Intercomparison of RSS and JAXA AMSR2
Brightness Temperature Calibrations
Kyle Hilburn and Chelle Gentemann
Remote Sensing Systems
Workshop on Optimal Estimation of Ocean, Ice, and Atmosphere Parameters
17 March 2015
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1
Introduction
• At the AMSR2 meeting in Tokyo, I
presented validation results for
Remote Sensing Systems (RSS)
retrievals of sea surface
temperature (SST) from AMSR2
• The results showed AMSR2 intercalibration accuracy of better than
0.1 K (compared to other RSS
satellite data records)
• For example, the figure shows a
time series of AMSRE (2002-2011)
and AMSR2 (2012-2014) (both in
red) and WindSat (blue) SST for the
Gulf of Mexico
• Note the excellent agreement in
annual cycle, inter-annual
variability, and overall mean
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2
Overview
• RSS and JAXA have produced two independent calibrations of AMSR2
• Several Kelvin differences exist between RSS and JAXA calibrations
• My presentation at the AMSR2 meeting showed similar large differences
between the RSS and JAXA nonlinearity corrections
• RSS: calibration to rain-free ocean radiative transfer model (RTM)
• This approach has also been applied to AMSR-E, GMI, SSMI, SSMIS, TMI, WindSat
• AMSR2 is the only satellite for which we’ve seen such large nonlinearities
• JAXA: nonlinearity determined from pre-launch measurements
• Key finding: Difference in nonlinearity corrections largely explains
differences between RSS and JAXA AMSR2 TB
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Background: AMSR2 Nonlinearity
• Red: JAXA correction developed
from pre-launch measurements
(Kasahara, 2013 X-Cal presentation)
• JAXA model is characterized by
maximum deflection at midpoint
parameter
• Black: RSS correction developed
post-launch from RTM using
WindSat
• RSS model is 5th order polynomial
• 6 and 7 GHz: RSS and JAXA agree
on positive deflections, but
disagree on magnitude
• 10, 18, 23, 36 GHz: JAXA has
positive deflections, while RSS has
negative deflections
• 89 GHz: RSS and JAXA agree that
nonlinearity is weak
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Background: Sources of Nonlinearity
• AMSR sensors are unique in their
nonlinearity
• AMSR: 1.0%
• AMSR2 Data Users Handbook, 2013
• AMSR-E Data Users Handbook, 2006
• AMSR-E nonlinearity only evident at 6 GHz
• WindSat: 0.1 %
• Gaiser et al., 2004
• GMI: 0.25%
• Draper et al., 2013
• SSM/I: 0.5%
• Yan et al., 2008
• The means that nonlinearity is single
largest term in AMSR2 calibration
• Nonlinearity in detector diode
• The typical assumption in MW
radiometer calibration is that output
voltage is proportional to the square of
the input voltage (square law), thus
directly proportional to input power
• This is only valid over range of input
powers and diode temperatures
• Expected to cause positive deflections
• Nonlinearity in amplifiers
• Low noise amplifier
• Intermediate frequency amplifier
• Warmer temperatures can cause gain
compression
• Expected to cause negative deflections
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5
Data
• AMSR2 has six feedhorns
•
•
•
•
•
•
6 and 7 GHz shared
10 GHz
18 and 23 GHz shared
36 GHz
89 GHz A
89 GHz B
• Shared horns have band-rejection filters and highpass filters to separate the frequencies
• Signal sent through low noise amplifier, then
bandwidth filter
• Additional amplification, demodulation, and
integration
• Temperature monitoring for auto gain control
• Two-point calibration
• High temperature source: 293 +/- 0.6 K
• Design much improved on AMSR-E
