Articles | Volume 17, issue 23
https://doi.org/10.5194/gmd-17-8773-2024
© Author(s) 2024. 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-17-8773-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
11 Dec 2024
Development and technical paper |
| 11 Dec 2024
| 11 Dec 2024
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
Marie Taufour
Laboratoire d'Aérologie, Université de Toulouse, UT3, CNRS, IRD, Toulouse, France
Jean-Pierre Pinty
Laboratoire d'Aérologie, Université de Toulouse, UT3, CNRS, IRD, Toulouse, France
Christelle Barthe
CORRESPONDING AUTHOR
Laboratoire d'Aérologie, Université de Toulouse, UT3, CNRS, IRD, Toulouse, France
Benoît Vié
Centre National de Recherches Météorologiques – UMR 3589, Toulouse, France
Chien Wang
Laboratoire d'Aérologie, Université de Toulouse, UT3, CNRS, IRD, Toulouse, France
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Cited articles
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. Meteor. Soc., 142, 347–362, https://doi.org/10.1002/qj.2572, 2016. a
Barnes, H. C. and Houze Jr., R. A.: Precipitation hydrometeor type relative to the mesoscale airflow in mature oceanic deep convection of the Madden-Julian Oscillation, J. Geophys. Res.-Atmos., 119, 13990–14014, https://doi.org/10.1002/2014JD022241, 2014. a
Barthe, C.: LIMAv2.0 in MesoNH-V5-6-0, Zenodo [code], https://doi.org/10.5281/zenodo.11393718, 2024. a
Barthe, C., Molinié, G., and Pinty, J.-P.: Description and first results of an explicit electrical scheme in a 3D cloud resolving model, Atmos. Res., 76, 95–113, https://doi.org/10.1016/j.atmosres.2004.11.021, 2005. a
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A mass-flux convection scheme for regional and global models, Q. J. Roy. Meteor. Soc., 127, 869–886, https://doi.org/10.1002/qj.49712757309, 2001. a
Beheng, K. D.: Microphysical properties of glaciating cumulus clouds: comparison of measurements with a numerical simulation, J. Geophys. Res.-Atmos., 113, 1377–1381, https://doi.org/10.1002/qj.49711347815, 1987. a
Brown, B. R., Bell, M. M., and Thompson, G.: Improvements to the snow melting process in a partially double moment microphysics parameterization, J. Adv. Model. Earth Sy., 9, 1150–1166, https://doi.org/10.1002/2016MS000892, 2017. a
Caumont, O., Ducrocq, V., Delrieu, G., Gosset, M., Pinty, J.-P., Parent du Châtelet, J., Andrieu, H., Lemaître, Y., and Scialom, G.: A radar simulator for high-resolution nonhydrostatic models, J. Atmos. Ocean. Tech., 23, 1049–1067, https://doi.org/10.1175/JTECH1905.1, 2006. a
Christensen, M. W. and Stephens, G. L.: Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships: Evidence of cloud deepening, J. Geophys. Res.-Atmos., 116, D3, https://doi.org/10.1029/2010JD014638, 2011. a
Cohard, J.-M. and Pinty, J.-P.: A comprehensive two-moment warm microphysical bulk scheme. II: 2D experiments with a non-hydrostatic model, Q. J. Roy. Meteor. Soc., 126, 1843–1859, https://doi.org/10.1002/qj.49712656614, 2000b. a
Cohard, J.-M., Pinty, J.-P., and Bedos, C.: Extending Twomey’s Analytical Estimate of Nucleated Cloud Droplet Concentrations from CCN Spectra, J. Atmos. Sci., 55, 3348–3357, https://doi.org/10.1175/1520-0469(1998)055<3348:ETSAEO>2.0.CO;2, 1998. a, b
Colella, P. and Woodward, P. R.: The Piecewise Parabolic Method (PPM) for gas-dynamical simulations, J. Comput. Phys., 54, 174–201, https://doi.org/10.1016/0021-9991(84)90143-8, 1984. a
