Articles | Volume 14, issue 3
Geosci. Model Dev., 14, 1511–1531, 2021
https://doi.org/10.5194/gmd-14-1511-2021
Geosci. Model Dev., 14, 1511–1531, 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 et al.

Related authors

Analytic characterization of random errors in spectral dual-polarized cloud radar observations
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-225,https://doi.org/10.5194/amt-2021-225, 2021
Preprint under review for AMT
Short summary
Improving the Representation of Aggregation in a Two-moment Microphysical Scheme with Statistics of Multi-frequency Doppler Radar Observations
Markus Karrer, Axel Seifert, Davide Ori, and Stefan Kneifel
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-382,https://doi.org/10.5194/acp-2021-382, 2021
Revised manuscript under review for ACP
Short summary
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021,https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary
Linkage among ice crystal microphysics, mesoscale dynamics, and cloud and precipitation structures revealed by collocated microwave radiometer and multifrequency radar observations
Jie Gong, Xiping Zeng, Dong L. Wu, S. Joseph Munchak, Xiaowen Li, Stefan Kneifel, Davide Ori, Liang Liao, and Donifan Barahona
Atmos. Chem. Phys., 20, 12633–12653, https://doi.org/10.5194/acp-20-12633-2020,https://doi.org/10.5194/acp-20-12633-2020, 2020
Short summary
PAMTRA 1.0: the Passive and Active Microwave radiative TRAnsfer tool for simulating radiometer and radar measurements of the cloudy atmosphere
Mario Mech, Maximilian Maahn, Stefan Kneifel, Davide Ori, Emiliano Orlandi, Pavlos Kollias, Vera Schemann, and Susanne Crewell
Geosci. Model Dev., 13, 4229–4251, https://doi.org/10.5194/gmd-13-4229-2020,https://doi.org/10.5194/gmd-13-4229-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Incorporation of volcanic SO2 emissions in the Hemispheric CMAQ (H-CMAQ) version 5.2 modeling system and assessing their impacts on sulfate aerosol over the Northern Hemisphere
Syuichi Itahashi, Rohit Mathur, Christian Hogrefe, Sergey L. Napelenok, and Yang Zhang
Geosci. Model Dev., 14, 5751–5768, https://doi.org/10.5194/gmd-14-5751-2021,https://doi.org/10.5194/gmd-14-5751-2021, 2021
Short summary
Efficient ensemble generation for uncertain correlated parameters in atmospheric chemical models: a case study for biogenic emissions from EURAD-IM version 5
Annika Vogel and Hendrik Elbern
Geosci. Model Dev., 14, 5583–5605, https://doi.org/10.5194/gmd-14-5583-2021,https://doi.org/10.5194/gmd-14-5583-2021, 2021
Short summary
Position correction in dust storm forecasting using LOTOS-EUROS v2.1: grid-distorted data assimilation v1.0
Jianbing Jin, Arjo Segers, Hai Xiang Lin, Bas Henzing, Xiaohui Wang, Arnold Heemink, and Hong Liao
Geosci. Model Dev., 14, 5607–5622, https://doi.org/10.5194/gmd-14-5607-2021,https://doi.org/10.5194/gmd-14-5607-2021, 2021
Short summary
Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: description and evaluation
Timofei Sukhodolov, Tatiana Egorova, Andrea Stenke, William T. Ball, Christina Brodowsky, Gabriel Chiodo, Aryeh Feinberg, Marina Friedel, Arseniy Karagodin-Doyennel, Thomas Peter, Jan Sedlacek, Sandro Vattioni, and Eugene Rozanov
Geosci. Model Dev., 14, 5525–5560, https://doi.org/10.5194/gmd-14-5525-2021,https://doi.org/10.5194/gmd-14-5525-2021, 2021
Short summary
Harmonized Emissions Component (HEMCO) 3.0 as a versatile emissions component for atmospheric models: application in the GEOS-Chem, NASA GEOS, WRF-GC, CESM2, NOAA GEFS-Aerosol, and NOAA UFS models
Haipeng Lin, Daniel J. Jacob, Elizabeth W. Lundgren, Melissa P. Sulprizio, Christoph A. Keller, Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Patrick C. Campbell, Barry Baker, Rick D. Saylor, and Raffaele Montuoro
Geosci. Model Dev., 14, 5487–5506, https://doi.org/10.5194/gmd-14-5487-2021,https://doi.org/10.5194/gmd-14-5487-2021, 2021
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.