Sea Surface Temperatures from the GOES-8 Geostationary Satellite

Sea Surface Temperatures from the GOES-8 Geostationary Satellite Richard Legeckis* and Tong Zhu + ABSTRACT The introduction of the 10-bit, five-band, multispectral visible and thermal infrared scanner on the National Oceanic and Atmospheric Administration s GOES-8 satellite in 1994 offers an opportunity to estimate sea surface temperatures from a geostationary satellite. The advantage of the Geostationary Operational Environmental Satellite (GOES) over the traditional Advanced Very High Resolution Radiometer is the 30-min interval between images, which can increase the daily quantity of cloud-free ocean observations. Linear regression coefficients are estimated for GOES-8 by using the sea surface temperatures derived from the NOAA-14 polar-orbiting satellite as the dependent variable and the GOES infrared split window channels and the satellite zenith angle as independent variables. The standard error between the polar and geostationary sea surface temperature is 0.35 C. Since the polar satellite sea surface temperature is estimated within 0.5 C relative to drifting buoy near-surface measurements, this implies that the GOES-8 infrared scanner can be used to estimate sea surface temperatures to better than 1.0 C relative to buoys. Daily composites of hourly GOES-8 sea surface temperatures are used to illustrate the capability of the GOES to produce improved cloud-free images of the ocean. Hourly time series reveal a 2 C diurnal surface temperature cycle in the eastern subtropical Pacific with a peak near 1200 LT. The rapid onset of coastal upwelling along the southern coast of Mexico during December of 1996 was resolved at hourly intervals. 1. Introduction *Office of Research and Applications, NOAA/NESDIS, Washington, D.C. + Research and Data Systems Corporation, Greenbelt, Maryland. Corresponding author address: Dr. Richard Legeckis, NOAA/ NESDIS, World Weather Building Room 102, Mail Code E/RA-3, Washington, DC 20233-9910. E-mail: RLegeckis@nesdis.noaa.gov In final form 24 March 1997. The National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellite systems have provided global estimates of sea surface temperatures (SST) since 1982 in an operational mode by combining multiple IR channels of the Advanced Very High Resolution Radiometer (AVHRR). McClain et al. (1985) derived the linear multichannel SST (MCSST) regression equations using collocated surface-drifting buoy measurements as an estimate of the bulk SST. Recently, the satellite SST data have been further improved by reprocessing in the NOAA NASA (National Aeronautic and Space Administration) Oceanic Pathfinder project and globalscale SST can now be estimated to about 0.5 C after 1 week of satellite observations. A review of the present status of satellite-derived SST is provided by Barton (1995). There is considerable complexity in the satellite estimates of SST since the buoys measure the bulk SST while the satellite views the surface skin temperatures as demonstrated by Schluessel et al. (1990). Satellite microwave measurements were used by Emery et al. (1994) to demonstrate the nonlinear relationship between the AVHRR channel differences and atmospheric water vapor. In addition, a single polar-orbiting satellite views the ocean twice each day so that diurnal variability may not be resolved. This condition is improved when there is more than one polar orbiter available. The accuracy of satellite SST depends greatly on the ability to define cloud-free ocean areas using objective automated procedures. In practice, these procedures sometimes fail at random intervals and in the presence of increased atmospheric aerosols from Saharan dust or volcanic eruptions. Nevertheless, progress has been made in the use of MCSST for glo- 1971

bal SST analysis as demonstrated by Reynolds and Marsico (1993). The initial estimation of SST using multispectral data from a geostationary atmospheric sounder on GOES-5 were made by Bates and Smith (1985). Improved data became available with the introduction of the GOES-8 satellite, which provides IR and visible channels similar to the AVHRR as described by Menzel and Purdom (1994). The GOES-8 is capable of providing images of the earth at 30-min intervals from a nadir location at longitude 75 W and the equator. For oceanic research, the repeated coverage of the same ocean area offers the possibility of detecting diurnal SST changes. Also, by removing rapidly moving clouds, the daily Geostationary Operational Environmental Satellite (GOES) composites can reveal more of the SST structure along the coasts of North and South America. To produce daily GOES image composites, the SST has to be estimated. Initially, an AVHRR linear split window equation was applied directly to the GOES channel data but a comparison with an AVHRR MCSST image taken within 30 min revealed a significant temperature bias. In general, regression equations are derived for each new AVHRR due to differences in sensor filter characteristics and other system-related variables. Therefore, it is to be