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Cited by 4Meteorological Phenomena and SimulationsAtmospheric aerosols and cloudsAtmospheric chemistry and aerosols

The impact of the mesh size and microphysics scheme on the representation of mid-level clouds in the ICON model in hilly and complex terrain

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Omanovic, Nadja, Goger, Brigitta, Lohmann, Ulrike·2024·Atmospheric Chemistry and Physics
DOI 10.5194/acp-24-14145-2024

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Abstract

The rise in computational power in recent years has enabled research and national weather services to conduct high-resolution simulations down to the kilometric (Δ x =𝒪(1 km)) and even to hectometric (Δ x =𝒪(100 m)) scales for both weather and climate applications. We investigate with the state-of-the-art numerical weather prediction model ICON how mid-level clouds are represented on a mesh size of 1 km and 65 m, respectively, and for two bulk microphysics schemes, namely one-moment and two-moment cloud microphysics. For this analysis, we leverage the abundant observational data from two independent field campaigns in Switzerland (CLOUDLAB; hilly terrain) and Austria (CROSSINN; complex terrain). With four case studies, we show that while the temperature fields around the campaign sites are well represented in both mesh sizes, the 65 m resolution simulates a more realistic vertical velocity structure that is beneficial for cloud formation. Therefore, the largest differences for the representation of clouds lie in the two mesh sizes. The 1 km simulation in hilly terrain does not capture the observed clouds in both cloud microphysics schemes. Here, the higher resolution of the vertical velocities in the 65 m proves to be crucial for representing the investigated cloud types, and the two-moment microphysics scheme in general performs better with respect to the cloud characteristics because it considers variations in cloud droplet and ice crystal number concentrations. In complex terrain, the differences between the mesh sizes and the cloud microphysics schemes are surprisingly small, but the 65 m simulations with two-moment cloud microphysics show the most realistic cloud representation.

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  • The CLOUDLAB experiments provided observational data to validate numerical weather models (e.g., ICON) under high ICNC (>100 L^-1) conditions, where vapor competition suppresses ice crystal growth.

    “The experiments provided observational data to validate numerical weather models (e.g., ICON; Omanovic et al., 2024) under high ICNC ( > 100 L -1 ) conditions, where vapor competition suppresses ice crystal growth (Fuchs et al., 2025; Henneberger et al., 2023)”

    From Past Field Experiments of Solar Radiation Modification: A Review of the Scientific and Technical Aspects for Governance (2026) · cites this paper as Omanovic et al., 2024

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  1. Adler, B., Babić, N., Kalthoff, N., and Wieser, A.: CROSSINN (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) – KITcube data sets [CHM 15k, GRAW, HATPRO2, Mobotix, Photos], KIT [data set], https://doi.org/10.5445/IR/1000127577, 2021a. a · DOI
  2. Adler, B., Gohm, A., Kalthoff, N., Babić, N., Corsmeier, U., Lehner, M., Rotach, M. W., Haid, M., Markmann, P., Gast, E., Tsaknakis, G., and Georgoussis, G.: CROSSINN: A Field Experiment to Study the Three-Dimensional Flow Structure in the Inn Valley, Austria, B. Am. Meteorol. Soc., 102, E38–E60, https://doi.org/10.1175/BAMS-D-19-0283.1, 2021b. a, b · DOI
  3. Allen, G., Coe, H., Clarke, A., Bretherton, C., Wood, R, Abel, S. J., Barrett, P., Brown, P., George, R., Freitag, S., McNaughton, C., Howell, S., Shank, L., Kapustin, V., Brekhovskikh, V., Kleinman, L., Lee, Y.-N., Springston, S., Toniazzo, T., Krejci, R., Fochesatto, J., Shaw, G., Krecl, P., Brooks, B., McMeeking, G., Bower, K. N., Williams, P. I., Crosier, J., Crawford, I., Connolly, P., Allan, J. D., Covert, D., Bandy, A. R., Russell, L. M., Trembath, J., Bart, M., McQuaid, J. B., Wang, J., and Chand, D.: South East Pacific atmospheric composition and variability sampled along 20° S during VOCALS-REx, Atmos. Chem. Phys., 11, 5237–5262, https://doi.org/10.5194/acp-11-5237-2011, 2011. a · DOI
