DOI: 10.57707/fmi-b2share.3dd0825ff4ed4d21b227df6becdfb41d
title: UCLALES-SALSA Simulation Data for the SPICULE-RF04b Cloud Case from "Secondary Ice Formation in Cumulus Congestus Clouds: Insights from Observations and Aerosol-Aware Large-Eddy Simulations"

description: Datasets include UCLALES-SALSA simulation outputs for a system of cumulus congestus (CuCg) clouds observed in 5 June 2021 over the Southern Great Plains in the United States of America during the Secondary Production of Ice in Cumulus Experiment (SPICULE) performed in May-June 2021. The selected cloud case was labeled as SPICULE_RF04b_20210605 to match the mission name. Airborne in situ observations used for model initialization and validation were presented by Lawson et al. (2023) and can be found in a public repository (https://www.eol.ucar.edu/field_projects/spicule). The simulation time interval goes from 18:30UTC to 21:30UTC including the first hour as a spinup period. The goal of the study was to understand the role of secondary ice formation in the glaciation of young cumulus congestus that lack sufficient ice nucleating particles using large-eddy-simulations that reproduced the observed changes in hydrometeor size distributions induced by secondary ice production due to droplet shattering (Phillips et al. 2017) , rime splintering (Hallet and Mossop, 1974) and ice-ice collisional breakup (Phillips et al. 2017) modified by Grzegorczyk et al. (2025).
description: Large-eddy simulations were performed using UCLALES–SALSA, a model that explicitly resolves aerosol–hydrometeor interactions through a sectional representation of aerosols, cloud droplets, rain droplets, and ice crystals. We used the DEV branch v2.0.0 of UCLALES-SALSA to include detailed microphysical descriptions of secondary ice production through the mechanisms of rime splintering, fragmentation of freezing drops in both modes of the relative size of colliding hydrometeors, and ice-ice collisional breakup (Calderón et al. 2025). 

Simulations were performed in two scenarios, one including only immersion freezing as an ice formation process, and another incorporating secondary ice formation via rime splintering, droplet shattering and ice-ice collisional breakup. Raw and conditionally sampled data obtained from simulations were compressed in two .zip files named as SPICULE-RF04b-20210605_SIP_OFF and SPICULE-RF04b_SIP_ON.
description: Simulations were performed in a model domain of 28.8 km x 28.8 km x 12 km with horizontal and vertical resolution of 300 m and 60 m respectively; and a maximum time step of 1 s. Each simulation ran 1 hour for spinup, and then in two hourly periods. Model outputs were taken every 30 s to follow closely the secondary ice formation in connection with rain development. The model domain size was selected based on the surface area covered during the SPICULE-RF04b flight mission. Convective buoyancy was emulated adding sensible and latent heat to the surface fluxes by mean of a Gaussian distribution with a maximum of 600 W/m2 and a linear variance of 2000 m around the cented of the model domain. This perturbation started after 1 hour of spinup.

Atmospheric properties used for model initialization were derived from ERA5 reanalyzed data on hourly data for 05 June 2021 for a horizontal domain of 1o by 1o close to Ada, OK, USA following flight trajectories relevant to the selected cloud case (Hersbach et al., 2023). Temperature and humidity profiles were modified to represent observed cloud base conditions (e.g. altitude, pressure and temperature). Atmospheric conditions at higher altitudes were modified to test different values convective available energy (CAPE) and equilibrium level (EL) or level of neutral buoyancy (LNB). This was essential to reach model closure with observed properties of the cloud tower. 
 
