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

2018 ◽  
Vol 75 (9) ◽  
pp. 3115-3137 ◽  
Author(s):  
Liping Luo ◽  
Ming Xue ◽  
Kefeng Zhu ◽  
Bowen Zhou
Keyword(s):  
Eastern China ◽  
Convective System ◽  
Distribution Characteristics ◽  
Grid Spacing ◽  
Particle Size Distributions ◽  
Growth Processes ◽  
Microphysics Scheme ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Size Sorting

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.

Tornadogenesis in a Simulated Mesovortex within a Mesoscale Convective System

2012 ◽  
Vol 69 (11) ◽  
pp. 3372-3390 ◽  
Author(s):  
Alexander D. Schenkman ◽  
Ming Xue ◽  
Alan Shapiro
Keyword(s):  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Surface Friction ◽  
Convective System ◽  
Grid Spacing ◽  
Near Surface ◽  
Advanced Regional Prediction System ◽  
Low Level ◽  
Regional Prediction ◽  
Mesoscale Convective

Abstract The Advanced Regional Prediction System (ARPS) is used to simulate a tornadic mesovortex with the aim of understanding the associated tornadogenesis processes. The mesovortex was one of two tornadic mesovortices spawned by a mesoscale convective system (MCS) that traversed southwestern and central Oklahoma on 8–9 May 2007. The simulation used 100-m horizontal grid spacing, and is nested within two outer grids with 400-m and 2-km grid spacing, respectively. Both outer grids assimilate radar, upper-air, and surface observations via 5-min three-dimensional variational data assimilation (3DVAR) cycles. The 100-m grid is initialized from a 40-min forecast on the 400-m grid. Results from the 100-m simulation provide a detailed picture of the development of a mesovortex that produces a submesovortex-scale tornado-like vortex (TLV). Closer examination of the genesis of the TLV suggests that a strong low-level updraft is critical in converging and amplifying vertical vorticity associated with the mesovortex. Vertical cross sections and backward trajectory analyses from this low-level updraft reveal that the updraft is the upward branch of a strong rotor that forms just northwest of the simulated TLV. The horizontal vorticity in this rotor originates in the near-surface inflow and is caused by surface friction. An additional simulation with surface friction turned off does not produce a rotor, strong low-level updraft, or TLV. Comparison with previous two-dimensional numerical studies of rotors in the lee of mountains shows striking similarities to the rotor formation presented herein. The findings of this study are summarized in a four-stage conceptual model for tornadogenesis in this case that describes the evolution of the event from mesovortexgenesis through rotor development and finally TLV genesis and intensification.

scholarly journals The Impact of a Stochastically Perturbing Microphysics Scheme on an Idealized Supercell Storm

2017 ◽  
Vol 146 (1) ◽  
pp. 95-118 ◽  
Author(s):  
Xiaoshi Qiao ◽  
Shizhang Wang ◽  
Jinzhong Min
Keyword(s):  
Stochastic Methods ◽  
Microphysics Scheme ◽  
Advanced Regional Prediction System ◽  
Recursive Filters ◽  
Supercell Storm ◽  
Regional Prediction ◽  
Double Moment ◽  
The Impact ◽  
Temperature Tendency ◽  
Stochastically Perturbed

Abstract The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH.

Ensemble Kalman Filter Analyses of the 29–30 May 2004 Oklahoma Tornadic Thunderstorm Using One- and Two-Moment Bulk Microphysics Schemes, with Verification against Polarimetric Radar Data

2012 ◽  
Vol 140 (5) ◽  
pp. 1457-1475 ◽  
Author(s):  
Youngsun Jung ◽  
Ming Xue ◽  
Mingjing Tong
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
General Structure ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Single Moment ◽  
Independent Observations

Abstract The performance of ensemble Kalman filter (EnKF) analysis is investigated for the tornadic supercell on 29–30 May 2004 in Oklahoma using a dual-moment (DM) bulk microphysics scheme in the Advanced Regional Prediction System (ARPS) model. The comparison of results using single-moment (SM) and DM microphysics schemes evaluates the benefits of using one over the other during storm analysis. Observations from a single operational Weather Surveillance Radar-1988 Doppler (WSR-88D) are assimilated and a polarimetric WSR-88D in Norman, Oklahoma (KOUN), is used to assess the quality of the analysis. Analyzed reflectivity and radial velocity in the SM and DM experiments compare favorably with independent radar observations in general. However, simulated polarimetric signatures obtained from analyses using a DM scheme agree significantly better with polarimetric signatures observed by KOUN in terms of the general structure, location, and intensity of the signatures than those generated from analyses using an SM scheme. These results demonstrate for the first time for a real supercell storm that EnKF data assimilation using a numerical model with an adequate microphysics scheme (i.e., a scheme that predicts at least two moments of the hydrometeor size distributions) is capable of producing polarimetric radar signatures similar to those seen in observations without directly assimilating polarimetric data. In such cases, the polarimetric data also serve as completely independent observations for the verification purposes.

