Articles | Volume 13, issue 9
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-13-4107-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Predicting the morphology of ice particles in deep convection using the super-droplet method: development and evaluation of SCALE-SDM 0.2.5-2.2.0, -2.2.1, and -2.2.2
Shin-ichiro Shima
CORRESPONDING AUTHOR
Graduate School of Simulation Studies, University of Hyogo, Kobe, Japan
RIKEN Center for Computational Science, Kobe, Japan
Yousuke Sato
Faculty of Science, Hokkaido University, Sapporo, Japan
RIKEN Center for Computational Science, Kobe, Japan
Akihiro Hashimoto
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba, Japan
Ryohei Misumi
National Research Institute for Earth Science and Disaster Resilience, Tsukuba, Japan
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Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
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A particle-based cloud model was developed for meter- to submeter-scale resolution in cloud simulations. Our new cloud model's computational performance is superior to a bin method and comparable to a two-moment bulk method. A highlight of this study is the 2 m resolution shallow cloud simulations over an area covering ∼10 km2. This model allows for studying turbulence and cloud physics at spatial scales that overlap with those covered by direct numerical simulations and field studies.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Pradeep Khatri, Atsushi Shimizu, Hitoshi Irie, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev., 14, 2235–2264, https://doi.org/10.5194/gmd-14-2235-2021, https://doi.org/10.5194/gmd-14-2235-2021, 2021
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This study compares performance of aerosol representation methods of the Japan Meteorological Agency's regional-scale nonhydrostatic meteorology–chemistry model (NHM-Chem). It indicates separate treatment of sea salt and dust in coarse mode and that of light-absorptive and non-absorptive particles in fine mode could provide accurate assessments on aerosol feedback processes.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-128, https://doi.org/10.5194/gmd-2018-128, 2018
Revised manuscript not accepted
Hazuki Arakida, Takemasa Miyoshi, Takeshi Ise, Shin-ichiro Shima, and Shunji Kotsuki
Nonlin. Processes Geophys., 24, 553–567, https://doi.org/10.5194/npg-24-553-2017, https://doi.org/10.5194/npg-24-553-2017, 2017
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This is the first study assimilating the satellite-based leaf area index observations every 4 days into a numerical model simulating the growth and death of individual plants. The newly developed data assimilation system successfully reduced the uncertainties of the model parameters related to phenology and carbon dynamics. It also provides better estimates of the present vegetation structure which can be used as the initial states for the simulation of the future vegetation change.
Sylwester Arabas and Shin-ichiro Shima
Nonlin. Processes Geophys., 24, 535–542, https://doi.org/10.5194/npg-24-535-2017, https://doi.org/10.5194/npg-24-535-2017, 2017
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The paper bridges cloud/aerosol modelling with bifurcation analysis. It identifies two nonlinear peculiarities in the differential equations describing formation of atmospheric clouds through vapour condensation on a population of aerosol particles. A key finding of the paper is an analytic estimate for the timescale of the process. The study emerged from discussions on the causes of hysteretic behaviour of the system that we observed in the results of numerical simulations.
Ruyi Zhang, Limin Zhou, Shin-ichiro Shima, and Huawei Yang
Geosci. Model Dev., 17, 6761–6774, https://doi.org/10.5194/gmd-17-6761-2024, https://doi.org/10.5194/gmd-17-6761-2024, 2024
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Solar activity weakly ionises Earth's atmosphere, charging cloud droplets. Electro-coalescence is when oppositely charged droplets stick together. We introduce an analytical expression of electro-coalescence probability and use it in a warm-cumulus-cloud simulation. Results show that charge cases increase rain and droplet size, with the new method outperforming older ones. The new method requires longer computation time, but its impact on rain justifies inclusion in meteorology models.
Chongzhi Yin, Shin-ichiro Shima, Lulin Xue, and Chunsong Lu
Geosci. Model Dev., 17, 5167–5189, https://doi.org/10.5194/gmd-17-5167-2024, https://doi.org/10.5194/gmd-17-5167-2024, 2024
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We investigate numerical convergence properties of a particle-based numerical cloud microphysics model (SDM) and a double-moment bulk scheme for simulating a marine stratocumulus case, compare their results with model intercomparison project results, and present possible explanations for the different results of the SDM and the bulk scheme. Aerosol processes can be accurately simulated using SDM, and this may be an important factor affecting the behavior and morphology of marine stratocumulus.
Toshiki Matsushima, Seiya Nishizawa, and Shin-ichiro Shima
Geosci. Model Dev., 16, 6211–6245, https://doi.org/10.5194/gmd-16-6211-2023, https://doi.org/10.5194/gmd-16-6211-2023, 2023
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Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Pradeep Khatri, Atsushi Shimizu, Hitoshi Irie, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev., 14, 2235–2264, https://doi.org/10.5194/gmd-14-2235-2021, https://doi.org/10.5194/gmd-14-2235-2021, 2021
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This study compares performance of aerosol representation methods of the Japan Meteorological Agency's regional-scale nonhydrostatic meteorology–chemistry model (NHM-Chem). It indicates separate treatment of sea salt and dust in coarse mode and that of light-absorptive and non-absorptive particles in fine mode could provide accurate assessments on aerosol feedback processes.
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Daisuke Goto, Yousuke Sato, Hisashi Yashiro, Kentaroh Suzuki, Eiji Oikawa, Rei Kudo, Takashi M. Nagao, and Teruyuki Nakajima
Geosci. Model Dev., 13, 3731–3768, https://doi.org/10.5194/gmd-13-3731-2020, https://doi.org/10.5194/gmd-13-3731-2020, 2020
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We executed a global aerosol model over 3 years with the finest grid size in the world. The results elucidated that global annual averages of parameters associated with the aerosols were generally comparable to those obtained from a low-resolution model (LRM), but spatiotemporal variabilities of the aerosol components and their associated parameters provided better results closer to the observations than those from the LRM. This study clarified the advantages of the high-resolution model.
Satoru Yamaguchi, Masaaki Ishizaka, Hiroki Motoyoshi, Sent Nakai, Vincent Vionnet, Teruo Aoki, Katsuya Yamashita, Akihiro Hashimoto, and Akihiro Hachikubo
The Cryosphere, 13, 2713–2732, https://doi.org/10.5194/tc-13-2713-2019, https://doi.org/10.5194/tc-13-2713-2019, 2019
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The specific surface area (SSA) of solid precipitation particles (PPs) includes detailed information of PP. This work is based on field measurement of SSA of PPs in Nagaoka, the city with the heaviest snowfall in Japan. The values of SSA strongly depend on wind speed (WS) and wet-bulb temperature (Tw) on the ground. An equation to empirically estimate the SSA of fresh PPs with WS and Tw was established and the equation successfully reproduced the fluctuation of SSA in Nagaoka.
Mizuo Kajino, Makoto Deushi, Tsuyoshi Thomas Sekiyama, Naga Oshima, Keiya Yumimoto, Taichu Yasumichi Tanaka, Joseph Ching, Akihiro Hashimoto, Tetsuya Yamamoto, Masaaki Ikegami, Akane Kamada, Makoto Miyashita, Yayoi Inomata, Shin-ichiro Shima, Kouji Adachi, Yuji Zaizen, Yasuhito Igarashi, Hiromasa Ueda, Takashi Maki, and Masao Mikami
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-128, https://doi.org/10.5194/gmd-2018-128, 2018
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Masashi Niwano, Teruo Aoki, Akihiro Hashimoto, Sumito Matoba, Satoru Yamaguchi, Tomonori Tanikawa, Koji Fujita, Akane Tsushima, Yoshinori Iizuka, Rigen Shimada, and Masahiro Hori
The Cryosphere, 12, 635–655, https://doi.org/10.5194/tc-12-635-2018, https://doi.org/10.5194/tc-12-635-2018, 2018
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We present a high-resolution regional climate model called NHM–SMAP applied to the Greenland Ice Sheet (GrIS). The model forced by JRA-55 reanalysis is evaluated using in situ data from automated weather stations, stake measurements,
and ice core obtained from 2011 to 2014. By utilizing the model, we highlight that the choice of calculation schemes for vertical water movement in snow and firn has an effect of up to 200 Gt/year in the yearly accumulated GrIS-wide surface mass balance estimates.
Hazuki Arakida, Takemasa Miyoshi, Takeshi Ise, Shin-ichiro Shima, and Shunji Kotsuki
Nonlin. Processes Geophys., 24, 553–567, https://doi.org/10.5194/npg-24-553-2017, https://doi.org/10.5194/npg-24-553-2017, 2017
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This is the first study assimilating the satellite-based leaf area index observations every 4 days into a numerical model simulating the growth and death of individual plants. The newly developed data assimilation system successfully reduced the uncertainties of the model parameters related to phenology and carbon dynamics. It also provides better estimates of the present vegetation structure which can be used as the initial states for the simulation of the future vegetation change.
Sylwester Arabas and Shin-ichiro Shima
Nonlin. Processes Geophys., 24, 535–542, https://doi.org/10.5194/npg-24-535-2017, https://doi.org/10.5194/npg-24-535-2017, 2017
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The study describes the updates of NOAA's current UFS-AQMv7 air quality forecast model by incorporating the latest scientific and structural changes in CMAQv5.4. An evaluation during the summer of 2023 shows that the updated model overall improves the simulation of MDA8 O3 by reducing the bias by 8%–12% in the contiguous US. PM2.5 predictions have mixed results due to wildfire, highlighting the need for future refinements.
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Most works have delved into convective weather nowcasting, and only a few works have discussed the nowcasting uncertainty for variables at the surface level. Hence, we proposed a method to estimate uncertainty. Generating appropriate noises associated with the characteristic of the error in analysis can simulate the uncertainty of nowcasting. This method can contribute to the estimation of near–surface analysis uncertainty in both nowcasting applications and ensemble nowcasting development.
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Geosci. Model Dev., 18, 1265–1286, https://doi.org/10.5194/gmd-18-1265-2025, https://doi.org/10.5194/gmd-18-1265-2025, 2025
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Normally, the Modular Earth Submodel System (MESSy) is linked to complete dynamic models to create chemical climate models. However, the modular concept of MESSy and the newly developed DWARF component presented here make it possible to create simplified models that contain only one or a few process descriptions. This is very useful for technical optimisation, such as porting to GPUs, and can be used to create less complex models, such as a chemical box model.
Edward C. Chan, Ilona J. Jäkel, Basit Khan, Martijn Schaap, Timothy M. Butler, Renate Forkel, and Sabine Banzhaf
Geosci. Model Dev., 18, 1119–1139, https://doi.org/10.5194/gmd-18-1119-2025, https://doi.org/10.5194/gmd-18-1119-2025, 2025
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An enhanced emission module has been developed for the PALM model system, improving flexibility and scalability of emission source representation across different sectors. A model for parametrized domestic emissions has also been included, for which an idealized model run is conducted for particulate matter (PM10). The results show that, in addition to individual sources and diurnal variations in energy consumption, vertical transport and urban topology play a role in concentration distribution.