• No evidence of direct solar intrusion for AMSR2
• Low temperature source: cold space mirror
• Time period of comparison is all of 2014
• Spatial domain of intercomparison is all
surface types including ocean, land, and ice
• Note: RSS calibration largely based on ocean
data, but this intercomparison is all surfaces
• JAXA Version 1.1
• RSS Version 7.2
• Version 7 = Meissner and Wentz [2012] RTM
• Used L1B files: not resampled
• JAXA files: separates ascending, descending
orbit segments, named by date and time
• RSS files: one whole orbit, named by RSS
orbit number
• One RSS file matches two JAXA files
• JAXA TAI time and RSS UTC time different by
8 leap seconds (1993-2014)
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Methodology
• Calibration to ocean RTM over rain-free scenes
• RSS RTM requires input: SST, wind speed and
direction, water vapor, and cloud water
• WindSat Version 7 specifies wind speed, water
vapor, and cloud water
• Important: retrieval algorithm is inverse of RTM to
within 0.1 K, this establishes absolute calibration
• NCEP for wind direction
• Reynolds OI for SST
• RTM gives TB, inverse of APC to get TA
• Spillover, cross-polarization, hot load offset, and
nonlinearity iteratively adjusted to minimize TA
differences over full mission
• Amazon comparisons to further constrain warm
end; strong nonlinearity complicates the
separation of nonlinearity and spillover effects
• RSS AMSR2 processing begins with JAXA L1A
• Reverse AGC to get counts, the fundamental
starting point
• To obtain cold reference, filter counts for RFI and
moon intrusion, and Earth spillover for low
frequencies
• To obtain hot reference, simple average of
thermistors, apply -1.3 K hot load offset to all
channels
• Reference temperatures convert counts to TA
• Along scan biases removed, generally 0.3 K or less,
except near scan edge for low frequencies
• APC converts TA to TB
• RSS and JAXA use somewhat different APC
coefficients, but nonlinearity correction is the
major source of differences between the two
calibrations
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Results: 6 and 7 GHz
• 6 GHz: TB diff (blue) of 1-2 K matches NL
correction diff (red) to within better
than 0.1 K
• Quadratic fit to TB diff has R2 > 0.96,
indicating mostly linear/quadratic shape
• RSS and JAXA agree on positive
deflections
Blue: JAXA-RSS TB
Cyan: +/- 1 SD JAXA-RSS TB
Red: JAXA-RSS nonlin. correction
Grey: Number of observations
• 7 GHz: TB diff (blue) of 2-3 K mostly
explained by NL diff (red) to within 0.51K
• Quadratic fit to TB diff has R2 > 0.96,
indicating mostly linear/quadratic shape
• Note the small increase with
temperature in the difference between
the TB and NL diffs
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Results: 10 and 23 GHz
• 10 GHz: TB diff (blue) of 3 K
• NL diff (red) explains shape of TB
diff, with residual diff of 1-1.5 K
• Quadratic fit to TB diff has R2 > 0.99,
indicating linear/quadratic shape
• 23 GHz: TB difference (blue) of 3-4 K
• NL diff (red) explains shape of TB
diff, with residual diff of 1-1.5 K
• Quadratic fit to TB diff has R2 > 0.99,
indicating linear/quadratic shape
Blue: JAXA-RSS TB
Cyan: +/- 1 SD JAXA-RSS TB
Red: JAXA-RSS nonlin. correction
Grey: Number of observations
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Results: 18 and 36 GHz
• 18 GHz: TB diff (blue) of 2-4 K mostly
explained by NL diff (red) to within 0.5-1 K
• Note the large polarization difference, the
V-pol diff about 2 K larger than H-pol diff
• Quadratic fit to TB diff has R2 > 0.99 for Vpol and R2 = 0.65 for H-pol.
