Sea Surface Temperatures from the GOES-8Geostationary Satellite

Sea Surface Temperatures from the GOES-8 Geostationary Satellite

Richard Legeckis* and

ABSTRACT

The introduction of the 10-bit, five-band, multispectral visible and thermal infrared scanner on the National Oce- anic 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 up welling along the southern coast of Mexico during December of 1996 was resolved at hourly intervals.

1. Introduction ministration) Oceanic Pathfinder project and global- scale SST can now be estimated to about 0.5°C after The National Oceanic and Atmospheric Adminis- 1 week of satellite observations. A review of the tration (NOAA) polar-orbiting satellite systems present status of satellite-derived SST is provided by have provided global estimates of sea surface temp- Barton (1995). eratures (SST) since 1982 in an operational mode by There is considerable complexity in the satellite combining multiple IR channels of the Advanced estimates of SST since the buoys measure the bulk SST Very High Resolution Radiometer (AVHRR). while the satellite views the surface skin temperatures McClain et al. (1985) derived the linear multichannel as demonstrated by Schluessel et al. (1990). Satellite SST (MCSST) regression equations using collocated microwave measurements were used by Emery et al. surface-drifting buoy measurements as an estimate (1994) to demonstrate the nonlinear relationship be- of the bulk SST. Recently, the satellite SST data tween the AVHRR channel differences and atmo- have been further improved by reprocessing in the spheric water vapor. In addition, a single polar-orbiting NOAA-NASA (National Aeronautic and Space Ad- 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 * Office of Research and Applications, NOAA/NESDIS, Wash- available. The accuracy of satellite SST depends ington, D.C. greatly on the ability to define cloud-free ocean areas +Research and Data Systems Corporation, Greenbelt, Maryland. using objective automated procedures. In practice, Corresponding author address: Dr. Richard Legeckis, NOAA/ NESDIS, World Weather Building Room 102, Mail Code E/RA-3, these procedures sometimes fail at random intervals Washington, DC 20233-9910. and in the presence of increased atmospheric aerosols E-mail: [email protected] from Saharan dust or volcanic eruptions. Nevertheless, In final form 24 March 1997. progress has been made in the use of MCSST for glo-

