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AGU Ocean Sciences Conference 28 February 2014, Honolulu, HI. Towards Stable and Consistent Long-Term SST and Brightness Temperature Records from Multiple AVHRRs and QCed in situ Data Advanced Clear-Sky Processor for Ocean AVHRR Reanalysis (ACSPO AVHRR RAN) Sasha Ignatov , Xinjia Zhou,
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AGU Ocean Sciences Conference28 February 2014, Honolulu, HI Towards Stable and Consistent Long-Term SST and Brightness Temperature Records from Multiple AVHRRs and QCed in situ Data Advanced Clear-Sky Processor for Ocean AVHRR Reanalysis (ACSPO AVHRR RAN) Sasha Ignatov, Xinjia Zhou, Boris Petrenko, Xingming Liang, Prasanjit Dash NOAA STAR, CIRA, GST Inc ACSPO AVHRR Reanalysis
NOAA Coral Reef Watch Program: Mark Eakin (Lead), Jacqueline Rauenzahn POES-GOES Blended Team: Eileen Maturi (Lead), Andy Harris, Jon Mittaz NOAA Ocean Remote Sensing Program: Paul DiGiacomo (Program Manager); Marilyn Yuen-Murphy (DPM) ACSPO SST Team: John Sapper, Yury Kihai, John Stroup NOAA in situ SST Quality Monitor (iQuam): Feng Xu NOAA Community Radiative Transfer (CRTM) Team: Mark Liu, Yong Chen, Yong Han, Fuzhong Weng Acknowledgements ACSPO AVHRR Reanalysis
Motivation and Objective of ACSPO RAN ACSPO – Advanced Clear-Sky Processor for Oceans • NOAA polar SST retrieval system • Operational with AVHRR (GAC, FRAC) and SNPP VIIRS • Experimental with Terra and Aqua MODIS • Fast CRTM is used to simulate TOA BTs, for improved cloud mask, physical SST retrievals, sensors monitoring, and CRTM validation Why ACSPO AVHRR Reanalysis? • NOAA Coral Reef Watch (CRW) and POES-GOES Blended Teams are ACSPO users • Long-term time series of ACSPO SST will be blended with GOES SST, and “L4 climatology” generated for CRW anomaly analyses Current Status (ACSPO-RAN1) • GAC data of NOAA-15, -16, -17, -18, -19, Metop-A , -B have been processed from 2002-pr and analyzed using NOAA Cal/Val Tools • Preliminary analyses summarized in this presentation ACSPO AVHRR Reanalysis
NOAA SST/Radiance Monitoring System SQUAM - SST Quality Monitor www.star.nesdis.noaa.gov/sod/sst/squam/ • Monitor SST Products (L2/3/4) for Self- and Cross-Consistency • Validate against in situ SSTs (from iQuam) iQuam - In situ Quality Monitor www.star.nesdis.noaa.gov/sod/sst/iquam/ • QC in situ SSTs • Monitor on the Web • Distribute to Users(including SQUAM) MICROS - Monitoring IR Clear-sky Radiances over Oceans for SST www.star.nesdis.noaa.gov/sod/sst/micros/ • Monitor Clear-sky ocean radiances for Self- / Cross-Consistency • Validate against CRTM simulations ACSPO AVHRR Reanalysis
SST Monitoring in SQUAM www.star.nesdis.noaa.gov/sod/sst/squam/ Nighttime data are shown (see SQUAM for daytime data) Dash, et al: SST Quality Monitor. JTECH, 2010. ACSPO AVHRR Reanalysis
ACSPO-RAN Metop-A/GAC L2 – OSTIA L4 1 October 2013 • Deviation from Reference SST is flat & close to 0 • Residual Cloud/Aerosol leakages & limb cooling (likely due to SST algorithm) are seen as cold spots ACSPO AVHRR Reanalysis
ACSPO-RAN Metop-A/GAC L2 – OSTIA L4 1 October 2013 • Overall shape of the histogram is close to Gaussian as expected • Skewed negatively due to residual cloud/aerosol contamination ACSPO AVHRR Reanalysis
Validation vs. iQuam in situ SSTs www.star.nesdis.noaa.gov/sod/sst/iquam/ Xu, Ignatov: In situ SST Quality Monitor. JTECH, 2014. ACSPO AVHRR Reanalysis
http://www.star.nesdis.noaa.gov/sod/sst/iquam/ ACSPO AVHRR Reanalysis
ACSPO-RAN Metop-A/GAC L2 – in situ SST 1 October 2013 • Match-ups of AVHRR GAC with in situ data ~2,300 fewer than with L4 • VAL stats maybe geographically biased & not globally representative ACSPO AVHRR Reanalysis
ACSPO-RAN Metop-A/GAC L2 – in situ SST 1 October 2013 • Shape close to Gaussian • Long tail on the left is indicative of residual cloud ACSPO AVHRR Reanalysis
Validation in SQUAM against iQuam SSTs Nighttime data are shown (see SQUAM for daytime data) ACSPO AVHRR Reanalysis
NIGHTACSPO-RAN Robust STD wrt. In situ SST • Typically, robust STDs are within (0.40±0.05)K • Some platforms are closer to this “norm” (Metop-A, -B, N-17, -18) • Other platforms (N-15, and some periods of N-16 and -19) are more deviant ACSPO AVHRR Reanalysis
NIGHTACSPO-RAN Median Bias wrt. In situ SST • Daily VAL vs. in situ SST is noisy (small match-up sample, geographic biases) • Some platforms are more stable (Metop-A, -B, N-17, -19) • Other platforms (N-15, -16, -19) are less stable ACSPO AVHRR Reanalysis
Brightness Temperatures Monitoring in MICROS www.star.nesdis.noaa.gov/sod/sst/micros/ Nighttime data are shown (see MICROS for daytime data) Liang and Ignatov: Monitoring IR Clear-sky Radiances over Ocean for SST. JTECH, 2011 ACSPO AVHRR Reanalysis
NIGHTDouble Differences SST (Ref = N-17/Metop-A) • Shape of DDs similar to VAL vs. In situ • Most stable N-17 and Metop-A were used as references; N-19 relatively stable • Least stable are N-15, -16 (after mid-2006), -18 (especially after mid-2011) ACSPO AVHRR Reanalysis
NIGHTDouble Differences Brightness Temp @3.7µm • General shape of biases vs. Reynolds similar to vs. In situ • Most stable Metop-A and -B, and N-17 and -19 • Least stable are N-15, -16 after 2006), -18 (after 2011) ACSPO AVHRR Reanalysis
ACSPO-RAN1 Summary • ~12 years of ACSPO AVHRR RAN1 • N-15, -16, -17, -18, -19, Metop-A, -B processed • Matched up with iQuam in situ SSTs • Displayed in SQUAM (SSTs) and in MICROS (BTs) • Working with POES-GOES Blended Team to test Observations & Critical Need • N-17, -19, Metop-A, -B SSTs more stable; N-15, -16, -18 less stable • Instabilities in SSTs are strongly linked with instabilities in BTs • Fixing AVHRR BTs (FCDR) is needed for high-quality SST CDR ACSPO-RAN2 is underway • Extend ACSPO-RAN time series (initially, to 1992) • ACSPO: Improved 1st guess (ERA-Interim profiles, CMC SST); Cloud mask; SST algorithms; Handling pre-KLM AVHRRs • iQuam v2 (time series 1981-pr, ARGO floats, improved QC) • SQUAM: Improved handling outliers; Efficiency; Display ACSPO AVHRR Reanalysis