Use of the SSU and AMSU-A observations in reanalyses
Shinya Kobayashi1,2, Marco Matricardi1, Dick Dee1 and Sakari Uppala111European Centre for Medium-Range Weather Forecasts, United Kingdom
2
Japan Meteorological Agency, Japan
Correspondence: Shinya.Kobayashi@ecmwf.int

INTRODUCTION
Biases in observations have to be taken into account to produce a time consistent atmospheric dataset that is
appropriate for climate research. Stratospheric temperature analyses in particular can be dominated by satellite
data and are strongly affected by biased observations. Changing bias characteristics are often reflected instratospheric temperature analyses as artificial jumps that correspond to satellite transitions. These artificial jumps
can mask the true climate signals and make it difficult to estimate reliable long-term temperature trends.
Biases in observations are the result of a number of processes that are not modelled properly in the assimilation
system. Satellites make in general indirect observations of physical properties of the earth system. Use of satellite
data in an assimilation system requires a fast radiative transfer model (RTM) that transforms the variables used in
the assimilation system into the observed quantities. There are several RTMs that are routinely used in numerical
weather prediction centres. When these RTMs are applied to early satellite instruments, biases can be generated
by a not proper characterization of the spectral response functions or by changes in the concentration of theradiatively active constituents not reflected in the RTMs. Those biases in the RTMs have to be addressed when wedeal with the biases in satellite observations used in reanalyses.
Stratospheric temperatures have been retrieved from satellite observations made by the series of the NOAA
polar orbiting satellites since 1978. Data from the TIROS-N to the NOAA-14 satellite have been used to retrieve
stratospheric temperatures by using the infrared channels of the Stratospheric Sounding Unit (SSU) instrument.
For the later satellites from NOAA-15 onward, SSU was replaced by a higher vertical resolution microwave
instrument, the Advanced Microwave Sounding Unit - A (AMSU-A). SSU radiances have previously been
directly assimilated in ERA-40 (Uppala et al. 2005) and JRA-25 (Onogi et al. 2007). Both reanalyses encountereddifficulties in utilising the SSU radiances due to the biases of the background model and the inadequacy of data
coverage for much of the period. Particularly problematic periods are the early 1980’s where SSUs had large
inter-satellite biases and the late 1990’s when the first AMSU-A instrument became available.
Nash and Forrester (1986) pointed out a number of reasons why biases in the SSU radiances should beexpected. One of the reasons is the change in time of the SSU spectral response function, which is not taken into
account in the Radiative Transfer model for TOVS (RTTOV) (Saunders et al. 1999), which is used in ERA-40 andJRA-25. It has also been earlier pointed out that the radiances of the AMSU-A stratospheric channels computedby RTTOV are significantly different from those computed by other RTMs due to the inclusion of the Zeeman
splitting effect (Garand et al. 2001).
In this paper, we utilise co-located observations produced by the Simultaneous Nadir Overpass (SNO)
technique (Cao et al. 2005) to evaluate the inter-satellite radiance differences for SSU, as well as between SSUand AMSU-A (Section 2). We then present a revised RTTOV model which can reproduce SSU and AMSU-Ainter-satellite radiance differences more accurately. The revised radiative transfer models are validated bysimulating the inter-satellite radiance differences for the co-located locations and comparing them with thoseevaluated by the SNO technique (Section 3). Section 4 demonstrates the impact of the changes in the radiativetransfer model on the stratospheric temperature analysis. Section 5 summarises the conclusions from theinvestigations.

2INTER-SATELLITE RADIANCE DIFFERENCESTo evaluate the inter-satellite biases between the SSU instruments, we utilise co-located observations producedby the SNO technique (Cao et al. 2005). This method compares the observations made by different satellites at thesame time and at the same location. Since both instruments sense the same atmospheric profiles, this comparison
provides us with reliable estimates of the inter-satellite biases with little ambiguity. The biases observed with thismethod are entirely attributable to differences in the radiometric and spectroscopic performance. Figure 1 shows
the time series of the inter-satellite biases between NOAA-6 and NOAA-7 for SSU channel 3 over the Antarctic.
The seasonal cycle in the time series is likely to be correlated with the lapse rate in the upper stratosphere,
indicating that the weighting functions are not identical. The inter-satellite radiance differences between SSU andAMSU-A was also investigated by the SNO technique (see Figure 2) and a significant discrepancy between themeasured radiance differences and those computed by RTTOV from background values was found in thespringtime and the wintertime, which suggests the need to review the radiative transfer modelling for both
instruments in RTTOV.