Gregor Ehrensperger, Thorsten Simon, Georg J. Mayr, and Tobias Hell
Geosci. Model Dev., 18, 1141–1153, https://doi.org/10.5194/gmd-18-1141-2025, https://doi.org/10.5194/gmd-18-1141-2025, 2025
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As lightning is a brief and localized event, it is not explicitly resolved in atmospheric models. Instead, expert-based auxiliary descriptions are used to assess it. This study explores how AI can improve our understanding of lightning without relying on traditional expert knowledge. We reveal that AI independently identified the key factors known to experts as essential for lightning in the Alps region. This shows how knowledge discovery could be sped up in areas with limited expert knowledge.
David Patoulias, Kalliopi Florou, and Spyros N. Pandis
Geosci. Model Dev., 18, 1103–1118, https://doi.org/10.5194/gmd-18-1103-2025, https://doi.org/10.5194/gmd-18-1103-2025, 2025
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The effect of the assumed atmospheric nucleation mechanism on particle number concentrations and size distribution was investigated. Two quite different mechanisms involving sulfuric acid and ammonia or a biogenic organic vapor gave quite similar results which were consistent with measurements at 26 measurement stations across Europe. The number of larger particles that serve as cloud condensation nuclei showed little sensitivity to the assumed nucleation mechanism.
Tim Radke, Susanne Fuchs, Christian Wilms, Iuliia Polkova, and Marc Rautenhaus
Geosci. Model Dev., 18, 1017–1039, https://doi.org/10.5194/gmd-18-1017-2025, https://doi.org/10.5194/gmd-18-1017-2025, 2025
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In our study, we built upon previous work to investigate the patterns artificial intelligence (AI) learns to detect atmospheric features like tropical cyclones (TCs) and atmospheric rivers (ARs). As primary objective, we adopt a method to explain the AI used and investigate the plausibility of learned patterns. We find that plausible patterns are learned for both TCs and ARs. Hence, the chosen method is very useful for gaining confidence in the AI-based detection of atmospheric features.
Felipe Cifuentes, Henk Eskes, Enrico Dammers, Charlotte Bryan, and Folkert Boersma
Geosci. Model Dev., 18, 621–649, https://doi.org/10.5194/gmd-18-621-2025, https://doi.org/10.5194/gmd-18-621-2025, 2025
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We tested the capability of the flux divergence approach (FDA) to reproduce known NOx emissions using synthetic NO2 satellite column retrievals from high-resolution model simulations. The FDA accurately reproduced NOx emissions when column observations were limited to the boundary layer and when the variability of the NO2 lifetime, the NOx : NO2 ratio, and NO2 profile shapes were correctly modeled. This introduces strong model dependency, reducing the simplicity of the original FDA formulation.
Stefano Ubbiali, Christian Kühnlein, Christoph Schär, Linda Schlemmer, Thomas C. Schulthess, Michael Staneker, and Heini Wernli
Geosci. Model Dev., 18, 529–546, https://doi.org/10.5194/gmd-18-529-2025, https://doi.org/10.5194/gmd-18-529-2025, 2025
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We explore a high-level programming model for porting numerical weather prediction (NWP) model codes to graphics processing units (GPUs). We present a Python rewrite with the domain-specific library GT4Py (GridTools for Python) of two renowned cloud microphysics schemes and the associated tangent-linear and adjoint algorithms. We find excellent portability, competitive GPU performance, robust execution on diverse computing architectures, and enhanced code maintainability and user productivity.
Pieter Rijsdijk, Henk Eskes, Arlene Dingemans, K. Folkert Boersma, Takashi Sekiya, Kazuyuki Miyazaki, and Sander Houweling
Geosci. Model Dev., 18, 483–509, https://doi.org/10.5194/gmd-18-483-2025, https://doi.org/10.5194/gmd-18-483-2025, 2025
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Clustering high-resolution satellite observations into superobservations improves model validation and data assimilation applications. In our paper, we derive quantitative uncertainties for satellite NO2 column observations based on knowledge of the retrievals, including a detailed analysis of spatial error correlations and representativity errors. The superobservations and uncertainty estimates are tested in a global chemical data assimilation system and are found to improve the forecasts.
Dario Di Santo, Cenlin He, Fei Chen, and Lorenzo Giovannini
Geosci. Model Dev., 18, 433–459, https://doi.org/10.5194/gmd-18-433-2025, https://doi.org/10.5194/gmd-18-433-2025, 2025
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This paper presents the Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), a computationally efficient tool that uses machine learning algorithms for sensitivity analysis in atmospheric models. It is tested with the Weather Research and Forecasting (WRF) model coupled with the Noah-Multiparameterization (Noah-MP) land surface model to investigate sea breeze circulation sensitivity to vegetation-related parameters.
Robert Schoetter, Robin James Hogan, Cyril Caliot, and Valéry Masson
Geosci. Model Dev., 18, 405–431, https://doi.org/10.5194/gmd-18-405-2025, https://doi.org/10.5194/gmd-18-405-2025, 2025
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Radiation is relevant to the atmospheric impact on people and infrastructure in cities as it can influence the urban heat island, building energy consumption, and human thermal comfort. A new urban radiation model, assuming a more realistic form of urban morphology, is coupled to the urban climate model Town Energy Balance (TEB). The new TEB is evaluated with a reference radiation model for a variety of urban morphologies, and an improvement in the simulated radiative observables is found.
Zebediah Engberg, Roger Teoh, Tristan Abbott, Thomas Dean, Marc E. J. Stettler, and Marc L. Shapiro
Geosci. Model Dev., 18, 253–286, https://doi.org/10.5194/gmd-18-253-2025, https://doi.org/10.5194/gmd-18-253-2025, 2025
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Contrails forming in some atmospheric conditions may persist and become strongly warming cirrus, while in other conditions may be neutral or cooling. We develop a contrail forecast model to predict contrail climate forcing for any arbitrary point in space and time and explore integration into flight planning and air traffic management. This approach enables contrail interventions to target high-probability high-climate-impact regions and reduce unintended consequences of contrail management.
Nils Eingrüber, Alina Domm, Wolfgang Korres, and Karl Schneider
Geosci. Model Dev., 18, 141–160, https://doi.org/10.5194/gmd-18-141-2025, https://doi.org/10.5194/gmd-18-141-2025, 2025
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Climate change adaptation measures like unsealings can reduce urban heat stress. As grass grid pavers have never been parameterized for microclimate model simulations with ENVI-met, a new parameterization was developed based on field measurements. To analyse the cooling potential, scenario analyses were performed for a densely developed area in Cologne. Statistically significant average cooling effects of up to −11.1 K were found for surface temperature and up to −2.9 K for 1 m air temperature.
Xuan Wang, Lei Bi, Hong Wang, Yaqiang Wang, Wei Han, Xueshun Shen, and Xiaoye Zhang
Geosci. Model Dev., 18, 117–139, https://doi.org/10.5194/gmd-18-117-2025, https://doi.org/10.5194/gmd-18-117-2025, 2025
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The Artificial-Intelligence-based Nonspherical Aerosol Optical Scheme (AI-NAOS) was developed to improve the estimation of the aerosol direct radiation effect and was coupled online with a chemical weather model. The AI-NAOS scheme considers black carbon as fractal aggregates and soil dust as super-spheroids, encapsulated with hygroscopic aerosols. Real-case simulations emphasize the necessity of accurately representing nonspherical and inhomogeneous aerosols in chemical weather models.
Lukas Pfitzenmaier, Pavlos Kollias, Nils Risse, Imke Schirmacher, Bernat Puigdomenech Treserras, and Katia Lamer
Geosci. Model Dev., 18, 101–115, https://doi.org/10.5194/gmd-18-101-2025, https://doi.org/10.5194/gmd-18-101-2025, 2025
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The Python tool Orbital-Radar transfers suborbital radar data (ground-based, airborne, and forward-simulated numerical weather prediction model) into synthetic spaceborne cloud profiling radar data, mimicking platform-specific instrument characteristics, e.g. EarthCARE or CloudSat. The tool's novelty lies in simulating characteristic errors and instrument noise. Thus, existing data sets are transferred into synthetic observations and can be used for satellite calibration–validation studies.
Mark Buehner, Jean-Francois Caron, Ervig Lapalme, Alain Caya, Ping Du, Yves Rochon, Sergey Skachko, Maziar Bani Shahabadi, Sylvain Heilliette, Martin Deshaies-Jacques, Weiguang Chang, and Michael Sitwell
Geosci. Model Dev., 18, 1–18, https://doi.org/10.5194/gmd-18-1-2025, https://doi.org/10.5194/gmd-18-1-2025, 2025
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The Modular and Integrated Data Assimilation System (MIDAS) software is described. The flexible design of MIDAS enables both deterministic and ensemble prediction applications for the atmosphere and several other Earth system components. It is currently used for all main operational weather prediction systems in Canada and also for sea ice and sea surface temperature analysis. The use of MIDAS for multiple Earth system components will facilitate future research on coupled data assimilation.
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024, https://doi.org/10.5194/gmd-17-8885-2024, 2024
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We developed a model to simulate polycyclic aromatic hydrocarbons (PAHs) from global to regional scales. The model can reproduce PAH distribution well. The concentration of BaP (indicator species for PAHs) could exceed the target values of 1 ng m-3 over some areas (e.g., in central Europe, India, and eastern China). The change in BaP is lower than that in PM2.5 from 2013 to 2018. China still faces significant potential health risks posed by BaP although the Action Plan has been implemented.
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024, https://doi.org/10.5194/gmd-17-8773-2024, 2024
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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.
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024, https://doi.org/10.5194/gmd-17-8639-2024, 2024
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A new parameterisation for dynamic anthropogenic heat and electricity consumption is described. The model reproduced the temporal variation in and spatial distributions of electricity consumption and temperature well in summer and winter. The partial air conditioning was the most critical factor, significantly affecting the value of anthropogenic heat emission.
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024, https://doi.org/10.5194/gmd-17-8495-2024, 2024
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To accurately characterize the spatiotemporal distribution of particulate matter <2.5 µm chemical components, we developed the Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v2.0 for chemical components with non-Gaussian and nonlinear properties. NAQPMS-PDAF v2.0 has better computing efficiency, excels when used with a small ensemble size, and can significantly improve the simulation performance of chemical components.
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024, https://doi.org/10.5194/gmd-17-8373-2024, 2024
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Chemical transport model simulations are combined with ozone observations to estimate the bias in ozone attributable to US anthropogenic sources and individual sources of US background ozone: natural sources, non-US anthropogenic sources, and stratospheric ozone. Results indicate a positive bias correlated with US anthropogenic emissions during summer in the eastern US and a negative bias correlated with stratospheric ozone during spring.
