Compilation of a High 

Resolution Sea Surface 

Temperature Dataset from 

Satellite Measurements for 

the Gulf of Mexico

Volume 1: Appendix

December 2003 

Prepared For: 

MMS 

Prepared By: 

Earth Tech, Inc. 

196 Baker Avenue 

Concord, Massachusetts 01742 

(978) 371-4000. 
TABLE OF CONTENTS 

Page 

1. INTRODUCTION 1

2. DATA COMPILATION 3

2.1 MM5 Modeling 3

2.2 SST Data Processing 4

3. RESULTS 8

3.1 SST over the United States. 8

3.2 SST over the Gulf of Mexico 11

3.3 Comparison with Data from other sources 16

4. REFERENCES 20
Page 1 

1. INTRODUCTION 

The purpose of this task is to develop a high-resolution sea surface temperature (SST) 

dataset to be employed in meteorological modeling using the Penn State University 

(PSU)/National Center for Atmospheric Research (NCAR) Fifth Generation Mesoscale 

Modeling System (MM5) [Grell et al., 1996]. The meteorological fields from MM5 will 

be used as part of a standard dataset to drive the CALPUFF dispersion modeling system 

[Scire et al., 2000a, b] in offshore and coastal applications in the Gulf of Mexico area. 

We present here a brief background on the need to have a high-resolution sea surface 

temperature data in meteorological modeling. In the following sections, we also discuss 

the approach taken in arriving at a daily SST dataset from high-resolution satellite 

measurements. 

Modeling the air quality impacts from sources offshore or in coastal areas is often 

influenced by mesoscale circulations such as the land/sea breeze. In addition, dispersion 

properties of the atmosphere offshore are typically quite different than those over land, 

making knowledge of the marine boundary layer structure critical for dispersion 

modeling for these sources. Coastal sites often experience a land/sea breeze circulation 

that exhibits a diurnal pattern with weak offshore winds at night and strong onshore 

winds during the day. This is in association with the diurnal heating and cooling of the 

landmass adjacent to the coastal water body. 

Sea breezes are a mesoscale phenomena occurring on spatial scales between 1 and 

200 kilometers (km). Owing to these spatial scales, coarse-scale numerical weather 

prediction models may not properly characterize detailed sea breeze circulations. 

Mesoscale meteorological modeling using models such as MM5 is often needed to 

accurately simulate these effects. More modeling work is needed to fully understand the 

meteorology of complex coastal environments [Cai et al., 2000; Rogers, 1995]. 

One key input to meteorological models simulating sea breeze circulations is the SST. Sea 

breeze strength and penetration depends on the difference between the air density over land 

and that in the marine layer. The properties of the marine layer strongly depend on the 

temperature at the surface of the underlying ocean. Hence, the larger the difference 

between the land temperature and the neighboring SST, the stronger will be the tendency 

for a sea breeze. As indicated previously, the sea breeze flows are of an order of a few 

kilometers to a few hundred kilometers. This requires that the spatial resolution of the 

available SST data be between these scales in order to accurately model the sea breeze 

effects. 

SST data is currently available on a variety of spatial and temporal scales. Details about 

the source and scales of the SST data are presented in Table 1. 
Page 2 

The purpose of this task is to compile a SST data on a resolution of ~36 km and ~4 km 

(at the equator) employing the Moderate Resolution Imaging Spectroradiometer 

(MODIS) measurements. 
Page 3 

2. DATA COMPILATION 

2.1 MM5 Modeling 

Under separate contract to the Minerals Management Service (MMS), Sonoma 

Technology, Inc. (STI) and PSU will run the MM5 meteorological model at 

36km/12km/4km nested grid meshes for the period October 1, 2000 to September 30, 

2001. This provides the standard three-dimensional meteorological dataset for use in 

dispersion modeling with CALPUFF. The SST data that will be used to force the lowerboundary in MM5 is being prepared by Earth Tech. under a contract with the MMS. As 

a reference, we have included the STI/PSU modeling domain for MM5. This is 

presented in Figure 1. 

Figure 1. MM5 modeling domains to be used in providing the standard meteorological 

data for use with the CALPUFF model. The blue, orange, and green boxes 

represent the three domains that will be used by STI/PSU. 

Briefly, there will be three domains employed in the MM5 modeling. The outer domain 

covering the whole United States will be at a 36 km grid spacing, while the inner two 

over the Gulf of Mexico and New Orleans would be at 12 and 4 km grid spacings, 

respectively. Further details about the model parameters and physics options can be 

obtained from the report from STI. 

