The Hillarys Transect (2): Validation of satellite-derived sea surface temperature in the Indian Ocean off Perth, Western Australia

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Continental Shelf Research 27 (2007) 1702–1718 www.elsevier.com/locate/csr

The Hillarys Transect (2): Validation of satellite-derived sea surface temperature in the Indian Ocean off Perth, Western Australia B. McAteea,, A. Pearceb, M. Lyncha, J. Daviesa, M. Boterhovena, B. Osbornea a

Curtin University of Technology, GPO Box U1987 Perth, Western Australia WA 6845, Australia b CSIRO Marine Research, Private Bag 5, Wembley, Western Australia WA 6913, Australia

Received 16 August 2005; received in revised form 24 January 2007; accepted 5 February 2007 Available online 21 February 2007

Abstract There has been a heightened interest in sea surface temperature (SST) measurements during the past two decades, particularly on a global scale, due largely to the advent of several El Nino episodes and increasing worldwide concern about global warming. Because of the continuous global measurements of SST that satellites can provide they play a fundamental role in acquiring the data sets necessary for studies of such climate processes. However, the satellite data still need to be validated against in situ measurements in order to assess the accuracy of satellite SST retrieval algorithms. Validation of such SST retrieval algorithms is the primary aim of the SST measurement program component of the Hillarys Transect. This paper describes a methodology for the validation of satellite-derived SST as well as the seasonal variation of SST off the coast of southern Western Australia. It discusses the factors which may affect the quality of in situ validation data and concludes that measurements of the bulk sea surface temperature (BSST) should be the preferred in situ data source for validation of satellite-based algorithms derived from floating buoy measurements. In this study BSST data possessed superior accuracy over the coincident radiometric sea surface skin temperature (SSST) data, and were found to be significantly better for validation purposes under wind speed conditions below 4 ms1 . r 2007 Elsevier Ltd. All rights reserved. PACS: 92.10.Bf; 92.10.Fj; 92.70.Jw; 93.30.Fd Keywords: SST; Satellite; Radiometer; Validation; Indian Ocean; Western Australia

1. Introduction The Hillarys Transect (Pearce et al., 2006; Fearns et al., 2007) sea surface temperature (SST) data set has been acquired on a quasi-monthly basis between Corresponding author. Tel.: +61 893870356.

E-mail address: [email protected] (B. McAtee). 0278-4343/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.csr.2007.02.001

December 1996 and December 1998, within which, four months (February, March, April and December) in 1997 were missed, and the August transect in 1998 was cut short due to bad weather. The dates on which the transects were carried out were selected based on there being a high degree of certainty that the fine, cloud-free conditions needed for the validation of satellite imagery would prevail for the duration of the transect. Even so, the satellite imagery on 6 of the 27

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transect days was definitely affected by cloud (Pearce et al., 2006). The data set captures the most part of two annual ocean temperature cycles. The transect heads offshore in a westerly direction to a maximum distance of 40 km along a latitude of 31 500 S, and the stations are spaced at 5 km intervals as shown in Fig. 1 (also see Pearce et al., 2006 for a more detailed description of the transect). The data set consists of both ‘skin’ (SSST) and ‘bulk’ (BSST) sea surface temperature measurements made using a number of different instruments. The instrumentation used includes a radiometer for SSST measurement, with both a thermistor and a mercury-in-glass thermometer for measuring BSST. The geographic location of the Hillarys Transects is important within a global context. The Hillarys Transect was included in the Global Ocean Data Assimilation Experiment High Resolution Sea Surface Temperature (GHRSST) validation program (Donlon, 2002). It was also a Sensor Intercomparison for Marine Biological and Interdisciplinary Ocean Studies (SIMBIOS) site (Mueller et al., 1998), and was located close to a solar photometer situated on Rottnest Island some 20 km to the south which is part of the Aerosol Robotic Network (AERONET) for the development of global aerosol climatology (Holben et al., 2001). Inclusion of the Hillarys site in such coordinated international programs makes it an important component in the calibration, validation and atmospheric correction of remotely sensed data for the marine environment. SST derived from satellite measurements is important as a boundary layer input to atmospheric

30m

40m

m

0m

50

10

H40

31°50'

H35 H30 H25 H20 H15 H10

H5 H0 HM

Rottnest Is.

32°00'S 0

10km

115°20'

SR

115°30'E

115°40'

Fig. 1. Map showing geographical location of the Hillarys Transect. HM denotes the Hillarys Marina and SR the Swan River. Adapted from Pearce et al. (2006).

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circulation and forecast models (Emery et al., 2001). It also has great importance in modeling the flux of gases between the ocean and atmosphere (Emery et al., 2001). SST therefore has a central role in predicting and understanding climate change and it is important that it is measured as accurately as possible. An accuracy of 0:3 K has been specified as necessary for using satellite-derived SST measurements in global climate studies (World Climate Research Programme, WCRP, 1984). Surface-atmosphere heat flux, wind speed, and remote measurement technique are all important in the interpretation of SST from satellite data (Barton et al., 2004). Validation of satellite-derived SST faces the same complications (Barton et al., 2004). As such, simple in situ measurements of the bulk temperature of the ocean often do not facilitate the highest quality validation data set (Barton, 2001; Barton et al., 2004). For this reason the validation component of the Hillarys Transects described in this paper focuses on the radiometric measurements of SSST, rather than BSST, and investigates the quality of validation achieved between remotely sensed SST and sea surface skin and bulk temperature data acquired in situ. This study focuses on the SST data acquired during transects in 1998, coinciding with the implementation of the calibration methodology for the radiometric data described in Section 3. In this work, the McMillin and Crosby (McMCR) (McMillin and Crosby, 1984) and multi-channel SST (MCSST) (McClain et al., 1985) algorithms were chosen for evaluation as the McMCR algorithm was found to be suitable for use in Perth coastal waters in a previous study (Pearce et al., 1989), while the MCSST algorithm was an operational SST algorithm for the AVHRR2 sensor aboard NOAA 14 satellite (McClain et al., 1985; Walton et al., 1998). Both of these SST products were routinely produced from the NOAA 14 sensor through the Western Australian Satellite Technology and Applications Consortium (WASTAC) satellite data reception site in Perth, so that data were readily accessible. The McMCR and MCSST algorithms were developed based on regression between satellitebased observations of SST against temperature estimates derived from floating buoys (Barton, 2001; Emery et al., 2001). As such, remotely sensed estimates of the top 10 mm21 mm of the ocean (Barton, 2001; Emery et al., 2001), or SSST, are used to estimate the bulk temperature which is

