Dataset and code for "Methods to evaluate subcolumn profiles based on two-point diagnostics"

Description of included files

WRF code base (wrf_clubb_silhs.tar.gz)

This WRF repo (using the stephens_et_al_2024_lidar_paper branch) can be used to generate both the high-resolution reproductions of LASSO runs with 60-second snapshot output (note that the resulting files will be quite large, it may be convenient to eliminate variables from the history stream), and the coarse-grained, essentially single-column model LASSO reproductions using CLUBB-SILHS.  The code base was compiled with gfortran for the CLUBB-SILHS runs, and with ifort for the LES runs.  The appropriate test case to compile is "em_crm", and within the wrf_clubb_silhs/WRF/test/em_crm directory there are two bash scripts that will automate the setup of the cases (and in the coarse-grained case, run the model as well).  The bash scripts are named runlasso_silhs.bash and runlasso_les.bash, for the coarse-grained and LES versions respectively.  For the coarse-grained CLUBB-SILHS cases, the user will need to modify the value of the vertical decorrelation coefficient (vert_decorr_coef) manually.  This parameter can be modified in wrf_clubb_silhs/WRF/test/clubb_input/tunable_parameters/silhs_parameters.in.

WRF-CLUBB-SILHS files

These are output files from coarse-grained versions of the LASSO LES runs, on a 4x4 grid, with CLUBB-SILHS active.  Two cases are included:

vdc_10.tar.gz.  This test uses the standard SILHS sampling algorithm, with a vertical decorrelation coefficient (VDC, or vert_decorr_coef in the SILHS source code) equal to 10.

vdc_10_cdf.tar.gz.  Uses new CDF sampling method described in the paper, with VDC=10.

vdc_30.tar.gz.  Uses standard sampling method, with VDC=30.

vdc_30_cdf.tar.gz.  Uses new CDF sampling method, with VDC=30.

clubb_release.tar.gz

This clubb_release git repo contains a branch, sample_w_using_invrs_cdf_tag_20240719, that includes code for the SILHS inverse CDF sampling method.

Python script:

The included python script will generate the figures and table shown in the paper, as long as the user has the WRF-CLUBB-SILHS files (included here) and the necessary LASSO and lidar files (not included).  Note that the sampling technique in the paper incorporates some randomness so the table values will not be the same as in the paper but should be statistically identical.  The script can also be found in the wrf_clubb_silhs.tar.gz code base, at wrf_clubb_silhs/WRF/scripts/stephens_et_al_2024_lidar_paper_figures.py.

As mentioned above, the WRF-CLUBB-SILHS runs can also be reproduced using the included WRF code base and the wrf_clubb_silhs/WRF/test/em_crm/runlasso_silhs.bash script.  

The raw lidar and value-added product with lidar statistics can be obtained from the ARM Data Discovery site (Data Discovery (arm.gov)).

The original LASSO files can be obtained through ARM's LASSO Bundle Browser (LASSO Bundle Browser (arm.gov)).

The LASSO reproductions with 60-second snapshot output would have to be generated by the user with the WRF code base included here and the wrf_clubb_silhs/WRF/test/em_crm/runlasso_les.bash script.