Presenter(s): Barry Baker, NOAA Air Resources Laboratory
The history and development of the NOAA FENGSHA dust emission scheme.

Aerosols have both direct and indirect effects on meteorology, atmospheric chemistry, human health, and ultimately the global energy budget with dust being a major contributor to the atmospheric aerosol burden. A great effort has been made to characterize the sources and mobilization of dust, however, current models still show large uncertainty as the modeling of mineral dust in the atmosphere is complex. The FENGSHA dust emission model, implemented into the operational NOAA National Air Quality Forecast Capability (NAQFC), and the new Global Ensemble Forecast System with Aerosols (GEFS-Aerosols), is a flexible emission model capable of predicting dust emissions across forecast scales. Here we will discuss the history and development of the FENGSHA dust emission scheme and its applications in the various operational and developmental UFS applications as well as tangential developments in tracking the erodibility of soils and the dust record.

Dr. Barry Baker is a physical research scientist at NOAA Air Resources Laboratory. He received his PhD from the University of Maryland Baltimore County (UMBC) and later received a postdoctoral fellowship through the National Research Council. He is now also on the global steering committee for the WMO Sand and Dust Storm group.

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Presenter(s): Dr. Zhan Zhang, Dr. Xuejin Zhang
A new operational hurricane prediction system for NOAA: UFS-based Hurricane Analysis and Forecast System (HAFS)

Hurricane Analysis and Forecast System (HAFS) is a Unified Forecast System(UFS) based hurricane application developed in the UFS R2O project. It is an atmosphere-ocean-wave coupled Tropical Cyclone prediction system. It has five salient features: (1) storm-following telescopic moving nests, (2) high-resolution physics configured for TC application, (3) storm inner-core Data Assimilation (DA) with vortex initialization, (4) atmosphere-ocean-wave coupling framework, and (5) intensive hurricane observational platforms to support the storm-scale DA system as well as the physics calibrations and system verifications/validations. HAFS is scheduled to become operational on June 27, 2023 to replace NOAA’s existing operational TC forecast systems, HWRF and HMON.

In this talk, we will present detailed configuration of the first version of HAFS operational implementation and the scientific evaluation of the three-year (2020-2022) retrospective experiments. We will also outline the continuous development activities of HAFS in the Hurricane Integrated Application Team and the future plans for HAFS in the next year and beyond.

Dr. Zhan Zhang obtained his Ph.D degree in tropical meteorology from Florida State University. He has worked at various research and operational organizations. He is the Hurricane Modelling team lead at NCEP/EMC, and the co-lead of NOAA’s Unified Forecast System (UFS) research and operation (R2O) Hurricane Application Team. He specializes in Numerical Weather Prediction modeling, especially in tropical cyclone modeling. His research interests include hurricane ensemble forecasts, vortex initialization, hurricane inner-core data assimilation.

Dr. Xuejin Zhang is a Meteorologist employed in NOAA’s Atlantic Oceanographic and Meteorological Laboratory’s Hurricane Research Division. He studies tropical cyclone forecast and simulation, land-air-sea interaction, regional climate, and parallel computing during his more than two-decade career. His expertise is in numerical algorithms, atmospheric dynamics, model initialization, and microphysics parameterization. He is currently leading the NOAA’s Unified Forecast System (UFS) R2O Hurricane Application Team. He obtained his Ph.D. in NC State University.

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Presenter: Patrick C. Burke
The vision of Warn-on-Forecast is to enable extended lead time for individual local severe weather hazards through design of new storm-scale probabilistic tools. Namely, the Warn-on-Forecast System (WoFS) employs rapid data assimilation, rapid forecast updates, and visualization of output at 5-minute resolution to provide movie-like probabilistic forecasts of individual storms on the 0-6 hour, “watch-to-warning” scale. Operating a relocatable, 900-km squared regional domain since 2017, the experimental 3-km WoFS has been shown to improve forecasts in testbed environments and to influence real-world decision-making and public threat communication at NWS national and local offices. WoFS migrated to the Microsoft Azure cloud in 2022, unlocking the capability to run multiple domains and/or larger domains. The NSSL double moment microphysics scheme designed in the WoF group is now available in the UFS, and WoFS is slated for an operational transition to the NWS, as part of the UFS, by late decade. Developing tools and processes within the UFS to support 5-15 minute data assimilation cycles, 30-minute relaunch cadence, and low-latency generation of unique ensemble visualizations will pave the way for future applications on a multitude of small-scale analysis and forecast problems.