-3200 -2400 -1600 -800 0 800 1600 2400

LCC East (km)

-2700

-2100

-1500

-900

-300

300

900

1500

2100

2700

LCC North (

km)
Page 4 

We prepared SST data over the United States at two resolutions. One at ~36 km grid 

spacing for the whole United States (for Domain 1) and the other at ~4 km grid spacing 

over the Gulf of Mexico (for Domains 2 and 3). We believe that the relative coarseness 

of the SST data over the Pacific Ocean or the Atlantic Ocean in this study would not 

affect the model predictions over the Gulf of Mexico. 

2.2 SST Data Processing 

In this section, we present the details of the approach we took in arriving at the final SST 

dataset. 

The MODIS SST data was chosen for daily processing. Data from the MODIS 

instrument is applied in deriving a variety of ocean products. Advanced Very High 

Resolution Radiometer (AVHRR) has been used in the past to estimate bulk sea 

temperature. MODIS improves on the capabilities of AVHRR in that ocean color can 

also be estimated from MODIS measurements. More details about the satellite, 

instrument and theoretical basis of calculation of SST are available at (http://modisocean.gsfc.nasa.gov/refs.html). 

The MODIS Level 3 Mapped data was used for our compilation. This data collection 

has 4.63 and 36 km spatial bins for the entire globe. Henceforth we will refer to the 

datasets as 4K (4.63 km) and 36K (36 km). The data is presented in a cylindrical 

equidistant map projection. In total, for 4K (36K) there exist 4096 (512) data grids in 

the meridional direction and 8192 (1024) grids in the zonal direction corresponding to 

approximately 4.63 (36) km at the equator. The data is available twice daily: one for the 

ascending and the other descending path of the satellite in its polar orbit. Moreover, the 

MODIS team has also processed the data to be output weekly. Each data file has a 

quality file associated with it. The data and the quality files are combined for a chosen 

level of quality (best, good, fair or poor) to obtain the final SST values. 

As is common with almost all sun-synchronous polar orbiting satellites, on a given day 

there exist significant gaps in the measured data over the tropics resulting from the 

orbital path. Moreover, the satellite cannot take measurements below thick clouds. 

Hence a large number of missing data points also exist over cloudy areas. One such area 

is Indonesia where thick cumulus clouds exist almost throughout the year. 

In the following paragraphs, we discuss the compilation of a contiguous SST dataset 

with grid spacings of 36 and 4 km starting with the MODIS 36 and 4 km data, 

respectively. 

Owing to the high thermal capacity of water, the diurnal cycle in the temperature of sea 

water at a given location is typically small. Over short time periods, it is generally 

reasonable to assume that there would be little variation in the sea surface temperature at 

a particular grid point. 
Page 5 

In arriving at the final SST for a particular day, we took into consideration the SST data 

for the three days prior and after the day in question. Hence we used up to a total of 

seven days of data in our calculation. Starting at the end (third day before and third day 

after) and approaching the current day, the global SST data was superimposed on top of 

each other. This can be represented by: 

SSTi

 = SSTi or SSTi+1 or SSTi-1 or SSTi+2 or SSTi-2 or SSTi+3 or SSTi-3 — Step 1

Where, i is the day for which the data has to be compiled. In the above equation, the 

global data array is filled from right to left and hence there is an increasing importance 

of the more recent values than the more distant ones. For example, if the SST data for 

Dayi

 at a particular grid point is missing, the value for Dayi-1 will be used as a 

replacement if available, otherwise, data for Dayi+1 is used, etc. 

After Step 1, the data gaps arising from the orbital path of the satellite are mostly 

eliminated. However gaps still exist over areas of thick clouds. As mentioned earlier, 

persistent clouds can prevent the measurement of SST for more than a week. Hence 

these gaps in the SST data are still not filled after Step 1. 

For the 36K dataset, most gaps are filled after Step 1. However any remaining gaps are 

filled in Step 3. Hence for this dataset, we jump directly to Step 3 and subsequent 

processing. The following description for Step 2 involves on the 4K dataset. 

The second step (Step 2) involves filling up the remaining gaps with the 36 km SST 

data. Owing to its coarser resolution, there are fewer spatial gaps in the 36 km data. 

