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Dynamical–Statistical Prediction of Week-2 Severe Weather for the United States

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  • 1 NOAA/NWS/NCEP/Climate Prediction Center, College Park, Maryland
  • | 2 Innovim LLC, Greenbelt, Maryland
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Abstract

A dynamical–statistical model is developed for forecasting week-2 severe weather (hail, tornadoes, and damaging winds) over the United States. The supercell composite parameter (SCP) is used as a predictor, which is derived from the 16-day dynamical forecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) model and represents the large-scale convective environments influencing severe weather. The hybrid model forecast is based on the empirical relationship between GEFS hindcast SCP and observed weekly severe weather frequency during 1996–2012, the GEFS hindcast period. Cross validations suggest that the hybrid model has a low skill for week-2 severe weather when applying simple linear regression method at 0.5° × 0.5° (latitude × longitude) grid data. However, the forecast can be improved by using the 5° × 5° area-averaged data. The forecast skill can be further improved by using the empirical relationship depicted by the singular value decomposition method, which takes into account the spatial covariations of weekly severe weather. The hybrid model was tested operationally in spring 2019 and demonstrated skillful forecasts of week-2 severe weather frequency over the United States.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hui Wang, hui.wang@noaa.gov

Abstract

A dynamical–statistical model is developed for forecasting week-2 severe weather (hail, tornadoes, and damaging winds) over the United States. The supercell composite parameter (SCP) is used as a predictor, which is derived from the 16-day dynamical forecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) model and represents the large-scale convective environments influencing severe weather. The hybrid model forecast is based on the empirical relationship between GEFS hindcast SCP and observed weekly severe weather frequency during 1996–2012, the GEFS hindcast period. Cross validations suggest that the hybrid model has a low skill for week-2 severe weather when applying simple linear regression method at 0.5° × 0.5° (latitude × longitude) grid data. However, the forecast can be improved by using the 5° × 5° area-averaged data. The forecast skill can be further improved by using the empirical relationship depicted by the singular value decomposition method, which takes into account the spatial covariations of weekly severe weather. The hybrid model was tested operationally in spring 2019 and demonstrated skillful forecasts of week-2 severe weather frequency over the United States.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Hui Wang, hui.wang@noaa.gov
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