5. Running the SRW App

This chapter explains how to set up and run the “out-of-the-box” case for the SRW Application. However, the steps are relevant to any SRW App experiment and can be modified to suit user goals. This chapter assumes that users have already built the SRW App by following the steps in Chapter 4. These steps are also applicable to containerized versions of the SRW App and assume that the user has completed all of Section 3.1.

The out-of-the-box SRW App case builds a weather forecast for June 15-16, 2019. Multiple convective weather events during these two days produced over 200 filtered storm reports. Severe weather was clustered in two areas: the Upper Midwest through the Ohio Valley and the Southern Great Plains. This forecast uses a predefined 25-km Continental United States (CONUS) domain (RRFS_CONUS_25km), the Global Forecast System (GFS) version 16 physics suite (FV3_GFS_v16 CCPP), and FV3-based GFS raw external model data for initialization.

Attention

The SRW Application has four levels of support. The steps described in this chapter will work most smoothly on preconfigured (Level 1) systems. This chapter can also serve as a starting point for running the SRW App on other systems (including generic Linux/Mac systems), but the user may need to perform additional troubleshooting.

The overall procedure for generating an experiment is shown in Figure 5.1, with the scripts to generate and run the workflow shown in red. Once the SRW App has been built, as described in Chapter 4, the steps to run a forecast are as follows:

1. Download and stage data

2. Optional: Configure a new grid

3. Generate a regional workflow experiment

   • Load the python environment for the regional workflow

   • Set the experiment configuration parameters

   • Optional: Plot the output

   • Optional: Configure METplus Verification Suite

4. Run the regional workflow

Fig. 5.1 Overall Layout of the SRW App Workflow

5.1. Download and Stage the Data

The SRW App requires input files to run. These include static datasets, initial and boundary conditions files, and model configuration files. On Level 1 systems, the data required to run SRW App tests are already available in the following locations:

Table 5.1 Data Locations for Level 1 Systems

Machine	File location
Cheyenne	/glade/work/epicufsrt/contrib/UFS_SRW_data/develop/input_model_data/
Gaea	/lustre/f2/dev/role.epic/contrib/UFS_SRW_data/develop/input_model_data/
Hera	/scratch1/NCEPDEV/nems/role.epic/UFS_SRW_data/develop/input_model_data/
Jet	/mnt/lfs4/HFIP/hfv3gfs/role.epic/UFS_SRW_data/develop/input_model_data/
NOAA Cloud	/contrib/EPIC/UFS_SRW_data/develop/input_model_data/
Orion	/work/noaa/epic-ps/role-epic-ps/UFS_SRW_data/develop/input_model_data/
WCOSS2	/lfs/h2/emc/lam/noscrub/UFS_SRW_App/develop/input_model_data/

For Level 2-4 systems, the data must be added to the user’s system. Detailed instructions on how to add the data can be found in Section 9.3. Sections 9.1 and 9.2 contain useful background information on the input and output files used in the SRW App.

5.2. Grid Configuration

The SRW App officially supports the four predefined grids shown in Table 5.2. The out-of-the-box SRW App case uses the RRFS_CONUS_25km predefined grid option. More information on the predefined and user-generated grid options can be found in Chapter 10. Users who plan to utilize one of the four predefined domain (grid) options may continue to Step 5.3. Users who plan to create a new custom predefined grid should refer to Section 10.2 for instructions. At a minimum, these users will need to add the new grid name to the valid_param_vals.yaml file and add the corresponding grid-specific parameters in the predef_grid_params.yaml file.

Table 5.2 Predefined Grids Supported in the SRW App

Grid Name	Grid Type	Quilting (write component)
RRFS_CONUS_25km	ESG grid	lambert_conformal
RRFS_CONUS_13km	ESG grid	lambert_conformal
RRFS_CONUS_3km	ESG grid	lambert_conformal
SUBCONUS_Ind_3km	ESG grid	lambert_conformal

5.3. Generate the Forecast Experiment

Generating the forecast experiment requires three steps:

1. Load the python environment for the regional workflow

2. Set experiment configuration parameters

3. Run a script to generate the experiment workflow

The first two steps depend on the platform being used and are described here for each Level 1 platform. Users will need to adjust the instructions to reflect their machine configuration if they are working on a Level 2-4 platform. Information in Chapter 11: Configuring the Workflow can help with this.

5.3.1. Load the Conda/Python Environment for the Regional Workflow

The workflow requires Python3 installed using conda, with the additional packages built in a separate conda evironment named regional_workflow. This environment has the following additional packages: PyYAML, Jinja2, f90nml, scipy, matplotlib, pygrib, cartopy. This conda/Python environment has already been set up on Level 1 platforms and can be activated in the following way:

source <path/to/etc/lmod-setup.sh/OR/lmod-setup.csh> <platform>
module use <path/to/modulefiles>
module load wflow_<platform>

where <platform> refers to a valid machine name (see Section 11.1).

Note

If users source the lmod-setup file on a system that doesn’t need it, it will not cause any problems (it will simply do a module purge).

A brief recipe for building the regional workflow environment on a Linux or Mac system can be found in Section 5.3.2.4.2.

The wflow_<platform> modulefile will then output instructions to activate the regional workflow. The user should run the commands specified in the modulefile output. The command may vary from system to system. For example, if the output says:

Please do the following to activate conda:
    > conda activate regional_workflow

then the user should run conda activate regional_workflow. This activates the regional_workflow conda environment, and the user typically sees (regional_workflow) in front of the Terminal prompt at this point.

5.3.1.1. Preparing the Workflow Environment on Non-Level 1 Systems

Users on non-Level 1 systems can copy one of the provided wflow_<platform> files and use it as a template to create a wflow_<platform> file that works for their system. The wflow_macos and wflow_linux template modulefiles are provided in the modulefiles directory. Modifications are required to provide paths for python, miniconda modules, module loads, conda initialization, and the path for user’s regional_workflow conda environment. After making modifications to a wflow_<platform> file, users can run the commands from Step 5.3.1 above to activate the regional workflow.

Note

conda needs to be initialized before running conda activate regional_workflow command. Depending on the user’s system and login setup, this may be accomplished in a variety of ways. Conda initialization usually involves the following command: source <conda_basedir>/etc/profile.d/conda.sh, where <conda_basedir> is the base conda installation directory.

5.3.2. Set Experiment Configuration Parameters

Each experiment requires certain basic information to run (e.g., date, grid, physics suite). This information is specified in config_defaults.yaml and in the user-specified config.yaml file. When generating a new experiment, the SRW App first reads and assigns default values from config_defaults.yaml. Then, it reads and (re)assigns variables from the user’s custom config.yaml file.

For background info on config_defaults.yaml, read Section 5.3.2.1, or jump to Section 5.3.2.2 to continue configuring the experiment.

5.3.2.1. Default configuration: config_defaults.yaml

Note

This section provides background information on available parameters and how the SRW App uses the config_defaults.yaml file. It is informative, but users do not need to modify config_defaults.yaml to run the out-of-the-box case for the SRW App. Therefore, users may skip to Step 5.3.2.2 to continue configuring their experiment.

