Articles | Volume 14, issue 5
https://doi.org/10.5194/gmd-14-2899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-2899-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new gas absorption optical depth parameterisation for RTTOV version 13
James Hocking
CORRESPONDING AUTHOR
Met Office, Fitzroy Road, Exeter, EX1 2PB, UK
Jérôme Vidot
CNRM, Université de Toulouse, Météo-France, CNRS, Lannion, France
Pascal Brunel
CNRM, Université de Toulouse, Météo-France, CNRS, Lannion, France
Pascale Roquet
CNRM, Université de Toulouse, Météo-France, CNRS, Lannion, France
Bruna Silveira
CNRM, Université de Toulouse, Météo-France, CNRS, Lannion, France
Emma Turner
Met Office, Fitzroy Road, Exeter, EX1 2PB, UK
Cristina Lupu
ECMWF, Shinfield Park, Reading, UK
Related authors
Rohit Mangla, Mary Borderies, Philippe Chambon, Alan Geer, and James Hocking
Atmos. Meas. Tech., 18, 2751–2779, https://doi.org/10.5194/amt-18-2751-2025, https://doi.org/10.5194/amt-18-2751-2025, 2025
Short summary
Short summary
This study provides a detailed description of the radar simulator available within version 13 of the RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) software. It is applied to the Météo-France global numerical weather prediction model, with the objective of simulating Dual-frequency Precipitation Radar reflectivity observations. Additionally, the simulation of the bright band is addressed and then successfully applied to model forecasts for the purpose of classifying NWP (numerical weather prediction) model columns between stratiform and convective categories.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Gloria Klein, Xavier Ceamanos, Jérôme Vidot, Didier Ramon, and Mustapha Moulana
EGUsphere, https://doi.org/10.5194/egusphere-2025-3263, https://doi.org/10.5194/egusphere-2025-3263, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This work investigates the impact of the Earth's sphericity on geostationary satellite observations, particularly in the context of the operational estimation of aerosol and land surface properties from the Meteosat Third Generation-Imager's Flexible Combined Imager. We demonstrate that the plane-parallel approximation widely used in fast radiative transfer codes can introduce significant biases in certain situations, mainly depending on the observing geometry and wavelength.
Rohit Mangla, Mary Borderies, Philippe Chambon, Alan Geer, and James Hocking
Atmos. Meas. Tech., 18, 2751–2779, https://doi.org/10.5194/amt-18-2751-2025, https://doi.org/10.5194/amt-18-2751-2025, 2025
Short summary
Short summary
This study provides a detailed description of the radar simulator available within version 13 of the RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) software. It is applied to the Météo-France global numerical weather prediction model, with the objective of simulating Dual-frequency Precipitation Radar reflectivity observations. Additionally, the simulation of the bright band is addressed and then successfully applied to model forecasts for the purpose of classifying NWP (numerical weather prediction) model columns between stratiform and convective categories.
Bruna Barbosa Silveira, Emma Catherine Turner, and Jérôme Vidot
Atmos. Meas. Tech., 17, 1279–1296, https://doi.org/10.5194/amt-17-1279-2024, https://doi.org/10.5194/amt-17-1279-2024, 2024
Short summary
Short summary
A fast radiative transfer model, used to speed up the full spectral simulation of meteorological satellite channels in weather forecast models, is tested using 25 000 modelled atmospheres. The differences between calculations from the fast and the high-resolution reference models are examined for nine historic weather satellite instruments. The study confirms that a reduced set of 83 atmospheric profiles is robust enough to estimate the scale of the differences obtained from the larger sample.
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Short summary
Satellite observations of radiation from the earth can have strong sensitivity to cloud and precipitation in the atmosphere, with applications in weather forecasting and the development of models. Computing the radiation received at the satellite sensor using radiative transfer theory requires a simulation of the optical properties of a volume containing a large number of cloud and precipitation particles. This article describes the physics used to generate these
bulkoptical properties.
Sebastien Massart, Niels Bormann, Massimo Bonavita, and Cristina Lupu
Geosci. Model Dev., 14, 5467–5485, https://doi.org/10.5194/gmd-14-5467-2021, https://doi.org/10.5194/gmd-14-5467-2021, 2021
Short summary
Short summary
Numerical weather predictions combine data from satellites with atmospheric forecasts. Some satellites measure the radiance emitted by the Earth's surface. To use this data, one must have knowledge of the surface properties, like the temperature of the thin layer above the surface. Error in this temperature leads to a misuse of the satellite data and affects the quality of the weather forecast. We updated our approach to better estimate this temperature, which should help improve the forecast.
