SkyLab experiments: Parameters and description

This page describes which parameters are available in SkyLab experiments, and the implementation of the four flagship experiments ran by the JSCDA team. You can see results from these on the JCSDA SkyLab website.

All the parameters for these experiments are contained within one yaml file, and running the create_experiment.py command sets up the generic workflow (EWOK: Experiments and Workflows Orchestration Kit) for this experiment.

More information about how to build and run SkyLab is available here: Running SkyLab.

The experiment yaml has to contain the following information:

workflow engine: # workflow manager, only `ecworkflow` is supported at the moment

workdir: # path to tmp work directory
flowdir: # path to tmp scripts directory

suite: # Choice between including path to `cyclingDA.py`, `skylab.py` (full DA), `hofx.py`, `forecast.py`..
model: # Choice between `qg`, `mpas`, `gfs`, `gfs_aero`, `geos_cf`, `ufs`..
model_path: # Path to model repository
modeltasks:  # Path to model specific tasks (get backgrounds, ..)
init_cycle: # Date for the first cycle of the experiment
last_cycle: # Date for the last cycle of the experiment
step_cycle: # Step between cycles
init_exp: # Reference for the first background to be read from DB, to initialize the experiment
initype: # Optional, `fc` will initialize from forecasts or from analysis

cost_function: # Cost function, choice between `3D-Var`, `4D-Var`, `3D-FGAT`,
window_length: # Window length
window_offset: # Offset between analysis and window start time
window_mode: # `3d` or `4d` depending on the cost function
cycling mode: # Optional: `non cycling, (will read backgrounds from provider instead of cycling)

forecast_length: # Forecast length after the variational task
forecast_output_frequency: # Forecast output frequency

obs_sampling: # Optional, `coarse` if the coarse observation file exists
OBSERVATIONS:
- Observation 1 # yaml configuration for observation type 1
- Observation 2 # yaml configuration for observation type 2

init_exp_obsbias: # Reference for the first obs bias to be read from DB
init_model_obsbias: # Model obs bias has been created from, e.g. `gfs`
init_provider_obsbias: # Provider obs bias has been created from. e.g. `noaa`
obsbias cycling mode: # Optional, `non cycling` will read bias correction from file at each cycle instead of cycling

AN_VARIABLES: # yaml configuration, list of analysis variables
BG_VARIABLES: #  yaml configuration, list of background variables
MODEL_VARIABLES: # yaml configuration, list of model variables

GEOMETRY: # yaml configuration, geometry and resolution for the outer loop, typically model dependent
MODEL: # yaml configuration, model to run in the forecast (and var steps if needed)

static_data: # Path to static files directory, e.g. static B, model files, or other needed files not found in r2d2 or elsewhere.
background_error_type: # Type of background error matrix, options are `hybrid`, `ensemble`, `static`
# ensemble B component parameters
BACKGROUND_ERROR_LOCALIZATION: # Yaml file for localizations
members: # Number of members in the ensemble B matrix
ensb_weight: # Weight of ensemble B matrix
ensemble_exp: # Reference to read ensemble backgrounds from DB
# static B component parameters
BACKGROUND_ERROR: # Yaml file for static B
staticb_weight: # Weight of static B

MINIMIZER: {algorithm: DRPLanczos} # Options are all the minimizers available in OOPS
ninner: 10 # Maximum number of iterations in quadratic minimization
reduc: 0.05 # Target norm reduction in quadratic minimization
MIN_GEOMETRY: # yaml configuration, geometry and resolution inside the quadratic minimization
LINEAR_MODEL: # yaml configuration, linear model to run in 4D-Var applications
diag: ombg # diagnostics in obs space, can be `oman`, `ombg`..

plots: # yaml configuration, activates log based and model-space plots

To add a new experiment we recommend starting from an existing experiment yaml file and modify it for your case to reduce the chance of introducing syntax errors.

Light versions of these experiments are also available. Using the same dates, algorithm, model, observations and observation operators, and background. Users can run them on a local machine (look for experiment-name-small.yaml).

1. skylab-aero.yaml

The skylab-aero experiment runs an EDA with 3 members at a c96 resolution, for 17 days in August 2021. At the moment it is non cycling and running the ID model (as a placeholder for future gfs-aero model integration). It is using a static B and a 3D-Var cost function. The four instruments being assimilated are:

  • viirs_npp

  • viirs_n20

  • modis_aqua

  • modis_terra

2. skylab-atm-land.yaml

The skylab-atm-land experiment runs a full DA system (deterministic and EDA) with 25 members at a c384 resolution for outer loops and c192 for inner loops, for 30 days in February-March 2022. It is non cycling and currently running the FV3-LM model (as a placeholder for future UFS model integration).

It is using a hybrid B matrix and 3D-Var cost functions for both the deterministic and the EDA. The observations currently assimilated are:

  • radiosonde_prepbufr

  • windborne

  • aircraft_prepbufr

  • satwinds_ssec_amv

  • buoy_ldm

  • synop_ldm

  • metar_ldm

  • ship_ldm

  • scatwind

  • gnssro_planetiq

  • gnssro_noaa_comm

  • gnssro

  • gnssro_spire

  • amsua_n19

  • amsua_n18

  • amsua_n15

  • amsua_metop-c

  • amsua_metop-b

  • cris-fsr_npp

  • cris-fsr_n20

  • iasi_metop-b

  • iasi_metop-c

  • atms_npp

  • atms_n20

  • mhs_n19

  • mhs_metop-c

  • mhs_metop-b

  • tms_tropics-01 (currently monitored only)

  • abi_g16_bt_64km

  • abi_g17_bt_64km

3. skylab-marine.yaml

The skylab-marine experiment runs a single DA system at 0.25 degrees resolution for outer loops and inner loops, for 30 days in August 2021. It is non cycling (waiting for future MOM6 model integration).

It is using a static B matrix and 3D-Var cost function. The observations currently assimilated are:

  • adt_3a

  • adt_3b

  • adt_c2

  • adt_j3

  • adt_sa

  • sst_avhrr_metop-b

  • sst_avhrr_metop-c

  • ocean_profile

  • icec_ssmis_f17

  • icec_ssmis_f18

4. skylab-trace-gas.yaml

The skylab-trace-gas experiment runs a single DA system at c90 resolution for outer loops and inner loops, for 10 days in August 2021. It is non cycling and running the ID model (as a placeholder for the geos-cf model integration).

It is using a static B matrix and 3D-Var cost function. The observations currently assimilated are:

  • tropomi_s5p_no2_tropo or tropomi_s5p_no2_total

  • mopitt_terra_co_total

  • tropomi_s5p_co_total