Articles | Volume 18, issue 12
https://doi.org/10.5194/amt-18-2751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-2751-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
Rohit Mangla
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
now at: Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore, Singapore
Mary Borderies
CORRESPONDING AUTHOR
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Philippe Chambon
CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
Alan Geer
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
James Hocking
Met Office, Fitzroy Rd., Exeter, UK
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David I. Duncan, Niels Bormann, Marijana Crepulja, Mohamed Dahoui, Alan J. Geer, Christophe Accadia, Sabatino Di Michele, Tim J. Hewison, and Ville Kangas
EGUsphere, https://doi.org/10.5194/egusphere-2026-712, https://doi.org/10.5194/egusphere-2026-712, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Satellite data used in weather forecast models needs to be of a very high quality. Previously, this has been delivered by bus-sized satellites. The new Arctic Weather Satellite shifts this paradigm, delivering high quality observations from a small satellite. Here we analyse the performance and test its impact with a state-of-the-art weather forecast model. It compares well to heritage instruments and has a positive impact on forecast skill.
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
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In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Chloé Radice, Hélène Brogniez, Pierre-Emmanuel Kirstetter, and Philippe Chambon
Atmos. Chem. Phys., 22, 3811–3825, https://doi.org/10.5194/acp-22-3811-2022, https://doi.org/10.5194/acp-22-3811-2022, 2022
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A novel probabilistic approach is proposed to evaluate relative humidity (RH) profiles simulated by an atmospheric model with respect to satellite-based RH defined from probability distributions. It improves upon deterministic comparisons by enhancing the information content to enable a finer assessment of each model–observation discrepancy, highlighting significant departures within a deterministic confidence range. Geographical and vertical distributions of the model biases are discussed.
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
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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.
Alan J. Geer
Atmos. Meas. Tech., 14, 5369–5395, https://doi.org/10.5194/amt-14-5369-2021, https://doi.org/10.5194/amt-14-5369-2021, 2021
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Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Alistair Bell, Pauline Martinet, Olivier Caumont, Benoît Vié, Julien Delanoë, Jean-Charles Dupont, and Mary Borderies
Atmos. Meas. Tech., 14, 4929–4946, https://doi.org/10.5194/amt-14-4929-2021, https://doi.org/10.5194/amt-14-4929-2021, 2021
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This paper presents work towards making retrievals on the liquid water content in fog and low clouds. Future retrievals will rely on a radar simulator and high-resolution forecast. In this work, real observations are used to assess the errors associated with the simulator and forecast. A selection method to reduce errors associated with the forecast is proposed. It is concluded that the distribution of errors matches the requirements for future retrievals.
James Hocking, Jérôme Vidot, Pascal Brunel, Pascale Roquet, Bruna Silveira, Emma Turner, and Cristina Lupu
Geosci. Model Dev., 14, 2899–2915, https://doi.org/10.5194/gmd-14-2899-2021, https://doi.org/10.5194/gmd-14-2899-2021, 2021
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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.
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021, https://doi.org/10.5194/amt-14-3427-2021, 2021
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Oriented nonspherical ice particles induce polarization that is ignored when cloud-sensitive satellite observations are used in numerical weather prediction systems. We present a simple approach for approximating particle orientation, requiring minor adaption of software and no additional calculation burden. With this approach, the system realistically simulates the observed polarization patterns, increasing the physical consistency between instruments with different polarizations.