  4. Andrejczuk, M., Reisner, J. M., Henson, B., Dubey, M. K., and Jeffery, C. A.: The potential impacts of pollution on a nondrizzling stratus deck: Does aerosol number matter more than type?, J. Geophys. Res.-Atmos., 113, D19204, https://doi.org/10.1029/2007JD009445, 2008. a · DOI
  5. Andrejczuk, M., Grabowski, W. W., Reisner, J., and Gadian, A.: Cloud-aerosol interactions for boundary layer stratocumulus in the Lagrangian Cloud Model, J. Geophys. Res.-Atmos., 115, D22214, https://doi.org/10.1029/2010JD014248, 2010. a, b · DOI
  6. Atlas, R. L., Bretherton, C. S., Blossey, P. N., Gettelman, A., Bardeen, C., Lin, P., and Ming, Y.: How Well Do Large-Eddy Simulations and Global Climate Models Represent Observed Boundary Layer Structures and Low Clouds Over the Summertime Southern Ocean?, J. Adv. Model. Earth Syst., 12, e2020MS002205, https://doi.org/10.1029/2020MS002205, 2020. a · DOI
  7. Baldauf, M., Seifert, A., Förstner, J., Majewski, D., Raschendorfer, M., and Reinhardt, T.: Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities, Mon. Weather Rev., 139, 3887–3905, https://doi.org/10.1175/MWR-D-10-05013.1, 2011. a, b · DOI
  8. Ban, N., Schmidli, J., and Schär, C.: Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations, J. Geophys. Res.-Atmos., 119, 7889–7907, https://doi.org/10.1002/2014JD021478, 2014. a · DOI
  9. Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a · DOI · Paper page
  10. Bergeron, T.: On the physics of clouds and precipitation, in: Proc. 5th Assembly UGGI, September 1935, Lisbon, Portugal, 15–180, 1935. a
  11. Bretherton, C. S. and Blossey, P. N.: Understanding Mesoscale Aggregation of Shallow Cumulus Convection Using Large-Eddy Simulation, J. Adv. Model. Earth Syst., 9, 2798–2821, https://doi.org/10.1002/2017MS000981, 2017. a · DOI
  12. Bretherton, C. S., Blossey, P. N., and Uchida, J.: Cloud droplet sedimentation, entrainment efficiency, and subtropical stratocumulus albedo, Geophys. Res. Lett., 34, L03813, https://doi.org/10.1029/2006GL027648, 2007. a · DOI
  13. Bryan, G. H. and Morrison, H.: Sensitivity of a Simulated Squall Line to Horizontal Resolution and Parameterization of Microphysics, Mon. Weather Rev., 140, 202–225, https://doi.org/10.1175/MWR-D-11-00046.1, 2012. a, b · DOI
  14. Buzzi, M.: Challenges in Operational Wintertime Weather Prediction at High Resolution in Complex Terrain, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-005698833, 2008. a · DOI
  15. Chow, F. K., Schär, C., Ban, N., Lundquist, K. A., Schlemmer, L., and Shi, X.: Crossing Multiple Gray Zones in the Transition from Mesoscale to Microscale Simulation over Complex Terrain, Atmosphere, 10, 274, https://doi.org/10.3390/atmos10050274, 2019. a · DOI
  16. Cooper, W. A.: Ice Initiation in Natural Clouds, in: Precipitation Enhancement, edited by: Braham, R. R. J., American Meteorological Society Boston, MA, Chicago, Illinois, USA, 29–32, ISBN 978-0-933876-65-1, https://doi.org/10.1007/978-1-935704-17-1, 1986. a, b, c · DOI
  17. Davies, T.: Lateral Boundary Conditions for Limited Area Models, Q. J. Royal Meteorol. Soc., 140, 185–196, https://doi.org/10.1002/qj.2127, 2014. a · DOI
  18. Delbeke, L., Wang, C., Tulet, P., Denjean, C., Zouzoua, M., Maury, N., and Deroubaix, A.: The impact of aerosols on stratiform clouds over southern West Africa: a large-eddy-simulation study, Atmos. Chem. Phys, 23, 13329–13354, https://doi.org/10.5194/acp-23-13329-2023, 2023. a · DOI
  19. DeMott, P. J., Prenni, A. J., McMeeking, G. R., Sullivan, R. C., Petters, M. D., Tobo, Y., Niemand, M., Möhler, O., Snider, J. R., Wang, Z., and Kreidenweis, S. M.: Integrating laboratory and field data to quantify the immersion freezing ice nucleation activity of mineral dust particles, Atmos. Chem. Phys, 15, 393–409, https://doi.org/10.5194/acp-15-393-2015, 2015. a · DOI
  20. Diamond, M. S., Saide, P. E., Zuidema, P., Ackerman, A. S., Doherty, S. J., Fridlind, A. M., Gordon, H., Howes, C., Kazil, J., Yamaguchi, T., Zhang, J., Feingold, G., and Wood, R.: Cloud adjustments from large-scale smoke-circulation interactions strongly modulate the southeastern Atlantic stratocumulus-to-cumulus transition, Atmos. Chem. Phys, 22, 12113–12151, https://doi.org/10.5194/acp-22-12113-2022, 2022. a · DOI
  21. Dipankar, A., Stevens, B., Heinze, R., Moseley, C., Zängl, G., Giorgetta, M., and Brdar, S.: Large Eddy Simulation Using the General Circulation Model ICON, J. Adv. Model. Earth Syst., 7, 963–986, https://doi.org/10.1002/2015MS000431, 2015. a · DOI
