Sensitivity of Real-Data Simulations of the 3 May 1999 Oklahoma City Tornadic Supercell and Associated Tornadoes to Multimoment Microphysics. Part I: Storm- and Tornado-Scale Numerical Forecasts

Abstract

Numerical predictions of the 3 May 1999 Oklahoma City, Oklahoma, tornadic supercell are performed within a real-data framework utilizing telescoping nested grids of 3-km, 1-km, and 250-m horizontal spacing. Radar reflectivity and radial velocity from the Oklahoma City WSR-88D are assimilated using a cloud analysis procedure coupled with a cycled 3DVAR system to analyze storms on the 1-km grid for subsequent forecast periods. Single-, double-, and triple-moment configurations of a multimoment bulk microphysics scheme are used in several experiments on the 1-km and 250-m grids to assess the impact of varying the complexity of the microphysics scheme on the storm structure, behavior, and tornadic activity (on the 250-m grid). This appears to be the first study of its type to investigate single-versus multimoment microphysics within a real-data context.It is found that the triple-moment scheme overall performs the best, producing the smallest track errors for the mesocyclone on the 1-km grid, and stronger and longer-lived tornado-like vortices (TLVs) on the 250-m grid, closest to the observed tornado. In contrast, the single-moment scheme with the default Marshall-Palmer rain intercept parameter performs poorly, producing a cold pool that is too strong, and only weak and short-lived TLVs. The results in the context of differences in latent cooling from evaporation and melting between the schemes, as well as implications for numerical prediction of tornadoes, are discussed. More generally, the feedbacks to storm thermodynamics and dynamics from increasing the prognostic detail of the hydrometeor size distributions are found to be important for improving the simulation and prediction of tornadic thunderstorms.

Explicit prediction of hail using multimoment microphysics schemes for a hailstorm of 19 March 2014 in eastern China

“…Simulated hydrometeor fields, precipitation, and storm dynamics are quite sensitive to the choice of the microphysics scheme. Several studies have demonstrated the advantages of multimoment schemes over one‐moment scheme [e.g., Milbrandt and Yau , ; Morrison et al ., ; Dawson et al ., , ; Bryan and Morrison , ]. For example, Dawson et al .…”

“…Simulated hydrometeor fields, precipitation, and storm dynamics are quite sensitive to the choice of the microphysics scheme. Several studies have demonstrated the advantages of multimoment schemes over one-moment scheme [e.g., Milbrandt and Yau, 2006a;Morrison et al, 2009;Dawson et al, 2010Dawson et al, , 2015Bryan and Morrison, 2012]. For example, Dawson et al [2010] found that a multimoment scheme produced improved predictions of supercell storm cold pool size and strength, and reflectivity structure, particularly in the forward flank region of the storm.…”

Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction

“…Many challenges still remain for PRD assimilation. The 20 May study demonstrated how the number of predicted moments in model microphysics schemes affect microphysical processes, where excessive size sorting known to occur with double moment microphysics schemes (Dawson et al, 2010;Morrison and Milbrandt, 2011;Dawson et al, 2015) had a significant impact on the effectiveness of PRD data assimilation. Also, the forward operators and microphysics schemes must be improved, specifically in regard to the treatment of frozen hydrometeors as well as mixed-phase hydrometeors, which most microphysics schemes do not predict.…”

Aboveground Thermodynamic Observations in Convective Storms from Balloonborne Probes Acting as Pseudo-Lagrangian Drifters

“…Using numerical simulations to explore the range of thermodynamic characteristics within storms is problematic as well, owing to the great sensitivity of the temperature and precipitation fields to the microphysics parameterization (e.g., Gilmore et al 2004;Snook and Xue 2008;Dawson et al 2010Dawson et al , 2015Bryan and Morrison 2012). It is not a stretch to say that an investigator can obtain almost any lowlevel buoyancy field in a simulated thunderstorm that she or he wants, simply by tuning the models microphysics parameterization.…”