Simultaneous Nadir Overpass Method for Inter-satellite Calibration of Radiometers

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• Simultaneous Nadir Overpass Method for
  Inter-satellite Calibration of Radiometers
• Changyong Cao
• NOAA/NESDIS/Center for Satellite Applications
  Research (STAR)
• Presented at the ASIC3 Workshop, May 16-18, 2006

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Global Temperature Trend from MSU - a typical
problem in time series analysis
Different merging procedure for removing
intersatellite biases can result in different
climate trends
5-day global ocean-averaged time series from NOAA
10 to 14 MSU L1B data with NESDIS operational
calibration
Courtesy of C. Zou
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Analyzing Intersatellite biases a critical step
in constructing time series for climate studies

• Bias factors ß f(t, n, s, e, l, v, o)
  eq. 1
• Where
• t observation time difference (including
  diurnal cycle effect)
• n off-nadir effects (both instrument and view
  path)
• s spatial differences, including geolocation,
  coregistration, alignment, scene uniformity,
  sensor modulation transfer functions (MTF) (and
  side lobe effects for microwave),
• e bias in the calibration system
  (blackbody/diffuser, PRT, mirror/reflector) and
  algorithm
• l nonlinearity
• v spectral response function (SRF) difference
  and uncertainty (frequency in microwave)
• o other factors, including human error
  calibration anomaly

The longterm stability of each factor must be
examined in climate studies
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The Simultaneous Nadir Overpass (SNO) method

• SNO every pair of POES satellites
• with different altitudes pass their orbital
  intersections within a few seconds regularly in
  the polar regions (predictable w/ SGP4)
• Precise coincidental pixel-by-pixel match-up data
  from radiometer pairs provide reliable long-term
  monitoring of instrument performance
• The SNO method has been used for operational
  on-orbit longterm monitoring of imagers and
  sounders (AVHRR, HIRS, AMSU) and for
  retrospective intersatellite calibration from
  1980 to 2003 to support climate studies
• The method is also expanded for SSM/I with
  Simultaneous Conical Overpasses (SCO)

SNOs occur regularly in the /- 70 to 80 latitude
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The SNO/SCO Procedure

• Predict SNOs between each pairs of satellites
  using the orbital perturbation model SGP4 and
  appropriate two-line-elements (TLEs) (Cao, et
  al., 2004)
• Download Level 1B data that contain SNO
  observations
• Criteria 1). At the SNO, the distance between
  the nadir pixels from the two satellites should
  be less than 1 pixel. 2). time difference
  between the nadir pixels from the two satellites
  should be less than 30 seconds.
• SNO data between satellites are matched
  pixel-by-pixel based on their latitude/longitude.
• Optimize match through radiance correlation to
  reduce the effect of navigation errors
• Statistics of the biases in radiance and
  brightness temperature/reflectance between two
  satellites are calculated for pixels within a
  small nadir window.
• The SNO time series of the biases and RMS are
  plotted.

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Assumption for Microwave instrumentsprecisely
matched frequency that never changes
NOAA16 vs. -17/AMSU/A Channel 5 (Mid-troposphere)
SNO Microwave example
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SNO microwave application Does NOAA18/AMSU have
a bias anomaly ?
AQUA-NOAA18
NOAA16-NOAA18
(AQUA-N18)- (AQUA-N16)
AQUA-NOAA16
SNO Microwave example
Intersatellite biases for AMSU on NOAA16, NOAA18,
and AQUA at SNOs Jul.-Dec., 2005
Courtesy of R. Iacovazzi
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SNO Time Series for Microwave Sounding Unit MSU
CH3
N10-N9
N12-N11
N9-N6
N7-N6
Instrument noise spec
N14-N12
N11-N10
N8-N7
SNO Microwave example
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SNO Derived Climate Trend from MSU
Trends for linear calibration algorithm 0.32 K
Decade-1
Trends for NESDIS operational calibration
algorithm 0.22 K Decade-1 (Vinnikov and Grody,
2003)
Trends for nonlinear calibration algorithm using
SNO cross calibration 0.17 K Decade-1
SNO Microwave example
Courtesy of C. Zou
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AVHRR VIS/NIR intersatellite bias at SNOs for
channel 1 (0.68 um)
N9-N8
N14-N12
N17-N16
N16-N15
N10-N9
N12-N11
N8-N7
N11-N10
N15-N14
SNO VIS/NIR example
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VIS/NIR Channles AVHRR/MODIS (0.68um) assumptions
linear, short term invariable gain
AVHRR/N18 MODIS/Aqua Sample
area

