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VIIRS-derived SST at the Naval Oceanographic Office: From evaluation to operation

VIIRS-derived SST at the Naval Oceanographic Office: From evaluation to operation . Jean-François Cayula a , Douglas May b , Bruce McKenzie b , Keith Willis b a Qinetiq North America, 1103 Balch Blvd., Suite 218, Stennis Space Center, MS 39529-6000

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VIIRS-derived SST at the Naval Oceanographic Office: From evaluation to operation

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  1. VIIRS-derived SST at the Naval Oceanographic Office: From evaluation to operation Jean-François Cayulaa, Douglas Mayb, Bruce McKenzieb, Keith Willisb aQinetiq North America, 1103 Balch Blvd., Suite 218, Stennis Space Center, MS 39529-6000 bNaval Oceanographic Office,1002 Balch Blvd., Stennis Space Center, MS 39522-5001 SPIE Defense Security and Sensing 2013, Ocean Sensing and Monitoring V Thermal Remote and In-Situ Sensing I Date: 5/1/2013 Time: 14:00 Location: Conv. Ctr. 350 The views expressed in this presentation are those of the authors and do not necessarily reflect the official policy or position of the Department of the Navy, Department of Defense, or US Government. Paper 8724-35

  2. Abstract The Naval Oceanographic Office (NAVOCEANO) produces Sea Surface Temperature (SST) retrievals from satellite data. NAVOCEANO also obtains satellite-derived SST data sets from other groups. To provide consistencyfor assimilation into analyses and models, all the SST data sets are evaluated for their accuracy with the same methodology. In this presentation, the focus is SST derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on-board the Suomi National Polar-orbiting Partnership (S-NPP) satellite. Of particular interest is the evaluation of NAVOCEANO-produced SST with its NAVOCEANO cloud mask (NCM), the VIIRS cloud mask (VCM), and VIIRS Environmental Data Record (EDR) SST. The evaluation results show that these products are in some ways comparable, with similar strengths and weaknesses, although they target different customers. For comparison, the reliability results for the Meteorological Operational (METOP-A) satellite-derived SST, which is a NAVOCEANO operational product, are presented. As a by-product of the NAVOCEANO VIIRS SST evaluation, the non-linear SST (NLSST) equations used to derive the SST values were found to be less than optimal, depending on the unit of the field temperature term. NAVOCEANO VIIRS SST employs an expanded NLSST equation, which in effect refines the approximation of the gamma term by adding an offset. In view of the evaluation results, NAVOCEANO VIIRS SST became operational in January 2013.

  3. Introduction • SST at NAVOCEANO • Input to ocean models • Processing AVHRR data from NOAA-19, Metop-A, Metop-B* • Processing Imager data from GOES-13, GOES-15, COMS-1* • SST data from other sources such as GHRSST

  4. Introduction • SST from VIIRS at NAVOCEANO • Preparation before availability of VIIRS data • MODIS (AQUA) • Proxy-VIIRS with “movicon” software then from GRAVITE • VIIRS data from S-NPP since January 2012, 2 sources: • GRAVITE (testing) • IDPS at AFWA (operational)

  5. SUOMI NPP VIIRS PROCESSING • 1,012 VIIRS granules each day received in near real time • Granules are processed individually • Process relies on M5 (0.67μm), M7 (0.87μm), M12 (3.7μm), M15 (10.7μm), and M16 (12.0μm) bands • SST retrievals are produced from cloud-screened 2x2 pixel unit arrays, 1.5 km spatial resolution. • Cloud tests for daytime and nighttime are listed in the next 2 slides.

  6. SUOMI NPP VIIRS PROCESSING Daytime cloud tests M7 Uniformity (max(REF)-min(REF)) ≤ 0.04 M15 Uniformity (max(RAD)-min(RAD)) ≤ 0.05 M16 Uniformity (max(RAD)-min(RAD)) ≤ 0.05 M7 Gross Cloud max(REF) ≤ 18% M15 Gross Cloud 270K ≤ min(BT) & max(BT) ≤ 310K M16 Gross Cloud 268K ≤ min(BT) & max(BT) ≤ 310K Reflected Ratio (avg(REF7)/avg(REF5)) ≤ .7 Visible Cloud Threshold avg(REF7) ≤ Threshold f(solz,satz,relaz) M15 minus M16 0≤ avg(BT15)-avg(BT16) ≤ Threshold f(BT15) Unreasonable SST -2 ≤ SST ≤ 35 SST Inter-comparison |NLSST-MCSST| ≤ 1.5 Climatology |SST-CLIM| ≤ 10

  7. SUOMI NPP VIIRS PROCESSING Nighttime cloud tests M15 Uniformity (max(RAD)-min(RAD)) ≤ 0.05 M16 Uniformity (max(RAD)-min(RAD)) ≤ 0.05 M12 Gross Cloud 268K ≤ min(BT) & max(BT) ≤ 310K M15 Gross Cloud 270K ≤ min(BT) & max(BT) ≤ 310K M16 Gross Cloud 268K ≤ min(BT) & max(BT) ≤ 310K Cirrus (MBT12avg-MBT16avg)/MBT16avg ≤ f(MBT15) Low Stratus (MBT16avg-MBT12avg) ≤ 0K M15 minus M16 0≤ (avg(BT15)-avg(BT16)) ≤ f(BT15) SST Inter-comparison comparing results of various SST equations Unreasonable SST (-2 ≤ SST ≤ 35) Climatology |SST-CLIM| ≤ 10 FLD |SST-((2*FLD+CLIM)/3))| < 2.5 Aerosol SST-FLD ≥ -1 & MCSST(12,15)-NLSST(15,16) ≤ .9 SST Delta |SST-First_SST| ≤ .6 in 10x6 pixel-window

  8. SST Calculations • MCSST and NLSST equations are the basis for SST calculations at NAVOCEANO. • Coefficients of equations are determined by linear regression against drifting buoy measurements.

