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Using Satellite Ground Operations Training To Develop An Algorithm Using Satellite Sea Surface Temperatures From The CERSER Ground Station To Predict NOAA North Carolina Coastal Buoy Temperatures Ignatius K. Williams & Rockson A. Armaah Center for Remote Sensing of Ice Sheets (CReSIS),

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  1. Using Satellite Ground Operations Training To Develop An Algorithm Using Satellite Sea Surface Temperatures From The CERSER Ground Station To Predict NOAA North Carolina Coastal Buoy Temperatures Ignatius K. Williams & Rockson A. Armaah Center for Remote Sensing of Ice Sheets (CReSIS), University of Kansas, Elizabeth City State University, Elizabeth City, Department of Oceanography & Fisheries, University of Ghana, Legon Mentors: Je’aime H. Powell / Kuchumbi Hayden 1 of 20

  2. Overview • Introduction • Objectives • Methodology • Results • Conclusion • Future work • References • Acknowledgements 2 of 20

  3. Key Terms • SST (Sea Surface Temperature) • ENVI (Environment for Visualizing Images) • ILWIS (Integrated Land Water Information System) • R2 value • AVHRR (Advanced Very High Resolution Radiometer) • TeraScan 3 of XX

  4. Abstract Elizabeth City State University currently operates a TeraScan Grounding station capable of receiving and processing imagery data collected by satellites managed by the National Oceanic and Atmospheric Administration (NOAA). The imagery received in the Infra-Red spectrum both measures sea surface temperatures and cloud cover for the eastern coast of North Carolina. Once the data sets were collected, they were statistically analyzed using the analysis of variance methodology and regression. Strong correlations were observed during the AVHRR-Buoy comparison for two of the three areas under the study. The NOAA-16 AVHRR SST emerged as the most consistent with the insitu data from the ORIN7 Buoy. This was due to its high coefficient of determination. TeraScan training received during the period also contributed knowledge on the processing of raw data to suit specific areas of interest. The processed data could then be exported to third party software such as ENVI and Google Earth while maintaining the specific data of interest. 4 of XX

  5. Introduction Elizabeth City State University – TeraScan Grounding station - National Oceanic and Atmospheric Administration (NOAA). (2002) 5 of 20

  6. Objectives • To find if there was a statistical difference in the SST readings from the satellite and buoy at 5% level of significance • Which satellite-buoy comparison had the highest correlation and, • Which NOAA satellite produced the most accurate SST reading for the best satellite-buoy comparison 6 of 20

  7. Methodology • Dataset Platform ( 22nd April, 2011- 14th June, 2011) • NOAA satellite data (http:// cerser.ecsu.edu/terascan) • Buoy data (http://ndbc.noaa.gov) • Visualization and SST extraction • Statistical analysis • Regression • Analysis of Variance (ANOVA) 7 of 20

  8. Results 8 of 20

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  16. Conclusion • NOAA AVHRR SST measurements were comparable to the SST of the HCGN7 and ORIN7 buoys • The AVHRR/ORIN7 buoy comparison was the most consistent with an R2 value of 0.74 • NOAA-16 AVHRR emerged most consistent with the ORIN buoy data (R2 = 0.855) 16 of 20

  17. TeraScan Training Achievements 17 of XX

  18. Future work • The readings can be improved in future studies by using the correct data formats (Geotiff on CERSER site) for the NOAA-AVHRR images • Use of raw or level 0 data using the appropriate channels in TeraScan to derive the SST product • The effect of atmospheric variables such as air temperature and humidity 18 of 20

  19. Acknowledgements 19 of 20

  20. Thanks for your attention Questions??? 20 of XX

  21. REFERENCES 21 of 20

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