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rttov 2008/05/09 14:40 rttov 2008/05/09 18:57 current
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HIRS, MSU, SSU, AMSU-A, AMSU-B, AVHRR, SSMI, VTPR, TMI, SSMIS, AIRS, HSB, MODIS, ATSR, AMSR, MVIRI, SEVIRI, GOES-Imager, GOES-Sounder, GMS/MTSAT imager, FY2-VISSR,  FY1-MVISR, WINDSAT, SSM/T-2, IASI, (CriS, VIIRS) HIRS, MSU, SSU, AMSU-A, AMSU-B, AVHRR, SSMI, VTPR, TMI, SSMIS, AIRS, HSB, MODIS, ATSR, AMSR, MVIRI, SEVIRI, GOES-Imager, GOES-Sounder, GMS/MTSAT imager, FY2-VISSR,  FY1-MVISR, WINDSAT, SSM/T-2, IASI, (CriS, VIIRS)
-//Documents partially used here (as at 6 May 2008)://+//Documents partly used here (as at 6 May 2008)://
  * www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rttov8_description.html,   * www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rttov8_description.html,
  * www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rtm_rttov9.html   * www.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rtm_rttov9.html
  * users_guide_91_v1.4.pdf   * users_guide_91_v1.4.pdf
 +
==== Fast RT model approach ==== ==== Fast RT model approach ====
-RTTOV contains a fast model of the transmittances of the atmospheric gases that is generated from accurate line-by-line or continuum transmittances (GENLN2 / kCarta / LBLRTM, or Liebe-89 MPM, resp.) for a set of diverse atmospheric profiles (43L TIGR profile dataset / t101L 52 profile ERA-40 dataset) over the wave-number range of specific channels/instruments in question. The monochromatic transmittances are convolved with the appropriate spectral response functions and are used to compute channel-specific regression coefficients by use of a selected set of predictors. These regression coefficients can then be used by a fast transmittance model to compute transmittances given any other input profile. (More detailed description see: www.metoffice.gov.uk/research/nwp/satellite/rtm/rt_lbl.html, as at 6 May 2008.) This parameterization of the transmittances makes the model computationally efficient and in principle should not add significantly to the errors generated by uncertainties in the spectroscopic data used by the line-by-line model. Assessing accuracy by means of comparing computations, e.g., for all the narrow channels of instrument ATOVS see www.metoffice.gov.uk/research/nwp/satellite/rtm/rttov_accuracy.html (as at 6 May 2008). The plot shows the accuracy of the RTTOV-6 and earlier RTTOV-5 model calculations for NOAA-15 ATOVS by comparing them with calculations by an accurate line-by-line model for a diverse set of different atmospheric profiles (noise equivalent temperature difference).+RTTOV contains a fast model of the transmittances of the atmospheric gases that is generated from accurate line-by-line or continuum transmittances, resp., (GENLN2 / kCarta / LBLRTM; Liebe-89 MPM) for a set of diverse atmospheric profiles (43L TIGR profile dataset / t101L 52 profile ERA-40 dataset) over the wave-number range of specific channels/instruments in question. The monochromatic transmittances are convolved with the appropriate spectral response functions and are used to compute channel-specific regression coefficients by use of a selected set of predictors. These regression coefficients can then be used by a fast transmittance model to compute transmittances given any other input profile. (More detailed description see: www.metoffice.gov.uk/research/nwp/satellite/rtm/rt_lbl.html, as at 6 May 2008.) This parameterization of the transmittances makes the model computationally efficient and in principle should not add significantly to the errors generated by uncertainties in the spectroscopic data used by the line-by-line model. Assessing accuracy by means of comparing computations, e.g., for all the narrow channels of instrument ATOVS see www.metoffice.gov.uk/research/nwp/satellite/rtm/rttov_accuracy.html (as at 6 May 2008). The plot shows the accuracy of the RTTOV-6 and earlier RTTOV-5 model calculations for NOAA-15 ATOVS by comparing them with calculations by an accurate line-by-line model for a diverse set of different atmospheric profiles (noise equivalent temperature difference).
The accuracy of simulating very broad channels (e.g. SEVIRI channel 4 at 3.9 microns) is addressed to be poor, here significant bias can occur. The accuracy of simulating very broad channels (e.g. SEVIRI channel 4 at 3.9 microns) is addressed to be poor, here significant bias can occur.
 
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