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Satellite-derived sea surface temperature inter-comparison: A case study
Adv. Space Res. Vol. 23, No. 8, pp. 1517-1523, 1999 Q 1999 COSPAR. Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0273-l 177/99 $20.00 + 0.00 PII: SO273-1177(99)00306-3
Pergsmon www.elsevier.nlAocate/asr
SATELLITE-DERIVED SEA SURFACE COMPARISON: A CASE STUDY I.J. Barton’
and W.J.
TEMPERATURE
INTER-
Skirving2
‘CSIRO Marine Research, Bobart, Tasmania 7000, Australia 2Australian Institute of Marine Science, Townsville, Queensland 4810, Australia
ABSTRACT During a one-week period in July/August 1997 an intensive campaign was held which involved the mea surement of the sea surface temperature (SST) from both satellites and ships off the coast of Townsville in tropical north-east Australia. The experiment was a pilot project of the Working Group on Calibration and Validation (WGCV) of the international Committee on Earth Observation Satellites (CEOS). The ship measurements were made with both infrared radiometers and in situ bulk surface temperature devices. The SST was also determined using the data from several sensors on different environmental satellites. The data analysis has demonstrated a reasonable agreement between the different measurements, but it has also highlighted some of the differences between the surface and space-based measurements and identified several difficulties in using surface-based data for satellite-derived data validation. The pilot project has also demonstrated the immense value of future similar inter-comparisons especially if the data are to be amalgamated into long-term data sets for climate research. 01999 COSPAR. Publishedby Elsevier Science Ltd. INTRODUCTION Future understanding of the complexities of the Earth’s climate will undoubtedly be linked to numerical models which portray the physical, chemical, and biological processes of the atmosphere, cryosphere, land, and ocean systems. For these models to accurately represent and predict subtle variations in climate, realistic global data sets will be required for both initialisation and validation of the model outputs. Accurate long-term data sets will also be required to confirm any decadal changes in the climate system (see for example Allen et al., 1994). For global climate models measurements are required over the entire globe and these must necessarily be provided by satellite instrumentation. The immense cost of obtaining surface-based measurements with the required spatial resolution, especially over the oceans of the southern hemisphere, is not a realistic option. Thus the major space agencies of the world are now planning the imminent launch of environmental satellites to provide the required data. One of the most basic parameters controlling the climate system is the sea surface temperature (SST) which is critical in defining the links between the ocean and the atmosphere. Fluxes of sensible and latent heat, radiative energy, and gas exchanges are critically dependent on the SST. At first look it may appear that 1517
I. J. Barton and W. J. Skirving
1518
the SST is an easily-measured parameter and should provide a direct link between the atmosphere and the ocean. However, small-scale processes at the air-sea interface provide large gradients of temperature over small vertical distances. In most situations radiative and evaporative heat losses provide a thin cool layer of water at the ocean surface. Thus the radiative temperature of the sea surface can be quite different to the bulk temperature of the water as measured by an in situ thermometer. This means that much care must be taken in the validation of satellite-derived SST and the amalgamation and application of different SST data sets. The Working Group on Calibration and Validation (WGCV) of the international Committee on Earth Obsercollaboration in the development vation Satellites (CEOS) is one of the major groups fostering international With the future launch of several environmental satellites of long-term data sets for climate applications. the WGCV has initiated two pilot projects to demonstrate the value of careful cross-calibration of data and data products obtained from these satellites. One project is focussed on .the thermal infrared spectral bands used for the determination of surface temperature, while the second involves the use of the visible/near infrared bands for estimating the reflectance of the Earth’s surface. For each project a typical ground-based test site has been selected and satellite and ground-based data for a set period have been collected. An area off the north-east tropical Australian coast near Townsville (19”S, 147’E) was chosen as the thermal infrared site and data were collected over a one-week period covering July and August 1997. The location and observation period were chosen for the following reasons. l The site has been selected as a long-term SST validation site for future environmental satellite sensors. A tourist ferry which operates a regular service between Townsville and the Outer Great Barrier Reef (a distance of 90 km) has been fitted with an infrared radiometer and a bulk-SST thermometer to provide continuous data over the next three years.
. The area is often free of cloud during l
Data from several satellites
the austral
would be available
winter when the Australian
is well to the north.
for the data analysis.
