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Advanced Clear-Sky Processor for Oceans NESDIS Operational Polar L1-to-L2 SST System

SST from Polar Orbiters: Use of NWP Outputs 5-7 March 2013, OSI SAF Workshop, Lannion, France. Advanced Clear-Sky Processor for Oceans NESDIS Operational Polar L1-to-L2 SST System Sasha Ignatov and John Sapper NOAA/NESDIS Critical ACSPO Developers

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Advanced Clear-Sky Processor for Oceans NESDIS Operational Polar L1-to-L2 SST System

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  1. SST from Polar Orbiters: Use of NWP Outputs 5-7 March 2013, OSI SAF Workshop, Lannion, France Advanced Clear-Sky Processor for Oceans NESDIS Operational Polar L1-to-L2 SST System Sasha Ignatov and John Sapper NOAA/NESDIS Critical ACSPO Developers Yury Kihai, John Stroup, Boris Petrenko, Xingming Liang ACSPO

  2. NESDIS Operational POES SST Products • Heritage Main Unit Task (MUT) - AVHRR • 1981 - pr (MCSST - McClain et al., 1985; NLSST - Walton et al., 1998) • 1993 - pr: Re-hosted to NAVO (“Shared Processing Agreement”). • Robust end-to-end system. No redesign since 1981: Data sub-sampling (2×2; HIRS FOV); No RTM. No LEO/GEO consistency. No reprocessing capability. • Advanced Clear-Sky Processor for Oceans (ACSPO) • Development started in late 2005 - Operational in May 2008 • Process all AVHRR pixels (GAC, FRAC). RTM & Reprocessing capabilities. • MODIS and VIIRS • NOAA responsible for JPSS IDPS SST. • Contractor’s Radiances (SDRs), Cloud Mask, and SST algorithms. • NOAA also generates heritage ACSPO SST product from VIIRS SDRs. • IDPS Fall-back. Benchmark to measure improvements. Transition for users. • Jan 2012: Included MODIS in ACSPO, simultaneously with VIIRS ACSPO

  3. ACSPO Motivation and Objectives • New Generation “Advanced” SST system • Radiative Transfer Model (RTM) capability • Use CRTM with 1st guess SST and GFS fields to generate 1st guess TOA clear-sky BTs & improve cloud mask/QC and SST retrievals • Flexibility/Consistency across platforms/sensors • Polar: AVHRR (GAC, FRAC), MODIS, and VIIRS Geo: MSG SEVIRI, GOES-R ABI • Reprocessing Capability • Operational system can be used for historical reprocessing • Every SST pixel processed (including full swath) • No sub-sampling is done in ACSPO system • Infrastructure in place for improved/RTM based SST and cloud mask • Use error characterization rather than quality flags • Cloud and Ice Mask optimized for SST • Code Modularized and Efficient ACSPO

  4. ACSPO Products • Primary: Clear-Sky Radiances (CSR) over Ocean in all sensor’ bands • Can be assimilated in NWP • Can be used for improved SST retrievals (e.g. JCSDA physical SST) • Quality of Clear-Sky Radiances monitored against CRTM • SST (derived from CSR in the Earth Emission Bands) • Currently, regression SST only (AVHRR, MODIS, VIIRS IDPS heritage) • Physical SST retrieval-ready (currently tested) • Hybrid Physical/Regression explored (e.g., Incremental Regression) • Aerosol (derived from CSR in solar reflectance bands) • Single-channel Aerosol retrievals (current NESDIS operational algorithm) • Aerosol product will be used to • monitor accuracy of CSR in solar reflectance bands • Explore aerosol correction to SST • Assist in Cloud Mask evaluation and improvement ACSPO 4

  5. AVHRR Operational Products generated since May 2008 NOAA16 (GAC-4km) – Unstable NOAA17 (GAC-4km) – Scan Motor stalled in Feb’10 NOAA18 (GAC-4km) – Stable (shows step-wise variations) NOAA19 (GAC-4km) – Stable Metop-A (FRAC-1km, GAC-4km) – Stable Archived at CLASS since 2011 Experimental Products generated since Jan’2012 NPP/VIIRS (0.75km) – Stable Terra/MODIS (1km) – Stable Aqua/MODIS (1km) – Stable Metop-B (GAC/GRAC) – launched in Sep 2012 – Stable ACSPO VIIRS will be operational within JDE system around Sep 2013 & will be archived with NODC/CLASS Platforms & Sensors Currently Processed by ACSPO ACSPO