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generally more representative of the mixed layer temperature, or BSST. This paper assesses the suitability of an inexpensive TASCO radiometer as a validation instrument for satellite-derived SST from the Advanced Very High Resolution Radiometer 2 (AVHRR2) sensor aboard the National Oceanic and Atmospheric Administration 14 (NOAA 14) polar orbiting satellite and describes the seasonal and cross-shelf variability of SST (both SSST and BSST) across the continental shelf off Hillarys. 2. Instrumentation and methods SSST and BSST measurements were recorded using several different techniques to allow comparison of results, and to compare SSST with BSST measurements. The suite of temperature measurements were carried out in parallel at each station. The instrumentation and procedure used for each of these measurements are described in the following sections. 2.1. TASCO radiometer The physics of SSST measurement using the TASCO radiometer (see Donlon et al., 1998 for further technical description of the TASCO instrument), under the assumption that the sea surface exhibits specular properties, is described by Eq. (1). The actual temperature measured by the TASCO radiometer, when aimed at the sea surface, comprises a combination of the infrared radiation emitted from the sea surface and that emitted downwards by the atmosphere and reflected from the sea surface into the field of view of the radiometer. The radiance from the sea surface may be extracted from the TASCO measurement according to the expression Btotal þ ½1  Bsky , (1)  where Btotal is the radiance leaving the sea surface measured by the TASCO radiometer ðW=ðm2 ster ðcm1 Þ4 ÞÞ, Bsea the radiance emitted by the sea surface ðW=ðm2 ster ðcm1 Þ4 ÞÞ, Bsky the downwelling sky radiance measured by the TASCO radiometer ðW=ðm2 ster ðcm1 Þ4 ÞÞ,  the emissivity of the sea surface, and it is implicit within Eq. (1) that Btotal , Bsea , and Bsky are measured at the band averaged wavelength for the TASCO radiometer. From Eq. (1), the corrections for the non-blackness of the sea surface (where the emissivity of the

sea surface is less than unity), and the associated reflected component, must be made using the equivalent blackbody radiances of the T total and T sky estimates measured by the TASCO radiometer. In order to apply Eq. (1) the measured T total and T sky were converted to their equivalent blackbody radiances via the Planck function, as described in Menzel (2001). Bsea , as given by Eq. (1), was finally converted to an estimate of the SSST via the inverse Planck function (Menzel, 2001); SSST ¼

c2 , l ln½c1 =½l5 Bsea þ 1

(2)

where c1 ¼ 1:191066E 8 ðW=ðm2 ster ðcm1 Þ4 ÞÞ, c2 ¼ 1:4388 K cm, and l is the band averaged wavelength for the TASCO radiometer. The TASCO radiometer used during the measurement program was receptive to thermal infrared radiation in the 7215 mm range, and had the response function shown in Fig. 2 (Donlon et al., 1998). The radiometer was used to make separate measurements of (i) the surface-leaving infrared radiation, Btotal from the sea surface, and (ii) the downwelling sky radiance, Bsky , and displays them as the brightness temperatures (Norman and Becker, 1995) T total and T sky , respectively, in units of  C. After being corrected for the calibration of the radiometer (see Section 3), these brightness temperature estimates may then be used to calculate Bsea using Eq. (1), and finally SSST via Eq. (2). The TASCO radiometer was used to measure the SSST from the side of the boat facing in the direction away from the sun (anti-solar). The measurement was made at an angle ðyÞ of 45 offnadir to ensure that the reading was not contaminated by the proximity of the vessel. The viewing angle was determined using a spirit level attached to

Bsea ¼

Fig. 2. Response function of TASCO radiometer.

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the side of the radiometer. However, at such viewing angles the sea surface emissivity begins to decrease significantly (Masuda et al., 1988) (see Fig. 3). A value of the sea surface emissivity of  ¼ 0:980 was used in this work. This value was calculated for the off-nadir angle of 45 , over wavelengths between 8 and 15 mm by band averaging the mean of the spectral emissivity data for sea water tabulated by Masuda et al. (1988) for zero wind speed at viewing angles of 40 and 50 , over the spectral response function of the TASCO radiometer. A sky temperature measurement was also made at a zenith angle of 45 in the anti-solar direction in order to determine the downwelling sky radiance in Eq. (1). This measurement is an important part of the methodology for radiometric SSST estimation as used by Barton et al. (2004) in their field program of TASCO radiometer measurements. 2.2. Mercury-in-glass thermometer and bucket sample The thermometers used to measure the BSST of the bucket sample were mercury-in-glass thermometers. Such thermometers were also used to measure the temperature of the blackbody calibration unit used in the SSST calibration process (see Section 3). They were calibrated to approximately 0:1  C (Pearce et al., 2006). A 15 L plastic bucket was deployed over the side of the boat at each station to collect an ocean sample from which a BSST estimate was taken using the mercury-in-glass thermometer. This estimate was compared with the estimate derived from the thermistor resistance measurement. A water sample was also taken from the bucket for salinity and chlorophyll analysis (see Pearce et al., 2006; Fearns et al., 2007). 2.3. Thermistor The thermistor designed for use in the validation of data from the moderate resolution imaging spectroradiometer (MODIS) was used to measure the BSST. This instrument was of the same type as that used by Barton et al. (2004) with an accuracy of 0:01  C over a temperature range of 0250  C. The thermistor was built into a construction ‘hard hat’ which floated upside down and positioned the thermistor approximately 3–4 cm below the surface. It had foam placed inside it to increase its buoyancy and stability. It was designed to float behind the

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vessel on station while attached to a multimeter on board the vessel, through which the resistance measurements were recorded. The conversion of thermistor resistance measurements to bulk temperature were performed using the equation T ¼  1:2037  ðlog RÞ3 þ 23:999  ðlog RÞ2  196:08  ðlog RÞ þ 529:85,

