Satellite Intercalibration for Continuity and Consistency

1. Satellite Intercalibration for Continuity and Consistency Changyong Cao, CEOS/WGCV Chair, NOAA Stephen Ungar, former CEOS/WGCV Chair, NASA Pascal Lecomte, CEOS/WGCV Vice Chair, ESA/ESRIN Mitch Goldberg, GSICS chair, NOAA

2. Interoperability and Data Quality Assurance • The success of GEOSS will depend on data and information providers accepting and implementing a set of interoperability arrangements, including technical specifications for collecting, processing, storing, and disseminating shared data, metadata, and products. (-- from the GEOSS 10 yr. Implementation plan) • Two important aspects of interoperability: • Data sharing/data accessibility • Data quality assurance/data usability • Calibration/validation is essential for data quality assurance and data usability

3. Current CEOS/WGCV Tasks GEOSS 9 SBAs GEO Tasks Climate Actions CEOS Constellations Cal/val process SI traceable (C-7) Spectrally resolved absolute radiances (A-5) Best practices (DA-06-09, O-18) LSI Constellation (T-1)Product cal/val support (T-5, O-9, O-11, O-12, O-14, EC-06-02) Reprocessing (CL-06-01, O-17, O-14)FCDR/ECV from AVHRR class instruments (T-4) In situ networks (C-9)Cal/val sites Cal/val portal DEM Interoperability (DA-07-01) GEO DA-06-02 (WGCV&IEEE) Data Quality Assurance Strategy GSICS support (C-8)

4. Data consistency is prerequisite for Satellite Data Assimilation in NWP: no radiance bias is allowed. Any bias between satellite observations and model calculations must be resolved (sources of bias: satellite observations vs. models). Current common practice: assume biases are from satellite observations and fudge the biases in weather predictions Bias estimates should be based on rigorous calibration, not guess work. Calibration for Numerical Weather Prediction (NWP) Cost function for satellite radiance data assimilation in NWP • where • x is a vector including all possible atmospheric and surface parameters. • I is the radiance vector • B is the error covariance matrix of background • E is the observation error covariance matrix • F is the radiative transfer model error matrix • Courtesy of Weng & Liu

5. Calibration for Climate Change Detection • Data consistency and continuity is essential for the “daunting task” of global climate change detection • Calibration stability requirements for temperature is on the order of 0.1 K per decade; albedo on the order of 1% per decade • These requirements are challenging the current state-of-the art in cal/val, as well as instrumentation • Example: the conclusions about mid troposphere warming trend using MSU is affected by the calibration method used Calibration 1: 0.32 K per decade Calibration 2: 0.22 K per decade Calibration 3: 0.17 K per decade After C. Zou

6. Each satellite has its own specifications and a limited life time. The creation of longterm time series for climate trending involves stitching together data from a number of satellites. Prelaunch cross calibration is not feasible in most cases, and prelaunch SI traceability is often lost postlaunch Currently on-orbit SI standard is not readily available. Future calibration transfer from on-orbit SI standards to all satellites will still require intersatellite calibration Inter-satellite calibration is a viable strategy to ensure consistency for a constellation of international satellites Inter-satellite calibration as a strategy for data quality assurance

7. Strategies for Satellite Intercalibration • Onboard calibration • Pros: no latency issue, good relative accuracy and self-consistency • Cons: cost, longterm degradation, absolute accuracy • Calibration/Validation Sites • Pros: SI traceable ground measurements possible • Cons: may not be stable in the longterm, uncertainties in atmospheric profiles, point vs. area measurements. • Many sites available for different applications • Instrumented sites: e.g. MOBY for ocean color • Stable natural targets (Libyan Desert, Greenland, Antarctic, Amazon forest, clouds) • Radiometric/Spatial/Spectral calibrations • Most useful for mid to High resolution instruments

8. Moon as a common target Pros: moon reflectance stability is unparalleled, Cons: good only for solar reflective bands; moon irradiance is currently not SI traceable. Simultaneous Nadir Overpass (SNO) Pros: applicable to all radiometers (VIS/NIR, IR, MW) Cons: calibration is relative to each other On-orbit SI traceable measurements IR/VIS Benchmark Missions (discussed later) SI traceable Lunar irradiance for the Solar bands GPS/RO Airborne radiometer underflight Pros: SI traceable calibration Cons: short duration, and view angle/path/footprint differences Cal/Val Portal Strategies for Satellite Intercalibration

9. World-wide Cal/Val Sites for Monitoring various sensors Cross calibration Integrated science applications Prime Sites for data collection Site description Surface Measurements FTP access via Cal/Val portals Supports GEO Tasks CEOS Calibration-Validation Sites African Desert Sites ALOS Cal/Val sites Courtesy of USGS Landsat Super sites

10. AMSU-A Brightness Temperatures over Amazon Rain Forest, July 2005 NOAA-16 Data: = Ascending, oo = Descending NOAA-18 Data: ++ = Ascending, xx = Descending Courtesy of T. Mo

11. The lunar surface is considered photometrically stable to < 10-8 per year (USGS). Moon calibration has been studied extensively by NASA for most major research instruments. Current conclusion: moon is excellent for stability trending but not good yet for absolute calibration. The USGS ROLO model has been used extensively but it has a number of limitations. NOAA has tested operational Lunar calibration for GOES Same phase angle vs. a variety of phase angles appears to be a major factor affecting uncertainty Lunar Calibration

12. Using the Moon for MODIS cross calibration Courtesy of J. Xiong, NASA

13. LEO vs. LEO and GEO vs. LEO inter-calibration Simultaneous Nadir Overpass (SNO) Intercalibrate GOES and LEO • Satellite intercalibration is critical for: • GSICS • CEOS Constellations • Benchmark missions