During the one-week observation period the Along Track Scanning Radiometer ERS-1 satellite would be reactivated by the European Space Agency to provide measurements. l
monsoon
(ATSR) instrument on the an extra set of space-based
This paper provides some initial results from the thermal infrared project. The paper will outline the measurement strategy and provide some preliminary results from the early data analysis of the measurements made on one day during the selected week. These results will be used to demonstrate the many benefits from conducting similar projects in the future. GROUND-BASED
MEASUREMENTS
For the infrared inter-comparison project data were obtained using instruments on two ships based in Townsville. The first was a small (10 m) double-hull motor vessel which was chartered for nine excursions during the one-week experiment. The other vessel was a high-speed tourist ferry (length 30 m) which operates between Townsville and Kelso Reef about 90 km north of Townsville (see Figure 1). The tourist ferry visited Kelso Reef on four days during the experiment. In this preliminary paper we only consider measurements from the smaller vessel taken over a period of one day. The smaller vessel was fitted with a suite of “off-the-shelf” infrared radiometers and devices for measuring water and air temperature. One Everest and two TASCO radiometers viewed the ocean surface at an incidence angle of 30’ while a third TASCO radiometer measured the downwelling sky radiance at a zenith angle of 30’. The SST at a depth of approximately 0.05 m was measured using two thermometers trailing
Satellite
Derived Sea Surface Temperature
1519
f
AUSTRALIA
‘,
Myrmidon Reef *
O-
10
20
CapeBowling Green
30
Kilometres
I 46”WE
147”OO’E
J
I
Fig. 1.
The study area showing the transect between Townsville and Kelso Reef.
over the vessel’s side; one was a platinum resistance thermometer (PRT) and the other a solid state device (AD590). During a measurement run these thermometers were deployed so they remained totally immersed in water that was unaffected by the presence of the vessel. The air temperature at a height of 2 m above the surface was also measured using a PRT. All data were logged on laptop computers using commercial Datataker units. Measurements were only made when the vessel was drifting or moving at a speed of less than 2 m s-r. Measurements from the smaller vessel were made at way points 5, 10 and 15 km north of Magnetic Island on the direct route between the island and Kelso Reef. The locations of these way points are given in Table 1. At each way point ship measurements were taken over a ten-minute period while the vessel slowly moved north-west with a following sea at a speed between 1 and 2 m s- l. This tactic was adopted to minimise the pitch and roll of the vessel.
Table 1.
Location of the Three Way Points where Ship Data were Collected
Way point number (Wp) 1
Distance north of Magnetic Is. (km) 5
Latitude
Longitude
(“S) 19.056
(OK) 146.897
2 3
10 15
19.002 18.955
146.903 146.912
1520
I. I. Barton and W. J. Skirving
SATELLITE MEASUREMENTS Data from five different satellite instruments have been obtained for comparison with the ground data. These instruments are the Advanced Very High Resolution Radiometer (AVHRR) on both the NOAA-12 and NOAA-14 satellites, the Along Track Scanning Radiometer (ATSR) on ERS-1, the ATSR-2 on ERS-2, and the Visible and Infrared Spin-scan Radiometer (VISSR) on GMS-5. Further data have been requested from the operators of other satellites with infrared channels but have not yet been received. During the one-week period all AVHRR data from both NOAA-12 and NOAA-14 were received by local stations at Townsville and Hobart (43 “S, 148’E). Local coast-line features were used to navigate the satellite data. AVHRR data are transmitted with lo-bit accuracy which is equivalent to 0.1 “C per bit at typical surface temperatures. The spatial resolution of the data is 1.1 km. During 1997 the ERS-1 satellite carrying the ATSR instrument has been maintained in a “stand-by” mode and is reactivated for a few days every 70 days. The dates of the experiment were set to include one of these periods when ATSR was operational. The ERS-2 satellite carrying ATSR-2 is in a similar orbit to ERS-1 but follows with a time lag of one day. Both satellites are in a basic three-day repeat orbit so several passes (ascending and descending ) were available for each instrument. The ATSR data have 1Zbit accuracy (0.025 “C) and have a similar spatial resolution to the AVHRR data. The ATSR and ATSR-2 data were obtained from the Rutherford Appleton Laboratory in the United Kingdom. Both infrared brightness temperatures and derived sea surface temperatures were provided. The VISSR data on GMS-5 were received by the Australian Bureau of Meteorology and small subsets covering the Townsville region were kindly supplied to the authors. The spatial resolution of the infrared channels of the VISSR instrument is 5 km and so contiguous pixels are used in our GMS data analysis. The data are transmitted from the satellite with only 8 bits per channel giving a digital step of approximately 0.4 “C in the brightness temperatures. These factors seriously limit the derived SST accuracy from GMS data as will be seen later. THE INTER-COMPARISONS Ship-based Thermometers and Infrared Radiometers The ship measurements for the day and night of July 30 are given in Table 2. The values are averages over a 5-minute period in the middle of each measurement run. Adverse weather conditions (wind and waves) limited the northerly extent of the morning excursion to way point 2. Problems with a data logging unit caused the loss of some Everest radiometer data on both excursions. Typical standard deviations of the measurements taken in the five-minute analysis period are 0.3 “C, 0.03 “C, 0.01 “C, 0.01 “C, and 0.01 “C for the sky Tasco, the SST Tascos, the Everest, the PRT, and the AD590 respectively. Differences between the various temperature measurements are given in Table 3. The two TASCO radiometers show good consistency and usually differ by less than 0.05 ‘C while the Everest radiometer shows a little more variation with the TASCOs, but is still well within 0.1 “C of the TASCO measurements. The two in situ thermometers (the in-water PRT and the AD590) also show differences of less than 0.1 “C. In this study the PRT measurements are assumed to be more accurate - an assumption supported by later measurements in the laboratory. The infrared radiometer measurements have been corrected for the reflection of downwelling sky radiation and thus give the true radiative (or skin) temperature of the water surface. For both the TASCOs and the Everest a surface emissivity of 0.986 at 30’ incidence angle and a central wavenumber of observation of lOOOcm_’ were assumed (Donlon et al., 1998). The sky radiance used was that measured by the upward looking TASCO radiometer.
