Explicit Prediction of Hail in a Long-Lasting Multicellular Convective System in Eastern China Using Multimoment Microphysics Schemes

Abstract

During the afternoon of 28 April 2015, a multicellular convective system swept southward through much of Jiangsu Province, China, over about 7 h, producing egg-sized hailstones on the ground. The hailstorm event is simulated using the Advanced Regional Prediction System (ARPS) at 1-km grid spacing. Different configurations of the Milbrandt–Yau microphysics scheme are used, predicting one, two, and three moments of the hydrometeor particle size distributions (PSDs). Simulated reflectivity and maximum estimated size of hail (MESH) derived from the simulations are verified against reflectivity observed by operational S-band Doppler radars and radar-derived MESH, respectively. Comparisons suggest that the general evolution of the hailstorm is better predicted by the three-moment scheme, and neighborhood-based MESH evaluation further confirms the advantage of the three-moment scheme in hail size prediction. Surface accumulated hail mass, number, and hail distribution characteristics within simulated storms are examined across sensitivity experiments. Results suggest that multimoment schemes produce more realistic hail distribution characteristics, with the three-moment scheme performing the best. Size sorting is found to play a significant role in determining hail distribution within the storms. Detailed microphysical budget analyses are conducted for each experiment, and results indicate that the differences in hail growth processes among the experiments can be mainly ascribed to the different treatments of the shape parameter within different microphysics schemes. Both the differences in size sorting and hail growth processes contribute to the simulated hail distribution differences within storms and at the surface.

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

“…The MY2 scheme is selected for this experiment because it has been used in multiple recent hail prediction studies (e.g., S16, L17, Luo et al 2017Luo et al , 2018. The MY2 and MY3 schemes use the same prognostic equations for hydrometeor mass mixing ratio and number concentration; however, MY3 also predicts hydrometeor Z; Z is used to diagnose the PSD shape parameter.…”

“…Many nonrotating storms produce large hail and cause extensive damage, one such hail event caused more than $1.4 billion in hail damage in Colorado (Fritz 2017). Studies such as Luo et al (2017) have evaluated the skill of next-day CAM forecasts for a severe-hail-producing pulse thunderstorm, but to our knowledge no studies have evaluated hail forecasts for nonrotating storms using a frequently updated, high-resolution ensemble forecast framework. In the future we plan to expand the number of case studies to include multiple hail events.…”

“…Extending the warning lead time for severe hail has the potential to mitigate hail-related damage, though this is challenging, as severe hail events often develop quickly. To extend severe weather warning lead time, the National Weather Service (NWS) is shifting from issuing severe weather warnings based upon detection by, for example, radar (warn-on-detection), to a paradigm where warnings will be issued based upon highresolution numerical weather prediction (NWP) model guidance (warn-on-forecast; Stensrud et al 2009Stensrud et al , 2013 Many studies analyze the skill of convectivescale forecasts; however, only a handful of studies (e.g., Milbrandt and Yau 2006;Snook et al 2016, hereafter S16;Luo et al 2017;Gagne et al 2017;Labriola et al 2017, hereafter L17;Adams-Selin and Ziegler 2016;Luo et al 2018) assess the skill of explicit hail prediction (hail size and coverage). Hail prediction using storm-scale NWP forecasts remains understudied because of the difficult and complex nature of hail storm prediction, hail growth processes, and challenges associated with hail forecast verification.…”

“…Multiple studies (e.g., S16; L17; Luo et al 2017Luo et al , 2018 have used predicted hail quantities (mass, number concentration) to identify regions where hail reaches the surface. Simulated radar products such as the maximum estimated size of hail (MESH; Witt et al 1998a) have also been used to derive surface hail size from model output (S16; L17; Luo et al 2017Luo et al , 2018. Another method, the Thompson hail method (Thompson et al 2018), uses model diagnosed hail PSDs to approximate the largest ''observable'' hailstone.…”

“…Another method, the Thompson hail method (Thompson et al 2018), uses model diagnosed hail PSDs to approximate the largest ''observable'' hailstone. Variants of this method have been used to diagnose the maximum surface hail size and distinguish regions of hail growth (Milbrandt and Yau 2006;S16;L17;Luo et al 2018). The skill of hail forecasts derived directly from model output is strongly dependent upon choice of MP scheme; to account for model biases, machine learning algorithms can be applied to CAM output to create storm-based hail forecasts (Gagne et al 2015(Gagne et al , 2017McGovern et al 2017).…”