Cited articles
Aonashi, K., Battaglia, A., Bolvin, D. T., Borderies, M., Chambon, P., Ferraro, R. R., Geer, A. J., Haddad, Z., Huffman, G. J., Ikuta, Y., Johnson, B. T., Kachi, M., Kidd, C., Kirstetter, P., Kubota, T., Kummerow, C., Louf, V., Maggioni, V., Mangla, R., Okamoto, K., Protat, A., and Shige, S.: A review of the different operational applica- tions of spaceborne precipitation radars within the International Precipitation Working Group (IPWG) community, IPWG, https://www.eorc.jaxa.jp/IPWG/reports/IPWG_review_applications_space-borne_precipitation_radars.pdf (last access: 10 June 2021), 2021. a
Augros, C., Caumont, O., Ducrocq, V., Gaussiat, N., and Tabary, P.: Comparisons between S-, C-and X-band polarimetric radar observations and convective-scale simulations of the HyMeX first special observing period, Q. J. Roy. Meteorol. Soc., 142, 347–362, 2016. a
Battaglia, A., Augustynek, T., Tanelli, S., and Kollias, P.: Multiple scattering identification in spaceborne W-band radar measurements of deep convective cores, J. Geophys. Res.-Atmos., 116, D19201, https://doi.org/10.1029/2011JD016142, 2011. a
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K., Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and Furukawa, K.: Spaceborne cloud and precipitation radars: Status, challenges, and ways forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020. a
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., William, K. D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13, https://doi.org/10.1029/2007JD009620, 2008. a, b
Bodas-Salcedo, A., Webb, M. J., Brooks, M. E., Ringer, M. A., William, K. D., Milton, S. F., and Wilson, D. R.: Evaluating cloud systems in the Met Office global forecast model using simulated CloudSat radar reflectivities, J. Geophys. Res., 113, D00A13, https://doi.org/10.1029/2007JD009620, 2008. a
Bodas-Salcedo, A., Webb, M., Bony, S., Chepfer, H., Dufresne, J.-L., Klein, S., Zhang, Y., Marchand, R., Haynes, J., Pincus, R., and John, V. O.: COSP: Satellite simulation software for model assessment, B. Am. Meteorol. Soc., 92, 1023–1043, 2011. a
Bohren, C. F. and Battan, L. J.: Radar backscattering of microwaves by spongy ice spheres, J. Atmos. Sci., 39, 2623–2628, 1982. a
Boodoo, S., Hudak, D., Donaldson, N., and Leduc, M.: Application of dual-polarization radar melting-layer detection algorithm, J. Appl. Meteorol. Climatol., 49, 1779–1793, 2010. a
Borderies, M., Caumont, O., Augros, C., Bresson, É., Delanoë, J., Ducrocq, V., Fourrié, N., Bastard, T. L., and Nuret, M.: Simulation of W-band radar reflectivity for model validation and data assimilation, Q. J. Roy. Meteorol. Soc., 144, 391–403, 2018. a
Botta, G., Aydin, K., and Verlinde, J.: Modeling of microwave scattering from cloud ice crystal aggregates and melting aggregates: A new approach, IEEE Geosci. Remote Sens. Lett., 7, 572–576, 2010. a
Bouyssel, F., Berre, L., Bénichou, H., Chambon, P., Girardot, N., Guidard, V., Loo, C., Mahfouf, J.-F., Moll, P., Payan, C., and Raspaud, D.: The 2020 global operational nwp data assimilation system at météo-france, Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV), 645–664, https://doi.org/10.1007/978-3-030-77722-7_25, 2022. a
Cintineo, R., Otkin, J. A., Xue, M., and Kong, F.: Evaluating the performance of planetary boundary layer and cloud microphysical parameterization schemes in convection-permitting ensemble forecasts using synthetic GOES-13 satellite observations, Mon. Weather Rev., 142, 163–182, 2014. a
Courtier, P.: The ARPEGE Project at Météo-France, in: Proc. of ECMWF seminar on Numerical Methods in Atmospheric Models, 9–13 September 1991, ECMWF: Reading, UK, 1991. a
Das, S. K., Deshpande, S. M., Krishna, U. M., Konwar, M., Kolte, Y. K., Chakravarty, K., Kalapureddy, M., and Sahoo, S.: Aspects of melting layer and fall streaks in stratiform cloud system over the Western Ghats, India from Ka-band polarimetric radar observations, Atmos. Res., 281, 106463, https://doi.org/10.1016/j.atmosres.2022.106463, 2023. a
Desroziers, G., Pouponneau, B., Thépaut, J.-N., Janisková, M., and Veersé, F.: Four-dimensional variational analyses of FASTEX situations using special observations, Q. J. Roy. Meteorol. Soc., 125, 3339–3358, 1999. a