14. Cross calibration between MODIS and AVHRR Consistency affecting NDVI, Aerosols, etc.

15. Using EOS/Hyperion SNO data to resolve spectral issues between MODIS and AVHRR • Analyze spectral response related biases between AVHRR and MODIS by using Hyperion data at the SNOs. • Joint effort involving NOAA, NASA, and USGS Hyperion spectra AVHRR MODIS ATSR AVHRR/MODIS 0.63 um Radiance ratio computed from Hyperion spectra 05/19/2007 23:47:00 80.5N,211.5E Time Diff=43 Sec SZA=61.8 Deg

16. MODIS/GOES inter-calibration compared with GOES Lunar calibration Degradation Rate for the Period: 4.4%/yr (MODIS) vs. 4.5%/yr (Moon) Courtesy of X. Wu

17. IASI and AIRS at the SNO • IASI and AIRS are atmospheric sounding instruments for weather applications (with good potential for climate). • IASI (8461 channels) and AIRS (2378 channels) provide spectrally resolved radiances in the 3.6-15.5 um spectral range. • Excellent agreement between them provides a quasi on-orbit standard Red= AIRS Black=IASI

18. LEO and GEO intercalibration- GOES Imager vs. IASI • GOES imager and IASI radiances comparison at nearly coincidental, and co-located ocean sites. • The IASI spectra are convolved with the GOES imager SRF to generate GOES imager measurements (bottom), compared with the original GOES observations (channel 4). • IASI has high spectral-resolution with better spectral and radiometric accuracy, and can be used as a reference for calibrating broadband GOES imagers.

19. GSICS • Global Space-based Inter-Calibration System (GSICS) • WMO Space Programme • GSICS Implementation Plan and Program formally endorsed at CGMS 34 (11/06) • Goal - Enhance calibration and validation of satellite observations and to intercalibrate critical components of global observing system

20. Bring together the meteorological agencies worldwide under interoperability for improved weather prediction and climate change detection Concrete steps to identify intersatellite biases and correct the biases in numerical weather predictions and climate trending. GSICS

21. Coordinated international intersatellite calibration program. Exchange of critical datasets for cal/val Best practices/requirements for monitoring observing system performance and for prelaunch characterization (with CEOS WGCV) Establish requirements for cal/val and advocate for benchmark systems (with CEOS WGCV) Quarterly reports of observing system performance, satellite intercalibration statistics and as warranted recommended solutions Improved sensor characterization and high quality radiances for NWP & CDRs GSICS Outcome

22. Climate Quality Calibration • 2006 Workshop report provides the roadmap to Achieving Satellite Instrument Calibration for Climate Change detection (USA) • Benchmark mission planning: • CLERREO (NASA) • TRUTHS (NPL) • SI traceable Lunar Irradiance (NIST) • Onboard benchmark sources using phase-change materials (Russia) Achieving Satellite Instrument Calibration for Climate Change

23. Cal/Val portal is the ESA response to the CEOS recommendation The CalVal Portal will support calibration by Supplying Satellite Data from multiple Sensors Supplying in-situ Measurements Supply Sensor Parameters Prepare input files for 6S given Sensor Parameters, Atmospheric Conditions, … Supply a platform to exchange results Give access to calibration tools (BEAM, 6S input file generation, 6S RTT) Web Portal Description of methodologies (IVOS for the 1st release) Description of instrument characteristics (SensorML) All GMES (Global Monitoring for Environment & Security) sensors will be supported (optical, radar, atm.Chem.) Access to EO data Designed to support all GMES sensors Version 1: MERIS FR and RR, AATSR, ALOS PRISM and AVNIR2 Version 2: additional Chris/Proba, Vegetation, … Access to in-situ data Local database Access to Nilu, Aeronet, external database (GECA) Access to tools CalVal Results User management, Forum, Help The Cal/Val Portal

25. Example use of the cal/val portal forCross calibrating ALOS and MERIS • MERIS: a reference! • Strong requirements for ocean colour • On board calibration • Clear sky conditions • Two types of sensors: • Spatial resolution=> homogeneous site • Spectral features • ALOS and MERIS extracted over the same stable sites available in the cal/val portal

26. GEO – CEOS Workshop on Quality Assurance of Calibration and Validation Processes Geneva, 2 – 4 October 2007 • Establish key elements needed to develop and implement a "data quality strategy" as required by GEO task DA-06-02 • Best practises in Cal / Val Processes. • Harmonisation and Standardisation of Quality Control and Calibration / Validation Processes. • The role of CEOS in the Certification of those processes. • Implementation Strategy.

27. Data quality assurance is essential for the success of GEOSS CEOS/WGCV and IEEE are leading this important GEO task (DA-06-02) Intersatellite calibration is an important part of data quality assurance Many techniques for intersatellite calibration have been developed, including using common cal/val sites, moon, SNOs, and absolute measurements All info will be included in the Cal/val portal Summary

28. Backup slides

29. WGCV CHAIR (NOAA)VICE CHAIR (ESA) SAR (CSA) IVOS (NPL) MS (ESA) TM (UCL) ACSG (GSFC) LPV (CNES) CEOS WGCV Subgroups Atmospheric Composition (ACSG) Chair Dr. B. Bojkov, GSFC Vice Chair Dr. J-C. Lambert, IASB/BIRA Infrared Visible Optical Sensors (IVOS) Chair Dr. N. Fox, NPL Land Product Validation (LPV) Chair Dr. F. Baret, CNES Vice Chair Dr. S. Garrigues Microwave Sensors Chair C. Buck,ESA Synthetic Aperture Radar (SAR) ChairDr. S. Srivastava, CSA Terrain Mapping (TM) Chair Prof. J. Peter Muller, UCL Future Chair Dr. Veljko Jovanovic