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC bal SST analysis as demonstrated by Reynolds and these areas, AVHRR SST images, instead of near-sur- Marsico (1993). face buoy measurements, were used to provide an es- The initial estimation of SST using multispectral timate of the SST for the derivation of the GOES-8 data from a geostationary atmospheric sounder on regression equations. This approach allowed the visual GOES-5 were made by Bates and Smith (1985). Im- comparison of the estimated SST as well as of the in- proved data became available with the introduction of dividual IR channels from both the AVHRR and the the GOES-8 satellite, which provides IR and visible GOES scanner. The interactive approach was useful channels similar to the AVHRR as described by for the evaluation of the GOES images and for detec- Menzel and Purdom (1994). The GOES-8 is capable tion of ocean features related to currents, eddies, up- of providing images of the earth at 30-min intervals welling, and time-dependent events. 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 di- 2. Comparison of GOES-8 and NOAA-14 urnal SST changes. Also, by removing rapidly mov- scanners ing clouds, the daily Geostationary Operational Environmental Satellite (GOES) composites can re- The NOAA-14 AVHRR and the GOES-8 scanners veal more of the SST structure along the coasts of have IR channels centered at 3.7, 10.8, and 11.9 fim North and South America. and 3.9,10.7, and 12 ^m, respectively. The shortwave To produce daily GOES image composites, the channels are both affected by reflected sunlight dur- SST has to be estimated. Initially, an AVHRR linear ing daylight hours, which limits SST computation split window equation was applied directly to the during daytime. There is only one visible GOES chan- GOES channel data but a comparison with an AVHRR nel while the AVHRR has both a visible and a near MCSST image taken within 30 min revealed a signifi- IR channel. The latter channel on the AVHRR is very cant temperature bias. In general, regression equations useful for land, cloud, and ocean discrimination as well are derived for each new AVHRR due to differences as for detection of aerosols that can introduce a bias in sensor filter characteristics and other system-related in the estimated SST. This advantage is missing on the variables. Therefore, it is to be expected that each GOES. For both instruments, the IR split window GOES scanner will also be unique and a project was channels near 11 and 12 fim will be referred to as chan- started to derive SST regression equations specifically nels 4 and 5, for convenience. for GOES-8. At the time, access to the GOES-9 data The instantaneous field of view (IFOV) of the were not available on a regular basis, so it will be AVHRR is about 1 km x 1 km. The AVHRR global evaluated in the future. area coverage (GAC) IR samples are formed by aver- The standard method for deriving the NOAA-14 aging four IFOV samples and skipping one along the SST regression equations is to use AVHRR data col- scan line and then skipping two of three scan lines. located with global measurements of bulk SST from Therefore, the individual GAC sample size is about 4 drifting buoys. However, a collocated GOES and buoy km x 1 km, but the effective spatial resolution of GAC dataset was not available and the GOES-8 view is re- data is about 4 km x 3 km at nadir. The GOES IFOV stricted to the ocean areas around North and South at nadir is about 4 km x 4 km. The difference in sample America so fewer buoys would be available for size has some effect on cloud tests that use the thermal colocation. Furthermore, to produce a collocated, homogeneity of adjacent samples to identify cloudy cloud-cleared GOES and buoy match-up database, areas. The GAC 4-km x 1-km samples can resolve automated cloud clearing algorithms had to be estab- smaller cloud-free areas by a factor of greater than 2 lished. Since the GOES channel noise levels (Menzel relative to the GOES. Both the GOES scanner and the and Purdom 1994) are greater than for the AVHRR, NOAA-14 AVHRR have ten-bit data samples and pro- the optimum parameters for automated cloud detec- vide calibrated temperature steps of about 0.1 °C per tion schemes are still under evaluation. digital count at 300 K. The GOES calibration method To overcome some of the limitations imposed by is described by Weinreb (1997). the higher GOES system noise levels and the lack of While the noise equivalent temperature of the a suitable GOES and buoy match-up database, a new AVHRR on NOAA-14 is less than 0.1° C at 300 K, the approach was taken. An interactive computer was used GOES-8 IR channels are noisier by a factor of 2 or 3. for visual identification of cloud-free ocean areas. In The AVHRR data are collected by a single detector

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC 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 x 3 km) is close to that of the GOES IR data (4 km x 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 FIG. 1. The GOES-8 satellite zenith angles and values of Z then mapped to a Mercator projection with a spatial relative to the satellite nadir at the equator and longitude 75°W. resolution of 4 km using nearest neighbor interpola- tion. The linear equations were more convenient for preparing images for display, while the nonlinear equa- in using the linear scaling factor of 0.125°C. It allows tions were used for regression computations since they all image data to be evaluated at nearly full spectral are more accurate. The SST values were scaled at resolution on the conventional and widely available 0.125°C per count so that an eight-bit image, with 8-bit computer monitors and it reduces the data vol- count values between 0 and 255, had temperatures ume in half from the original 10-bit input that is usu- between 0° and 31.875°C. The University of Miami ally stored as a 16-bit value in archive data. software system was used to navigate, process, and The Mercator-mapped GOES and GAC images map the GAC data at the National Environmental Sat- were navigated by alignment of coastal landmarks ellite, Data and Information System (NESDIS). within an uncertainty of one sample in the vicinity of The GOES ten-bit images were created using the data extraction. However, it was noticed that in some McIDAS software provided to NESDIS by the Uni- cases the alignment of the image at the center produced versity of Wisconsin. The GOES images were also colocation navigation errors at the edge of the image mapped to Mercator projections as above. Hourly data of up to three samples between GOES and GAC. By were extracted for regions of interest and the tempera- avoiding misaligned areas, a navigation accuracy of tures were scaled to 0.125 per count for the three indi- about 4 km was achieved in most cases. The collocated vidual IR channels. This linear scaling produces only GOES images used for regression computations were a small error when compared to the nonlinear ten-bit usually obtained within 1 h of the AVHRR data. GOES input values that were computed from raw ra- To complete the dataset, image files of the GOES diance values. For example, the differences between satellite zenith angle, shown in Fig. 1, were created. adjacent input counts of the GOES 10-bit data for The image size was set to 640 x 500 samples to facili- channels 4 and 5 at 0°, 17°, and 25°C are about 0.15°, tate image display and data extraction on a graphics 0.125°, and 0.116°C, respectively. Therefore, for tem- computer. The small image size required less com- peratures below 17°C, the use of the 0.125°C count"1 puter memory allocation and allowed the sequential scaling factor provides greater resolution than is pro- review of many hourly images for selected areas of vided by the input. At 25 °C the linear scaling intro- interest using video movie loops. The areas for the duces an error of only 0.009°C in the GOES data and study are shown in Fig. 2 and include the Gulf of is negligible when compared to the GOES thermal Maine in the Northeast (40°N, 75°W), the Southeast channel noise illustrated below. There are two benefits coast of the United States (32°N, 75°W), the Gulf of