ð3Þ

where R is the thermistor resistance measurement ðOÞ, and T the BSST ð CÞ. Eq. (3) was derived by fitting a polynomial to the calibration data provided with the thermistor by Minnett (pers. comm.). The thermistor was deployed from the stern of the boat and allowed time to equilibrate with the ocean temperature. Resistance measurement readings were taken using a high impedance Fluke multimeter at each minute over a 4 min period. These readings were subsequently converted to an ocean temperature via Eq. (3) back at the laboratory. The bulk ocean temperature assigned for the station was then recorded post-cruise as the average of the temperatures calculated from the resistance measured at the 3 and 4 min marks. The variability in the resistance measurement over the 4 min period was also used as an estimate of the uncertainty value for the BSST. 3. Calibration of radiometric data Due to the dependence of Bsea on both Btotal and Bsky , and hence T total and T sky , as illustrated by Eqs. (1) and (2), the accuracy with which T total in particular is known is critical when aiming to measure an accurate value of the SSST for satellite validation purposes. For this reason, the calibration of the TASCO radiometer is of great importance, and needs to be known as precisely as possible. Investigation has shown that the accuracy of the TASCO radiometer is dependent upon the operating temperature of the instrument itself. In order to characterize the change in response of the radiometer with operating temperature, a similar experiment to the CASOTS radiometer calibration experiment carried out by Donlon et al. (2002) was conducted. The TASCO radiometer measured the brightness temperature of a water-filled blackbody calibration unit for a range of ambient temperatures ð202 40  CÞ. At each ambient temperature, the temperature of the blackbody calibration unit was varied

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over a range comparable to that which may be expected in the annual cycle of SST in nearshore waters of the Indian Ocean off Perth, Western Australia ð15225  CÞ (Pearce et al., 2006). A strong linear relationship ðr2 ¼ 0:93Þ was found to exist between the difference between the TASCO radiometer estimate of the blackbody temperature and the true blackbody temperature (T-BB) (where BB is measured by a mercury-in-glass-thermometer) and the difference between the ambient air temperature (AT) and the blackbody temperature (AT-BB) (both also measured by mercury-in-glassthermometers). These calibration data are plotted in Fig. 4. Regression of these data gives T  BB ¼ ð0:089  0:005Þ½AT  BB þ ð0:248  0:023Þ,

ð4Þ

where T is the TASCO radiometer measurement of the blackbody temperature, AT the ambient air temperature measured by a mercury-in-glass thermometer, which is assumed to be the operating temperature of the instrument, and BB the blackbody temperature as measured by a second mercury-in-glass thermometer. Eq. (4) can be readily rearranged to give T ¼ BB þ ð0:089  0:005Þ½AT  BB þ ð0:248  0:023Þ.

accuracy. During the Hillarys Transects we did not have such conditions in the field. Although our calibration methodology takes into account change in the ambient temperature, a large component of uncertainty may be introduced to our measurements through the field calibration process compared to the equivalent process under laboratory conditions. Using our calibration methodology the uncertainty in the temperature measurements made using the TASCO radiometer were of the order of 0:1  C. This estimate of uncertainty for TASCO-derived temperature measurements flows into the error analysis for TASCO radiometerderived measurements of SSST, as described in the following section. 4. Uncertainties in radiometric SSST measurement As the radiometric SSST data set is to be used for validating satellite SSST measurements, it is important that there also exists an accurate estimate of the uncertainty in the data. The uncertainty in the value of the SSST may be written as dSSST ¼

qSSST qSSST qSSST de þ dy, dBsea þ qBsea qe qy (6)

ð5Þ

A calibrated estimate of T total may then be obtained by substituting the value of T total , measured by the TASCO radiometer, for BB in Eq. (5). Similarly, the estimate of T sky made by the radiometer may be corrected for the calibration of the TASCO radiometer by substituting T sky for BB in Eq. (5). It should be noted that correction of T sky using Eq. (5) assumes that the regression relationship between TBB and AT-BB may be extrapolated to extremely cold temperatures of the order of 40  C for the sky temperature measurement. However, a 1  C change in T sky causes only a corresponding 0:01  C in T sea under conditions typical of those encountered during the Hillarys Transects. The impact of any non-linearity in the calibration of the radiometer at very cold temperatures is then unlikely to be significant in the final SSST result. It is pertinent to note that results from intercomparison studies between blackbody calibration units used for the calibration of SSST measuring radiometers such as that described by Rice et al. (2004) identified a stable ambient temperature as being important for achieving optimal calibration

where the partial derivative qSSST=qBsea may be evaluated through Eq. (2). Similarly qSSST=qe may be evaluated through Eq. (1). By applying some simple calculus, qSSST=qy may be written as qSSST qSSST qe ¼  . qy qe qy

(7)

The quantity qe=qy may then be evaluated from a plot such as that in Fig. 3.

Fig. 3. Plot of change in sea surface emissivity with increasing off-nadir angle of observation.

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Similarly to dSSST, dBsea in Eq. (6) may be described by the expression dBsea ¼

qBsea qBsea qBsea de, dBsky þ dBtotal þ qBsky qBtotal qe

(8)

where the partial derivatives qBsea =qBsky , qBsea = qBtotal , and qBsea =qBe may be evaluated through Eq. (1). In this work, the estimation of dBtotal and dBsky was based upon the uncertainty in the measurements of T total and T sky which are evaluated through the calibration equation for the radiometer described by Eq. (5) in Section 3 (each were of the order of 0:1  C). Eqs. (6) and (8) show that in order to minimize dSSST it is necessary that the sea surface emissivity and the viewing angle are accurately known. As we use angular measurements of Btotal and Bsea , an understanding of how changing the zenith angle of the view of the sea surface effects the value of sea surface emissivity (see Fig. 3) is critical. From the figure, at large zenith angles, the gradient of the sea surface emissivity curve increases. Accordingly through Eqs. (6)–(8), any uncertainty in the zenith angle will subsequently have a larger impact on the accuracy of the SSST measurement at larger zenith angles. Using the basic method of measuring the zenith angle of observation of the radiometer with a spirit level adopted in this work, such accuracy is assumed to be limited to dy ¼ 5 . Given this a priori estimate of dy, de may be obtained from the curve in Fig. 3 since de=dy may be read from Fig. 3. At 45 , de is approximately 0:04 and makes a large contribution to the value of qSSST. In this work we have used sea surface emissivity data from Masuda et al. (1988) for which the wind speed was zero. Consequently, in this study the value of de may also include a component due to the impact of wind-related roughening of the sea surface, for the viewing geometry we have used (Donlon et al., 1998). Table 1 gives the calculated uncertainty of the radiometric SSST data from cruises during 1998 using Eqs. (6)–(8), with dy ¼ 5 , and de=dy determined for y ¼ 45 from Fig. 3 (Fig. 4). 5. Results and validation of satellite SST algorithms 5.1. SSST The complete SST data set from the Hillarys Transect during 1998, comprising both SSST and BSST measurements, is shown in Fig. 5. The time

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Table 1 Value of dSSST ð CÞ for each month in 1998 Month

dSSST

Month

dSSST

Jan. Feb. Mar. Apr. May Jun.

0.6 0.6 0.5 0.6 0.7 0.7

Jul. Aug. Sep. Oct. Nov. Dec.

