Cori quick guide for running e3sm_diags v2¶

1. Installation¶

We will use the e3sm_unifed environment to install. For the latest stable release or if you don’t have access to e3sm analysis machines, please instead refer to Latest stable release.

Most of the E3SM analysis software is maintained with an Anaconda metapackage (E3SM unified environment). If you have an account on Cori, then to get all of the tools in the metapackage in your path, use the activation command below. (Change .sh to .csh for csh shells.)

Below, we also provide the paths for observational data needed by e3sm_diags (<obs_path>), and some sample model data for testing (<test_data_path>). Both <obs_path> and <test_data_path> have two subdirectories: /climatology and /time-series for climatology and time-series data respectively.

Also listed below are paths where the HTML files (<html_path>) must be located to be displayed at their corresponding web addresses (<web_address>).

<activation_command>: source /global/cfs/cdirs/e3sm/software/anaconda_envs/load_latest_e3sm_unified.sh

<obs_path>: /global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/

<test_data_path>: /global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/

<html_path>: /global/cfs/cdirs/e3sm/www/<username>/

<web_address>: http://portal.nersc.gov/cfs/e3sm/<username>/

2. Config and run¶

Running the annual mean latitude-longitude contour set¶

Copy and paste the below code into run_e3sm_diags.py using your favorite text editor. Adjust any options as you like.

Tip: Some of E3SM’s analysis machines (Acme1, Anvil, Compy, Cori) have web servers setup to host html results. On Cori, create the directory /global/cfs/cdirs/e3sm/www/<username>/ using your username. Set results_dir to /global/cfs/cdirs/e3sm/www/<username>/doc_examples/lat_lon_demo in run_e3sm_diags.py below. Then, you can view results via a web browser here: http://portal.nersc.gov/cfs/e3sm/<username>/doc_examples/lat_lon_demo
import os
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.run import runner

param = CoreParameter()

param.reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/'
param.test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/climatology/'
param.test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'
param.seasons = ["ANN"]   #all seasons ["ANN","DJF", "MAM", "JJA", "SON"] will run,if comment out"

prefix = '/global/cfs/cdirs/e3sm/www/<username>/doc_examples/'
param.results_dir = os.path.join(prefix, 'lat_lon_demo')
# Use the following if running in parallel:
#param.multiprocessing = True
#param.num_workers = 32

# Use below to run all core sets of diags:
#runner.sets_to_run = ['lat_lon','zonal_mean_xy', 'zonal_mean_2d', 'polar', 'cosp_histogram', 'meridional_mean_2d']
# Use below to run lat_lon map only:
runner.sets_to_run = ['lat_lon']
runner.run_diags([param])

Run in serial with:

                              python run_e3sm_diags.py

                            

The above run has the same results as running e3sm_diags -p lat_lon_params.py using the code below for lat_lon_params.py:

                              reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/'
test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/climatology/'

test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'

sets = ["lat_lon"]
seasons = ["ANN"]

# 'mpl' for matplotlib plots
backend = 'mpl'

# Name of folder where all results will be stored.
results_dir = '/global/cfs/cdirs/e3sm/www/<username>/doc_examples/lat_lon_demo'

                            

The new way of running (no -p) is implemented in version 2.0.0, preparing e3sm_diags to accomodate more diagnostics sets with set-specific parameters.

To enable multiprocessing rather than running in serial, the program will need to be run in an interactive session on compute nodes, or as a batch job.

Interactive session on compute nodes¶

First, request an interactive session with a single node (32 cores with Cori Haswell, 68 cores with Cori KNL) for one hour (running this example should take much less than this). If obtaining a session takes too long, try to use the debug partition. Note that the maximum time allowed for that partition is 00:30:00.

                                salloc --nodes=1 --partition=regular --time=01:00:00 -C haswell

                              

Once the session is available, launch E3SM Diagnostics, to activate e3sm_unified:

                                source /global/cfs/cdirs/e3sm/software/anaconda_envs/load_latest_e3sm_unified.sh
python run_e3sm_diags.py --multiprocessing --num_workers=32

We could have also set these multiprocessing parameters in the run_e3sm_diags.py as well but we’re showing that you can still submit parameters via the command line.

Batch job¶

Alternatively, you can also create a script and submit it to the batch system. Copy and paste the code below into a file named diags.bash.

                                #!/bin/bash -l
#SBATCH --job-name=diags
#SBATCH --output=diags.o%j
#SBATCH --partition=regular
#SBATCH --account=acme
#SBATCH --nodes=1
#SBATCH --time=01:00:00
#SBATCH -C haswell

source /global/cfs/cdirs/e3sm/software/anaconda_envs/load_latest_e3sm_unified.sh
python run_e3sm_diags.py --multiprocessing --num_workers=32

                              

And then submit it:

                                sbatch diags.bash

                              

View the status of your job with squeue -u <username>. Here’s the meaning of some values under the State (ST) column:

PD: Pending
R: Running
CA: Cancelled
CD: Completed
F: Failed
TO: Timeout
NF: Node Failure

View results on the web¶

Once the run is completed, open http://portal.nersc.gov/cfs/e3sm/<username>/doc_examples/lat_lon_demo/viewer/index.html to view the results. If you don’t see the results, you may need to set proper permissions. Run chmod -R 755 /global/cfs/cdirs/e3sm/www/<username>/.

