Details:

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Carpenter, Noah T. ; Gao, Jidong ; Clark, Adam ; Burke, Patrick ; Skinner, Patrick ; ... More +

  Carpenter, Noah T. ; Gao, Jidong ; Clark, Adam ; Burke, Patrick ; Skinner, Patrick ; Wang Yunheng ; Knopfmeier, Kent ; Pan, Sijie ; Matilla, Brian ; Martin, Joshua Less -
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; GSL (Global Systems Laboratory) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations)

  OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; GSL (Global Systems Laboratory) ; CIWRO (Cooperative Institute for Severe and High-Impact Weather Research and Operations) Less -
• Description:
  This study compares real-time forecasts produced by the Warn-on-Forecast System (WoFS) and a hybrid ensemble and variational data assimilation and prediction system (WoF-Hybrid) for 31 events during 2021. Object-based verification is used to quantify and compare strengths and weaknesses of WoFS ensemble forecasts with 3-km horizontal grid spacing and WoF-Hybrid deterministic forecasts with 1.5-km horizontal grid spacing. The goal of such comparison is to provide evidence as to whether WoF-Hybrid has performance characteristics that complement or improve upon those of WoFS. Results indicate that both systems provide similar accuracy for timing and placement of thunderstorm objects identified using simulated reflectivity. WoF-Hybrid provides more accurate forecasts of updraft helicity tracks. Differences in forecast quality are case dependent; the largest difference in accuracy favoring WoF-Hybrid occurs in eight cases identified as “high-impact” by the quantity of National Weather Service Local Storm Reports, while WoFS performance is favored at short lead times for 10 “moderate-” and 13 “low-impact” events. WoF-Hybrid reflectivity objects are closer in size and location to observed objects. However, a higher thunderstorm overprediction bias is identified in WoF-Hybrid, particularly early in the forecast. Two severe weather events are selected for detailed investigation. In the case of 26 May, both systems had similar skill; however, for 10 December, WoF-Hybrid forecasts significantly outperformed WoFS forecasts. These results show improved performance for WoF-Hybrid over WoFS under certain regimes that warrants further investigation. To understand reasons for these differences will help incorporate higher-resolution modeling into Warn-on-Forecast systems.
• Keywords:
  [+]

  Numerical Weather Forecasting Weather Forecasting Data Assimilation Model Evaluation Model Performance Numerical Weather Prediction Short-range Prediction
• Source:
  Wea. Forecasting, 40, 451–469
• DOI:
  https://doi.org/10.1175/WAF-D-24-0143.1
• Format:
  PDF
• Document Type:
  Journal Article
• Funding:
  Grant no. NA11OAR4320072
• Rights Information:
  Other
• Compliance:
  Submitted
• Main Document Checksum:
  [+]

  urn:sha-512:17f8406d4321efc1e1ed90bf59c5147044088229e64c74333491aa3cc3ce8c07d1f0c8797a713ef9ee3ba9ea5119585a94f4fed26adf31a942aa620fc7ec990d
• File Type:

• Supporting Files:
  No Additional Files