Aerosol properties were derived from droplet size distributions measured below cloud base altitude with the Passive Cavity Aerosol Spectrometer Probe (PCASP-100X) (UCAR/NCAR, 2025). Dry particle size distributions were calculated inverting the kappa-Köhler relation at the temperature and relative humidity of observations. We assumed that dry aerosol particles were spherical and internally mixed with sulphate species and mineral dust in volumetric fractions of 0.901 and 0.099. This chemical composition corresponds to a species-based kappa value of 0.5496 numerically equivalent to the average hygroscopicity parameter kappa (AOSCCNSMPSKAPPA) derived from Cloud Condensation Nuclei Counter and Scanning-Mobility Particle Sizer measurements at the ARM station in the Southern Great Plains, USA performed on 05 June 2021 (Kulkarni and Shilling, 2024). We used a contact angle distribution centered at 132 ± 20 degrees to account for ice nucleating abilities like those reported for mineral dust as in Savre et al. (2015). 
 
PCASP-derived dry aerosol distributions at below cloud altitude were fitted to a multimodal lognormal distribution. Since PCASP measurements do not account for aerosol particles with dry diameter below 100 nm, we added a submicron particle mode centered at 0.0055 µm corresponding to summer average values reported for the ARM station SGP, USA and consistent with frequent events of new particle formation (Marinescu et al., 2019). The final size distribution used for model initialization has four particle modes centered at [0.0055 µm, 0.090 µm, 0.440 µm, 1.05 µm], estandar deviation of [2.8, 1.44, 1.44, 1.42] and total number concentration of [1085., 810., 2.475, 4.96] mg-1. Simulations were initialized assuming that the aerosol loading follows the vertical variability of PCASP total aerosol number concentrations. 

Secondary ice formation rates were simulated using the parameterization for ice multiplication factors of Hallet and Mossop (1974) in the case of rime splintering, Phillips et al. (2018) in the case of droplet shattering and Phillips et al. (2017) in the case of ice-ice collisional breakup with modification proposed by by Grzegorczyk et al. (2025). ​ 

Details to run the simulation is given in the readme_SPICULE_RF04b_simulations.txt and data needed was included in each experiment folder.
description: Datasets are organized according the simulation scenarios, SIP-OFF and SIP-ON. Raw data is given separately from conditionally sampled data. Raw data is divided in hourly intervals. 

*** RAW DATA
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Description: raw for the simulation scenario only with primary ice production (PIP) 

Simulation name Simulation time (s) Container name
----------------------------------------------------------------------------------------------------------------------------------------------------------
PIP_Naz_noseed_30s 3600-7200 spicule.rf04b.20210605.sip.off.h1.raw 
PIP_Naz_noseed_30s_2 7200-10800 spicule.rf04b.20210605.sip.off.h2.raw
------------------------------------------------------------------------------------------------------------------------------------------------------------ 


Description: raw data for the simulation scenario including primary ice production (PIP) and secondary ice 
production via droplet shattering, rime splintering and ice-ice collisional breakup. 

Simulation name Simulation time (s) Container name
----------------------------------------------------------------------------------------------------------------------------------------------------------
SIP_Naz_noseed_30s 3600-7200 spicule.rf04b.20210605.sip.on.h1.raw 
SIP_Naz_noseed_30s_2 7200-10800 spicule.rf04b.20210605.sip.on.h2.raw
------------------------------------------------------------------------------------------------------------------------------------------------------------

*** CONDITIONALLY SAMPLED DATA
Conditionally sampled data from each simulation scenario is divided in two containers: spicule.rf04b.20210605.sip.***.cond
Each container has the same type of files per simulation and files have been named according the following nomenclature: 