  6. ACSPO Versions • V1.00 (May 2008) • AVHRR GAC from NOOA-18 and Metop-A • V1.02 (Sep 2008) • Weekly 1° Reynolds SST  0.25° daily; CRTM Alpha  v1; Planck-weighted CRTM coefficients; Black surface model  Fresnel’s • V1.10 (Apr 2009) • Started processing Metop-A FRAC; Implemented improved cloud mask; Day/Night switch over SZA changed from 85° to 90° • V1.20 (Aug 2009) • 8km land-sea mask replaced with 1km; ocean-to-land distances added; Code upgraded • V1.30 (Mar 2010) • Clear-sky mask upgraded; SST coefficients uniformly recalculated • V1.40 (Dec 2010) • CRTM upgraded to 1.2 to 2.02; OSTIA input capability added; code upgrades towards modularization and parallelization; • V2.00 (May 2012) • Major code restructuring towards meeting the FORTRN 2K standards • V2.20 (Mar 2013) (intermediate 2.10 was not operationalized) • Currently under testing – several major code upgrades ACSPO

  7. ACSPO v1.00 vs. Heritage NESDIS SST (Metop-A GAC 3 Jan 2008 Daytime) Heritage NESDIS SST product ACSPO SST product • GAC: Resolution 8 km; VZA<53° • 6.6×104 SST observations • 8.3% ocean covered in 1 day @0.3° Lat/Lon • GAC: Resolution 4 km; Full Swath • 2.1×106 SST observations • 32.7% ocean covered in 1 day @0.3° Lat/Lon ACSPO Provides more retrievals than heritage system, with comparable accuracy ACSPO

  8. ACSPO v1.00 Evaluation • Quality of ACSPO products - Higher density and equal or better accuracy than heritage product ACSPO

  9. ACSPO 1.10: Gulf of CaliforniaMetOp-A, 12 May 2009, 5:10-5:20 UTC (Night) 1km Full Resolution Area Coverage (FRAC) ‘AVHRR – Reynolds’ SST 4km Global Area Coverage (GAC) ‘AVHRR – Reynolds’ SST ACSPO v1.10 FRAC

  10. ACSPO 1.20: New 1 km Land Mask(Scene in the Mediterranean Sea) Pink: High resolution coastline from CDAT (CoastWatch Data Tool, http://coastwatch.noaa.gov/) Green: 1km USGS Land Mask Grey: Cloud mask on the SST field Water is black on the land to water picture Water is white on the water to land pictures The new 1km mask match the CDAT coastal line much closer ACSPO v1.20 (GAC & FRAC)

  11. ACSPO 1.20: Ocean-to-Land Distance(Example in the Mediterranean Sea) Pink: High resolution coastline from CDAT (CoastWatch Data Tool, http://coastwatch.noaa.gov/) Green: 1km USGS Land mask Grey: Cloud mask on the SST field White: Water with D>50km off the land The ocean-to-land layer can be used to select/avoid coastal areas ACSPO v1.20 (GAC & FRAC)

  12. ACSPO v1.20: Land-to-Ocean Distance(Example in the Mediterranean Sea) Pink: High resolution coastline from CDAT (CoastWatch Data Tool, http://coastwatch.noaa.gov/) Green: 1km USGS Land mask Grey: Cloud mask on the SST field Black: Water The land-to-ocean layer can be used in case satellite geolocation is off ACSPO v1.20 (GAC & FRAC)

  13. Inland Water-to-Land Distance(Example in the Mediterranean Sea) The inland water-to-ocean layer can be used to identify inland waters ACSPO v1.20 (GAC & FRAC)

  14. Composite map of “Regression – Daily Reynolds (AVHRR) SST”, ACSPO 1.20 Metop-A, 7 January 2010, Night In ACSPO 1.20 regression SST was calculated using coefficients derived in NESDIS heritage (MUT) system in the range of view zenith angle -54°<θ<+54°. As a result, ACSPO SST was biased cold at scan edges resulting in cold stripes on the map. ACSPO v1.30 (GAC & FRAC)

  15. Composite map of “Regression – Daily Reynolds (AVHRR) SST”, ACSPO 1.30 Metop-A, 7 January 2010, Night In ACSPO v1.30, the coefficients were re-derived using ACSPO data in the full AVHRR swath. As a result, dependency of regression SST on satellite zenith angle is now minimized. ACSPO v1.30 (GAC & FRAC)

  16. SST bias as a function of satellite zenith angle in ACSPO 1.20 and ACSPO 1.30 ACSPO 1.20 ACSPO 1.30 NIGHTTIME NIGHT: The dependencies of SST bias on view zenith angle in ACSPO 1.30 are more flat and consistent across multiple platforms. ACSPO v1.30 (GAC & FRAC)

  17. SST bias as a function of satellite zenith angle in ACSPO 1.20 and ACSPO 1.30 ACSPO 1.20 ACSPO 1.30 DAYTIME DAY: The dependencies of retrieved SSTs on view zenith angle in ACSPO 1.30 are more flat and consistent across multiple platforms. *Note that quality of both NOAA-16 and -17 AVHRR radiances is currently suboptimal, due to scan motor problems. ACSPO v1.30 (GAC & FRAC)