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC ber 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 FIG. 2. The four major areas used for GAC and GOES data its characteristics are described by Weinreb (1997). To colocation. The location of SST time series is identified by A-E. illustrate the magnitude of the noise, collocated data were extracted from each satellite along the two track lines shown in Fig. 4. Temperature differences (chan- Mexico (25 °N, 90°W), and the Pacific Ocean off Cen- nels 4-5) were extracted from the images along two tral America (12°N, 95°W). In summary, for a given tracks with a width of one sample and a length of 180 area, there is an AVHRR SST image, one GOES vis- samples in Figs. 5 and 6 for the west to east track and ible image and three IR images, and a GOES satellite in Figs. 7 and 8 for the north to south track. The dif- zenith angle image. In some cases, the AVHRR indi- ferences between channel temperatures are due to dif- vidual channel images were prepared to allow com- ferent atmospheric absorption in the spectral windows, parison with the GOES channel data. 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 4. Data extraction from images appear noise free. The two GOES channels are not well correlated at short spatial scales and channel 5 To prepare a set of collocated values of the GAC appears to produce the largest noise signal. The larg- and the GOES data, the images were inspected visu- est GOES noise levels appear in the north to south ally to define ocean areas that were cloud free. In the track of Fig. 8 since this track cuts across the zonally Gulf of Mexico, the thermal structure associated with oriented stripping in the images. Although the stan- the Loop Current was easily recognizable and was dard deviation of the GOES differences for the entire used as one of the target areas. During daytime, the length of each track in Fig. 6 and Fig. 8 is nearly the GOES visible image and the 3.9-^m IR image were same, it is evident that, for shorter segments of the also used for cloud detection. Clouds were easily rec- data, the noise variability for channel differences is ognizable in the visible images by their elevated re- greatest in Fig. 8 and the standard deviation would flectance values, while in the 3.9-^m images, clouds increase accordingly. usually appeared warmer than the adjacent water dur- The range of variables in the present collocated sat- ing daytime. Review of difference images (channels ellite dataset is limited to some extent by the satellite- 4-5) was also useful in detecting areas of uniform at- viewing geometry. For example, the high GOES zenith mospheric moisture or cloud contamination. angles in Fig. 1 are associated with low temperatures When a large cloud-free ocean area was identified, along the northeastern coasts of the United States and collocated data were extracted at random locations. Canada, which tend to be cloudy. The coldest water The data were collected from 12 March to 12 Decem- was usually found above the zenith angle of 50° and

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC 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 admit- ted that obtaining a wide range of parameters turned out to be more dif- ficult 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 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 Nearly 40 GOES and GAC data- (3°C) was added to the GOES image to approximately match the AVHRR SST color sets provided 7413 collocated cloud- scale. The track line is shown for reference and also appears in Fig. 4. free samples between 12 March and 12 December 1996 in waters around North America, and these are summa- rized in Table 1. The available GAC SST extended day and night observations by linear regression with from 5° to 31°C, the GOES split window temperature NOAA-14 AVHRR GAC nonlinear SST (NLSST) as differences (4-5) from 0.3° to 3.2°C, and the GOES the dependent variable and the GOES T4, T45, and Z as zenith angles from 15° to 50°. These parameter ranges the independent variables with all temperatures de- do not cover all the possible oceanic and atmospheric fined in degrees Celsius (°C): conditions, and the resulting regression results are limited by the available input. The collocated temperature GOES SST = A + B(T4) + C(TJ + D(T )(Z), (1) values were averaged in boxes of lxl to 11x11 samples prior to estimating the regression. The stan- where T4 = GOES 1l-fjm channel, T5 = GOES 12-/um dard error of the regression decreased as the box size channel, T45 = T4 - Ts, Z= sec (0) - 1, 0 = GOES sat- increased. The results presented here are for a box ellite zenith angle at the earth's surface, and N = num- average of 7 x 7 since improvements were negligible ber of collocated samples. above that box size. The GOES SST equation coeffi- The linear regression for all collocated samples for cients (A, B, C, D) were determined for the combined both day and night resulted in the following GOES