  22. Doms, G., Förstner, J., Heise, E., Herzog, H.-J., Mironov, D., Raschendorfer, M., Reinhardt, T., Ritter, B., Schrodin, R., Schulz, J.-P., and Vogel, G.: A Description of the Nonhydrostatic Regional COSMO-Model Part II: Physical Parameterizations, COSMO Model, DWD, 41–87, https://doi.org/10.5676/DWD_pub/nwv/cosmo-doc_6.00_II, 2021. a, b, c, d, e · DOI
  23. Duynkerke, P. G., de Roode, S. R., van Zanten, M. C., Calvo, J., Cuxart, J., Cheinet, S., Chlond, A., Grenier, H., Jonker, P. J., Köhler, M., Lenderink, G., Lewellen, D., Lappen, C. L., Lock, A. P., Moeng, C. H., Muller, F., Olmeda, D., Piriou, J. M., Sánchez, E., and Sednev, I.: Observations and numerical simulations of the diurnal cycle of the EUROCS stratocumulus case, Q. J. Roy. Meteorol. Soc., 130, 3269–3296, https://doi.org/10.1256/qj.03.139, 2004. a · DOI
  24. Endo, S., Fridlind, A. M., Lin, W., Vogelmann, A. M., Toto, T., Ackerman, A. S., McFarquhar, G. M., Jackson, R. C., Jonsson, H. H., and Liu, Y.: RACORO Continental Boundary Layer Cloud Investigations: 2. Large-eddy Simulations of Cumulus Clouds and Evaluation with in Situ and Ground-Based Observations, J. Geophys. Res.-Atmos., 120, 5993–6014, https://doi.org/10.1002/2014JD022525, 2015. a · DOI
  25. Feingold, G., Walko, R., Stevens, B., and Cotton, W.: Simulations of marine stratocumulus using a new microphysical parameterization scheme, Atmos. Res., 47–48, 505–528, https://doi.org/10.1016/S0169-8095(98)00058-1, 1998. a · DOI
  26. Gerber, F., Besic, N., Sharma, V., Mott, R., Daniels, M., Gabella, M., Berne, A., Germann, U., and Lehning, M.: Spatial variability in snow precipitation and accumulation in COSMO–WRF simulations and radar estimations over complex terrain, The Cryosphere, 12, 3137–3160, https://doi.org/10.5194/tc-12-3137-2018, 2018. a · DOI
  27. Goger, B. and Dipankar, A.: The impact of mesh size, turbulence parameterization, and land-surface-exchange scheme on simulations of the mountain boundary layer in the hectometric range, Q. J. Roy. Meteorol. Soc., 150, 3853–3873, https://doi.org/10.1002/qj.4799, 2024. a, b · DOI
  28. Goger, B., Rotach, M. W., Gohm, A., Fuhrer, O., Stiperski, I., and Holtslag, A. A. M.: The Impact of Three-Dimensional Effects on the Simulation of Turbulence Kinetic Energy in a Major Alpine Valley, Bound.-Lay. Meteorol., 168, 1–27, https://doi.org/10.1007/s10546-018-0341-y, 2018. a · DOI
  29. Goger, B., Rotach, M. W., Gohm, A., Stiperski, I., Fuhrer, O., and de Morsier, G.: A New Horizontal Length Scale for a Three-Dimensional Turbulence Parameterization in Mesoscale Atmospheric Modeling over Highly Complex Terrain, J. Appl. Meteorol. Clim., 58, 2087–2102, https://doi.org/10.1175/JAMC-D-18-0328.1, 2019. a · DOI
  30. Goger, B., Stiperski, I., Nicholson, L., and Sauter, T.: Large-eddy simulations of the atmospheric boundary layer over an Alpine glacier: Impact of synoptic flow direction and governing processes, Q. J. Roy. Meteorol. Soc., 148, 1319–1343, https://doi.org/10.1002/qj.4263, 2022. a · DOI
  31. Grabowski, W. W.: Comparison of Eulerian Bin and Lagrangian Particle-Based Microphysics in Simulations of Nonprecipitating Cumulus, J. Atmos. Sci., 77, 3951–3970, https://doi.org/10.1175/JAS-D-20-0100.1, 2020. a · DOI
  32. Hande, L. B., Engler, C., Hoose, C., and Tegen, I.: Seasonal variability of Saharan desert dust and ice nucleating particles over Europe, Atmos. Chem. Phys., 15, 4389–4397, https://doi.org/10.5194/acp-15-4389-2015, 2015. a · DOI
  33. Heim, C., Panosetti, D., Schlemmer, L., Leuenberger, D., and Schär, C.: The Influence of the Resolution of Orography on the Simulation of Orographic Moist Convection, Mon. Weather Rev., 148, 2391–2410, https://doi.org/10.1175/MWR-D-19-0247.1, 2020. a · DOI
  34. Heinze, R., Dipankar, A., Carbajal Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Di Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C. I., Muppa, S. K., Neggers, R. A. J., Orlandi, E., Pantillon, F., Pospichal, B., Röber, N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D., and Quaas, J.: Large-eddy simulations over Germany using ICON: A comprehensive evaluation, Q. J. Roy. Meteorol. Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017a. a, b, c · DOI
  35. Heinze, R., Moseley, C., Böske, L. N., Muppa, S. K., Maurer, V., Raasch, S., and Stevens, B.: Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign, Atmos. Chem. Phys., 17, 7083–7109, https://doi.org/10.5194/acp-17-7083-2017, 2017b. a · DOI