  18. Daytime M-O Bias @3.7µm (ACSPO v1.30) In ACSPO v1.30: An unrealistic cold M-O bias (~ -20K) in Ch3b found in sun glint area and a warm bias (~ +5K) elsewhere, due to inaccurate surface model (quasi-Lambertian) used in CRTM 1.2. ACSPO v1.40 (GAC & FRAC)

  19. Daytime M-O Bias @3.7µm (ACSPO v1.40) In ACSPO v1.40: Specular model adopted in CRTM 2.02 instead of quasi-Lambertian significantly reduces daytime M-O biases in Ch3b, due to improved CRTM performance. ams.confex.com/ams/pdfpapers/170593.pdf ACSPO v1.40 (GAC & FRAC)

  20. Code Redesign and Optimization in ACSPO v2.00 • The goal of ACSPO v.2.00 development has been redesign and optimization of the ACSPO code while retaining all the functionality of the previous ACSPO v.1.40. • This included: • Moving away from legacy CLAVR-based design and code, which • Was designed for AVHRR cloud mask, not SST • Used numerous global common blocks • Had messy interfaces (e.g., data passed via global variables) • Contained a lot of redundant code • Was not very amenable for use with other sensors, ancillary files, and output formats • Development of separate, independent modules for each major ACSPO function, providing • Better overall structure • Clean, well-defined interfaces • Minimum of need to know details of entire system • Possibility of simultaneous development of different modules ACSPO v2.00

  21. Code Redesign and Optimization in ACSPO v2.00 (Continued) • Elimination of global variables, which • Are confusing and error-prone • Easily lead to unintended side-effects (i.e., changing a value in one part of code and having it unintentionally affect some other part of code) • Movement of satellite/device independent functions in special library module, which can be compiled as static library and used for ACSPO and other projects • Elimination of redundant code • Easier maintenance • Replacement of old style FORTRAN 77 code with FORTRAN 95-2003 style • Better suited for modern compiler and auto parallel optimization • Overall functionality the same as in previous version, but with improved graceful degradation support against problems with input ancillary data ACSPO v2.00

  22. Configuration Files in ACSPO v.2.00 • In ACSPO v.2.00, the majority of satellite/device specific constants and conditions were moved from code to configuration files. This added the following capabilities: • Parameters and thresholds can be adjusted without code modification and recompilation • Processing can be data driven (e.g., providing capability to use same code for multiple sensors) • The main features of ACSPO 2.00 configuration files are: • One configuration file defined for program and one for each module • All attributes and layers defined in program-level configuration file • Interfaces between modules and attributes/layers defined in program-level configuration file • Configuration files store module-specific constants and thresholds that would otherwise be specified in the code ACSPO v2.00

  23. Long Term SST Monitoring in SST Quality Monitor (SQUAM) www.star.nesdis.noaa.gov/sod/sst/squam/ VIIRS SST - ACSPO and IDPS

  24. NIGHT STD DEV wrt. Reynolds L4 Warm-Up Cool-Down Event IDPS shows larger STD • AVHRR & MODIS SSTs are consistent • ACSPO VIIRS is consistent with MODIS & AVHRR • VIIRS IDPS SST shows larger STD • Large peaks due to suboptimal performance of Ice Mask VIIRS SST - ACSPO and IDPS

  25. NIGHT STD DEV wrt. In situ drifters • AVHRR & MODIS SSTs are consistent • ACSPO VIIRS is consistent with MODIS & AVHRR • VIIRS EDR shows larger STD, out of spec • Large peaks due to suboptimal performance of Ice Mask VIIRS SST - ACSPO and IDPS

  26. DAY STD DEV wrt. Reynolds L4 IDPS shows much larger STD Warm-Up Cool-Down Event • AVHRR & MODIS SSTs are consistent • ACSPO VIIRS is consistent with MODIS & AVHRR • VIIRS EDR shows much larger STD, out of spec • Large peaks due to suboptimal performance of Ice Mask VIIRS SST - ACSPO and IDPS

  27. DAY STD DEV wrt. In situ drifters • AVHRR & MODIS SSTs are consistent • ACSPO VIIRS is consistent with MODIS & AVHRR • VIIRS IDPS SST shows larger STD, out of spec • Large peaks due to suboptimal performance of Ice Mask VIIRS SST - ACSPO and IDPS

  28. ACSPO Plans • Optimize for maintenance, operations, reprocessing • Modularized, efficient code • More efficient RTM and I/O; Parallel processing • Handling various first-guess fields in CRTM • Reprocessing • Reprocess AVHRR GAC N15, 16, 17, 18,N19, Metop-A & B: 2004-pr (for NOAA Coral Reef Watch) • Work towards setting up VIIRS/MODIS reprocessing capability • Explore new Bands • Explore bands at 8,7 and 4 µm; 3.7 during the daytime • Explore aerosol RTM and physical correction • Use GOCART/NAAPS in conjunction with CRTM • Improve/Explore new Algorithms • Bias Correction; Cloud Masking; RTM based SSTs ACSPO

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