Bulletin of the American Meteorological Society 19 57

Unauthenticated | Downloaded 10/08/21 03:17 PM UTC 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 074 1915 1907 91 094 1945 1851 314 102 1845 1908 115 344 1845 1924 75 FIG. 4. The images of channel differences (4-5) for AVHRR 345 2045 1910 61 GAC and GOES-8 data on 14 March 1996 shown in Fig. 3. The 346 0745 0720 421 zonal banding in the GOES image is attributed to noise. The data along the two track lines are shown in Figs. 5-8. 346 1845 1858 172 Northeast Gulf Stream-Gulf of Maine shelf SST coefficients in (1): A = -0.3977, B = 1.0595, C = 115 1945 1850 72 1.6425, D = 0.8526, as shown in Fig. 9. The standard 135 0745 0651 195 error of the difference between the GOES SST and the 151 1845 1845 150 GAC SST is 0.35°C and the linear fit to the GOES 152 0645 0709 110 channels 4 and 5 shows the increase of the split win- 178 0745 0726 135 dow temperature differences with increasing GAC 178 1745 1714 255 SST. There is a lack of collocated values below 5°C 233 0745 0731 174 and a relatively small number between 5° and 10°C, 233 1745 1719 217 mainly due to the lack of low-temperature values in 289 0745 0725 164 cloud-free ocean areas in the present dataset. Addi- tional datasets will be required to extend the analysis 290 0745 0714 371 to all types of ocean and atmospheric conditions. 290 1845 1843 224 However, it appears that the approach provides use- 291 0645 0703 124 ful results. An independent set of about 1000 collo- 291 1845 1832 118 cated values, with GOES SST temperatures between 300 1845 1834 68 9° and 28°C, was used to verify (1). The residual dif- 301 1845 1823 124 ferences had a mean value of 0.06°C and a standard error of0.43°C.

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC FIG. 5. The temperature data extracted west to east along the FIG. 6. The temperature data extracted west to east along the track line shown in Fig. 4 from channels 4 and 5 of the AVHRR track line shown in Fig. 4 from channels 4 and 5 of the GOES-8 GAC at 0750 UTC on 14 March 1996. at 0815 UTC on 14 March 1996.

The GAC SST used as the dependent variable was SST (LSST) split window equations for NOAA-14 computed using the operational NLSST split window with T4 defined in degrees Celsius (°C), equations for NOAA-14 for day and night separately and has the form LSST = A + B(T4) + C(T45) + D(T45)(Z), (3)

NLSST = A + B(T4) + C(Ts)(T45) + D(T45)(Z), (2) where the linear NOAA-14 coefficients at night are A = -1.134, B = 1.029088, C = 2.275385, D = 0.152561, where T4 is in kelvins (K), T& is in degrees Celsius (°C), and the linear NOAA-14 coefficients during the day and the NOAA-14 nonlinear coefficients at night are are A = -0.533, B = 1.017342, C = 2.139588, D defined as A = -253.428, B = 0.933209, C = 0.078095, = 0.779706. and D = 0.738128. The NOAA-14 nonlinear daytime There are several alternative equations to the daily coefficients are defined as A = -255.165, B = GOES SST defined by (1). For example, one could test 0.939813, C = 0.076066, D = 0.801458, and the separate day and night equations, introduce the non- AVHRR variables are similar to those defined for linear term (r)(r45) as defined in (2), or eliminate the GOES in (1), except that T$ is the approximate tem- dependence on the zenith angle term (Z). However, perature of the ocean target, which must be known these efforts are best left for future work when addi- before the NLSST computation is made. This surface tional collocated GOES datasets and in situ drifting temperature (7) can be estimated by using the linear buoy surface temperatures become available. After all,

FIG. 7. Same as Fig. 5 except for the north to south track of FIG. 8. Same as Fig. 6 except for the north to south track of AVHRR GAC. GOES-8.