Configuration parameters in the config_defaults.yaml file appear in Table 5.3. Some of these default values are intentionally invalid in order to ensure that the user assigns valid values in the user-specified config.yaml file. Any settings provided in config.yaml will override the settings in config_defaults.yaml. There is usually no need for a user to modify the default configuration file. Additional information on the default settings can be found in the config_defaults.yaml file comments and in Chapter 11.

Table 5.3 Configuration variables specified in the config_defaults.yaml script

Group Name	Configuration variables
User	RUN_ENVIR, MACHINE, MACHINE_FILE, ACCOUNT
Platform	WORKFLOW_MANAGER, NCORES_PER_NODE, BUILD_MOD_FN, WFLOW_MOD_FN, BUILD_VER_FN, RUN_VER_FN, SCHED, DOMAIN_PREGEN_BASEDIR, ENV_INIT_SCRIPTS_FPS, PRE_TASK_CMDS, PARTITION_DEFAULT, QUEUE_DEFAULT, PARTITION_HPSS, QUEUE_HPSS, PARTITION_FCST, QUEUE_FCST, RUN_CMD_UTILS, RUN_CMD_FCST, RUN_CMD_POST, SLURM_NATIVE_CMD, MODEL, MET_INSTALL_DIR, METPLUS_PATH, MET_BIN_EXEC, CCPA_OBS_DIR, MRMS_OBS_DIR, NDAS_OBS_DIR
Workflow	WORKFLOW_ID, USE_CRON_TO_RELAUNCH, CRON_RELAUNCH_INTVL_MNTS, EXPT_BASEDIR, EXPT_SUBDIR, EXEC_SUBDIR, DOT_OR_USCORE, EXPT_CONFIG_FN, CONSTANTS_FN, RGNL_GRID_NML_FN, FV3_NML_BASE_SUITE_FN, FV3_NML_YAML_CONFIG_FN, FV3_NML_BASE_ENS_FN, FV3_EXEC_FN, DIAG_TABLE_TMPL_FN, FIELD_TABLE_TMPL_FN, DATA_TABLE_TMPL_FN, MODEL_CONFIG_TMPL_FN, NEMS_CONFIG_TMPL_FN, FCST_MODEL, WFLOW_XML_FN, GLOBAL_VAR_DEFNS_FN, EXTRN_MDL_VAR_DEFNS_FN, WFLOW_LAUNCH_SCRIPT_FN, WFLOW_LAUNCH_LOG_FN, CCPP_PHYS_SUITE, GRID_GEN_METHOD, DATE_FIRST_CYCL, DATE_LAST_CYCL, INCR_CYCL_FREQ, FCST_LEN_HRS, GET_OBS, MAXTRIES_VX_ENSGRID_PROB_REFC, PREEXISTING_DIR_METHOD, VERBOSE, DEBUG, COMPILER
NCO	envir, NET, model_ver, RUN, OPSROOT
task_make_grid	GRID_DIR, ESGgrid_LON_CTR, ESGgrid_LAT_CTR, ESGgrid_DELX, ESGgrid_DELY, ESGgrid_NX, ESGgrid_NY, ESGgrid_PAZI, ESGgrid_WIDE_HALO_WIDTH, GFDLgrid_LON_T6_CTR, GFDLgrid_LAT_T6_CTR, GFDLgrid_NUM_CELLS, GFDLgrid_STRETCH_FAC, GFDLgrid_REFINE_RATIO, GFDLgrid_ISTART_OF_RGNL_DOM_ON_T6G, GFDLgrid_IEND_OF_RGNL_DOM_ON_T6G, GFDLgrid_JSTART_OF_RGNL_DOM_ON_T6G, GFDLgrid_JEND_OF_RGNL_DOM_ON_T6G, GFDLgrid_USE_NUM_CELLS_IN_FILENAMES
task_make_orog	KMP_AFFINITY_MAKE_OROG, OMP_NUM_THREADS_MAKE_OROG OMP_STACKSIZE_MAKE_OROG, OROG_DIR
task_make_sfc_climo	KMP_AFFINITY_MAKE_SFC_CLIMO, OMP_NUM_THREADS_MAKE_SFC_CLIMO, OMP_STACKSIZE_MAKE_SFC_CLIMO, SFC_CLIMO_DIR
task_get_extrn_ics	EXTRN_MDL_NAME_ICS, EXTRN_MDL_ICS_OFFSET_HRS, FV3GFS_FILE_FMT_ICS, EXTRN_MDL_SYSBASEDIR_ICS, USE_USER_STAGED_EXTRN_FILES, EXTRN_MDL_SOURCE_BASEDIR_ICS, EXTRN_MDL_FILES_ICS, EXTRN_MDL_FILES_ICS, EXTRN_MDL_FILES_ICS, EXTRN_MDL_DATA_STORES, NOMADS, NOMADS_file_type
task_get_extrn_lbcs	EXTRN_MDL_NAME_LBCS, LBC_SPEC_INTVL_HRS, EXTRN_MDL_LBCS_OFFSET_HRS, FV3GFS_FILE_FMT_LBCS, EXTRN_MDL_SYSBASEDIR_LBCS, USE_USER_STAGED_EXTRN_FILES, EXTRN_MDL_SOURCE_BASEDIR_LBCS, EXTRN_MDL_FILES_LBCS, EXTRN_MDL_DATA_STORE, NOMADS, NOMADS_file_type
task_make_ics	KMP_AFFINITY_MAKE_ICS, OMP_NUM_THREADS_MAKE_ICS, OMP_STACKSIZE_MAKE_ICS, USE_FVCOM, FVCOM_WCSTART, FVCOM_DIR, FVCOM_FILE
task_make_lbcs	KMP_AFFINITY_MAKE_LBCS, OMP_NUM_THREADS_MAKE_LBCS, OMP_STACKSIZE_MAKE_LBCS
task_run_fcst	KMP_AFFINITY_RUN_FCST, OMP_NUM_THREADS_RUN_FCST, OMP_STACKSIZE_RUN_FCST, DT_ATMOS, RESTART_INTERVAL, WRITE_DOPOST, LAYOUT_X, LAYOUT_Y, BLOCKSIZE, QUILTING, PRINT_ESMF, WRTCMP_write_groups, WRTCMP_write_tasks_per_group, WRTCMP_cen_lon, WRTCMP_cen_lat, WRTCMP_lon_lwr_left, WRTCMP_lat_lwr_left, WRTCMP_lon_upr_rght, WRTCMP_lat_upr_rght, WRTCMP_dlon, WRTCMP_dlat, WRTCMP_stdlat1, WRTCMP_stdlat2, WRTCMP_nx, WRTCMP_ny, WRTCMP_dx, WRTCMP_dy, PREDEF_GRID_NAME, USE_MERRA_CLIMO, SFC_CLIMO_FIELDS, FIXgsm, FIXaer, FIXlut, TOPO_DIR, SFC_CLIMO_INPUT_DIR, SYMLINK_FIX_FILES, FNGLAC, FNMXIC, FNTSFC, FNSNOC, FNZORC, FNAISC, FNSMCC, FNMSKH, FIXgsm_FILES_TO_COPY_TO_FIXam, FV3_NML_VARNAME_TO_FIXam_FILES_MAPPING, FV3_NML_VARNAME_TO_SFC_CLIMO_FIELD_MAPPING, CYCLEDIR_LINKS_TO_FIXam_FILES_MAPPING
task_run_post	KMP_AFFINITY_RUN_POST, OMP_NUM_THREADS_RUN_POST, OMP_STACKSIZE_RUN_POST, SUB_HOURLY_POST, DT_SUB_HOURLY_POST_MNTS, USE_CUSTOM_POST_CONFIG_FILE, CUSTOM_POST_CONFIG_FP, POST_OUTPUT_DOMAIN_NAME
Global	USE_CRTM, CRTM_DIR, DO_ENSEMBLE, NUM_ENS_MEMBERS, NEW_LSCALE, DO_SHUM, ISEED_SHUM, SHUM_MAG, SHUM_LSCALE, SHUM_TSCALE, SHUM_INT, DO_SPPT, ISEED_SPPT, SPPT_MAG, SPPT_LOGIT, SPPT_LSCALE, SPPT_TSCALE, SPPT_INT, SPPT_SFCLIMIT, USE_ZMTNBLCK, DO_SKEB, ISEED_SKEB, SKEB_MAG, SKEB_LSCALE, SKEP_TSCALE, SKEB_INT, SKEBNORM, SKEB_VDOF, DO_SPP, ISEED_SPP, SPP_VAR_LIST, SPP_MAG_LIST, SPP_LSCALE, SPP_TSCALE, SPP_SIGTOP1, SPP_SIGTOP2, SPP_STDDEV_CUTOFF, DO_LSM_SPP, LSM_SPP_TSCALE, LSM_SPP_LSCALE, ISEED_LSM_SPP, LSM_SPP_VAR_LIST, LSM_SPP_MAG_LIST, HALO_BLEND