Cotton, W. R., Tripoli, G. J., Rauber, R. M., and Mulvihill, E. A.: Numerical Simulation of the Effects of Varying Ice Crystal Nucleation Rates and Aggregation Processes on Orographic Snowfall, J. Clim. Appl. Meteorol., 25, 1658–1680, https://doi.org/10.1175/1520-0450(1986)025<1658:NSOTEO>2.0.CO;2, 1986. a
Crameri, F.: Scientific colour maps, Zenodo [data set], https://doi.org/10.5281/zenodo.1243862, 2018. a
Crameri, F., Shephard, G., and Heron, P.: The misuse of colour in science communication, Nat. Commun., 11, 5444, https://doi.org/10.1038/s41467-020-19160-7, 2020. a
Cuxart, J., Bougeault, P., and Redelsperger, J.-L.: A turbulence scheme allowing for mesoscale and large-eddy simulations, Q. J. Roy. Meteor. Soc., 126, 1–30, https://doi.org/10.1002/qj.49712656202, 2000. a, b
Dawson, D. T., Xue, M., Milbrandt, J. A., and Yau, M. K.: Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms, Mon. Weather Rev., 138, 1152–1171, https://doi.org/10.1175/2009MWR2956.1, 2010. a
Delanoë, J., Protat, A., Testud, J., Bouniol, D. Heymsfield, A. J., Bansemer, A., Brown, P. R. A., and Forbes, R. M.: Statistical properties of the normalized ice particle size distribution, J. Geophys. Res.-Atmos., 110, d10201, https://doi.org/10.1029/2004JD005405, 2005. a
Delanoë, J., Heymsfield, A. J., Protat, A., Bansemer, A., and Hogan, R. J.: Normalized particle size distribution for remote sensing application, J. Geophys. Res.-Atmos., 119, 4204–4227, https://doi.org/10.1002/2013JD020700, 2014. a
Doswell III, C. A. and Burgess, D. W.: Tornadoes and Toraadic Storms: a Review of Conceptual Models, American Geophysical Union (AGU), 161–172, https://doi.org/10.1029/GM079p0161, 1993. a
Ducrocq, V., Braud, I., Davolio, S., Ferretti, R., Flamant, C., Jansa, A., Kalthoff, N., Richard, E., Taupier-Letage, I., Ayral, P.-A., Belamari, S., Berne, A., Borga, M., Boudevillain, B., Bock, O., Boichard, J.-L., Bouin, M.-N., Bousquet, O., Bouvier, C., Chiggiato, J., Cimini, D., Corsmeier, U., Coppola, L., Cocquerez, P., Defer, E., Delanoë, J., Di Girolamo, P., Doerenbecher, A., Drobinski, P., Dufournet, Y., Fourrié, N., Gourley, J. J., Labatut, L., Lambert, D., Coz, J. L., Marzano, F. S., Molinié, G., Montani, A., Nord, G., Nuret, M., Ramage, K., Rison, W., Roussot, O., Said, F., Schwarzenboeck, A., Testor, P., Baelen, J. V., Vincendon, B., Aran, M., and Tamayo, J.: HyMeX-SOP1: The Field Campaign Dedicated to Heavy Precipitation and Flash Flooding in the Northwestern Mediterranean, B. Am. Meteorol. Soc., 95, 1083–1100, https://doi.org/10.1175/BAMS-D-12-00244.1, 2014. a
Farley, R. D., Price, P. E., Orville, H. D., and Hirsch, J. H.: On the Numerical Simulation of Graupel/Hall Initiation via the Riming of Snow in Bulk Water Microphysical Cloud Models, J. Appl. Meteorol., 28, 1128–1131, https://doi.org/10.1175/1520-0450(1989)028<1128:OTNSOG>2.0.CO;2, 1989. a
Ferrier, B. S., Tao, W.-K., and Simpson, J.: A Double-Moment Multiple-Phase Four-Class Bulk Ice Scheme. Part II: Simulations of Convective Storms in Different Large-Scale Environments and Comparisons with other Bulk Parameterizations, J. Atmos. Sci., 52, 1001–1033, https://doi.org/10.1175/1520-0469(1995)052<1001:ADMMPF>2.0.CO;2, 1995. a
Ferrier, S.: A Double-Moment Multiple-Phase Four-Class Bulk Ice Scheme. Part I: Description, J. Atmos. Sci., 51, 249–280, https://doi.org/10.1175/1520-0469(1994)051<0249:ADMMPF>2.0.CO;2, 1994. a, b, c, d
Foote, G. B. and Du Toit, P. S.: Terminal Velocity of Raindrops Aloft, J. Appl. Meteorol., 8, 249–253, https://doi.org/10.1175/1520-0450(1969)008<0249:TVORA>2.0.CO;2, 1969. a