19 57 Bulletin of the American Meteorological Society

Unauthenticated | Downloaded 10/08/21 03:17 PM UTC the GOES data are relatively new and require inspec- tion 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) FIG. 9. The linear regression for GOES SST as defined by (1) was applied directly to each sample of the GOES im- for collocated night and day data from 13 March to 12 December ages and then the warmest water samples were retained 1996. The AVHRR GAC (NLSST) defined by (2) is the dependent in the composite. An example of a GOES SST warm- variable. The linear fits to GOES TA and T5 data are shown. est water composite of 21 images on 11 April 1996 is shown in Fig. 10, along with the single AVHRR been removed from the Gulf of Mexico in the GOES MCSST image at 0750 UTC. Most of the clouds have 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 T4 homogeneity test for a 2 x 2 sample box that is identified as cloudy if the difference between samples in the 2 x 2 box is greater than 0.75°C. The SST is computed for each retained sample using (1) and for each value of T4, the T45 term is an average of available cloud-free samples in a 5 x 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 interpo- lation with a 3 x 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 FIG. 10. The warmest sample daily composite on 11 April 1996 of the 21 GOES Tehuantepec on the Pacific Side of Cen- SST images defined by (1) and the single AVHRR GAC SST image defined by (3) tral America from 20 to 21 December at 0750 UTC. 1996 in Fig. 13. In each case, most

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC clouds are removed except in persistently cloudy areas such as the ITCZ in the southern part of Fig. 13. Com- posites 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 up welling. At this frame rate, the clouds so beloved by meteo- rologists appear as high-frequency noise when compared to the low-frequency oceanic thermal patterns. Observing the complexity of the movement of low-fre- FIG. 11. The average hourly composite of GOES-8 SST images defined by (1) from quency ocean temperature features is 0000 to 2300 UTC on 14 October 1996. both interesting and inspiring, and initially only requires the availability of the GOES chan- played in time lapse on a computer monitor at the rate nel 4 and proper computer video resources. So buckle of about 15 frames s_1. The color scale was set to re- up your seat belts and go for a ride. veal small ocean temperature changes in the channel 4 images. The rapid cycling of the images revealed the development of low-frequency oceanic upwelling 7. Diurnal SST variability while the clouds moved very rapidly and had the ap- pearance of high frequency noise. Due to the use of 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 na- ture 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 FIG. 12. The average hourly composite of GOES-8 SST images defined by (1) of GOES hourly images was dis- between 0000 and 2300 UTC from 9 to 13 December 1996.

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC ated 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 instanta- neously. This greatly facilitates data selection and evaluation since one has to locate an ocean area that is suffi- ciently 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 FIG. 13. The average hourly composite of GOES-8 SST images defined by (1) of Tehuantepec as indicated by loca- between 0000 and 2300 UTC from 20 to 21 December 1996. tion A in Fig. 2. During the first three days, the ocean area off the coast was the high frame rate, it was then noticed that there was relatively cloud free and the diurnal temperature cycle an apparent modulation of the ocean thermal field that is evident on both land and ocean. A 3 x 3 sample was synchronized with the diurnal warming and cool- median filter was applied to the image in Fig. 14 to ing of the land along the coast of Mexico. remove small-scale residual clouds. The time-space The diurnal cycle on land was very evident because image can be sampled at any location to reveal the of the large diurnal surface temperature changes and magnitude of the temporal temperature changes as the relatively cloud-free conditions at this time. The shown in Fig. 15, at location A in Fig. 2, about 75 km diurnal ocean cycle was not evident at first for several offshore and the same distance inland. The ocean sur- reasons. The ocean temperature changes are relatively face temperatures in GOES channel 4 have a range of small, the intermittent clouds tend to produce distract- about 2°C and are correlated with the peak heating on ing data gaps, and the fluctuations of the SST pattern land near 1200 LT (1800 UTC). Since only channel 4 appeared to be related only to the strong upwelling was available in this dataset, another location was in- events. A fortuitous change in the color scale used to vestigated below for SST changes. display the images during data analysis revealed the To obtain the diurnal SST cycle, another space- correlation of the diurnal land and ocean fluctuations, time image was created from 15 to 24 December 1996 especially outside the upwelling area. In effect, what in Fig. 16 and includes an upwelling event in the Gulf at first appeared to be changes in the SST patterns due of Tehuantepec. In this case, the regression equation to advection were actually diurnal ocean surface tem- (1) was applied to the GOES images as follows. A perature changes. cloud test was first applied to each GOES image by To demonstrate the magnitude of the diurnal ocean testing the uniformity of channel 4 in 2 x 2 boxes. The cycle from the GOES channel 4 images, an interactive threshold of the uniformity between sample pairs in the computer technique used in medical research to view box was set at 0.75°C. This is higher than the unifor- three-dimensional objects was employed. Researchers mity test threshold of 0.5°C used for AVHRR but was at the National Institutes of Health utilize computer required to make allowances for the larger GOES infra- graphics to produce a series of two-dimensional slices red noise level. For data that passed this cloud test, the of a three-dimensional human body to investigate spa- T45 differences were averaged in a 5 x 5 box to reduce tial changes in human anatomy. In the present case, noise in the SST. The T4 values were not averaged ini- the GOES data are also three-dimensional. Each tially to preserve the details in the ocean temperature GOES image has two spatial dimensions and time patterns. The space-time image in Fig. 16 was then forms the third dimension at hourly intervals. By ac- extracted from west to east along latitude 14.5°N with cumulating data from the same line on a sequence of the center of the data line at longitude 95 °W and pass- GOES images, a new space-time image can be cre- ing through points B and C as shown in Fig. 2.