0.6 0.8 0.7 0.6 0.7 0.4

Fig. 4. Plot illustrating linear relationship between T-BB and AT-BB.

series shows the change in the offshore sea surface skin and bulk temperature gradient from the coast to the continental shelf over the course of a year. See the color plates in Pearce et al. (2006) for satellite images of the corresponding area. The station located in the nearshore lagoon a few hundred meters from the shore is denoted H0, the station 5 km offshore is denoted as H5, and the 10 km H10, etc. The station 5 km offshore is also repeated at the end of the transect (H5R) to help gauge any change in the SSST measured using the TASCO radiometer during the survey, and to allow the calculation of a correction factor to be applied to the measured SSST to correct for any change in the SSST between the time of the radiometer measurement and the satellite overpass some 4 h later. During the summer months (Figs. 5a,b,l) there is only a slight SSST gradient with a tendency for the SSST to be cooler further from the coast. The change in SST over the transect is generally no more than 2  C at this time. The warmer inshore water may be due to the shallow inshore bathymetry which allows greater solar heating to occur relative to the deeper offshore water. For further discussion of the general oceanography of the area the reader is directed to the accompanying paper by Pearce et al. (2006).

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Fig. 5. Offshore SST profiles for each month during 1998. During summer the SSST is warmer inshore and cooler offshore. The TASCO radiometer measurements are shown by triangles, the bulk temperature of the bucket sample by asterisks and the thermistor data by squares. H5R is the repeated measurement station.

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SST (°C)

24 21 18 15 H0

H10

H20 Station

H30

H40

H0

H10

H20 Station

H30

H40

H0

H10

H20 Station

H30

H40

H0

H10

H20 Station

H30

H40

SST (°C)

24 21 18 15

SST (°C)

24 21 18 15

SST (°C)

24 21 18 15

Fig. 6. Validation of satellite SST results from January, March, June and October 1998. The TASCO radiometer measurements are shown as the triangles, the McMCR algorithm estimates are shown by the asterisks and the MCSST algorithm by the diamonds. The dashed line represents the in situ measurements with a correction applied for the time lag between the time of the measurement and the satellite overpass.

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Progressing into the autumn (Figs. 5c,d,e) and winter (Figs. 5f,g,h), there is an increase in the SSST gradient, with warmer SSST offshore due to the presence of the Leeuwin Current and coastal cooling (heat loss) (Pearce et al., 2006). The influence of the Leeuwin Current on the SSST profile begins to become apparent in March, and by the time of its peak flow, in winter, the SSST difference between inshore and offshore water is of the order of 426  C. This strong gradient is particularly apparent in the plots for June–August (Figs. 5f–h), though the August transect was cut short due to bad weather. The SSST gradient subsides in Spring as does the flow of the Leeuwin Current, and the SSST profile along the transect flattens as it begins to return to the Summer pattern. In general, the SSST gradient measured in situ using the TASCO radiometer is captured by the coincident satellite estimates. However, the in situ and satellite-based SSST profiles are often offset from one another (see Fig. 6b for example). Table 2 shows the bias and RMS error between the in situ and satellite-derived SSST estimates obtained using the McMCR and MCSST satellitebased algorithms for the months in 1998 for which the satellite view of the transect stations was not obviously contaminated by cloud. In the 1998 data set the satellite-derived data for May, August and November were definitely cloud-affected and were not included in the analysis presented in the tables. During February the data may have been contaminated by the presence of thin cirrus clouds which were noted during the transect but were not readily identifiable in the satellite imagery.

Table 2 Comparison between SSST estimates from the TASCO radiometer (TAS), McMillin and Crosby SST algorithm (McMCR) and the multichannel SST algorithm (MCSST) using AVHRR data for dates of the Hillarys Transect during 1998 for which there was cloud-free satellite imagery Month

Jan. Feb. Mar. Apr. Jun. Jul. Sept. Oct. Dec.

Bias ð CÞ

RMS ð CÞ

TAS–McMCR

TAS–MCSST

McMCR–MCSST

TAS–McMCR

TAS–MCSST

McMCR–MCSST

0.22 0.32 1.60 1.19 0.34 0.34 0.67 0.24 0.20

0.46 0.89 1.57 1.21 0.49 0.06 0.41 0.11 0.33

0.69 1.21 0.04 0.03 0.15 0.28 0.26 0.13 0.52

0.36 0.47 1.80 1.30 0.49 0.51 0.77 0.51 0.39

0.56 1.00 1.76 1.33 0.62 0.36 0.55 0.48 0.47

0.73 1.28 0.05 0.04 0.16 0.31 0.28 0.14 0.56

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The estimated accuracy of SSST measurements made using the TASCO radiometer (see Table 1) are of the order of the uncertainties in the SST estimates derived from global satellite algorithms such as those of the McMCR algorithm (1 K) and the MCSST algorithm (0.7 K) (Walton et al., 1998; Barton, 1995). However, a qualitative comparison between in situ and satellite-derived SST may still be made from the Hillarys Transect data set. The satellite-derived SST estimates using the McMCR and MCSST algorithms are compared with the in situ TASCO radiometer measurements for January, March, June and October of 1998 in Fig. 6. These months have been selected as they are representative of the seasonal SST profiles measured during the cruises (the satellite-based SST images from the NOAA 14 satellite for the days corresponding to the cruise dates in January and October of 1998 may be viewed in Figure 7 of Pearce et al., 2006). The two satellite-derived SST data sets overestimate the in situ measurements in most cases in Table 1, but are usually similar to each other and agree to within the estimates of accuracy quoted for their use (1 K for the McMCR algorithm, McMillin and Crosby, 1984, and 0.7 K for the MCSST, Walton et al., 1998; Barton, 1995), as shown by the column headed McMCR–MCSST in the RMS half of Table 2. The exception to this occurred during February where there was possible contamination by cirrus clouds. In general, data from the McMCR algorithm compare more favorably to the TASCO-derived SSST data, and are equal to, or cooler than, the equivalent estimates from the MCSST algorithm. For the set of measurements made during 1998 in Table 2 the results for the McMCR algorithm showed an RMS error of 0:73  C about a bias of 0:40  C. For the MCSST algorithm there was an RMS error of 0:79  C about a bias of 0:61  C when compared to the TASCO measurements. The bias commonly observed between the in situ and satellite measurements may be largely due to the solar heating of the near-surface of the ocean in the intervening time period between making the SSST estimate using the TASCO radiometer and the overpass time of the satellite. The overpass time of the AVHRR-2 is approximately 1400 h (local time), while the cruise is generally completed by this time. The effect of the diurnal thermal stratification (Donlon, 2002) which may occur during this time lag is particularly evident in Fig. 5c, for March of 1998, which shows that the SSST measured by the