Goren, T. and Rosenfeld, D.: Satellite observations of ship emission induced transitions from broken to closed cell marine stratocumulus over large areas, J. Geophys. Res.-Atmos., 117, D17, https://doi.org/10.1029/2012JD017981, 2012. a
Hall, W. D.: A Detailed Microphysical Model Within a Two-Dimensional Dynamic Framework: Model Description and Preliminary Results, J. Atmos. Sci., 37, 2486–2507, https://doi.org/10.1175/1520-0469(1980)037<2486:ADMMWA>2.0.CO;2, 1980. a
Harrington, J. Y., Meyers, M. P., Walko, R. L., and Cotton, W. R.: Parameterization of Ice Crystal Conversion Processes Due to Vapor Deposition for Mesoscale Models Using Double-Moment Basis Functions. Part I: Basic Formulation and Parcel Model Results, J. Atmos. Sci., 52, 4344–4366, https://doi.org/10.1175/1520-0469(1995)052<4344:POICCP>2.0.CO;2, 1995. a, b, c
Heymsfield, A. J., Bansemer, A., Field, P. R., Durden, S. L., Stith, J. L., Dye, J. E., Hall, W., and Grainger, C. A.: Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitating Clouds: Results from In Situ Observations in TRMM Field Campaigns, J. Atmos. Sci., 59, 3457–3491, https://doi.org/10.1175/1520-0469(2002)059<3457:OAPOPS>2.0.CO;2, 2002. a
Heymsfield, A. J., Schmitt, C., Bansemer, A., and Twohy, C. H.: Improved Representation of Ice Particle Masses Based on Observations in Natural Clouds, J. Atmos. Sci., 67, 3303–3318, https://doi.org/10.1175/2010JAS3507.1, 2010. a
Heymsfield, A. J., Schmitt, C., and Bansemer, A.: Ice Cloud Particle Size Distributions and Pressure-Dependent Terminal Velocities from In Situ Observations at Temperatures from 0° to −86 °C, J. Atmos. Sci., 70, 4123–4154, https://doi.org/10.1175/JAS-D-12-0124.1, 2013. a
Hoarau, T., Barthe, C., Tulet, P., Claeys, M., Pinty, J.-P., Bousquet, O., Delanoë, J., and Vié, B.: Impact of the Generation and Activation of Sea Salt Aerosols on the Evolution of Tropical Cyclone Dumile, J. Geophys. Res.-Atmos., 123, 8813–8831, https://doi.org/10.1029/2017JD028125, 2018a. a, b
Hoarau, T., Pinty, J.-P., and Barthe, C.: A representation of the collisional ice break-up process in the two-moment microphysics LIMA v1.0 scheme of Meso-NH, Geosci. Model Dev., 11, 4269–4289, https://doi.org/10.5194/gmd-11-4269-2018, 2018b. a
Hogan, R. J. and Bozzo, A.: A Flexible and Efficient Radiation Scheme for the ECMWF Model, J. Adv. Model. Earth Sy., 10, 1990–2008, https://doi.org/10.1029/2018MS001364, 2018. a
Kajikawa, M. and Heymsfield, A. J.: Aggregation of Ice Crystals in Cirrus, J. Atmos. Sci., 46, 3108–3121, https://doi.org/10.1175/1520-0469(1989)046<3108:AOICIC>2.0.CO;2, 1989. a
Khain, A., Ovtchinnikov, M., Pinsky, M., Pokrovsky, A., and Krugliak, H.: Notes on the state-of-the-art numerical modeling of cloud microphysics, Atmos. Res., 55, 159–224, https://doi.org/10.1016/S0169-8095(00)00064-8, 2000. a
Khairoutdinov, M. and Kogan, Y.: A New Cloud Physics Parameterization in a Large-Eddy Simulation Model of Marine Stratocumulus, Mon. Weather Rev., 128, 229–243, https://doi.org/10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2, 2000. a
Klemp, J. B. and Wilhelmson, R. B.: The Simulation of Three-Dimensional Convective Storm Dynamics, J. Atmos. Sci., 35, 1070–1096, https://doi.org/10.1175/1520-0469(1978)035<1070:TSOTDC>2.0.CO;2, 1978. a
Kumjian, M. R. and Ryzhkov, A. V.: Polarimetric Signatures in Supercell Thunderstorms, J. Appl. Meteorol. Clim., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1, 2008. a