  36. Henneberger, J., Ramelli, F., Spirig, R., Omanovic, N., Miller, A. J., Fuchs, C., Zhang, H., Bühl, J., Hervo, M., Kanji, Z. A., Ohneiser, K., Radenz, M., Rösch, M., Seifert, P., and Lohmann, U.: Seeding of Supercooled Low Stratus Clouds with a UAV to Study Microphysical Ice Processes – An Introduction to the CLOUDLAB Project, B. Am. Meteorol. Soc., 104, E1962–E1979, https://doi.org/10.1175/BAMS-D-22-0178.1, 2023. a, b · DOI · Paper page
  37. Hentgen, L., Ban, N., Kröner, N., Leutwyler, D., and Schär, C.: Clouds in Convection-Resolving Climate Simulations Over Europe, J. Geophys. Res.-Atmos., 124, 3849–3870, https://doi.org/10.1029/2018JD030150, 2019. a · DOI
  38. Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a · DOI · Paper page
  39. Hoffmann, F.: The Effect of Spurious Cloud Edge Supersaturations in Lagrangian Cloud Models: An Analytical and Numerical Study, Mon. Weather Rev., 144, 107–118, https://doi.org/10.1175/MWR-D-15-0234.1, 2016. a · DOI
  40. Hogan, R. J. and Bozzo, A.: A Flexible and Efficient Radiation Scheme for the ECMWF Model, J. Adv. Model, Earth Syst., 10, 1990–2008, https://doi.org/10.1029/2018MS001364, 2018. a · DOI
  41. Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A. K., Paccini, L., Peinado, A., Praturi, D. S., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C. C., von Storch, J.-S., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J., and Stevens, B.: ICON-Sapphire: simulating the components of the Earth system and their interactions at kilometer and subkilometer scales, Geosci. Model Dev., 16, 779–811, https://doi.org/10.5194/gmd-16-779-2023, 2023. a, b · DOI
  42. Howes, C., Saide, P. E., Coe, H., Dobracki, A., Freitag, S., Haywood, J. M., Howell, S. G., Gupta, S., Uin, J., Kacarab, M., Kuang, C., Leung, L. R., Nenes, A., McFarquhar, G. M., Podolske, J., Redemann, J., Sedlacek, A. J., Thornhill, K. L., Wong, J. P. S., Wood, R., Wu, H., Zhang, Y., Zhang, J., and Zuidema, P.: Biomass-burning smoke's properties and its interactions with marine stratocumulus clouds in WRF-CAM5 and southeastern Atlantic field campaigns, Atmos. Chem. Phys., 23, 13911–13940, https://doi.org/10.5194/acp-23-13911-2023, 2023. a · DOI
  43. Huang, Y., Dong, X., Kay, J. E., Xi, B., and McIlhattan, E. A.: The Climate Response to Increased Cloud Liquid Water over the Arctic in CESM1: A Sensitivity Study of Wegener–Bergeron–Findeisen Process, Clim. Dynam., 56, 3373–3394, https://doi.org/10.1007/s00382-021-05648-5, 2021. a, b · DOI
  44. Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Baltink, H. K., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a · DOI
  45. Jeong, J.-H., Witte, M. K., and Smalley, M.: Effects of Wind Shear and Aerosol Conditions on the Organization of Precipitating Marine Stratocumulus Clouds, Q. J. Roy. Meteorol. Soc., 128, e2023JD039081, https://doi.org/10.1029/2023JD039081, 2023. a · DOI
  46. Jiang, H., Feingold, G., and Cotton, W.: Simulations of aerosol-cloud-dynamical feedbacks resulting from entrainment of aerosol into the marine boundary layer during the Atlantic Stratocumulus Transition Experiment, J. Geophys. Res.-Atmos., 107, 4813, https://doi.org/10.1029/2001JD001502, 2002. a · DOI
  47. Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo, D. J., and Krämer, M.: Overview of Ice Nucleating Particles, Meteorol. Monogr., 58, 1.1–1.33, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0006.1, 2017. a, b · DOI
  48. Kay, J. E., Wall, C., Yettella, V., Medeiros, B., Hannay, C., Caldwell, P., and Bitz, C.: Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM), J. Climate, 29, 4617–4636, https://doi.org/10.1175/JCLI-D-15-0358.1, 2016. a, b · DOI
  49. Khain, A., Rosenfeld, D., Pokrovsky, A., Blahak, U., and Ryzhkov, A.: The role of CCN in precipitation and hail in a mid-latitude storm as seen in simulations using a spectral (bin) microphysics model in a 2D dynamic frame, Atmos. Res., 99, 129–146, https://doi.org/10.1016/j.atmosres.2010.09.015, 2011. a · DOI
  50. Klaus, D., Dethloff, K., Dorn, W., Rinke, A., and Wu, D. L.: New Insight of Arctic Cloud Parameterization from Regional Climate Model Simulations, Satellite-Based, and Drifting Station Data, Geophys. Res. Lett., 43, 5450–5459, https://doi.org/10.1002/2015GL067530, 2016. a, b · DOI
  51. Kondo, M., Sato, Y., Inatsu, M., and Katsuyama, Y.: Evaluation of Cloud Microphysical Schemes for Winter Snowfall Events in Hokkaido: A Case Study of Snowfall by Winter Monsoon, SOLA, 17, 74–80, https://doi.org/10.2151/sola.2021-012, 2021. a, b · DOI