• Reflectance Min Max
  Mean Stdev
• Band 1 AVHRR 0.4301 0.4728 0.4523
  0.008894
• Band 1 MODIS 0.4800 0.5401
  0.5113 0.012135

For this area with 205 samples, the difference
between MODIS and AVHRR is about 13, at 99
confidence level with uncertainty /-0.4.
Spectral differences is not the main contributor
to the this discrepancy, according to radiative
transfer calculations. Good example of
calibration traceability issue.
SNO VIS/NIR example
Lat79.82, SZA82.339996, cos(sza)0.13,
TimeDiff 26 sec, Uncertainty due to SZA diff
0.1,
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Discrepancies between MODIS and AVHRR SNO time
series for channel 1 (0.68um) (N16 vs. Aqua)
North pole South pole
Cos(sza)
SNO VIS/NIR example
Different on-orbit calibration traceability
causes discrepancies between MODIS and AVHRR.
Seasonal variation may be related to SRF
difference, polarization, BRDF effects
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SNO application operational longterm monitoring
of all POES radiometers
AVHRR 0.86um channel (with vicarious calibration)
N-16 coeff. update
N-17 coeff. update
Solar zenith angle problem
SNO VIS/NIR example
Biases can be very small for sensors with same
SRF, despite water vapor impact
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Further Reduction in Uncertainties

• SRF differences and uncertainties
• BRDF of snow ice (especially at high SZA)
• Polarization differences at high SZA
• MTF difference (impact of shadow)
• AVHRR calibration seasonal uncertainties?
• Combination of the above
• Hyperspectral observations such as AVIRIS and
  Hyperion are helpful

Antarctic snow
Sea ice
Desert
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AVHRR CH4 (11.5um) SNO Time SeriesNOAA-9 to
NOAA-17, 1987 to 2003
Infrared
Nonlinearity error
Brightness temperature difference (K)
SNO Infrared example
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Intersatellite Spectral Difference and its
effect on climate trending (HIRS NOAA15/16)
SNO Infrared example
Seasonal biases are highly correlated with the
lapse rate, suggesting that the small differences
in the spectral response functions plays an
important role for the biases (Cao, et al.,
JTECH, 2005)
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Inter-calibrating AIRS and NOAA16/HIRS
Small but persistent HIRS warm bias

• Bias is scene temperature dependent
• Possible causes nonlinearity, spectral response
  uncertainties, or blackbody.

Scene temperature changes with season
SNO Infrared example
Courtesy of Wang, et al
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The SNO process to support climate studies
SNO time series reveals intersatellite biases
Find the root cause of the biases (blackbody,
PRT, reflector, nonlinearity, spectral
difference/uncertainty, etc) (see equation 1).
Requires dialogs between scientists engineers
Feedback to vendors for climate quality
instrumentation
Correct the biases
SNO time series confirms no bias
Climate change detection
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More SNO opportunities
Desirable well-calibrated identical radiometers
in low inclination orbits (i.e., TRMM and
International Space Station) to calibrate polar
radiometers at SNOs in the low latitudes.
SNOs between International satellites are
valuable for establishing international on-orbit
standards and implementing GEOSS
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Summary

• SNO - an enabling methodology for improving
  intersatellite calibration. Works well for the
  microwave, visible/near infrared, and infrared
  instruments.
• A simple, unambiguous, and robust method that
  produces highly repeatable results.
• Very useful for on-orbit verification and
  longterm monitoring of instrument performance,
  improving the calibration consistency of
  historical data to support climate studies, and
  establishing the calibration links between
  operational satellite radiometers.
• The SNOs will bring together all the satellite
  radiometers and become an important tool for the
  implementation of GEOSS.

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Acknowledgements

• This study is partially funded by
• The Integrated Program Office (IPO) under the
  Internal Government Studies (IGS) Program
• The Environmental Services Data and Information
  Management (ESDIM) of NOAAs GeoSpatial Data and
  Climate Services (GDCS) group, and
• The Product Systems Development and
  Implementation (PSDI) program of NOAA/NESDIS/OSD.
• Thanks are extended to M. Goldberg, F. Weng, J.
  Sullivan, R. Iacovazzi, L. Wang, P. Ciren, F. Yu,
  and X. Hui for their contributions and support.
• The contents presented here are solely the
  opinions of the authors and do not constitute a
  statement of policy, decision, or position on
  behalf of NOAA or the U. S. Government.