expected that each GOES scanner will also be unique and a project was started to derive SST regression equations specifically for GOES-8. At the time, access to the GOES-9 data were not available on a regular basis, so it will be evaluated in the future. The standard method for deriving the NOAA-14 SST regression equations is to use AVHRR data collocated with global measurements of bulk SST from drifting buoys. However, a collocated GOES and buoy dataset was not available and the GOES-8 view is restricted to the ocean areas around North and South America so fewer buoys would be available for colocation. Furthermore, to produce a collocated, cloud-cleared GOES and buoy match-up database, automated cloud clearing algorithms had to be established. Since the GOES channel noise levels (Menzel and Purdom 1994) are greater than for the AVHRR, the optimum parameters for automated cloud detection schemes are still under evaluation. To overcome some of the limitations imposed by the higher GOES system noise levels and the lack of a suitable GOES and buoy match-up database, a new approach was taken. An interactive computer was used for visual identification of cloud-free ocean areas. In these areas, AVHRR SST images, instead of near-surface buoy measurements, were used to provide an estimate of the SST for the derivation of the GOES-8 regression equations. This approach allowed the visual comparison of the estimated SST as well as of the individual IR channels from both the AVHRR and the GOES scanner. The interactive approach was useful for the evaluation of the GOES images and for detection of ocean features related to currents, eddies, upwelling, and time-dependent events. 2. Comparison of GOES-8 and NOAA-14 scanners The NOAA-14 AVHRR and the GOES-8 scanners have IR channels centered at 3.7, 10.8, and 11.9 µm and 3.9, 10.7, and 12 µm, respectively. The shortwave channels are both affected by reflected sunlight during daylight hours, which limits SST computation during daytime. There is only one visible GOES channel while the AVHRR has both a visible and a near IR channel. The latter channel on the AVHRR is very useful for land, cloud, and ocean discrimination as well as for detection of aerosols that can introduce a bias in the estimated SST. This advantage is missing on the GOES. For both instruments, the IR split window channels near 11 and 12 µm will be referred to as channels 4 and 5, for convenience. The instantaneous field of view (IFOV) of the AVHRR is about 1 km 1 km. The AVHRR global area coverage (GAC) IR samples are formed by averaging four IFOV samples and skipping one along the scan line and then skipping two of three scan lines. Therefore, the individual GAC sample size is about 4 km 1 km, but the effective spatial resolution of GAC data is about 4 km 3 km at nadir. The GOES IFOV at nadir is about 4 km 4 km. The difference in sample size has some effect on cloud tests that use the thermal homogeneity of adjacent samples to identify cloudy areas. The GAC 4-km 1-km samples can resolve smaller cloud-free areas by a factor of greater than 2 relative to the GOES. Both the GOES scanner and the NOAA-14 AVHRR have ten-bit data samples and provide calibrated temperature steps of about 0.1 C per digital count at 300 K. The GOES calibration method is described by Weinreb (1997). While the noise equivalent temperature of the AVHRR on NOAA-14 is less than 0.1 C at 300 K, the GOES-8 IR channels are noisier by a factor of 2 or 3. The AVHRR data are collected by a single detector 1972 Vol. 78, No. 9, September 1997

for each channel, while the GOES IR data are obtained by two detectors for each channel during each scan. The GOES system noise can be attributed both to the differences between the two detectors as well as to the GOES instrument and orbit characteristics as described by Menzel and Purdom (1994). For example, since the GOES is 36 000 km distant from the earth, the energy reaching the detector from a single IFOV on earth is much less than for a comparable IFOV of the AVHRR at an altitude of 850 km. 3. Data preparation For this study, the AVHRR GAC data were used since the GAC spatial resolution (4 km 3 km) is close to that of the GOES IR data (4 km 4 km). The AVHRR ten-bit data were converted to SST images, using both the linear and nonlinear NOAA-14 split window equations with a zenith angle term, and were then mapped to a Mercator projection with a spatial resolution of 4 km using nearest neighbor interpolation. The linear equations were more convenient for preparing images for display, while the nonlinear equations were used for regression computations since they are more accurate. The SST values were scaled at 0.125 C per count so that an eight-bit image, with count values between 0 and 255, had temperatures between 0 and 31.875 C. The University of Miami software system was used to navigate, process, and map the GAC data at the National Environmental Satellite, Data and Information System (NESDIS). The GOES ten-bit images were created using the McIDAS software provided to NESDIS by the University of Wisconsin. The