Using Double Differences in MICROS for Cross-Sensor Consistency Checks http://www.star.nesdis.noaa.gov/sod/sst/micros/ Xingming Liang1,2, Sasha Ignatov1 and Korak Saha1,2 1NOAA/NESDIS/STAR 2CSU/CIRA GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 1 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Acknowledgments Advanced Clear-Sky Processor for Oceans (ACSPO; NESDIS Sea Surface Temperature System): Sensor Radiances over Oceans with Clear-Sky Mask and QC J. Sapper, Y. Kihai, B. Petrenko, J. Stroup, P. Dash, F. Xu, M. Bouali – NESDIS SST Team Sensor Characterization & Cross-platform Consistency, including Double Differences (DD) F. Wu, F. Yu, C. Cao, L. Wang, F. Weng, M. Goldberg, T. Hewison, J. Xiong, X. Hu, T. Chang – GSICS Community Radiative Transfer Model (CRTM) F. Weng, Y. Han, Q. Liu, P. Van Delst, Y. Chen, D. Groff – CRTM N. Nalli - Surface emissivity model GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 2 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Outline MICROS overview MICROS Double-Differences (DD) Relative Merits: SNO, Hyper-Spectral DDs Conclusion Future plans GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 3 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 MICROS Overview Objectives Sensors monitored System set-up & Processing time MICROS Hightlights Ways to present M-O bias GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 4 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 MICROS Objectives Monitor clear-sky sensor radiances (BTs) over global ocean in NRT (“OBS”) , against CRTM with first-guess input fields (“Model”) Understand & minimize M-O biases in BT & SST minimize need for empirical “bias correction” Evaluate sensor radiances for stability Check for cross-platform consistency GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 5 of 27

Platforms/Sensors monitored in MICROS Routinely processing 5 AVHRRs 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 NPP/VIIRS Terra/MODIS Aqua/MODIS MSG/SEVIRI GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 6 of 27

System Set-Up & Processing Time Fully automated Scripted/Cronned Back-up processing ACSPO: Identify clear-sky pixels (Fortran 95) MICROS: Generate stats (IDL) Post to web: Html/JS/JQuery/JQplot Processing Time (ACSPO/MICROS): Process 24hrs of Day (N-2) 5 GAC AVHRRs (NOAA16-19, MetopA) 1 FRAC AVHRR (Metop-A) MODIS/Terra & Aqua VIIRS/NPP ACSPO/MICROS 3/0.5 4/1 10/3 (under opt.) GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 7 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Model - Obs Advanced Clear-Sky Processor for Oceans (ACSPO) “M” = MODEL Clear-Sky BT (currently, in SST bands only) Calculated using CRTM, with first guess SST (daily 0.25º Reynolds) and upper air fields (NCEP GFS 6hr 1º) as input Fast CRTM allows for real-time processing “O”= OBS: Clear-Sky Ocean Sensor BTs Clear-Sky Ocean pixels identified using ACSPO Cloud Mask and QC Monitoring IR Clear-sky Radiances over Oceans for SST Calculates M-O bias & Runs global daily statistics on it Processing fully automated, performed in NRT Also, Double-Differences calculated w.r.t. a Reference sensor Graphic summaries reported on the web http://www.star.nesdis.noaa.gov/sod/sst/micros/ GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 8 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 MICROS Highlights End-to-end system Web-Based Near-Real Time MICROS Both conventional & robust statistics used Statistical analyses performed in global clear-sky ocean domain Analyses stratified by Day/Night Only Night data used for sensor 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 GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 9 of 27

Ways to present M-O Bias Maps Four ways to present M-O Biases in MICROS Histograms Time series Dependencies GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 10 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Maps The M-O biases: Close to zero; Uniformly distributed GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 11 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Dependencies View Angle dependencies of M-O bias: Close to zero GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 13 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Histograms Near-Gaussian # clear-sky oceans pixels ~3Million/night (global GAC) M-O bias is close to zero In fact, it’s slightly warm: Expected (Discussed next slide) Cross-platform biases close to zero (~0.2 K) Overpass times from 9:30pm-5am (Diurnal effects) Errors in sensor SRFs (CRTM coefficients) & CAL GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 12 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Time Series for GAC M-O bias in Ch3B M-O Biases in Ch3B BT are in sync with SST oscillations ACSPO version V1.30 V1.40 V1.10 V1.00 V1.02 Warm M-O biases result from: (1) Missing aerosols; (2) Using bulk SST (instead of skin); (3) Using daily mean Reynolds SST (to represent nighttime SST); (4) Residual cloud Temporal variability: Due to unstable Reynolds SST (input into CRTM) N16: Out of family/Unstable (CAL problems) N17: Scan motor spiked in Feb’2010 SST Biases (Regression-Reynolds) GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 14 of 27

Double Differences (DD) Cross-platform consistency in MICROS Day-to-day noise and spurious variability hinder accurate measurement of cross-platform bias Double-differences (DD) employed to differentiate 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 GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 15 of 27

DDs (Ref = Metop-A) Cross-platform consistency in IR37 Double Differences (DDs) in IR37 NOAA16 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 N16: unstable N17: +0.01 ± 0.02 K (scan motor failed Feb’10) N18: +0.04 ± 0.05 K (not very stable) N19: -0.06 ± 0.02 K GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 16 of 27

