The First Hybrid NOAA Hazardous Weather Testbed Spring Forecasting Experiment for Advancing Severe Weather Prediction

Adam J. Clark NOAA/OAR National Severe Storms Laboratory, and School of Meteorology, University of Oklahoma, Norman, Oklahoma;

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Israel L. Jirak NOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma;

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Timothy A. Supinie NOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma;

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Kent H. Knopfmeier NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Jake Vancil NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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David Jahn NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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David Harrison NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Allison Lynn Brannan NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Christopher D. Karstens NOAA/NWS/NCEP Storm Prediction Center, and School of Meteorology, University of Oklahoma, Norman, Oklahoma;

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Eric D. Loken NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Nathan A. Dahl NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Makenzie Krocak NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma;

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David Imy NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma;

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Andrew R. Wade NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Jeffrey M. Milne NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, and School of Meteorology, University of Oklahoma, Norman, Oklahoma

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Kimberly A. Hoogewind NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Pamela L. Heinselman NOAA/OAR National Severe Storms Laboratory, and School of Meteorology, University of Oklahoma, Norman, Oklahoma;

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Montgomery Flora NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Joshua Martin NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Brian C. Matilla NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Joseph C. Picca NOAA/NWS/NCEP Storm Prediction Center, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Patrick S. Skinner NOAA/OAR National Severe Storms Laboratory, and Cooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma;

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Patrick Burke NOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma;

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Open access

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Adam J. Clark, adam.clark@noaa.gov

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Adam J. Clark, adam.clark@noaa.gov

The 2023 NOAA Hazardous Weather Testbed Spring Forecasting Experiment

What:

Within a hybrid framework of in-person and virtual participation, over 125 forecasters and researchers engaged in real-time, experimental severe weather forecasting activities and model evaluations, which included 1) the Rapid Refresh Forecast System, 2) the Model for Prediction Across Scales, 3) the Warn-on-Forecast System, and 4) innovative postprocessing strategies.

When:

1 May–2 June 2023

Where:

Norman, Oklahoma, and Online

After three years of fully virtual experiments, the 2023 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (SFE 2023) resumed in a hybrid format, with 50 of 127 participants contributing remotely. The hybrid format is ideal because virtual participation engages those unable to attend physically, while in-person participation enables science-based discussions and establishing new collaborations, thus expanding the SFE scope in the number and diversity of participants. SFEs, co-led by the NWS/Storm Prediction Center (SPC) and OAR/National Severe Storm Laboratory (NSSL), aim to accelerate research-to-operations (R2O) by testing new prediction capabilities, studying how end-users apply and interpret severe weather guidance, and conducting numerous model evaluations. SFE 2023 results have particular importance as NOAA’s Unified Forecast System (UFS) initiative moves forward with the Rapid Refresh Forecast System (RRFS), NOAA’s first formally designed convection-allowing model (CAM) ensemble, which is scheduled for operational implementation in 2025.

SFE 2023 forecasting activities included issuing experimental 1) severe weather outlooks for days 1–4 and 2) short-term mesoscale discussions using NSSL’s Warn-on-Forecast System (WoFS). Model evaluations used the 41-member Community Leveraged Unified Ensemble (CLUE; Clark et al. 2018), which included deterministic and ensemble components of RRFS, medium-range CAM ensembles with 7-day forecasts, the Model for Prediction Across Scales (MPAS), and a 1-km Weather Research and Forecasting Model configuration for tornado and severe wind forecasting. Other evaluations explored mesoscale analyses and machine learning (ML) approaches for generating severe weather guidance from CAMs and the Global Ensemble Forecast System (GEFS).

Some highlights of the 2023 SFE

In detailed blind (i.e., model labels were hidden) subjective and objective assessments, the RRFS had inferior performance relative to its operational deterministic and ensemble counterparts, the High-Resolution Rapid Refresh (HRRR) and High-Resolution Ensemble Forecast System (HREF), respectively. Participants noted issues with overforecast storm intensity and spurious convection, which tests at NSSL have shown are directly related to characteristics of the Finite Volume Cubed Sphere (FV3) model core used in RRFS. While similar performance was found between HRRR and RRFS in SFE 2022, the 2023 results were likely impacted by implementation of radar data assimilation in 2023, which introduced the problems with storm intensity and spurious convection near initialization time that were linked to errors at later hours in the forecast. These storm intensity issues were also present in 2022, but not fully manifest until the first diurnal convective maximum, so that errors had not had a chance to grow and significantly impact the day 1 forecast. Furthermore, 2023 results were likely affected by a relatively quiet weather regime with weakly forced events in which the RRFS issues are more apparent.

Because of irreconcilable issues NSSL identified with FV3 for rapid data assimilation and prediction at convection-allowing scales (i.e., grid spacing ≤ 3 km), NSSL began testing MPAS in early 2023 as a replacement for WRF in its next-generation WoFS. The tests consisted of three regional, CONUS-domain MPAS configurations to assess whether MPAS could simulate storms with the same fidelity as WRF. After this short development period, SFE 2023 evaluations indicated MPAS configurations performed very well. MPAS runs initialized from RRFS performed better than the RRFS itself, and MPAS performance was competitive with the HRRR. NOAA is in the process of leveraging these results to improve future versions of RRFS.

SFE 2023 included assessment of 10-member FV3-based and 5-member MPAS ensembles with forecasts to 7 and 5 days, respectively. In comparisons of FV3-based and MPAS five-member ensembles at day 3–5 lead times, the MPAS ensemble received significantly higher mean subjective ratings. Nonetheless, at times, forecast value was noted in the FV3-based CAM ensembles out to day 7.

More information on the 2023 SFE

In this first hybrid SFE, progress was made in key areas to accelerate R2O and assist evidence-based decision-making for models and tools that improve operational severe weather forecasts. The full 2023 SFE Summary Report is at https://hwt.nssl.noaa.gov/sfe/2023/docs/HWT_SFE_2023_Prelim_Findings_v1.pdf.

Acknowledgments.

This work comprised regular duties at federally funded NOAA/NSSL for Clark, Imy, Heinselman, and Burke, and NOAA/SPC for Jirak, Supinie, and Karstens. Knopfmeier, Vancil, Jahn, Harrison, Brannan, Loken, Dahl, Krocak, Wade, Milne, Hoogewind, Flora, Martin, Matilla, Picca, and Skinner were funded by NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma (OU) Cooperative Agreement NA21OAR4320204, U.S. Department of Commerce. OU’s Institute for Public Policy Research and Analysis also funded Krocak.

Reference

Clark, A. J., and Coauthors, 2018: The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 99, 14331448, https://doi.org/10.1175/BAMS-D-16-0309.1.

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  • Clark, A. J., and Coauthors, 2018: The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. Bull. Amer. Meteor. Soc., 99, 14331448, https://doi.org/10.1175/BAMS-D-16-0309.1.

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