GOES images were also mapped to Mercator projections as above. Hourly data were extracted for regions of interest and the temperatures were scaled to 0.125 per count for the three individual IR channels. This linear scaling produces only a small error when compared to the nonlinear ten-bit GOES input values that were computed from raw radiance values. For example, the differences between adjacent input counts of the GOES 10-bit data for channels 4 and 5 at 0, 17, and 25 C are about 0.15, 0.125, and 0.116 C, respectively. Therefore, for temperatures below 17 C, the use of the 0.125 C count 1 scaling factor provides greater resolution than is provided by the input. At 25 C the linear scaling introduces an error of only 0.009 C in the GOES data and is negligible when compared to the GOES thermal channel noise illustrated below. There are two benefits FIG. 1. The GOES-8 satellite zenith angles and values of Z relative to the satellite nadir at the equator and longitude 75 W. in using the linear scaling factor of 0.125 C. It allows all image data to be evaluated at nearly full spectral resolution on the conventional and widely available 8-bit computer monitors and it reduces the data volume in half from the original 10-bit input that is usually stored as a 16-bit value in archive data. The Mercator-mapped GOES and GAC images were navigated by alignment of coastal landmarks within an uncertainty of one sample in the vicinity of data extraction. However, it was noticed that in some cases the alignment of the image at the center produced colocation navigation errors at the edge of the image of up to three samples between GOES and GAC. By avoiding misaligned areas, a navigation accuracy of about 4 km was achieved in most cases. The collocated GOES images used for regression computations were usually obtained within 1 h of the AVHRR data. To complete the dataset, image files of the GOES satellite zenith angle, shown in Fig. 1, were created. The image size was set to 640 500 samples to facilitate image display and data extraction on a graphics computer. The small image size required less computer memory allocation and allowed the sequential review of many hourly images for selected areas of interest using video movie loops. The areas for the study are shown in Fig. 2 and include the Gulf of Maine in the Northeast (40 N, 75 W), the Southeast coast of the United States (32 N, 75 W), the Gulf of 1973

FIG. 2. The four major areas used for GAC and GOES data colocation. The location of SST time series is identified by A E. Mexico (25 N, 90 W), and the Pacific Ocean off Central America (12 N, 95 W). In summary, for a given area, there is an AVHRR SST image, one GOES visible image and three IR images, and a GOES satellite zenith angle image. In some cases, the AVHRR individual channel images were prepared to allow comparison with the GOES channel data. 4. Data extraction from images To prepare a set of collocated values of the GAC and the GOES data, the images were inspected visually to define ocean areas that were cloud free. In the Gulf of Mexico, the thermal structure associated with the Loop Current was easily recognizable and was used as one of the target areas. During daytime, the GOES visible image and the 3.9-µm IR image were also used for cloud detection. Clouds were easily recognizable in the visible images by their elevated reflectance values, while in the 3.9-µm images, clouds usually appeared warmer than the adjacent water during daytime. Review of difference images (channels 4 5) was also useful in detecting areas of uniform atmospheric moisture or cloud contamination. When a large cloud-free ocean area was identified, collocated data were extracted at random locations. The data were collected from 12 March to 12 December 1996 along the eastern coast of the United States, the Gulf of Mexico, the Caribbean Sea, and the coasts of Mexico. For example, images of the Loop Current and coastal waters in the Gulf of Mexico are shown in Fig. 3 for the AVHRR SST and the GOES-8 channel 4. The colors of the two images were matched approximately by adding 24 counts (3 C) to the GOES channel 4 image. This difference provides an estimate of the correction that will have to be provided by a regression equation. Similar ocean and cloud patterns are evident in both the GOES and GAC images. The track line in Fig. 3 will be used below for illustrating differences in collocated data values. To illustrate the differences in the quality of the GAC and GOES data, the difference images of channels 4 and 5 for each satellite are shown in Fig. 4. The GOES-8 channel difference reveals a noise pattern (zonal banding) that is not apparent in the AVHRR data. The GOES instrument produces the noise, and its characteristics are described by Weinreb (1997). To illustrate the magnitude of the noise, collocated data were extracted from each satellite along the two track lines shown in Fig. 4. Temperature differences (channels 4 5) were extracted from the images along two tracks with a width of one sample and a length of 180 samples in Figs. 5 and 6 for the west