Blended Probabilistic Tornado Forecasts Combining Climatological Frequencies with NSSLWRF Ensemble Forecasts

Details

• Journal Title:
  Weather and Forecasting
• Personal Author:
  Gallo, B. T. ; Clark, A. J. ; Smith, B. T. ; Thompson, R. L. ; Jirak, I. ; Dembek, S. R.
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research) ; NSSL (National Severe Storms Laboratory) ; CIMMS (Cooperative Institute for Mesoscale Meteorological Studies) ; NWS (National Weather Service) ; NCEP (National Centers for Environmental Prediction)
• Description:
  Attempts at probabilistic tornado forecasting using convection-allowing models (CAMs) have thus far used CAM attribute [e.g., hourly maximum 2–5-km updraft helicity (UH)] thresholds, treating them as binary events—either a grid point exceeds a given threshold or it does not. This study approaches these attributes probabilistically, using empirical observations of storm environment attributes and the subsequent climatological tornado occurrence frequency to assign a probability that a point will be within 40 km of a tornado, given the model-derived storm environment attributes. Combining empirical frequencies and forecast attributes produces better forecasts than solely using mid- or low-level UH, even if the UH is filtered using environmental parameter thresholds. Empirical tornado frequencies were derived using severe right-moving supercellular storms associated with a local storm report (LSR) of a tornado, severe wind, or severe hail for a given significant tornado parameter (STP) value from Storm Prediction Center (SPC) mesoanalysis grids in 2014–15. The NSSL–WRF ensemble produced the forecast STP values and simulated right-moving supercells, which were identified using a UH exceedance threshold. Model-derived probabilities are verified using tornado segment data from just right-moving supercells and from all tornadoes, as are the SPC-issued 0600 UTC tornado probabilities from the initial day 1 forecast valid 1200–1159 UTC the following day. The STP-based probabilistic forecasts perform comparably to SPC tornado probability forecasts in many skill metrics (e.g., reliability) and thus could be used as first-guess forecasts. Comparison with prior methodologies shows that probabilistic environmental information improves CAM-based tornado forecasts.
• Keywords:
  Forecasting Tornadoes
• Source:
  Weather and Forecasting, 33(2), 443-460
• DOI:
  https://doi.org/10.1175/waf-d-17-0132.1
• Document Type:
  Journal Article
• Funding:
  Grant no. NA11OAR4320072
• Rights Information:
  Other
• Compliance:
  CHORUS
• Main Document Checksum:
  urn:sha256:b57b5fab211359c01e4a1597f6b9bfcbcee0c65e2e5d25d9a8c1bd1473b1878c
• Download URL:
  https://repository.library.noaa.gov/view/noaa/48337/noaa_48337_DS1.pdf
• File Type:
  [PDF - 2.14 MB ]