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 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.

Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double-Moment Microphysics Schemes and EnKF Radar Data Assimilation

2017 ◽  
Vol 145 (6) ◽  
pp. 2257-2279 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan A. Snook ◽  
Guifu Zhang
Keyword(s):  
Mesoscale Convective System ◽  
Radar Data ◽  
Convective Precipitation ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Verification Methods ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective ◽  
Single Moment ◽  
Double Moment

Abstract Ensemble-based probabilistic forecasts are performed for a mesoscale convective system (MCS) that occurred over Oklahoma on 8–9 May 2007, initialized from ensemble Kalman filter analyses using multinetwork radar data and different microphysics schemes. Two experiments are conducted, using either a single-moment or double-moment microphysics scheme during the 1-h-long assimilation period and in subsequent 3-h ensemble forecasts. Qualitative and quantitative verifications are performed on the ensemble forecasts, including probabilistic skill scores. The predicted dual-polarization (dual-pol) radar variables and their probabilistic forecasts are also evaluated against available dual-pol radar observations, and discussed in relation to predicted microphysical states and structures. Evaluation of predicted reflectivity (Z) fields shows that the double-moment ensemble predicts the precipitation coverage of the leading convective line and stratiform precipitation regions of the MCS with higher probabilities throughout the forecast period compared to the single-moment ensemble. In terms of the simulated differential reflectivity (ZDR) and specific differential phase (KDP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The ZDR from individual ensemble members indicates better raindrop size sorting along the leading convective line in the double-moment ensemble. Various commonly used ensemble forecast verification methods are examined for the prediction of dual-pol variables. The results demonstrate the challenges associated with verifying predicted dual-pol fields that can vary significantly in value over small distances. Several microphysics biases are noted with the help of simulated dual-pol variables, such as substantial overprediction of KDP values in the single-moment ensemble.

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 Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

scholarly journals Analysis and Forecast of a Tornadic Thunderstorm Using Multiple Doppler Radar Data, 3DVAR, and ARPS Model

2013 ◽  
Vol 2013 ◽  
pp. 1-18
Author(s):  
Edward Natenberg ◽  
Jidong Gao ◽  
Ming Xue ◽  
Frederick H. Carr
Keyword(s):  
Doppler Radar ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Radar Data ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Upper Level ◽  
Using Data ◽  
Nwp Model ◽  
The Impact

A three-dimensional variational (3DVAR) assimilation technique developed for a convective-scale NWP model—advanced regional prediction system (ARPS)—is used to analyze the 8 May 2003, Moore/Midwest City, Oklahoma tornadic supercell thunderstorm. Previous studies on this case used only one or two radars that are very close to this storm. However, three other radars observed the upper-level part of the storm. Because these three radars are located far away from the targeted storm, they were overlooked by previous studies. High-frequency intermittent 3DVAR analyses are performed using the data from five radars that together provide a more complete picture of this storm. The analyses capture a well-defined mesocyclone in the midlevels and the wind circulation associated with a hook-shaped echo. The analyses produced through this technique are used as initial conditions for a 40-minute storm-scale forecast. The impact of multiple radars on a short-term NWP forecast is most evident when compared to forecasts using data from only one and two radars. The use of all radars provides the best forecast in which a strong low-level mesocyclone develops and tracks in close proximity to the actual tornado damage path.

scholarly journals Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data

2011 ◽  
Vol 139 (11) ◽  
pp. 3446-3468 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Radar Data ◽  
Convective System ◽  
Foundation Engineering ◽  
X Band ◽  
Regional Prediction ◽  
Mesoscale Convective

Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with a dense X-band radar network that provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of four radars in the CASA Integrated Project-1 (IP-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding Weather Surveillance Radars-1988 Doppler (WSR-88Ds) on 8–9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; two tornadoes rated as category 1 on the enhanced Fujita scale (EF-1) and one tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA. To quantify the impacts of CASA radar data in storm-scale NWP, a set of data assimilation experiments were performed using the Advanced Regional Prediction System (ARPS) ensemble Kalman filter (EnKF) system configured with full model physics and high-resolution terrain. Data from four CASA IP-1 radars and five WSR-88Ds were assimilated in some of the experiments. The ensemble contained 40 members, and radar data were assimilated every 5 min for 1 h. While the assimilation of WSR-88D data alone was able to produce a reasonably accurate analysis of the convective system, assimilating CASA data in addition to WSR-88D data is found to improve the representation of storm-scale circulations, particularly in the lowest few kilometers of the atmosphere, as evidenced by analyses of gust front position and comparison of simulated Vr with observations. Assimilating CASA data decreased RMS innovation of the resulting ensemble mean analyses of Z, particularly in early assimilation cycles, suggesting that the addition of CASA data allowed the EnKF system to more quickly achieve a good result. Use of multiple microphysics schemes in the forecast ensemble was found to alleviate underdispersion by increasing the ensemble spread. This work is the first assimilating real CASA data into an NWP model using EnKF.