However, the underlying retrieval algorithm is similar for both the 36 km and the 4 km 

datasets. Moreover, the 36 km data comprises of a spatial average of approximately 81 

adjacent 4 km data cells. Hence any gaps in the 4 km data would be substantially 

reduced in the 36 km dataset. 

Thus, 

SSTi = SSTi or (SSTi)36 km or (SSTi+1)36 km or (SSTi-1)36 km or (SSTi+2)36 

km or (SSTi-2)36 km or (SSTi+3)36 km or (SSTi-3)36 km — Step 2

Where, SSTi is the 4 km SST value, and the 36 km values are as labeled. 

As noted in Table 1, MODIS SST data is available daily as well as weekly. The weekly 

data is an average of the daily data over the period of interest. Weekly data has most of 

the data gaps eliminated except for regions with thick cumulus clouds. After Step 2, we 

fill any remaining gaps with weekly data that may be available. After Steps 1 and 2, if 

any gaps still exist in the data, filling by the weekly data are expected to be 

inconsequential. However, our compilation of SST from Steps 1 and 2 and that obtained 
Page 6 

from the MODIS team may differ slightly. Thus to be on the safer side, we fill the SST 

data from Step 1 with the MODIS weekly data obtained from the MODIS website. 

Thus, 

 SSTi = SSTi (from Step 2) or SSTwi — Step 3

Where, i is for the current day and wi is the week closest to the current day i. 

The final step (Step 4) in obtaining a complete 4 km dataset is to fill the remaining gaps 

– mostly over land and the land-sea boundary. In this case, in order to obtain values at 

grid cells of missing data, we interpolate data from neighboring grid cells lying under a 

given radius of influence. In our calculations, we used a 20-grid cell radius of influence. 

It should be noted that the SST values over land do not exist. However to ingest this 

dataset in a prognostic model like MM5, a contiguous dataset should be available. The 

preprocessing steps in MM5 introduce a land-sea mask to eliminate values over land. 

The final compiled product is converted into a GRIB format (WMO), using the gribw 

software (http://wesley.wwb.noaa.gov/gribw.html). The GRIB format is used worldwide 

in many applications and is compatible with the preprocessing software used in MM5 

modeling. The 4K dataset resolution obtained after the MODIS SST compilation is ~ 

0.043845o × 0.043845o. The GRIB format can hold information about the spatial 

resolution in millilatitudes (10-3) and millilongitudes. Creating a GRIB file at the native 

resolution would truncate the spatial resolution to 0.043o × 0.043o. Hence the spatial 

resolution in the 4K GRIB file was converted to 0.05o

 × 0.05o. We do not believe this 

would affect the spatial distribution of SST. The 36K grib files were left unchanged. 
Page 7 

Table 1. Sources and Resolution of Currently Available SST Data 

No. Source 

Spatial 

Resolution 

Temporal 

Resolution Methodology Web

1 Final Analysis Data (FNL) 1o × 1o 00, 06, 12, 18 Z Data assimilation NCEP Tropospheric Ana

http://dss.ucar.edu/datase

2 Real-time, global, sea surface 

temperature analysis (RTG_SST) 

0.5o × 0.5o Daily Analysis of satellite 

measurements, surface observations and subsequent interpolation 

http://polar.ncep.noaa.go

3 AVHRR Pathfinder Data 9, 18, 54 km Daily, weekly, 

monthly 

Satellite measurements http://podaac.jpl.nasa.gov

4 AVHRR Multi-channel SST 

(MCSST) 

18 km Weekly Satellite measurements http://podaac.jpl.nasa.gov

5 MODIS SST 4 km Daily, weekly Satellite measurements http://modis-ocean.gsfc.n6 NAVOCEANO MCSST 9 km Daily Satellite measurements http://podaac.jpl.nasa.gov7 NCEP Reynolds Optimally 

Interpolated (OI) SST 

1o × 1o Weekly, monthly Satellite measurements 

8 Along-Track Scanning 

Radiometer (ATSR) 

1 km Satellite measurements http://www.atsr.rl.ac.uk/a
Page 8 

3. RESULTS 

3.1 SST over the United States. 

Our compilation of the 36K data for the whole United States is presented in Figure 2 (2a 

through 2d). For comparison, we employed the NCEP Reynolds OI SST data (Source 7 

in Table 1) [Reynolds and Smith, 1994] and the Real Time Global SST data (Source 2 in 

Table 1) [Thiébaux et al., 2001]. Our compilation of the MODIS SST and the RTG SST 

was available daily, while the Reynolds SST was available weekly. Moreover, the RTG 

SST data compilation started in February 2001. Hence comparison of MODIS SST prior 

to February 2001 was done only with the Reynolds SST. 