1521
Satellite Derived Sea Surface Temperature
Table 2.
Wp 1 2 1 1 2 3 2 1
Table 3 is widely activity. day and
Details of the Ship Data Collected During Temperatures are in Degrees Centigrade.
Local time 0948 1016 1055 2139 2217 2250 2336 0013
Tair 20.31 20.93 20.95 20.73 20.85 20.90 20.73 20.24
SST (PRT) 21.26 21.52 21.33 21.46 21.67 21.75 21.60 21.39
SST (TASCOl) 21.00 21.29 21.15 21.23 21.44 21.49 21.37 21.15
SST (TASCOZ) 20.93 21.29 21.15 21.18 21.41 21.49 21.35 21.15
on July 30. All
SST (Everest) 20.94 21.21 21.43 21.52 21.39 21.15
Sky (TASC03) -25.95 -26.61 -25.83 -21.89 -22.91 -22.80 -23.11 -23.35
shows that the bulk-skin temperature difference is generally between 0.2 and 0.3 “C - a value that accepted for an open ocean surface where the near-surface layers are well mixed by wind and wave There is no evidence of any diurnal effect in the bulk-skin temperature differences. For both the night excursions the water is between 0.5 and 1.0 “C warmer than the air.
Table 3. Differences Wp 1 2 1 1 2 3 2 1
Satellite
SST (AD590) 21.27 21.59 21.39 21.44 21.69 21.77 21.65 21.44
the Two Excursions
TASCOlTASC02 0.07 0.00 0.00 0.05 0.03 0.00 0.02 0.00
and Ship-based
TASCOlEverest 0.06 0.08
0.01 -0.03 -0.02 0.00
Between PRTAD590 -0.01 -0.07 -0.06 0.02 -0.02 -0.02 -0.05 -0.05
the Various PRTTASCOl 0.26 0.23 0.18 0.23 0.23 0.26 0.23 0.24
Ship-Based PRTTASCO2 0.33 0.23 0.18 0.28 0.26 0.26 0.25 0.24
Measurements PRTEverest 0.32 0.31 0.24 0.23 0.21 0.24
AirTASCOl -0.69 -0.36 -0.20 -0.50 -0.59 -0.59 -0.64 -0.91
Measurements
The derivation of accurate SST values using satellite infrared measurements is only possible in regions which are free of cloud. For all the satellite data used in this study the satellite images were scanned visually to detect any presence of cloud, and cloud-affected areas were not included in the inter-comparison. Standard SST algorithms were applied to the satellite data to estimate the SST. For AVHRR data the standard Non-linear SST (NLSST) algorithms provided by NOAA were used. These algorithms estimate SST with a typical error of 0.5 “C. The AVHRR algorithm coefficients are derived using standard regression techniques with a set of coincident AVHRR brightness temperatures and in situ buoy measurements. Thus the algorithms are tuned to provide an estimate of the bulk SST even though the satellite radiometers sample the surface skin temperature. For the two ATSR instruments the SST algorithms were derived using an atmospheric transmission model with a set of atmospheric profiles of temperature, pressure and water vapor. Thus the ATSR provides a direct measure of the surface skin temperature. With this basic difference in algorithm development it is expected that the skin SST, as provided by ATSR, should generally be lower than the AVHRR SST by 0.2 to 0.3 “C.
I. J. Barton and W. J. Skirving
1522
Table 4.
Satellite and Ship Measurements of SST for July 30, 1997. The Times of the Ground Measurements are Given in the Left-Most Column while the Observation Times for the Satellite Measurements are Given in the Second Row of the Table. Due to Some Cloud Contamination Data from the NOAA-12 Satellite were not Available on July 30 so Data from the Following Day are Used.
