Published May 22, 2025 | Version v1
Dataset Metadata-only

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"

Creators

  • 1. Finnish Meteorological Institute, Kuopio, Finland
  • 2. Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 3. Finnish Meteorological Institute, Helsinki, Finland
  • 4. SPEC Incorporated, Boulder, Colorado, USA

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).

Methods

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.

Technical info

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.

Table of contents

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>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

Other

├── [ 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 │

Other

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.

Additional details

Identifiers

URL
https://etsin.fairdata.fi/dataset/6317de8c-804a-4a23-b2db-5a3ad63c195c
b2rec
50ab4fa1e7b94909b29e31c9ef0e8618

Funding

National Center of Meteorology, Abu Dhabi, UAE under the UAE Research Program for Rain Enhancement Science
Horizon Europe Programme
CERTAINTY (Cloud-aERosol inTeractions \& their impActs IN The earth sYstem) Grant Agreement No.101137680
Horizon Europe Programme
CleanCloud (Clouds and climate transitioning to post-fossil aerosol regime) Grant Agreement no. 101137639
Academy of Finland
Marine organic aerosol and its impacts on clouds and climate Grant no. 322532

FMI metadata

Link to external data location (URL)
https://a3s.fi/spicule.rf04b.20210605.sip.off.h1.raw/index.html, https://a3s.fi/spicule.rf04b.20210605.sip.off.h2.raw/index.html, https://a3s.fi/spicule.rf04b.20210605.sip.on.h1.raw/index.html, https://a3s.fi/spicule.rf04b.20210605.sip.on.h2.raw/index.html, https://a3s.fi/spicule.rf04b.20210605.sip.off.cond/index.html, https://a3s.fi/spicule.rf04b.20210605.sip.on.cond/index.html
Process step
Raw data comprises model outputs that were sampled every 30 s along model domain and simulation time, Derived data comprises model outputs from grid points with cloudy conditions defined as total water content (sum of liquid water and ice water contents) larger than a threshold value of 0.01 g/m3. Sub-datasets are derived after different procedures such as horizontal averaging operations, vertical integration or redistribution in different bin schemes as indicated in the Technical Info section.
Lineage
Original data from simulation outputs and derived data after conditional sampling.
Model
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
Parameter
  • Parameter name: T or temp
  • Parameter unit: K
  • Parameter description: Absolute temperature
  • Parameter name: P or press
  • Parameter unit: Pa
  • Parameter description: Atmospheric pressure
  • Parameter name: dn
  • Parameter unit: kg/m3
  • Parameter description: Moist air density
  • Parameter name: theta
  • Parameter unit: K
  • Parameter description: Potential temperature
  • Parameter name: uwind
  • Parameter unit: m/s
  • Parameter description: West-east component of the wind velocity
  • Parameter name: vwind
  • Parameter unit: m/s
  • Parameter description: North-south component of the wind velocity
  • Parameter name: wwind
  • Parameter unit: m/s
  • Parameter description: Vertical component of the wind velocity
  • Parameter name: RH or rh
  • Parameter unit: Dimensionless [0-1]
  • Parameter description: Relative humidity over liquid water
  • Parameter name: RHi or rhi
  • Parameter unit: Dimensionless [0-1]
  • Parameter description: Relative humidity over ice water
  • Parameter name: rp
  • Parameter unit: kg/kg
  • Parameter description: Water vapor mass mixing ratio
  • Parameter name: rc
  • Parameter unit: kg/kg
  • Parameter description: Cloud water mass mixing ratio (including water in aerosol)
  • Parameter name: srp
  • Parameter unit: kg/kg
  • Parameter description: Drizzle/rain water mass mixing ratio
  • Parameter name: ri
  • Parameter unit: kg/kg
  • Parameter description: Pristine or non-rimed ice mass mixing ratio
  • Parameter name: riri
  • Parameter unit: kg/kg
  • Parameter description: Rimed ice mass mixing ratio
  • Parameter name: RimeFrac
  • Parameter unit: Dimensionless 0-1]
  • Parameter description: Fraction of rimed ice calculated in terms of the mass mixing ratios of pristine and rimed ice as riri/(ri+riri)
  • Parameter name: LWC
  • Parameter unit: kg/m3