Di Michele, S., Ahlgrimm, M., Forbes, R., Kulie, M., Bennartz, R., Janisková, M., and Bauer, P.: Interpreting an evaluation of the ECMWF global model with CloudSat observations: Ambiguities due to radar reflectivity forward operator uncertainties, Q. J. Roy. Meteorol. Soc., 138, 2047–2065, 2012. a, b
Ebert, E. E., Janowiak, J. E., and Kidd, C.: Comparison of near-real-time precipitation estimates from satellite observations and numerical models, B. Am. Meteorol. Soc., 88, 47–64, 2007. a
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018. a
Fabry, F. and Sun, J.: For how long should what data be assimilated for the mesoscale forecasting of convection and why? Part I: On the propagation of initial condition errors and their implications for data assimilation, Mon. Weather Rev., 138, 242–255, 2010. a
Galligani, V., Prigent, C., Defer, E., Jimenez, C., and Eriksson, P.: The impact of the melting layer on the passive microwave cloud scattering signal observed from satellites: A study using TRMM microwave passive and active measurements, J. Geophys. Res.-Atmos., 118, 5667–5678, 2013. a
Maxwell Garnett, J. M.: XII. Colours in metal glasses and in metallic films, Philos. T. R. Soc. A, Containing Papers of a Mathematical or Physical Character, 203, 385–420, 1904. a
Geer, A. J.: Physical characteristics of frozen hydrometeors inferred with parameter estimation, Atmos. Meas. Tech., 14, 5369–5395, https://doi.org/10.5194/amt-14-5369-2021, 2021. a
Geer, A. J. and Baordo, F.: Improved scattering radiative transfer for frozen hydrometeors at microwave frequencies, Atmos. Meas. Tech., 7, 1839–1860, https://doi.org/10.5194/amt-7-1839-2014, 2014. a, b
Geer, A. J., Bauer, P., Lonitz, K., Barlakas, V., Eriksson, P., Mendrok, J., Doherty, A., Hocking, J., and Chambon, P.: Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0, Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021, 2021. a, b, c, d, e, f, g
Giangrande, S. E., Krause, J. M., and Ryzhkov, A. V.: Automatic designation of the melting layer with a polarimetric prototype of the WSR-88D radar, J. Appl. Meteorol. Climatol., 47, 1354–1364, 2008. a
Hamada, A. and Takayabu, Y. N.: Improvements in detection of light precipitation with the Global Precipitation Measurement dual-frequency precipitation radar (GPM DPR), J. Atmos. Ocean. Technol., 33, 653–667, 2016. a
Hashino, T., Satoh, M., Hagihara, Y., Kubota, T., Matsui, T., Nasuno, T., and Okamoto, H.: Evaluating cloud microphysics from NICAM against CloudSat and CALIPSO, J. Geophys. Res.-Atmos., 118, 7273–7292, 2013. a
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G. L.: A multipurpose radar simulation package: QuickBeam, B. Am. Meteorol. Soc., 88, 1723–1728, 2007. a
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Kidd, C., Nelkin, E. J., Sorooshian, S., Tan, J., and Xie, P.: NASA global precipitation measurement (GPM) integrated multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD) Version, 4, 2018. a
Iguchi, T., Seto, S., Meneghini, R., Yoshida, N., Awaka, J., Le, M., Chandrasekar, V., and Kubota, T.: GPM/DPR level-2 algorithm theoretical basis document, NASA Goddard Space Flight Center, https://www.eorc.jaxa.jp/GPM/doc/algorithm/ATBD_DPR_201811_with_Appendix3b.pdf (last access: January 2023), 2010. a, b, c, d, e, f
Iguchi, T., Matsui, T., Tao, W.-K., Khain, A. P., Phillips, V. T., Kidd, C., L’Ecuyer, T., Braun, S. A., and Hou, A.: WRF–SBM simulations of melting-layer structure in mixed-phase precipitation events observed during LPVEx, J. Appl. Meteorol. Climatol., 53, 2710–2731, 2014. a
Ikuta, Y., Okamoto, K., and Kubota, T.: One-dimensional maximum-likelihood estimation for spaceborne precipitation radar data assimilation, Quarterly J. Roy. Meteorol. Soc., 147, 858–875, 2021a. a
Ikuta, Y., Satoh, M., Sawada, M., Kusabiraki, H., and Kubota, T.: Improvement of the Cloud Microphysics Scheme of the Mesoscale Model at the Japan Meteorological Agency Using Spaceborne Radar and Microwave Imager of the Global Precipitation Measurement as Reference, Mon. Weather Rev., 149, 3803–3819, 2021b. a
Johnson, B. T., Olson, W. S., and Skofronick-Jackson, G.: The microwave properties of simulated melting precipitation particles: sensitivity to initial melting, Atmos. Meas. Tech., 9, 9–21, https://doi.org/10.5194/amt-9-9-2016, 2016. a, b, c, d