Local time 0948 1016 1055 2139 2217 2250 2336 0013
Wp 1 2 1 1 2 3 2 1
ATSR 1033 21.46 21.57 21.46
ATSR-2 2257
NlCAVHRR NlZ-AVHRR GMS* GMS* 1420 1900 31/7 1032 2233 22.26 20.98 20.87 22.47 21.00 20.87 22.26 20.98 20.87 21.33 22.26 20.98 17.67 21.50 22.47 21.00 17.67 21.65 22.65 21.20 19.91 21.50 22.47 21.00 17.67 21.33 22.26 20.98 17.67 * Digitisation step of approx 2 “C in derived SST values
SST TASCO-1 21.00 21.29 21.15 21.23 21.44 21.49 21.37 21.15
SST PRT 21.26 21.52 21.33 21.46 21.67 21.75 21.60 21.39
The two infrared channels on the GMS instrument are not well-suited for estimating SST mainly due to digitisation effects. A digitisation step of near 0.5 “C in each brightness temperature translates to a step of near 2 “C in SST when the data are used with a typical split-window SST algorithm. For this analysis a simple split-window algorithm was developed using a radiative transfer model (Barton et al., 1989) and then the algorithm was used to derive an estimate of the skin SST using the GMS data. DISCUSSION The SST measurements from the ship and satellites are all presented in Table 4. For the morning measurements there is reasonable agreement between the satellite data with the GMS estimates being lower than the ship measurements while those from NOAA-14 are higher. The ATSR estimates are 0.45 “C higher than the skin SST measurements of the ship-borne radiometers and are just slightly higher than the bulk measurements. Both the GMS and NOAA-12 estimates are marginally lower than the ship radiometer measurements. The NOAA-14 estimates are almost exactly 1.0 “C higher than the bulk temperature measurements. For the evening measurements a similar pattern exists except that the ATSR-2 estimates are now in between the skin and bulk measurements made from the ship. The effect of digitisation in the GMS estimates can be seen in the Table. The increase of 2.24 “C in the results for way point 3 is caused by a decrease of one count in the GMS 11 pm channel signal (equivalent to an increase of 0.45 “C in the brightness temperature). Care must also be taken with digitisation effects when dealing with AVHRR data. Barton (1989) that variations in estimated SST can be as large as 0.4 “C when multi-channel SST algorithms are AVHRR data on a pixel by pixel basis. Techniques that involve some spatial averaging of the data with this problem, but if there are only small horizontal gradients in SST then averaging of the temperatures may not help. All the measurements in Table 4 show good relative increases with the distance away from the coast.
agreement
with location.
has shown applied to may assist brightness
In all cases the temperature
The comparisons discussed in this paper have been made with measured and satellite-derived SST values. However, it is also possible to compare brightness temperatures by calculating the expected radiances at the satellite using an atmospheric radiative transfer model with vertical profiles of pressure, water vapor and temperature as measured by radiosondes. Throughout the week of this experiment the Bureau of Meteorol-
Satellite Derived Sea Surface Temperature
I523
ogy launched radiosondes from their station close to the coast at Townsville. Sondes were launched at times of ship data collection and thus it will be possible in future analyses to compare brightness temperature measurements. This will be a useful comparison as it will then be possible to detect anomalies in satellite data due to spectral leaks or poor on-board calibration. Such errors are extremely difficult to detect without comparison with other satellite data. Any minor biases in the model-derived brightness temperatures should be present for all radiometers, but anomalous measurements by any one instrument should be detectable.
CONCLUSIONS A preliminary analysis of coincident satellite and ground based data has shown the potential of data intercomparisons in assessing instrument performance and the accuracies associated with the derivation of geophysical parameters from satellite data. For the CEOS Infrared Pilot Project the early analysis suggests that 1. The GMS measurement of SST is too noisy to be of use in future climate studies. Future GMS satellites should have at least lo-bit data digitisation. 2. The AVHRR instruments on the NOAA satellites give a reasonable measure of SST - in our limited analysis NOAA-14 measurements were too high while those for NOAA-12 were too low. 3. The ATSR instruments give measurements that are close to the ship measurements but in the data analysed here the satellite measurements appeared to be too warm by 0.3 “C. This is just within the range where data are deemed to be useful for climate research. 4. The ship measurements showed a uniform cool skin layer with a temperature between 0.2 and 0.3 cooler than the bulk temperature just below the surface. Further analysis of the full data set will be used to confirm these preliminary results. The measurements reported here highlight the immense value in careful validation of satellite data products. In many situations it is not possible to monitor changes in satellite instrument performance or characteristics after launch. Further inter-comparisons can only assist in improving the global data sets that will be an integral part of our future understanding of climate and climate change. ACKNOWLEDGMENTS Radiosonde and GMS data were kindly supplied by the Australian Bureau of Meteorology. Thanks must also go to the operators of the two vessels who allowed the fixture of instruments and recording equipment to their ships. REFERENCES Allen, M.R., C.T.Mutlow, G.M.C. Blumberg, J.R. Christy, R.T. McNider, and D.T. Llewellyn-Jones, Global Change Detection, Nature, 370,24 (1994). Barton, I.J., Digitisation Effects in AVHRR and MCSST Data, Remote Sens. Enve’ron., 29, 87 (1989). Barton, I.J., A.M. Zavody, D.M. O’Brien, D.R. Cutten, R.W. Saunders and D.T. Llewellyn-Jones, Theoretical Algorithms for Satellite-Derived Sea Surface Temperatures, J. Geophys. Res., 94, D3,3365 (1989). Donlon,C.J., S.J. Keogh, D.J. Baldwin, I.S. Robinson, I. Ridley, T. Sheasby, I.J. Barton, E.F. Bradley, T.J. Nightingale, and W. Emery, Solid State Radiometer Measurements of Sea Surface Skin Temperature, J. Atmos and Ocean. Tech., 15,775 (1998).