Figure 1 Inter-satellite biases between NOAA-6 andNOAA-7 for SSU channel 3 over the Antarctic.

Figure 2 Difference between SSU3 on NOAA-11 andAMSU-A14 on NOAA-15 radiances.

3

REVISED RADIATIVE TRANSFER MODELS
3.1
SSU
SSU is a 3-channel infrared radiometer designed to measure the radiance emitted by stratospheric carbon
dioxide (Miller et al. 1980). The instrument utilises the pressure modulation technique by which a signal isderived by viewing the atmosphere through an absorption cell within which the pressure of the carbon dioxide canbe modulated. The spectral performance of the instrument depends on the mean pressure within the cell and thus
its long-term stability is crucial for time-consistent observations. The mean cell pressure was monitored routinely
by the Met Office at 6-month intervals and upon the launch of each new spacecraft. Figure 3 shows the daily
values interpolated from Met Office’s 6-monthly estimations by using a linear relationship between the mean cellpressure and the modulation frequency.

Figure 3 Daily values of the mean cell pressures of the SSU instruments.

Figure 4 shows the typical changes to the weighting functions caused by the CO2 cell pressure loss. Theseweighting functions were computed for U.S. standard atmosphere 1976 by using the LBLRTM line-by-line
radiative transfer model (Clough et al. 2004) and the HITRAN line parameter database (Rothman et al. 2005). The
impact of CO2 cell pressure loss is significant in channel 2 and channel 3.

Figure 4 Impact of the changes of the mean cell pressure on the weighting functions.

To assess the effectiveness of taking into account the CO2 cell pressure loss, the inter-satellite biases at theco-located locations were simulated and compared with those evaluated by the SNO technique. Apart from themean cell pressure, changes in the atmospheric CO2 concentration were taken into account. The atmosphericprofiles corresponding to the co-located observations were obtained from ERA-40. Figure 5 shows the time seriesof the inter-satellite biases computed by LBLRTM for the same pair of the satellites as shown in Figure 1 together
with those evaluated by the SNO technique. The seasonal cycle of the inter-satellite biases is well captured in thesimulation for channel 2 (not shown) and channel 3 (see Figure 5), indicating that the mean cell pressure is adominant factor in determining the instrument performance. For channel 1 (not shown), changes of the mean cellpressure have little impact on the computed radiances, indicating a possibility of other dominant causes of thebiases, e.g. uncertainties in the band-pass filter and the calibration algorithm. The remaining difference between
the measured and computed inter-satellite biases in channel 2 and channel 3 could be due to similar causes.
Further examination is needed to identify the causes of the remaining biases. Nevertheless, taking into account thechanges of the mean cell pressure is expected to reduce the biases in the RTM.

Figure 5 Same as Figure 1 for the computed inter-satellite biases.

3.2
AMSU-A
AMSU-A is a multi-channel microwave radiometer designed with the aim to be able to calculate the verticaltemperature profiles from about 3 hPa (45 km) pressure height to the Earth’s surface (Goodrum, Kidwell andWinston 2000). The AMSU-A stratospheric channels measure the radiance originating from the 60-Ghz oxygenabsorption lines. These absorption lines result from magnetic-dipole transitions and exhibit Zeeman splitting
because of the terrestrial magnetic field. This splitting is appreciable for line widths at the low pressures. Thiseffect is important for the upper atmospheric channels that cover frequencies within a few MHz from the centre of
the absorption lines (Rosenkranz and Staelin 1988).