Lac, C., Chaboureau, J.-P., Masson, V., Pinty, J.-P., Tulet, P., Escobar, J., Leriche, M., Barthe, C., Aouizerats, B., Augros, C., Aumond, P., Auguste, F., Bechtold, P., Berthet, S., Bielli, S., Bosseur, F., Caumont, O., Cohard, J.-M., Colin, J., Couvreux, F., Cuxart, J., Delautier, G., Dauhut, T., Ducrocq, V., Filippi, J.-B., Gazen, D., Geoffroy, O., Gheusi, F., Honnert, R., Lafore, J.-P., Lebeaupin Brossier, C., Libois, Q., Lunet, T., Mari, C., Maric, T., Mascart, P., Mogé, M., Molinié, G., Nuissier, O., Pantillon, F., Peyrillé, P., Pergaud, J., Perraud, E., Pianezze, J., Redelsperger, J.-L., Ricard, D., Richard, E., Riette, S., Rodier, Q., Schoetter, R., Seyfried, L., Stein, J., Suhre, K., Taufour, M., Thouron, O., Turner, S., Verrelle, A., Vié, B., Visentin, F., Vionnet, V., and Wautelet, P.: Overview of the Meso-NH model version 5.4 and its applications, Geosci. Model Dev., 11, 1929–1969, https://doi.org/10.5194/gmd-11-1929-2018, 2018. a, b
Lafore, J. P., Stein, J., Asencio, N., Bougeault, P., Ducrocq, V., Duron, J., Fischer, C., Héreil, P., Mascart, P., Masson, V., Pinty, J. P., Redelsperger, J. L., Richard, E., and Vilà-Guerau de Arellano, J.: The Meso-NH Atmospheric Simulation System. Part I: adiabatic formulation and control simulations, Ann. Geophys., 16, 90–109, https://doi.org/10.1007/s00585-997-0090-6, 1998. a, b
Lau, K., Kim, M., and Kim, K.: Asian summer monsoon anomalies induced by aerosol direct forcing: the role of the Tibetan Plateau, Clim. Dynam., 26, 855–864, https://doi.org/10.1007/s00382-006-0114-z, 2006. a
Lawson, R. P., Woods, S., and Morrison, H.: The Microphysics of Ice and Precipitation Development in Tropical Cumulus Clouds, J. Atmos. Sci., 72, 2429–2445, https://doi.org/10.1175/JAS-D-14-0274.1, 2015. a
Lee, S. S.: Effect of Aerosol on Circulations and Precipitation in Deep Convective Clouds, J. Atmos. Sci., 69, 1957–1974, https://doi.org/10.1175/JAS-D-11-0111.1, 2012. a
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Bulk Parameterization of the Snow Field in a Cloud Model, J. Clim. Appl. Meteorol., 22, 1065–1092, https://doi.org/10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2, 1983. a
Long, A. B.: Solutions to the Droplet Collection Equation for Polynomial Kernels, J Atmos. Sci., 31, 1040–1052, https://doi.org/10.1175/1520-0469(1974)031<1040:STTDCE>2.0.CO;2, 1974. a
Masson, V., Le Moigne, P., Martin, E., Faroux, S., Alias, A., Alkama, R., Belamari, S., Barbu, A., Boone, A., Bouyssel, F., Brousseau, P., Brun, E., Calvet, J.-C., Carrer, D., Decharme, B., Delire, C., Donier, S., Essaouini, K., Gibelin, A.-L., Giordani, H., Habets, F., Jidane, M., Kerdraon, G., Kourzeneva, E., Lafaysse, M., Lafont, S., Lebeaupin Brossier, C., Lemonsu, A., Mahfouf, J.-F., Marguinaud, P., Mokhtari, M., Morin, S., Pigeon, G., Salgado, R., Seity, Y., Taillefer, F., Tanguy, G., Tulet, P., Vincendon, B., Vionnet, V., and Voldoire, A.: The SURFEXv7.2 land and ocean surface platform for coupled or offline simulation of earth surface variables and fluxes, Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, 2013. a
McFarquhar, G. M., Baumgardner, D., Bansemer, A., Abel, S. J., Crosier, J., French, J., Rosenberg, P., Korolev, A., Schwarzoenboeck, A., Leroy, D., Um, J., Wu, W., Heymsfield, A. J., Twohy, C., Detwiler, A., Field, P., Neumann, A., Cotton, R., Axisa, D., and Dong, J.: Processing of Ice Cloud In Situ Data Collected by Bulk Water, Scattering, and Imaging Probes: Fundamentals, Uncertainties, and Efforts toward Consistency, Meteor. Mon., 58, 11.1–11.33, https://doi.org/10.1175/amsmonographs-d-16-0007.1, 2017. a
