Explicit Ensemble Prediction of Hail in 19 May 2013 Oklahoma City Thunderstorms and Analysis of Hail Growth Processes with Several Multimoment Microphysics Schemes

Abstract

Hail forecast evaluations provide important insight into microphysical treatment of rimed ice. In this study we evaluate explicit 0–90-min EnKF-based storm-scale (500-m horizontal grid spacing) hail forecasts for a severe weather event that occurred in Oklahoma on 19 May 2013. Forecast ensembles are run using three different bulk microphysics (MP) schemes: the Milbrandt–Yau double-moment scheme (MY2), the Milbrandt–Yau triple-moment scheme (MY3), and the NSSL variable density-rimed ice double-moment scheme (NSSL). Output from a hydrometeor classification algorithm is used to verify surface hail size forecasts. All three schemes produce forecasts that predict the coverage of severe surface hail with moderate to high skill, but exhibit less skill at predicting significant severe hail coverage. A microphysical budget analysis is conducted to better understand hail growth processes in all three schemes. The NSSL scheme uses two-variable density-rimed ice categories to create large hailstones from dense, wet growth graupel particles; however, it is noted the scheme underestimates the coverage of significant severe hail. Both the MY2 and MY3 schemes produce many small hailstones aloft from unrimed, frozen raindrops; in the melting layer, hailstones become much larger than observations because of the excessive accretion of water. The results of this work highlight the importance of using a MP scheme that realistically models microphysical processes.

Understanding Hail in the Earth System

“…Gagne et al, ; Labriola et al, ). While not yet widely applied operationally, both approaches do appear to provide a reasonable predictions of hail size (Gagne et al, ; Jewell & Brimelow, ; Labriola et al, ). One example is the application of HAILCAST that has been implemented in high‐resolution WRF model output based on simulated storms (Adams‐Selin & Ziegler, ).…”

“…Brimelow et al, ; Brimelow et al, ; Jewell & Brimelow, ), or alternatively using explicit microphysics to forecast graupel or hail size from simulated updrafts (e.g. Gagne et al, ; Labriola et al, ). While not yet widely applied operationally, both approaches do appear to provide a reasonable predictions of hail size (Gagne et al, ; Jewell & Brimelow, ; Labriola et al, ).…”

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

“…Hail size information can be extracted from the CAM predicted microphysical state variables. The Thompson hail size algorithm (Thompson et al 2018;Gagne et al 2019) uses hail PSDs predicted by MP schemes to approximate the maximum observable hail size at each grid point; variants of this method have been used to verify forecasts and understand process-level hail growth and decay processes (Milbrandt and Yau 2006a;Snook et al 2016;Labriola et al 2017Labriola et al ,2019Luo et al 2018). Surveys conducted during the SFE indicate that forecasters find the additional information provided by explicit hail size forecasts useful relative to surrogate fields (e.g., UH) (Gallo et al 2017).…”

“…The U.S. Next Generation Weather Radar (NEXRAD) system (Crum et al 1993) is an observational platform capable of capturing the evolution of severe hail within thunderstorms in three dimensions. Radar-derived hail products have been used to verify hail forecasts in several recent studies (e.g., Gagne et al 2015Gagne et al , 2017Snook et al 2016;Labriola et al 2017Labriola et al , 2019Luo et al 2017Luo et al , 2018 and are often used operationally to diagnose maximum hail size (Cintineo et al 2012). Radar-derived hail proxies, such as hydrometeor classification algorithm (HCA; Park et al 2009;Putnam et al 2017) output, are preferable to surface reports because they produce high-resolution surface hail size estimates that are not subject to population biases or gaps in data over rural areas (Cintineo et al 2012).…”

“…For calibrated radar observations, HCA membership functions have been found to classify hail size within a 120-km range of a radar with greater skill than other radarderived hail products such as the maximum estimated size of hail (MESH) (Ortega et al 2016). Following Labriola et al (2019), HCA output is used to verify forecasts in this study both subjectively and objectively.…”