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC 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. 16. The diurnal SST cycle in Fig. 18 has a range FIG. 14. The space-time image of hourly GOES-8 channel 4 of about 2°C but is considerably more noisy than the along the north to south track passing through location A in Fig. 2 temperature cycle in channel 4 in Fig. 15. Neverthe- from 11 to 16 November 1996. The surface diurnal temperature less, this shows that it is possible to monitor the ocean cycle is evident on land and ocean and the time series at location diurnal SST cycle with GOES in some subtropical A and on coastal land is shown in Fig. 15. The local time (1200 ocean areas and this surface signal may be a useful LT) corresponds to 1800 UTC. input for ocean-atmosphere interaction models. An attempt was made to extract the diurnal cycle At this stage in the processing, the resulting space- off the East Coast of the United States in the warm core time SST image already revealed the temporal vari- of the Gulf Stream and adjacent shelf waters at loca- ability of upwelling and the diurnal ocean cycles tions D and E in Fig. 2. The GOES channel 4 tempera- eastward of the upwelling on the computer monitor. tures are shown in Fig. 19. While a diurnal cycle of However, there still remained a considerable number about 1°C can be seen in the shelf waters, the cycle at of random data gaps where the cloud test had elimi- the Gulf Stream location is not clear. It is possible that nated data. The smaller gaps eastward of the upwelling the rapid advection along the Gulf Stream masks the area were removed by using a 3 x 3 median filter. The diurnal changes. 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 8* Conclusions cloud-cleared GOES SST in Fig. 16 is the result of the above procedure. However, the GOES SST cycle in It has been demonstrated that GOES-8 offers some Fig. 16 is noisier than the channel 4 cycle in Fig. 14. unique capabilities for ocean observations. GOES SST The GOES SST time series of the upwelling is daily regression equations can be derived by using the extracted from Fig. 16 and the large 6°C drop in SST NOAA-14 AVHRR SST images as a surface tempera- due to an upwelling event in the Gulf of Tehuantepec ture reference and they provide a reasonable estimate is shown in Fig. 17. The diurnal cycle was not distinct of SST. The results can be improved by increasing the in this time series but is detectable in the space-time size of the database to include a wider range of envi- image in Fig. 16 after some tuning of the color scale. ronmental parameters. The present regression estimate The diurnal SST cycle at longitude 93°W eastward of is limited to the availability of NOAA polar orbiter the upwelling is shown in Fig. 18. In this case, land data twice a day. It does not replace the direct vali- temperatures are from the GOES channel 4 since the dation of GOES SST equations with surface bulk SST processing altered the land temperatures in SST measurements, but it does provide a conve-

19 57 Bulletin of the American Meteorological Society

Unauthenticated | Downloaded 10/08/21 03:17 PM UTC 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.