scholarly journals Effects of Under-Resolved Convective Dynamics on the Evolution of a Squall Line

2019 ◽  
Vol 148 (1) ◽  
pp. 289-311 ◽  
Author(s):  
Adam Varble ◽  
Hugh Morrison ◽  
Edward Zipser
Keyword(s):  
Early Stage ◽  
Deep Convection ◽  
Mesoscale Convective System ◽  
Squall Line ◽  
Cold Pool ◽  
Convective System ◽  
Stratiform Region ◽  
Mesoscale Convective ◽  
Upper Level ◽  
Resolution Simulation

Abstract Simulations of a squall line observed on 20 May 2011 during the Midlatitude Continental Convective Clouds Experiment (MC3E) using 750- and 250-m horizontal grid spacing are performed. The higher-resolution simulation has less upshear-tilted deep convection and a more elevated rear inflow jet than the coarser-resolution simulation in better agreement with radar observations. A stronger cold pool eventually develops in the 250-m run; however, the more elevated rear inflow counteracts the cold pool circulation to produce more upright convective cores relative to the 750-m run. The differing structure in the 750-m run produces excessive midlevel front-to-rear detrainment, reinforcing excessive latent cooling and rear inflow descent at the rear of the stratiform region in a positive feedback. The contrasting mesoscale circulations are connected to early stage deep convective draft differences in the two simulations. Convective downdraft condensate mass, latent cooling, and downward motion all increase with downdraft area similarly in both simulations. However, the 750-m run has a relatively greater number of wide and fewer narrow downdrafts than the 250-m run averaged to the same 750-m grid, a consequence of downdrafts being under-resolved in the 750-m run. Under-resolved downdrafts in the 750-m run are associated with under-resolved updrafts and transport mid–upper-level zonal momentum downward to low levels too efficiently in the early stage deep convection. These results imply that under-resolved convective drafts in simulations may vertically transport air too efficiently and too far vertically, potentially biasing buoyancy and momentum distributions that impact mesoscale convective system evolution.

scholarly journals Identifikasi Karakteristik Quasi-Linear Convective System Pada Maret Sampai Mei 2017 Di Wilayah Pangkalan Bun Berbasis Radar Cuaca

2019 ◽  
Vol 4 ◽  
pp. 110
Author(s):  
Meldisa Putri Maulidyah ◽  
Rossian Nursiddiq Islamiardi ◽  
Rezky Fajar Maulana ◽  
Kristian Adi Putra Tamba ◽  
Imma Redha Nugraheni ◽  
...  
Keyword(s):  
Coastal Plain ◽  
Descriptive Analysis ◽  
Mesoscale Convective System ◽  
Heavy Rain ◽  
Radar Data ◽  
Horizontal Wind ◽  
Convective System ◽  
Raw Data ◽  
Mesoscale Convective ◽  
Strong Winds

<p><strong>Abstract: </strong>Quasi Linear Convective System (QLCS) is one of the phenomena of meso-scale convective weather systems (MCS), which are linear in shape with an unspecified leftime and potentially bad weather in the form of heavy rain and strong winds. This research will identify, analyze, and characterize QLCS in the Pangkalan Bun region, Central Kalimantan, as a research location with a period of March to May 2017 using raw data radar data base of Pangkalanbun type C-Band single polarization type Selex SI Gematronik. Method of research was conducted in a descriptive analysis with a description of the QLCS temporally and spatially. The results showed the most duration was 30-60 minutes. The location of the QLCS formation is dominant in the coastal plain or lowland areas. The type of formation of QLCS is dominant broken line.</p><p><strong>Abstrak: </strong>Quasi Linear Convective System (QLCS) merupakan salah satu fenomena dari sistem cuaca konvektif skala meso atau Mesoscale Convective System (MCS) yang berbentuk linear dengan masa hidup tidak ditentukan dan berpotensi cuaca buruk berupa hujan lebat dan angin kencang. Pada penelitian ini akan mengidentifikasi, menganalisis, dan mengarakteristikan QLCS di wilayah cakupan radar Pangkalan Bun, Kalimantan Tengah sebagai lokasi penelitian dengan jangka waktu bulan Maret sampai Mei tahun 2017 menggunakan raw data radar cuaca Pangkalan Bun tipe C-Band jenis polarisasi tunggal Selex SI Gematronik. Metode yang dilakukan dalam penelitian ini adalah analisis deskriptif produk Column Max (CMAX), Combined Moment (CM), Strom Structure Analysis (SSA), Severe Weather Indicator (SWI), dan Horizontal WInd (HWIND). Hasil penelitian menunjukkan durasi pembentukan QLCS terbanyak terjadi dalam rentang 30-60 menit dengan lokasi pembentukan QLCS dominan pada area coastal plain atau dataran rendah. Tipe pembentukan QLCS dominan broken line dan banyak terjadi di pagi hari.</p>