 Figure 2a. 

Figure 2. Horizontal plot of SST values over the Gulf of Mexico (units = oC). (a) 

October 18, 2000, (b) January 17, 2001, (c) April 18, 2001, and (d) July 18, 

2001. The weekly MODIS and RTG data is obtained by averaging seven 

days of daily data that include three days before and after the day of interest. 

Spatial resolutions of MODIS, Reynolds and RTG SST data are 0.35 × 0.35, 1 

× 1, and 0.5 × 0.5 degrees, respectively. 

MODIS

Reynolds
Page 9 

In Figure 2, weekly average values for MODIS SST are shown for October 18, 2000, 

January 17, 2001, April 18, 2001, and July 18, 2001. The weekly averages were 

calculated from the daily values three days before and after the day in question, for a 

total of seven days. Similar approach was taken for the RTG SST data that is available 

daily. 

As seen in Figure 2a, large-scale features in the SST values are captured in both the 

MODIS and the Reynolds SST data. For example, warmer Gulf Stream temperatures 

(24-27 o

C) extending from North Carolina to the mid Atlantic Ocean are seen in both 

data. The coarseness of the Reynolds data is clearly visible in the bottom plot. This 

coarse resolution is also responsible for the lack of colder waters (9-12 o

C) along the 

western coast of the United States, which are seen in the MODIS data. Another 

interesting feature observed in the MODIS data that is not visible in the Reynolds data is 

the colder water temperatures in Lake Superior. Although, this feature is not relevant to 

the present study, such differences in two datasets are important in future work where 

modeling near coastal regions need to be done. 

 Figure 2b. 

MODIS

Reynolds
Page 10 

In January, colder water temperatures are seen along the US Gulf coast in the MODIS 

SST. This feature is absent in the Reynolds dataset. In April, a small eddy feature off 

the Eastern US is seen in both the MODIS and RTG, but not in the Reynolds. Finer 

resolution of the MODIS and RTG datasets thus has the capability of capturing such 

small-scale features in ocean temperatures. Finally, in July, colder water temperatures 

off the coast of California are seen in all three datasets. However, the MODIS data 

exhibits greater details than the other two datasets. 

 Figure 2c. 

MODIS

Reynolds

RTG
Page 11 

 Figure 2d. 

3.2 SST Over the Gulf of Mexico 

Domains 2 and 3 in MM5 modeling are located over the Gulf of Mexico. Therefore, we 

examined the SST data in detail over the Gulf of Mexico. We used the 4K data for this 

purpose. This analysis is presented in Figure 3 (3a through 3d) and is similar to the one 

presented in Figure 2. 

MODIS

Reynolds

RTG
Page 12 

As seen in Figure 3, the MODIS SST values for October 18, 2000 are more or less 

comparable to the Reynolds SST values. Warm water temperatures (27-30o

C) are 

evident closer to the tropics and south and east of Key West. These warm temperatures 

also extend in the middle of the Gulf of Mexico. The MODIS SST data exhibits peculiar 

features not observed in the Reynolds data. For example, the water temperatures in the 

proximity of coastal United States tend to be lower in the MODIS SST than the 

Reynolds SST dataset. While the average water temperature near the southern coast of 

the United States from the Reynolds SST is between 24 and 27oC, those values from the 

MODIS SST data tend to be between 21 and 24oC. This difference can be attributed to 

the relative coarseness of the Reynolds SST data. 

 Figure 3a. 

Figure 3. Horizontal plot of SST values over the Gulf of Mexico (units = oC). (a) 

October 18, 2000, (b) January 17, 2001, (c) April 18, 2001, and (d) July 18, 

2001. The weekly MODIS and RTG data is obtained by averaging seven 

days of daily data that include three days before and after the day of interest. 

Spatial resolutions of MODIS, Reynolds and RTG SST data are 0.05 × 0.05, 1 

× 1, and 0.5 × 0.5 degrees, respectively. 

MODIS

Reynolds
Page 13 

Similar features are also observed in the January 17, 2001 comparison. While the open 

ocean temperatures of 24-27o

C are comparable in both datasets, the coastal values from 

MODIS tend to be biased lower. Also, an unique feature observed in the MODIS data is 

the isolated maxima/minima over the shallow waters south of northwest Cuba. The 

colder water temperature along the coastal United States could also be a result of the 

ocean depth. This needs to be investigated in the future. 

