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M onitoring of I R C lear-sky R adiances over O ceans for S ST

Xingming Liang 1,2 , Sasha Ignatov 1 and Korak Saha 1,2 1 NOAA/NESDIS/STAR 2 CSU/CIRA. M onitoring of I R C lear-sky R adiances over O ceans for S ST. http://www.star.nesdis.noaa.gov/sod/sst/micros/. CIRA Research and Service Initiative Award , 21 November 2011. Slide 1 of 37.

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M onitoring of I R C lear-sky R adiances over O ceans for S ST

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  1. Xingming Liang1,2, Sasha Ignatov1 and Korak Saha1,2 1NOAA/NESDIS/STAR 2CSU/CIRA Monitoring of IR Clear-sky Radiances over Oceans for SST http://www.star.nesdis.noaa.gov/sod/sst/micros/ CIRA Research and Service Initiative Award, 21 November 2011 Slide 1 of 37

  2. Acknowledgments Advanced Clear-Sky Processor for Oceans (ACSPO): Sensor Radiances over Oceans with Clear-Sky Mask and Quality Control J. Sapper, Y. Kihai, B. Petrenko, J. Stroup, P.Dash, and F. xu – SST Team Community Radiative Transfer Model (CRTM) F. Weng, Y. Han, Q. Liu, P. Van Delst, Y. Chen, D. Groff: CRTM N. Nalli: Surface emissivity model Sensor calibration & cross-platform consistency C. Cao, L. Wang, F. Wu, F. Weng: GSICS (Global Satellite-based Inter-Calibration System) CIRA Research and Service Initiative Award, 21 November 2011 Slide 2 of 37

  3. Outline MICROS objectives Summary of MICROS Using MICROS Conclusion and future plans CIRA Research and Service Initiative Award, 21 November 2011 Slide 3 of 37

  4. MICROS Objectives Monitor clear-sky sensor radiances (BTs) over global ocean (“OBS”) in NRT for stability and cross-platform consistency, against CRTM with first-guess input fields (“Model”) Fully understand & minimize M-O biases in BT&SST (minimize need for ‘bias correction’) Diagnose SST products Validate CRTM performance Evaluate sensor radiances for Stability Cross-platform consistency CIRA Research and Service Initiative Award, 21 November 2011 Slide 4 of 37

  5. Summary of MICROS • Sensors monitored in MICROS • System set-up and processing time • MICROS functionalities CIRA Research and Service Initiative Award, 21 November 2011 Slide 5 of 37

  6. Platforms/Sensorsmonitored in MICROS Routinely processing AVHRR Jul’2008-on Metop-A (GAC and FRAC - Good) NOAA19 (Good) NOAA18 (Good) NOAA17 (stopped processing 2/10; sensor issues) NOAA16 (out of family) Under testing / In pipeline VIIRS Proxy MODIS (Terra & Aqua) MSG/SEVIRI CIRA Research and Service Initiative Award, 21 November 2011 Slide 6 of 37

  7. System Set-Up & Processing Time Automated scripted/cronned ACSPO to identify clear-sky (F95) MICROS to generate stats (IDL) Posted to web (Html/JS/JQuery/JQplot) • Processing Time: (ACSPO/MICROS) • Start at 2am on Day(N); Process 24hrs of Day (N-2) • 5 GAC AVHRRs: 3/0.5hrs; 1 FRAC AVHRR (Metop): 4/1hrs • MODIS (Terra & Aqua): 4/1 hrs each • VIIRS proxy: 10/3 hrs -> under optimization CIRA Research and Service Initiative Award, 21 November 2011 Slide 7 of 37

  8. MICROS Functionalities • MICROS Highlights • 4 ways to present M-O biases • Maps • Histograms • Dependencies • Time Series • (including Double Differences) CIRA Research and Service Initiative Award, 21 November 2011 Slide 8 of 37

  9. MICROS Highlights • Statistical analyses performed in global clear-sky ocean domain • End-to-end system MICROS • Web-based NRT tool • Both conventional & robust statistics used • Analyses separated by Day/Night • Only Night data used for SDR analyses • Presently, daytime data not used due to sub-optimal treatment of solar reflectance & diurnal cycle • Double-differences used to evaluate sensor radiances for cross-platform consistency CIRA Research and Service Initiative Award, 21 November 2011 Slide 9 of 37