5.3.2.2. User-specific configuration: config.yaml

The user must specify certain basic experiment configuration information in a config.yaml file located in the ufs-srweather-app/ush directory. Two example templates are provided in that directory: config.community.yaml and config.nco.yaml. The first file is a minimal example for creating and running an experiment in community mode (with RUN_ENVIR set to community). The second is an example for creating and running an experiment in the NCO (operational) mode (with RUN_ENVIR set to nco). The community mode is recommended in most cases and is fully supported for this release. The operational/NCO mode is typically used by developers at the Environmental Modeling Center (EMC) and at the Global Systems Laboratory (GSL) working on pre-implementation testing for the Rapid Refresh Forecast System (RRFS). Table 5.4 compares the configuration variables that appear in the config.community.yaml with their default values in config_default.yaml.

Table 5.4 Configuration variables specified in the config.community.yaml script

Parameter	Default Value	config.community.yaml Value
RUN_ENVIR	“nco”	“community”
MACHINE	“BIG_COMPUTER”	“hera”
ACCOUNT	“project_name”	“an_account”
MODEL	“”	“FV3_GFS_v16_CONUS_25km”
METPLUS_PATH	“”	“”
MET_INSTALL_DIR	“”	“”
CCPA_OBS_DIR	“”	“”
MRMS_OBS_DIR	“”	“”
NDAS_OBS_DIR	“”	“”
EXPT_SUBDIR	“”	“test_community”
CCPP_PHYS_SUITE	“FV3_GFS_v16”	“FV3_GFS_v16”
DATE_FIRST_CYCL	“YYYYMMDDHH”	‘2019061518’
DATE_LAST_CYCL	“YYYYMMDDHH”	‘2019061518’
FCST_LEN_HRS	24	12
PREEXISTING_DIR_METHOD	“delete”	“rename”
VERBOSE	true	true
COMPILER	“intel”	“intel”
EXTRN_MDL_NAME_ICS	“FV3GFS”	“FV3GFS”
FV3GFS_FILE_FMT_ICS	“nemsio”	“grib2”
EXTRN_MDL_NAME_LBCS	“FV3GFS”	“FV3GFS”
FV3GFS_FILE_FMT_LBCS	“nemsio”	“grib2”
LBC_SPEC_INTVL_HRS	6	6
WTIME_RUN_FCST	“04:30:00”	“02:00:00”
QUILTING	true	true
PREDEF_GRID_NAME	“”	“RRFS_CONUS_25km”
DO_ENSEMBLE	false	false
NUM_ENS_MEMBERS	1	2

To get started, make a copy of config.community.yaml. From the ufs-srweather-app directory, run:

cd /path/to/ufs-srweather-app/ush
cp config.community.yaml config.yaml

The default settings in this file include a predefined 25-km CONUS grid (RRFS_CONUS_25km), the GFS v16 physics suite (FV3_GFS_v16 CCPP), and FV3-based GFS raw external model data for initialization.

Note

Users who have a previous configuration using the former shell-script-based can convert their config.sh file to a config.yaml file by running:

./config_utils.py -c $PWD/config.sh -t $PWD/config_defaults.yaml -o yaml >config.yaml

Use caution when upgrading your experiment in this way, as many intervening changes in the workflow have occurred since the python changeover.

Next, users should edit the new config.yaml file to customize it for their machine. At a minimum, users must change the MACHINE and ACCOUNT variables. Then, they can choose a name for the experiment directory by setting EXPT_SUBDIR. If users have pre-staged initialization data for the experiment, they can set USE_USER_STAGED_EXTRN_FILES: true, and set the paths to the data for EXTRN_MDL_SOURCE_BASEDIR_ICS and EXTRN_MDL_SOURCE_BASEDIR_LBCS. If the modulefile used to set up the build environment in Section 4.4 uses a GNU compiler, check that the line COMPILER: "gnu" appears in the workflow: section of the config.yaml file. On platforms where Rocoto and cron are available, users can automate resubmission of their experiment workflow by adding the following lines to the workflow: section of the config.yaml file:

USE_CRON_TO_RELAUNCH: true
CRON_RELAUNCH_INTVL_MNTS: 3

Note

Generic Linux and MacOS users should refer to Section 5.3.2.4 for additional details on configuring an experiment and python environment.

Detailed information on additional parameter options can be viewed in Chapter 11: Configuring the Workflow. Additionally, information about the four predefined Limited Area Model (LAM) Grid options can be found in Chapter 10: Limited Area Model (LAM) Grids.

On Level 1 systems, the following fields typically need to be updated or added to the appropriate section of the config.yaml file in order to run the out-of-the-box SRW App case:

user:
   MACHINE: hera
   ACCOUNT: an_account
workflow:
   EXPT_SUBDIR: test_community
task_get_extrn_ics:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_ICS: "/path/to/UFS_SRW_App/develop/input_model_data/<model_type>/<data_type>/<YYYYMMDDHH>"
task_get_extrn_lbcs:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_LBCS: "/path/to/UFS_SRW_App/develop/input_model_data/<model_type>/<data_type>/<YYYYMMDDHH>"

where:

• MACHINE refers to a valid machine name (see Section 11.1 for options).

• ACCOUNT refers to a valid account name. Not all systems require a valid account name, but most do.

Hint

To determine an appropriate ACCOUNT field for Level 1 systems, run groups, and it will return a list of projects you have permissions for. Not all of the listed projects/groups have an HPC allocation, but those that do are potentially valid account names.

• EXPT_SUBDIR is changed to an experiment name of the user’s choice.

• </path/to/> is the path to the SRW App data on the user’s machine (see Section 5.1).

