------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- *** CASE DESCRIPTION ------------------------- We selected a cumulus congestus (CuCg) cloud observed during the flight RF04b in the Secondary Production of Ice in Cumulus Experiment (SPICULE) carried out in June 2021 over the Southern Great Plains in the United States of America. Our selection was driven by the existence of a very detailed set of airborne in situ and remote observations of cloud microphysical properties showing evidence of secondary ice production. Measurements from cloud optical probes and holographic particle imaging systems indicated the presence of fragmented frozen drops and small columnar particles coexisting which it is consistent with findings of laboratory experiments focused on rime splintering (RS), droplet shattering (DS), and ice-ice collisional breakup (IIBR) mechanisms. This case was documented in Lawson et al. (2023) 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 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ *** MODEL VERSION ------------------------- All simulations ran with the DEV branch v2.0.0 of UCLALES-SALSA updated on 07-04-2025 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. There is an emulator for the electrostatic enhancement of collision kernels to add emissions of charged aerosol particles during seeding operations. The current version allows to introduce point, surface of volume sources of aerosol particles with or without electric effects on the collision-coalescence processes. As a technical detail we also now allow charge to be carried from aerosol to cloud droplets during cloud droplet formation process 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 ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- *** MODEL SETUP ------------------------- 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 rain development. The model domain size was selected based on the surface area covered simultaneously by the GV and Learjet flights on June 05, 2021. This time interval coincides with the early stage of the cloud system in which ice multiplication was observed in the rising cloud tower. Convective buoyancy was simulated with a perturbation in surface fluxes that follows a Gaussian distribution centered at the domain center with a maximum of 600 W/m2 and a spatial variance of 2000 m. Surface fluxes were increased after the spinup period. 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). ​ 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. 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. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- *** SIMULATIONS SCENARIOS with vertically variable aerosol loading​ CAPE = 763.7 J/kg LCL [hPa, oC]​ 895.36 17.15​ EL [hPa, oC]​ 318.93 -30.24​ --------------------------------------------------------------------------------------- Main Folder : SPICULE_RF04b_20210605_SIP_OFF -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Description: raw and conditionally sampled data for the simulation scenario only with primary ice production (PIP) Simulation name Simulation time (s) -------------------------------------------------------------------------------------------- PIP_Naz_noseed_30s 3600-7200 PIP_Naz_noseed_30s_2 7200-10800 -------------------------------------------------------------------------------------------- Main Folder : SPICULE_RF04b_20210605_SIP_ON -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Description: raw and conditionally sampled 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) ------------------------------------------------------------------------------------------- SIP_Naz_noseed_30s 3600-7200 SIP_Naz_noseed_30s_2 7200-10800 ------------------------------------------------------------------------------------------ --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- *** DETAILS TO RUN SIMULATIONS ------------------------------- 1. Download or clone the UCLALES-SALSA-DEV-v.2.0.0 branch from https://github.com/UCLALES-SALSA/UCLALES-SALSA/releases/tag/v2.0.0 2. Copy the files contained in the folder /simulation_initialization/ (a) Builder of the namelist of input and output variables needed to run the simulation: - runles_spicule_hour_0: to run the spinup period and generate the history file needed to run all scenarios - runles_spicule_hour_1: to run the first simulation hour - runles_spicule_hour_2: to run the second simulation hour (b) sounding : vertical profiles of atmospheric properties - First line: atmospheric properties at surface level, pressure in hPa, temperature in kelvin, specific humidity, horizontal wind speed components (i.e. east-west u and north-south v) - First column: altitude in meters above ground - Second column: potential temperature or liquid potential temperature in kelvin depending on the simulation settings in the runles - Third column: specific humidity in kg/kg - Fourth and fifth columns: horizontal wind components (i.e. east-west u and north-south