[ container ] spicule.rf04b.20210605.sip.***.cond

├── [ ] Cloudy_SPICULE_RF04b_20210605_simulation_name.nc 
│   └──-------- Raw data is conditionally sampled to mask non-cloudy conditions and downdrafts.
│ Grid points with cloudy conditions have total water content(TWC=LWC+IWC) above a threshold value of 0.01 g/m3 and
│ vertical wind velocity above a threshold value of 0.02 m/s.
│ Files contain just variables with x,y,z,t dependencies.
│
├── [ ] Cloudy_SPICULE_RF04b_20210605_simulation_name_Ndba_xy_ave.nc 
│   └──--------  Horizontal average values in cloudy points of droplet number concentrations including cloud droplets and precipitation droplets.
│ This information is derived from the Experiment_Name_Ndba.nc file.
│ This file contains droplet number concentrations with bin,z,t dependencies that have been conditionally sampled
│ for cloudy conditions (TWC&gt;0.01g/m3). 
│   
├── [ ] Prop2D_SPICULE_RF04b_20210605_simulation_name.nc 
│   └──------- It contains time series of horizontal fields of cloud properties (x,y) (e.g. liquid water path, cloud top altitude)
│ derived from conditionally sampled data in grid points with cloudy conditions corresponding to
│ total water content (TWC=LWC+IWC) above a threshold value of 0.01 g/m3.
│ Non-cloudy model columns are masked if the total water path (TWP) is below a threshold value of 50 g/m2.
│ This file contains just variables with x,y,t dependencies.
│
├── [ ] SPICULE_RF04b_20210605_simulation_name_Ndba.nc 
│   └──--------  Raw data of cloud droplets and precipitation droplets (i.e. Ncba, Dwcba, Npba, Dwpba) is resampled into a common
│ size bin scheme. Remember that cloud droplet properties are given in the aerosol size bin scheme based on dry particle size.
│ This file contains droplet number concentrations with bin x,y,z,t dependencies that have not been conditionally sampled for cloudy conditions.
│ The information must be combined with the previous files. 
│ 
├── [ ] readme_SPICULE_RF04b_simulations.txt
│   └──--------  Detailed description of the integrated cloud case study
└── [ ] simulation_initialization.zip
 ├── [ ] datafiles
 │   ├── [ ] aerosol_case_SPICULE_RF04b.nc : vertical profile of aerosol properties
 │   ├ ...
 │   ├── [ ] kmls.lay : atmospheric properties used for radiative transfer calculations
 │   ├──...
 │ 
 ├── [ ] runles_spicule_hour_* : auxiliary file to build the NAMELIST
 └── [ ] soundin_spicule : profile of atmospheric properties

Important: Ice size distributions can be read directly from the raw data Simulation Name .... _Niba.nc but must be sampled for cloudy conditions using the variable "Cloudy" in Cloudy_SPICULE_RF04b_20210605_simulation_name.nc
description: ├── [ Container name] spicule.rf04b.20210605.sip.***.raw
│   └──-------- It contains simulation outputs in its original state obtained after the post-processing as netcdf files without any manipulation or cleaning. Filenames correspond to the experiment name followed by the name of the binned variable if suitable. Binned variables describe the number concentration of hydrometeors and the size │ of cloud droplets, precipitation droplets and ice crystals. The bin scheme has the resolution given in the settings for the model simulation. The experiment name is composed of the name of the field campaign followed by the flight identification number, the date and the main model settings (i.e.ice formation mechanism, │ vertical profile of aerosol loading, no seeding material, sampling time frequency, hour).
│   ├── [ ] Simulation Name .... _Dwaba.nc Wet diameter of aerosol particles in regime A 
│   ├── [ ] Simulation Name .... _Dwcba.nc Wet diameter of cloud droplets formed from aerosol particles in regime A
│   ├── [ ] Simulation Name .... _Dwiba.nc Maximum length of ice particles
│   ├── [ ] Simulation Name .... _Dwpba.nc Wet diameter of precipitation droplets (drizzle + rain)
│   ├── [ ] Simulation Name .... _Naba.nc Number concentration of aerosol particles in regime A
│   ├── [ ] Simulation Name .... _.nc Scalar variables (e.g. vapor water mixing ratio (rp), vertical wind, etc.). Each property is given at every grid point of the model domain (i.e rp(z,x,y,t))
│   ├── [ ] Simulation Name .... _Ncba.nc Number concentration of droplets formed from aerosol particles in regime A
│   ├── [ ] Simulation Name .... _Niba.nc Number concentration of ice particles
│   ├── [ ] Simulation Name .... _Npba.nc Number concentration of precipitation droplets (drizzle + rain)
│   ├── [ ] Simulation Name .... _.ps.nc Horizontal average of Scalar variables
│   ├── [ ] Simulation Name .... _.ts.nc Time series of cloud field properties
│
description: References 