• <model_type> refers to a subdirectory containing the experiment data from a particular model. Valid values on Level 1 systems correspond to the valid values for EXTRN_MDL_NAME_ICS and EXTRN_MDL_NAME_LBCS (see Chapter 11 for options).

• <data_type> refers to one of 3 possible data formats: grib2, nemsio, or netcdf.

• <YYYYMMDDHH> refers to a subdirectory containing data for the cycle date (in YYYYMMDDHH format).

Note

On JET, users should also add PARTITION_DEFAULT: xjet and PARTITION_FCST: xjet to the platform: section of the config.yaml file.

For example, to run the out-of-the-box experiment on Gaea, add or modify variables in the user, workflow, task_get_extrn_ics, and task_get_extrn_lbcs sections of config.yaml (unmodified variables are not shown in this example):

user:
   MACHINE: gaea
   ACCOUNT: hfv3gfs
workflow:
   EXPT_SUBDIR: run_basic_srw
task_get_extrn_ics:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_ICS: /lustre/f2/dev/role.epic/contrib/UFS_SRW_data/develop/input_model_data/FV3GFS/grib2/2019061518
   EXTRN_MDL_DATA_STORES: disk
task_get_extrn_lbcs:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_LBCS: /lustre/f2/dev/role.epic/contrib/UFS_SRW_data/develop/input_model_data/FV3GFS/grib2/2019061518
   EXTRN_MDL_DATA_STORES: disk

To determine whether the config.yaml file adjustments are valid, users can run the following script from the ush directory:

./config_utils.py -c $PWD/config.yaml -v $PWD/config_defaults.yaml

A correct config.yaml file will output a SUCCESS message. A config.yaml file with problems will output a FAILURE message describing the problem. For example:

INVALID ENTRY: EXTRN_MDL_FILES_ICS=[]
FAILURE

Note

The regional workflow must be loaded for the config_utils.py script to validate the config.yaml file.

Valid values for configuration variables should be consistent with those in the ush/valid_param_vals.yaml script. In addition, various sample configuration files can be found within the subdirectories of tests/WE2E/test_configs.

To configure an experiment and python environment for a general Linux or Mac system, see the next section. To configure an experiment to run METplus verification tasks, see Section 5.3.2.5. Otherwise, skip to Section 5.3.3 to generate the workflow.

5.3.2.3. Plotting Configuration (optional)

An optional Python plotting task (PLOT_ALLVARS) can be activated in the workflow to generate plots for the FV3-LAM post-processed GRIB2 output over the CONUS. It generates graphics plots for a number of variables, including:

• 2-m temperature

• 2-m dew point temperature

• 10-m winds

• 250 hPa winds

• Accumulated precipitation

• Composite reflectivity

• Surface-based CAPE/CIN

• Max/Min 2-5 km updraft helicity

• Sea level pressure (SLP)

This workflow task can produce both plots from a single experiment and difference plots that compare the same cycle from two experiments. When plotting the difference, the two experiments must be on the same domain and available for the same cycle starting date/time and forecast hours. Other parameters may differ (e.g., the experiments may use different physics suites).

5.3.2.3.1. Cartopy Shapefiles

The Python plotting tasks require a path to the directory where the Cartopy Natural Earth shapefiles are located. The medium scale (1:50m) cultural and physical shapefiles are used to create coastlines and other geopolitical borders on the map. On Level 1 systems, this path is already set in the system’s machine file using the variable FIXshp. Users on other systems will need to download the shapefiles and update the path of $FIXshp in the machine file they are using (e.g., $SRW/ush/machine/macos.yaml for a generic MacOS system, where $SRW is the path to the ufs-srweather-app directory). The subset of shapefiles required for the plotting task can be obtained from the SRW Data Bucket. The full set of medium-scale (1:50m) Cartopy shapefiles can be downloaded here.

5.3.2.3.2. Task Configuration

Users will need to add or modify certain variables in config.yaml to run the plotting task(s). At a minimum, to activate the plot_allvars tasks, users must add it to the default list of taskgroups under the rocoto: tasks: section.

rocoto:
  tasks:
    taskgroups: '{{ ["parm/wflow/prep.yaml", "parm/wflow/coldstart.yaml", "parm/wflow/post.yaml", "parm/wflow/plot.yaml"]|include }}'

Users may also wish to adjust the start, end, and increment value for the plotting task. For example:

task_plot_allvars:
   PLOT_FCST_START: 0
   PLOT_FCST_INC: 6
   PLOT_FCST_END: 12

If the user chooses not to set these values, the default values will be used (see Section 11.15).

Note

If a forecast starts at 18h, this is considered the 0th forecast hour, so “starting forecast hour” should be 0, not 18.

When plotting output from a single experiment, no further adjustments are necessary. The output files (in .png format) will be located in the experiment directory under the $CDATE/postprd subdirectory where $CDATE corresponds to the cycle date and hour in YYYYMMDDHH format (e.g., 2019061518).

5.3.2.3.2.1. Plotting the Difference Between Two Experiments

When plotting the difference between two experiments (expt1 and expt2), users must set the COMOUT_REF template variable in expt2’s config.yaml file to point at forecast output from the expt1 directory. For example, in community mode, users can set COMOUT_REF as follows in the expt2 configuration file:

task_plot_allvars:
   COMOUT_REF: '${EXPT_BASEDIR}/expt1/${PDY}${cyc}/postprd'

This will ensure that expt2 can produce a difference plot comparing expt1 and expt2. In community mode, using default directory names and settings, $COMOUT_REF will resemble /path/to/expt_dirs/test_community/2019061518/postprd. Additional details on the plotting variables are provided in Section 11.15.

The output files (in .png format) will be located in the postprd directory for the experiment.

5.3.2.4. User-Specific Configuration on a Generic Linux/MacOS System

The configuration process for Linux and MacOS systems is similar to the process for other systems, but it requires a few extra steps.

Note

Examples in this subsection presume that the user is running in the Terminal with a bash shell environment. If this is not the case, users will need to adjust the commands to fit their command line application and shell environment.

5.3.2.4.1. Install/Upgrade Mac-Specific Packages

MacOS requires the installation of a few additional packages and, possibly, an upgrade to bash. Users running on MacOS should execute the following commands:

bash --version
brew install bash       # or: brew upgrade bash
brew install coreutils
brew gsed               # follow directions to update the PATH env variable

5.3.2.4.2. Creating a conda Environment on Linux and Mac

Users need to create a conda regional_workflow environment. The environment can be stored in a local path, which could be a default location or a user-specified location (e.g. $HOME/condaenv/venvs/ directory). (To determine the default location, use the conda info command, and look for the envs directories list.) A brief recipe for creating a virtual conda environment on non-Level 1 platforms:

conda create --name regional_workflow python=<python3-conda-version>
conda activate regional_workflow
conda install -c conda-forge f90nml
conda install jinja2
conda install pyyaml
# install packages for graphics environment
conda install scipy
conda install matplotlib
conda install -c conda-forge pygrib
conda install cartopy
# verify the packages installed
conda list
conda deactivate

where <python3-conda-version> is a numeric version (e.g. 3.9.12) in the conda base installation resulting from the query python3 --version.

