WOD are associated with as much metadata as possible, and every ocean data value has a quality control flag associated with it. The WOD is a product of the U.S. National Oceanographic Data Center and its co-located World Data Center for Oceanography. However, the WOD exists because of the international oceanographic data exchange that has occurred under the auspices of the Intergovernmental Oceanographic Commission (IOC) and the International Council of Science (ICSU) World Data Center (WDC) system. World Data Centers are part of the ICSU World Data System.
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The remotely sensed sea surface temperature (SST) estimated from the 4-km-resolution Pathfinder SST algorithm is compared to a SST locally measured by the Marine Atmospheric Emitted Radiance Interferometer (MAERI) during five oceanographic cruises in the Atlantic and Pacific Oceans, in conditions ranging from Arctic to equatorial. The Pathfinder SST is a product of the satellite-based Advanced Very High Resolution Radiometer, while the MAERI is an infrared radiometric interferometer with continuous onboard calibration that can provide highly accurate (better than 0.05°C) in situ skin temperatures during extended shipboard deployments. Matchups, which are collocated (within 4 km) and coincident (±40 min during the day; ±120 min during the night) data, from these two different sources under cloud-free conditions are compared. The average difference between the MAERI and Pathfinder SSTs is found to be 0.07 ±0.31°C from 219 matchups during the low- and midlatitude cruises; inclusion of 80 more matchups from the Arctic comparisons produces an average global difference of 0.14 ±0.36°C. The MAERI-Pathfinder differences compare favorably with the average midlatitude differences between the MAERI skin SST and other bulk SST estimates commonly available for these cruises such as the research vessels' thermosalinograph SST (0.12 ±0.17°C) and the weekly National Centers for Environmental Prediction optimally interpolated SST analysis (0.41 ±0.58°C). While not representative of all possible oceanic and atmospheric regimes, the accuracy of the Pathfinder SST estimates under the conditions sampled by the five cruises is found to be at least twice as good as previously demonstrated.
A brief outline of the basic concepts of cloud filtering and atmospheric attenuation corrections used in the Multi-channel Sea Surface Temperature (MCSST) method is given. The operational MCSST procedures and products are described in detail. The comparative performance of AVHRR-based MCSST'S is discussed via the use of the results of the JPL Satellite-Derived Sea Surface Temperature workshops. For the four data periods there is surprisingly good correspondence in the sign and location of the major monthly mean SST anomaly features derived from MCSST's and those from a screened set of ship-based SST's. With the partial exception of the one data period severely affected in some areas by volcanic aerosol from El Chichon eruptions, global statistical measures of the MCSST anomalies relative to the the ship data are as follows: biases, 0.3–0.4°C (MCSST lower than ship); standard deviations, 0.5–0.6°C; and cross-correlations, +0.3 to +0.7. A refined technique in use with NOAA 9 data in 1985 has yielded consistent biases and rms differences near −0.1°C and 0.5°C, respectively.
Sea surface temperatures (SSTs), computed from sensor systems on the National Oceanographic and Atmospheric Administration (NOAA) polar-orbiting satellites, are compared with surface skin temperatures (from an infrared radiometer mounted on a ship) and subsurface temperature measurements. Three split window retrieval methods using channels 4 and 5 of the NOAA 7 advanced very high resolution radiometer (AVHRR) sensor were investigated. These methods were (1) using AVHRR alone, (2) using AVHRR with atmospheric temperature and water vapor profiles from the TIROS operational vertical sounder (TOVS), and (3) using AVHRR and data from the high-resolution infrared sounder (HIRS). TOVS sensors (including HIRS) are carried by the same satellite as the AVHRR and provide simultaneous corrections for the AVHRR-based SST estimates. The importance of scan angle correction to define the correct atmospheric path is discussed, and the improvement of SST retrievals using sensor combinations is demonstrated with satellite versus ship skin temperature mean differences ranging from 0.55° to 0.73°C for AVHRR alone, from -0.39° to 0.71°C for AVHRR plus TOVS, and from 0.22° to 0.33°C for AVHRR plus HIRS. The improved SST accuracy by AVHRR plus HIRS is due to additional correction for the atmospheric water vapor and temperature structures, made possible with some of the HIRS channels. Significant differences between ship skin and subsurface temperatures were observed, with the mean deviation being 0.2°C for a range of temperature differences between -0.25° and 0.6°C.
Large diurnal sea surface warming exceeding 1°C is common in the western North Atlantic Ocean and is often of large horizontal extent. These events correlate closely with very light winds and high insolation. In the area investigated, 17°-40°N and 55°-80°W, the largest warming is found in the western portion of the ridge associated with the Azores-Bermuda high, where the lowest wind speeds are observed. The distribution of warming events shows that the largest number occur between June and August, when insolation is highest and percent cloud cover and wind speed are low. The most probable latitude of warming events moves north from approximately 25°N in spring to near 30°N in summer, a shift similar to that seen in the minimum of the climatological winds. Local areas have a probability as high as 30% for diurnal warming in excess of 1°C in the summer. The net heat flux into the ocean, calculated by using monthly mean values for low latitudes in the summer, excluding diurnal warming events, is biased consistently high by as much as 5 W/m2 relative to the same values calculated with warming events included.
Extensive oceanographic and atmospheric observations obtained during three independent experiments in the Atlantic Ocean are used to demonstrate the relationship between wind speed and the temperature deviation DeltaT, which is defined as the sea surface skin temperature (SSST) minus the subsurface bulk sea surface temperature (BSST). At wind speeds 1.5 K are common during periods of high insolation. The variability of DeltaT at night is reduced and extreme cool skin temperatures of 6 ms-1, the variability of DeltaT is diminished and the mean value of DeltaT approximates a cool bias of -0.14K+/-0.1K. We conclude that BSST measurements obtained at wind speeds >6 ms-1, when corrected for a small (-0.14 K) cool bias, are representative of the SSST and can be used with confidence to validate satellite derived SSST. When the wind speed is
People around the world depend on the resources provided by the ocean to support life. But global-scale damage to the coral reefs, a large and integral part of the ocean environment that supports a variety of sea life, is a frightening prospect that may unfold in the coming years. Recently, a phenomenon called coral bleaching has raised concerns about the deteriorating conditions in the world's oceans and the implications for life on our planet.Coral bleaching occurs as coral tissue expels zooxanthellae, a symbiotic algae that resides in the structure of the coral and is essential to its survival. The widespread nature of the bleaching threatens the state of the environment.