scholarly journals Observation of Quasi-2-Day Convective Disturbances in the Equatorial Indian Ocean during DYNAMO

2018 ◽  
Vol 75 (9) ◽  
pp. 2867-2888 ◽  
Author(s):  
Hungjui Yu ◽  
Richard H. Johnson ◽  
Paul E. Ciesielski ◽  
Hung-Chi Kuo
Keyword(s):  
Indian Ocean ◽  
System Development ◽  
Equatorial Indian Ocean ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Distinct Pattern ◽  
Convective System ◽  
Zonal Scale ◽  
Central Indian Ocean ◽  
Mesoscale Convective

Abstract This study examines the westward-propagating convective disturbances with quasi-2-day intervals of occurrence identified over Gan Island in the central Indian Ocean from mid- to late October 2011 during the Dynamics of the Madden–Julian Oscillation (DYNAMO) field campaign. Atmospheric sounding, satellite, and radar data are used to develop a composite of seven such disturbances. Composites and spectral analyses reveal that 1) the quasi-2-day convective events comprise westward-propagating diurnal convective disturbances with phase speeds of 10–12 m s−1 whose amplitudes are modulated on a quasi-2-day time scale on a zonal scale of ~1000 km near the longitudes of Gan; 2) the cloud life cycle of quasi-2-day convective disturbances shows a distinct pattern of tropical cloud population evolution—from shallow to deep to stratiform convection; 3) the time scales of mesoscale convective system development and boundary layer modulation play essential roles in determining the periodicity of the quasi-2-day convective events; and 4) in some of the quasi-2-day events there is evidence of counterpropagating (westward and eastward) cloud systems along the lines proposed by Yamada et al. Based on these findings, an interpretation is proposed for the mechanisms for the quasi-2-day disturbances observed during DYNAMO that combines concepts from prior studies of this phenomenon over the western Pacific and Indian Oceans.

A new parameterization of the accretion of cloud water by snow and its evaluation through simulations of mesoscale convective systems

2020 ◽  
Author(s):  
Han-Gyul Jin ◽  
Jong-Jin Baik
Keyword(s):  
Mesoscale Convective System ◽  
Cloud Droplet ◽  
Spherical Shape ◽  
Heavy Precipitation ◽  
Cloud Water ◽  
Squall Line ◽  
Convective System ◽  
Cloud Droplets ◽  
Convective Systems ◽  
Mesoscale Convective

&lt;p&gt;A new parameterization of the accretion of cloud water by snow for use in bulk microphysics schemes is derived by analytically solving the stochastic collection equation (SCE), where the theoretical collision efficiency for individual snowflake&amp;#8211;cloud droplet pairs is applied. The snowflake shape is assumed to be nonspherical with the mass- and area-size relations suggested by an observational study. The performance of the new parameterization is compared to two parameterizations based on the continuous collection equation, one with the spherical shape assumption for snowflakes (SPH-CON), and the other with the nonspherical shape assumption employed in the new parameterization (NSP-CON). In box model simulations, only the new parameterization reproduces a relatively slow decrease in the cloud droplet number concentration which is predicted by the direct SCE solver. This results from considering the preferential collection of cloud droplets depending on their sizes in the new parameterization based on the SCE. In idealized squall-line simulations using a cloud-resolving model, the new parameterization predicts heavier precipitation in the convective core region compared to SPH-CON, and a broader area of the trailing stratiform rain compared to NSP-CON due to the horizontal advection of greater amount of snow in the upper layer. In the real-case simulations of a line-shaped mesoscale convective system that passed over the central Korean Peninsula, the new parameterization predicts higher frequencies of light precipitation rates and lower frequencies of heavy precipitation rates. The relatively large amount of upper-level snow in the new parameterization contributes to a broadening of the area with significant snow water path.&lt;/p&gt;

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