  52. Korolev, A.: Limitations of the Wegener–Bergeron–Findeisen Mechanism in the Evolution of Mixed-Phase Clouds, J. Atmos. Sci., 64, 3372–3375, https://doi.org/10.1175/JAS4035.1, 2007. a · DOI
  53. Korolev, A. V. and Mazin, I. P.: Supersaturation of Water Vapor in Clouds, J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2, 2003. a · DOI
  54. Kovačević, N. and Ćurić, M.: Precipitation Sensitivity to the Mean Radius of Drop Spectra: Comparison of Single- and Double-Moment Bulk Microphysical Schemes, Atmosphere, 6, 451–473, https://doi.org/10.3390/atmos6040451, 2015. a, b · DOI
  55. Kretzschmar, J., Salzmann, M., Mülmenstädt, J., and Quaas, J.: Arctic Clouds in ECHAM6 and Their Sensitivity to Cloud Microphysics and Surface Fluxes, Atmos. Chem. Phys, 19, 10571–10589, https://doi.org/10.5194/acp-19-10571-2019, 2019. a, b · DOI
  56. Lehner, M., Rotach, M. W., and Obleitner, F.: A Method to Identify Synoptically Undisturbed, Clear-Sky Conditions for Valley-Wind Analysis, Bound.-Lay. Meteorol., 173, 435–450, https://doi.org/10.1007/s10546-019-00471-2, 2019. a · DOI
  57. Leutwyler, D., Fuhrer, O., Lapillonne, X., Lüthi, D., and Schär, C.: Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19, Geosci. Model Dev., 9, 3393–3412, https://doi.org/10.5194/gmd-9-3393-2016, 2016. a · DOI
  58. Li, X.-Y., Wang, H., Christensen, M. W., Chen, J., Tang, S., Kirschler, S., Crosbie, E., Ziemba, L. D., Painemal, D., Corral, A. F., Mccauley, K. A., Dmitrovic, S., Sorooshian, A., Fenn, M., Schlosser, J. S., Stamnes, S., Hair, J. W., Cairns, B., Moore, R., Ferrare, R. A., Shook, M. A., Choi, Y., Diskin, G. S., Digangi, J., Nowak, J. B., Robinson, C., Shingler, T. J., Lee Thornhill, K., and Voigt, C.: Process Modeling of Aerosol-Cloud Interaction in Summertime Precipitating Shallow Cumulus Over the Western North Atlantic, J. Geophys. Res.-Atmos., 129, e2023JD039489, https://doi.org/10.1029/2023JD039489, 2024. a · DOI
  59. Lilly, D. K.: On the numerical simulation of buoyant convection, Tellus, 14, 148–172, https://doi.org/10.1111/j.2153-3490.1962.tb00128.x, 1962. a · DOI
  60. Liu, X., Xie, S., Boyle, J., Klein, S. A., Shi, X., Wang, Z., Lin, W., Ghan, S. J., Earle, M., Liu, P. S. K., and Zelenyuk, A.: Testing Cloud Microphysics Parameterizations in NCAR CAM5 with ISDAC and M-PACE Observations, J. Geophys. Res.-Atmos., 116, D00T11, https://doi.org/10.1029/2011JD015889, 2011. a, b · DOI
  61. Lohmann, U., Lüönd, F., and Mahrt, F.: An Introduction to Clouds: From the Microscale to Climate, in: 1st Edn., Cambridge University Press, https://doi.org/10.1017/CBO9781139087513, 2016. a, b, c, d · DOI
  62. Louis, J.-F.: A parametric model of vertical eddy fluxes in the atmosphere, Bound.-Lay. Meteorol., 17, 187–202, https://doi.org/10.1007/BF00117978, 1979. a · DOI
  63. McIlhattan, E. A., L'Ecuyer, T. S., and Miller, N. B.: Observational Evidence Linking Arctic Supercooled Liquid Cloud Biases in CESM to Snowfall Processes, J. Climate, 30, 4477–4495, https://doi.org/10.1175/JCLI-D-16-0666.1, 2017. a, b · DOI
  64. Mellado, J. P., Bretherton, C. S., Stevens, B., and Wyant, M. C.: DNS and LES for Simulating Stratocumulus: Better Together, J. Adv. Model. Earth Syst., 10, 1421–1438, https://doi.org/10.1029/2018MS001312, 2018. a · DOI
  65. Mikkola, J., Sinclair, V. A., Bister, M., and Bianchi, F.: Daytime along-valley winds in the Himalayas as simulated by the Weather Research and Forecasting (WRF) model, Atmos. Chem. Phys., 23, 821–842, https://doi.org/10.5194/acp-23-821-2023, 2023. a · DOI
  66. Miyamoto, Y., Kajikawa, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita, H.: Deep moist atmospheric convection in a subkilometer global simulation, Geophys. Res. Lett., 40, 4922–4926, https://doi.org/10.1002/grl.50944, 2013. a · DOI
  67. Moseley, C., Pscheidt, I., Cioni, G., and Heinze, R.: Impact of resolution on large-eddy simulation of midlatitude summertime convection, Atmos. Chem. Phys., 20, 2891–2910, https://doi.org/10.5194/acp-20-2891-2020, 2020. a · DOI
  68. NASA/METI/AIST/Japan Spacesystems and US/Japan ASTER Science Team: ASTER Global Digital Elevation Model, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/ASTGTM.002, 2009. a · DOI
  69. Omanovic, N., Ferrachat, S., Fuchs, C., Henneberger, J., Miller, A. J., Ohneiser, K., Ramelli, F., Seifert, P., Spirig, R., Zhang, H., and Lohmann, U.: Evaluating the Wegener–Bergeron–Findeisen process in ICON in large-eddy mode with in situ observations from the CLOUDLAB project, Atmos. Chem. Phys., 24, 6825–6844, https://doi.org/10.5194/acp-24-6825-2024, 2024a. a, b, c · DOI