  9. SST Calculations Disregarding the angle correction, the 2-channel split-window equation can be expressed [5] as (1) with, (2) When γ is assumed constant, eq. 1 is the MCSST [6] form (3)

  10. SST Calculations However in [7], γ was found to be correlated with the surface temperature field. In particular for the 2-channel split-window equation (4) is a reasonable approximation. This leads to the standard NLSST [7] form (5)

  11. SST Calculations • Switch from Celsius to Kelvin for Tfield shows the dependence of NLSST equation on temperature unit / implicit offset • Suggests (6) • Varying offset while fitting data shows effect on accuracy

  12. SST Calculations Taking into account (6) in eq. (1) and bringing back the correction term for the view angle lead to the expanded NLSST form of the split-window equation (7) Finally, (6) is rewritten in a form more appropriate to estimate the equation coefficients (8)

  13. SST Calculations At night, NAVOCEANO uses a triple-window equation which can be modified like the daytime equation to explicitly show the offset (9) Again, (9) is rewritten in a form more appropriate to estimate the equation coefficients (10)

  14. SST Calculations • Varying the value of the offset in (9) while fitting data shows effect on accuracy. The RMS error for the standard NLSST equation is shown for an offset of 0⁰C while that for the MCSST equation is shown when the offset tends toward infinity.

  15. Evaluation NAVOCEANO evaluates all satellite-derived SST data by matching them to drifting buoys Criteria for matching: Criteria for matching: 25 km maximum distance 25 km maximum distance 4h maximum time separation 4h maximum time separation Confidently clear Probably clear 3 categories: Probably cloud contaminated Statistics based on 1 month of data

  16. Evaluation March 20, 2013 daytime reliability statistics RMS errors similar for all categories/products IDPS EDR restrictive for best category

  17. Evaluation March 20, 2013 nighttime reliability statistics RMS errors similar for all categories/products lower than daytime MetOp-A less restrictive, IDPS EDR more restrictive for best category

  18. Conclusion After a year of testing the performance, NAVOCEANO VIIRS SST was found to be similar to other NAVOCEANO operational products and suitable to be ingested by the Navy ocean models. S-NPP VIIRS SST at NAVOCEANO was declared operational at the end of January 2013. Expanded NLSST removes dependence on temperature unit that affects the accuracy of the standard NLSST and allows selection of optimal offset rather than implicit offset.

  19. References [1] Hurlburt, H.E., Brassington, G.B., Drillet, Y., Kamichi, M., Benkiran, M., Bourdalle-Badie, R., Chassignet, E.P., Jacobs, G.A., Le Galloudec, O., Lellouche, J.-M., Metzger, E.J., Oke, P.R., Pugh, T.F., Schiller, A., Smedstad, O.M., Tranchant, B., Tsuino, H., Usui, N., and Wallcraft, A.J., High-Resolution Global and Basin-Scale Ocean Analyses and Forecasts, Oceanography, 22(3), 110-127 (2009). [2] Vogel, R.L., Privette, J.L., and Yu, Y., “Creating Proxy VIIRS Data from MODIS: Spectral Transformations for Mid- and Thermal-Infrared Bands,” IEEE Trans. Geosci. Remote Sens., 46(11), 3768-3782 (2008). [3] McKenzie, B., May, D, Cayula, J.-F., and Willis, K., "Initial results of NPP VIIRS SST processing at NAVOCEANO," Proc. SPIE 8372, Ocean Sensing and Monitoring IV, 83720H (June 11, 2012); doi:10.1117/12.922955; http://dx.doi.org/10.1117/12.922955 [4] Heckmann, G., Grant, K.D., and Mulligan, J.E., Key Features of the Deployed NPP/NPOESS Ground System, Abstract # IN43A-1378 presented at 2010 Fall Meeting, AGU, San Francisco, CA (2010). [5] McMillin, L,. and Crosby, D., Theory and validation of the multiple window sea surface temperature technique, J. Geophys. Res., 89, 3655-3661 (1984). [6] McClain, E.P., Pichel, W., and Walton, C., Comparative performance of AVHRR-based multichannel sea surface temperatures, J. Geophys. Res., 90, 11,587-11,601 (1985). [7] Walton, C., Pichel, W., Sapper, J., and May, D., The Development and Operational Application of Nonlinear Algorithms for the Measurement of Sea Surface Temperatures with the NOAA Polar-Orbiting Environmental Satellites, J. Geophys. Res., 103(12), 27,999-28,012 (1998). [8] Cayula, J.-F., May, D., McKenzie, B, Olszewski, D., and Willis, K, Reliability Estimates for Real-Time Sea Surface Temperature, Sea Technology, 45(2), 67-73 (2004).

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