DDs in other bands Cross-platform consistency in IR11&12 Double Differences in IR11 N16: Unstable in all 3 bands N17: biased +0.05K high in IR11; -0.03 K low in IR12 N18: biased -0.02K low in IR11; +0.06K high in IR12 N19: biased -0.07K low in IR11; -0.09K low in IR12 N18: Similar pattern in IR11 and IR12 with IR37. Double Differences in IR12 Cross-platform biases are due to CAL errors SRFs deviation from those used in CRTM Local time differences (diurnal cycle in SST/GFS) Work is underway to attribute the causes & reconcile platforms GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 17 of 27

GSICS Inter-Calibration Methodologies Simultaneous Nadir Overpasses Hyper-Spectral Double-Differences (integrate HS radiances with wide- band spectral response) MICROS Double Differences (integrate RTM simulations with wide-band spectral response) – Fit? GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 18 of 27

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/ GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 19 of 27

Cross-platform consistency in GSICS: 2 of 2 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) GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 20 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 MICROS vs SNO & HS DDs MICROS DDs SNO Hyper-Spectral (HS) DDs Real-time Online NRT Jul’2008-present 2002-08 No Domain Global ocean Clear-Sky only Full sensor swath Polar areas Ice/Ocean/Land All-Sky Nadir only Global match-ups Nadir Only NOBS ~3Mln/Day (AVHRR GAC) Several match-ups/Day Sensors-specific QC ACSPO Clear-Sky Mask No QC Data Distribution Gaussian Asymmetric Transfer Standard CRTM; No match-up in space/time required Direct Comparisons; Match-up in space/time required GOES; Match-up in space/time required Effect of Solar reflectance Currently, only used during nighttime (no daytime) Renders data in mid-IR (Ch3B) unusable Shortwave bands not always covered by HS measurements Spectral Response Considered Not considered Cross-platform bias precision ~0.01 K ~1K ~0.1K MICROS supplements GSICS Inter-calibration Techniques GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 21 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Conclusions M-O biases & Double-Differences (DD) in MICROS Functional with 5 AVHRR; Terra/Aqua MODIS & NPP/VIIRS being tested DDs Cancel out most errors in M-O biases AVHRR Cross-sensor biases (DDs): ~10-2K to ~10-1K Cross-sensor biases (DDs) are due to errors in Sensor Calibration Sensor Spectral Response Functions CRTM Coefficients Need to unscramble cross-platform biases seen in MICROS DDs With GSICS Colleagues (T. Chang, F. Wu, F. Yu) – Sensor Cal and SRFs CRTM Colleagues – Verify CRTM coefficients MICROS DDs supplement GSICS Hyper-Spectral DDs and SNO Global clear-sky night ocean domain Difference between M and O: Narrow Gaussian distribution, centered at ~0 Large data volume (GAC: 3M pixels / 24hr): Instrumental to beat down noise No collocation with other sensors required GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 22 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Future Plans 1 Extend MICROS to more polar sensors and GOES Work with GSICS Colleagues to Reconcile cross-platform differences Evaluate MICROS DDs for consistency w/Hyper-Spectral DDs & SNO AVHRRs Continue monitoring all sensors in orbit; Add Metop-B (May 2012) Extend back in time to include all AVHRR (1978-pr) Add Terra/Aqua MODIS & NPP VIIRS in MICROS Fine tune CRTM and ACSPO Cloud Mask Evaluate for stability & cross-consistency with AVHRRs Reprocess MODIS historical data back to 2000. (where is L1B data ?) Add new sensors GEO: MSG SEVIRI in progress; and GOES-R/ABI (~2015) ATSR (NRA joint proposal w/JPL/S. Hook and U. Leicester/G. Corlett) Discussions underway w/CMA on FY1/VIRR & FY3/MERIS GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 23 of 27

PROXY MODIS & VIIRS streams MICROS ver 5 PROXY MODIS & VIIRS streams MODIS & VIIRS proxy have been trended in MICROS5 since mid-2011 Quantitative analyses pending fine-tuning ACSPO Processor Will report MODIS-VIIRS- AVHRR consistency in 2013 GSICS Meeting MICROS Version 5: In preparation for launch of NPP/VIIRS in Oct’2011, MICROS5 was set up Added proxy NPP VIIRS & Proxy Terra/Aqua MODIS Added interactive plots for flexible display of multiple platforms GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 24 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Future Plans 2 Extend MICROS to Include Reflectance Bands Aerosol Quality Monitor (AQUAM) was set up to prepare for adding aerosol in CRTM GOCART and NAAPS identified as sources of 3D aerosol fields inputs into CRTM Initially, use solar reflectance bands to evaluate DDs and CRTM/GOCART&NAAPS First-guess reflectances will improve ACSPO clear-sky mask Subsequently, extend aerosol analyses into thermal IR bands M-O bias in emission bands will become closer to zero & STD reduced DDs in emission bands are expected to be improved GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 25 of 27

Improve Accuracy of MICROS DDs Future Plans 3 Improve Accuracy of MICROS DDs DDs largely cancel out uncertain/unknown factors However, they can be further improved by Using more accurate first guess fields (SST, GFS) Using Improved CRTM (especially daytime) Modeling diurnal variation in first-guess SST So far, checked sensitivity of DDs to first-guess SST (major factor in SST bands) Overall, very small  MICROS DDs are reliable However, temporal noise may be reduced and DDs estimated more accurately From MICROS paper: http://www.star.nesdis.noaa.gov/sod/sst/micros_v5/pdf/JTECH-D-10-05023.pdf GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 26 of 27

GSICS Annual Meeting, Beijing, 5-8 March 2012 Thank you! GSICS Annual Meeting, Beijing, 5-8 March 2012 Slide 27 of 27