TASCO radiometer at the repeated H5 station was of the order of 1:522:0  C warmer than the earlier measurement made on the outgoing leg of the transect. Further, discussion of the time lag and associated diurnal heating among the different in situ measurements made during the transect and the satellite overpass time may be found in Pearce et al. (2006). To correct the SSST measurements made using the TASCO radiometer for the effects of this time lag, for each transect in 1998 the difference between the initial SSST measurement at the H5 station and that at the H5 station repeated on the incoming leg of the transect was computed. Since the time between these two measurements was known, the rate of change of SSST could be estimated assuming a linear heating rate. Based on the rate of change in SSST, the SSST at each station in the transect at the time of the satellite overpass may be estimated by assuming the rate of change to be constant and extrapolating forward from the time at which the SSST was measured at each station during the transect to the time of the satellite overpass. The in situ SSST data with this correction applied are shown by the dashed line in the plots of Fig. 6. When these corrections were applied, the mean bias over the year between the in situ SSST measurements and the results from McMCR and MCSST algorithms was improved, although the mean RMS between the in situ and satellite-based estimates remained relatively unchanged. The comparison between the in situ and satellite-based results, as for Table 2, but with the correction for the time lag between the in situ measurements and the satellite overpass applied, is given in Table 3. This table shows that the main impact of the inclusion of the correction factor to the data is to improve the level of agreement between the SST estimates from the MCSST algorithm and TASCO measurements of SSST in the summer months when large amounts of solar heating could be expected inshore. Although a large amount of the observed bias over the whole data set is removed by the application of the lag correction, such improvements are not consistent throughout the year or along the transect so there are clearly other factors also affecting the satellite and in situ match-ups. As noted by Barton et al. (2004), the effects of wind speed and surface-atmosphere heat flux may also contribute to such differences. The effects of wind on the radiometric SSST measurement is discussed in more detail in Section 6.2.

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Table 3 Comparison between SSST estimates from the TASCO radiometer (TAS), McMillin and Crosby SST algorithm (McMCR) and the multichannel SST algorithm (MCSST) as for Table 2, but with the correction for time lag between in situ measurement and satellite overpass time applied Month

RMS ð CÞ

TAS–McMCR

TAS–MCSST

McMCR–MCSST

TAS–McMCR

TAS–MCSST

McMCR–MCSST

0.64 0.94 0.72 1.14 0.30 0.02 0.28 0.54 0.30

0.05 0.26 0.69 1.17 0.45 0.26 0.02 0.67 0.22

0.69 1.21 0.04 0.03 0.15 0.28 0.26 0.13 0.52

0.76 1.06 0.83 1.25 0.47 0.41 0.38 0.64 0.45

0.33 0.49 0.80 1.28 0.58 0.46 0.26 0.77 0.38

0.73 1.28 0.05 0.04 0.16 0.31 0.28 0.14 0.56

26

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SST MCSST (°C)

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14 14

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r2=0.87

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Fig. 7. The results from the (a) McMCR algorithm and (b) MCSST algorithm plotted against the SSST measured by the TASCO radiometer.

The set of satellite-derived SST using the McMCR and MCSST algorithms for 1998 is plotted against the data measured using the TASCO radiometer, with the correction applied, in Fig. 7. The figure shows that similar results are achieved by both algorithms. The plot of results from the McMCR algorithm versus the TASCO-derived SSST (Fig. 7a) shows an RMS error of 0:63  C about a bias of 0:01  C. For the MCSST algorithm the RMS error is 0:60  C with a bias of 0:19  C (Fig. 7b). Both of these results are in agreement with the results presented in Table 3. 5.2. BSST BSST follows the same seasonal cycle identified for SSST in the previous section, as shown by Fig. 5.

During the Hillarys Transects measurements of BSST were duplicated using different instruments. BSST measurements derived from the thermistor ð0:01  CÞ and the mercury-in-glass thermometer ð0:1  CÞ estimate of the bucket sample show good agreement to within the uncertainty of each measurement. Of the complete set of 1998 BSST data 75% of the measurements agree to within this level of uncertainty. Fig. 8 shows the mercury-in-glass thermometer estimates of BSST from the bucket sample plotted against the thermistor estimates. The two data sets show a correlation coefficient of 0.84, and there is a bias of 0:2  C between the thermometer and thermistor measurements. The scattering of outlying points in Fig. 8 are mostly due to water penetrating into the thermistor connections. This causes changes in conductivity and hence fluctuations in the resistance measurements. A further source of error is the possibility of the thermistor drifting into the warm exhaust water from the vessel. For these reasons the thermometer measurement of BSST from the bucket sample was probably the more reliable of the two measurements, although removal of the bucket sample from its original environment may have an effect on the temperature of the sample through the change in the impact of physical parameters such as wind speed and sea state (Paulson et al., 1972). The McMCR and MCSST algorithms are global algorithms based upon regression between satellitederived estimates of SSST and drifting buoy data (Barton, 1995). The buoy data are a measure of BSST, rather than the skin temperature, of the

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ocean. During the Hillarys Transects, the simplicity of BSST measurement methodology using a bucket sample and mercury-in-glass thermometer provided a more accurate estimate of BSST ð0:1 KÞ compared to the SSST data where Table 1 shows a mean accuracy of 0:7 K for measurement of the skin temperature. Table 4 shows the equivalent data to those in Table 2 but with the BSST estimate taken from the bucket sample in place of the TASCO estimate. The data show that in most cases the comparison with the results from the McMCR and MCSST algorithms is as good or better than the equivalent comparisons using the TASCOderived data. When a correction for time lag between in situ and satellite measurements based on the change in BSST observed over the duration of the transect is

25

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applied to the BSST data (see Table 5) the agreement between the satellite-derived estimates of SST and the BSST data are improved, as for the SSST case described by Table 3. The most significant improvement is for the data from the March transect, while agreement between the BSST and MCSST data is generally improved throughout the year. Fig. 9 shows the results from the McMCR and MCSST algorithms plotted against the set of bucket temperature measurements for 1998. Both the McMCR and MCSST algorithms show very high correlations with the bucket sample of r2 ¼ 0:95 and 0.98, respectively. The comparison between the SST estimates from the McMCR algorithm and the bucket samples with the correction applied show an RMS error of 0:60  C around a bias of 0:12  C. The same comparison for the MCSST algorithm shows an RMS error of 0:43  C about a bias of 0:09  C, in agreement with the results presented in Table 5. The comparisons between the satellite-derived SSTs and in situ measurements are discussed further in Section 6.2.