5.3.2.4.3. Configuring an Experiment on General Linux and MacOS Systems

Optional: Install Rocoto

Note

Users may install Rocoto if they want to make use of a workflow manager to run their experiments. However, this option has not yet been tested on MacOS and has had limited testing on general Linux plaforms.

Configure the SRW App:

Configure an experiment using a template. Copy the contents of config.community.yaml into config.yaml:

cd /path/to/ufs-srweather-app/ush
cp config.community.yaml config.yaml

In the config.yaml file, set MACHINE: macos or MACHINE: linux, and modify the account and experiment info. For example:

user:
   RUN_ENVIR: community
   MACHINE: macos
   ACCOUNT: user
workflow:
   EXPT_SUBDIR: test_community
   PREEXISTING_DIR_METHOD: rename
   VERBOSE: true
   COMPILER: gnu
task_get_extrn_ics:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_ICS: /path/to/input_model_data/FV3GFS/grib2/2019061518
   EXTRN_MDL_DATA_STORES: disk
task_get_extrn_lbcs:
   USE_USER_STAGED_EXTRN_FILES: true
   EXTRN_MDL_SOURCE_BASEDIR_LBCS: /path/to/input_model_data/FV3GFS/grib2/2019061518
   EXTRN_MDL_DATA_STORES: disk
task_run_fcst:
   PREDEF_GRID_NAME: RRFS_CONUS_25km
   QUILTING: true

Due to the limited number of processors on MacOS systems, users must also configure the domain decomposition parameters directly in the section of the predef_grid_params.yaml file pertaining to the grid they want to use. Domain decomposition needs to take into account the number of available CPUs and configure the variables LAYOUT_X, LAYOUT_Y, and WRTCMP_write_tasks_per_group accordingly.

The example below is for systems with 8 CPUs:

task_run_fcst:
   LAYOUT_X: 3
   LAYOUT_Y: 2
   WRTCMP_write_tasks_per_group: 2

Note

The number of MPI processes required by the forecast will be equal to LAYOUT_X * LAYOUT_Y + WRTCMP_write_tasks_per_group.

For a machine with 4 CPUs, the following domain decomposition could be used:

task_run_fcst:
   LAYOUT_X: 3
   LAYOUT_Y: 1
   WRTCMP_write_tasks_per_group: 1

Configure the Machine File

Configure a macos.yaml or linux.yaml machine file in ufs-srweather-app/ush/machine based on the number of CPUs (NCORES_PER_NODE) in the system (usually 8 or 4 in MacOS; varies on Linux systems). Job scheduler (SCHED) options can be viewed here. Users must also set the path to the fix file directories.

platform:
   # Architecture information
   WORKFLOW_MANAGER: none
   NCORES_PER_NODE: 8
   SCHED: none
   # Run commands for executables
   RUN_CMD_FCST: 'mpirun -np ${PE_MEMBER01}'
   RUN_CMD_POST: 'mpirun -np 4'
   RUN_CMD_SERIAL: time
   RUN_CMD_UTILS: 'mpirun -np 4'
   # Commands to run at the start of each workflow task.
   PRE_TASK_CMDS: '{ ulimit -a; }'

task_make_orog:
   # Path to location of static input files used by the make_orog task
   TOPO_DIR: path/to/FIXgsm/files

task_make_sfc_climo:
   # Path to location of static surface climatology input fields used by sfc_climo_gen
   SFC_CLIMO_INPUT_DIR: path/to/FIXgsm/files

task_run_fcst:
   FIXaer: /path/to/FIXaer/files
   FIXgsm: /path/to/FIXgsm/files
   FIXlut: /path/to/FIXlut/files

data:
   # Used by setup.py to set the values of EXTRN_MDL_SOURCE_BASEDIR_ICS and EXTRN_MDL_SOURCE_BASEDIR_LBCS
   FV3GFS: /Users/username/DATA/UFS/FV3GFS

The data: section of the machine file can point to various data sources that the user has pre-staged on disk. For example:

data:
   FV3GFS:
      nemsio: /Users/username/DATA/UFS/FV3GFS/nemsio
      grib2: /Users/username/DATA/UFS/FV3GFS/grib2
      netcdf: /Users/username/DATA/UFS/FV3GFS/netcdf
   RAP: /Users/username/DATA/UFS/RAP/grib2
   HRRR: /Users/username/DATA/UFS/HRRR/grib2

This can be helpful when conducting multiple experiments with different types of data.

5.3.2.5. Configure METplus Verification Suite (Optional)

Users who want to use the METplus verification suite to evaluate their forecasts need to add additional information to their config.yaml file. Other users may skip to the next section.

Attention

METplus installation is not included as part of the build process for this release of the SRW App. However, METplus is preinstalled on many Level 1 & 2 systems. For the v2.1.0 release, METplus use is supported on systems with a functioning METplus installation, although installation itself is not supported. For more information about METplus, see Section 8.4.

Note

If METplus users update their METplus installation, they must update the module load statements in ufs-srweather-app/modulefiles/tasks/<machine>/run_vx.local file to correspond to their system’s updated installation:

module use -a </path/to/met/modulefiles/>
module load met/<version.X.X>

To use METplus verification, the path to the MET and METplus directories must be added to config.yaml:

platform:
   METPLUS_PATH: </path/to/METplus/METplus-4.1.0>
   MET_INSTALL_DIR: </path/to/met/10.1.0>

To turn on verification tasks in the workflow, include the parm/wflow/verify.yaml file in the rocoto: tasks: taskgroups: section of config.yaml.

rocoto:
  tasks:
    taskgroups: '{{ ["parm/wflow/prep.yaml", "parm/wflow/coldstart.yaml", "parm/wflow/post.yaml", "parm/wflow/verify.yaml"]|include }}'

The verify.yaml file includes the definitions of several common verification tasks by default. They are independent of each other, so users may want to turn some off depending on the needs of their experiment. Note that the ENSGRID and ENSPOINT tasks apply only to ensemble model verification. Additional verification tasks appear in Table 5.6.

To turn off a task, simply include its entry from verify.yaml as an empty YAML entry. For example, to turn off PointStat tasks:

rocoto:
  tasks:
    taskgroups: '{{ ["parm/wflow/prep.yaml", "parm/wflow/coldstart.yaml", "parm/wflow/post.yaml", "parm/wflow/verify.yaml"]|include }}'
  metatask_vx_ens_member:
    metatask_PointStat_mem#mem#:

More information about configuring the rocoto: section can be found in Section 13.

If users have access to NOAA HPSS but have not pre-staged the data, the default verify.yaml taskgroup will activate the tasks, and the workflow will attempt to download the appropriate data from NOAA HPSS. In this case, the *_OBS_DIR paths must be set to the location where users want the downloaded data to reside.

Users who do not have access to NOAA HPSS and do not have the data on their system will need to download CCPA, MRMS, and NDAS data manually from collections of publicly available data, such as the ones listed here.