Li Fang, Jianbing Jin, Arjo Segers, Ke Li, Ji Xia, Wei Han, Baojie Li, Hai Xiang Lin, Lei Zhu, Song Liu, and Hong Liao
Geosci. Model Dev., 17, 8267–8282, https://doi.org/10.5194/gmd-17-8267-2024, https://doi.org/10.5194/gmd-17-8267-2024, 2024
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Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.
Mijie Pang, Jianbing Jin, Arjo Segers, Huiya Jiang, Wei Han, Batjargal Buyantogtokh, Ji Xia, Li Fang, Jiandong Li, Hai Xiang Lin, and Hong Liao
Geosci. Model Dev., 17, 8223–8242, https://doi.org/10.5194/gmd-17-8223-2024, https://doi.org/10.5194/gmd-17-8223-2024, 2024
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The ensemble Kalman filter (EnKF) improves dust storm forecasts but faces challenges with position errors. The valid time shifting EnKF (VTS-EnKF) addresses this by adjusting for position errors, enhancing accuracy in forecasting dust storms, as proven in tests on 2021 events, even with smaller ensembles and time intervals.
Mike Bush, David L. A. Flack, Huw W. Lewis, Sylvia I. Bohnenstengel, Chris J. Short, Charmaine Franklin, Adrian P. Lock, Martin Best, Paul Field, Anne McCabe, Kwinten Van Weverberg, Segolene Berthou, Ian Boutle, Jennifer K. Brooke, Seb Cole, Shaun Cooper, Gareth Dow, John Edwards, Anke Finnenkoetter, Kalli Furtado, Kate Halladay, Kirsty Hanley, Margaret A. Hendry, Adrian Hill, Aravindakshan Jayakumar, Richard W. Jones, Humphrey Lean, Joshua C. K. Lee, Andy Malcolm, Marion Mittermaier, Saji Mohandas, Stuart Moore, Cyril Morcrette, Rachel North, Aurore Porson, Susan Rennie, Nigel Roberts, Belinda Roux, Claudio Sanchez, Chun-Hsu Su, Simon Tucker, Simon Vosper, David Walters, James Warner, Stuart Webster, Mark Weeks, Jonathan Wilkinson, Michael Whitall, Keith D. Williams, and Hugh Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-201, https://doi.org/10.5194/gmd-2024-201, 2024
Revised manuscript accepted for GMD
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RAL configurations define settings for the Unified Model atmosphere and Joint UK Land Environment Simulator. The third version of the Regional Atmosphere and Land (RAL3) science configuration for kilometre and sub-km scale modelling represents a major advance compared to previous versions (RAL2) by delivering a common science definition for applications in tropical and mid-latitude regions. RAL3 has more realistic precipitation distributions and improved representation of clouds and visibility.
Prabhakar Namdev, Maithili Sharan, Piyush Srivastava, and Saroj Kanta Mishra
Geosci. Model Dev., 17, 8093–8114, https://doi.org/10.5194/gmd-17-8093-2024, https://doi.org/10.5194/gmd-17-8093-2024, 2024
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Inadequate representation of surface–atmosphere interaction processes is a major source of uncertainty in numerical weather prediction models. Here, an effort has been made to improve the Weather Research and Forecasting (WRF) model version 4.2.2 by introducing a unique theoretical framework under convective conditions. In addition, to enhance the potential applicability of the WRF modeling system, various commonly used similarity functions under convective conditions have also been installed.
Andrew Gettelman, Richard Forbes, Roger Marchand, Chih-Chieh Chen, and Mark Fielding
Geosci. Model Dev., 17, 8069–8092, https://doi.org/10.5194/gmd-17-8069-2024, https://doi.org/10.5194/gmd-17-8069-2024, 2024
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Supercooled liquid clouds (liquid clouds colder than 0°C) are common at higher latitudes (especially over the Southern Ocean) and are critical for constraining climate projections. We compare a single-column version of a weather model to observations with two different cloud schemes and find that both the dynamical environment and atmospheric aerosols are important for reproducing observations.
Yujuan Wang, Peng Zhang, Jie Li, Yaman Liu, Yanxu Zhang, Jiawei Li, and Zhiwei Han
Geosci. Model Dev., 17, 7995–8021, https://doi.org/10.5194/gmd-17-7995-2024, https://doi.org/10.5194/gmd-17-7995-2024, 2024
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This study updates the CESM's aerosol schemes, focusing on dust, marine aerosol emissions, and secondary organic aerosol (SOA) . Dust emission modifications make deflation areas more continuous, improving results in North America and the sub-Arctic. Humidity correction to sea-salt emissions has a minor effect. Introducing marine organic aerosol emissions, coupled with ocean biogeochemical processes, and adding aqueous reactions for SOA formation advance the CESM's aerosol modelling results.
Lucas A. McMichael, Michael J. Schmidt, Robert Wood, Peter N. Blossey, and Lekha Patel
Geosci. Model Dev., 17, 7867–7888, https://doi.org/10.5194/gmd-17-7867-2024, https://doi.org/10.5194/gmd-17-7867-2024, 2024
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Marine cloud brightening (MCB) is a climate intervention technique to potentially cool the climate. Climate models used to gauge regional climate impacts associated with MCB often assume large areas of the ocean are uniformly perturbed. However, a more realistic representation of MCB application would require information about how an injected particle plume spreads. This work aims to develop such a plume-spreading model.
Leonardo Olivetti and Gabriele Messori
Geosci. Model Dev., 17, 7915–7962, https://doi.org/10.5194/gmd-17-7915-2024, https://doi.org/10.5194/gmd-17-7915-2024, 2024
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Data-driven models are becoming a viable alternative to physics-based models for weather forecasting up to 15 d into the future. However, it is unclear whether they are as reliable as physics-based models when forecasting weather extremes. We evaluate their performance in forecasting near-surface cold, hot, and windy extremes globally. We find that data-driven models can compete with physics-based models and that the choice of the best model mainly depends on the region and type of extreme.
David C. Wong, Jeff Willison, Jonathan E. Pleim, Golam Sarwar, James Beidler, Russ Bullock, Jerold A. Herwehe, Rob Gilliam, Daiwen Kang, Christian Hogrefe, George Pouliot, and Hosein Foroutan
Geosci. Model Dev., 17, 7855–7866, https://doi.org/10.5194/gmd-17-7855-2024, https://doi.org/10.5194/gmd-17-7855-2024, 2024
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This work describe how we linked the meteorological Model for Prediction Across Scales – Atmosphere (MPAS-A) with the Community Multiscale Air Quality (CMAQ) air quality model to form a coupled modelling system. This could be used to study air quality or climate and air quality interaction at a global scale. This new model scales well in high-performance computing environments and performs well with respect to ground surface networks in terms of ozone and PM2.5.
Markus Kunze, Christoph Zülicke, Tarique Adnan Siddiqui, Claudia Christine Stephan, Yosuke Yamazaki, Claudia Stolle, Sebastian Borchert, and Hauke Schmidt
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-191, https://doi.org/10.5194/gmd-2024-191, 2024
Revised manuscript accepted for GMD
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We present the Icosahedral Nonhydrostatic (ICON) general circulation model with upper atmosphere extension with the physics package for numerical weather prediction (UA-ICON(NWP)). The parameters for the gravity wave parameterizations were optimized, and realistic modelling of the thermal and dynamic state of the mesopause regions was achieved. UA-ICON(NWP) now shows a realistic frequency of major sudden stratospheric warmings and well-represented solar tides in temperature.
Giulio Mandorli and Claudia J. Stubenrauch
Geosci. Model Dev., 17, 7795–7813, https://doi.org/10.5194/gmd-17-7795-2024, https://doi.org/10.5194/gmd-17-7795-2024, 2024
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In recent years, several studies focused their attention on the disposition of convection. Lots of methods, called indices, have been developed to quantify the amount of convection clustering. These indices are evaluated in this study by defining criteria that must be satisfied and then evaluating the indices against these standards. None of the indices meet all criteria, with some only partially meeting them.
Wonbae Bang, Jacob Carlin, Kwonil Kim, Alexander Ryzhkov, Guosheng Liu, and Gyuwon Lee
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-179, https://doi.org/10.5194/gmd-2024-179, 2024
Revised manuscript accepted for GMD
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Microphysics model-based diagnosis such as the spectral bin model (SBM) recently has been attempted to diagnose winter precipitation types. In this study, the accuracy of SBM-based precipitation type diagnosis is compared with other traditional methods. SBM have relatively higher accuracy about snow and wetsnow events whereas lower accuracy about rain event. When microphysics scheme in the SBM was optimized for the corresponding region, accuracy about rain events was improved.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Jianyu Lin, Tie Dai, Lifang Sheng, Weihang Zhang, Shangfei Hai, and Yawen Kong
EGUsphere, https://doi.org/10.5194/egusphere-2024-3321, https://doi.org/10.5194/egusphere-2024-3321, 2024
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The effectiveness of assimilation system and its sensitivity to ensemble member size and length of assimilation window have been investigated. This study advances our understanding about the selection of basic parameters in the four-dimension local ensemble transform Kalman filter assimilation system and the performance of ensemble simulation in a particulate matter polluted environment.
Samiha Binte Shahid, Forrest G. Lacey, Christine Wiedinmyer, Robert J. Yokelson, and Kelley C. Barsanti
Geosci. Model Dev., 17, 7679–7711, https://doi.org/10.5194/gmd-17-7679-2024, https://doi.org/10.5194/gmd-17-7679-2024, 2024
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The Next-generation Emissions InVentory expansion of Akagi (NEIVA) v.1.0 is a comprehensive biomass burning emissions database that allows integration of new data and flexible querying. Data are stored in connected datasets, including recommended averages of ~1500 constituents for 14 globally relevant fire types. Individual compounds were mapped to common model species to allow better attribution of emissions in modeling studies that predict the effects of fires on air quality and climate.
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-157, https://doi.org/10.5194/gmd-2024-157, 2024
Revised manuscript accepted for GMD
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This study combines Machine Learning with Concentration-Weighted Trajectory Analysis to quantify regional transport PM2.5. From 2013–2020, local emissions dominated Beijing's pollution events. The Air Pollution Prevention and Control Action Plan reduced regional transport pollution, but the eastern region showed the smallest decrease. Beijing should prioritize local emission reduction while considering the east region's contributions in future strategies.