 Figure 3b. 

MODIS

Reynolds
Page 14 

 Figure 3c. 

MODIS

Reynolds

RTG
Page 15 

 Figure 3d. 

MODIS

Reynolds

RTG
Page 16 

3.3 Comparison with Data from Other Sources 

Further validation of the SST data from MODIS was done by comparisons with surface 

based observations. Hourly ocean buoy data over the Gulf of Mexico is available from 

the National Data Buoy Center (http://www.ndbc.noaa.gov). Data over the Gulf of 

Mexico was extracted from the observed data and plotted against that derived from our 

compilations. The locations of the buoys are shown in Figure 4 and Table 2. This 

evaluation is presented in Figure 5 (a through j). The hourly ocean buoy data at these 

locations was averaged to a daily value and plotted against the daily values from the 

MODIS dataset. The data used in the analysis was for a time period October 2000 to 

September 2001. Since the MODIS data was only partly available for June and July 

2001, SST values during these two months were not included in our analysis. Also, 

missing days from the buoy data along with corresponding days in the MODIS data were 

also omitted from the analysis. 

Figure 4. Locations of the stations from which the buoy SST data was obtained. The ‘+’ 

sign gives the geographic location, while the number in the parenthesis is the 

station number. Note that most of the stations are located near the United 

States only. 
Page 17 

From Table 2 and Figure 5, it can be seen that the satellite-derived SST values are in 

very good comparison with the observed SST from buoys. Cross correlation coefficients 

between the MODIS and buoy SST values exceed 0.9 for all stations except Galveston. 

This represents an excellent match between what is calculated by the MODIS algorithms 

and that observed at the ocean surface. The relatively lower correlation between the two 

SST data at Galveston is unknown and needs to be studied more. 
Page 18 

Table 2. Correlation Coefficients MODIS Data Versus Buoy Data 

Station 

Number Station Name Latitude (N) Longitude (W) 

Number of 

Points 

Correlation 

Coefficient 

41009 Canaveral West 28.5 80.18 302 0.958 

42001 Mid Gulf 25.92 89.68 304 0.967 

42003 E Gulf 26.01 85.91 304 0.957 

42007 Biloxi 30.09 88.77 304 0.992 

42019 Freeport 27.91 95.36 304 0.940 

42020 Corpus Christi 26.95 96.7 276 0.962 

42035 Galveston 29.25 94.41 248 0.828 

42036 West Tampa 28.51 84.51 304 0.991 

42039 Pensacola 28.8 86.06 268 0.982 

42040 Mobile South 29.21 88.20 292 0.971 
Page 19 

Figure 5. Comparison between MODIS and buoy SST values. The points represent 

data for a period of one year (October 2000 – September 2001, except June – 

July 2001). Missing data in buoy observations are removed. Days 

corresponding to the missing buoy data days are also removed from the 

MODIS data. Also shown in the plots are 1:1, 1:2 and 2:1 lines. The name 

and location of the station is shown above each plot. 
Page 20 

4. REFERENCES

Cai, X. –M, and D. G. Steyn, 2000: Modelling study of sea breezes in a complex coastal 

environment, Atmospheric Environment, 34, 2873-2886. 

Grell, G., J. Dudhia, and D. Stauffer, 1996: A description of the fifth generation Penn 

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR, 117 pp, 1996. 

Reynolds, R. W. and T. M. Smith, 1994: Improved global sea surface temperature 

analyses using optimum interpolation. Journal of Climate, 7, 929-948. 

Rogers, D. P., 1995: Coastal meteorology, Reviews of Geophysics, 33. 

Scire J. S., F. R. Robe, M. E. Fernau, and R. J. Yamartino, 2000a: A User's Guide for the 

CALMET Meteorological Model (Version 5.0). Earth Tech, Inc., Concord, 

Massachusetts. 

Scire, J. S., D. G. Strimaitis and R. J. Yamartino, 2000b: A User's Guide for the 

CALPUFF Dispersion Model (Version 5.0). Earth Tech, Inc., Concord, Massachusetts. 

Thiébaux, H. J., B. Katz and W. Wang, 2001: New sea surface temperature analysis 

implemented at NCEP, preprint from the NWP/WAF conference, Fort Lauderdale, 

Florida.