Pergsmon www.elsevier.nlAocate/asr
SATELLITE-DERIVED SEA SURFACE COMPARISON: A CASE STUDY I.J. Barton’
and W.J.
TEMPERATURE
INTER-
Skirving2
‘CSIRO Marine Research, Bobart, Tasmania 7000, Australia 2Australian Institute of Marine Science, Townsville, Queensland 4810, Australia
ABSTRACT During a one-week period in July/August 1997 an intensive campaign was held which involved the mea surement of the sea surface temperature (SST) from both satellites and ships off the coast of Townsville in tropical north-east Australia. The experiment was a pilot project of the Working Group on Calibration and Validation (WGCV) of the international Committee on Earth Observation Satellites (CEOS). The ship measurements were made with both infrared radiometers and in situ bulk surface temperature devices. The SST was also determined using the data from several sensors on different environmental satellites. The data analysis has demonstrated a reasonable agreement between the different measurements, but it has also highlighted some of the differences between the surface and space-based measurements and identified several difficulties in using surface-based data for satellite-derived data validation. The pilot project has also demonstrated the immense value of future similar inter-comparisons especially if the data are to be amalgamated into long-term data sets for climate research. 01999 COSPAR. Publishedby Elsevier Science Ltd. INTRODUCTION Future understanding of the complexities of the Earth’s climate will undoubtedly be linked to numerical models which portray the physical, chemical, and biological processes of the atmosphere, cryosphere, land, and ocean systems. For these models to accurately represent and predict subtle variations in climate, realistic global data sets will be required for both initialisation and validation of the model outputs. Accurate long-term data sets will also be required to confirm any decadal changes in the climate system (see for example Allen et al., 1994). For global climate models measurements are required over the entire globe and these must necessarily be provided by satellite instrumentation. The immense cost of obtaining surface-based measurements with the required spatial resolution, especially over the oceans of the southern hemisphere, is not a realistic option. Thus the major space agencies of the world are now planning the imminent launch of environmental satellites to provide the required data. One of the most basic parameters controlling the climate system is the sea surface temperature (SST) which is critical in defining the links between the ocean and the atmosphere. Fluxes of sensible and latent heat, radiative energy, and gas exchanges are critically dependent on the SST. At first look it may appear that 1517
I. J. Barton and W. J. Skirving
1518
the SST is an easily-measured parameter and should provide a direct link between the atmosphere and the ocean. However, small-scale processes at the air-sea interface provide large gradients of temperature over small vertical distances. In most situations radiative and evaporative heat losses provide a thin cool layer of water at the ocean surface. Thus the radiative temperature of the sea surface can be quite different to the bulk temperature of the water as measured by an in situ thermometer. This means that much care must be taken in the validation of satellite-derived SST and the amalgamation and application of different SST data sets. The Working Group on Calibration and Validation (WGCV) of the international Committee on Earth Obsercollaboration in the development vation Satellites (CEOS) is one of the major groups fostering international With the future launch of several environmental satellites of long-term data sets for climate applications. the WGCV has initiated two pilot projects to demonstrate the value of careful cross-calibration of data and data products obtained from these satellites. One project is focussed on .the thermal infrared spectral bands used for the determination of surface temperature, while the second involves the use of the visible/near infrared bands for estimating the reflectance of the Earth’s surface. For each project a typical ground-based test site has been selected and satellite and ground-based data for a set period have been collected. An area off the north-east tropical Australian coast near Townsville (19”S, 147’E) was chosen as the thermal infrared site and data were collected over a one-week period covering July and August 1997. The location and observation period were chosen for the following reasons. l The site has been selected as a long-term SST validation site for future environmental satellite sensors. A tourist ferry which operates a regular service between Townsville and the Outer Great Barrier Reef (a distance of 90 km) has been fitted with an infrared radiometer and a bulk-SST thermometer to provide continuous data over the next three years.
. The area is often free of cloud during l
Data from several satellites
the austral
would be available
winter when the Australian
is well to the north.
for the data analysis.
During the one-week observation period the Along Track Scanning Radiometer ERS-1 satellite would be reactivated by the European Space Agency to provide measurements. l
monsoon
(ATSR) instrument on the an extra set of space-based
This paper provides some initial results from the thermal infrared project. The paper will outline the measurement strategy and provide some preliminary results from the early data analysis of the measurements made on one day during the selected week. These results will be used to demonstrate the many benefits from conducting similar projects in the future. GROUND-BASED
MEASUREMENTS
For the infrared inter-comparison project data were obtained using instruments on two ships based in Townsville. The first was a small (10 m) double-hull motor vessel which was chartered for nine excursions during the one-week experiment. The other vessel was a high-speed tourist ferry (length 30 m) which operates between Townsville and Kelso Reef about 90 km north of Townsville (see Figure 1). The tourist ferry visited Kelso Reef on four days during the experiment. In this preliminary paper we only consider measurements from the smaller vessel taken over a period of one day. The smaller vessel was fitted with a suite of “off-the-shelf” infrared radiometers and devices for measuring water and air temperature. One Everest and two TASCO radiometers viewed the ocean surface at an incidence angle of 30’ while a third TASCO radiometer measured the downwelling sky radiance at a zenith angle of 30’. The SST at a depth of approximately 0.05 m was measured using two thermometers trailing
Satellite
Derived Sea Surface Temperature
1519
f
AUSTRALIA
‘,
Myrmidon Reef *
O-
10
20
CapeBowling Green
30
Kilometres
I 46”WE
147”OO’E
J
I
Fig. 1.