  • Parameter description: Liquid water content
  • Parameter name: IWC
  • Parameter unit: kg/m3
  • Parameter description: Ice water content
  • Parameter name: TWC
  • Parameter unit: kg/m3
  • Parameter description: Total water content
  • Parameter name: rrate
  • Parameter unit: W/m2 and also in mm/h
  • Parameter description: Liquid precipitation flux
  • Parameter name: irrate
  • Parameter unit: W/m2 and also in mm/h calculated using ice density of 400 kg/m3
  • Parameter description: Frozen precipitation flux
  • Parameter name: lhf
  • Parameter unit: W/m2
  • Parameter description: Latent heat flux at surface level
  • Parameter name: shf
  • Parameter unit: W/m2
  • Parameter description: Sensible heat flux at surface level
  • Parameter name: Naa
  • Parameter unit: #/kg
  • Parameter description: Total number concentration of aerosol particles in regime A
  • Parameter name: Nca
  • Parameter unit: #/kg
  • Parameter description: Total number concentration of cloud droplets formed from aerosol particles in regime A
  • Parameter name: Np
  • Parameter unit: #/kg
  • Parameter description: Total number concentration of drizzle/rain droplets
  • Parameter name: Ni
  • Parameter unit: #/kg and #/m3
  • Parameter description: Total number concentration of ice particles
  • Parameter name: INC
  • Parameter unit: #/L
  • Parameter description: Ice number concentration or total number concentration of ice particles
  • Parameter name: Dwaa
  • Parameter unit: m
  • Parameter description: Count mean diameter in wet size for aerosol particles in regime A
  • Parameter name: Dwca
  • Parameter unit: m
  • Parameter description: Count mean diameter in wet size for cloud droplets formed from aerosols in regime A
  • Parameter name: Dwpa
  • Parameter unit: m
  • Parameter description: Count mean diameter in wet size for drizze/rain droplets
  • Parameter name: Dwia
  • Parameter unit: m
  • Parameter description: Count mean diameter for ice particles
  • Parameter name: CDNC
  • Parameter unit: #/m3
  • Parameter description: Cloud droplet number concentrations droplets with diameter 2 um<D<80 um
  • Parameter name: CNC
  • Parameter unit: #/m3
  • Parameter description: Cloud droplet number concentrations droplets with diameter D>2 um
  • Parameter name: Naba
  • Parameter unit: #/kg
  • Parameter description: Binned number concentrations of aerosol particle in the size distribution A or regime A
  • Parameter name: Ncba
  • Parameter unit: #/kg
  • Parameter description: Binned number concentrations of cloud droplets in the size distribution A or regime A
  • Parameter name: Npba
  • Parameter unit: #/kg
  • Parameter description: Binned number concentrations of drizzle/rain droplets
  • Parameter name: Niba
  • Parameter unit: #/kg
  • Parameter description: Binned number concentrations of ice particles
  • Parameter name: Dwaba
  • Parameter unit: m
  • Parameter description: Binned wet diameter of aerosol particles in the size distribution A or regime A
  • Parameter name: Dwcba
  • Parameter unit: m
  • Parameter description: Binned wet diameter of cloud droplets in the size distribution A or regime A
  • Parameter name: Dwpba
  • Parameter unit: m
  • Parameter description: Binned wet diameter of drizzle/rain droplets
  • Parameter name: Dwiba
  • Parameter unit: m
  • Parameter description: Binned size of ice particles as maximum length
  • Parameter name: Ndba
  • Parameter unit: #/kg
  • Parameter description: Binned number concentrations of droplets (cloud, drizzle, rain)
  • Parameter name: s_n_siprmspl
  • Parameter unit: #/kg/s
  • Parameter description: Number concentration of secondary ice particles produced by rime splintering per unit time
  • Parameter name: s_n_sipdrfr
  • Parameter unit: #/kg/s
  • Parameter description: Number concentration of secondary ice particles produced by droplet shattering per unit time
  • Parameter name: s_n_sipiibr
  • Parameter unit: #/kg/s
  • Parameter description: Number concentration of secondary ice particles produced by ice-ice collisional breakup per unit time
  • Parameter name: SIP-RMSPL
  • Parameter unit: #/kg/m3
  • Parameter description: Number concentration of secondary ice particles produced by rime splintering per unit time
  • Parameter name: SIP-DS
  • Parameter unit: #/kg/m3
  • Parameter description: Number concentration of secondary ice particles produced by droplet shattering per unit time