21

6. Discussion 19

6.1. Radiometer calibration Bias = -0.2°C r2 = 0.84°C

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Fig. 8. Comparison of BSST measurements between the bucket sample and the thermistor.

Our results emphasize the need for frequent calibration of the TASCO radiometer, ideally with blackbodies at both ambient and elevated temperatures, as suggested by Barton et al. (2004). Calibration of TASCO radiometers elsewhere has shown that they exhibit a measurement bias which is related to the temperature of the target (Prata and Cechet, 1998). This bias appears similar to that

Table 4 Comparison between bucket temperature estimates of BSST, McMillin and Crosby SST algorithm (McMCR) and the multi-channel SST algorithm (MCSST) as for Table 2 Month

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Bias ð CÞ

RMS ð CÞ

BSST–McMCR

BSST–MCSST

McMCR–MCSST

BSST–McMCR

BSST–MCSST

McMCR–MCSST

0.26 0.24 1.37 1.15 0.07 0.47 0.75 0.21 0.25

0.43 0.97 1.34 1.18 0.22 0.19 0.49 0.08 0.28

0.69 1.21 0.04 0.03 0.15 0.28 0.26 0.13 0.52

0.43 0.47 1.52 1.25 0.29 0.60 0.84 0.54 0.42

0.56 1.10 1.48 1.29 0.39 0.33 0.59 0.51 0.41

0.73 1.28 0.05 0.04 0.16 0.31 0.28 0.14 0.56

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Table 5 Comparison between bucket temperature estimates of BSST, McMillin and Crosby SST algorithm (McMCR) and the multi-channel SST algorithm (MCSST) as for Table 4 but with the correction for time lag between in situ measurement and satellite overpass time applied Month

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McMCR–MCSST

BSST–McMCR

BSST–MCSST

McMCR–MCSST

0.67 1.06 0.22 0.57 0.34 0.31 0.25 0.20 0.17

0.02 0.15 0.19 0.60 0.19 0.03 0.01 0.33 0.36

0.69 1.21 0.04 0.03 0.15 0.28 0.26 0.13 0.52

0.83 1.23 0.33 0.64 0.46 0.50 0.35 0.45 0.39

0.41 0.56 0.29 0.67 0.37 0.31 0.23 0.54 0.49

0.73 1.28 0.05 0.04 0.16 0.31 0.28 0.14 0.56

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Fig. 9. The results from the (a) McMCR algorithm and (b) MCSST algorithm plotted against BSST measured from the bucket sample using the mercury-in-glass thermometer.

observed during our calibration work. However, using the calibration methodology developed in this work, we can be confident that the effects of such instrument bias on our measurements have been minimized. Intercomparisons with other radiometers used for SSST validation purposes such as those described by Barton et al. (2004) have shown that a standard deviation of up to 0.2 K may be expected in temperature measurements made using TASCO radiometers when well-calibrated. We then expect that our measurement methodology contributes approximately 0.5 K to the estimates of uncertainty in our SSST measurements, based on a mean of 0:7 K from Table 1. The largest sources of such error are the impacts of viewing geometry on the choice of sea surface emissivity and may also include fluctuations in the ambient temperature

which affect both the radiometer (Prata and Cechet, 1998) and the blackbody calibration unit (Rice et al., 2004). Subpixel variations in SSST were found to be between 0.1 and 0.2 K by Pearce et al. (2006) and may also contribute to the RMS errors and biases observed in Figs. 7 and 9. Careful use of well-calibrated blackbody targets to calibrate radiometers in the field, such as the TASCO radiometer used in this work, could result in validation data sets that have uncertainties within 0:1  C (Rice et al., 2004). In our work, both the estimates of T total and T sky possess uncertainties of this order and there is a sizeable contribution of uncertainty related to the choice of sea surface emissivity. As such, even after improved calibration of the radiometer and thorough analysis of the TASCO-derived SSST, the level of uncertainty in the data remains too large for quantitative satellite validation to a level suitable for climate studies. Importantly, however, our calibration methodology characterized the changing relationship between the response of the TASCO radiometer, ambient temperature of operation and temperature of the target. As a result of this calibration work, we can be confident that we have the best possible SSST validation data set given our relatively simple methodology, which was constrained by our operating budget. In the future, findings from calibration work such as that by Rice et al. (2004) and Barton et al. (2004) may be incorporated to improve the accuracy of our SSST measurements closer to climate quality. The value of sea surface emissivity used in the calibration process is also important and needs to be

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accurately defined. Currently, radiometric SSST measurements are made at an off-nadir viewing angle of 45 , and this angle is only roughly estimated. From Fig. 3 this angle marks the beginning of the rapid decline in the value of the sea surface emissivity. Hence, uncertainties in the viewing angle at 45 cause a larger uncertainty in the emissivity than for viewing angles around 25230 where the gradient of the emissivity curve is smaller. As such, in the future, radiometric SSST measurements should be fixed at an accurately measured off-nadir viewing angle between 25 and 30 . Even using the current methodology, however, if the uncertainty in the angle of observation was to be reduced from 5 to 1 , the mean uncertainty in SSST measured by the TASCO radiometer would only be reduced from approximately 0:7 to 0:4 K. 6.2. Validation of satellite-derived SST In general, our work has shown that the estimated accuracy of SSST measurements made using the TASCO radiometer (see Table 1) were of the order of the uncertainties in the SST estimates derived for global satellite algorithms such as those of the McMCR algorithm (1 K) and the MCSST algorithm (0.7 K) (Walton et al., 1998; Barton, 1995) so that we were unable to perform a quantitative validation analysis of the satellitederived SST. Because BSST measurements are logistically and technically more simple to make, satellite SST algorithms are commonly based on such data from floating buoy and ships of opportunity (Barton, 2001; Emery et al., 2001). However, as satellite SSTs are estimates of the skin temperature from the top 10 mm to 1 mm of the ocean (Barton, 2001; Emery et al., 2001) the physics that connects the skin and bulk temperatures is generally ignored by such algorithms (Emery et al., 2001). Our SSST measurements made using the TASCO radiometer show smaller biases against the satellitederived SST than the bulk temperature estimates from the bucket sample, while the RMS errors for the radiometric measurements of SSST are comparable to those for BSST using the mercury-in-glass thermometer. This suggests that the radiometer in general provides an estimate of the SSST which is closer to the value of the SST sensed by the satellite, although there are more sources of uncertainty in the radiometric measurement methodology which result in a larger RMS error (see Figs. 7 and 9).

