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 Next slide please 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 Next slide please 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 Next slide please 3 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 Next slide please 4 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 Next slide please 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) Next slide please 6 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 Next slide please 7 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 Next slide please 8 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 Next slide please 9 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 Next slide please Blue: JAXA-RSS TB Cyan: +/- 1 SD JAXA-RSS TB Red: JAXA-RSS nonlin. correction Grey: Number of observations 10 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 Next slide please 11 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 Next slide please 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 12 • 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 Next slide please Intra-Orbit Position 10V LNA Problem Orbit Number 13 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 Next slide please 14 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 Next slide please 15 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 Next slide please 16 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 Next slide please 17 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 Next slide please 18 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! 19
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