Meyers, M. P., Walko, R. L., Harrington, J. Y., and Cotton, W. R.: New RAMS cloud microphysics parameterization. Part II: The two-moment scheme, Atmos. Res., 45, 3–39, https://doi.org/10.1016/S0169-8095(97)00018-5, 1997. a
Milbrandt, J. A. and Yau, M. K.: A Multimoment Bulk Microphysics Parameterization. Part II: A Proposed Three-Moment Closure and Scheme Description, J. Atmos. Sci., 62, 3065–3081, https://doi.org/10.1175/JAS3535.1, 2005. a, b
Milbrandt, J. A. and Yau, M. K.: A Multimoment Bulk Microphysics Parameterization. Part III: Control Simulation of a Hailstorm, J. Atmos. Sci., 63, 3114–3136, https://doi.org/10.1175/JAS3816.1, 2006. a
Milbrandt, J. A., Yau, M. K., Mailhot, J., Bélair, S., and McTaggart-Cowan, R.: Simulation of an Orographic Precipitation Event during IMPROVE-2. Part II: Sensitivity to the Number of Moments in the Bulk Microphysics Scheme, Mon. Weather Rev., 138, 625–642, https://doi.org/10.1175/2009MWR3121.1, 2010. a
Milbrandt, J. A., Morrison, H., II, D. T. D., and Paukert, M.: A Triple-Moment Representation of Ice in the Predicted Particle Properties (P3) Microphysics Scheme, J. Atmos. Sci., 78, 439–458, https://doi.org/10.1175/JAS-D-20-0084.1, 2021. a
Mitra, S. K., Vohl, O., Ahr, M., and Pruppacher, H. R.: 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, https://doi.org/10.1175/1520-0469(1990)047<0584:AWTATS>2.0.CO;2, 1990. a
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997. a
Morcrette, J.-J.: Radiation and cloud radiative properties in the European Centre for Medium Range Weather Forecasts forecasting system, J. Geophys. Res.-Atmos., 96, 9121–9132, https://doi.org/10.1029/89JD01597, 1991. a
Morrison, H. and Milbrandt, J. A.: Parameterization of Cloud Microphysics Based on the Prediction of Bulk Ice Particle Properties. Part I: Scheme Description and Idealized Tests, J. Atmos. Sci., 72, 287–311, https://doi.org/10.1175/JAS-D-14-0065.1, 2015. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137, 991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a, b
Murakami, M.: Numerical Modeling of Dynamical and Microphysical Evolution of an Isolated Convective Cloud, J. Meteorol. Soc. Jpn. Ser. II, 68, 107–128, https://doi.org/10.2151/jmsj1965.68.2_107, 1990. a, b
Nickerson, E. C., Richard, E., Rosset, R., and Smith, D. R.: The Numerical Simulation of Clouds, Rains and Airflow over the Vosges and Black Forest Mountains: A Meso-β Model with Parameterized Microphysics, Mon. Weather Rev., 114, 398–414, https://doi.org/10.1175/1520-0493(1986)114<0398:TNSOCR>2.0.CO;2, 1986. a
Noppel, H., Blahak, U., Seifert, A., and Beheng, K. D.: Simulations of a hailstorm and the impact of CCN using an advanced two-moment cloud microphysical scheme, Atmos. Res., 96, 286–301, https://doi.org/10.1016/j.atmosres.2009.09.008, 2010. a
Pergaud, J., Masson, V., Malardel, S., and Couvreux, F.: A Parameterization of Dry Thermals and Shallow Cumuli for Mesoscale Numerical Weather Prediction, Bound.-Lay. Meteorol., 132, 83, https://doi.org/10.1007/s10546-009-9388-0, 2009. a
Phillips, V. T. J., DeMott, P. J., and Andronache, C.: An Empirical Parameterization of Heterogeneous Ice Nucleation for Multiple Chemical Species of Aerosol, J. Atmos. Sci., 65, 2757–2783, https://doi.org/10.1175/2007JAS2546.1, 2008. a