Both the McMCR and the MCSST algorithms are global SST algorithms, and they produce similar results (see Tables 2, 3). But, as the coefficients in the algorithms are determined from a global average of atmospheric conditions and buoy match-ups, the closeness of the satellite SST product to the in situ measurements depends upon the similarity between this global average of atmospheric conditions and the prevailing atmospheric state at the time at which the measurements are made. It is also the case that the NOAA 14 overpass occurs 1–2 h after the completion of the transect and up to 4 h after measurements made at the inshore stations. Solar heating of the near-surface of the ocean during the temporal lag between in situ and satellite SST observations appears to be a major cause of offsets between the in situ and satellite observations in Fig. 6. It is possible to at least partially account for this offset using the repeated SSST measurement at station H5R shown in the plots of Fig. 5. The correction of the in situ SSST for diurnal heating during the lag time between the completion of the transect and the satellite overpass does improve the fit between the in situ and satellite-derived SST estimates to some extent (Tables 2 and 3). Similar improvement was also obtained through lag correction of the BSST. However, it is the case that such offsets are not uniform along the transect due to the existence of temperature gradients, changes in depth and wind speed and direction as well as water body movements. Clearly, such a correction factor is more complex than purely a linear interpolation based on the change in SSST/BSST over a few hours. Wind speed has a noticeable impact on stratification (Donlon et al., 1999, 2002) and is a factor which may also need to be taken into account. Improvement between satellite and in situ matchups after the application of the lag correction is most obvious in the results for March given in Tables 2 and 3 and 4 and 5. Although for March the match-ups between the satellite and in situ data are some of the poorest of the 1998 data set, the improvement after the application of the correction is greater than for other data. This is due to the combination of the low wind speeds for the March transect ( 3 ms1 or below, except for the H5R station) as shown by Fig. 10 and the maximum ambient air temperature of approximately 36  C facilitating solar heating. The reader is directed to Figure 4 of Pearce et al. (2006) for further details on the wind conditions encountered during the Hillarys

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Transects. February was also a low wind speed transect with a maximum ambient air temperature of approximately 36  C. For the February data, the lag correction improved the match-ups between satellite and in situ skin temperature, particularly for the MCSST data. However, the data may have been affected by the periodic presence of thin cirrus clouds during the transect, as noted previously. During other months, where the winds were strong, the data in Tables 2–5 show that there was very little difference between using the BSST estimate from the bucket sample or the TASCO derived skin temperature for validation of the McMCR and MCSST algorithms. Fig. 11 shows the bias between the in situ SSST and BSST measurements and results from the McMCR and MCSST algorithms for the same months as in Fig. 6. The wind speed is also plotted to show its impact on the comparisons. The data presented in Fig. 11, in combination with Figure 12 from Pearce et al. (2006), suggest that a wind speed of approximately 4 ms1 may be the point at which skin and bulk temperature estimates are of equal quality for validation purposes. Donlon et al. (2002) suggest a wind speed threshold of 6 ms1 as being the point at which the skin–bulk temperature difference is well quantified, optimizing the use of BSST measurements for satellite-derived SST validation. It is pertinent to note that the formation of Langmuir cells after about 3 ms1 (Beer, 1983, pp. 123–125) may promote near-surface mixing leading to the convergence between SSST and BSST observed in this work. The data plotted in Fig. 11 also show that where wind speeds are higher the magnitude of the bias between the in situ SSST and BSST and satellite-derived SST data are comparable, in agreement with the data presented in Tables 2–5 referred to earlier. As noted by Barton (2001), the understanding of the effects of wind speed on the validation of satellite-derived SST may improve in the future once estimates of wind speed are readily available from satellites. 7. Conclusions The Hillarys Transects have acquired a unique interdisciplinary oceanographic data set in the coastal waters off Perth, Western Australia. The radiometric component of the measurement program described in this paper has acquired data which reveal the seasonally cyclic nature of the SSST gradients between inshore waters and those

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offshore near the continental shelf. The BSST measurements made show corresponding trends in subsurface waters. The associated satellite images provided in Pearce et al. (2006) show the SST patterns through which the transect passes and describes how these are also seasonally driven. The analysis presented in this paper has also revealed that SST patterns and gradients observed via satellite-remote sensing may be related to the basic subsurface temperature structures which also occur seasonally in Perth coastal waters. From the perspective of validation of satellitederived SST imagery, this work has shown that although the radiometric calibration of the TASCO radiometer used in this work appears to be on a par

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with larger studies, such as that described by Barton et al. (2004), the methodology presented here requires refinement in the future so as to limit the uncertainty in the measurements of SSST introduced through our simplistic estimation of the zenith angle at which the sea surface is viewed by the radiometer. The uncertainty in viewing geometry has an associated impact on the chosen value of the sea surface emissivity which propagates into a significant contribution to the final uncertainty n our measurement of SSST using the TASCO radiometer. Our results have shown that on average, over the course of a year, inclusion of temperature correction to the in situ TASCO radiometer and BSST data to account for the lag between time of acquisition and the satellite overpass improves the bias between the in situ SSST and BSST data sets and the satellitederived SST data. However, the RMS error remains virtually unchanged and our results show that the effectiveness of the correction is seasonal and dependent upon wind speed. In particular, we have found that below a wind speed of 4 ms1 there is definite separation between SSST and BSST. In these low wind speed scenarios; (1) lag time between in situ and satellite-based measurements must be accounted for; (2) satellite-based SST algorithms derived using floating buoy data such as the McMCR and MCSST algorithms evaluated in this study clearly estimate the BSST; and (3) in situ measurements of BSST should be the preferred source of validation data. Above 4 ms1 our results show that there is little difference between using either the TASCO radiometer data or the BSST data from the bucket sample for validation of the satellite SSST algorithms applied, which is suggestive of increased mixing between the sea surface skin layer and subsurface waters. In the windy conditions frequently encountered during the Hillarys Transects we conclude that the BSST estimates should be the preferred source of validation data due to their superior accuracy over the radiometric results obtained in this study using the TASCO radiometer and methodology described in this paper. More highly calibrated, viewing positioned, and temperature-controlled instruments are in use, such as the Marine-Atmosphere Emitted Radiance Interferometer (M-AERI, Smith et al., 1996; Post et al., 1997), and appear from our results to be more suited to a measurement program like future Hillarys Transects in order to achieve sea surface