  70. Palmer, T.: The primacy of doubt: Evolution of numerical weather prediction from determinism to probability, J. Adv. Model. Earth Syst., 9, 730–734, https://doi.org/10.1002/2017MS000999, 2017. a · DOI
  71. Panosetti, D., Böing, S., Schlemmer, L., and Schmidli, J.: Idealized Large-Eddy and Convection-Resolving Simulations of Moist Convection over Mountainous Terrain, J. Atmos. Sci., 73, 4021–4041, https://doi.org/10.1175/JAS-D-15-0341.1, 2016. a · DOI
  72. Perez, A. B., Diamond, M. S., Bender, F. A. M., Devasthale, A., Schwarz, M., Savre, J., Tonttila, J., Kokkola, H., Lee, H., Painemal, D., and Ekman, A. M. L.: Comparing the simulated influence of biomass burning plumes on low-level clouds over the southeastern Atlantic under varying smoke conditions, Atmos. Chem. Phys, 24, 4591–4610, https://doi.org/10.5194/acp-24-4591-2024, 2024. a · DOI
  73. 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, b, c · DOI
  74. Pressel, K. G., Mishra, S., Schneider, T., Kaul, C. M., and Tan, Z.: Numerics and subgrid-scale modeling in large eddy simulations of stratocumulus clouds, J. Adv. Model. Earth Syst., 9, 1342–1365, https://doi.org/10.1002/2016MS000778, 2017. a · DOI
  75. Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, in: 1st Edn., Springer, Dordrecht, https://doi.org/10.1007/978-94-009-9905-3, 1978. a · DOI
  76. Roberts, G. C., Day, D. A., Russell, L. M., Dunlea, E. J., Jimenez, J. L., Tomlinson, J. M., Collins, D. R., Shinozuka, Y., and Clarke, A. D.: Characterization of particle cloud droplet activity and composition in the free troposphere and the boundary layer during INTEX-B, Atmos. Chem. Phys., 10, 6627–6644, https://doi.org/10.5194/acp-10-6627-2010, 2010. a · DOI
  77. Rohanizadegan, M., Petrone, R. M., Pomeroy, J. W., Kosovic, B., Muñoz-Esparza, D., and Helgason, W. D.: High-Resolution Large-Eddy Simulations of Flow in the Complex Terrain of the Canadian Rockies, Earth Space Sci., 10, e2023EA003166, https://doi.org/10.1029/2023EA003166, 2023. a · DOI
  78. Rotach, M. W., Stiperski, I., Fuhrer, O., Goger, B., Gohm, A., Obleitner, F., Rau, G., Sfyri, E., and Vergeiner, J.: Investigating Exchange Processes over Complex Topography: The Innsbruck Box (i-Box), B. Am. Meteorol. Soc., 98, 787–805, https://doi.org/10.1175/BAMS-D-15-00246.1, 2017. a · DOI
  79. Sandu, I. and Stevens, B.: On the Factors Modulating the Stratocumulus to Cumulus Transitions, J. Atmos. Sci., 68, 1865–1881, https://doi.org/10.1175/2011JAS3614.1, 2011. a · DOI
  80. Sandu, I., Brenguier, J.-L., Geoffroy, O., Thouron, O., and Masson, V.: Aerosol impacts on the diurnal cycle of marine stratocumulus, J. Atmos. Sci., 65, 2705–2718, https://doi.org/10.1175/2008JAS2451.1, 2008. a · DOI
  81. Sato, Y., Nishizawa, S., Yashiro, H., Miyamoto, Y., Kajikawa, Y., and Tomita, H.: Impacts of cloud microphysics on trade wind cumulus: which cloud microphysics processes contribute to the diversity in a large eddy simulation?, Prog. Earth Planet. Sci., 2, 23, https://doi.org/10.1186/s40645-015-0053-6, 2015. a · DOI
  82. Schär, C., Fuhrer, O., Arteaga, A., Ban, N., Charpilloz, C., Di Girolamo, S., Hentgen, L., Hoefler, T., Lapillonne, X., Leutwyler, D., Osterried, K., Panosetti, D., Rüdisühli, S., Schlemmer, L., Schulthess, T. C., Sprenger, M., Ubbiali, S., and Wernli, H.: Kilometer-Scale Climate Models: Prospects and Challenges, B. Am. Meteorol. Soc., 101, E567–E587, https://doi.org/10.1175/BAMS-D-18-0167.1, 2020. a · DOI
  83. Schemann, V. and Ebell, K.: Simulation of mixed-phase clouds with the ICON large-eddy model in the complex Arctic environment around Ny-Ålesund, Atmos. Chem. Phys., 20, 475–485, https://doi.org/10.5194/acp-20-475-2020, 2020. a · DOI
  84. Schemann, V., Ebell, K., Pospichal, B., Neggers, R., Moseley, C., and Stevens, B.: Linking Large-Eddy Simulations to Local Cloud Observations, J. Adv. Model. Earth Syst., 12, e2020MS002209, https://doi.org/10.1029/2020MS002209, 2020. a, b, c, d · DOI
  85. Schmidli, J., Böing, S., and Fuhrer, O.: Accuracy of Simulated Diurnal Valley Winds in the Swiss Alps: Influence of Grid Resolution, Topography Filtering, and Land Surface Datasets, Atmosphere, 9, 196, https://doi.org/10.3390/atmos9050196, 2018. a, b · DOI