Phillips, V. T. J., Demott, P. J., Andronache, C., Pratt, K. A., Prather, K. A., Subramanian, R., and Twohy, C.: Improvements to an Empirical Parameterization of Heterogeneous Ice Nucleation and Its Comparison with Observations, J. Atmos. Sci., 70, 378–409, https://doi.org/10.1175/JAS-D-12-080.1, 2013. a, b
Pianezze, J., Barthe, C., Bielli, S., Tulet, P., Jullien, S., Cambon, G., Bousquet, O., Claeys, M., and Cordier, E.: A New Coupled Ocean-Waves-Atmosphere Model Designed for Tropical Storm Studies: Example of Tropical Cyclone Bejisa (2013–2014) in the South-West Indian Ocean, J. Adv. Model. Earth Sy., 10, 801–825, https://doi.org/10.1002/2017MS001177, 2018. a
Pinty, J. P. and Jabouille, P.: A mixed-phase cloud parameterization for use in mesoscale non hydrostatic model: simulations of a squall line and of orographic precipitations, in: Proceedings of Conf. on Cloud Physics, Amer. Meteor. Soc Everett, WA, 217–220, https://worldcat.org/oclc/39920280 (last access: 10 December 2024), 1998. a, b
Pruppacher, H. and Klett, J.: Microphysics of Clouds and Precipitation, Kluwer Academic Publishers, Dordrecht, The Netherlands, https://doi.org/10.1007/978-0-306-48100-0, 1997. a
Reisner, J., Rasmussen, R. M., and Bruintjes, R. T.: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model, Q. J. Roy. Meteor. Soc., 124, 1071–1107, https://doi.org/10.1002/qj.49712454804, 1998. a
Schnaiter, M., Järvinen, E., Vochezer, P., Abdelmonem, A., Wagner, R., Jourdan, O., Mioche, G., Shcherbakov, V. N., Schmitt, C. G., Tricoli, U., Ulanowski, Z., and Heymsfield, A. J.: Cloud chamber experiments on the origin of ice crystal complexity in cirrus clouds, Atmos. Chem. Phys., 16, 5091–5110, https://doi.org/10.5194/acp-16-5091-2016, 2016. a
Seifert, A. and Beheng, K.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 2: Maritime vs. continental deep convective storms, Meteorol. Atmos. Phys., 92, 67–82, https://doi.org/10.1007/s00703-005-0113-3, 2006a. a, b
Seifert, A. and Beheng, K. D.: A two-moment cloud microphysics parameterization for mixed-phase clouds. Part 1: Model description, Meteorol. Atmos. Phys., 92, 45–66, https://doi.org/10.1007/s00703-005-0112-4, 2006b. a
Seifert, A., Khain, A., Pokrovsky, A., and Beheng, K. D.: A comparison of spectral bin and two-moment bulk mixed-phase cloud microphysics, Atmos. Res., 80, 46–66, https://doi.org/10.1016/j.atmosres.2005.06.009, 2006. a, b
Simpson, E. L., Connolly, P. J., and McFiggans, G.: Competition for water vapour results in suppression of ice formation in mixed-phase clouds, Atmos. Chem. Phys., 18, 7237–7250, https://doi.org/10.5194/acp-18-7237-2018, 2018. a
Simpson, J.: Cumulus Clouds: Interactions Between Laboratory Experiments and Observations as Foundations for Models, Springer Netherlands, Dordrecht, 399–412, ISBN 978-94-017-2241-4, https://doi.org/10.1007/978-94-017-2241-4_22, 1983. a
Srivastava, R. C.: Parameterization of Raindrop Size Distributions, J. Atmos. Sci., 35, 108–117, https://doi.org/10.1175/1520-0469(1978)035<0108:PORSD>2.0.CO;2, 1978. a
Straka, J. M. and Mansell, E. R.: A Bulk Microphysics Parameterization with Multiple Ice Precipitation Categories, J. Appl. Meteorol., 44, 445–466, https://doi.org/10.1175/JAM2211.1, 2005. a, b
Tao, W.-K., Chen, J.-P., Li, Z., Wang, C., and Zhang, C.: Impact of aerosols on convective clouds and precipitation, Rev. Geophys., 50, 2, https://doi.org/10.1029/2011RG000369, 2012. a