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skin temperature validation data sets of climate quality. Acknowledgments The authors would like to thank Dr. Peter Minnett from the University of Miami for the loan of the Moderate Resolution Spectroradiometer validation thermistor (YSI Model 44032) used in this work. References Barton, I.J., 1995. Satellite-derived sea surface temperatures: current status. Journal of Geophysical Research 100, 8777–8790. Barton, I.J., 2001. Interpretation of satellite-derived sea surface temperatures. Advances in Space Research 28, 165–170. Barton, I.J., Minnett, P.J., Maillett, K.A., Donlon, C.J., Hook, S.J., Jessup, A.T., Nightingale, T.J., 2004. The Miami2001 infrared radiometer calibration and intercomparison. Part 2: shipboard results. Journal of Atmospheric and Oceanic Technology 21, 268–283. Beer, T., 1983. Environmental Oceanography: An Introduction to the Behaviour of Coastal Waters. Pergamon Press, New South Wales, Australia. Donlon, C.J., 2002. In: Proceedings from the Second GODAE High Resolution SST Pilot Project Workshop, Workshop Proceedings from the Institute for Environment and Sustainability GHRSST/5, Inland and Marine Waters Unit, TP272, I-21020, ISPRA, Italy. Donlon, C.J., Keogh, S.J., Baldwin, D.J., Robinson, I.S., Ridley, I., Sheasby, T., Barton, I.J., Bradley, E.F., Nightingale, T.J., Emery, W., 1998. Solid-state radiometer measurements of sea surface skin temperature. Journal of Atmospheric and Oceanic Technology 15, 775–787. Donlon, C.J., Minnett, P.J., Nightingale, T.J., Fielder, L., Fisher, C.G., Baldwin, D., Robinson, I.S., 1999. The calibration and intercalibration of sea-going infrared radiometer systems using a low cost blackbody cavity. Journal of Atmospheric and Oceanic Technology 16, 1183–1197. Donlon, C.J., Nightingale, T.J., Gentemann, C., Barton, I.J., Ward, B., Murray, M.J., 2002. Toward improved validation of satellite sea surface skin temperature measurements for climate research. Journal of Climate 15, 353–370. Emery, W.J., Castro, S., Wick, G.A., Schleussel, P., Donlon, C., 2001. Estimating sea surface temperature from infrared satellite and in situ temperature data. Bulletin of the American Meteorological Society 82, 2773–2785. Fearns, P.R., Twomey, L., Zakiyah, U., Helleren, S., Vincent, W., Lynch, M.J., 2007. The Hillarys Transect (3): optical and chlorophyll relationships across the continental shelf off Perth. Continental Shelf Research, in press. Holben, B.N., Tanre´, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J., Chatenet, B., Lavenue, F., Kaufman, Y.J., Vande Castle, J., Setzer, A., Markham, B., Clark, D., Frouin, R., Halthore, R., Karnieli, A., O’Neill, N.T., Pietrass, C., Pinker, R.T., Voss, K., Zibordi, G., 2001. An emerging ground-based aerosol

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climatology: aerosol optical depth from AERONET. Journal of Geophysical Research 106, 12067–12097. Masuda, K.T., Takashima, T., Takayama, Y., 1988. Emissivity of pure and sea waters for the model seas surface in the infrared window regions. Remote Sensing of Environment 24, 313–329. McClain, E.P., Pichel, W.G., Walton, C.C., 1985. Comparative performance of AVHRR-based multichannel sea surface temperatures. Journal of Geophysical Research 90, 11587–11601. McMillin, L.M., Crosby, D.S., 1984. Theory and validation of the multiple window sea surface temperature technique. Journal of Geophysical Research 89, 3655–3661. Menzel, W.P., 2001. Applications with meteorological satellites. Technical Document, World Meteorological Organization, WMO/TD No. 1078. Mueller, J., McClain, C., Caffrey, B., Feldman, G., 1998. The NASA SIMBIOS Program. Backscatter 9. Norman, J.M., Becker, F., 1995. Terminology in the thermal infrared remote sensing of natural surfaces. Remote Sensing Review 12, 159–173. Paulson, C.A., Leavitt, E., Fleagle, R.G., 1972. Air–sea transfer of momentum, heat and water determined from profile measurements during BOMEX. Journal of Physical Oceanography 2, 487–497. Pearce, A.F., Prata, A.J., Manning, C.R., 1989. Comparison of NOAA/AVHRR-2 sea surface temperatures with surface measurements in coastal waters. International Journal of Remote Sensing 10, 37–52. Pearce, A.F., Lynch, M.J., Hanson, C.E., 2006. The Hillarys Transect (1): seasonal and cross-shelf variability of physical and chemical water properties off Perth, Western Australia, 1996–98. Continental Shelf Research 26, 1689–1729.

Post, M.J., Fairall, C.W., Snider, J.B., Yong, H., White, A.B., Ecklund, W.L., Weickmann, K.M., Quinn, P.K., Cooper, D.I., Sekelsky, S.M., McIntosh, R.E., Minnett, P.J., Knuteson, R.O., 1997. The combined sensor program: an air–sea science mission in the central and western pacific ocean. Bulletin of the American Meteorological Society 78, 2797–2815. Prata, A.J., Cechet, B., 1998. Miami infrared radiometer workshop. In: Workshop Proceedings of the Miami Infrared Radiometer Workshop, CSIRO Atmospheric Research, Aspendale, Victoria, Australia, 2–6 March 1998. Rice, J.P., Butler, J.J., Johnson, B.C., Minnett, P.J., Maillett, K.A., Nightingale, T.J., Hook, S.J., Abtahi, A., Donlon, C.J., Barton, I.J., 2004. The Miami2001 infrared radiometer calibration and intercomparison. Part 1: laboratory calibration of blackbody targets. Journal of Atmospheric and Oceanic Technology 21, 258–267. Smith, W.L., Knuteson, R.O., Revercomb, H.E., Feltz, W., Howell, H.B., Menzel, W.P., Nalli, N.R., Brown, O., Brown, J., Minnett, P., McKeown, W., 1996. Observations of the infrared radiative properties of the ocean-implications for the measurement of sea surface temperature via satellite remote sensing. Bulletin of the American Meteorological Society 77, 41–51. Walton, C.C., Pichel, W.G., Sapper, J.F., May, D.A., 1998. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. Journal of Geophysical Research 103, 27999–28012. WCRP, 1984. Report on the TOGA workshop on sea surface temperature and net radiation, World Climate Research Programme, World Meteorological Organization WCP-92, Geneva, Switzerland.