Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS

1. SPIE Defense, Security and Sensing29 April – 3 May 2013, Baltimore, MD Evaluation and Selection of SST Regression Algorithms for JPSS VIIRS Boris Petrenko1,2, Sasha Ignatov1, Yury Kihai1,2, XingMing Liang1,3, and John Stroup1,4 1NOAA/NESDIS/STAR; 2GST, Inc.; 3CIRA; 4STG,Inc. SST Algorithms for JPSS VIIRS

2. Background and Objectives • Currently, NOAA generates two global L2 SST products from S-NPP VIIRS data, using two processing systems: • Interface Data Processing Segment (IDPS) • Advanced Clear-Sky Processor for Oceans (ACSPO) • The performance of both products wrt in situ and L4 SST is different; performance of IDPS SST is suboptimal • The goals of this study are: • To evaluate existing operational and prospective SST algorithms • To select optimal regression SST algorithms for VIIRS In this presentation, only daytime algorithms are discussed. For nighttime SST algorithms, the main result will be presented SST Algorithms for JPSS VIIRS

3. Requirements to VIIRS SST algorithm The VIIRS SST algorithm should: • Be of regression type, rather than RTM-based (IDPS does not support RTM simulations) • Use full VIIRS space and time resolution (no averaging SST over large areas and long time intervals) • Use full VIIRS swath (no restriction on view zenith angle, VZA) SST Algorithms for JPSS VIIRS

4. Basic Daytime Equation • The daytime SST algorithms use modifications of “basic” NLSST equation Ts = a0 + a1*T11 + a2* ΔT* (Ts0-273.15)+ a3* Δ T*Sθ, S θ=1/sec(θ)-1, ΔT= T11 - T12 • Main trends in modifications: • Using coefficients dependent on some proxies of atmospheric attenuation (ΔT, latitude, S θ) • Different ways of handling first guess SST Ts0 (in K, in C, with adjustable offset, as a separate regressor) SST Algorithms for JPSS VIIRS

5. Daytime Retrieval Algorithms (1) • ACSPO (NOAA/NESDIS/STAR): Basic NLSST equation, Ts0 in C • Pathfinder (U. of Miami and NOAA/NESDIS/NODC): • Basic NLSST equation, Ts0 in C, • separate sets of coefficients for “Dry” (ΔT ≤ 0.5 K) and “Moist” conditions (ΔT ≥ 0.9 K) • Interpolation for ΔT for 0.5 < Δ T < 0.9: • Current IDPS algorithm: • Same as Pathfinder Ts0 in K • LATBAND (U. Miami): • Basic NLSST equation, Ts0 in C, • separate sets of coefficients for latitudinal bands: -90° ≤ lat < -40°, -40° ≤ lat < -20°,-20° ≤ lat < 0°, 0° ≤ lat < 20°, 20° ≤ lat < 40°, 40° ≤ lat < 90° SST Algorithms for JPSS VIIRS

6. Daytime Retrieval Algorithms (2) • OSI-SAF: - The coefficients depend on Sθ: Ts = (a1 + a2 S θ) T11 + [a3 + a4 (Ts0 – 273.15) + a5 S θ] ΔT + a6Sθ + a0 • NAVO: - Adjustable offset at Ts0: Ts = a0 + a1*T11 + a2* ΔT* Ts0 + a3* ΔT+ a4*Δ T*Sθ • NRL: - Ts0 is a separate regressor (in K): Ts = a0 + a1*T11 + a2* ΔT* + a4*Δ T*Sθ+ a5Ts0 SST Algorithms for JPSS VIIRS

7. Retrieval Characteristics • Bias, B, wrt in situ SST • Standard Deviation, σ , wrt in situ SST • Sensitivity, μ, of retrieved SST to true SST (C. Merchant, 2009). • μcharacterizes the capability of algorithm to reproduce SST variations • Ideally, μ≈1 • μ is calculated by differentiating the algorithm’s equation • Derivatives of BTin terms of SST are calculated with RTM. SST Algorithms for JPSS VIIRS

8. Calculation of Retrieval Characteristics • Bias and SD were calculated from dataset of matchups (MDS) of VIIRS BTs with in situ SST • MDS was collected during 10 months (15 Apr 2012 – 14 Feb 2013); no calibration trends found during this time period. • Sensitivities were calculated with the Community Radiative Transfer Model within ACSPO for all VIIRS pixels for 24 Aug 2012 • B, σ and μ were tabulated as 2D functions of VZA and TPW SST Algorithms for JPSS VIIRS