Patrick Burke worked as a National Weather Service forecaster, including 5 years as a Lead Forecaster at the Weather Prediction Center. In 2020, he brought his operational experience to the Warn-on-Forecast (WoF) research group at the National Severe Storms Laboratory; Patrick is the WoF Program Lead. He holds an MS in meteorology from the University of Oklahoma. Growing up in the severe weather culture of Oklahoma he has always been motivated to help people stay safe during severe storm and flash flood events. In his spare time, Patrick enjoys running and writing music.
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Presenter: Dr. Ben Green
Subseasonal prediction remains a uniquely challenging problem because the timescale involved cannot take much advantage of the memory imparted by atmospheric initial conditions (leveraged for predictions shorter than ~2 weeks), or of the slowly-evolving boundary forcings (leveraged for predictions longer than ~3 months). Regardless, interest in subseasonal prediction has grown substantially over the past decade owing to the identification of so-called “forecasts of opportunity” and the potential benefits of these forecasts to numerous sectors of society. Recognizing the demand for subseasonal forecasts, NOAA has been developing a fully-coupled Earth system model under the Unified Forecast System (UFS) framework which will be responsible for global (ensemble) predictions at lead times of 0-35 days. The development has involved several prototype coupled UFS runs consisting of bimonthly initializations over a 7-year period for a total of 168 cases.

This webinar presents results from a study that leverages these existing baseline prototypes to isolate the impact of substituting (one-at-a-time) parameterizations for convection, microphysics, and boundary layer on 35-d forecasts. It is found that no particular configuration of atmospheric physics within the coupled UFS is uniformly better or worse for subseasonal prediction, based on several metrics including mean-state biases and skill scores for the Madden-Julian Oscillation, precipitation, and 2-m temperature. Importantly, the spatial patterns of many “first-order” biases (e.g., impact of convection on precipitation) are remarkably similar between the end of the first week and weeks 3-4, indicating that some subseasonal biases may be mitigated through tuning at shorter timescales. An additional convective parameterization test using a different baseline shows that attempting to generalize specific results within UFS may be misguided.

Dr. Ben Green is a Research Scientist at the University of Colorado CIRES and works at the NOAA Global Systems Laboratory. He has been affiliated with these institutions since 2015, when he joined the team as a postdoc. He is a member of the GSL subseasonal-to-seasonal branch and works on the coupled UFS.
His research interests focus on numerical modeling of the Earth system at multiple timescales, with a particular emphasis on parameterizations for air-sea interaction. Ben has his Bachelor, Master, and PhD degrees in meteorology from Penn State University.

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Presenter: Lucas Harris (NOAA/GFDL)
NOAA has unified around two world-leading modeling systems to carry out its minutes-to-millennia mission: the Unified Forecast System for weather applications and NWS operations, and the GFDL Seamless Modeling Suite for climate-focused prediction, projection, and understanding. These share several major community components, including the FV3 Dynamical Core and the MOM6 Ocean Model. The NOAA Research Global-Nest Initiative is a multi-institution partnership to develop multiscale "digital twins" of the earth system for medium-range and subseasonal prediction and for ultra-high resolution climate modeling. This initiative will take advantage of new capabilities in our foundational technologies (FV3, SHiELD, FMS, Pace, etc.) to create global-nested convective-scale models, global storm-resolving models, and experimental kilometer and sub-kilometer scale process models. Results on cloud-radiation-prediction interactions, hurricanes, severe weather, the Madden-Julian Oscillation, and more will be shown. Progress on public availability, technology transfer, simulation on GPUs, and on large-eddy scale modeling will also be described, as will how this effort is acting to improve and extend both the UFS and SMS.

Lucas Harris has been interested in the weather since he was five years old. He is the Deputy Division Leader of the Weather and Climate Dynamics Division at NOAA’s Geophysical Fluid Dynamics Laboratory. His research is focused on development and application of the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3) to create new models for frontier weather and climate problems. He holds a PhD in Atmospheric Sciences and an MS in Applied Mathematics, both from the University of Washington, and previously worked at Princeton University and NCAR.

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Presenter:  Tara Jensen is Project Manager II at NCAR/RAL
With the evolution of weather and climate prediction using the Unified Forecast System (UFS), verification and evaluation activities are critical for the success of Research to Operations (R2O) across the UFS community. The enhanced Model Evaluation Tools (MET) framework (METplus) is at the core of an expanding cross-section of the community evaluation activities.

The METplus system consists of several components for the computation of traditional statistics based on gridded forecasts and either a gridded analysis or point-based observations. The system also incorporates an analysis system for aggregating statistics and plotting graphical results. These tools are designed to be highly flexible to allow for quick adaption to meet additional evaluation and diagnostic needs. A suite of python wrappers have been implemented in METplus to facilitate a low-level automation and configuration of the system, and to enhance the pre-existing plotting capabilities.

This presentation will briefly highlight advances in the recent METplus coordinated 5.0.0 release. It will also address the challenges and successes of community contributions and established capability to facilitate the advancement of innovations through UFS stages and gates.

Tara Jensen is Project Manager II at NCAR/RAL and the Deputy for the NCAR node of the Developmental Testbed Center. For the past 8 years, she has been leading the DTC verification team in the development of the enhanced Model Evaluation Tools verification and diagnostics framework (METplus). This framework has its roots in Tropospheric weather and Tropical Cyclone verification but over the years has been extended to also specifically include metrics for applications such as Subseasonal to Seasonal, Atmospheric Composition, Marine and Cryosphere. Tara and her team have also been working with SWPC over the past three years to guide development of METplus for use in Space Weather model evaluation.
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