Articles | Volume 14, issue 3
https://doi.org/10.5194/gmd-14-1511-2021
https://doi.org/10.5194/gmd-14-1511-2021
Model description paper
 | 
17 Mar 2021
Model description paper |  | 17 Mar 2021

snowScatt 1.0: consistent model of microphysical and scattering properties of rimed and unrimed snowflakes based on the self-similar Rayleigh–Gans approximation

Davide Ori, Leonie von Terzi, Markus Karrer, and Stefan Kneifel

Related authors

A Microwave Scattering Database of Oriented Ice and Snow Particles: Supporting Habit-Dependent Growth Models and Radar Applications (McRadar 1.0.0)
Leonie von Terzi, Davide Ori, and Stefan Kneifel
EGUsphere, https://doi.org/10.5194/egusphere-2025-3910,https://doi.org/10.5194/egusphere-2025-3910, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Moisture budget estimates derived from airborne observations in an Arctic atmospheric river during its dissipation
Henning Dorff, Florian Ewald, Heike Konow, Mario Mech, Davide Ori, Vera Schemann, Andreas Walbröl, Manfred Wendisch, and Felix Ament
Atmos. Chem. Phys., 25, 8329–8354, https://doi.org/10.5194/acp-25-8329-2025,https://doi.org/10.5194/acp-25-8329-2025, 2025
Short summary
Observed and modeled Arctic airmass transformations during warm air intrusions and cold air outbreaks
Manfred Wendisch, Benjamin Kirbus, Davide Ori, Matthew D. Shupe, Susanne Crewell, Harald Sodemann, and Vera Schemann
EGUsphere, https://doi.org/10.5194/egusphere-2025-2062,https://doi.org/10.5194/egusphere-2025-2062, 2025
Short summary
Investigating KDP signatures inside and below the dendritic growth layer with W-band Doppler Radar and in situ snowfall camera
Anton Kötsche, Alexander Myagkov, Leonie von Terzi, Maximilian Maahn, Veronika Ettrichrätz, Teresa Vogl, Alexander Ryzhkov, Petar Bukovcic, Davide Ori, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2025-734,https://doi.org/10.5194/egusphere-2025-734, 2025
Short summary
Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds
Theresa Kiszler, Davide Ori, and Vera Schemann
Atmos. Chem. Phys., 24, 10039–10053, https://doi.org/10.5194/acp-24-10039-2024,https://doi.org/10.5194/acp-24-10039-2024, 2024
Short summary

Cited articles

Accadia, C., Mattioli, V., Colucci, P., Schlüssel, P., D'Addio, S., Klein, U., Wehr, T., and Donlon, C.: Microwave and Sub-mm Wave Sensors: A European Perspective, Springer International Publishing, Cham, 83–98, https://doi.org/10.1007/978-3-030-24568-9_5, 2020. a
Battaglia, A. and Kollias, P.: Evaluation of differential absorption radars in the 183 GHz band for profiling water vapour in ice clouds, Atmos. Meas. Tech., 12, 3335–3349, https://doi.org/10.5194/amt-12-3335-2019, 2019. a
Böhm, J.: A general hydrodynamic theory for mixed-phase microphysics. Part I: Drag and fall speed of hydrometeors, Atmos. Res., 27, 253–274, 1992. a, b, c, d
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, John Wiley & Sons, Inc., New York, USA, 1983. a, b, c, d
Brath, M., Ekelund, R., Eriksson, P., Lemke, O., and Buehler, S. A.: Microwave and submillimeter wave scattering of oriented ice particles, Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, 2020. a, b, c
Download
Short summary
Snowflakes have very complex shapes, and modeling their properties requires vast computing power. We produced a large number of realistic snowflakes and modeled their average properties by leveraging their fractal structure. Our approach allows modeling the properties of big ensembles of snowflakes, taking into account their natural variability, at a much lower cost. This enables the usage of remote sensing instruments, such as radars, to monitor the evolution of clouds and precipitation.
Share