In RTTOV, the Zeeman splitting effect is represented by the scalar approximation described by Liebe et al.
(1993), which models the effect simply by increasing the line-broadening parameter for the O2 absorption lines.
Figure 6 shows a comparison of the attenuation rates computed by three different radiative transfer models for thefrequencies around the pass bands of AMSU-A channel 14. The radiative transfer models used are with 1) thescalar approximation as in RTTOV, 2) the explicit representation of the Zeeman splitting effect and 3) no Zeeman
splitting effect. All of these models are based on a version of the Liebe line-by-line model (Rayer 2001), andutilised Millimeter-wave Propagation Model (MPM) 89 (Liebe 1989) for water vapour spectra and MPM92(Liebe et al. 1992) for oxygen spectra. The scalar approximation proved to be very accurate at the frequencies
near the centre of the absorption line. In the frequencies within the pass bands, however, this approximation
overestimates the attenuation rates, which results in anomalous upward shift of its weighting function (see Figure7). The weighting function of the no Zeeman effect model is much closer to that of the explicit Zeeman effectmodel, indicating that the calculations of the no Zeeman effect model would be more accurate than those of thescalar approximation model. Although the explicit Zeeman effect model is undoubtedly the most accurate model,
the implementation of this model into RTTOV requires a long term effort. As a practical short-term solution, we
simply excluded the Zeeman splitting effect from the line-by-line computations used to train the fast RTM.

Figure 6 Attenuation rates at the frequencies around thepass bands of AMSU-A channel 14

Figure 7 Weighting functions for AMSU-A channel 14.

Figure 8 shows the time series of the inter-satellite radiance differences computed by the revised radiativetransfer model for the same pair of satellites as shown in Figure 2 together with those evaluated by the SNOtechnique. Remarkable agreement between the measured and the computed radiance differences indicates asignificant improvement of the accuracy of the RTM.

Figure 8 Difference between SSU3 on NOAA-11 and AMSU-A14 on NOAA-15 radiances.
Left: RTTOV was used for the AMSU-A computations. Right: No Zeeman effect model was used.

4IMPACT ON THE STRATOSPHERIC TEMPERATURE ANALYSIS
To assess the impact of the new RTTOV coefficients for AMSU-A on the analysis fields, assimilation
experiments have been performed. The control assimilation utilised the ERA-Interim system configuration(T255L60) with the current RTTOV coefficients, and the new RT experiment utilised exactly the same system
configuration except for the new RTTOV coefficients for AMSU-A. Figure 9 shows the evolution of the verticaltemperature structures in the Polar Regions. The control assimilation tends to create spurious peaks around modellevels 6 and 10 (2 and 5 hPa respectively) when the strong polar vortex develops in winter. This is because theweighting function for AMSU-A channel 14 in the current RTTOV is located too high, which results in too warm
radiance simulations when the mesosphere is warmer than the stratosphere. Such a situation occurs in the Polar
Regions in winter. In the new RT experiment, these spurious peaks have been reduced and the verticaltemperature structure varies more smoothly with seasons. The monthly averaged zonal mean temperatures also
demonstrate the significant reduction of the spurious peaks in the Polar Regions (not shown).

Figure 9 Evolution of the vertical temperature structures over the Antarctic from September 1998 to July 1999
in the control assimilation (top) and the new RT experiment (bottom). The vertical axes represent model levels.

5CONCLUSION
For a better use of the SSU and AMSU-A observations in reanalyses, this study addressed the biases in theradiative transfer modelling for these instruments. The radiative transfer coefficients for SSU in RTTOV were
updated using more appropriate spectral response functions and introducing variable CO2 as a state vector inRTTOV. For AMSU-A, the inaccurate treatment of the Zeeman splitting effect in RTTOV was found to result in aoverestimation of the optical depths. To correct this, the radiative transfer coefficients for AMSU-A were also
updated by not taking into account the Zeeman splitting effect. The updated RTTOV was validated by computing