Users who have already staged the observation data needed for METplus (i.e., the CCPA, MRMS, and NDAS data) on their system should set the path to this data and turn off the corresponding task by including them with no entry in config.yaml.

platform:
   CCPA_OBS_DIR: /path/to/UFS_SRW_App/develop/obs_data/ccpa/proc
   MRMS_OBS_DIR: /path/to/UFS_SRW_App/develop/obs_data/mrms/proc
   NDAS_OBS_DIR: /path/to/UFS_SRW_App/develop/obs_data/ndas/proc
rocoto:
  tasks:
    taskgroups: '{{ ["parm/wflow/prep.yaml", "parm/wflow/coldstart.yaml", "parm/wflow/post.yaml", "parm/wflow/verify.yaml"]|include }}'
    task_get_obs_ccpa:
    task_get_obs_mrms:
    task_get_obs_ndas:

5.3.3. Generate the Regional Workflow

Run the following command from the ufs-srweather-app/ush directory to generate the workflow:

./generate_FV3LAM_wflow.py

The last line of output from this script, starting with */1 * * * * or */3 * * * *, can be saved and used later to automatically run portions of the workflow if users have the Rocoto workflow manager installed on their system.

This workflow generation script creates an experiment directory and populates it with all the data needed to run through the workflow. The flowchart in Figure 5.2 describes the experiment generation process. First, generate_FV3LAM_wflow.py runs the setup.py script to set the configuration parameters. Second, it symlinks the time-independent (fix) files and other necessary data input files from their location to the experiment directory ($EXPTDIR). Third, it creates the input namelist file input.nml based on the input.nml.FV3 file in the parm directory. Lastly, it creates the workflow XML file FV3LAM_wflow.xml that is executed when running the experiment with the Rocoto workflow manager.

The setup.py script reads three other configuration scripts in order: (1) config_defaults.yaml (Section 5.3.2.1), (2) config.yaml (Section 5.3.2.2), and (3) set_predef_grid_params.py. If a parameter is specified differently in these scripts, the file containing the last defined value will be used.

The generated workflow will appear in $EXPTDIR, where EXPTDIR=${EXPT_BASEDIR}/${EXPT_SUBDIR}. These variables were specified in config_defaults.yaml and config.yaml in Step 5.3.2. The settings for these paths can also be viewed in the console output from the ./generate_FV3LAM_wflow.py script or in the log.generate_FV3LAM_wflow file, which can be found in $EXPTDIR.

Fig. 5.2 Experiment Generation Description

5.3.4. Description of Workflow Tasks

Note

This section gives a general overview of workflow tasks. To begin running the workflow, skip to Step 5.4

Figure 5.3 illustrates the overall workflow. Individual tasks that make up the workflow are specified in the FV3LAM_wflow.xml file. Table 5.5 describes the function of each baseline task. The first three pre-processing tasks; MAKE_GRID, MAKE_OROG, and MAKE_SFC_CLIMO; are optional. If the user stages pre-generated grid, orography, and surface climatology fix files, these three tasks can be skipped by removing the prep.yaml file from the default taskgroups entry in the config.yaml file before running the generate_FV3LAM_wflow.py script:

rocoto:
  tasks:
    taskgroups: '{{ ["parm/wflow/coldstart.yaml", "parm/wflow/post.yaml"]|include }}'

Fig. 5.3 Flowchart of the Workflow Tasks

The FV3LAM_wflow.xml file runs the specific j-job scripts (jobs/JREGIONAL_[task name]) in the prescribed order when the experiment is launched via the launch_FV3LAM_wflow.sh script or the rocotorun command. Each j-job task has its own source script (or “ex-script”) named exregional_[task name].sh in the scripts directory. Two database files named FV3LAM_wflow.db and FV3LAM_wflow_lock.db are generated and updated by the Rocoto calls. There is usually no need for users to modify these files. To relaunch the workflow from scratch, delete these two *.db files and then call the launch script repeatedly for each task.

Table 5.5 Baseline Workflow Tasks in the SRW App

Workflow Task	Task Description
make_grid	Pre-processing task to generate regional grid files. Only needs to be run once per experiment.
make_orog	Pre-processing task to generate orography files. Only needs to be run once per experiment.
make_sfc_climo	Pre-processing task to generate surface climatology files. Only needs to be run once per experiment.
get_extrn_ics	Cycle-specific task to obtain external data for the initial conditions (ICs)
get_extrn_lbcs	Cycle-specific task to obtain external data for the lateral boundary conditions (LBCs)
make_ics	Generate initial conditions from the external data
make_lbcs	Generate LBCs from the external data
run_fcst	Run the forecast model (UFS Weather Model)
run_post	Run the post-processing tool (UPP)

In addition to the baseline tasks described in Table 5.5 above, users may choose to run some or all of the METplus verification tasks. These tasks are described in Table 5.6 below.

Table 5.6 Verification (VX) Workflow Tasks in the SRW App

Workflow Task	Task Description
GET_OBS_CCPA	Retrieves and organizes hourly CCPA data from NOAA HPSS. Can only be run if verify.yaml is included in a tasksgroups list and user has access to NOAA HPSS data.
GET_OBS_NDAS	Retrieves and organizes hourly NDAS data from NOAA HPSS. Can only be run if verify.yaml is included in a tasksgroups list and user has access to NOAA HPSS data.
GET_OBS_MRMS	Retrieves and organizes hourly MRMS composite reflectivity and echo top data from NOAA HPSS. Can only be run if verify.yaml is included in a tasksgroups list and user has access to NOAA HPSS data.
VX_GRIDSTAT	Runs METplus grid-to-grid verification for 1-h accumulated precipitation
VX_GRIDSTAT_REFC	Runs METplus grid-to-grid verification for composite reflectivity
VX_GRIDSTAT_RETOP	Runs METplus grid-to-grid verification for echo top
VX_GRIDSTAT_##h	Runs METplus grid-to-grid verification for 3-h, 6-h, and 24-h (i.e., daily) accumulated precipitation. Valid values for ## are 03, 06, and 24.
VX_POINTSTAT	Runs METplus grid-to-point verification for surface and upper-air variables
VX_ENSGRID	Runs METplus grid-to-grid ensemble verification for 1-h accumulated precipitation. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_REFC	Runs METplus grid-to-grid ensemble verification for composite reflectivity. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_RETOP	Runs METplus grid-to-grid ensemble verification for echo top. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_##h	Runs METplus grid-to-grid ensemble verification for 3-h, 6-h, and 24-h (i.e., daily) accumulated precipitation. Valid values for ## are 03, 06, and 24. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_MEAN	Runs METplus grid-to-grid verification for ensemble mean 1-h accumulated precipitation. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_PROB	Runs METplus grid-to-grid verification for 1-h accumulated precipitation probabilistic output. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_MEAN_##h	Runs METplus grid-to-grid verification for ensemble mean 3-h, 6-h, and 24h (i.e., daily) accumulated precipitation. Valid values for ## are 03, 06, and 24. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_PROB_##h	Runs METplus grid-to-grid verification for 3-h, 6-h, and 24h (i.e., daily) accumulated precipitation probabilistic output. Valid values for ## are 03, 06, and 24. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_PROB_REFC	Runs METplus grid-to-grid verification for ensemble probabilities for composite reflectivity. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSGRID_PROB_RETOP	Runs METplus grid-to-grid verification for ensemble probabilities for echo top. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSPOINT	Runs METplus grid-to-point ensemble verification for surface and upper-air variables. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSPOINT_MEAN	Runs METplus grid-to-point verification for ensemble mean surface and upper-air variables. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.
VX_ENSPOINT_PROB	Runs METplus grid-to-point verification for ensemble probabilities for surface and upper-air variables. Can only be run if DO_ENSEMBLE: true and verify_ensgrid.yaml is included in a taskgroups list.