to east track and in Figs. 7 and 8 for the north to south track. The differences between channel temperatures are due to different atmospheric absorption in the spectral windows, the different IFOV of samples from each satellite scanner, and the increased GOES-8 noise. The AVHRR channel 4 and 5 data are very closely correlated and appear noise free. The two GOES channels are not well correlated at short spatial scales and channel 5 appears to produce the largest noise signal. The largest GOES noise levels appear in the north to south track of Fig. 8 since this track cuts across the zonally oriented stripping in the images. Although the standard deviation of the GOES differences for the entire length of each track in Fig. 6 and Fig. 8 is nearly the same, it is evident that, for shorter segments of the data, the noise variability for channel differences is greatest in Fig. 8 and the standard deviation would increase accordingly. The range of variables in the present collocated satellite dataset is limited to some extent by the satelliteviewing geometry. For example, the high GOES zenith angles in Fig. 1 are associated with low temperatures along the northeastern coasts of the United States and Canada, which tend to be cloudy. The coldest water was usually found above the zenith angle of 50 and 1974 Vol. 78, No. 9, September 1997

in areas that were often cloudy. The low zenith angles are in areas of higher temperatures, except where cool water is found due to upwelling off the Pacific coast of Central America. The areas of high zenith angle and high temperature occur in the subequatorial Atlantic and Pacific, but these areas were also very cloudy. The daily shift of the polar-orbiting satellite orbit can also prevent data colocation at specific sites since the AVHRR data appear on the edge of the site for 2 out of 9 days. Also, the AVHRR GAC sample size increases with the polar-orbiting satellite zenith angle and these data were avoided by limiting the GAC zenith angle to 45. Finally, it must be admitted that obtaining a wide range of parameters turned out to be more difficult than first anticipated. Large clear-ocean areas usually have a similar air mass and as a result many collocated values were similar to each other. However, a sufficient quantity of collocated data were obtained to produce representative regression results. 5. GOES-8 daily SST regression equations Nearly 40 GOES and GAC datasets provided 7413 collocated cloudfree samples between 12 March and 12 December 1996 in waters around North America, and these are summarized in Table 1. The available GAC SST extended from 5 to 31 C, the GOES split window temperature differences (4 5) from 0.3 to 3.2 C, and the GOES zenith angles from 15 to 50. These parameter ranges do not cover all the possible oceanic and atmospheric conditions, and the resulting regression results are limited by the available input. The collocated temperature values were averaged in boxes of 1 1 to 11 11 samples prior to estimating the regression. The standard error of the regression decreased as the box size increased. The results presented here are for a box average of 7 7 since improvements were negligible above that box size. The GOES SST equation coefficients (A, B, C, D) were determined for the combined FIG. 3. The warm waters of the Loop Current in the Gulf of Mexico on 14 March 1996 for the AVHRR GAC SST and for the GOES-8 channel 4. A bias of 24 counts (3 C) was added to the GOES image to approximately match the AVHRR SST color scale. The track line is shown for reference and also appears in Fig. 4. day and night observations by linear regression with NOAA-14 AVHRR GAC nonlinear SST (NLSST) as the dependent variable and the GOES T 4, T 45, and Z as the independent variables with all temperatures defined in degrees Celsius ( C): GOES SST = A + B(T 4 ) + C(T 45 ) + D(T 45 )(Z), (1) where T 4 = GOES 11-µm channel, T 5 = GOES 12-µm channel, T 45 = T 4 T 5, Z = sec (Ø) 1, Ø = GOES satellite zenith angle at the earth s surface, and N = number of collocated samples. The linear regression for all collocated samples for both day and night resulted in the following GOES 1975

TABLE 1. Julian day (JD) during 1996, GOES and GAC UTC, and number of collocated data for the areas in Fig. 2. JD GOES hr GAC hr N Central American eastern subtropical Pacific 319 0845 0850 271 319 2045 2001 176 321 0845 0828 347 321 1945 1939 183 347 0845 0801 342 Gulf of Mexico Loop Current, shelf, Caribbean 072 1945 1931 311 073 0815 0802 242 074 1915 1907 1314 102 1845 1908 31 349 1945 1846 181 Southeast Gulf Stream Sargasso Sea shelf FIG. 4. The images of channel differences (4 5) for AVHRR GAC and GOES-8 data on 14 March 1996 shown in Fig. 3. The zonal banding in the GOES image is attributed to noise. The data along the two track lines are shown in Figs. 5 8. SST coefficients in (1): A = 0.3977, B = 1.0595, C = 1.6425, D = 0.8526, as shown in Fig. 9. The standard error of the difference between the GOES SST and the GAC SST is 0.35 C and the linear fit to the GOES channels 4 and 5 shows the increase of the split