Johnson, B. T. and Boukabara, S. A.: Development of the Community Active Sensor Module (CASM): Forward Simulation, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2016-154, in review, 2016. a
Johnson, B. T., Dang, C., Stegmann, P., Liu, Q., Moradi, I., and Auligne, T.: The Community Radiative Transfer Model (CRTM): Community-focused collaborative model development accelerating research to operations, B. Am. Meteorol. Soc., 104, E1817–E1830, 2023. a
Karrer, M., Dias Neto, J., von Terzi, L., and Kneifel, S.: Melting Behavior of Rimed and Unrimed Snowflakes Investigated With Statistics of Triple-Frequency Doppler Radar Observations, J. Geophys. Res.-Atmos., 127, e2021JD035907, https://doi.org/10.1029/2021JD035907, 2022. a
Kollias, P. and Albrecht, B.: Why the melting layer radar reflectivity is not bright at 94 GHz, Geophys. Res. Lett., 32, L24818, https://doi.org/10.1029/2005GL024074, 2005. a, b, c
Kotsuki, S., Terasaki, K., and Miyoshi, T.: GPM/DPR precipitation compared with a 3.5-km-resolution NICAM simulation, Sola, 10, 204–209, 2014. a
Kotsuki, S., Terasaki, K., Satoh, M., and Miyoshi, T.: Ensemble-Based Data Assimilation of GPM DPR Reflectivity: Cloud Microphysics Parameter Estimation With the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), J. Geophys. Res.-Atmos., 128, e2022JD037447, https://doi.org/10.1029/2022JD037447, 2023. a
Le, M. and Chandrasekar, V.: Precipitation type classification method for dual-frequency precipitation radar (DPR) onboard the GPM, IEEE T. Geosci. Remote Sens., 51, 1784–1790, 2012. a
Le, M. and Chandrasekar, V.: Graupel and hail identification algorithm for the dual-frequency precipitation radar (DPR) on the GPM core satellite, J. Meteorol. Soc. JPN II, 99, 49–65, 2021. a
Le, M., Chandrasekar, V., and Biswas, S.: An algorithm to identify surface snowfall from GPM DPR observations, IEEE T. Geosci. Remote Sens., 55, 4059–4071, 2017. a
Levene, H.: Contributions to probability and statistics, Essays in honor of Harold Hotelling, 278, 292, 1960. a
Lhermitte, R. M.: Observation of rain at vertical incidence with a 94 GHz Doppler radar: An insight on Mie scattering, Geophys. Res. Lett., 15, 1125–1128, 1988. a
Li, Y., Zipser, E. J., Krueger, S. K., and Zulauf, M. A.: Cloud-resolving modeling of deep convection during KWAJEX. Part I: Comparison to TRMM satellite and ground-based radar observations, Mon. Weather Rev., 136, 2699–2712, 2008. a
Liao, L., Meneghini, R., Nowell, H. K., and Liu, G.: Scattering computations of snow aggregates from simple geometrical particle models, IEEE J. Select. Top. Appl. Earth Observ. Remote Sens., 6, 1409–1417, 2013. a
Liu, C. and Zipser, E. J.: The global distribution of largest, deepest, and most intense precipitation systems, Geophys. Res. Lett., 42, 3591–3595, 2015. a
Liu, G.: A database of microwave single-scattering properties for nonspherical ice particles, B. Am. Meteorol. Soc., 89, 1563–1570, 2008. a
Liu, N. and Liu, C.: Global distribution of deep convection reaching tropopause in 1 year GPM observations, J. Geophys. Res.-Atmos., 121, 3824–3842, 2016. a
Lopez, P.: Implementation and validation of a new prognostic large-scale cloud and precipitation scheme for climate and data-assimilation purposes, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 128, 229–257, 2002. a
Mai, L., Yang, S., Wang, Y., and Li, R.: Impacts of Shape Assumptions on Z–R Relationship and Satellite Remote Sensing Clouds Based on Model Simulations and GPM Observations, Remote Sens., 15, 1556, https://doi.org/10.3390/rs15061556, 2023. a
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A comparison of simulated cloud radar output from the multiscale modeling framework global climate model with CloudSat cloud radar observations, J. Geophys. Res.-Atmos., 114, D00A20, https://doi.org/10.1029/2008JD009790, 2009. a
Marshall, J. S. and Palmer, W. M.: The distribution of raindrops with size, J. Meteorol., 5, 165–166, 1948. a
Matsui, T., Iguchi, T., Li, X., Han, M., Tao, W.-K., Petersen, W., L'Ecuyer, T., Meneghini, R., Olson, W., Kummerow, C. D., Hou, A. Y., Schwaller, M. R., Stocker, E. F., and Kwiatkowski, J.: GPM satellite simulator over ground validation sites, B. Am. Meteorol. Soc., 94, 1653–1660, 2013. a
Mätzler, C.: Thermal microwave radiation: applications for remote sensing, Iet, 52, 584 pp., https://doi.org/10.1049/PBEW052E, 2006. a, b
