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Forecasting the 8 May 2017 Severe Hail Storm in Denver, Colorado, at a Convection-Allowing Resolution: Understanding Rimed Ice Treatments in Multimoment Microphysics Schemes and Their Effects on Hail Size Forecasts

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  • 1 School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
  • | 2 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
  • | 3 School of Meteorology, and Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
  • | 4 Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma
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Abstract

Day-ahead (20–22 h) 3-km grid spacing convection-allowing model forecasts are performed for a severe hail event that occurred in Denver, Colorado, on 8 May 2017 using six different multimoment microphysics (MP) schemes including: the Milbrandt–Yau double-moment (MY2), Thompson (THO), NSSL double-moment (NSSL), Morrison double-moment graupel (MOR-G) and hail (MOR-H), and Predicted Particle Properties (P3) schemes. Hail size forecasts diagnosed using the Thompson hail algorithm and storm surrogates predict hail coverage. For this case hail forecasts predict the coverage of hail with a high level of skill but underpredict hail size. The storm surrogate updraft helicity predicts the coverage of severe hail with the most skill for this case. Model data are analyzed to assess the effects of microphysical treatments related to rimed ice. THO uses diagnostic equations to increase the size of graupel within the hail core. MOR-G and MOR-H predict small rimed ice aloft; excessive size sorting and increased fall speeds cause MOR-H to predict more and larger surface hail than MOR-G. The MY2 and NSSL schemes predict large, dense rimed ice particles because both schemes predict separate hail and graupel categories. The NSSL scheme predicts relatively little hail for this case; however, the hail size forecast qualitatively improves when the maximum size of both hail and graupel is considered. The single ice category P3 scheme only predicts dense hail near the surface while above the melting layer large concentrations of low-density ice dominate.

© 2019 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: Jonathan Labriola, j.labriola@ou.edu

Abstract

Day-ahead (20–22 h) 3-km grid spacing convection-allowing model forecasts are performed for a severe hail event that occurred in Denver, Colorado, on 8 May 2017 using six different multimoment microphysics (MP) schemes including: the Milbrandt–Yau double-moment (MY2), Thompson (THO), NSSL double-moment (NSSL), Morrison double-moment graupel (MOR-G) and hail (MOR-H), and Predicted Particle Properties (P3) schemes. Hail size forecasts diagnosed using the Thompson hail algorithm and storm surrogates predict hail coverage. For this case hail forecasts predict the coverage of hail with a high level of skill but underpredict hail size. The storm surrogate updraft helicity predicts the coverage of severe hail with the most skill for this case. Model data are analyzed to assess the effects of microphysical treatments related to rimed ice. THO uses diagnostic equations to increase the size of graupel within the hail core. MOR-G and MOR-H predict small rimed ice aloft; excessive size sorting and increased fall speeds cause MOR-H to predict more and larger surface hail than MOR-G. The MY2 and NSSL schemes predict large, dense rimed ice particles because both schemes predict separate hail and graupel categories. The NSSL scheme predicts relatively little hail for this case; however, the hail size forecast qualitatively improves when the maximum size of both hail and graupel is considered. The single ice category P3 scheme only predicts dense hail near the surface while above the melting layer large concentrations of low-density ice dominate.

© 2019 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: Jonathan Labriola, j.labriola@ou.edu
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