window temperature differences with increasing GAC SST. There is a lack of collocated values below 5 C and a relatively small number between 5 and 10 C, mainly due to the lack of low-temperature values in cloud-free ocean areas in the present dataset. Additional datasets will be required to extend the analysis to all types of ocean and atmospheric conditions. However, it appears that the approach provides useful results. An independent set of about 1000 collocated values, with GOES SST temperatures between 9 and 28 C, was used to verify (1). The residual differences had a mean value of 0.06 C and a standard error of 0.43 C. 074 1915 1907 91 094 1945 1851 314 102 1845 1908 115 344 1845 1924 75 345 2045 1910 61 346 0745 0720 421 346 1845 1858 172 Northeast Gulf Stream Gulf of Maine shelf 115 1945 1850 72 135 0745 0651 195 151 1845 1845 150 152 0645 0709 110 178 0745 0726 135 178 1745 1714 255 233 0745 0731 174 233 1745 1719 217 289 0745 0725 164 290 0745 0714 371 290 1845 1843 224 291 0645 0703 124 291 1845 1832 118 300 1845 1834 68 301 1845 1823 124 1976 Vol. 78, No. 9, September 1997

FIG. 5. The temperature data extracted west to east along the track line shown in Fig. 4 from channels 4 and 5 of the AVHRR GAC at 0750 UTC on 14 March 1996. FIG. 6. The temperature data extracted west to east along the track line shown in Fig. 4 from channels 4 and 5 of the GOES-8 at 0815 UTC on 14 March 1996. The GAC SST used as the dependent variable was computed using the operational NLSST split window equations for NOAA-14 for day and night separately and has the form NLSST = A + B(T 4 ) + C(T S )(T 45 ) + D(T 45 )(Z), (2) where T 4 is in kelvins (K), T s is in degrees Celsius ( C), and the NOAA-14 nonlinear coefficients at night are defined as A = 253.428, B = 0.933209, C = 0.078095, and D = 0.738128. The NOAA-14 nonlinear daytime coefficients are defined as A = 255.165, B = 0.939813, C = 0.076066, D = 0.801458, and the AVHRR variables are similar to those defined for GOES in (1), except that T s is the approximate temperature of the ocean target, which must be known before the NLSST computation is made. This surface temperature (T s ) can be estimated by using the linear SST (LSST) split window equations for NOAA-14 with T 4 defined in degrees Celsius ( C), LSST = A + B(T 4 ) + C(T 45 ) + D(T 45 )(Z), (3) where the linear NOAA-14 coefficients at night are A = 1.134, B = 1.029088, C = 2.275385, D = 0.752567, and the linear NOAA-14 coefficients during the day are A = 0.533, B = 1.017342, C = 2.139588, D = 0.779706. There are several alternative equations to the daily GOES SST defined by (1). For example, one could test separate day and night equations, introduce the nonlinear term (T s )(T 45 ) as defined in (2), or eliminate the dependence on the zenith angle term (Z). However, these efforts are best left for future work when additional collocated GOES datasets and in situ drifting buoy surface temperatures become available. After all, FIG. 7. Same as Fig. 5 except for the north to south track of AVHRR GAC. FIG. 8. Same as Fig. 6 except for the north to south track of GOES-8. 1977

the GOES data are relatively new and require inspection from different points of view. The AVHRR-based SST regression equations were introduced in 1982 and are still being refined and evaluated. 6. GOES-8 daily SST composites The GOES-8 SST equation (1) was applied to hourly GOES data, and composite images were made by two methods to remove clouds in the final result. In the first method, no cloud test is used and Eq. (1) was applied directly to each sample of the GOES images and then the warmest water samples were retained in the composite. An example of a GOES SST warmest water composite of 21 images on 11 April 1996 is shown in Fig. 10, along with the single AVHRR MCSST image at 0750 UTC. Most of the clouds have FIG. 10. The warmest sample daily composite on 11 April 1996 of the 21 GOES SST images defined by (1) and the single AVHRR GAC SST image defined by (3) at 0750 UTC. FIG. 9. The linear regression for GOES SST as defined by (1) for collocated night and day data from 13 March to 12 December 1996. The AVHRR GAC (NLSST) defined by (2) is the dependent variable. The linear fits to GOES T 4 and T 5 data are shown. been removed from the Gulf of Mexico in the GOES composite in Fig. 10. The similar composite of the two available AVHRR images (day and night) for this day was more cloudy than the GOES in Fig. 10. The SST of the daily warmest composite is elevated by about 1 C relative to the nighttime AVHRR image, possibly due to diurnal warming, which will be demonstrated in the next section. The second composite method first applies a cloud test to each image by using a T 4 homogeneity test for a 2 2 sample box that is identified as cloudy if the difference between samples in the 2 2 box is greater than 0.75 C. The SST is computed for each retained sample using (1) and for each value of T 4, the T 45 term is an average of available cloud-free samples in a 5 5 box. The samples are then averaged with time to produce an average SST of the cloud-free data for the final composite. Remaining gaps due to removed clouds are filled by interpolation with a 3 3 median filter. An example of this approach is shown for the Gulf Stream and the Gulf of Maine on 14 October 1996 in Fig. 11, for the Gulf Stream and the Sargasso Sea from 9 to 13 December 1996 in Fig. 12, and for upwelling in the Gulf of Tehuantepec on the Pacific side of Central America from 20 to 21 December 1996 in Fig. 13. In each case, most 1978 Vol. 78, No. 9, September 1997