The study area showing the transect between Townsville and Kelso Reef.
over the vessel’s side; one was a platinum resistance thermometer (PRT) and the other a solid state device (AD590). During a measurement run these thermometers were deployed so they remained totally immersed in water that was unaffected by the presence of the vessel. The air temperature at a height of 2 m above the surface was also measured using a PRT. All data were logged on laptop computers using commercial Datataker units. Measurements were only made when the vessel was drifting or moving at a speed of less than 2 m s-r. Measurements from the smaller vessel were made at way points 5, 10 and 15 km north of Magnetic Island on the direct route between the island and Kelso Reef. The locations of these way points are given in Table 1. At each way point ship measurements were taken over a ten-minute period while the vessel slowly moved north-west with a following sea at a speed between 1 and 2 m s- l. This tactic was adopted to minimise the pitch and roll of the vessel.
Table 1.
Location of the Three Way Points where Ship Data were Collected
Way point number (Wp) 1
Distance north of Magnetic Is. (km) 5
Latitude
Longitude
(“S) 19.056
(OK) 146.897
2 3
10 15
19.002 18.955
146.903 146.912
1520
I. I. Barton and W. J. Skirving
SATELLITE MEASUREMENTS Data from five different satellite instruments have been obtained for comparison with the ground data. These instruments are the Advanced Very High Resolution Radiometer (AVHRR) on both the NOAA-12 and NOAA-14 satellites, the Along Track Scanning Radiometer (ATSR) on ERS-1, the ATSR-2 on ERS-2, and the Visible and Infrared Spin-scan Radiometer (VISSR) on GMS-5. Further data have been requested from the operators of other satellites with infrared channels but have not yet been received. During the one-week period all AVHRR data from both NOAA-12 and NOAA-14 were received by local stations at Townsville and Hobart (43 “S, 148’E). Local coast-line features were used to navigate the satellite data. AVHRR data are transmitted with lo-bit accuracy which is equivalent to 0.1 “C per bit at typical surface temperatures. The spatial resolution of the data is 1.1 km. During 1997 the ERS-1 satellite carrying the ATSR instrument has been maintained in a “stand-by” mode and is reactivated for a few days every 70 days. The dates of the experiment were set to include one of these periods when ATSR was operational. The ERS-2 satellite carrying ATSR-2 is in a similar orbit to ERS-1 but follows with a time lag of one day. Both satellites are in a basic three-day repeat orbit so several passes (ascending and descending ) were available for each instrument. The ATSR data have 1Zbit accuracy (0.025 “C) and have a similar spatial resolution to the AVHRR data. The ATSR and ATSR-2 data were obtained from the Rutherford Appleton Laboratory in the United Kingdom. Both infrared brightness temperatures and derived sea surface temperatures were provided. The VISSR data on GMS-5 were received by the Australian Bureau of Meteorology and small subsets covering the Townsville region were kindly supplied to the authors. The spatial resolution of the infrared channels of the VISSR instrument is 5 km and so contiguous pixels are used in our GMS data analysis. The data are transmitted from the satellite with only 8 bits per channel giving a digital step of approximately 0.4 “C in the brightness temperatures. These factors seriously limit the derived SST accuracy from GMS data as will be seen later. THE INTER-COMPARISONS Ship-based Thermometers and Infrared Radiometers The ship measurements for the day and night of July 30 are given in Table 2. The values are averages over a 5-minute period in the middle of each measurement run. Adverse weather conditions (wind and waves) limited the northerly extent of the morning excursion to way point 2. Problems with a data logging unit caused the loss of some Everest radiometer data on both excursions. Typical standard deviations of the measurements taken in the five-minute analysis period are 0.3 “C, 0.03 “C, 0.01 “C, 0.01 “C, and 0.01 “C for the sky Tasco, the SST Tascos, the Everest, the PRT, and the AD590 respectively. Differences between the various temperature measurements are given in Table 3. The two TASCO radiometers show good consistency and usually differ by less than 0.05 ‘C while the Everest radiometer shows a little more variation with the TASCOs, but is still well within 0.1 “C of the TASCO measurements. The two in situ thermometers (the in-water PRT and the AD590) also show differences of less than 0.1 “C. In this study the PRT measurements are assumed to be more accurate - an assumption supported by later measurements in the laboratory. The infrared radiometer measurements have been corrected for the reflection of downwelling sky radiation and thus give the true radiative (or skin) temperature of the water surface. For both the TASCOs and the Everest a surface emissivity of 0.986 at 30’ incidence angle and a central wavenumber of observation of lOOOcm_’ were assumed (Donlon et al., 1998). The sky radiance used was that measured by the upward looking TASCO radiometer.