  10. Ways to present M-O Bias Maps Four ways to present M-O Biases in MICROS Histograms Time series Dependencies CIRA Research and Service Initiative Award, 21 November 2011 Slide 10 of 37

  11. Maps The M-O bias is close to zero and uniformly distributed CIRA Research and Service Initiative Award, 21 November 2011 Slide 11 of 37

  12. Histograms Distribution: Near-Gaussian # clear-sky pixels ~3Mln/night, over global oceans (GAC) Warm M-O biases: Expected (explained on slide 18) Cross-platform biases are within ~0.2 K Overpass times range from 9:30pm-5am – diurnal effects Errors in sensor SRFs (CRTM coefficients) & Calibration cause cross-platform biases CIRA Research and Service Initiative Award, 21 November 2011 Slide 12 of 37

  13. Dependencies VZA dependencies for M-O median bias: Close to zero • Standard Deviations and frequency plots supplement mean M-O bias analyses CIRA Research and Service Initiative Award, 21 November 2011 Slide 13 of 37

  14. MICROS Applications • Validate and improve ACSPO • Monitor sensor radiances • Validate and improve CRTM CIRA Research and Service Initiative Award, 21 November 2011 Slide 14 of 37

  15. ACSPO Validation and Improvement CIRA Research and Service Initiative Award, 21 November 2011 Slide 15 of 37

  16. ACSPO • ACSPO: Advanced Clear-Sky Processor over Oceans • Newly developed at NESDIS to replace heritage SST system • ACSPO generates: • Clear-Sky Ocean Radiances, SST, and Aerosol products • CRTM (GFS and FG-SST as input) used to simulate sensor BTs • Model BTs used to improve cloud mask and physical SST, and monitor sensor performance • Monitoring M-O bias in MICROS to improve ACSPO performance • Minimization of M-O bias • Check for stability and cross-platform consistency SST team has released near 10 ACSPO versions since early 2008. All versions were tested on MICROS before they were released. CIRA Research and Service Initiative Award, 21 November 2011 Slide 16 of 37

  17. ACSPO version update CRTM r577 CRTM r577 From MICROS paper: http://www.star.nesdis.noaa.gov/sod/sst/micros_v5/pdf/JTECH-D-10-05023.pdf CRTM V1.1, Daily V.1 SST CRTM V1.1, Daily V.1 SST • Using daily instead of weekly Reynolds SST in ACSPO v1.02 significantly improved global STDs • Using CRTM v1.1 instead of rev577 improved the VZA dependencies, and brought NOAA-16 Ch3B back in family (Liu et al., 2009) • ACSPO cloud mask is robust with respect to CRTM and its input • The updated cloud mask in ACSPO v1.10 significantly improved VZA dependencies CIRA Research and Service Initiative Award, 21 November 2011 Slide 17 of 37

  18. Global distribution ACSPO V1.0: Weekly SST ACSPO V1.02: Daily SST Using daily instead of weekly Reynolds SST in ACSPO v1.02 resulted in a more uniform distribution of M-O bias CIRA Research and Service Initiative Award, 21 November 2011 Slide 18 of 37

  19. Time Series Stability of M-O bias in Ch3B M-O Biases in Ch3B BT are consistent with SST oscillations ACSPO version V1.30 V1.40 V1.10 V1.00 V1.02 • Warm M-O biases is a combined effect of: Missing aerosols; Using bulk SST (instead of skin); Using daily mean Reynolds SST (to represent nighttime SST); Residual cloud. • Temporary variability : Due to unstable Reynolds SST input to CRTM. (see Slide 30) • N16: Out of family/Unstable (CAL problems). • N17: Scan motor spiked in Feb’2010. SST Biases (Regression-Reynolds) CIRA Research and Service Initiative Award, 21 November 2011 Slide 19 of 37

  20. Ambient clear-sky dependence Exponential Fit: • NCSOP -> ∞, M-O -> A • NCSOP -> 0, M-O -> A+B • B: Amplitude of M-O bias • C: Drop-off rate • M-O bias changes significantly from “cloudy” to “confident clear-sky” • This analysis may be helpful to guide improvement to ACSPO CM • Including clear-sky dependencies in MICROS is underway From Liang et. al, 2009: http://www.star.nesdis.noaa.gov/sod/sst/micros_v5/pdf/JGR_2008_CRTM_ACSPO.pdf CIRA Research and Service Initiative Award, 21 November 2011 Slide 20 of 37