clouds are removed except in persistently cloudy areas such as the ITCZ in the southern part of Fig. 13. Composites offer the possibility of improving data coverage of ocean areas that are partly cloudy and of filling gaps in the less frequent AVHRR images. An interesting approach to GOES data evaluation is to view the hourly images, preferably at full spectral resolution, in a time-lapse mode. At video frame rates above 15 frames s 1, it is possible to distinguish the advection of surface thermal features due to ocean currents and upwelling. At this frame rate, the clouds so beloved by meteorologists appear as high-frequency noise when compared to the low-frequency oceanic thermal patterns. Observing the complexity of the movement of low-frequency ocean temperature features is both interesting and inspiring, and initially only requires the availability of the GOES channel 4 and proper computer video resources. So buckle up your seat belts and go for a ride. 7. Diurnal SST variability FIG. 11. The average hourly composite of GOES-8 SST images defined by (1) from 0000 to 2300 UTC on 14 October 1996. One of the unique capabilities of the GOES is the acquisition of full earth views at intervals of 30 min. This allows the investigation of diurnal ocean SST cycles. This possibility was realized during the investigation of the intense upwelling events off the Pacific coast of Mexico at the Gulf of Tehuantepec shown in Fig. 13. The upwelling occurs during intense offshore wind events that can decrease SST rapidly as far as 500 km offshore within a 24-h period (McCreary et al. 1989). The GOES infrared imagery at hourly intervals provided excellent resolution of the time-dependent nature of the upwelling events during December 1996, and SST time series were extracted at locations A, B, and C in Fig. 2. To visualize the upwelling, a series of GOES hourly images was displayed in time lapse on a computer monitor at the rate of about 15 frames s 1. The color scale was set to reveal small ocean temperature changes in the channel 4 images. The rapid cycling of the images revealed the development of low-frequency oceanic upwelling while the clouds moved very rapidly and had the appearance of high frequency noise. Due to the use of FIG. 12. The average hourly composite of GOES-8 SST images defined by (1) between 0000 and 2300 UTC from 9 to 13 December 1996. 1979

FIG. 13. The average hourly composite of GOES-8 SST images defined by (1) between 0000 and 2300 UTC from 20 to 21 December 1996. the high frame rate, it was then noticed that there was an apparent modulation of the ocean thermal field that was synchronized with the diurnal warming and cooling of the land along the coast of Mexico. The diurnal cycle on land was very evident because of the large diurnal surface temperature changes and the relatively cloud-free conditions at this time. The diurnal ocean cycle was not evident at first for several reasons. The ocean temperature changes are relatively small, the intermittent clouds tend to produce distracting data gaps, and the fluctuations of the SST pattern appeared to be related only to the strong upwelling events. A fortuitous change in the color scale used to display the images during data analysis revealed the correlation of the diurnal land and ocean fluctuations, especially outside the upwelling area. In effect, what at first appeared to be changes in the SST patterns due to advection were actually diurnal ocean surface temperature changes. To demonstrate the magnitude of the diurnal ocean cycle from the GOES channel 4 images, an interactive computer technique used in medical research to view three-dimensional objects was employed. Researchers at the National Institutes of Health utilize computer graphics to produce a series of two-dimensional slices of a three-dimensional human body to investigate spatial changes in human anatomy. In the present case, the GOES data are also three-dimensional. Each GOES image has two spatial dimensions and time forms the third dimension at hourly intervals. By accumulating data from the same line on a sequence of GOES images, a new space time image can be created that reveals the diurnal ocean temperature cycle along the selected line. Since this is done interactively on a graphics computer, the new space time image is created nearly instantaneously. This greatly facilitates data selection and evaluation since one has to locate an ocean area that is sufficiently cloud free to provide temporal data continuity, which is not readily apparent from static images. The space time image of GOES channel 4 from 11 to 16 November 1996 