• Shape of 18H has substantial contribution
from higher order terms
• 36 GHz: TB diff (blue) of 2-3K is mostly
explained by NL diff (red) to within 0.6 K
• Quadratic fit to TB diff has R2 > 0.98,
indicating linear/quadratic shape
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Blue: JAXA-RSS TB
Cyan: +/- 1 SD JAXA-RSS TB
Red: JAXA-RSS nonlin. correction
Grey: Number of observations
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Results: 89 GHz
• 89A: TB diff (blue) of 0.4-0.7 K is explained
at cold end by NL diff (red) with residual
diffs of 0.3 K
• Quadratic fit to TB diff has R2 = 0.83 for Vpol and 0.96 at H-pol indicates
contribution of higher order terms for Vpol
• RSS and JAXA agree on small nonlinearity
Blue: JAXA-RSS TB
Cyan: +/- 1 SD JAXA-RSS TB
Red: JAXA-RSS nonlin. correction
Grey: Number of observations
• 89B: TB diff (blue) of 0.5-1 K is explained
at cold end by NL diff (red) with residual
diffs of 0.5 K
• Quadratic fit to TB diff has R2 = 0.99 for Vpol and 0.88 for H-pol indicates
contribution from higher order terms for
H-pol
• RSS and JAXA agree on small nonlinearity
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Summary of Results
• Table provides:
• JAXA minus RSS mean
TB difference
• Standard deviation (SD)
• Coefficients for
quadratic polynomial
regression of JAXA-RSS
difference as a function
of JAXA TB
• Correlation coefficient
for the regression
• TB range of regression
• Double difference (DD)
residual JAXA-RSS TB
difference minus JAXARSS nonlinearity
correction
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Chan
Mean
SD
C2
C1
C0
R2
Range
DD
06V
1.08
0.63
-3.893E-05
8.122E-03
1.170E+00
0.968
156, 310
-0.02
06H
1.64
0.54
-5.349E-05
1.298E-02
1.194E+00
0.992
74, 290
-0.06
07V
1.89
0.43
-6.722E-05
2.492E-02
-2.825E-01
0.969
158, 314
0.80
07H
3.16
0.75
-2.194E-04
7.361E-02
-1.722E+00
0.994
76, 292
0.59
10V
3.28
0.94
-2.032E-04
7.253E-02
-2.599E+00
0.999
166, 312
1.41
10H
2.88
0.62
-1.545E-04
5.056E-02
-4.337E-01
0.999
80, 292
1.24
18V
4.00
1.17
-4.293E-04
1.717E-01
-1.240E+01
0.996
172, 312
0.72
18H
1.70
0.56
7.558E-05
-3.272E-02
4.790E+00
0.649
96, 296
0.38
23V
3.50
0.72
-3.353E-04
1.422E-01
-1.106E+01
0.990
170, 310
1.43
23H
3.78
0.87
-1.557E-04
5.029E-02
3.257E-01
0.997
112, 298
1.13
36V
1.71
0.48
-1.265E-04
4.981E-02
-2.917E+00
0.986
166, 312
0.62
36H
2.84
0.68
-1.672E-04
5.914E-02
-1.976E+00
0.995
128, 298
0.29
89AV
0.36
0.49
2.749E-05
-1.269E-02
1.799E+00
0.830
164, 308
0.31
89AH
0.73
0.55
2.744E-05
-2.063E-02
4.025E+00
0.968
148, 300
0.21
89BV
0.85
0.45
-2.202E-06
-3.402E-03
1.872E+00
0.994
166, 308
0.57
89BH
0.58
0.51
5.228E-05
-2.793E-02
4.208E+00
0.885
148, 300
0.40
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• Although not evident in results shown so far, there is
isolated problem in 10V
• Figure shows 10V TA measured minus RTM AMSR2
closure versus orbit number and intra-orbit position
• “Closure” means RTM input is from AMSR2 retrievals
• Detects features that are inconsistent with RTM
•
•
•
•
White circle: warm anomaly in mission plot 1.4 K
Starts at orbit 8500, December 22, 2013
Southern Hemisphere summer near Antarctica
Anomaly in cold counts and in LNA temperature counts,
reducing cold counts makes problem worse
• Problem happens when 10V LNA counts are at maximum
value
• Believe the problem is a temperature related
nonlinearity
• Red areas around North Pole in winter are due to larger
errors in modeling cold SST and high wind
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Intra-Orbit Position
10V LNA Problem
Orbit Number
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Comparison with Kachi
Kachi, Imaoka, Maeda, Okuyama, Naoki, Hori, Kasahara, and Ito, 2014: Calibration and validation results of the