Lucie Bakels, Daria Tatsii, Anne Tipka, Rona Thompson, Marina Dütsch, Michael Blaschek, Petra Seibert, Katharina Baier, Silvia Bucci, Massimo Cassiani, Sabine Eckhardt, Christine Groot Zwaaftink, Stephan Henne, Pirmin Kaufmann, Vincent Lechner, Christian Maurer, Marie D. Mulder, Ignacio Pisso, Andreas Plach, Rakesh Subramanian, Martin Vojta, and Andreas Stohl
Geosci. Model Dev., 17, 7595–7627, https://doi.org/10.5194/gmd-17-7595-2024, https://doi.org/10.5194/gmd-17-7595-2024, 2024
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Computer models are essential for improving our understanding of how gases and particles move in the atmosphere. We present an update of the atmospheric transport model FLEXPART. FLEXPART 11 is more accurate due to a reduced number of interpolations and a new scheme for wet deposition. It can simulate non-spherical aerosols and includes linear chemical reactions. It is parallelised using OpenMP and includes new user options. A new user manual details how to use FLEXPART 11.
Bjarke Tobias Eisensøe Olsen, Andrea Noemi Hahmann, Nicolás González Alonso-de-Linaje, Mark Žagar, and Martin Dörenkämper
EGUsphere, https://doi.org/10.5194/egusphere-2024-3123, https://doi.org/10.5194/egusphere-2024-3123, 2024
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Low-level jets (LLJs) are strong winds in the lower atmosphere, important for wind energy as turbines get taller. This study compares a weather model (WRF) with real data across five North and Baltic Sea sites. Adjusting the model improved accuracy over the widely-used ERA5. In key offshore regions, LLJs occur 10–15 % of the time and significantly boost wind power, especially in spring and summer, contributing up to 30 % of total capacity in some areas.
Jaroslav Resler, Petra Bauerová, Michal Belda, Martin Bureš, Kryštof Eben, Vladimír Fuka, Jan Geletič, Radek Jareš, Jan Karel, Josef Keder, Pavel Krč, William Patiño, Jelena Radović, Hynek Řezníček, Matthias Sühring, Adriana Šindelářová, and Ondřej Vlček
Geosci. Model Dev., 17, 7513–7537, https://doi.org/10.5194/gmd-17-7513-2024, https://doi.org/10.5194/gmd-17-7513-2024, 2024
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Detailed modeling of urban air quality in stable conditions is a challenge. We show the unprecedented sensitivity of a large eddy simulation (LES) model to meteorological boundary conditions and model parameters in an urban environment under stable conditions. We demonstrate the crucial role of boundary conditions for the comparability of results with observations. The study reveals a strong sensitivity of the results to model parameters and model numerical instabilities during such conditions.
Cited articles
Abade, G. C., Grabowski, W. W., and Pawlowska, H.: Broadening of cloud droplet
spectra through eddy hopping: Turbulent entraining parcel simulations,
J. Atmos. Sci., 75, 3365–3379,
https://doi.org/10.1175/JAS-D-18-0078.1, 2018. a, b, c
Alfonso, L. and Raga, G. B.: The impact of fluctuations and correlations in droplet growth by collision–coalescence revisited – Part 1: Numerical calculation of post-gel droplet size distribution, Atmos. Chem. Phys., 17, 6895–6905, https://doi.org/10.5194/acp-17-6895-2017, 2017. a
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
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, c
Arabas, S. and Shima, S.-i.: Large-eddy simulations of trade wind cumuli using
particle-based microphysics with monte Carlo coalescence, J. Atmos. Sci., 70, 2768–2777, https://doi.org/10.1175/JAS-D-12-0295.1, 2013. a, b
Arabas, S. and Shima, S.: On the CCN (de)activation nonlinearities, Nonlin. Processes Geophys., 24, 535–542, https://doi.org/10.5194/npg-24-535-2017, 2017. a
Árnason, G. and Brown, P. S.: Growth of Cloud Droplets by Condensation:
A Problem in Computational Stability, 28, 72–77
https://doi.org/10.1175/1520-0469(1971)028<0072:GOCDBC>2.0.CO;2,
1971. a
Auer, A. H.: Distribution of Graupel and Hail With Size, Mon. Weather Rev., 100, 325–328, https://doi.org/10.1175/1520-0493-100-05-0325, 1972. a, b
Auer, A. H. and Veal, D. L.: The Dimension of Ice Crystals in Natural Clouds,
J. Atmos. Sci., 27, 919–926,
https://doi.org/10.1175/1520-0469(1970)027<0919:TDOICI>2.0.CO;2, 1970. a
Bailey, M. and Hallett, J.: Growth rates and habits of ice crystals between −20 and −70 ∘C, J. Atmos. Sci., 61, 514–544,
https://doi.org/10.1175/1520-0469(2004)061<0514:GRAHOI>2.0.CO;2, 2004. a
Baran, A. J.: From the single-scattering properties of ice crystals to climate
prediction: A way forward, Atmos. Res., 112, 45–69, https://doi.org/10.1016/j.atmosres.2012.04.010, 2012. a, b
Beard, K. V.: Terminal Velocity And Shape Of Cloud And Precipitation Drops
Aloft, J. Atmos. Sci., 33, 851–864,
https://doi.org/10.1175/1520-0469(1976)033<0851:TVASOC>2.0.CO;2, 1976. a
Beard, K. V. and Ochs, H. T.: Collisions between Small Precipitation Drops.
Part II: Formulas for Coalescence, Temporary Coalescence, and Satellites,
J. Atmos. Sci., 52, 3977–3996,
https://doi.org/10.1175/1520-0469(1995)052<3977:CBSPDP>2.0.CO;2, 1995. a
Beheng, K. D.: The evolution of raindrop spectra: A review of microphysical
essentials, in: Rainfall: State of the Science, Wiley Blackwell, 29–48,
https://doi.org/10.1029/2010GM000957, 2010. a
Böhm, J. P.: A general hydrodynamic theory for mixed-phase microphysics.
Part III: Riming and aggregation, Atmos. Res., 28, 103–123,
https://doi.org/10.1016/0169-8095(92)90023-4, 1992a. a
Böhm, J. P.: A general hydrodynamic theory for mixed-phase microphysics.
Part II: collision kernels for coalescence, Atmos. Res., 27,
275–290, https://doi.org/10.1016/0169-8095(92)90036-A, 1992b. a, b, c, d
Böhm, J. P.: Theoretical collision efficiencies for riming and aerosol
impaction, Atmos. Res., 32, 171–187,
https://doi.org/10.1016/0169-8095(94)90058-2, 1994. a
Böhm, J. P.: Reply to Comment on “Revision and clarification of ‘A
general hydrodynamic theory for mixed-phase microphysics' [Böhm J.P.,
1999, Atmos. Res. 52, 167–176]”, Atmos. Res., 69, 289–293,
https://doi.org/10.1016/j.atmosres.2003.10.001,
2004. a, b, c
Bott, A.: A flux method for the numerical solution of the stochastic
collection equation, J. Atmos. Sci., 55, 2284–2293,
https://doi.org/10.1175/1520-0469(1998)055<2284:AFMFTN>2.0.CO;2, 1998. a, b
Brown, A. R., Derbyshire, S. H., and Mason, P. J.: Large‐eddy simulation of
stable atmospheric boundary layers with a revised stochastic subgrid model,
Q. J. Roy. Meteor. Soc., 120, 1485–1512,
https://doi.org/10.1002/qj.49712052004, 1994. a
Brown, P. R. A. and Francis, P. N.: Improved Measurements of the Ice Water
Content in Cirrus Using a Total-Water Probe, J. Atmos.
Ocean. Tech., 12, 410–414,
https://doi.org/10.1175/1520-0426(1995)012<0410:imotiw>2.0.co;2, 1995. a
Chen, S., Yau, M. K., and Bartello, P.: Turbulence effects of collision
efficiency and broadening of droplet size distribution in cumulus clouds,
J. Atmos. Sci., 75, 203–217,
https://doi.org/10.1175/JAS-D-17-0123.1, 2018. a, b
Connolly, P. J., Möhler, O., Field, P. R., Saathoff, H., Burgess, R., Choularton, T., and Gallagher, M.: Studies of heterogeneous freezing by three different desert dust samples, Atmos. Chem. Phys., 9, 2805–2824, https://doi.org/10.5194/acp-9-2805-2009, 2009. a
Connolly, P. J., Emersic, C., and Field, P. R.: A laboratory investigation into the aggregation efficiency of small ice crystals, Atmos. Chem. Phys., 12, 2055–2076, https://doi.org/10.5194/acp-12-2055-2012, 2012. a, b
Cotton, W. R., Bryan, G., and van den Heever, S. C.: Storm and Cloud Dynamics
– The Dynamics of Clouds and Precipitating Mesoscale Systems, International Geophysics, vol. 99, Elsevier, 2nd edn.,
https://doi.org/10.1016/S0074-6142(10)09918-3,
2010. a
Cui, Z., Carslaw, K. S., Yin, Y., and Davies, S.: A numerical study of aerosol
effects on the dynamics and microphysics of a deep convective cloud in a
continental environmental, J. Geophys. Res.-Atmos., 111, D05201,
https://doi.org/10.1029/2005JD005981, 2006. a
Davis, M. H.: Collisions of Small Cloud Droplets: Gas Kinetic Effects,
J. Atmos. Sci., 29, 911–915,
https://doi.org/10.1175/1520-0469(1972)029<0911:coscdg>2.0.co;2, 1972. a
De Boer, G., Morrison, H., Shupe, M. D., and Hildner, R.: Evidence of liquid
dependent ice nucleation in high-latitude stratiform clouds from surface
remote sensors, Geophys. Res. Lett., 38, L01803,
https://doi.org/10.1029/2010GL046016, 2011. a