9. Bias of SST – in situ SST vs VZA and TPW OSI-SAF ACSPO LATBAND PATHFINDER • The bias is most uniform for OSI-SAF • NRL SST Bias is also relatively flat • For other algorithms, biases strongly vary at large VZAs IDPS NAVO NRL SST Algorithms for JPSS VIIRS

10. SD of SST – in situ SST vs VZA and TPW OSI-SAF ACSPO LATBAND PATHFINDER IDPS NAVO NRL • For all algorithms (except NRL), the SDs grow towards large VZAs • The rate of this growth increases with TPW • For NRL SST, SD is flat and small SST Algorithms for JPSS VIIRS

11. Sensitivity to true SST as a function of VZA and TPW OSI-SAF ACSPO LATBAND PATHFINDER NAVO IDPS NRL • Generally, μ<1 and it decreases with VZA and TPW, but differently for different algorithms • Sensitivity to true SST is very low for NRL algorithm SST Algorithms for JPSS VIIRS

12. Bias, SD and Sensitivity as Functions of TPW along the Slant Line of Sight, STPW=TPW×sec(VZA) BIAS SD SENSITIVITY • STPW characterizes atmospheric absorption along the line of sight. • Bias of OSI-SAF SST is most uniform • SD for NRL algorithm is smallest, but sensitivity is unacceptably low • OSI-SAF SST provides best combination of SST accuracy, precision and sensitivity SST Algorithms for JPSS VIIRS

13. Analysis of Geographic Distributions of Retrieval Characteristics • LUT values of bias and SD were interpolated to pixels values of VZA and TPW for August 24 2012 • Pixel values of sensitivity were calculated with CRTM within ACSPO • Composite maps of Bias, SD and sensitivity were produced with 0.8° × 0.8° lat/lon spatial resolution TPW TPW VZA |VZA| SST Algorithms for JPSS VIIRS

14. Geographic Distribution of Bias • OSI-SAF SST biases are most uniform (<0.1 K) • Other algorithms show higher spatial variability of SST biases OSI-SAF LATBAND OSI-SAF LATBAND PATHFINDER ACSPO PATHFINDER ACSPO IDPS NAVO IDPS SST Algorithms for JPSS VIIRS

15. Geographic Distribution of SD • All algorithms show high spatial variability of SST SD OSI-SAF LATBAND PATHFINDER ACSPO NAVO IDPS SST Algorithms for JPSS VIIRS

16. Geographic Distribution of Sensitivity to True SST • All algorithms show high spatial variability of sensitivity to true SST • Typically, μ is lower in the tropics than in high latitudes. • This is especially the case for LATBAND algorithm • For IDPS SST, μ>1 in high latitudes • All algorithms show high spatial variability of OSI-SAF LATBAND PATHFINDER ACSPO NAVO IDPS SST Algorithms for JPSS VIIRS

17. Evaluation of Overall Algorithms Performance • The geographical distributions of Bias, SD and sensitivity are significantly non-uniform. • The requirements to the retrieval characteristics are not necessarily met at every element of the ocean surface • Evaluation of overall algorithm’s performance should account for favorability of distributions of retrieval characteristics • Our approach to algorithm evaluation: • Pose requirements on retrieval characteristics • Determine a Quality Retrieval Domain (QRD) - the part of the ocean, within which the predefined specifications aremet. • Select the algorithm with largest QRD SST Algorithms for JPSS VIIRS

18. Quality Retrieval Domains for Daytime SST Algorithms (24 August, 2012) • The QRDs are shown for the following specifications: • Bias < 0.1 K • SD < 0.4 K • 0.8<Sensitivity < 1.1 • The QRD is significantly different for different algorithms; it is an informative measure of algorithms’ performance • The maximum QRD is provided by the OSI-SAF algorithm OSI-SAF (82.2%) LATBAND (71.1%) PATHFINDER (75.4%) ACSPO (58.7%) IDPS (60.4%) NAVO (62.8%) SST Algorithms for JPSS VIIRS

19. Summary • SST retrieval characteristics – bias, SD of retrieved SST and its sensitivity to true SST - significantly vary in space • New metrics - Quality Retrieval Domain – was introduced to evaluate overall performance of SST algorithms • The OSI-SAF algorithm was selected for implementation for VIIRS because it has provided the maximum QRD under reasonable specifications on retrieval characteristics. • Although we presented results for daytime only, the OSI-SAF nighttime algorithm also has been optimal in terms of QRD and was selected for VIIRS. • This result shows that accounting for angular dependencies of regression coefficients is more efficient way to improve SST retrievals than using dependencies of coefficients on proxies of water vapor content. SST Algorithms for JPSS VIIRS