Advanced Microwave Scanning Radiometer 2 (AMSR2), EUMETSAT, 22-26 September, Geneva, Switzerland
• Black: JAXA – RSS AMSR2 TB
• Red: Kachi et al. provide linear
fit for AMSR2 - TMI
• 10V: good match over all TB,
except at hot end
• 10H: good match in center
• 18V: matches at cold end,
slope right, but offset colder
• 18H: matches at cold end
• 23V: matches at warm end
• 36V: offset warmer
• 36H: matches at warm end
• 89: slope matches, offset
warmer
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Comparison with JAXA
JAXA, 2014: Results of intercalibration between AMSR2 and TMI/AMSR-E (AMSR2 Version 1.1), March 12, 2014,
Available online: http://www.wmo.int/pages/prog/sat/meetings/documents/GSICS-EP-15_Doc_05_JAXA-AMSR2XcalResults.pdf, accessed 13 February 2015
• Black: JAXA – RSS AMSR2 TB
• JAXA provides linear fit
• Red solid: AMSR2 - TMI
• Red dash: AMSR2 - AMSR-E
• TMI has larger slope than
AMSR-E at low frequencies
(6-36 GHz)
• AMSR-E has larger slope at
89 GHz than TMI
• 6V, 6H: slope similar, but
offset cooler
• 10, 18, 23, 36: slopes similar,
offset cooler, matches best at
cold end
• 89: TMI slopes match best
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Comparison with Alsweiss
Alsweiss, Jelenak, Chang, Park, and Meyers, 2015: Inter-calibration results of the Advanced Microwave Scanning
Radiometer-2 over ocean. IEEE J. Selected Topics Appl. Earth Obs. Rem. Sens., in press.
• Black: JAXA – RSS AMSR2 TB
• Alsweiss et al. provide
quadratic polynomial for
AMSR2 – TMI diffs
• Red solid: ascending
• Red dash: descending
• Temperature of best match
goes from cold end at 10
GHz to warm end at 89 GHz
with increasing frequency,
following the mode of TB
histogram
• Slopes do not match our
results
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Comparison with Singh
Singh, Piyush, Varma, and Rajput, 2014: Radiative transfer based cross-calibration of passive microwave
measurements obtained by satellites. Intl. Geosci. Rem. Sens. Symposium, 13-18 July, Québec, Canada.
• Black: JAXA – RSS AMSR2 TB
• Singh et al. provide quadratic
polynomial for TMI TB vs
AMSR2 TB
• Red: AMSR2 - TMI
• Completely different results;
flipping the sign makes
better agreement for
agreement for 10H, 18V, and
18H; and worse agreement
for 10V, 23V, and 36V
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Summary and Discussion
• JAXA-RSS mean differences are as large as 4 K
• JAXA is warmer than RSS for all channels
• Standard deviations across all TB are as large as 1 K
• At a given temperature, standard deviations as large as 0.5 K
• Nonlinearity explains most of differences, except at 10 and 23 GHz that have
double difference residuals greater than 1 K
• JAXA nonlinearity is generally larger than RSS
• Quadratic fit is very good match to shape of TB differences, except for 18H,
which has higher order nonlinearity contributions in RSS correction
• JAXA and RSS agree on positive deflections for 6 and 7 GHz
• JAXA and RSS agree on small nonlinearity at 89 GHz
• Disagree on sign of deflection: 10, 18, 23, and 36 GHz
• JAXA deflection is positive for all channels
• RSS deflection is negative for these channels
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Conclusions
• The several Kelvin differences between JAXA and RSS TB from AMSR2
are mostly due to differences in nonlinearity characterizations
• However, both calibrations find nonlinearity of several Kelvin
• Climate monitoring requires 0.1-0.2 K accuracy
• Achieving this accuracy is complicated by large nonlinearity
• Differences between JAXA and RSS raise possibility of temperature
dependent nonlinearity in radiometer components
• Linearity design requirements of 0.1% would greatly solve the
problem
Thank you!
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