Demange, G., Zapolsky, H., Patte, R., and Brunel, M.: A phase field model for snow crystal growth in three dimensions, npj Computational Materials, 3, 15, https://doi.org/10.1038/s41524-017-0015-1, 2017. a, b
DeVille, R. E., Riemer, N., and West, M.: Weighted Flow Algorithms (WFA) for
stochastic particle coagulation, J. Comput. Phys., 230,
8427–8451, https://doi.org/10.1016/j.jcp.2011.07.027, 2011. a
Dunnavan, E. L., Jiang, Z., Harrington, J. Y., Verlinde, J., Fitch, K.,
Garrett, T. J., Dunnavan, E. L., Jiang, Z., Harrington, J. Y., Verlinde, J.,
Fitch, K., and Garrett, T. J.: The Shape and Density Evolution of Snow
Aggregates, J. Atmos. Sci., 76, 3919–3940,
https://doi.org/10.1175/JAS-D-19-0066.1, 2019. a
Durant, A. J. and Shaw, R. A.: Evaporation freezing by contact nucleation
inside-out, Geophys. Res. Lett., 32, 1–4,
https://doi.org/10.1029/2005GL024175, 2005. a
Dziekan, P. and Pawlowska, H.: Stochastic coalescence in Lagrangian cloud microphysics, Atmos. Chem. Phys., 17, 13509–13520, https://doi.org/10.5194/acp-17-13509-2017, 2017. a, b, c, d
Dziekan, P., Waruszewski, M., and Pawlowska, H.: University of Warsaw Lagrangian Cloud Model (UWLCM) 1.0: a modern large-eddy simulation tool for warm cloud modeling with Lagrangian microphysics, Geosci. Model Dev., 12, 2587–2606, https://doi.org/10.5194/gmd-12-2587-2019, 2019. a
Erfani, E. and Mitchell, D. L.: Growth of ice particle mass and projected area during riming, Atmos. Chem. Phys., 17, 1241–1257, https://doi.org/10.5194/acp-17-1241-2017, 2017. a, b, c, d
Field, P. R., Heymsfield, A. J., and Bansemer, A.: A test of ice
self-collection kernels using aircraft data, J. Atmos. Sci., 63, 651–666, https://doi.org/10.1175/JAS3653.1, 2006. a
Field, P. R., Lawson, R. P., Brown, P. R. A., Lloyd, G., Westbrook, C.,
Moisseev, D., Miltenberger, A., Nenes, A., Blyth, A., Choularton, T.,
Connolly, P., Buehl, J., Crosier, J., Cui, Z., Dearden, C., DeMott, P.,
Flossmann, A., Heymsfield, A., Huang, Y., Kalesse, H., Kanji, Z. A., Korolev,
A., Kirchgaessner, A., Lasher-Trapp, S., Leisner, T., McFarquhar, G.,
Phillips, V., Stith, J., and Sullivan, S.: Secondary Ice Production: Current
State of the Science and Recommendations for the Future, Meteorological
Monographs, 58, 7.1–7.20, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1, 2017. a, b, c
Findeisen, W. and Findeisen, E.: Investigations on the ice splinter formation
on rime layers (A contribution to the origin of storm electricity and to the
microstructure of cumulonimbi), Meteorol. Z, 60, 145–154, 1943. a
Fletcher, N. H.: Active Sites and Ice Crystal Nucleation, J. Atmos. Sci., 26, 1266–1271,
https://doi.org/10.1175/1520-0469(1969)026<1266:asaicn>2.0.co;2, 1969. a
Gillespie, D. T.: The Stochastic Coalescence Model for Cloud Droplet Growth,
J. Atmos. Sci., 29, 1496–1510,
https://doi.org/10.1175/1520-0469(1972)029<1496:tscmfc>2.0.co;2, 1972. a
Grabowski, W. W. and Abade, G. C.: Broadening of Cloud Droplet Spectra through
Eddy Hopping: Turbulent Adiabatic Parcel Simulations, J. Atmos. Sci., 74, 1485–1493, https://doi.org/10.1175/JAS-D-17-0043.1,
2017. a, b, c
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, b, c
Hall, W. D. and Pruppacher, H. R.: Survival Of Ice Particles Falling From
Cirrus Clouds In Subsaturated Air, J. Atmos. Sci., 33,
1995–2006, https://doi.org/10.1175/1520-0469(1976)033< 1995:TSOIPF>2.0.CO;2, 1976. a
Hallett, J. and Mason, B. J.: The influence of temperature and supersaturation
on the habit of ice crystals grown from the vapour, P. Roy. Soc. Lond. A Mat., 247,
440–453, https://doi.org/10.1098/rspa.1958.0199, 1958. a
Hallett, J. and Mossop, S. C.: Production of secondary ice particles during
the riming process, Nature, 249, 26–28, https://doi.org/10.1038/249026a0, 1974. a
Hardy, K. R.: The Development of Raindrop-size Distributions and Implications
Related to the Physics of Precipitation, J. Atmos. Sci., 20, 299–312,
https://doi.org/10.1175/1520-0469(1963)020<0299:TDORSD>2.0.CO;2, 1963. a
Harrington, J. Y., Moyle, A., Hanson, L. E., and Morrison, H.: On calculating
deposition coefficients and aspect-ratio evolution in approximate models of
ice crystal vapor growth, J. Atmos. Sci., 76,
1609–1625, https://doi.org/10.1175/JAS-D-18-0319.1, 2019. a, b, c
Hashino, T. and Tripoli, G. J.: The Spectral Ice Habit Prediction System
(SHIPS). Part I: Model description and simulation of the vapor deposition
process, J. Atmos. Sci., 64, 2210–2237,
https://doi.org/10.1175/JAS3963.1, 2007. a, b, c, d
Hashino, T. and Tripoli, G. J.: The Spectral Ice Habit Prediction System
(SHIPS). Part IV: Box model simulations of the habit-dependent aggregation
process, J. Atmos. Sci., 68, 1142–1161,
https://doi.org/10.1175/2011JAS3667.1, 2011b. a, b, c, d
Heymsfield, A.: Ice Crystal Terminal Velocities, J. Atmos. Sci., 29, 1348–1357,
https://doi.org/10.1175/1520-0469(1972)029<1348:ictv>2.0.co;2, 1972. a, b
Heymsfield, A. J.: The Characteristics of Graupel Particles in Northeastern
Colorado Cumulus Congestus Clouds, J. Atmos. Sci., 35,
284–295, https://doi.org/10.1175/1520-0469(1978)035<0284:TCOGPI> 2.0.CO;2, 1978. a
Heymsfield, A. J.: A comparative study of the rates of development of
potential graupel and hail embryos in High Plains storms, J. Atmos. Sci., 39, 2867–2897,
https://doi.org/10.1175/1520-0469(1982)039<2867:ACSOTR>2.0.CO;2, 1982. a
Heymsfield, A. J. and Kajikawa, M.: Improved Approach To Calculating Terminal
Velocities Of Plate-Like Crystals And Graupel, J. Atmos. Sci., 44, 1088–1099,
https://doi.org/10.1175/1520-0469(1987)044<1088:AIATCT>2.0.CO;2, 1987. a, b
Heymsfield, A. J. and Pflaum, J. C.: A quantitative assessment of the accuracy
of techniques for calculating graupel growth., J. Atmos. Sci., 42, 2264–2274,
https://doi.org/10.1175/1520-0469(1985)042<2264:AQAOTA>2.0.CO;2, 1985. a, b, c, d
Heymsfield, A. J. and Westbrook, C. D.: Advances in the Estimation of Ice
Particle Fall Speeds Using Laboratory and Field Measurements, J.
Atmos. Sci., 67, 2469–2482, https://doi.org/10.1175/2010JAS3379.1, 2010. a
Heymsfield, A. J., Lewis, S., Bansemer, A., Iaquinta, J., Miloshevich, L. M.,
Kajikawa, M., Twohy, C., and Poellot, M. R.: A general approach for deriving
the properties of cirrus and stratiform ice cloud particles, J.
Atmos. Sci., 59, 3–29,
https://doi.org/10.1175/1520-0469(2002)059<0003:AGAFDT>2.0.CO;2, 2002. a, b
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
Higuchi, K.: On The Coalescence Between Plane Snow Crystals, J.
Meteorol., 17, 239–243,
https://doi.org/10.1175/1520-0469(1960)017<0239:otcbps>2.0.co;2, 1960. a, b
Hoffmann, F.: On the limits of Köhler activation theory: how do collision and coalescence affect the activation of aerosols?, Atmos. Chem. Phys., 17, 8343–8356, https://doi.org/10.5194/acp-17-8343-2017, 2017. a
Hoffmann, F., Yamaguchi, T., and Feingold, G.: Inhomogeneous mixing in
lagrangian cloud models: Effects on the production of precipitation embryos,
J. Atmos. Sci., 76, 113–133,
https://doi.org/10.1175/JAS-D-18-0087.1, 2019. a, b, c
Hoose, C. and Möhler, O.: Heterogeneous ice nucleation on atmospheric aerosols: a review of results from laboratory experiments, Atmos. Chem. Phys., 12, 9817–9854, https://doi.org/10.5194/acp-12-9817-2012, 2012. a
Hoose, C., Kristjánsson, J. E., Chen, J.-P., Hazra, A., Hoose, C.,
Kristjánsson, J. E., Chen, J.-P., and Hazra, A.: A
Classical-Theory-Based Parameterization of Heterogeneous Ice Nucleation by
Mineral Dust, Soot, and Biological Particles in a Global Climate Model,
J. Atmos. Sci., 67, 2483–2503,
https://doi.org/10.1175/2010JAS3425.1, 2010. a
Hubbard, J. B. and Douglas, J. F.: Hydrodynamic friction of arbitrarily shaped
Brownian particles, Physical Review E, 47, R2983, https://doi.org/10.1103/PhysRevE.47.R2983,
1993. a
Jaruga, A. and Pawlowska, H.: libcloudph++ 2.0: aqueous-phase chemistry extension of the particle-based cloud microphysics scheme, Geosci. Model Dev., 11, 3623–3645, https://doi.org/10.5194/gmd-11-3623-2018, 2018. a, b, c
Jensen, E. and Pfister, L.: Transport and freeze-drying in the tropical
tropopause layer, J. Geophys. Res.-Atmos., 109, D02207,
https://doi.org/10.1029/2003JD004022, 2004. a, b
Jiang, Z., Oue, M., Verlinde, J., Clothiaux, E. E., Aydin, K., Botta, G., and
Lu, Y.: What can we conclude about the real aspect ratios of ice particle
aggregates from two-dimensional images?, J. Appl. Meteorol.