  21. Aerosol Effects on BTs and SST • Aerosol Quality Monitor (AQUAM) is being set up to prepare for adding aerosol in CRTM • GOCART and NAAPS initially selected as two prospective inputs into CRTM • Initially, use solar reflectance bands to evaluate CRTM/GOCART&NAAPS • First-guess reflectances will improve ACSPO clear-sky mask • Subsequently, extend aerosol analyses into thermal IR bands • M-O bias in thermal bands will become closer to zero & STD reduced CIRA Research and Service Initiative Award, 21 November 2011 Slide 21 of 37

  22. Monitoring Sensor Radiances CIRA Research and Service Initiative Award, 21 November 2011 Slide 22 of 37

  23. Time series Stability of M-O bias in Ch4&5 M-O Biases in Ch4 BT biases show high degree of stability & cross-platform consistency Suggest that CAL and SRF are fairly stable in time Similar to Ch3B, warm M-O biases seen in Ch4 & Ch5 “Unphysical” changes in time (cf. Ch3B in slide 18) Biases are more stable in Ch4 & 5 (atmosphere more opaque) M-O Biases in Ch5 CIRA Research and Service Initiative Award, 21 November 2011 Slide 23 of 37

  24. MODIS and VIIRS • ~5 months of MODIS & VIIRS proxy data are available in MICROS5 • Terra-Aqua consistency is ~ 0.3K at 3.7 µm band • VIIRS “out-of-family” behavior uncovered using MICROS analyses • Due to inaccurate radiance-to-BT conversion, resolved in ACSPO MICROS Version 5: • In preparation for launch of NPP/VIIRS • Added MODIS (Terra & Aqua); checked for consistency with AVHRR • Added NPP/VIIRS proxy (End-to-end test) • Added Interactive plots to provide flexible overlays for multiple platforms CIRA Research and Service Initiative Award, 21 November 2011 Slide 24 of 37

  25. Double Differences (DD)Cross-platform consistency in MICROS • Day-to-day noise and spurious variability hinder accurate quantitative cross-platform bias • Double-differencing (DD) technique employed to extract the “cross-platform bias” signal from “noise” • Metop-A used as a Reference Satellite • Stable; Overpass time close to N17/Terra • CRTM (Reynolds SST) is used as a ‘Transfer Standard’. • DDs cancel out/minimize effect of systematic errors & instabilities in BTs and SSTs arising from e.g.: • Errors/Instabilities in reference SST & GFS; • Missing aerosol; • Possible systemic biases in CRTM; • Updates to ACSPO algorithm. CIRA Research and Service Initiative Award, 21 November 2011 Slide 25 of 37

  26. DDs (Ref = Metop-A)Cross-platform consistency in Ch3B Double Differences (DDs) in Ch3B V1.30 V1.40 V1.02 V1.10 V1.00 • DDs cancel out most errors/noise in M-O biases • Relative to Metop-A , biases are • N17: +0.01 ± 0.02 K (stopped working Feb’10) • N18: +0.04 ± 0.05 K • N19: -0.06 ± 0.02 K • N16: unstable CIRA Research and Service Initiative Award, 21 November 2011 Slide 26 of 37

  27. DDs (Reference = Metop-A)Cross-platform consistency in Ch4&5 Double Differences in Ch4 • N16: Unstable in all 3 bands • N17: biased 0.05K high in Ch4; -0.03 K low in Ch5 • N18: biased -0.02K low in Ch4; +0.06K high in Ch5 • N19: biased -0.07K low in Ch4; -0.09K low in Ch5 Double Differences in Ch5 • Cross-platform biases are due to • CAL errors • SRFs deviation from those used in CRTM • Local time differences (diurnal cycle in SST) • Work is underway to attribute the causes and reconcile platforms CIRA Research and Service Initiative Award, 21 November 2011 Slide 27 of 37

  28. Simultaneous Nadir Overpasses (SNO) Cross-platform consistency in GSICS: 1 of 2 • SNO matches two satellites in space and time at nadir • Objectives • Eliminate uncertainties associated with • Atmospheric path • View geometry • Time difference • And estimate cross sensor inconsistency SNO in Ch3 (NOAA16~ NOAA17) SNO in Ch4 (NOAA16~ NOAA17) From SNO web: www.star.nesdis.noaa.gov/smcd/spb/calibration/sno/ CIRA Research and Service Initiative Award, 21 November 2011 Slide 28 of 37

