Probabilistic Verification of Storm Prediction Center Convective Outlooks

Probabilistic Verification of Storm Prediction Center Convective Outlooks AbstractEight years’ worth of day 1 and 4.5 years’ worth of day 2–3 probabilistic convective outlooks from the Storm Prediction Center (SPC) are converted to probability grids spanning the continental United States (CONUS). These results are then evaluated using standard probabilistic forecast metrics including the Brier skill score and reliability diagrams. Forecasts are gridded in two different ways: one with a high-resolution grid and interpolation between probability contours and another on an 80-km-spaced grid without interpolation. Overall, the highest skill is found for severe wind forecasts and the lowest skill is observed for tornadoes; for significant severe criteria, the opposite discrepancy is observed, with highest forecast skill for significant tornadoes and approximately no overall forecast skill for significant severe winds. Highest climatology-relative skill is generally observed over the central and northern Great Plains and Midwest, with the lowest—and often negative—skill seen in the West, southern Texas, and the Atlantic Southeast. No discernible year-to-year trend in skill was identified; seasonally, forecasts verified the best in the spring and late autumn and worst in the summer and early autumn. Forecasts are also evaluated in CAPE-versus-shear parameter space; forecasts struggle most in very low shear but also in high-shear, low-CAPE environments. In aggregate, forecasts for all variables verified more skillfully using interpolated probability grids, suggesting utility in interpreting forecasts as a continuous field. Forecast reliability results depend substantially on the interpretation of the forecast fields, but day 1 and day 2–3 tornado outlooks consistently exhibit an underforecast bias. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Weather and Forecasting American Meteorological Society

Probabilistic Verification of Storm Prediction Center Convective Outlooks

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0434
D.O.I.
10.1175/WAF-D-17-0104.1
Publisher site
See Article on Publisher Site

Abstract

AbstractEight years’ worth of day 1 and 4.5 years’ worth of day 2–3 probabilistic convective outlooks from the Storm Prediction Center (SPC) are converted to probability grids spanning the continental United States (CONUS). These results are then evaluated using standard probabilistic forecast metrics including the Brier skill score and reliability diagrams. Forecasts are gridded in two different ways: one with a high-resolution grid and interpolation between probability contours and another on an 80-km-spaced grid without interpolation. Overall, the highest skill is found for severe wind forecasts and the lowest skill is observed for tornadoes; for significant severe criteria, the opposite discrepancy is observed, with highest forecast skill for significant tornadoes and approximately no overall forecast skill for significant severe winds. Highest climatology-relative skill is generally observed over the central and northern Great Plains and Midwest, with the lowest—and often negative—skill seen in the West, southern Texas, and the Atlantic Southeast. No discernible year-to-year trend in skill was identified; seasonally, forecasts verified the best in the spring and late autumn and worst in the summer and early autumn. Forecasts are also evaluated in CAPE-versus-shear parameter space; forecasts struggle most in very low shear but also in high-shear, low-CAPE environments. In aggregate, forecasts for all variables verified more skillfully using interpolated probability grids, suggesting utility in interpreting forecasts as a continuous field. Forecast reliability results depend substantially on the interpretation of the forecast fields, but day 1 and day 2–3 tornado outlooks consistently exhibit an underforecast bias.

Journal

Weather and ForecastingAmerican Meteorological Society

Published: Feb 18, 2018

References

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