is shown in Fig. 14 for a line that extends southward about 450 km off the coast of Mexico, west of the Gulf of Tehuantepec as indicated by location A in Fig. 2. During the first three days, the ocean area off the coast was relatively cloud free and the diurnal temperature cycle is evident on both land and ocean. A 3 3 sample median filter was applied to the image in Fig. 14 to remove small-scale residual clouds. The time space image can be sampled at any location to reveal the magnitude of the temporal temperature changes as shown in Fig. 15, at location A in Fig. 2, about 75 km offshore and the same distance inland. The ocean surface temperatures in GOES channel 4 have a range of about 2 C and are correlated with the peak heating on land near 1200 LT (1800 UTC). Since only channel 4 was available in this dataset, another location was investigated below for SST changes. To obtain the diurnal SST cycle, another space time image was created from 15 to 24 December 1996 in Fig. 16 and includes an upwelling event in the Gulf of Tehuantepec. In this case, the regression equation (1) was applied to the GOES images as follows. A cloud test was first applied to each GOES image by testing the uniformity of channel 4 in 2 2 boxes. The threshold of the uniformity between sample pairs in the box was set at 0.75 C. This is higher than the uniformity test threshold of 0.5 C used for AVHRR but was required to make allowances for the larger GOES infrared noise level. For data that passed this cloud test, the T 45 differences were averaged in a 5 5 box to reduce noise in the SST. The T 4 values were not averaged initially to preserve the details in the ocean temperature patterns. The space time image in Fig. 16 was then extracted from west to east along latitude 14.5 N with the center of the data line at longitude 95 W and passing through points B and C as shown in Fig. 2. 1980 Vol. 78, No. 9, September 1997

FIG. 15. The diurnal ocean temperature cycle as a time series of GOES-8 channel 4 temperatures from Fig. 14 at location A in Fig. 2 and on coastal land from 0000 UTC 11 November to 0600 UTC 14 November 1996. FIG. 14. The space time image of hourly GOES-8 channel 4 along the north to south track passing through location A in Fig. 2 from 11 to 16 November 1996. The surface diurnal temperature cycle is evident on land and ocean and the time series at location A and on coastal land is shown in Fig. 15. The local time (1200 LT) corresponds to 1800 UTC. At this stage in the processing, the resulting space time SST image already revealed the temporal variability of upwelling and the diurnal ocean cycles eastward of the upwelling on the computer monitor. However, there still remained a considerable number of random data gaps where the cloud test had eliminated data. The smaller gaps eastward of the upwelling area were removed by using a 3 3 median filter. The result was recomposed with the unfiltered SST data to restore any warm values removed by the median filter. The relatively smooth space time image of cloud-cleared GOES SST in Fig. 16 is the result of the above procedure. However, the GOES SST cycle in Fig. 16 is noisier than the channel 4 cycle in Fig. 14. The GOES SST time series of the upwelling is extracted from Fig. 16 and the large 6 C drop in SST due to an upwelling event in the Gulf of Tehuantepec is shown in Fig. 17. The diurnal cycle was not distinct in this time series but is detectable in the space time image in Fig. 16 after some tuning of the color scale. The diurnal SST cycle at longitude 93 W eastward of the upwelling is shown in Fig. 18. In this case, land temperatures are from the GOES channel 4 since the SST processing altered the land temperatures in Fig. 16. The diurnal SST cycle in Fig. 18 has a range of about 2 C but is considerably more noisy than the temperature cycle in channel 4 in Fig. 15. Nevertheless, this shows that it is possible to monitor the ocean diurnal SST cycle with GOES in some subtropical ocean areas and this surface signal may be a useful input for ocean atmosphere interaction models. An attempt was made to extract the diurnal cycle off the East Coast of the United States in the warm core of the Gulf Stream and adjacent shelf waters at locations D and E in Fig. 2. The GOES channel 4 temperatures are shown in Fig. 19. While a diurnal cycle of about 1 C can be seen in the shelf waters, the cycle at the Gulf Stream location is not clear. It is possible that the rapid advection along the Gulf Stream masks the diurnal changes. 8. Conclusions It has been demonstrated that GOES-8 offers some unique capabilities for ocean observations. GOES SST daily regression equations can be derived by using the NOAA-14 AVHRR SST images as a surface temperature reference and they provide a reasonable estimate of SST. The results can be improved by increasing the size of the database to include a wider range of environmental parameters. The present regression estimate is limited to the availability of NOAA polar orbiter data twice a day. It does not replace the direct validation of GOES SST equations with surface bulk SST measurements, but it does provide a conve- 1981

