Data for: Bulk microphysics schemes may perform better with a unified cloudrain category
Data files
Mar 21, 2024 version files 1.41 MB

README.md

summary_mat.zip
Abstract
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 warmphase 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.
README: Data for: Bulk microphysics schemes may perform better with a unified cloudrain category
Corresponding Author Information
Name: Arthur Z Hu
ORCID: 0000000321464325
Institution: University of California, Davis
Email: azqhu@ucdavis.edu, zh2611@columbia.edu
Principal Investigator Information
Name: Adele L Igel
ORCID: 000000024845594X
Institution: University of California, Davis
Email: aigel@ucdavis.edu
Date of data collection: 20230921
SHARING/ACCESS INFORMATION
NA
DATA & FILE OVERVIEW
File List:
Raw model output files:
Can be found under https://farm.cse.ucdavis.edu/~arthurhu/uampx/
./UvsS_KiD/
(UAMP and SAMP cases from KiD, a 1D single column model) conftest_fullmic/
(tests used to find the best configuration of UAMP, 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_mamp_bamp+sbm_uAdele_c0101_v0001.nc
(model output in netCDF) fullmic/
(UAMP and SAMP outputs from a full microphysics model) fullmic_*m/
(2m = SAMP, 4m = UAMP) *_*/
(AMP or bin, SBM or TAU) Na*/
(initial aerosol concentration) w*/
(maximum vertical velocity) KiD_mamp_bamp+sbm_uAdele_c0101_v0001.nc
(model output in netCDF) sedonly/
(UAMP and SAMP outputs from a model with only sedimentation turned on) sed2l_*m/
(2m = SAMP, 4m = UAMP) *_*/
(AMP or bin, SBM or TAU) KiD_mamp_bamp+sbm_uAdele_c0101_v0001.nc
(model output in netCDF)./UvsS_RAMS
(UAMP and SAMP cases from RAMS, a 3D model) SCMS0*_*/
(00=SAMP, 045=UAMP, 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) aA19950722154500g1.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 46)
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)
DATASPECIFIC INFORMATION
softwarespecific 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 UAMP vs. SAMP in KiD: 16 initial conditions and 4 schemes run in both UAMP and SAMP. 96 cases in total.
Comparing UAMP vs. SAMP 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 UAMP vs. SAMP in RAMS: 4 initial conditions and 2 schemes run in both UAMP and SAMP. 12 cases in total.
Missing data codes:
NaN
or 999
./summary_mat/fullmic_fullmic_*m_pfm.mat
UAMP's (4m) and SAMP'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 AMPbin 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 UAMP 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
Methods
This dataset is generated using KiD_AMP and the Regional Atmospheric Modeling System (RAMS). The simulation output is further summarized and visualized using MATLAB.