  29. Hyper-Spectral (HS) DDs Cross-platform consistency in GSICS: 2 of 2 • HS DD use GOES as the transfer standard to match up each pair of satellites in space and time at nadir (Wang and Cao, 2008; Hewison and Konig, 2008) (from GSICS Quarterly v2. 2008) CIRA Research and Service Initiative Award, 21 November 2011 Slide 29 of 37

  30. MICROS vs. SNO & HS DD CIRA Research and Service Initiative Award, 21 November 2011 Slide 30 of 37

  31. Validation and improvement of CRTM and its input CIRA Research and Service Initiative Award, 21 November 2011 Slide 31 of 37

  32. The effect of input SST Reynolds - OSTIA Large spurious variations disappear when OSTIA is used instead of Reynolds Smaller RSD indicate that OSTIA captures spatial SST variability better than Reynolds DDs slight improved (minimally sensitive to the input SST) From MICROS paper: http://www.star.nesdis.noaa.gov/sod/sst/micros_v5/pdf/JTECH-D-10-05023.pdf CIRA Research and Service Initiative Award, 21 November 2011 Slide 32 of 37

  33. CRTM daytime performance CRTM V1.1 • Quasi-Lambertian surfacemodel used in CRTM v1 resulted in unrealistic cold M-O bias ~-20 K in sun glint areas and a smaller warm bias ~+5 K outside glint in AVHRR Ch3B band at 3.7um • Using specular reflectance model dramatically improves the daytime M-O biases (Liang, et. al, 2010) • Based on MICROS analyses, CRTM v2 employs improved solar reflectance model • Cox-Munk empirical will be fined-tuned to minimize the remaining M-O bias CRTM V2.0 CIRA Research and Service Initiative Award, 21 November 2011 Slide 33 of 37

  34. Out of band issue in NOAA16 Ch3B CRTM V1.1 out of band leakage CRTM r577 From Liu et. al, 2009.: http://www.star.nesdis.noaa.gov/sod/sst/micros_v5/pdf/JGR_2008_CRTM_ACSPO.pdf • NOAA16 Ch3B is 0.3 K out of family when using CRTM r577. • This anomaly is due to the combination effect: • Out of band leakage in Ch3B; • Absence data above 10hPa in GFS; • Linear assuming for above 10hPa in CRTM r577. • The anomaly fixed in CRTM v1.1. CIRA Research and Service Initiative Award, 21 November 2011 Slide 34 of 37

  35. Conclusion • MICROS used to monitor M-O bias • End-to-end system • NRT web-based tool • Functional with5 AVHRR, 2 MODIS, and VIIRS/NPP proxy • Diagnostics of SST products (ACSPO) • All ACSPO versions tested in MICROS before official release • Ambient clear-sky analysis used to guide improvement to cloud mask • Monitoring of sensor stability and cross-platform consi. • BT and SST biases stable in time • N16 out-of-family & unstable • DDs cancel out most errors/noise • Cross-platform DDs ~10-2K, due to errors in CAL and/or SRFs (or CRTM coefficients) • Validation and improvement to CRTM and its input • Variability in M-O biases due to unstable Reynolds SST • Specular reflectance model significantly improve daytime performances. CIRA Research and Service Initiative Award, 21 November 2011 Slide 35 of 37

  36. Future Plans • Continue monitoring AVHRRs, MODIS, VIIRS • Replace VIIRS Proxy with real VIIRS • Evaluate sensors for stability and cross-consistency, in NRT • Improve accuracy of MICROS DDs • Use more accurate first guess fields (SST and GFS) • Improve ACSPO cloud mask • Improve CRTM accuracy (especially daytime) • Fully understand and minimize cross-platform biases • Add aerosol in CRTM • Continue exploring daytime M-O performance • Model diurnal variation (DV) in first-guess SST input to CRTM • Improve sensor radiances (Calibration, SRF) • Add MSG/SEVIRI • Add Metop-B AVHRR once becomes available (Apr 2012) CIRA Research and Service Initiative Award, 21 November 2011 Slide 36 of 37

  37. Thank you! CIRA Research and Service Initiative Award, 21 November 2011 Slide 37 of 37

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