FIG. 17. The upwelling in the Gulf of Tehuantepec as a time series of GOES-8 SST from Fig. 16 at latitude 14.5 N and longitude 95 W, location B in Fig. 2, from 15 to 24 December 1996. FIG. 16. The space time image of hourly GOES-8 SST defined by (1) along the west-to-east track at latitude 14.5 N from 15 to 24 December 1996. The local time (1200 LT) corresponds to 1800 UTC. Temperature time series at B and C in Fig. 2 are shown in Figs. 17 and 18. nient method of estimating SST from geostationary satellites without the need for a collocated buoy database. The composite of hourly GOES images improves cloud clearing of ocean areas relative to the two daily views provided by a polar-orbiting satellite. Daily composites of the warmest GOES SST samples reveals a persistently warmer composite image and implies that the diurnal surface temperature cycle dominates the composite. An alternative composite method is to eliminate clouds in individual images and then form average SST composites. The effects of the GOES thermal channel noise can be reduced by a spatial average of the split window channel differences used in (1) to estimate SST. There is a need for an objective method of cloud clearing the GOES data in the presence of the elevated noise evident in the thermal channels. The diurnal ocean surface temperature cycle was resolved in the GOES SST and channel 4 images. The detection of this cycle was aided by the ability to rapidly view hourly image sequences at full spectral resolution. The near instantaneous creation of space time images at selected sites on an image was also very useful. Time space images off the Pacific coast of Central America during December 1996 reveal that the ocean diurnal cycle reaches a maxima at about 1200 LT and the GOES SST has a 2 C diurnal temperature range. Improving the noise characteristics and the spatial resolution of GOES IR data could greatly facilitate the identification of cloud-free ocean areas and allow the monitoring of diurnal oceanic surface temperature cycles. In addition, if all of the internationally operated geostationary satellite scanners were improved, daily global SST composite images could be created FIG. 18. The ocean diurnal temperature cycle east of the Gulf of Tehuantepec as a time series of GOES-8 SST from Fig. 16 at latitude 14.5 N and longitude 93 W, location C in Fig. 2, from 15 to 24 December 1996. The land data are from GOES channel 4. 1982 Vol. 78, No. 9, September 1997

and diurnal cycles could be estimated to improve forecast models. That is probably a task for the next generation of scientists and engineers, unless a miracle happens in our lifetime. One can only hope. Acknowledgments. This study was supported by the NOAA Satellite Ocean Remote Sensing (NSORS) program. GOES calibration status is available from MWeinreb@nesdis.noaa.gov; Satellite images were evaluated using the National Institutes of Health (NIH) Image Software, available at zippy.nimh.nih.gov; The IDL Software from Research Systems Inc. was used for regression of the collocated data; Pathfinder SST is available at podaac@podaac.jpl.nasa.gov. The equation for the GOES satellite zenith angles was provided by Dan Tarpley. Thanks to Paul Chang for improving access to the GOES data, to Doug May for evaluation of the regression equation, and to Ian Barton for reviewing the manuscript. References FIG. 19. The GOES-8 channel 4 time series at locations D and E in Fig. 2 for the Gulf Stream and coastal shelf waters from 14 to 16 October 1996. The land temperatures are for coastal land in Virginia. Barton, I. J., 1995: Satellite-derived sea surface temperatures: Current status. J. Geophys. Res., 100(C5), 8777 8790. Bates, J. J., and W. L. Smith, 1985: Sea surface temperature: Observations from geostationary satellites. J. Geophys. Res., 90(C6), 11 609 11 618. Emery, W. J., Y. Yunyue, G. A. Wick, P. Schluessel, and R. W. Reynolds, 1994: Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation. J. Geophys. Res., 99, 5219 5236. McClain, E. P., W. G. Pichel, and C. C. Walton, 1985: Comparative performance of AVHRR-based multichannel sea surface temperatures. J. Geophys. Res., 90, 11 586 11 601. McCreary, J. P., H. S. Lee, and D. B. Enfield, 1989: The response of the coastal ocean to strong offshore winds: With application to circulations in the Gulfs of Tehuantepec and Papagayo. J. Mar. Res., 47, 81 109. Menzel, W. P., and J. F. Purdom, 1994: Introducing GOES-1: The first of a new generation of geostationary operational environmental satellites. Bull. Amer. Meteor. Soc., 75, 757 781. Reynolds, R. W., and D. C. Marsico, 1993: An improved real-time global sea surface temperature analysis. J. Climate, 6, 114 119. Schluessel, P., W. J. Emery, H. Grassl, and T. Mammen 1990: On the bulk-skin temperature difference and its impact on satellite remote sensing of sea surface temperatures. J. Geophys. Res., 95, 13 341 13 356. Weinreb, M., 1997: Operational calibration of the images and sounders on GOES-8 and -9 satellites. NOAA Tech. Memo. NESDIS 44, 1 32. 1983