Climatol., 56, 725–734, https://doi.org/10.1175/JAMC-D-16-0248.1, 2017. a
Johansen, A., Youdin, A. N., and Lithwick, Y.: Adding particle collisions to
the formation of asteroids and Kuiper belt objects via streaming
instabilities, Astron. Astrophys., 537, A125,
https://doi.org/10.1051/0004-6361/201117701, 2012. a, b, c, d
Jonas, P. R.: The collision efficiency of small drops, Q. J. Roy. Meteor. Soc., 98, 681–683, https://doi.org/10.1002/qj.49709841717, 1972. a
Kajikawa, M.: Observation of the Falling Motion of Early Snowflakes, J. Meteorol. Soc. Jpn., 67, 731–738,
https://doi.org/10.2151/jmsj1965.67.5_731, 1989. a, b
Kajikawa, M.: Characteristics of the aggregation of needle snow crystals,
J. Jpn. Soc. Snow Ice, 57, 349–355, 1995. 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
Kamra, A. K., Bhalwankar, R. V., and Sathe, A. B.: Spontaneous breakup of
charged and uncharged water drops freely suspended in a wind tunnel, J. Geophys. Res., 96, 17159–17168, https://doi.org/10.1029/91jd01475, 1991. a
Kanji, Z. A., Ladino, L. A., Wex, H., Boose, Y., Burkert-Kohn, M., Cziczo,
D. J., and Krämer, M.: Overview of Ice Nucleating Particles,
Meteor. Mon., 58, 1.1–1.33,
https://doi.org/10.1175/amsmonographs-d-16-0006.1, 2017. a, b
Khain, A., Pokrovsky, A., Pinsky, M., Seifert, A., and Phillips, V.:
Simulation of effects of atmospheric aerosols on deep turbulent convective
clouds using a spectral microphysics mixed-phase cumulus cloud model. Part I:
Model description and possible applications, J. Atmos. Sci., 61, 2963–2982, https://doi.org/10.1175/JAS-3350.1, 2004. a, b, c
Khain, A. P. and Pinsky, M.: Physical Processes in Clouds and Cloud Modeling,
Cambridge University Press, https://doi.org/10.1017/9781139049481, 2018. a, b, c, d
Khain, A. P., Beheng, K. D., Heymsfield, A., Korolev, A., Krichak, S. O.,
Levin, Z., Pinsky, M., Phillips, V., Prabhakaran, T., Teller, A., Van Den
Heever, S. C., and Yano, J. I.: Representation of microphysical processes
in cloud-resolving models: Spectral (bin) microphysics versus bulk
parameterization, Rev. Geophys., 53, 247–322, https://doi.org/10.1002/2014RG000468, 2015. a, b, c
Khvorostyanov, V. I. and Curry, J. A.: Terminal Velocities of Droplets and
Crystals: Power Laws with Continuous Parameters over the Size Spectrum,
J. Atmos. Sci., 59, 1872–1884,
https://doi.org/10.1175/1520-0469(2002)059<1872:TVODAC>2.0.CO;2, 2002. a
Khvorostyanov, V. I. and Curry, J. A.: The theory of ice nucleation by
heterogeneous freezing of deliquescent mixed CCN. Part I: Critical radius,
energy and nucleation rate, J. Atmos. Sci., 61,
2676–2691, https://doi.org/10.1175/JAS3266.1, 2004. a
Khvorostyanov, V. I. and Curry, J. A.: The theory of ice nucleation by
heterogeneous freezing of deliquescent mixed CCN. Part II: Parcel model
simulation, J. Atmos. Sci., 62, 261–285,
https://doi.org/10.1175/JAS-3367.1, 2005. a
Khvorostyanov, V. I. and Curry, J. A.: Thermodynamics, kinetics, and
microphysics of clouds, Cambridge University Press,
https://doi.org/10.1017/CBO9781139060004, 2014. a
Kikuchi, K., Kameda, T., Higuchi, K., and Yamashita, A.: A global
classification of snow crystals, ice crystals, and solid precipitation based
on observations from middle latitudes to polar regions, Atmos. Res., 132–133, 460–472, https://doi.org/10.1016/j.atmosres.2013.06.006, 2013. a
Knight, N. C. and Heymsfield, A. J.: Measurement and interpretation of
hailstone density and terminal velocity, J. Atmos. Sci., 40, 1510–1516,
https://doi.org/10.1175/1520-0469(1983)040<1510:MAIOHD>2.0.CO;2, 1983. a, b
Kobayashi, T.: The growth of snow crystals at low supersaturations,
Philos. Mag., 6, 1363–1370, https://doi.org/10.1080/14786436108241231, 1961. a
Kogan, Y. L.: The simulation of a convective cloud in a 3-D model with
explicit microphysics. Part I: model description and sensitivity
experiments, J. Atmos. Sci., 48, 1160–1189,
https://doi.org/10.1175/1520-0469(1991)048<1160:TSOACC>2.0.CO;2, 1991. a
Köhler, H.: The nucleus in and the growth of hygroscopic droplets,
T. Faraday Soc., 32, 1152–1161,
https://doi.org/10.1039/TF9363201152, 1936. a
Koop, T., Luo, B., Tsias, A., and Peter, T.: Water activity as the determinant
for homogeneous ice nucleation in aqueous solutions, Nature, 406, 611–614,
https://doi.org/10.1038/35020537, 2000. a
Korolev, A. and Isaac, G.: Roundness and aspect ratio of particles in ice
clouds, J. Atmos. Sci., 60, 1795–1808,
https://doi.org/10.1175/1520-0469(2003)060<1795:RAAROP>2.0.CO;2, 2003. a, b, c
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteo. Monogr., 58, 5.1–5.50, https://doi.org/10.1175/amsmonographs-d-17-0001.1, 2017. a
Kumai, M.: Formation of Ice Crystals and Dissipation of Supercooled Fog by
Artificial Nucleation, and Variations of Crystal Habit at Early Growth
Stages, J. Appl. Meteorol., 21, 579–587,
https://doi.org/10.1175/1520-0450(1982)021<0579:FOICAD>2.0.CO;2, 1982. a
Lasher-Trapp, S. G., Cooper, W. A., and Blyth, A. M.: Broadening of droplet
size distributions from entrainment and mixing in a cumulus cloud, Q.
J. Roy. Meteor. Soc., 131, 195–220,
https://doi.org/10.1256/qj.03.199, 2005. a
Lawson, R. P., Pilson, B., Baker, B., Mo, Q., Jensen, E., Pfister, L., and Bui, P.: Aircraft measurements of microphysical properties of subvisible cirrus in the tropical tropopause layer, Atmos. Chem. Phys., 8, 1609–1620, https://doi.org/10.5194/acp-8-1609-2008, 2008. a, b
Lew, J. K. and Pruppacher, H. R.: A Theoretical Determination of the Capture
Efficiency of Small Columnar Ice Crystals by Large Cloud Drops, J. Atmos. Sci., 40, 139–145,
https://doi.org/10.1175/1520-0469(1983)040<0139:ATDOTC>2.0.CO;2, 1983. a
Lew, J. K., Kingsmill, D. E., and Montague, D. C.: A Theoretical Study of the Collision Efficiency of Small Planar Ice Crystals Colliding with Large
Supercooled Water Drops, J. Atmos. Sci., 42, 857–862,
https://doi.org/10.1175/1520-0469(1985)042<0857:atsotc>2.0.co;2, 1985. a
Li, X. Y., Brandenburg, A., Haugen, N. E., and Svensson, G.: Eulerian and
Lagrangian approaches to multidimensional condensation and collection,
J. Adv. Model. Earth Sy., 9, 1116–1137,
https://doi.org/10.1002/2017MS000930, 2017. a
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
Lin, C. L. and Lee, S. C.: Collision Efficiency of Water Drops in the
Atmosphere, J. Atmos. Sci., 32, 1412–1418,
https://doi.org/10.1175/1520-0469(1975)032<1412:CEOWDI>2.0.CO;2, 1975. a
Locatelli, J. D. and Hobbs, P. V.: Fall speeds and masses of solid
precipitation particles, J. Geophys. Res., 79, 2185–2197,
https://doi.org/10.1029/jc079i015p02185, 1974. a, b, c, d
Low, R. D. H.: A Generalized Equation for the Solution Effect in Droplet
Growth, J. Atmos. Sci., 26, 608–611,
https://doi.org/10.1175/1520-0469(1969)026<0608:agefts>2.0.co;2, 1969. a
Low, T. B. and List, R.: Collision, Coalescence and Breakup of Raindrops. Part
I: Experimentally Established Coalescence Efficiencies and Fragment Size
Distributions in Breakup, J. Atmos. Sci., 39,
1591–1606, https://doi.org/10.1175/1520-0469(1982)039< 1591:CCABOR>2.0.CO;2, 1982. a
Magono, C. and Lee, C. W.: Meteorological Classification of Natural Snow
Crystals, Journal of the Faculty of Science, Hokkaido University. Series 7,
Geophysics, II, 321–335, 1966. a
Magono, C. and Nakamura, T.: Aerodynamic Studies of Falling Snowflakes,
J. Meteorol. Soc. Jpn., 43, 139–147,
https://doi.org/10.2151/jmsj1965.43.3_139, 1965. a
Marcolli, C.: Deposition nucleation viewed as homogeneous or immersion freezing in pores and cavities, Atmos. Chem. Phys., 14, 2071–2104, https://doi.org/10.5194/acp-14-2071-2014, 2014. a
Marcolli, C.: Pre-activation of aerosol particles by ice preserved in pores, Atmos. Chem. Phys., 17, 1595–1622, https://doi.org/10.5194/acp-17-1595-2017, 2017. a, b
Maruyama, K. I. and Fujiyoshi, Y.: Monte Carlo simulation of the formation of
snowflakes, J. Atmos. Sci., 62, 1529–1544,
https://doi.org/10.1175/JAS3416.1, 2005. a, b
Mason, B. J. and Ramanadham, R.: Modification of the size distribution of
falling raindrops by coalescence, Q. J. Roy.
Meteor.l Soc., 80, 388–394, https://doi.org/10.1002/qj.49708034508, 1954. a
Mazloomi Moqaddam, A., Chikatamarla, S. S., and Karlin, I. V.: Simulation of
Droplets Collisions Using Two-Phase Entropic Lattice Boltzmann Method,
J. Stat. Phys., 161, 1420–1433,
https://doi.org/10.1007/s10955-015-1329-3, 2015. a
Milbrandt, J. A. and Morrison, H.: Parameterization of cloud microphysics
based on the prediction of bulk ice particle properties. Part III:
Introduction of multiple free categories, J. Atmos. Sci., 73, 975–995, https://doi.org/10.1175/JAS-D-15-0204.1, 2016. a
Miller, T. L. and Young, K. C.: A Numerical Simulation of Ice Crystal Growth
from the Vapor Phase, J. Atmos. Sci., 36, 458–469,
https://doi.org/10.1175/1520-0469(1979)036<0458:ansoic>2.0.co;2, 1979. a
Misumi, R., Hashimoto, A., Murakami, M., Kuba, N., Orikasa, N., Saito, A.,
Tajiri, T., Yamashita, K., and Chen, J. P.: Microphysical structure of a
developing convective snow cloud simulated by an improved version of the
multi-dimensional bin model, Atmos. Sci. Lett., 11, 186–191,
https://doi.org/10.1002/asl.268, 2010. a, b, c, d
Mitchell, D. L.: Use of mass- and area-dimensional power laws for determining
precipitation particle terminal velocities, J. Atmos. Sci., 53, 1710–1723,
https://doi.org/10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2, 1996. a, b, c
Mitchell, D. L., Zhang, R., and Pitter, R. L.: Mass-dimensional relationships
for ice particles and the influence of riming on snowfall rates, J.
