High‐resolution NU‐WRF simulations of a deep convective‐precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations

Abstract

The Goddard microphysics was recently improved by adding a fourth ice class (frozen drops/hail).This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of ice/snow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries. Key Points:• New 4ICE microphysics scheme is implemented in a regional scale model • Radar reflectivities and rain rate intensities are sensitive to the microphysics scheme • The new 4ICE scheme produces radar structures superior to original 4ICE and 3ICE schemes (2016), High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations,

High‐Resolution, Multilayer Modeling of Singapore's Urban Climate Incorporating Local Climate Zones

“…Other physical parameterization schemes include Rapid Radiative Transfer longwave radiation scheme (Mlawer et al, ), Dudhia shortwave radiation scheme (Dudhia, ), Bougeault and Lacarrere turbulent scheme (Bougeault & Lacarrere, ), Monin‐Obukhov surface layer scheme (Monin & Obukhov, ), Goddard microphysics scheme (Tao et al, ), and Kain‐Fritsch cumulus scheme (Kain, ; for the two outer domains only). The time step for this simulation was 81 s for the outermost domain and reduced at a 3:1 ratio for subsequent child domain.…”

POLARRIS: A POLArimetric Radar Retrieval and Instrument Simulator

“…Once a single‐particle 4 × 4 Mueller scattering matrix ( S , mm 2 ) is generated (see the equations in Vivekanandan et al ()), it is integrated over the particle size distributions (PSDs) for each species class in the model to derive a size‐integrated 4 × 4 Mueller scattering matrix (S| i , mm 2 /m 3 ): where i represents each particular hydrometeor species, and N ( D ) represents the particle number density (m −4 ) for a given particle diameter D (m). In typical bulk microphysics schemes with four ice categories such as the Goddard 4ICE scheme (Lang et al, ; Tao et al, ), the hydrometeor species are cloud, rain, ice crystals, snow aggregates, graupel, and hail. In the HUCM SBM scheme (Khain et al, ), i represents liquid droplets, three types of ice‐crystal shapes (column, dendrite, and plate), snow aggregates, graupel, or hail.…”

Influence of bulk microphysics schemes upon Weather Research and Forecasting (WRF) version 3.6.1 nor'easter simulations

“…Snow growth in GCE6 is further augmented by its assumption of water saturation for the vapor growth of cloud ice to snow (Reeves and Dawson, 2013;Lang et al, 2014). GCE7 addressed the vapor growth issue of GCE6 by introducing snow size and density mapping, snow breakup interactions, a relative humidity (RH)-based correction factor, and a new vertical-velocitydependent ice supersaturation assumption (Lang el al., 2007(Lang el al., , 2011(Lang el al., , 2014Chern et al, 2016;Tao et al, 2016). Despite the reduced efficiency of vapor growth of cloud ice to snow due to both the new RH correction factor and the ice supersaturation adjustment, the new-snow mapping and enhanced cloud ice-to-snow auto-conversion in GCE7 offset this potential reduction, which kept GCE snowfall mixing ratios higher than those in non-GCE BMPSs.…”

“…Graupel mixing ratios are the lowest in GCE7 due to the net effect of its additions despite the inclusion of a new graupel size map. In particular, the combination of the new snow size map (decrease snow size aloft, increases snow surface area, and enhances vapor growth), the addition of deposition conversion processes (graupel/hail particles experiencing deposition growth at lower temperatures are converted to snow), and a reduction in super-cooled droplets available for riming (cloud-ice generation is augmented; see below) all favor snow growth at the expense of graupel (Lang et al, 2014;Chern et al, 2016;Tao et al, 2016). Consistent with Reeves and Dawson (2013), WSM6 and WDM6 graupel-mixing ratio values are typically 30-50 % of their snow counterparts.…”

“…GCE7 blunts cloud ice-to-snow conversion rates using a RH correction factor that is dependent upon ice supersaturation, which is itself dependent up vertical velocity. Additionally, GCE7 also includes contact and immersion freezing terms , makes the cloud-ice collection by snow efficiency a function of snow size (Lang et al, , 2014, sets a maximum limit on cloud-ice particle size , makes ice nuclei concentrations follows the Cooper curve (Cooper, 1986;Tao et al, 2016), and allows cloud ice to persist in ice subsaturated conditions (i.e., RH for ice ≥ 70 %) (Lang et al, , 2014. Despite the increased cloud ice-to-snow auto-conversion rates in GCE7 (Lang et al, 2014;Tao et al, 2016), precipitable cloudice amounts nearly doubled relative to GCE6 (See Fig.…”