Cited articles
Andrey-Andrés, J., Fourrié, N., Guidard, V., Armante, R., Brunel, P., Crevoisier, C., and Tournier, B.: A simulated observation database to assess the impact of the IASI-NG hyperspectral infrared sounder, Atmos. Meas. Tech., 11, 803–818, https://doi.org/10.5194/amt-11-803-2018, 2018. a
Bucholtz, A.: Rayleigh-scattering calculations for the terrestrial atmosphere,
Appl. Optics, 34, 2765–2773, 1995. a
Chen, Y., Weng, F., Han, Y., and Liu, Q.: Validation of the community radiative
transfer model (CRTM) by using CloudSat Data, J. Geophys. Res., 113,
2156–2202, 2008. a
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J.,
Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative
transfer modeling: a summary of the AER codes, Short Communication, J. Quant.
Spectrosc. Ra., 91, 233–244, 2005. a
Crevoisier, C., Clerbaux, C., Guidard, V., Phulpin, T., Armante, R., Barret, B., Camy-Peyret, C., Chaboureau, J.-P., Coheur, P.-F., Crépeau, L., Dufour, G., Labonnote, L., Lavanant, L., Hadji-Lazaro, J., Herbin, H., Jacquinet-Husson, N., Payan, S., Péquignot, E., Pierangelo, C., Sellitto, P., and Stubenrauch, C.: Towards IASI-New Generation (IASI-NG): impact of improved spectral resolution and radiometric noise on the retrieval of thermodynamic, chemistry and climate variables, Atmos. Meas. Tech., 7, 4367–4385, https://doi.org/10.5194/amt-7-4367-2014, 2014. a
Ding, S., Yang, P., Weng, F., Liu, Q., Han, Y., van Delst, P., Li, J., and
Baum, B.: Validation of the community radiative transfer model, J. Quant.
Spectrosc. Ra., 112, 1050–1064, 2011. a
Eresmaa, R. and McNally, A. P.: Diverse profile datasets from the ECMWF
137-level short-range forecasts, Report, NWP SAF, available at:
https://nwp-saf.eumetsat.int/site/download/documentation/rtm/nwpsaf-ec-tr-017.pdf
(last access: 16 November 2020), 2014. a
Eyre, J. R. and Woolf, H.: Transmittance of atmospheric gases in the microwave
region, Appl. Optics, 27, 3244–3249, 1988. a
Ghent, D. J., Corlett, G. K., Göttsche, F.-M., and Remedios, J. J.: Global
Land Surface Temperature From the Along‐Track Scanning Radiometers, J.
Geophys. Res.-Atmos., 27, 12167–12193, https://doi.org/10.1002/2017JD027161,
2017. a
Havemann, S., Thelen, J.-C., Taylor, J. P., and Harlow, R. C.: The
Havemann-Taylor Fast Radiative Transfer Code (HT-FRTC): A multipurpose code
based on principal components, J. Quant. Spectrosc. Ra., 220,
180–192, https://doi.org/10.1016/j.jqsrt.2018.09.008, 2018. a
Hocking, J.: A visible/infrared multiple scattering model for RTTOV, Report,
NWP SAF, available at:
https://nwp-saf.eumetsat.int/publications/tech_reports/nwpsaf-mo-tr-031.pdf
(last access: 16 November 2020), 2016. a
Hocking, J., Brunel, P., Vidot, J., Roquet, P., and Turner, E.: RTTOV comparisons with LBL simulations, available at: https://nwp-saf.eumetsat.int/site/software/rttov/download/coefficients/comparison-with-lbl-simulations/, last access: 18 May 2021. a
Liebe, H. J.: MPM – An atmospheric millimeter-wave propagation model, Int. J. Infrared Milli Waves, 10, 631–650, https://doi.org/10.1007/BF01009565, 1989. a
Lupu, C. and Geer, A.: Operational implementation of RTTOV-11 in the IFS,
Technical Memorandum 748, ECMWF, 2015. a
Lupu, C. and Wilhelmsson, T.: A guide to simulated satellite images in the IFS,
Research department memorandum rd16-064, 10 pp., ECMWF, available at:
https://software.ecmwf.int/wiki/display/FCST/Simulated+satellite+data?preview=/55127736/97382070/A_guide_to_simulated_satellite_images_in_the_IFS.pdf (last access: 16
November 2020), 2016. a
Matricardi, M.: A principal component based version of the RTTOV fast radiative
transfer model, Q. J. Roy. Meteor. Soc., 136, 1823–1835,
https://doi.org/10.1002/qj.680, 2010. a
McMillin, L. M. and Fleming, H. E.: Atmospheric transmittance of an absorbing
gas: a computationally fast and accurate transmittance model for absorbing