v) (c) datafiles (folder) : it contains the background radiation profile (i.e. kmls.lay in this study) and also the vertical profile for aerosol loading (i.e. aerosol_case_SPICULE_Rf04b.nc) 3. More instructions how to compile and run in https://github.com/UCLALES-SALSA/UCLALES-SALSA/blob/DEV/README and https://github.com/UCLALES-SALSA/UCLALES-SALSA/blob/DEV/les-post-processing/readme_postprocessing.txt ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ *** FOLDER DESCRIPTION ------------------------- Folder: raw_data ---------------- 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). Experiment name Description --------------------------------------------------------------------------------------------------------------------------------------------- SPICULE_RF04b_20210605_PIP_Naz_noseed_30s Simulation of the first hour after spinup and convection initiation (i.e. 3600 s to 7200 s) considering only primary ice formation and vertical variability in the initial aerosol loading. There is no seeding material added. Outputs are sampled every 30 s. SPICULE_RF04b_20210605_PIP_Naz_noseed_30s_2 Simulation of the second hour after spinup and convection initiation (i.e. 7230 s to 10800 s) considering only primary ice formation and vertical variability in the initial aerosol loading. There is no seeding material added. Outputs are sampled every 30 s. SPICULE_RF04b_20210605_SIP_Naz_noseed_30s Simulation of the first hour after spinup and convection initiation (i.e. 3600 s to 7200 s) considering primary and secondary ice formation and vertical variability in the initial aerosol loading. There is no seeding material added. Outputs are sampled every 30 s. SPICULE_RF04b_20210605_SIP_Naz_noseed_30s_2 Simulation of the second hour after spinup and convection initiation (i.e. 7230 s to 10800 s) considering primary and secondary ice formation and vertical variability in the initial aerosol loading. There is no seeding material added. Outputs are sampled every 30 s. Cloud Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------- General properties Experiment_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)) Experiment_name.ps.nc Horizontal averages of cloud properties in cloudy grid points (i.e.liquid water content > 0.01 g/m3)) Each property is given as function of z and t Experiment_name.ts.nc Time series of cloud properties in cloudy grid points (i.e.cloud liquid water path, rainwater path) Each property is given as function of t Aerosol particles Experiment_name_Naba.nc Number concentration of aerosol particles in regime A Experiment_name_Dwaba.nc Wet diameter of aerosol particles in regime A Cloud droplets Experiment_name_Ncba.nc Number concentration of droplets formed from aerosol particles in regime A Experiment_name_Dwcba.nc Wet diameter of cloud droplets formed from aerosol particles in regime A Drizzle-rain droplets Experiment_name_Npba.nc Number concentration of precipitation droplets (drizzle + rain) Experiment_name_Dwpba.nc Wet diameter of precipitation droplets (drizzle + rain) Ice particles* Experiment_name_Niba.nc Number concentration of ice particles Experiment_name_Dwiba.nc Maximum length of ice particles * IMPORTANT: Size distributions of ice particles can be plotted as given by the *_Niba.nc files. Size bins are separated by a constant volume ratio of 3.0 ----------------------------------------- Folder name: Cloudy_vars_xyzt ----------------------------------------- Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------------- Cloudy_Experiment_name.nc Raw data is conditionally sampled to mask non-cloudy conditions. Grid points with cloudy conditions have total water content (TWC=LWC+IWC) above a threshold value of 0.01 g/m3. This file contains just variables with x,y,z,t dependencies. ----------------------------------------- Folder name: Cloudy_updrafts_vars_xyzt ----------------------------------------- Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------------- Cloudy_updrafts_Experiment_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. This file contains just variables with x,y,z,t dependencies. ----------------------------------------- Folder name: Cloudy_vars_xyt ----------------------------------------- Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------------- Cloudy_2D_Experiment_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. ----------------------------------------- Folder name: Raw_vars_bin_xyzt ----------------------------------------- Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------------- Experiment_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. ----------------------------------------- Folder name: Cloudy_vars_bin_zt ----------------------------------------- Filename Description -------------------------------------------------------------------------------------------------------------------------------------------------------------- Cloudy_Experiment_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>0.01g/m3).