WoFS Ensemble Configuration

Up through and including 2021, WoFS has used the Advanced Weather Research and Forecast (WRF-ARW core) dynamic solver developed in collaboration with NCAR and Global Systems Laboratory (GSL). The WRF code used by WoFS in 2021 is based on the code used by the High-Resolution Rapid Refresh (HRRR) model in 2020, and is modified to meet the requirements for accurate short-term (0–6 hour) forecasts. One modification is the use of NSSL double moment cloud microphysics.

WoFS uses data assimilation tools developed by the WoF research team for ensemble Kalman Filter assimilation of high-resolution Multi-Radar, Multi-Sensor radar reflectivity and radial velocity data and satellite data. The 2021 configuration of WoFS consists of a 36-member ensemble with a regional domain (900 x 900 km) centered around the area of expected high impact weather for a particular day. WoFS operates at a convection-allowing 3-km horizontal resolution with 15-min data assimilation intervals, and assimilates available WSR-88D radar data, Geostationary Operational Environmental Satellite (GOES)-16 and 17 satellite, and conventional observations. High resolution radar and satellite-derived observations are resampled from their native resolution to a 5-km grid prior to assimilation.

Initial and lateral boundary conditions (ICs/LBCs) are generated from an experimental HRRR Ensemble analysis and forecast developed by collaborators from GSL. Each of the 36 WoFS ensemble members has a unique combination of IC/LBCs, boundary layer physics scheme, and radiation scheme, which helps to maintain ensemble spread through the cycling period. Outlier thresholds are applied to reduce the impact of potential non-representative observations to the system.

Forecasts are generated using the first 18 members, at selected times, to create probabilistic high impact weather forecasts, which provide a measure of uncertainty not available from a single deterministic forecast. The experimental WoFS is run over a limited region of the country, about the size of the four corners states (Utah, Colorado, Arizona, and New Mexico), and is only run on select days of interest or during organized experiments. Note that the model domain can be moved around to anywhere in the lower 48 United States that severe weather is expected.

WoF Hybrid:

In parallel with the baseline WoFS, there is an additional deterministic forecast member, called WoF Hybrid, which is of higher resolution and is built from a different modeling approach. WoF Hybrid is an efficient, weather-adaptive, hybrid three-dimensional variational and Ensemble Kalman Filter analysis and forecast system. The system incorporates flow-dependent background error covariances estimated from the ensemble forecasts of the baseline WoFS, but provides a 1.5km deterministic analysis and forecast component that can be regarded as a complement to the baseline WoFS. One can think of this as mirroring the way that other coupled model systems attempt to provide one high-resolution, skillful, control forecast member to complement the associated ensemble (e.g., GFS and GEFS). Keep in mind that WoF Hybrid is produced in a different manner than the baseline WoF System, and its output is not incorporated into probabilistic fields from the baseline system. Images below illustrate how to access output from WoF Hybrid on the WoFS web viewer.

wofs hybrid configuration
wofs hybrid configuration

Future configurations:

The WoFS effort has enjoyed a period of success using a stable configuration from 2018-2021, but modeling within NOAA continues to evolve. To remain consistent with the Unified Forecast System and associated changes taking place at NSSL’s partner laboratory, GSL, we will undergo a period of exploratory research to create a new WoFS that uses the Finite-Volume Cubed-Sphere (FV3) dynamic core and uses ICs/BCs from the new Rapid Refresh Forecast System (RRFS). The switch to new ICs/BCs will take place in 2022, and it is hoped that a test version using FV3 will also be available.

Workflow diagrams for WoFS:

Below is a flowchart of the workflow for a) WoFS and b) WoF Hybrid. The 36-member WoFS and its companion model, WoF Hybrid, are continuously cycled every 15 minutes. Forecasts from both systems, launched at the top of each hour, are projected 6-h, and forecasts from WoFS, launched at the bottom of each hour, are projected 3-h. All WoFS forecasts are derived from 18 ensemble members. HRRRE members provide boundary conditions for WoFS, and the HRRRE control member provides boundary conditions for WoF Hybrid. HRRRE also provides initial background fields for both systems.

wofs workflow diagram
wofs workflow diagram

Table comparing WoFS, WoF Hybrid, and HRRRE in 2021:

  HRRRE WoFS WoF Hybrid
Model Version WRF-ARW v3.9+ WRF-ARW v3.9+ WRF-ARW v3.9+
Grid Dimensions 1800 x 1060 x 50 300 x 300 x 50 600 x 600 x 50
Grid Resolution 3 km 3 km 1.5 km
EnKF cycling 36-mem. w/ GSI-EnKF every 1 hr 36-mem. w/ GSI-EnKF every 15 min One deterministic DA cy*cle every 15 min
  • Prepbufr conventional observations
  • MRMS radar reflectivity
  • Prepbufr conventional observations
  • Oklahoma Mesonet (when available)
  • MRMS reflectivity 20 dBZ; radar ‘zeroes’
  • MRMS radial velocity (3-km grid w/mask)
  • GOES-16 cloud-water path
  • GOES-16 clear sky radiances
  • Prepbufr conventional observations
  • Oklahoma Mesonet (when available)
  • Raw radar reflectivity 15 dBZ; radar ‘zeroes’
  • Raw radial velocity
  • GOES-16 GLM data
  • Radar data derived VAD profile
Microphysics Thompson (aerosol aware) NSSL double-moment NSSL double-moment
LSM RUC (Smirnova) RUC (Smirnova) RUC (Smirnova)

The table compares configuration between the HRRRE and WoFS ensembles and the WoF Hybrid single member, deterministic model. There are many similarities, and HRRRE forecasts are used as initial conditions and lateral boundary conditions for WoFS. WoF Hybrid, in turn, derives its initial conditions from WoFS. Note that WoFS and WoF Hybrid assimilate a greater variety of observational data than HRRRE, including WSR-88D radial velocity from MRMS and GOES-16 satellite data, and use NSSL double moment microphysics, which can generate more accurate model depictions of convection by better representing hydrometeor distributions.


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