Appl. Meteorol., 29, 153–163,
https://doi.org/10.1175/1520-0450(1990)029<0153:MDRFIP>2.0.CO;2, 1990. a, b
Morrison, H. and Grabowski, W. W.: A novel approach for representing ice
microphysics in models: Description and tests using a kinematic framework,
J. Atmos. Sci., 65, 1528–1548,
https://doi.org/10.1175/2007JAS2491.1, 2008. a
Morrison, H. and Grabowski, W. W.: An improved representation of rimed snow
and conversion to graupel in a multicomponent bin microphysics scheme,
J. Atmos. Sci., 67, 1337–1360,
https://doi.org/10.1175/2010JAS3250.1, 2010. a, b, c, d
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, b, c
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van
Diedenhoven, B., and Xue, L.: Confronting the challenge of modeling cloud
and precipitation microphysics, J. Adv. Model. Earth
Sy., 45, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020. a, b, c, d, e, f
Mosimann, L., Weingartner, E., and Waldvogel, A.: An Analysis of Accreted Drop Sizes and Mass on Rimed Snow Crystals, J. Atmos. Sci.,
51, 1548–1558, https://doi.org/10.1175/1520-0469(1994)051< 1548:aaoads>2.0.co;2, 1994. a
Murray, B. J., O'Sullivan, D., Atkinson, J. D., and Webb, M. E.: Ice
nucleation by particles immersed in supercooled cloud droplets, 41, 6519–6554,
https://doi.org/10.1039/c2cs35200a, 2012. a
Murray, W. A. and List, R.: Freezing of Water Drops, J. Glaciol.,
11, 415–429, https://doi.org/10.3189/s0022143000022371, 1972. a
Nakaya, U.: Snow Crystals: Natural and Artificial, Harvard Univ. Press,
1954. a
Naumann, A. K. and Seifert, A.: A Lagrangian drop model to study warm rain
microphysical processes in shallow cumulus, J. Adv. Model.
Earth Sy., 7, 1136–1154, https://doi.org/10.1002/2015MS000456, 2015. a
Niedermeier, D., Shaw, R. A., Hartmann, S., Wex, H., Clauss, T., Voigtländer, J., and Stratmann, F.: Heterogeneous ice nucleation: exploring the transition from stochastic to singular freezing behavior, Atmos. Chem. Phys., 11, 8767–8775, https://doi.org/10.5194/acp-11-8767-2011, 2011. a
Niedermeier, D., Ervens, B., Clauss, T., Voigtländer, J., Wex, H.,
Hartmann, S., and Stratmann, F.: A computationally efficient description of
heterogeneous freezing: A simplified version of the Soccer ball model,
Geophys. Res. Lett., 41, 736–741, https://doi.org/10.1002/2013GL058684, 2014. a
Niedermeier, D., Augustin-Bauditz, S., Hartmann, S., Wex, H., Ignatius, K., and Stratmann, F.: Can we define an asymptotic value for the ice active surface site density for heterogeneous ice nucleation?, J. Geophys.
Res., 120, 5036–5046, https://doi.org/10.1002/2014JD022814, 2015. a, b
Niederreiter, H.: Quasi-Monte Carlo methods and pseudo-random numbers, B. Am. Math. Soc., 84, 957–1041, 1978. a
Niehaus, J., Becker, J. G., Kostinski, A., and Cantrell, W.: Laboratory
measurements of contact freezing by dust and bacteria at temperatures of
mixed-phase clouds, J. Atmos. Sci., 71, 3659–3667,
https://doi.org/10.1175/JAS-D-14-0022.1, 2014. a
Niemand, M., Möhler, O., Vogel, B., Vogel, H., Hoose, C., Connolly, P.,
Klein, H., Bingemer, H., Demott, P., Skrotzki, J., and Leisner, T.: A
particle-surface-area-based parameterization of immersion freezing on desert
dust particles, J. Atmos. Sci., 69, 3077–3092,
https://doi.org/10.1175/JAS-D-11-0249.1, 2012. a, b, c, d
Nishizawa, S., Yashiro, H., Sato, Y., Miyamoto, Y., and Tomita, H.: Influence of grid aspect ratio on planetary boundary layer turbulence in large-eddy simulations, Geosci. Model Dev., 8, 3393–3419, https://doi.org/10.5194/gmd-8-3393-2015, 2015. a, b, c
Noh, Y., Oh, D., Hoffmanna, F., and Raasch, S.: A cloud microphysics
parameterization for shallow cumulus clouds based on Lagrangian cloud model
simulations, J. Atmos. Sci., 75, 4031–4047,
https://doi.org/10.1175/JAS-D-18-0080.1, 2018. a
Okawa, D.: Improvement of the super-droplet method using recursive multiple
collision algorithm, Master's thesis, University of Hyogo, 2015. a
Onishi, R. and Seifert, A.: Reynolds-number dependence of turbulence enhancement on collision growth, Atmos. Chem. Phys., 16, 12441–12455, https://doi.org/10.5194/acp-16-12441-2016, 2016. a
Ormel, C. W. and Spaans, M.: Monte Carlo Simulation of Particle Interactions
at High Dynamic Range: Advancing beyond the Googol, Astrophys.
J., 684, 1291–1309, https://doi.org/10.1086/590052, 2008. a, b
Paoli, R., Hélie, J., and Poinsot, T.: Contrail formation in aircraft
wakes, J. Fluid Mech., 502, 361–373,
https://doi.org/10.1017/S0022112003007808, 2004. a, b
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007. a
Phillips, V. T., Pokrovsky, A., and Khain, A.: The influence of time-dependent
melting on the dynamics and precipitation production in maritime and
continental storm clouds, J. Atmos. Sci., 64, 338–359,
https://doi.org/10.1175/JAS3832.1, 2007. a, b
Phillips, V. T., Formenton, M., Bansemer, A., Kudzotsa, I., and Lienert, B.: A
parameterization of sticking efficiency for collisions of snow and graupel
with ice crystals: Theory and comparison with observations, J. Atmos. Sci., 72, 4885–4902, https://doi.org/10.1175/JAS-D-14-0096.1, 2015. a
Phillips, V. T., Yano, J. I., and Khain, A.: Ice multiplication by breakup in
ice-ice collisions. Part I: Theoretical formulation, J. Atmos. Sci., 74, 1705–1719, https://doi.org/10.1175/JAS-D-16-0224.1, 2017. a
Pinsky, M., Khain, A., and Shapiro, M.: Collision Efficiency of Drops in a
Wide Range of Reynolds Numbers: Effects of Pressure on Spectrum Evolution,
J. Atmos. Sci., 58, 742–764,
https://doi.org/10.1175/1520-0469(2001)058<0742:CEODIA>2.0.CO;2, 2001. a
Pope, S. B.: Lagrangian PDF Methods for Turbulent Flows, Ann. Rev.
Fluid Mech., 26, 23–63, https://doi.org/10.1146/annurev.fl.26.010194.000323, 1994. a
Prat, O. P., Barros, A. P., and Testik, F. Y.: On the Influence of Raindrop
Collision Outcomes on Equilibrium Drop Size Distributions, J. Atmos. Sci., 69, 1534–1546, https://doi.org/10.1175/JAS-D-11-0192.1, 2012. a
Przybylo, V. M., Sulia, K. J., Schmitt, C. G., Lebo, Z. J., and May, W. C.:
The ice Particle and Aggregate Simulator (IPAS). Part I: Extracting
dimensional properties of ice-ice aggregates for microphysical
parameterization, J. Atmos. Sci., 76, 1661–1676,
https://doi.org/10.1175/JAS-D-18-0187.1, 2019. a
Rasmussen, R. and Pruppacher, H. R.: A wind tunnel and theoretical study of
the melting behavior of atmospheric ice particles. I: a wind tunnel study of
frozen drops of radius less than 500 micrometers., J. Atmos. Sci., 39, 152–158,
https://doi.org/10.1175/1520-0469(1982)039<0152:AWTATS>2.0.CO;2, 1982. a, b
Rasmussen, R. M. and Heymsfield, A. J.: A generalized form for impact
velocities used to determine graupel accretional densities, J.
Atmos. Sci., 42, 2275–2279,
https://doi.org/10.1175/1520-0469(1985)042<2275:AGFFIV>2.0.CO;2, 1985. a, b, c
Rasmussen, R. M. and Heymsfield, A. J.: Melting and Shedding of Graupel and
Hail. Part I: Model Physics, J. Atmos. Sci., 44,
2754–2763, https://doi.org/10.1175/1520-0469(1987)044< 2754:masoga>2.0.co;2, 1987. a
Riechelmann, T., Noh, Y., and Raasch, S.: A new method for large-eddy
simulations of clouds with Lagrangian droplets including the effects of
turbulent collision, New J. Phys., 14,
https://doi.org/10.1088/1367-2630/14/6/065008, 2012. a, b, c
Rogers, R. R. and Yau, M. K.: A Short Course in Cloud Physics,
Butterworth-Heinemann, 3rd edn., 1989. a
Roscoe, R.: XXXI. The flow of viscous fluids round plane obstacles, The
London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science,
40, 338–351, https://doi.org/10.1080/14786444908561255, 1949. a
Rosenfeld, D. and Woodley, W. L.: Deep convective clouds with sustained
supercooled liquid water down to −37.5 ∘C, Nature, 405, 440–442, https://doi.org/10.1038/35013030,
2000. a
Sato, Y., Nakajima, T., Suzuki, K., and Iguchi, T.: Application of a Monte
Carlo integration method to collision and coagulation growth processes of
hydrometeors in a bin-type model, J. Geophys. Res., 114,
D09215, https://doi.org/10.1029/2008JD011247, 2009. a
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, b
Sato, Y., Shima, S.-i., and Tomita, H.: A grid refinement study of trade wind cumuli simulated by a Lagrangian cloud microphysical model: the super-droplet method, Atmos. Sci. Lett., 18, 359–365, https://doi.org/10.1002/asl.764,
2017. a
Sato, Y., Shima, S.-i., and Tomita, H.: Numerical Convergence of Shallow
Convection Cloud Field Simulations: Comparison Between Double-Moment Eulerian
and Particle-Based Lagrangian Microphysics Coupled to the Same Dynamical
Core, J. Adv. Model. Earth Sy., 10, 1495–1512,
https://doi.org/10.1029/2018MS001285, 2018. a
Schilling, V., Siano, S., and Etling, D.: Dispersion of aircraft emissions due to wake vortices in stratified shear flows: A two-dimensional numerical
study, J. Geophys. Res.-Atmos., 101, 20965–20974,
https://doi.org/10.1029/96JD02013, 1996. a
Schmidt, D. P. and Rutland, C. J.: A New Droplet Collision Algorithm, J. Comput. Phys., 164, 62–80, https://doi.org/10.1006/jcph.2000.6568, 2000. a
Schmitt, C. G. and Heymsfield, A. J.: The dimensional characteristics of ice
crystal aggregates from fractal geometry, J. Atmos. Sci., 67, 1605–1616, https://doi.org/10.1175/2009JAS3187.1, 2010. a, b, c, d
Scotti, A., Meneveau, C., and Lilly, D. K.: Generalized Smagorinsky model for anisotropic grids, Phys. Fluids A, 5, 2306–2308,
https://doi.org/10.1063/1.858537, 1993. a
Seeßelberg, M., Trautmann, T., and Thorn, M.: Stochastic simulations as a
benchmark for mathematical methods solving the coalescence equation,
Atmos. Res., 40, 33–48, https://doi.org/10.1016/0169-8095(95)00024-0, 1996. a, b
Seifert, A., Khain, A., Blahak, U., and Beheng, K. D.: Possible effects of
collisional breakup on mixed-phase deep convection simulated by a spectral
(bin) cloud model, J. Atmos. Sci., 62, 1917–1931,
https://doi.org/10.1175/JAS3432.1, 2005. a
Seifert, A., Leinonen, J., Siewert, C., and Kneifel, S.: The Geometry of Rimed Aggregate Snowflakes: A Modeling Study, J. Adv. Model.