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References

• SNO methodology
• Cao, C., P. Ciren, M. Goldberg, F. Weng, and C.
  Zou, 2005, Simultaneous Nadir Overpasses for
  NOAA-6 to NOAA-17 satellites from 1980 to 2003
  for the intersatellite calibration of
  radiometers, NOAA Technical Report
• Cao, C., M. Weinreb, and H. Xu, 2004, Predicting
  simultaneous nadir overpasses among
  polar-orbiting meteorological satellites for the
  intersatellite calibration of radiometers. 
  Journal of Atmospheric and Oceanic Technology,
  Vol. 21, April 2004, pp. 537-542.
• Applications to Infrared soundersCao, C., H.
  Xu, J. Sullivan, L. McMillin, P. Ciren, and Y.
  Hou, 2005, Intersatellite radiance biases for the
  High Resolution Infrared Radiation Sounders
  (HIRS) onboard NOAA-15, -16, and -17 from
  simultaneous nadir observations. Journal of
  Atmospheric and Oceanic Technology, Vol.22, No.
  4, pp. 381-395.
• Cao, C, and P. Ciren, 2004, Inflight spectral
  calibration of HIRS using AIRS observations, 13th
  conference on Satellite Meteorology and
  Oceanography, Sept. 20-23, 2004, Norfolk, VA.
• Ciren, P. and C. Cao, 2003, First comparison of
  radiances measured by AIRS/AQUA and
  HIRS/NOAA-16-17, Proceedings of the
  International ATOVS Working Group Conference,
  ITSC XIII, Sainte Adele, Canada, Oct. 29, - Nov.
  4, 2003.
• Applications to Microwave sounders and climate
  trending
• Zou, C., M. Goldberg, Z. Cheng, N. Grody, J.
  Sullivan, C. Cao, and D. Tarpley, 2004, MSU
  channel 2 brightness temperature trend when
  calibrated using the simultaneous nadir overpass
  method, submitted to JGR.
• Applications to Imaging radiometers
• Cao, C., and A. Heidinger, 2002,
  Inter-Comparison of the Longwave Infrared
  Channels of MODIS and AVHRR/NOAA-16 using
  Simultaneous Nadir Observations at Orbit
  Intersections, Earth Observing Systems, VII,
  Edited by W. Barnes, Proceedings of SPIE Vol.
  4814, pp. 306-316. Seattle, WA. 
• Heidinger, A, C. Cao, and J. Sullivan,
  2002, Using MODIS to calibrate AVHRR reflectance
  channels, Journal of Geophysical Research, Vol.
  107, No. D23, 4702.
• Wu, A., X. Xiong, C. Cao, X. Wu, W. Barnes,
  2004, Inter-comparison of radiometric calibration
  of Terra and Aqua MODIS 11um and 12 um bands,
  Proceedings of SPIE, 2004, Denver, CO.

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Development of the SNO Methodology

• STK Orbital tracking (before 1999)
• TERRA and NOAA satellite close approach near
  Alaska (2000)
• Investigating user allegation on AVHRR N14/N16
  bias (2001)
• HIRS SNO study paper attempt (Cao, et al 2001),
  and NOAA17/HIRS OV, 2002
• MODIS/AVHRR Study (Cao and Heidinger 2002, SPIE
  Heidinger, et al 2002, JGR)

• Grid based SNOs and PATMOS-x (Heidinger)
• MODIS/AVHRR collaborative study with MODIS MCST
  (J. Xiong, A. Wu)

• Extended SNO prediction capability with SGP4
  (Cao, et al, 2003- 2004)
• Operational Instrument performance monitoring
  for HIRS, AMSU, and AVHRR (2003, online)
• SNO time series analysis ESDIM project HIRS,
  MSU and AVHRR 1980-2003 SNOs (2004-2005, Cao, et
  al, 2005,JTECH, NOAA Tech)

Independently AVHRR coincidental matching
studies at Langley (Doelling, et al)

• Microwave recalibration for climate trend (Zou,
  et al, 2005)
• SCO time series(Weng, et al)

• Infrared spectral calibration at SNOs using AIRS
  (Wang, Ciren, Cao, 2004-2006)

• MODIS traceable calibration for AVHRR VIS/NIR
  channels (Heidinger, et al)

• Backbone for the Integrated Cal/Val System for
  NPP/NPOESS (2005)
• Establishing on-orbit calibration traceability
  and reference networks
• International collaboration to support GOESS