Tip: Once you’re on the webpage for a specific plot, click on the ‘Output Metadata’ drop down menu to view the metadata for the displayed plot. Running that command allows the displayed plot to be recreated. Changing any of the options will modify just that resulting figure.

Running all the core diagnostics sets¶

Core diagnostics set includes: lat_lon, zonal_mean_xy, zonal_mean_2d, polar, cosp_histogram, meridional_mean_2d. These diags share a common parameter space (core parameters). To run all these sets without defining set-specific parameters (e.g. plev for zonal_mean_2d and meridional_mean_2d.), replace the runner.sets_to_run line in run_e3sm_diags.py with the one below:

                              runner.sets_to_run = ['lat_lon','zonal_mean_xy', 'zonal_mean_2d', 'polar', 'cosp_histogram', 'meridional_mean_2d']

                            

Running area mean time series set¶

In v2.0.0, the time series set was implemented to support regional averaged time series plotting using monthly mean time series input. This set is enabled if monthly mean time series is processed as documented here.

A run_e3sm_diags.py example for running area mean time series alone:

                              import os
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.parameter.area_mean_time_series_parameter import AreaMeanTimeSeriesParameter
from acme_diags.run import runner

param = CoreParameter()

param.reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/time-series/'
param.test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/time-series/E3SM_v1/'
param.test_name = 'e3sm_v1'

prefix = '/global/cfs/cdirs/e3sm/www/<username>/doc_examples/'
param.results_dir = os.path.join(prefix, 'area_mean_with_obs')
# Use the following if running in parallel:
#param.multiprocessing = True
#param.num_workers =  40

# We're passing in this new object as well, in
# addition to the CoreParameter object.

ts_param = AreaMeanTimeSeriesParameter()
#ts_param.ref_names = ['none']   # Using this setting will plot only the model data, not the observation data
ts_param.start_yr = '2002'
ts_param.end_yr = '2008'

runner.sets_to_run = ['area_mean_time_series']
runner.run_diags([param, ts_param])

                            

This set can also be ran with the core diagnostics sets, so that all the plots are shown in one viewer. The following is an example to run all sets:

                              import os
from acme_diags.parameter.core_parameter import CoreParameter
from acme_diags.parameter.area_mean_time_series_parameter import AreaMeanTimeSeriesParameter
from acme_diags.run import runner

param = CoreParameter()

param.reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/climatology/'
param.test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/test_model_data_for_acme_diags/climatology/'
param.test_name = '20161118.beta0.FC5COSP.ne30_ne30.edison'
param.multiprocessing = True
param.num_workers = 40
prefix = '/global/cfs/cdirs/e3sm/www/<username>/doc_examples'
param.results_dir = os.path.join(prefix, 'all_sets')

#
##Set specific parameters for new sets
ts_param = AreaMeanTimeSeriesParameter()
ts_param.reference_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/time-series/'
ts_param.test_data_path = '/global/cfs/cdirs/e3sm/acme_diags/obs_for_e3sm_diags/time-series/E3SM_v1/'
ts_param.test_name = 'e3sm_v1'
ts_param.start_yr = '2002'
ts_param.end_yr = '2008'

runner.sets_to_run = ['lat_lon','zonal_mean_xy', 'zonal_mean_2d', 'polar', 'cosp_histogram', 'meridional_mean_2d', 'area_mean_time_series']
runner.run_diags([param, ts_param])

                            

Advanced: Running custom diagnostics¶

The following steps are for ‘advanced’ users, who want to run custom diagnostics. So, most users will not run the software like this.

By default, with e3sm_diags, a built in set of variables are defined for each diagonostics sets. To do a short run, e.g. only running through a subset of variables, a configuration file is needed to customize the run.

In the following example, only precipitation and surface sea temperature are run to compare with model and obs for lat_lon set. Create mydiags.cfg file as below.

Check Available Parameters for all available parameters.

For a larger configuration file example, look here for the cfg file that was used to create all of the latitude-longitude sets.

                              [#]
sets = ["lat_lon"]
case_id = "GPCP_v2.3"
variables = ["PRECT"]
ref_name = "GPCP_v2.3"
reference_name = "GPCP"
seasons = ["ANN", "DJF", "MAM", "JJA", "SON"]
regions = ["global"]
test_colormap = "WhiteBlueGreenYellowRed.rgb"
reference_colormap = "WhiteBlueGreenYellowRed.rgb"
diff_colormap = "BrBG"
contour_levels = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16]
diff_levels = [-5, -4, -3, -2, -1, -0.5, 0.5, 1, 2, 3, 4, 5]

                            

Run E3SM diagnostics with the -d parameter. Use the above run script. And run as following:

                              python run_e3sm_diags.py -d mydiags.cfg

                            