1. Simultaneous Nadir Overpass Method for Inter-satellite Calibration of Radiometers
Changyong Cao
NOAA/NESDIS/Center for Satellite Applications & Research (STAR)
Presented at the ASIC^3 Workshop, May 16-18, 2006

2. 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
This map shows a 5-day and global ocean averaged time series for channel 2 from NOAA 10, 11, 12, and 14. These time series are obtained from the 1B data generated from the NESDIS operational calibration algorithm. What we see here is that there is a large intersatellite biases on the order of 0.5 to 1K in overlapping observations. When people use these time series to derive trend, these biases must be removed to construct a single time series. However, different approaches to remove these biases lead to different results.
5-day global ocean-averaged time series from NOAA 10 to 14 MSU L1B data with NESDIS operational calibration
Courtesy of C. Zou

3. Analyzing Intersatellite biases – a critical step in constructing time series for climate studies
Bias factors: β = f(t, n, s, ε, 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),
ε = 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

4. 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

5. 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.

6. Assumption for Microwave instruments: precisely matched frequency that never changes
NOAA16 vs. -17/AMSU/A
Channel 5 (Mid-troposphere)
SNO Microwave example

7. 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

8. SNO Time Series for Microwave Sounding Unit MSU CH3
N10-N9
N12-N11
N7-N6
N9-N6
Instrument noise spec
N11-N10
N14-N12
N8-N7
SNO Microwave example

9. 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)
With the small biases, trends obtained from the time series should be more reliable. These three plots show the trends for the three different calibration method. The right hand side is the value of the trend. When constructing single time series, only the constant intersatellite biases were removed with respect to the reference satellite NOAA 10. The top one is with linear calibration, and the middle one is the NESDIS algorithm which has medium nonlinearity, the last is the SNO calibration which has the largest nonlinearity effect in the calibration algorithm. It is very clear here that with increasing nonlinearity in the calibration algorithm, the trends get smaller. So here we not only obtain the trend, but also we understand somehow why we get this trend.
Trends for nonlinear calibration algorithm using SNO cross calibration
0.17 K Decade-1
SNO Microwave example
Courtesy of C. Zou

10. AVHRR VIS/NIR intersatellite bias at SNOs for channel 1 (0.68 um)
SNO VIS/NIR example

11. VIS/NIR Channles AVHRR/MODIS (0.68um)
assumptions: linear, short term invariable gain
AVHRR/N MODIS/Aqua Sample area
Reflectance Min Max Mean Stdev
Band 1 AVHRR
Band 1 MODIS
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
Lat=79.82, SZA= , cos(sza)=0.13, TimeDiff 26 sec, Uncertainty due to SZA diff 0.1%,

12. Discrepancies between MODIS and AVHRR SNO time series for channel 1 (0
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

13. AVHRR 0.86um channel (with vicarious calibration)
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

14. 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

15. AVHRR CH4 (11.5um) SNO Time Series NOAA-9 to NOAA-17, 1987 to 2003
Infrared
AVHRR CH4 (11.5um) SNO Time Series NOAA-9 to NOAA-17, 1987 to 2003
Nonlinearity error
Brightness temperature difference (K)
SNO Infrared example

16. 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)

17. 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

18. 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

19. 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

20. 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.

21. 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 NOAA’s 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.

22. 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
Applications to Infrared sounders: Cao, 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
Cao, C, and P. Ciren, 2004, Inflight spectral calibration of HIRS using AIRS observations, 13th conference on Satellite Meteorology and Oceanography, Sept , 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 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.

23. 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)
Extended SNO prediction capability with SGP4 (Cao, et al, )
“Operational” Instrument performance monitoring for HIRS, AMSU, and AVHRR (2003, online)
SNO time series analysis ESDIM project: HIRS, MSU and AVHRR SNOs ( , Cao, et al, 2005,JTECH, NOAA Tech)
Grid based SNOs and PATMOS-x (Heidinger)
MODIS/AVHRR collaborative study with MODIS MCST (J. Xiong, A. Wu)
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, )
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