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 FIG. 16. The space-time image of hourly GOES-8 SST defined ocean diurnal cycle reaches a maxima at about by (1) along the west-to-east track at latitude 14.5°N from 15 to 1200 LT and the GOES SST has a 2°C diurnal tem- 24 December 1996. The local time (1200 LT) corresponds to 1800 perature range. UTC. Temperature time series at B and C in Fig. 2 are shown in Improving the noise characteristics and the spatial Figs. 17 and 18. resolution of GOES IR data could greatly facilitate the identification of cloud-free ocean areas and allow the nient method of estimating SST from geostationary monitoring of diurnal oceanic surface temperature satellites without the need for a collocated buoy cycles. In addition, if all of the internationally oper- database. ated geostationary satellite scanners were improved, The composite of hourly GOES images improves daily global SST composite images could be created 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 FIG. 18. The ocean diurnal temperature cycle east of the Gulf detection of this cycle was aided by the ability to rap- of Tehuantepec as a time series of GOES-8 SST from Fig. 16 at idly view hourly image sequences at full spectral reso- latitude 14.5°N and longitude 93°W, location C in Fig. 2, from lution. The near instantaneous creation of space-time 15 to 24 December 1996. The land data are from GOES channel 4.

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Unauthenticated | Downloaded 10/08/21 03:17 PM UTC and diurnal cycles could be estimated to improve fore- cast 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 [email protected]; 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 [email protected]. 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 re- FIG. 19. The GOES-8 channel 4 time series at locations D and viewing the manuscript. 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. References

Barton, I. J., 1995: Satellite-derived sea surface temperatures: tion to circulations in the Gulfs of Tehuantepec and Papagayo. Current status. J. Geophys. Res., 100(C5), 8777-8790. J. Mar. Res., 47, 81-109. Bates, J. J., and W. L. Smith, 1985: Sea surface temperature: Menzel, W. P., and J. F. Purdom, 1994: Introducing GOES-1: The Observations from geostationary satellites. J. Geophys. Res., first of a new generation of geostationary operational environ- 90(C6), 11 609-11 618. mental satellites. Bull. Amer. Meteor. Soc., 75,757-781. Emery, W. J., Y. Yunyue, G. A. Wick, P. Schluessel, and R. W. Reynolds, R. W., and D. C. Marsico, 1993: An improved real-time Reynolds, 1994: Correcting infrared satellite estimates of sea global sea surface temperature analysis. J. Climate, 6,114-119. surface temperature for atmospheric water vapor attenuation. Schluessel, P., W. J. Emery, H. Grassl, and T. Mammen 1990: On J. Geophys. Res., 99, 5219-5236. the bulk-skin temperature difference and its impact on satel- McClain, E. P., W. G. Pichel, and C. C. Walton, 1985: Compara- lite remote sensing of sea surface temperatures. J. Geophys. tive performance of AVHRR-based multichannel sea surface Res., 95, 13 341-13 356. temperatures. J. Geophys. Res., 90, 11 586-11 601. Weinreb, M., 1997: Operational calibration of the images and McCreary, J. P., H. S. Lee, and D. B. Enfield, 1989: The response sounders on GOES-8 and -9 satellites. NOAA Tech. Memo. of the coastal ocean to strong offshore winds: With applica- NESDIS 44, 1-32.

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Bulletin of the American Meteorological Society 19 57

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HISTORICAL ESSAYS ON METEOROLOGY, 1919-1995 The Diamond Anniversary History Volume of the American Meteorological Society Edited by James Rodger Fleming Foreword by Warren M. Washington As part of its 75th Anniversary, the American Meteorological Society initiated a history book— a collection of 20 essays that chronicle achievements in the field of meteorology in many specialized areas, including basic and applied research, the private sector, and education. These essays celebrate a period of disciplinary formation and remarkable growth in the field of meteorology, and an era of expanding theoretical, observational, and institutional horizons. They constitute a sampling of what has been learned, where we stand, and where we might be going—in research, in education, and in the private sector. This is a book of meteorological discovery and innovation, designed to value the past in order to inspire the future. ©1996 American Meteorological Society. Hardcover, B&W, 618 pp., $60 list/$25 member (including shipping and handling). Please send prepaid orders to: Order Department, American Meteorological Society, 45 Beacon St., Boston, MA 02108-3693.

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