Lawson, R. P., Korolev, A. v, DeMott, P. J., Heymsfield, A. J., Bruintjes, R. T., Wolff, C. A., Woods, S., Patnaude, R. J., Jensen, J. B., Moore, K. A., Heckman, I., Rosky, E., Haggerty, J., Perkins, R. J., Fisher, T., & Hill, T. C. J. (2023). The Secondary Production of Ice in Cumulus Experiment (SPICULE). Bulletin of the American Meteorological Society, 104(1), E51–E76. https://doi.org/10.1175/BAMS-D-21-0209.1 
Hallet, 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.

Phillips, V. T. J., Patade, S., Gutierrez, J., and Bansemer, A.: Secondary Ice Production by Fragmentation of Freezing Drops: Formulation and Theory, Journal of the Atmospheric Sciences, 75, 3031–3070, https://doi.org/10.1175/JAS-D-17-0190.1, 2018.

Phillips, V. T. J., Yano, J.-I., and Khain, A.: Ice Multiplication by Breakup in Ice–Ice Collisions. Part I: Theoretical Formulation, Journal of the Atmospheric Sciences, 74, 1705–1719, https://doi.org/10.1175/JAS-D-16-0224.1, 2017b.

Grzegorczyk, P., Wobrock, W., Canzi, A., Niquet, L., Tridon, F., and Planche, C.: Investigating secondary ice production in a deep convective cloud with a 3D bin microphysics model: Part I - Sensitivity study of microphysical processes representations, Atmospheric Research, 313, 107 774, https://doi.org/10.1016j.atmosres.2024.107774, 2025a.

Calderón, S. M., Tonttila, J., Raatikainen, T., Ahola, J., Kokkola, H., & Romakkaniemi, S. (2025). UCLALES-SALSA: large-eddy-simulations with aerosol-cloud-ice-precipitation interactions (2.0.0). Zenodo. https://doi.org/10.5281/zenodo.15179737

UCLALES-SALSA Developers. (2025). UCLALES-SALSA (Version 2.0.0) [Computer software]. GitHub. https://github.com/UCLALES-SALSA/UCLALES-SALSA/releases/tag/v2.0.0

UCAR/NCAR - Earth Observing Laboratory. 2023. SPICULE: Low Rate (LRT - 1 sps) Navigation, State Parameter, and Microphysics Flight-Level Data. Version 2.2. UCAR/NCAR - Earth Observing Laboratory. https://doi.org/10.26023/SXJ1-0JC5-0Y0V. Accessed 19 May 2025. 

Marinescu, P. J., Levin, E. J. T., Collins, D., Kreidenweis, S. M., and van den Heever, S. C.: Quantifying aerosol size distributions and their temporal variability in the Southern Great Plains, USA, Atmospheric Chemistry and Physics, 19, 11 985–12 006, https://doi.org/10.5194/acp-19-11985-2019, 2019.

Hersbach, H., Bell, B., Berrisford, P., Biavatti, G., Horáyi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,Schepers, D., Simmons, A., Dee, C., Soci, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, https://doi.org/10.24381/cds.bd0915c6, 2024

Kulkarni, G., Levin, M., and Shilling, J.: Atmospheric Radiation Measurement (ARM) user facility. CCN Counter derived hygroscopicity parameter kappa (AOSCCNSMPSKAPPA), 2017-04-12 to 2025-01-14, Southern Great Plains (SGP) Lamont, OK (Extended and Colocated with C1) (E13), https://doi.org/10.5439/1729907, accessed on 2024/09/24, 2024

Savre, J., Ekman, A. M. L., and Svensson, G.: Technical note: Introduction to MIMICA, a large-eddy simulation solver for cloudy planetary boundary layers, Journal of Advances in Modeling Earth Systems, 6, 630–649, https://doi.org/10.1002/2013MS000292, 2014.