The Second Real-Time, Virtual Spring Forecasting Experiment to Advance Severe Weather Prediction

Details

• Journal Title:
  Bulletin of the American Meteorological Society
• Personal Author:
  Clark, Adam J. ; Jirak, Israel L. ; Gallo, Burkely T. ; Knopfmeier, Kent H. ; Roberts, Brett ; Krocak, Makenzie ; Vancil, Jake ; Hoogewind, Kimberly A. ; Dahl, Nathan A. ; Loken, Eric D. ; Jahn, David ; Harrison, David ; Imy, David ; Burke, Patrick ; Wicker, Louis J. ; Skinner, Patrick S. ; Heinselman, Pamela L. ; Marsh, Patrick ; Wilson, Katie A. ; Dean, Andrew R. ; Creager, Gerald J. ; Jones, Thomas A. ; Gao, Jidong ; Wang, Yunheng ; Flora, Montgomery ; Potvin, Corey K. ; Kerr, Christopher A. ; Yussouf, Nusrat ; Martin, Joshua ; Guerra, Jorge ; Matilla, Brian C. ; Galarneau, Thomas J.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction) ; SPC (Storm Prediction Center) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies)
• Description:
  The 2021 NOAA Hazardous Weather Testbed Spring Forecasting Experiment (2021 SFE) was held virtually 3 May–4 June 2021. SFEs are co-led by the NWS/Storm Prediction Center (SPC) and OAR/National Severe Storms Laboratory (NSSL), and test new severe weather prediction tools, study how end users apply severe weather guidance to forecasting, and facilitate experiments for optimizing convection-allowing model (CAM) ensemble design informing Unified Forecast System (UFS; https://ufscommunity.org) development. Well-vetted virtual meeting tools enabled the most SFE participation ever: 133 forecasters, researchers, and students from around the world. Gathering a diverse community to learn from one another has been a hallmark of SFEs that has continued in virtual experiments. However, science-based discussions and establishing new collaborations are more difficult in the virtual environment. Nevertheless, lessons learned could benefit a future hybrid approach involving both in-person and virtual participation.
• Keywords:
  Machine Learning Weather Forecasting Machine Learning Weather Forecasting Ensembles Forecast Verification Model Evaluation Operational Forecasting Postprocessing
• Source:
  Bulletin of the American Meteorological Society, 103(4), E1114-E1116
• DOI:
  https://doi.org/10.1175/BAMS-D-21-0239.1
• Document Type:
  Journal Article
• Funding:
  Grant no. NA11OAR4320072
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:4da8dce2d19487e56b8f463b6681259b6e644b8cb8e53c22d2838092abd5c4a1
• Download URL:
  https://repository.library.noaa.gov/view/noaa/45430/noaa_45430_DS1.pdf
• File Type:
  [PDF - 235.58 KB ]