  • Parameter name: SIP-IIBR
  • Parameter unit: #/kg/m3
  • Parameter description: Number concentration of secondary ice particles produced by ice-ice collisional breakup per unit time
  • Parameter name: Cloudy
  • Parameter unit: Dimensionless [0 or 1]
  • Parameter description: Dummy variable to indicate cloudy conditions defined as TWC>0.01 g/m3
  • Parameter name: Alphaiw
  • Parameter unit: Dimensionless [0-1]
  • Parameter description: Ratio of ice water content to total water content
  • Parameter name: DropN
  • Parameter unit: #/m3
  • Parameter description: Total number concentration of droplets with D > 2 um in cloudy points
  • Parameter name: PrecipN
  • Parameter unit: #/m3
  • Parameter description: Total number concentration of droplets with D>150 um
  • Parameter name: DropDmean
  • Parameter unit: m
  • Parameter description: Count mean diameter for droplets with D > 2 um in cloudy points
  • Parameter name: PrecipDmean
  • Parameter unit: m
  • Parameter description: Count mean diameter for droplets with D > 150 um in cloudy points
  • Parameter name: IceDmean
  • Parameter unit: m
  • Parameter description: Count mean maximum length for ice particles in cloudy points
  • Parameter name: DropMVD
  • Parameter unit: m
  • Parameter description: Mean volume diameter for droplets with D > 2 um in cloudy points
  • Parameter name: PrecipMVD
  • Parameter unit: m
  • Parameter description: Mean volume diameter for droplets with D > 150 um in cloudy points
  • Parameter name: DropED
  • Parameter unit: m
  • Parameter description: Effective diameter for droplets with D > 2 um in cloudy points
  • Parameter name: PrecipED
  • Parameter unit: m
  • Parameter description: Effective diameter for droplets with D > 150 um in cloudy points
  • Parameter name: DropIntD
  • Parameter unit: m #/m3
  • Parameter description: Integral diameter or DropN*DropDmean for droplets with D > 2 um
  • Parameter name: PrecipIntD
  • Parameter unit: m #/m3
  • Parameter description: Integral diameter or PrecipN*PrecipDmean for droplets with D > 150 um
  • Parameter name: IceIntD
  • Parameter unit: m #/m3
  • Parameter description: Integral diameter or IceN*IceDmean for ice particles
  • Parameter name: aea
  • Parameter unit: m
  • Parameter description: Lower limits of size bins used in aerosol size distribution A
  • Parameter name: cla
  • Parameter unit: m
  • Parameter description: Lower limits of size bins used in cloud droplet size distribution A
  • Parameter name: prc
  • Parameter unit: m
  • Parameter description: Lower limits of size bins used in drizzle/rain size distribution
  • Parameter name: ice
  • Parameter unit: m
  • Parameter description: Lower limits of size bins used in ice particle size distribution
  • Parameter name: s_n_activ
  • Parameter unit: #/kg/s
  • Parameter description: Number concentration of aerosol particles activated per unit time
  • Parameter name: s_m_autoc
  • Parameter unit: kg/kg/s
  • Parameter description: Autoconversion rate for all droplets
  • Parameter name: s_m_autoc50
  • Parameter unit: kg/kg/s
  • Parameter description: Autoconversion rate for droplets with diameter larger than 50 um
  • Parameter name: s_m_autoc80
  • Parameter unit: kg/kg/s
  • Parameter description: Autoconversion rate for droplets with diameter larger than 80 um
  • Parameter name: s_m_accr
  • Parameter unit: kg/kg/s
  • Parameter description: Accretion rate for all droplets
  • Parameter name: s_m_accr50
  • Parameter unit: kg/kg/s
  • Parameter description: Accretion rate for droplets with diameter larger than 50 um
  • Parameter name: s_m_accr80
  • Parameter unit: kg/kg/s
  • Parameter description: Autoconversion rate for droplets with diameter larger than 80 um
Data levels (meter, hectoPascal, degree, sigma pressure levels, other) in vertical direction(+/-) for example 1500 m or 850 hPa
  • Level: From surface up to 11.8 km with 60 m vertical resolution
Resolution
300
Resolution unit
m
Topic category
climatologyMeteorologyAtmosphere

Temporal Coverage

Ranges:

Start date:
End date: 2021-06-05

Spans:

Span:Every 30 s

Locations

Vicinity of the Canadian River, north of Ada, Oklahoma, United States of America Point:
(Lon: 34.95, Lat: -96.6 )

Vicinity of the Canadian River, north of Ada, Oklahoma, United States of America