Mech, M., Maahn, M., Kneifel, S., Ori, D., Orlandi, E., Kollias, P., Schemann, V., and Crewell, S.: PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere, Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020, 2020. a
Meneghini, R. and Liao, L.: Effective dielectric constants of mixed-phase hydrometeors, J. Atmos. Ocean. Technol., 17, 628–640, 2000. a
Mitra, S., Vohl, O., Ahr, M., and Pruppacher, H.: A wind tunnel and theoretical study of the melting behavior of atmospheric ice particles. IV: Experiment and theory for snow flakes, J. Atmos. Sci., 47, 584–591, 1990. a
Moradi, I., Johnson, B., Stegmann, P., Holdaway, D., Heymsfield, G., Gelaro, R., and McCarty, W.: Developing a Radar Signal Simulator for the Community Radiative Transfer Model, IEEE T. Geosci. Remote Sens., 61, https://doi.org/10.1109/TGRS.2023.3330067, 2023. a, b
Olson, W., Bauer, P., Kummerow, C. D., Hong, Y., and Tao, W.-K.: A melting-layer model for passive/active microwave remote sensing applications. Part II: Simulation of TRMM observations, J. Appl. Meteorol. Climatol., 40, 1164–1179, 2001. a
Park, S.: IFS Doc-Physical Processes; IFS Documentation CY45R1, no. 1, https://doi.org/10.21957/4whwo8jw0, 2018. a
Petty, G. W. and Huang, W.: Microwave backscatter and extinction by soft ice spheres and complex snow aggregates, J. Atmos. Sci., 67, 769–787, 2010. a
Ringer, M., Edwards, J., and Slingo, A.: Simulation of satellite channel radiances in the Met Office Unified Model, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Appl. Meteorol. Phys. Oceanogr., 129, 1169–1190, 2003. a
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78–97, 2008. a
Romatschke, U.: Melting layer detection and observation with the NCAR airborne W-band radar, Remote Sens., 13, 1660, https://doi.org/10.21957/4whwo8jw0, 2021. a
RTTOV: RTTOV version 13, EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), RTTOV [code], https://nwp-saf.eumetsat.int/site/software/rttov/rttov-v13/ (last access: January, 2023), 2025. a
Sassen, K., Campbell, J. R., Zhu, J., Kollias, P., Shupe, M., and Williams, C.: Lidar and triple-wavelength Doppler radar measurements of the melting layer: A revised model for dark-and brightband phenomena, J. Appl. Meteorol., 44, 301–312, 2005. a
Sassen, K., Matrosov, S., and Campbell, J.: CloudSat spaceborne 94 GHz radar bright bands in the melting layer: An attenuation-driven upside-down lidar analog, Geophys. Res. Lett., 34, L16818, https://doi.org/10.1029/2007GL030291, 2007. a
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
Skofronick-Jackson, G., Petersen, W. A., Berg, W., Kidd, C., Stocker, E. F., Kirschbaum, D. B., Kakar, R., Braun, S. A., Huffman, G. J., Iguchi, T., Kirstetter, P. E., Kummerow, C., Meneghini, R., Oki, R., Olson, W. S., Takayabu, Y. N., Furukawa, K., and Wilheit, T.: The Global Precipitation Measurement (GPM) mission for science and society, B. Am. Meteorol. Soc., 98, 1679–1695, 2017. a
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, 2002. a
Sun, Y., Dong, X., Cui, W., Zhou, Z., Fu, Z., Zhou, L., Deng, Y., and Cui, C.: Vertical structures of typical Meiyu precipitation events retrieved from GPM-DPR, J. Geophys. Res.-Atmos., 125, e2019JD031466, https://doi.org/10.1029/2019JD031466, 2020. a, b
Szyrmer, W. and Zawadzki, I.: Modeling of the melting layer. Part I: Dynamics and microphysics, J. Atmos. Sci., 56, 3573–3592, 1999. a
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterization in large-scale models, Mon. Weather Rev., 117, 1779–1800, 1989. a
Zhang, A. and Fu, Y.: Life cycle effects on the vertical structure of precipitation in East China measured by Himawari-8 and GPM DPR, Mon. Weather Rev., 146, 2183–2199, 2018. a
Zhang, J., Langston, C., and Howard, K.: Brightband identification based on vertical profiles of reflectivity from the WSR-88D, J. Atmos. Ocean. Technol., 25, 1859–1872, 2008. a
Zimmerman, D. W. and Zumbo, B. D.: Rank transformations and the power of the Student t test and Welch t'test for non-normal populations with unequal variances, Can. J. Exp. Psychol., 47, 523, https://doi.org/10.1037/h0078850, 1993. a
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.
This study provides a detailed description of the radar simulator available within version 13 of...