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Bridges to Operations: Initial Operational Implementation of the UFS Hurricane Application

Spring 2023

The initial operational implementation of the Hurricane Analysis and Forecast System (HAFS) for tropical cyclone (TC) forecasting was recently approved for operations by the NOAA National Centers for Environmental Prediction (NCEP) Central Operations (NCO) in advance of the 2023 Atlantic basin hurricane season. HAFS is the operational instantiation of the Unified Forecast System (UFS) hurricane application, with a focus on transitioning TC modeling research to operations. HAFS is an atmosphere-ocean-wave coupled TC forecast system, featuring convection-allowing high-resolution storm-following nests, vortex initialization, inner-core data assimilation, and TC-calibrated model physics.

The development of HAFS began in 2019, when increasingly complex configurations of HAFS (HAFSv0.0 through HAFSv0.3) were run and evaluated during the Hurricane Forecast Improvement Project (HFIP) real-time demonstration (see figure below), providing the groundwork for HAFSv1.0.

Timeline of HAFS development, adapted from EMC.

For the initial operational capability of HAFS (HAFSv1.0), two distinct configurations were selected to replace the existing regional operational hurricane forecast systems, Hurricane Weather Research and Forecast (HWRF) and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic Model (HMON). The HAFSv1.0a (HFSA) configuration will replace HWRF; whereas the HAFSv1.0b (HFSB) configuration will replace HMON. Both HAFS configurations include a storm-centric domain with one moving nest at 6 km and 2 km, respectively, 81 vertical levels, and a 2-hPa model top. Additionally, both configurations employ four-dimensional ensemble variational (4DEnVar) data assimilation with warm-cycling vortex initialization (VI) and two-way HYbrid Coordinate Ocean Model (HYCOM) ocean coupling.

For HFSA, unique features include a slightly larger parent domain than HFSB, one-way wave (WAVEWATCH III, WW3) coupling, a greater maximum wind threshold for VI, and up to seven storms run for all global basins. The HFSB configuration does not include wave coupling, and runs up to five storms for National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) basins only. In order to provide additional diversity, HFSA and HFSB apply two different physics suites. The major differences between the HFSA and HFSB physics suites stem from the planetary boundary layer (PBL) and microphysics schemes, with the HFSB configuration employing an enhanced TC PBL option (Chen et al. 2022) and the Thompson double-moment microphysics scheme, as opposed to the GFDL single-moment microphysics for the HFSA configuration. Both HAFS configurations demonstrated improved track and intensity skill relative to the current operational hurricane models over a 3-year retrospective period covering all storms in the Atlantic and Eastern Pacific basins, leading to the operational implementation of HAFS as part of NOAA NCO.

HAFS demonstrated improved track and intensity skill relative to the current operational hurricane models over a 3-year retrospective period covering all storms in the Atlantic and Eastern Pacific basins, leading to the operational implementation of HAFS as part of NOAA NCO. This significant milestone was achieved through a collaborative effort.

Throughout this process, the DTC provided software management and community support to ensure that distributed development efforts for HAFS were achievable through strong code governance and a developer support system. In addition to their community engagement role, DTC also conducts independent testing and evaluation (T&E), which focuses on providing value-added evaluations for HAFS forecasts during the pre-implementation process. For HAFSv1.0, the DTC used the enhanced Model Evaluation Tools (METplus) to provide TC-centric evaluations such as track, intensity, rapid intensification, large-scale, and quantitative precipitation forecast (QPF) verification. These evaluations provided additional evidence and support for the HAFSv1.0 implementation into operations. For example, the evaluation provided confidence in the implementation of Thompson microphysics for one HAFS configuration based on the improved precipitation structure when compared to that produced by the GFDL microphysics.

To further support these efforts, the DTC Visitor Program is supporting three community principal investigators (PI) to work on projects aimed at the transition of research developments to operations and ultimately improved HAFS forecasts. Projects by Andrew Hazelton (Atlantic Oceanographic and Meteorological Laboratory (AOML)/Hurricane Research Division (HRD) and University of Miami (UM)/Cooperative Institute for Marine and Atmospheric Studies (CIMAS)), Mike Iacono and John Henderson (Atmospheric and Environmental Research), and Shaowu Bao (Coastal Carolina University) all aim to provide improvements and diversity to the physics parameterizations used in the HAFS configurations. As preparations for HAFSv2.0 are gearing up, these visitor projects, aligned with continued DTC T&E activities, are well positioned to impact the next HAFS implementation, planned for 2024. 

This significant milestone was achieved through a collaborative effort, led by NOAA NCEP’s Environmental Modeling Center (EMC), including active development and verification efforts from 

  • NOAA Atlantic Oceanographic and Meteorological Laboratory's Hurricane Research Division and Physics Oceanography Divisions,
  • NOAA Geophysical Fluid Dynamics Laboratory, 
  • NOAA National Hurricane Center,
  • Developmental Testbed Center, 
  • National Center for Atmospheric Research, 
  • Naval Research Laboratory, 
  • University of Oklahoma, 
  • University of Miami / Cooperative Institute for Marine and Atmospheric Studies, 
  • University of Maryland, 
  • State University of New York  / University at Albany, and
  • University of Alabama Huntsville.

For more news related to HAFS, see the Lead Story article: The CCPP Goes Operational.

Contributed by Kathryn Newman and Linlin Pan