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Just What is “Good”? Musings on Hail Forecast Verification Through Evaluation of FV3-HAILCAST Hail Forecasts

Rebecca D. Adams-SelinaVerisk Atmospheric and Environmental Research, Bellevue, Nebraska

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Christina KalbbResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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Tara JensenbResearch Applications Laboratory, National Center for Atmospheric Research, Boulder, Colorado

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John HendersoncVerisk Atmospheric and Environmental Research, Lexington, Massachusetts

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Tim SupiniedCenter for Analysis and Prediction of Storms, Norman, Oklahoma

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Lucas HarriseNOAA/OAR Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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Yunheng WangfCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
gNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma

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Burkely T. GallofCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
hNOAA/NWS/NCEP Storm Prediction Center, Norman, Oklahoma

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Adam J. ClarkiNOAA/OAR National Severe Storms Laboratory, Norman, Oklahoma
jSchool of Meteorology, University of Oklahoma, Norman, Oklahoma

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Abstract

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast.

The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods.

Corresponding author: Rebecca Adams-Selin, rselin@aer.com

Abstract

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast.

The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods.

Corresponding author: Rebecca Adams-Selin, rselin@aer.com
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