  86. Schmidt, H., Rast, S., Bao, J., Cassim, A., Fang, S.-W., Jimenez-de la Cuesta, D., Keil, P., Kluft, L., Kroll, C., Lang, T., Niemeier, U., Schneidereit, A., Williams, A. I. L., and Stevens, B.: Effects of Vertical Grid Spacing on the Climate Simulated in the ICON461.5-Sapphire Global Storm-Resolving Model, Geosci. Model Dev., 17, 1563–1584, https://doi.org/10.5194/gmd-17-1563-2024, 2024. a · DOI
  87. Schöni, F.: ICON Model Evaluation: A Cold Air Pool Case Study, MS thesis, ETH Zurich, https://doi.org/10.3929/ethz-b-000611435, 2023. a · DOI
  88. Schulz, J.-P. and Vogel, G.: Improving the Processes in the Land Surface Scheme TERRA: Bare Soil Evaporation and Skin Temperature, Atmosphere, 11, 513, https://doi.org/10.3390/atmos11050513, 2020. a · DOI
  89. Schulze, B. C., Charan, S. M., Kenseth, C. M., Kong, W., Bates, K. H., Williams, W., Metcalf, A. R., Jonsson, H. H., Woods, R., Sorooshian, A., Flagan, R. C., and Seinfeld, J. H.: Characterization of Aerosol Hygroscopicity Over the Northeast Pacific Ocean: Impacts on Prediction of CCN and Stratocumulus Cloud Droplet Number Concentrations, Earth Space Sci., 7, e2020EA001098, https://doi.org/10.1029/2020EA001098, 2020. a · DOI
  90. 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, 2006. a, b, c · DOI
  91. Shima, S., Kusano, K., Kawano, A., Sugiyama, T., and Kawahara, S.: The super-droplet method for the numerical simulation of clouds and precipitation: a particle-based and probabilistic microphysics model coupled with a non-hydrostatic model, Q. J. Roy. Meteorol. Soc., 135, 1307–1320, https://doi.org/10.1002/qj.441, 2009. a · DOI
  92. Siebesma, A. P., Bretherton, C. S., Brown, A., Chlond, A., Cuxart, J., Duynkerke, P. G., Jiang, H. L., Khairoutdinov, M., Lewellen, D., Moeng, C. H., Sanchez, E., Stevens, B., and Stevens, D. E.: A large eddy simulation intercomparison study of shallow cumulus convection, J. Atmos. Sci., 60, 1201–1219, https://doi.org/10.1175/1520-0469(2003)60<1201:ALESIS>2.0.CO;2, 2003. a · DOI
  93. Simmel, M., Trautmann, T., and Tetzlaff, G.: Numerical solution of the stochastic collection equation – comparison of the Linear Discrete Method with other methods, Atmos. Res., 61, 135–148, https://doi.org/10.1016/S0169-8095(01)00131-4, 2002. a · DOI
  94. Singh, S., Kalthoff, N., and Gantner, L.: Sensitivity of convective precipitation to model grid spacing and land-surface resolution in ICON, Q. J. Roy. Meteorol. Soc., 147, 2709–2728, https://doi.org/10.1002/qj.4046, 2021. a · DOI
  95. Smagorinsky, J.: General circulation experiments with the primitive equations, Mon. Weather Rev., 91, 99–164, https://doi.org/10.1175/1520-0493(1963)091<0099:GCEWTP>2.3.CO;2, 1963. a · DOI
  96. Stevens, B., Aquistapache, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D., Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas, V., Biercamp, J., Brueck, M., Brune, S., Buehler, S. A., Burkhardt, U., Cioni, G., Costa-Surós, M., Crewell, S., Crüger, T., Deneke, H., Friedrichs, P., Henken, C. C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N., Köhler, M., van Laar, T. W., Li, P., Löhnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C., Naumann, A. K., Peters, K., Poll, S., Quaas, J., Röber, N., Rochetin, N., Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijewski, M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J., Tessiot, O., Vercauteren, N., Vial, J., Voigt, A., and Zängl, G.: The Added Value of Large-eddy and Storm-resolving Models for Simulating Clouds and Precipitation, J. Meteorol. Soc. Jappn. Ser. II, 98, 395–435, https://doi.org/10.2151/jmsj.2020-021, 2020. a · DOI
  97. Stevens, B., Feingold, G., Cotton, W. R., and Walko, R. L.: Elements of the microphysical structure of numerically simulated nonprecipitating stratocumulus, J. Atmos. Sci., 53, 980–1006, https://doi.org/10.1175/1520-0469(1996)053<0980:EOTMSO>2.0.CO;2, 1996. a · DOI
  98. Stevens, D. E. and Bretherton, C. S.: Effects of resolution on the simulation of stratocumulus entrainment, Q. J. Roy. Meteorol. Soc., 125, 425–439, https://doi.org/10.1002/qj.49712555403, 1999. a, b · DOI
  99. Strauss, C., Ricard, D., Lac, C., and Verrelle, A.: Evaluation of Turbulence Parametrizations in Convective Clouds and Their Environment Based on a Large-Eddy Simulation, Q. J. Roy. Meteorol. Soc., 145, 3195–3217, https://doi.org/10.1002/qj.3614, 2019. a · DOI