5.4. Run the Workflow

The workflow can be run using the Rocoto workflow manager (see Section 5.4.1) or using standalone wrapper scripts (see Section 5.4.2).

Attention

If users are running the SRW App on a system that does not have Rocoto installed (e.g., Level 3 & 4 systems, such as MacOS or generic Linux systems), they should follow the process outlined in Section 5.4.2 instead of the instructions in this section.

5.4.1. Run the Workflow Using Rocoto

The information in this section assumes that Rocoto is available on the desired platform. All official HPC platforms for the UFS SRW App release make use of the Rocoto workflow management software for running experiments. However, if Rocoto is not available, it is still possible to run the workflow using stand-alone scripts according to the process outlined in Section 5.4.2.

There are three ways to run the workflow with Rocoto: (1) automation via crontab (2) by calling the launch_FV3LAM_wflow.sh script, and (3) by manually issuing the rocotorun command.

Note

Users may find it helpful to review Chapter 12 to gain a better understanding of Rocoto commands and workflow management before continuing, but this is not required to run the experiment.

Optionally, an environment variable can be set to navigate to the $EXPTDIR more easily. If the login shell is bash, it can be set as follows:

export EXPTDIR=/<path-to-experiment>/<directory_name>

If the login shell is csh/tcsh, it can be set using:

setenv EXPTDIR /<path-to-experiment>/<directory_name>

5.4.1.1. Automated Option

The simplest way to run the Rocoto workflow is to automate the process using a job scheduler such as Cron. For automatic resubmission of the workflow at regular intervals (e.g., every 3 minutes), the user can add the following commands to their config.yaml file before generating the experiment:

USE_CRON_TO_RELAUNCH: true
CRON_RELAUNCH_INTVL_MNTS: 3

This will automatically add an appropriate entry to the user’s cron table and launch the workflow. Alternatively, the user can add a crontab entry manually using the crontab -e command. As mentioned in Section 5.3.3, the last line of output from ./generate_FV3LAM_wflow.py (starting with */3 * * * *), can be pasted into the crontab file. It can also be found in the $EXPTDIR/log.generate_FV3LAM_wflow file. The crontab entry should resemble the following:

*/3 * * * * cd <path/to/experiment/subdirectory> && ./launch_FV3LAM_wflow.sh called_from_cron="TRUE"

where <path/to/experiment/subdirectory> is changed to correspond to the user’s $EXPTDIR. The number 3 can be changed to a different positive integer and simply means that the workflow will be resubmitted every three minutes.

Hint

• On NOAA Cloud instances, */1 * * * * (or CRON_RELAUNCH_INTVL_MNTS: 1) is the preferred option for cron jobs because compute nodes will shut down if they remain idle too long. If the compute node shuts down, it can take 15-20 minutes to start up a new one.

• On other NOAA HPC systems, admins discourage the */1 * * * * due to load problems. */3 * * * * (or CRON_RELAUNCH_INTVL_MNTS: 3) is the preferred option for cron jobs on non-NOAA Cloud systems.

To check the experiment progress:

cd $EXPTDIR
rocotostat -w FV3LAM_wflow.xml -d FV3LAM_wflow.db -v 10

After finishing the experiment, open the crontab using crontab -e and delete the crontab entry.

Note

On Orion, cron is only available on the orion-login-1 node, so users will need to work on that node when running cron jobs on Orion.

The workflow run is complete when all tasks have “SUCCEEDED”. If everything goes smoothly, users will eventually see a workflow status table similar to the following:

CYCLE              TASK                   JOBID         STATE        EXIT STATUS   TRIES   DURATION
==========================================================================================================
201906151800       make_grid              4953154       SUCCEEDED         0          1          5.0
201906151800       make_orog              4953176       SUCCEEDED         0          1         26.0
201906151800       make_sfc_climo         4953179       SUCCEEDED         0          1         33.0
201906151800       get_extrn_ics          4953155       SUCCEEDED         0          1          2.0
201906151800       get_extrn_lbcs         4953156       SUCCEEDED         0          1          2.0
201906151800       make_ics               4953184       SUCCEEDED         0          1         16.0
201906151800       make_lbcs              4953185       SUCCEEDED         0          1         71.0
201906151800       run_fcst               4953196       SUCCEEDED         0          1       1035.0
201906151800       run_post_f000          4953244       SUCCEEDED         0          1          5.0
201906151800       run_post_f001          4953245       SUCCEEDED         0          1          4.0
...
201906151800       run_post_f012          4953381       SUCCEEDED         0          1          7.0

If users choose to run METplus verification tasks as part of their experiment, the output above will include additional lines after run_post_f012. The output will resemble the following but may be significantly longer when using ensemble verification:

CYCLE              TASK                   JOBID          STATE       EXIT STATUS   TRIES   DURATION
==========================================================================================================
201906151800       make_grid              30466134       SUCCEEDED        0          1          5.0
...
201906151800       run_post_f012          30468271       SUCCEEDED        0          1          7.0
201906151800       run_gridstatvx         30468420       SUCCEEDED        0          1         53.0
201906151800       run_gridstatvx_refc    30468421       SUCCEEDED        0          1        934.0
201906151800       run_gridstatvx_retop   30468422       SUCCEEDED        0          1       1002.0
201906151800       run_gridstatvx_03h     30468491       SUCCEEDED        0          1         43.0
201906151800       run_gridstatvx_06h     30468492       SUCCEEDED        0          1         29.0
201906151800       run_gridstatvx_24h     30468493       SUCCEEDED        0          1         20.0
201906151800       run_pointstatvx        30468423       SUCCEEDED        0          1        670.0

5.4.1.2. Launch the Rocoto Workflow Using a Script

Users who prefer not to automate their experiments can run the Rocoto workflow using the launch_FV3LAM_wflow.sh script provided. Simply call it without any arguments from the experiment directory:

cd $EXPTDIR
./launch_FV3LAM_wflow.sh

This script creates a log file named log.launch_FV3LAM_wflow in $EXPTDIR or appends information to the file if it already exists. The launch script also creates the log/FV3LAM_wflow.log file, which shows Rocoto task information. Check the end of the log file periodically to see how the experiment is progressing:

tail -n 40 log.launch_FV3LAM_wflow

In order to launch additional tasks in the workflow, call the launch script again; this action will need to be repeated until all tasks in the workflow have been launched. To (re)launch the workflow and check its progress on a single line, run:

./launch_FV3LAM_wflow.sh; tail -n 40 log.launch_FV3LAM_wflow

This will output the last 40 lines of the log file, which list the status of the workflow tasks (e.g., SUCCEEDED, DEAD, RUNNING, SUBMITTING, QUEUED). The number 40 can be changed according to the user’s preferences. The output will look similar to this:

CYCLE                    TASK                       JOBID        STATE   EXIT STATUS   TRIES  DURATION
======================================================================================================
201906151800        make_grid         druby://hfe01:33728   SUBMITTING             -       0       0.0
201906151800        make_orog                           -            -             -       -         -
201906151800   make_sfc_climo                           -            -             -       -         -
201906151800    get_extrn_ics         druby://hfe01:33728   SUBMITTING             -       0       0.0
201906151800   get_extrn_lbcs         druby://hfe01:33728   SUBMITTING             -       0       0.0
201906151800         make_ics                           -            -             -       -         -
201906151800        make_lbcs                           -            -             -       -         -
201906151800         run_fcst                           -            -             -       -         -
201906151800      run_post_00                           -            -             -       -         -
201906151800      run_post_01                           -            -             -       -         -
201906151800      run_post_02                           -            -             -       -         -
201906151800      run_post_03                           -            -             -       -         -
201906151800      run_post_04                           -            -             -       -         -
201906151800      run_post_05                           -            -             -       -         -
201906151800      run_post_06                           -            -             -       -         -

Summary of workflow status:
~~~~~~~~~~~~~~~~~~~~~~~~~~

  0 out of 1 cycles completed.
  Workflow status:  IN PROGRESS

If all the tasks complete successfully, the “Workflow status” at the bottom of the log file will change from “IN PROGRESS” to “SUCCESS”. If certain tasks could not complete, the “Workflow status” will instead change to “FAILURE”. Error messages for each task can be found in the task log files located in $EXPTDIR/log.

The workflow run is complete when all tasks have “SUCCEEDED”, and the rocotostat command outputs a table similar to the one above.

5.4.1.3. Launch the Rocoto Workflow Manually

Load Rocoto

Instead of running the ./launch_FV3LAM_wflow.sh script, users can load Rocoto and any other required modules manually. This gives the user more control over the process and allows them to view experiment progress more easily. On Level 1 systems, the Rocoto modules are loaded automatically in Step 5.3.1. For most other systems, users can load a modified wflow_<platform> modulefile, or they can use a variant on the following commands to load the Rocoto module:

module use <path_to_rocoto_package>
module load rocoto

Some systems may require a version number (e.g., module load rocoto/1.3.3)

Run the Rocoto Workflow

After loading Rocoto, cd to the experiment directory and call rocotorun to launch the workflow tasks. This will start any tasks that do not have a dependency. As the workflow progresses through its stages, rocotostat will show the state of each task and allow users to monitor progress:

cd $EXPTDIR
rocotorun -w FV3LAM_wflow.xml -d FV3LAM_wflow.db -v 10
rocotostat -w FV3LAM_wflow.xml -d FV3LAM_wflow.db -v 10

The rocotorun and rocotostat commands above will need to be resubmitted regularly and repeatedly until the experiment is finished. In part, this is to avoid having the system time out. This also ensures that when one task ends, tasks dependent on it will run as soon as possible, and rocotostat will capture the new progress.

If the experiment fails, the rocotostat command will indicate which task failed. Users can look at the log file in the log subdirectory for the failed task to determine what caused the failure. For example, if the make_grid task failed, users can open the make_grid.log file to see what caused the problem:

cd $EXPTDIR/log
vi make_grid.log

Note

If users have the Slurm workload manager on their system, they can run the squeue command in lieu of rocotostat to check what jobs are currently running.

5.4.2. Run the Workflow Using Stand-Alone Scripts

The regional workflow can be run using standalone shell scripts in cases where the Rocoto software is not available on a given platform. If Rocoto is available, see Section 5.4.1 to run the workflow using Rocoto.

Attention

When working on an HPC system, users should allocate a compute node prior to running their experiment. The proper command will depend on the system’s resource manager, but some guidance is offered in Section 3.1.4. It may be necessay to reload the regional workflow (see Section 5.3.1). It may also be necessary to load the build_<platform>_<compiler> scripts as described in Section 4.4.2.

1. cd into the experiment directory. For example, from ush, presuming default directory settings:

   cd ../../expt_dirs/test_community
   

2. Set the environment variable $EXPTDIR for either bash or csh, respectively:

   export EXPTDIR=`pwd`
   setenv EXPTDIR `pwd`
   

3. Set the PDY and cyc environment variables. PDY refers to the first 8 characters (YYYYMMDD) of the DATE_FIRST_CYCL variable defined in the config.yaml. cyc refers to the last two digits of DATE_FIRST_CYCL (HH) defined in config.yaml. For example, if the config.yaml file defines DATE_FIRST_CYCL: '2019061518', the user should run:

   export PDY=20190615 && export cyc=18
   

   before running the wrapper scripts.

4. Copy the wrapper scripts from the ush directory into the experiment directory. Each workflow task has a wrapper script that sets environment variables and runs the job script.

   cp <path/to>/ufs-srweather-app/ush/wrappers/* .
   

5. Set the OMP_NUM_THREADS variable.

   export OMP_NUM_THREADS=1
   

6. Run each of the listed scripts in order. Scripts with the same stage number (listed in Table 5.7) may be run simultaneously.

   ./run_make_grid.sh
   ./run_get_ics.sh
   ./run_get_lbcs.sh
   ./run_make_orog.sh
   ./run_make_sfc_climo.sh
   ./run_make_ics.sh
   ./run_make_lbcs.sh
   ./run_fcst.sh
   ./run_post.sh
   

Each task should finish with error code 0. For example:

End exregional_get_extrn_mdl_files.sh at Wed Nov 16 18:08:19 UTC 2022 with error code 0 (time elapsed: 00:00:01)

Check the batch script output file in your experiment directory for a “SUCCESS” message near the end of the file.

Table 5.7 List of tasks in the regional workflow in the order that they are executed. Scripts with the same stage number may be run simultaneously. The number of processors and wall clock time is a good starting point for Cheyenne or Hera when running a 48-h forecast on the 25-km CONUS domain. For a brief description of tasks, see Table 5.5.

Stage/	Task Run Script	Number of Processors	Wall Clock Time (H:mm)
1	run_get_ics.sh	1	0:20 (depends on HPSS vs FTP vs staged-on-disk)
1	run_get_lbcs.sh	1	0:20 (depends on HPSS vs FTP vs staged-on-disk)
1	run_make_grid.sh	24	0:20
2	run_make_orog.sh	24	0:20
3	run_make_sfc_climo.sh	48	0:20
4	run_make_ics.sh	48	0:30
4	run_make_lbcs.sh	48	0:30
5	run_fcst.sh	48	0:30
6	run_post.sh	48	0:25 (2 min per output forecast hour)

Users can access log files for specific tasks in the $EXPTDIR/log directory. To see how the experiment is progressing, users can also check the end of the log.launch_FV3LAM_wflow file from the command line:

tail -n 40 log.launch_FV3LAM_wflow

Hint

If any of the scripts return an error that “Primary job terminated normally, but one process returned a non-zero exit code,” there may not be enough space on one node to run the process. On an HPC system, the user will need to allocate a(nother) compute node. The process for doing so is system-dependent, and users should check the documentation available for their HPC system. Instructions for allocating a compute node on NOAA HPC systems can be viewed in Section 3.1.4 as an example.

Note

On most HPC systems, users will need to submit a batch job to run multi-processor jobs. On some HPC systems, users may be able to run the first two jobs (serial) on a login node/command-line. Example scripts for Slurm (Hera) and PBS (Cheyenne) resource managers are provided (sq_job.sh and qsub_job.sh, respectively). These examples will need to be adapted to each user’s system. Alternatively, some batch systems allow users to specify most of the settings on the command line (with the sbatch or qsub command, for example).