1521
Satellite Derived Sea Surface Temperature
Table 2.
Wp 1 2 1 1 2 3 2 1
Table 3 is widely activity. day and
Details of the Ship Data Collected During Temperatures are in Degrees Centigrade.
Local time 0948 1016 1055 2139 2217 2250 2336 0013
Tair 20.31 20.93 20.95 20.73 20.85 20.90 20.73 20.24
SST (PRT) 21.26 21.52 21.33 21.46 21.67 21.75 21.60 21.39
SST (TASCOl) 21.00 21.29 21.15 21.23 21.44 21.49 21.37 21.15
SST (TASCOZ) 20.93 21.29 21.15 21.18 21.41 21.49 21.35 21.15
on July 30. All
SST (Everest) 20.94 21.21 21.43 21.52 21.39 21.15
Sky (TASC03) -25.95 -26.61 -25.83 -21.89 -22.91 -22.80 -23.11 -23.35
shows that the bulk-skin temperature difference is generally between 0.2 and 0.3 “C - a value that accepted for an open ocean surface where the near-surface layers are well mixed by wind and wave There is no evidence of any diurnal effect in the bulk-skin temperature differences. For both the night excursions the water is between 0.5 and 1.0 “C warmer than the air.
Table 3. Differences Wp 1 2 1 1 2 3 2 1
Satellite
SST (AD590) 21.27 21.59 21.39 21.44 21.69 21.77 21.65 21.44
the Two Excursions
TASCOlTASC02 0.07 0.00 0.00 0.05 0.03 0.00 0.02 0.00
and Ship-based
TASCOlEverest 0.06 0.08
0.01 -0.03 -0.02 0.00
Between PRTAD590 -0.01 -0.07 -0.06 0.02 -0.02 -0.02 -0.05 -0.05
the Various PRTTASCOl 0.26 0.23 0.18 0.23 0.23 0.26 0.23 0.24
Ship-Based PRTTASCO2 0.33 0.23 0.18 0.28 0.26 0.26 0.25 0.24
Measurements PRTEverest 0.32 0.31 0.24 0.23 0.21 0.24
AirTASCOl -0.69 -0.36 -0.20 -0.50 -0.59 -0.59 -0.64 -0.91
Measurements
The derivation of accurate SST values using satellite infrared measurements is only possible in regions which are free of cloud. For all the satellite data used in this study the satellite images were scanned visually to detect any presence of cloud, and cloud-affected areas were not included in the inter-comparison. Standard SST algorithms were applied to the satellite data to estimate the SST. For AVHRR data the standard Non-linear SST (NLSST) algorithms provided by NOAA were used. These algorithms estimate SST with a typical error of 0.5 “C. The AVHRR algorithm coefficients are derived using standard regression techniques with a set of coincident AVHRR brightness temperatures and in situ buoy measurements. Thus the algorithms are tuned to provide an estimate of the bulk SST even though the satellite radiometers sample the surface skin temperature. For the two ATSR instruments the SST algorithms were derived using an atmospheric transmission model with a set of atmospheric profiles of temperature, pressure and water vapor. Thus the ATSR provides a direct measure of the surface skin temperature. With this basic difference in algorithm development it is expected that the skin SST, as provided by ATSR, should generally be lower than the AVHRR SST by 0.2 to 0.3 “C.
I. J. Barton and W. J. Skirving
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Table 4.
Satellite and Ship Measurements of SST for July 30, 1997. The Times of the Ground Measurements are Given in the Left-Most Column while the Observation Times for the Satellite Measurements are Given in the Second Row of the Table. Due to Some Cloud Contamination Data from the NOAA-12 Satellite were not Available on July 30 so Data from the Following Day are Used.