Taufour, M., Vié, B., Augros, C., Boudevillain, B., Delanoë, J., Delautier, G., Ducrocq, V., Lac, C., Pinty, J.-P., and Schwarzenböck, A.: Evaluation of the two-moment scheme LIMA based on microphysical observations from the HyMeX campaign, Q. J. Roy. Meteor. Soc., 144, 1398–1414, https://doi.org/10.1002/qj.3283, 2018. a, b, c
Thompson, G., Rasmussen, R. M., and Manning, K.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis, Mon. Weather Rev., 132, 519–542, https://doi.org/10.1175/1520-0493(2004)132<0519:EFOWPU>2.0.CO;2, 2004. a
van den Heever, S. C., Stephens, G. L., and Wood, N. B.: Aerosol Indirect Effects on Tropical Convection Characteristics under Conditions of Radiative–Convective Equilibrium, J. Atmos. Sci., 68, 699–718, https://doi.org/10.1175/2010JAS3603.1, 2011. a
Voldoire, A., Decharme, B., Pianezze, J., Lebeaupin Brossier, C., Sevault, F., Seyfried, L., Garnier, V., Bielli, S., Valcke, S., Alias, A., Accensi, M., Ardhuin, F., Bouin, M.-N., Ducrocq, V., Faroux, S., Giordani, H., Léger, F., Marsaleix, P., Rainaud, R., Redelsperger, J.-L., Richard, E., and Riette, S.: SURFEX v8.0 interface with OASIS3-MCT to couple atmosphere with hydrology, ocean, waves and sea-ice models, from coastal to global scales, Geosci. Model Dev., 10, 4207–4227, https://doi.org/10.5194/gmd-10-4207-2017, 2017. a
Walko, R., Cotton, W., Meyers, M., and Harrington, J.: New RAMS cloud microphysics parameterization part I: the single-moment scheme, Atmos. Res., 38, 29–62, https://doi.org/10.1016/0169-8095(94)00087-T, 1995. a, b, c
Wang, C. and Chang, J. S.: A three-dimensional numerical model of cloud dynamics, microphysics, and chemistry: 1. Concepts and formulation, J. Geophys. Res.-Atmos., 98, 14827–14844, https://doi.org/10.1029/92JD01393, 1993. a
Wang, P. K. and Ji, W.: Collision Efficiencies of Ice Crystals at Low–Intermediate Reynolds Numbers Colliding with Supercooled Cloud Droplets: A Numerical Study, J. Atmos. Sci., 57, 1001–1009, https://doi.org/10.1175/1520-0469(2000)057<1001:CEOICA>2.0.CO;2, 2000. a
Wisner, C., Orville, H. D., and Myers, C.: A Numerical Model of a Hail-Bearing Cloud, J. Atmos. Sci., 29, 1160–1181, https://doi.org/10.1175/1520-0469(1972)029<1160:ANMOAH>2.0.CO;2, 1972. a
Wurtz, J., Bouniol, D., Vié, B., and Lac, C.: Evaluation of the AROME model's ability to represent ice crystal icing using in situ observations from the HAIC 2015 field campaign, Q. J. Roy. Meteor. Soc., 147, 2796–2817, https://doi.org/10.1002/qj.4100, 2021. a
Wurtz, J., Bouniol, D., and Vié, B.: Improvements to the parametrization of snow in AROME in the context of ice crystal icing, Q. J. Roy. Meteor. Soc., 149, 878–893, https://doi.org/10.1002/qj.4437, 2023. a
Ziegler, C. L.: Retrieval of Thermal and Microphysical Variables in Observed Convective Storms. Part 1: Model Development and Preliminary Testing, J. Atmos. Sci., 42, 1487–1509, https://doi.org/10.1175/1520-0469(1985)042<1487:ROTAMV>2.0.CO;2, 1985. a
Short summary
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols) microphysics scheme. We have focused on collection processes, where the hydrometeor number transfer is often estimated in proportion to the mass transfer. The impact of these parameterizations on a convective system and the prospects for more realistic estimates of secondary parameters (reflectivity, hydrometeor size) are shown in a first test on an idealized case.
We have developed a complete two-moment version of the LIMA (Liquid Ice Multiple Aerosols)...
