DOI: 10.5061/dryad.1g1jwsv48
title: Data for: Bulk microphysics schemes may perform better with a unified cloud-rain category

description: Bulk microphysics schemes continue to face challenges due in part to the
 necessary simplification of hydrometeor properties and processes that are
 inherent to any parameterization. In all operational bulk schemes, one
 such simplification is the division of liquid water into two subcategories
 (cloud and rain) when predicting the evolution of warm clouds. It was
 previously found that biases in collisional growth in a bulk scheme with
 these separate liquid water categories can be mitigated with a unified
 liquid water category in which cloud and rain are contained within the
 same category. In this study, we examine the effect of artificially
 separating the liquid water category on other microphysical processes and
 in more realistic settings. Both our idealized 1D and 3D results show that
 a unified category bulk scheme is fundamentally better at predicting the
 timing and intensity of rain from warm-phase cumulus clouds compared to a
 traditional (separate) category bulk scheme. This is because a unified
 category bulk scheme allows a bimodal distribution to exist within one
 traditional "rain" category, whereas separate category bulk
 schemes only have one mode per category. This advantage allows the unified
 bulk scheme to retain the information of the largest droplets even as they
 fall through a layer of small raindrops. A separate category bulk scheme
 fails to represent this bimodal feature in comparison.
description: This dataset is generated using KiD_AMP and the Regional
 Atmospheric Modeling System (RAMS). The simulation output is further
 summarized and visualized using MATLAB. 
description: # Data for: Bulk microphysics schemes may perform better with a unified
 cloud-rain category ### Corresponding Author Information Name: Arthur Z Hu
 ORCID: 0000-0003-2146-4325 Institution: University of California, Davis
 Email: [azqhu@ucdavis.edu,](mailto:azqhu@ucdavis.edu)
 [zh2611@columbia.edu](mailto:zh2611@columbia.edu) ### Principal
 Investigator Information Name: Adele L Igel ORCID: 0000-0002-4845-594X
 Institution: University of California, Davis Email:
 [aigel@ucdavis.edu](mailto:aigel@ucdavis.edu) Date of data collection:
 2023-09-21 ## SHARING/ACCESS INFORMATION NA ## DATA & FILE OVERVIEW
 ### File List: Raw model output files: Can be found under
 [https://farm.cse.ucdavis.edu/~arthurhu/uampx/](https://farm.cse.ucdavis.edu/~arthurhu/uampx/) * `./UvsS_KiD/` (U-AMP and S-AMP cases from KiD, a 1D single column model) * ` |- conftest_fullmic/` (tests used to find the best configuration of U-AMP, unnecessary for reproducing the figures in the paper) * ` |- fullmic_Na*w*/` (Na = aerosol concentration in [1/mg], w=max vertical velocity in [m/s]) * ` |- *_*/` (AMP or bin, SBM or TAU) * ` |- pmomxy*-*/` (the two predicted moments, orders matters technically for AMP but doesn't matter unified bulk schemes in principle) * ` |- spcr*-*/` (the two assumed shape parameter) * ` |- KiD_m-amp_b-amp+sbm_u-Adele_c-0101_v-0001.nc` (model output in netCDF) * ` |- fullmic/` (U-AMP and S-AMP outputs from a full microphysics model) * ` |- fullmic_*m/` (2m = S-AMP, 4m = U-AMP) * ` |- *_*/` (AMP or bin, SBM or TAU) * ` |- Na*/` (initial aerosol concentration) * ` |- w*/` (maximum vertical velocity) * ` |- KiD_m-amp_b-amp+sbm_u-Adele_c-0101_v-0001.nc` (model output in netCDF) * ` |- sedonly/` (U-AMP and S-AMP outputs from a model with only sedimentation turned on) * ` |- sed2l_*m/` (2m = S-AMP, 4m = U-AMP) * ` |- *_*/` (AMP or bin, SBM or TAU) * ` |- KiD_m-amp_b-amp+sbm_u-Adele_c-0101_v-0001.nc` (model output in netCDF) * `./UvsS_RAMS` (U-AMP and S-AMP cases from RAMS, a 3D model) * ` |- SCMS0*_*/` (00=S-AMP, 045=U-AMP, 64:6 and 4 are the two assumed shape parameter, 66: 6 and 6 are the two assumed shape parameter) * ` |- Na*/` (initial aerosol concentration) * ` |- *_*/` (AMP or bin, SBM or TAU) * ` |- a-A-1995-07-22-154500-g1.h5` (model output in HDF5) note: scripts in this folder requires external functions in ./figure_scripts/ext_funs/ easiest way to include those functions is `addpath(ext_funs/)` or start a MATLAB instance directly from ./figure scripts/ Processed files: * `./summary_mat/` (processed files from KiD, details below) * ` |- fullmic_fullmic_*m_pfm.mat` (used to generate figure 3) * ` |- fullmic_scores_conftest.mat` (used to generate figure 4-6) Figure scripts: * `./figure_scripts/` * `|- f*.m` (scripts to plot the figures) * `|- ext_funs/` (external functions required for the scripts above) * `|- plots/` (output directory for the figures) * `|- vids/` (output directory for the supplemental videos) ### DATA-SPECIFIC INFORMATION software-specific information needed to interpret the data: MATLAB (2021b) or later #### Raw model output files ##### Number of variables: Depending on the microphysics scheme and model. ##### Variable List: Can be viewed with `ncdump -h *.nc` for KiD outputs and `h5dump -n *h5` for RAMS outputs (need to install the hdf5 toolkit via, e.g., `brew install hdf5`). ##### Number of cases: Finding the best configuration in KiD: 16 initial conditions and 4 schemes, 56 different moment combinations for the 2 AMP schemes, and 25 different assumed shape parameters for each moment combination. 44832 cases in total. Comparing U-AMP vs. S-AMP in KiD: 16 initial conditions and 4 schemes run in both U-AMP and S-AMP. 96 cases in total. Comparing U-AMP vs. S-AMP in KiD (sedimentation only): 8 cases. Finding the best configuration in RAMS: 4 initial conditions and 2 schemes, 12 different moment combinations and 9 different assumed shape parameter for AMP. 436 cases in total. Comparing U-AMP vs. S-AMP in RAMS: 4 initial conditions and 2 schemes run in both U-AMP and S-AMP. 12 cases in total. ##### Missing data codes: `NaN` or `-999` ### `./summary_mat/fullmic_fullmic_*m_pfm.mat` U-AMP's (4m) and S-AMP's (2m) performance summary ##### Note: Each file is organized as a MATLAB structure ### Number of variables: 5 ##### Variable List: * `pfm.cloud_M1_path`: cloud water path [kg/m2] * `pfm.rain_M1_path`: rain water path [kg/m2] * `pfm.liq_M1_path`: liquid water path [kg/m2] * `pfm.mean_surface_ppt`: mean surface precipitation [mm/hr] * `pfm.Dm_r`: mean diameter of the larger mode [um] * `pfm.misc`: miscellaneous variables (e.g., initial conditions) (for each variable above except for `pfm.misc`): * `pfm.[i].sbm`: results from SBM * `pfm.[i].tau`: results from TAU (for each variable above except): * `pfm.[i].[j].mr`: mean AMP/bin ratio of var[i] across the simulation * `pfm.[i].[j].md`: mean AMP-bin difference of var[i] across the simualtion * `pfm.[i].[j].rsq`: R^2 between AMP and bin * `pfm.[i].[j].mpath_bin`: mean value of var[i] from bin (variables below are not used in the analysis) * `pfm.[i].[j].mpath_amp`: mean value of var[i] from AMP * `pfm.[i].[j].msd_amp`: difference between mean and initial value of var[i] from AMP * `pfm.[i].[j].msd_bin`: difference between mean and initial value of var[i] from bin * `pfm.[i].[j].sval_amp`: initial value of var[i] from AMP * `pfm.[i].[j].sval_bin`: initial value of var[i] from bin * `pfm.[i].[j].er`: AMP/bin ratio at the end of the simulation for var[i] * `pfm.[i].[j].maxr`: AMP/bin ratio of max(var[i]) * `pfm.[i].[j].serr`: ratio between the ratio between final and initial value of var[i] between AMP and bin ... **or in python `(AMP[-1]/AMP[0]) / (bin[-1]/bin[0])`** ### Number of cases: 16 ### Missing data codes: `NaN` or `-999` ### `./summary_mat/fullmic_scores_conftest.mat` (Evaluation of different U-AMP configurations) ##### Note: Organized as a MATLAB structure ### Number of variables: 8 ### Variable List: * `score_trim(i).cloud_M1_path`: cloud water path [kg/m2] * `score_trim(i).rain_M1_path`: rain water path [kg/m2] * `score_trim(i).liq_M1_path`: liquid water path [kg/m2] * `score_trim(i).mean_surface_ppt`: mean surface precipitation [mm/hr] * `score_trim(i).Dm_c`: mean diameter of the smaller (cloud) mode [um] * `score_trim(i).Dm_r`: mean diameter of the larger (rain) mode [um] * `score_trim(i).Dm_w`: mean diameter (overall) [um] * `score_trim(i).dm_rain_coll`: autoconversion rate [kg/kg/s] note: (i) from 1 to 16, representing the 16 initial conditions cases. ##### Number of cases: 16 ### Missing data codes: `NaN` or `-999`