scholarly journals The Analysis and Prediction of the 8–9 May 2007 Oklahoma Tornadic Mesoscale Convective System by Assimilating WSR-88D and CASA Radar Data Using 3DVAR

2011 ◽  
Vol 139 (1) ◽  
pp. 224-246 ◽  
Author(s):  
Alexander D. Schenkman ◽  
Ming Xue ◽  
Alan Shapiro ◽  
Keith Brewster ◽  
Jidong Gao
Keyword(s):  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Squall Line ◽  
Convective System ◽  
Data Types ◽  
Advanced Regional Prediction System ◽  
Engineering Research ◽  
Regional Prediction ◽  
Mesoscale Convective

Abstract The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8–9 May 2007. The simulation uses a 1000 km × 1000 km domain with 2-km horizontal grid spacing. The ARPS three-dimensional variational data assimilation (3DVAR) is used to assimilate a variety of data types. All experiments assimilate routine surface and upper-air observations as well as wind profiler and Oklahoma Mesonet data over a 1-h assimilation window. A subset of experiments assimilates radar data. Cloud and hydrometeor fields as well as in-cloud temperature are adjusted based on radar reflectivity data through the ARPS complex cloud analysis procedure. Radar data are assimilated from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network as well as from the Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) network of four X-band Doppler radars. Three-hour forecasts are launched at the end of the assimilation window. The structure and evolution of the forecast MCS and LEV are markedly better throughout the forecast period in experiments in which radar data are assimilated. The assimilation of CASA radar data in addition to WSR-88D data increases the structural detail of the modeled squall line and MCS at the end of the assimilation window, which appears to yield a slightly better forecast track of the LEV.

Tornadogenesis in a High-Resolution Simulation of the 8 May 2003 Oklahoma City Supercell

2013 ◽  
Vol 71 (1) ◽  
pp. 130-154 ◽  
Author(s):  
Alexander D. Schenkman ◽  
Ming Xue ◽  
Ming Hu
Keyword(s):  
Dominant Role ◽  
Oklahoma City ◽  
Prediction System ◽  
Grid Spacing ◽  
Surface Drag ◽  
Near Surface ◽  
Vertical Vorticity ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
First Time

Abstract A 50-m-grid-spacing Advanced Regional Prediction System (ARPS) simulation of the 8 May 2003 Oklahoma City tornadic supercell is examined. A 40-min forecast run on the 50-m grid produces two F3-intensity tornadoes that track within 10 km of the location of the observed long-track F4-intensity tornado. The development of both simulated tornadoes is analyzed to determine the processes responsible for tornadogenesis. Trajectory-based analyses of vorticity components and their time evolution reveal that tilting of low-level frictionally generated horizontal vorticity plays a dominant role in the development of vertical vorticity near the ground. This result represents the first time that such a mechanism has been shown to be important for generating near-surface vertical vorticity leading to tornadogenesis. A sensitivity simulation run with surface drag turned off was found to be considerably different from the simulation with drag included. A tornado still developed in the no-drag simulation, but it was much shorter lived and took a substantially different track than the observed tornadoes as well as the simulated tornadoes in the drag simulation. Tilting of baroclinic vorticity in an outflow surge may have played a role in tornadogenesis in the no-drag simulation.

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

2009 ◽  
Vol 48 (9) ◽  
pp. 1790-1802 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Relative Humidity ◽  
Wind Direction ◽  
Meteorological Data ◽  
Logistic Models ◽  
Satellite Observations ◽  
Lapse Rate ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Surface Models

Abstract A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

2014 ◽  
Vol 142 (1) ◽  
pp. 141-162 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan Snook ◽  
Guifu Zhang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Cold Pool ◽  
Convective System ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Mesoscale Convective ◽  
Double Moment

Abstract Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

scholarly journals Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
John R. Lawson ◽  
William A. Gallus ◽  
Corey K. Potvin
Keyword(s):  
Mesoscale Convective System ◽  
Real Data ◽  
The United States ◽  
Convective System ◽  
Grid Spacing ◽  
Structure Amplitude ◽  
Cold Pools ◽  
Bow Echo ◽  
Mesoscale Convective ◽  
Horizontal Grid

The bow echo, a mesoscale convective system (MCS) responsible for much hail and wind damage across the United States, is associated with poor skill in convection-allowing numerical model forecasts. Given the decrease in convection-allowing grid spacings within many operational forecasting systems, we investigate the effect of finer resolution on the character of bowing-MCS development in a real-data numerical simulation. Two ensembles were generated: one with a single domain of 3-km horizontal grid spacing, and another nesting a 1-km domain with two-way feedback. Ensemble members were generated from their control member with a stochastic kinetic-energy backscatter scheme, with identical initial and lateral-boundary conditions. Results suggest that resolution reduces hindcast skill of this MCS, as measured with an adaptation of the object-based Structure–Amplitude–Location method. The nested 1-km ensemble produces a faster system than in both the 3-km ensemble and observations. The nested 1-km simulation also produced stronger cold pools, which could be enhanced by the increased (fractal) cloud surface area with higher resolution, allowing more entrainment of dry air and hence increased evaporative cooling.

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