Local time 0948 1016 1055 2139 2217 2250 2336 0013
Wp 1 2 1 1 2 3 2 1
ATSR 1033 21.46 21.57 21.46
ATSR-2 2257
NlCAVHRR NlZ-AVHRR GMS* GMS* 1420 1900 31/7 1032 2233 22.26 20.98 20.87 22.47 21.00 20.87 22.26 20.98 20.87 21.33 22.26 20.98 17.67 21.50 22.47 21.00 17.67 21.65 22.65 21.20 19.91 21.50 22.47 21.00 17.67 21.33 22.26 20.98 17.67 * Digitisation step of approx 2 “C in derived SST values
SST TASCO-1 21.00 21.29 21.15 21.23 21.44 21.49 21.37 21.15
SST PRT 21.26 21.52 21.33 21.46 21.67 21.75 21.60 21.39
The two infrared channels on the GMS instrument are not well-suited for estimating SST mainly due to digitisation effects. A digitisation step of near 0.5 “C in each brightness temperature translates to a step of near 2 “C in SST when the data are used with a typical split-window SST algorithm. For this analysis a simple split-window algorithm was developed using a radiative transfer model (Barton et al., 1989) and then the algorithm was used to derive an estimate of the skin SST using the GMS data. DISCUSSION The SST measurements from the ship and satellites are all presented in Table 4. For the morning measurements there is reasonable agreement between the satellite data with the GMS estimates being lower than the ship measurements while those from NOAA-14 are higher. The ATSR estimates are 0.45 “C higher than the skin SST measurements of the ship-borne radiometers and are just slightly higher than the bulk measurements. Both the GMS and NOAA-12 estimates are marginally lower than the ship radiometer measurements. The NOAA-14 estimates are almost exactly 1.0 “C higher than the bulk temperature measurements. For the evening measurements a similar pattern exists except that the ATSR-2 estimates are now in between the skin and bulk measurements made from the ship. The effect of digitisation in the GMS estimates can be seen in the Table. The increase of 2.24 “C in the results for way point 3 is caused by a decrease of one count in the GMS 11 pm channel signal (equivalent to an increase of 0.45 “C in the brightness temperature). Care must also be taken with digitisation effects when dealing with AVHRR data. Barton (1989) that variations in estimated SST can be as large as 0.4 “C when multi-channel SST algorithms are AVHRR data on a pixel by pixel basis. Techniques that involve some spatial averaging of the data with this problem, but if there are only small horizontal gradients in SST then averaging of the temperatures may not help. All the measurements in Table 4 show good relative increases with the distance away from the coast.
agreement
with location.
has shown applied to may assist brightness
In all cases the temperature
The comparisons discussed in this paper have been made with measured and satellite-derived SST values. However, it is also possible to compare brightness temperatures by calculating the expected radiances at the satellite using an atmospheric radiative transfer model with vertical profiles of pressure, water vapor and temperature as measured by radiosondes. Throughout the week of this experiment the Bureau of Meteorol-
Satellite Derived Sea Surface Temperature
I523
ogy launched radiosondes from their station close to the coast at Townsville. Sondes were launched at times of ship data collection and thus it will be possible in future analyses to compare brightness temperature measurements. This will be a useful comparison as it will then be possible to detect anomalies in satellite data due to spectral leaks or poor on-board calibration. Such errors are extremely difficult to detect without comparison with other satellite data. Any minor biases in the model-derived brightness temperatures should be present for all radiometers, but anomalous measurements by any one instrument should be detectable.
CONCLUSIONS A preliminary analysis of coincident satellite and ground based data has shown the potential of data intercomparisons in assessing instrument performance and the accuracies associated with the derivation of geophysical parameters from satellite data. For the CEOS Infrared Pilot Project the early analysis suggests that 1. The GMS measurement of SST is too noisy to be of use in future climate studies. Future GMS satellites should have at least lo-bit data digitisation. 2. The AVHRR instruments on the NOAA satellites give a reasonable measure of SST - in our limited analysis NOAA-14 measurements were too high while those for NOAA-12 were too low. 3. The ATSR instruments give measurements that are close to the ship measurements but in the data analysed here the satellite measurements appeared to be too warm by 0.3 “C. This is just within the range where data are deemed to be useful for climate research. 4. The ship measurements showed a uniform cool skin layer with a temperature between 0.2 and 0.3 cooler than the bulk temperature just below the surface. Further analysis of the full data set will be used to confirm these preliminary results. The measurements reported here highlight the immense value in careful validation of satellite data products. In many situations it is not possible to monitor changes in satellite instrument performance or characteristics after launch. Further inter-comparisons can only assist in improving the global data sets that will be an integral part of our future understanding of climate and climate change. ACKNOWLEDGMENTS Radiosonde and GMS data were kindly supplied by the Australian Bureau of Meteorology. Thanks must also go to the operators of the two vessels who allowed the fixture of instruments and recording equipment to their ships. REFERENCES Allen, M.R., C.T.Mutlow, G.M.C. Blumberg, J.R. Christy, R.T. McNider, and D.T. Llewellyn-Jones, Global Change Detection, Nature, 370,24 (1994). Barton, I.J., Digitisation Effects in AVHRR and MCSST Data, Remote Sens. Enve’ron., 29, 87 (1989). Barton, I.J., A.M. Zavody, D.M. O’Brien, D.R. Cutten, R.W. Saunders and D.T. Llewellyn-Jones, Theoretical Algorithms for Satellite-Derived Sea Surface Temperatures, J. Geophys. Res., 94, D3,3365 (1989). Donlon,C.J., S.J. Keogh, D.J. Baldwin, I.S. Robinson, I. Ridley, T. Sheasby, I.J. Barton, E.F. Bradley, T.J. Nightingale, and W. Emery, Solid State Radiometer Measurements of Sea Surface Skin Temperature, J. Atmos and Ocean. Tech., 15,775 (1998).