Earth Sy., 11, 712–731, https://doi.org/10.1029/2018MS001519, 2019. a, b, c, d
Seiki, T. and Nakajima, T.: Aerosol effects of the condensation process on a
convective cloud simulation, J. Atmos. Sci., 71,
833–853, https://doi.org/10.1175/JAS-D-12-0195.1, 2014. a
Shaw, R. A., Durant, A. J., and Mi, Y.: Heterogeneous surface crystallization
observed in undercooled water, J. Phys. Chem. B, 109,
9865–9868, https://doi.org/10.1021/jp0506336, 2005. a, b
Shima, S.-i.: Shima-Lab/SCALE-SDM_mixed-phase_Shima2019 SCALE-SDM_0.2.5-2.2.2 (Version SCALE-SDM_0.2.5-2.2.2), Zenodo, https://doi.org/10.5281/zenodo.3483650, 2020. a
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. Meteor.
Soc., 135, 1307–1320, https://doi.org/10.1002/qj.441, 2009. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p
Shima, S.-i., Hasegawa, K., and Kusano, K.: Preliminary numerical study on the
cumulus-stratus transition induced by the increase of formation rate of
aerosols, Low Temperature Science, 72, 249–264,
available at: http://hdl.handle.net/2115/55063 (last access: 26 August 2020), 2014. a
Shirgaonkar, A. and Lele, S.: Large Eddy Simulation of Early Stage Contrails:
Effect of Atmospheric Properties, in: 44th AIAA Aerospace Sciences Meeting
and Exhibit, American Institute of Aeronautics and Astronautics, Reston,
Virigina, https://doi.org/10.2514/6.2006-1414, 2006. a, b
Shupe, M. D., Daniel, J. S., de Boer, G., Eloranta, E. W., Kollias, P., Long,
C. N., Luke, E. P., Turner, D. D., and Verlinde, J.: A focus on mixed-phase
clouds, B. Am. Meteorol. Soc., 89, 1549–1562,
https://doi.org/10.1175/2008BAMS2378.1, 2008. a
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
Smoluchowski, M.: Drei Vorträge über Diffusion, Brownsche
Molekularbewegung und Koagulation von Kolloidteilchen, Physik. Z., 17,
557–571, 585–599, 1916. a
Squires, P.: The growth of cloud drops by condensation. I. General
characteristics, Aust. J. Chem., 5, 59–86,
https://doi.org/10.1071/CH9520059, 1952. a
Srivastava, R. C.: On the Role of Coalescence between Raindrops in Shaping
Their Size Distribution1, J. Atmos. Sci., 24, 287–292,
https://doi.org/10.1175/1520-0469(1967)024<0287:OTROCB>2.0.CO;2, 1967. a
Starr, D. O. and Cox, S. K.: Cirrus clouds. Part I: a cirrus cloud model.,
J. Atmos. Sci., 42, 2663–2681,
https://doi.org/10.1175/1520-0469(1985)042<2663:CCPIAC>2.0.CO;2, 1985. a, b
Steinke, I., Hoose, C., Möhler, O., Connolly, P., and Leisner, T.: A new temperature- and humidity-dependent surface site density approach for deposition ice nucleation, Atmos. Chem. Phys., 15, 3703–3717, https://doi.org/10.5194/acp-15-3703-2015, 2015. a
Stevens, B. and Lenschow, D. H.: Observations, Experiments, and Large Eddy
Simulation, B. Am. Meteorol. Soc., 82, 283–294,
https://doi.org/10.1175/1520-0477(2001)082<0283:OEALES>2.3.CO;2, 2001. a
Straub, W., Beheng, K. D., Seifert, A., Schlottke, J., and Weigand, B.:
Numerical Investigation of Collision-Induced Breakup of Raindrops. Part II:
Parameterizations of Coalescence Efficiencies and Fragment Size
Distributions, J. Atmos. Sci., 67, 576–588,
https://doi.org/10.1175/2009JAS3175.1, 2010. a
Takahashi, T., Endoh, T., Wakahama, G., and Fukuta, N.: Vapor diffusional
growth of free-falling snow crystals between-3 and-23 C, J.
Meteorol. Soc. Jpn., 69, 15–30, 1991. a
Ullrich, R., Hoose, C., Möhler, O., Niemand, M., Wagner, R.,
Höhler, K., Hiranuma, N., Saathoff, H., and Leisner, T.: A new ice
nucleation active site parameterization for desert dust and soot, J. Atmos. Sci., 74, 699–717, https://doi.org/10.1175/JAS-D-16-0074.1, 2017. a, b
Um, J., McFarquhar, G. M., Hong, Y. P., Lee, S.-S., Jung, C. H., Lawson, R. P., and Mo, Q.: Dimensions and aspect ratios of natural ice crystals, Atmos. Chem. Phys., 15, 3933–3956, https://doi.org/10.5194/acp-15-3933-2015, 2015. a
Unterstrasser, S. and Sölch, I.: Optimisation of the simulation particle number in a Lagrangian ice microphysical model, Geosci. Model Dev., 7, 695–709, https://doi.org/10.5194/gmd-7-695-2014, 2014. a
VanZanten, M. C., Stevens, B., Nuijens, L., Siebesma, A. P., Ackerman, A. S.,
Burnet, F., Cheng, A., Couvreux, F., Jiang, H., Khairoutdinov, M., Kogan, Y.,
Lewellen, D. C., Mechem, D., Nakamura, K., Noda, A., Shipway, B. J.,
Slawinska, J., Wang, S., and Wyszogrodzki, A.: Controls on precipitation and
cloudiness in simulations of trade-wind cumulus as observed during RICO,
J. Adv. Model. Earth Sy., 3, M06001, https://doi.org/10.1029/2011MS000056,
2011. a, b
Vardiman, L.: The Generation of Secondary Ice Particles in Clouds by
Crystal–Crystal Collision, J. Atmos. Sci., 35,
2168–2180, https://doi.org/10.1175/1520-0469(1978)035< 2168:tgosip>2.0.co;2, 1978. a
Vohl, O., Mitra, S. K., Wurzler, S., Diehl, K., and Pruppacher, H. R.:
Collision efficiencies empirically determined from laboratory investigations
of collisional growth of small raindrops in a laminar flow field,
Atmos. Res., 85, 120–125, https://doi.org/10.1016/j.atmosres.2006.12.001,
2007. a
von Blohn, N., Diehl, K., Mitra, S. K., and Borrmann, S.: Riming of graupel:
Wind tunnel investigations of collection kernels and growth regimes, J. Atmos. Sci., 66, 2359–2366, https://doi.org/10.1175/2009JAS2969.1,
2009. a
Wang, L. P., Ayala, O., Rosa, B., and Grabowski, W. W.: Turbulent collision
efficiency of heavy particles relevant to cloud droplets, New J.
Phys., 10, 075013, https://doi.org/10.1088/1367-2630/10/7/075013, 2008.
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, b, c
Wang, P. K. and Pruppacher, H. R.: Acceleration to Terminal Velocity of Cloud
and Raindrops, J. Appl. Meteorol., 16, 275–280,
https://doi.org/10.1175/1520-0450(1977)016<0275:ATTVOC>2.0.CO;2, 1977. a
Westbrook, C. D., Ball, R. C., Field, P. R., and Heymsfield, A. J.: Theory of growth by differential sedimentation, with application to snowflake
formation, Physical Review E, 70, 021403 , https://doi.org/10.1103/PhysRevE.70.021403,
2004a. a, b
Westbrook, C. D., Ball, R. C., Field, P. R., and Heymsfield, A. J.:
Universality in snowflake aggregation, Geophys. Res. Lett., 31, L15104,
https://doi.org/10.1029/2004GL020363, 2004b. a, b
Wex, H., DeMott, P. J., Tobo, Y., Hartmann, S., Rösch, M., Clauss, T., Tomsche, L., Niedermeier, D., and Stratmann, F.: Kaolinite particles as ice nuclei: learning from the use of different kaolinite samples and different coatings, Atmos. Chem. Phys., 14, 5529–5546, https://doi.org/10.5194/acp-14-5529-2014, 2014. a
Wex, H., Augustin-Bauditz, S., Boose, Y., Budke, C., Curtius, J., Diehl, K., Dreyer, A., Frank, F., Hartmann, S., Hiranuma, N., Jantsch, E., Kanji, Z. A., Kiselev, A., Koop, T., Möhler, O., Niedermeier, D., Nillius, B., Rösch, M., Rose, D., Schmidt, C., Steinke, I., and Stratmann, F.: Intercomparing different devices for the investigation of ice nucleating particles using Snomax® as test substance, Atmos. Chem. Phys., 15, 1463–1485, https://doi.org/10.5194/acp-15-1463-2015, 2015. a, b
Wicker, L. J. and Skamarock, W. C.: Time-splitting methods for elastic models using forward time schemes, Mon. Weather Rev., 130, 2088–2097,
https://doi.org/10.1175/1520-0493(2002)130<2088:TSMFEM>2.0.CO;2, 2002. a
Xue, L., Fan, J., Lebo, Z. J., Wu, W., Morrison, H., Grabowski, W. W., Chu, X.,
Geresdi, I., North, K., Stenz, R., Gao, Y., Lou, X., Bansemer, A.,
Heymsfield, A. J., McFarquhar, G. M., and Rasmussen, R. M.: Idealized
simulations of a squall line from the MC3E field campaign applying three bin
microphysics schemes: Dynamic and thermodynamic structure, Mon. Weather Rev., 145, 4789–4812, https://doi.org/10.1175/MWR-D-16-0385.1, 2017. a
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for
fluids, J. Comput. Phys., 31, 335–362,
https://doi.org/10.1016/0021-9991(79)90051-2, 1979. a
Short summary
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds based on kinetic description and subsequently demonstrated that a large-eddy simulation of a cumulonimbus which predicts ice particle morphology without assuming ice categories or mass–dimension relationships is possible. Our results strongly support the particle-based modeling methodology’s efficacy for simulating mixed-phase clouds.
Using the super-droplet method, we constructed a detailed numerical model of mixed-phase clouds...