  100. Tonttila, J., Maalick, Z., Raatikainen, T., Kokkola, H., Kuhn, T., and Romakkaniemi, S.: UCLALES-SALSA v1.0: a large-eddy model with interactive sectional microphysics for aerosol, clouds and precipitation, Geosci. Model Dev., 10, 169–188, https://doi.org/10.5194/gmd-10-169-2017, 2017. a · DOI
  101. Tukiainen, S., O'Connor, E., and Korpinen, A.: CloudnetPy: A Python Package for Processing Cloud Remote Sensing Data, J. Open Source Softw., 5, 2123, https://doi.org/10.21105/joss.02123, 2020. a · DOI
  102. Twohy, C. H., Anderson, J. R., Toohey, D. W., Andrejczuk, M., Adams, A., Lytle, M., George, R. C., Wood, R., Saide, P., Spak, S., Zuidema, P., and Leon, D.: Impacts of aerosol particles on the microphysical and radiative properties of stratocumulus clouds over the southeast Pacific Ocean, Atmos. Chem. Phys., 13, 2541–2562, https://doi.org/10.5194/acp-13-2541-2013, 2013. a · DOI
  103. Umek, L., Gohm, A., Haid, M., Ward, H. C., and Rotach, M. W.: Large eddy simulation of foehn-cold pool interactions in the Inn Valley during PIANO IOP2, Q. J. Roy. Meteor. Soc., 147, 944–982, https://doi.org/10.1002/qj.3954, 2021. a · DOI
  104. Van Weverberg, K., Goudenhoofdt, E., Blahak, U., Brisson, E., Demuzere, M., Marbaix, P., and van Ypersele, J.-P.: Comparison of one-moment and two-moment bulk microphysics for high-resolution climate simulations of intense precipitation, Atmos. Res., 147–148, 145–161, https://doi.org/10.1016/j.atmosres.2014.05.012, 2014. a · DOI
  105. Voordendag, A., Goger, B., Prinz, R., Sauter, T., Mölg, T., Saigger, M., and Kaser, G.: A novel framework to investigate wind-driven snow redistribution over an Alpine glacier: combination of high-resolution terrestrial laser scans and large-eddy simulations, The Cryosphere, 18, 849–868, https://doi.org/10.5194/tc-18-849-2024, 2024. a · DOI
  106. 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 · DOI
  107. Wang, Z.-C., Yin, B., Min, Q., and Zhu, L.: Temperature-dependence of the near-UV absorption of water vapor in the 290–350 nm range, J. Quant. Spectrosc. Ra., 28, 108204, https://doi.org/10.1016/j.jqsrt.2022.108204, 2022. a · DOI
  108. Witte, M. K., Morrison, H., Davis, A. B., and Teixeira, J.: Limitations of Bin and Bulk Microphysics in Reproducing the Observed Spatial Structure of Light Precipitation, J. Atmos. Sci., 79, 161–178, https://doi.org/10.1175/JAS-D-21-0134.1, 2022. a · DOI
  109. Xue, H., Feingold, G., and Stevens, B.: Aerosol effects on clouds, precipitation, and the organization of shallow cumulus convection, J. Atmos. Sci., 65, 392–406, https://doi.org/10.1175/2007JAS2428.1, 2008. a · DOI
  110. Yamaguchi, T. and Feingold, G.: Technical note: Large-eddy simulation of cloudy boundary layer with the Advanced Research WRF model, J. Adv. Model. Earth Syst., 4, M09003, https://doi.org/10.1029/2012MS000164, 2012. a · DOI
  111. Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) Modelling Framework of DWD and MPI-M: Description of the Non-Hydrostatic Dynamical Core, Q. J. Roy. Meteorol. Soc., 141, 563–579, https://doi.org/10.1002/qj.2378, 2015. a · DOI
  112. Zhang, Y., Klein, S. A., Fan, J., Chandra, A. S., Kollias, P., Xie, S., and Tang, S.: Large-Eddy Simulation of Shallow Cumulus over Land: A Composite Case Based on ARM Long-Term Observations at Its Southern Great Plains Site, J. Atmos. Sci., 74, 3229–3251, https://doi.org/10.1175/JAS-D-16-0317.1, 2017. a, b · DOI

Authors
Omanovic, N., Goger, B., & Lohmann, U.
Affiliations
ETH Zurich (CH)
Corresponding author
  • Omanovic, Nadja
  • Goger, Brigitta
ORCID
Venue
Atmospheric Chemistry and Physics
Publisher
Copernicus GmbH
Published
2024-12-19
Volume
24
Issue
24
Pages
14145-14175
ISSN
  • 1680-7316 (ISSN-L)
  • 1680-7324
DOI
10.5194/acp-24-14145-2024
OpenAlex
W4405571114
Cited by
4
References
115
Citations by year
2026: 1 · 2025: 2 · 2024: 1
FWCI
0.73
Type
journal article
Language
en
Open access
Licence
Locations
  • Atmospheric chemistry and physics (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection
Funders
European Commission (101021272)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
  • Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric aerosols and clouds
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
Keywords
mid-level clouds, ICON model, cloud microphysics schemes, two-moment microphysics, grid resolution, hilly terrain, complex terrain, vertical velocity, cloud droplet number concentration, ice crystal number concentration, CLOUDLAB, high-resolution numerical weather prediction

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