gases with constant mixing ratios in inhomogeneous atmospheres, Appl. Optics,
15, 358–363, 1976. a
McMillin, L. M., Crone, L. J., and Kleespies, T. J.: Atmospheric transmittance
of an absorbing gas. 5. Improvements to the OPTRAN approach, Appl. Optics,
34, 8396–8399, 1995. a
McMillin, L. M., Xiong, X., Han, Y., Kleespies, T. J., and Delst, P. V.:
Atmospheric transmittance of an absorbing gas. 7. Further improvements to the
OPTRAN 6 approach, Appl. Optics, 45, 2028–2034, https://doi.org/10.1364/AO.45.002028,
2006. a
Moncet, J.-L., Uymin, G., Liang, P., and Lipton, A.: Fast and accurate
radiative transfer in the thermal regime by simultaneous optimal spectral
sampling over all channels, J. Atmos. Sci., 72, 2262–2641,
https://doi.org/10.1175/JAS-D-14-0190.1, 2015. a
Rothman, L. S., Gordon, I. E., Babikov, Y., Barbe, A., Chris Benner, D., Bernath, P. F., Birk, M., Bizzocchi, L., Boudon, V., Brown, L. R., Campargue, A., Chance, K., Cohen, E. A., Coudert, L. H., Devi, V. M., Drouin, B. J., Fayt, A., Flaud, J.-M., Gamache, R. R., Harrison, J. J., Hartmann, J.-M., Hill, C., Hodges, J. T., Jacquemart, D., Jolly, A., Lamouroux, J., Le Roy, R. J., Li, G., Long, D. A., Lyulin, O. M., Mackie, C. J., Massie, S. T., Mikhailenko, S., Müller, H. S. P., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V., Perrin, A., Polovtseva, E. R., Richard, C., Smith, M. A. H., Starikova, E., Sung, K., Tashkun, S., Tennyson, J., Toon, G. C., Tyuterev, Vl. G., and Wagner, G.: The HITRAN2012
molecular spectroscopic database, J. Quant. Spectrosc. Ra., 130,
4–50, https://doi.org/10.1016/j.jqsrt.2013.07.002, 2013. a
Saunders, R., Matricardi, M., and Brunel, P.: An improved fast radiative
transfer model for assimilation of satellite radiance observations, Q. J.
Roy. Meteor. Soc., 125, 1407–1425, https://doi.org/10.1002/qj.1999.49712555615, 1999. a
Saunders, R., Brunel, P., English, S., Bauer, P., O'Keefe, U., Francis, P. N.,
and Rayer, P.: RTTOV-8 science and validation report, available at:
https://nwp-saf.eumetsat.int/site/download/documentation/rtm/rttov8_svr.pdf
(last access: 16 November 2020), 2006.
a
Saunders, R., Hocking, J., Rundle, D., Rayer, P., Havemann, S., Matricardi, M.,
Geer, A., Lupu, C., Brunel, P., and Vidot, J.: RTTOV v12 science and
validation report, available at:
https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov12/rttov12_svr.pdf
(last access: 16 November 2020), 2017. a, b
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a, b, c, d
Saunders, R., Hocking, J., Turner, E., Havemann, S., Geer, A., Lupu, C., Vidot,
J., Chambon, P., Köpken-Watts, C., Scheck, L., Stiller, O., Stumpf, C., and
Borbas, E.: RTTOV v13 science and validation report, available at:
https://nwp-saf.eumetsat.int/site/download/documentation/rtm/docs_rttov13/rttov13_svr.pdf,
last access: 16 November 2020. a
Scheck, L.: Comparison of MFASIS and RTTOV-DOM, Report, NWP SAF, available at:
https://nwp-saf.eumetsat.int/publications/vs_reports/nwpsaf-mo-vs-054.pdf
(last access: 16 November 2020), 2016. a
Turner, E., Rayer, P., and Saunders, R.: AMSUTRAN: A microwave transmittance
code for satellite remote sensing, J. Quant. Spectrosc. Ra.,
227, 117–129, https://doi.org/10.1016/j.jqsrt.2019.02.013, 2019. a
Short summary
RTTOV is a fast radiative transfer model for simulating passive satellite-based observations at visible, infrared, and microwave wavelengths. A core part of the model is a parameterisation of the absorption of radiation by the various gases present in the atmosphere. We present a new parameterisation that performs well compared to the existing one in terms of accuracy and can be developed further more easily. The new parameterisation is implemented in the latest release, RTTOV v13.0.
RTTOV is a fast radiative transfer model for simulating passive satellite-based observations at...