the inter-satellite radiance differences for the co-located locations and comparing them with those evaluated by
the SNO technique. The results demonstrated a clear reduction of the biases due to the improvement of theaccuracy of the radiances simulated by the updated radiative transfer model. This improvement of the radiativetransfer modelling for SSU and AMSU-A is expected to increase significantly the time consistency ofstratospheric temperature analyses.
ACKNOWLEDGEMENTThe authors wish to thank H. Brindley (Imperial College) and A. Geer (ECMWF) for their help with theinstrument characteristics of SSU and R. Saunders (Met Office) for his support and advice on this work.
REFERENCES
Cao, C., H. Xu, J. Sullivan, L. McMillin, P. Ciren, and Y. T. Hou, 2005: Intersatellite Radiance Biases for theHigh-Resolution Infrared Radiation Sounders (HIRS) on board NOAA-15, -16, and -17 from Simultaneous Nadir
Observations. J. Atmos. Oceanic Technol., 22, 381-395.
Clough SA, Shephard MW, Mlawer EJ, Delamere JS, Iacono MJ, Cady-Pereira K, Boukabara S, Brown PD, 2004:
Atmospheric radiative transfer modeling: a summary of the AER codes. J Quant. Spectrosc. and Radiat Transfer,91, 233-244.
Garand, L., D. S. Turner, M. Larocque, J. Bates, S. Boukabara, P. Brunel, F. Chevallier, G. Deblonde,1 R. Engelen, M.
Hollingshead, D. Jackson, G. Jedlovec, J. Joiner, T. Kleespies, D. S. McKague, L. McMillin, J.-L. Moncet, J. R.
Pardo, P. J. Rayer, E. Salathe, R. Saunders, N. A. Scott, P. Van Delst, and H. Woolf, 2001: Radiance and Jacobianintercomparison of radiative transfer models applied to HIRS and AMSU channels. J. Geophys. Res., 106,24,017-24,031.
Goodrum G., K. B. Kidwell, and W. Winston, 2000: NOAA KLM USER'S GUIDE. [Online] Available:
http://www2.ncdc.noaa.gov/docs/klm/cover.htm
Liebe, H. J., 1989: MPM - an atmospheric millimeter-wave propagation model. Int. J. Infrared Milli., 10, 631-650.
Liebe, H. J., Rosenkranz, P. W., and Hufford, G. A., 1992: Atmospheric 60-GHz oxygen spectrum: new laboratory
measurements and line parameters. J. Quant. Spectrosc. Radiat. Transfer, 48, 629-643.
Liebe, H. J., Hufford, G. A., Cotton, M. G., 1993: Propagation modeling of moist air and suspended water/ice particles
at frequencies below 1000 GHz. in Proc. NATO/AGARD Wave Propagation Panel, 52nd Meeting, Paper No.
3/1-10, Mallorca, Spain, May 1993.
Miller, D. E., J. L. Brownscombe, G. P. Carruthers, D. P. Pick, and K. H. Stewart, 1980: Operational temperaturesounding of the stratosphere. Philos. Trans. Roy. Soc. London, 296, 65-71.
Nash J. and G. F. Forrester 1986: Long-term monitoring of stratospheric temperature trends using radiancemeasurements obtained by the TIROS-N series of NOAA spacecraft. Adv. Space Res., 6, 37-44.
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori,K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji and R. Taira 2007: The JRA-25
Reanalysis. J. Meteor. Soc. Japan, 85, 369-432.
Rayer,P.
J.,
2001:
Microwave
transmittancemodelsfor
RTTOV.
[Online]Available:
http://www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rtm_reports.html
Rosenkranz, P. W., and Staelin, D. H., 1988: Polarised thermal microwave emission from oxygen in the mesosphere.
Radio Sci., 23, 721-729.
Rothman, L.S., D. Jacquemart, A. Barbe, D. Chris Benner, M. Birk, L.R. Brown, M.R. Carleer, C. Chackerian, Jr., K.
Chance, L.H. Couderth, 2, V. Danai, V.M. Devic, J.-M. Flaud, R.R. Gamache, A. Goldman, J.-M. Hartmann, K.W.
Jucks, A.G. Maki, J.-Y. Mandin, S.T. Massie, J. Orphal, A. Perrin, C.P. Rinsland, M.A.H. Smith, J. Tennyson, R.N.
Tolchenov, R.A. Toth, J. Vander Auwera, P. Varanasi and G. Wagner 2005: The HITRAN 2004 molecular
spectroscopic database. J Quant. Spectrosc. and Radiat Transfer, 96, 139-204.
Saunders, R., Matricardi, M. and Brunel, P., 1999: An improved fast radiative transfer model for assimilation of satelliteradiance observations. Q. J. R. Meteorol. Soc., 125, 1407-1425
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., da Costa Bechtold V., Fiorino, M., Gibson, J. K, Haseler, J.,
Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K.,
Balmaseda, M. A., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof,A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E.
M., Jenne, R., McNally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A, Saunders, R. W., Simon, P., Sterl, A.,
Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P. and Woollen, J., 2005: The ERA-40 re-analysis. Quart. J. R.
Meteorol. Soc., 131, 2961-3012.