naval oceanography data decision superiority 1 approved for public release, distribution unlimited...

Naval Oceanography Data Decision Superiority 1

Approved for Public Release, Distribution Unlimited

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 39529-6000bNaval 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 IDate: 5/1/2013 Time: 14:00

Location: Conv. Ctr. 350

Paper 8724-35

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.



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 consistency for 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.



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



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)



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.



SUOMI NPP VIIRS PROCESSINGDaytime 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



SUOMI NPP VIIRS PROCESSINGNighttime cloud tests

M15 Uniformity (max(RAD)-min(RAD)) ≤ 0.05M16 Uniformity (max(RAD)-min(RAD)) ≤ 0.05M12 Gross Cloud 268K ≤ min(BT) & max(BT) ≤ 310KM15 Gross Cloud 270K ≤ min(BT) & max(BT) ≤ 310KM16 Gross Cloud 268K ≤ min(BT) & max(BT) ≤ 310KCirrus (MBT12avg-MBT16avg)/MBT16avg ≤ f(MBT15)Low Stratus (MBT16avg-MBT12avg) ≤ 0KM15 minus M16 0≤ (avg(BT15)-avg(BT16)) ≤ f(BT15)SST Inter-comparison comparing results of various SST equationsUnreasonable SST (-2 ≤ SST ≤ 35)Climatology |SST-CLIM| ≤ 10FLD |SST-((2*FLD+CLIM)/3))| < 2.5Aerosol SST-FLD ≥ -1 & MCSST(12,15)-NLSST(15,16) ≤ .9SST Delta |SST-First_SST| ≤ .6 in 10x6 pixel-window



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.



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)



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)



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



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)



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)



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 0 C while that for the MCSST equation is ⁰shown when the offset tends toward infinity.



Evaluation

NAVOCEANO evaluates all satellite-derived SST data by matching them to drifting buoys

Criteria for matching:25 km maximum distance

4h maximum time separationCriteria for matching:

25 km maximum distance

4h maximum time separation

3 categories:

Confidently clear

Probably clear

Probably cloud contaminated

Statistics based on 1 month of data



Evaluation

March 20, 2013 daytime reliability statistics S-NPP VIIRS SST matches RMS error Bias

Confidently clear 15,868 0.44 0.02

Probably clear 2,917 0.77 0.07

Probably cloud cont. 375 1.69 -0.46

MetOp-A FRAC SST matches RMS error Bias

Confidently clear 18,412 0.46 -0.14

Probably clear 658 0.99 -0.38


IDPS EDR SST matches RMS error Bias

High 5,621 0.42 0.03

Degraded 18,185 0.77 -0.24

Excluded 28,270 1.78 -1.30

RMS errors similar for all categories/products

IDPS EDR restrictive for best category



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

S-NPP VIIRS SST matches RMS error Bias


Probably clear 2,917 0.81 -.016


MetOp-A FRAC SST matches RMS error Bias


Probably clear 299 1.19 -0.86


IDPS EDR SST matches RMS error Bias

High 6,882 0.31 -0.08

Degraded 17,320 0.48 -0.18

Excluded 28,738 2.02 -1.68



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.



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).

naval oceanography data decision superiority 1 approved for public release, distribution unlimited...

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