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.

Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data

2012 ◽  
Vol 140 (7) ◽  
pp. 2126-2146 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract This study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8–9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential). Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physics uncertainty and effectively plays a role of flow-dependent inflation (in precipitation regions) during EnKF data assimilation. The ensemble predicts with high probability (approximately 0.65) the near-surface mesovortex associated with the first of three reported tornadoes. Though a bias toward stronger precipitation is noted in the ensemble forecasts, all experiments produce skillful probabilistic forecasts of radar reflectivity on a 0–3-h time scale as evaluated by multiple probabilistic verification metrics. These results suggest that both the inclusion of CASA radar data and use of a mixed-microphysics ensemble during EnKF data assimilation positively impact the skill of 2–3-h ensemble forecasts of mesovortices, despite having little impact on the quality of precipitation forecasts (analyzed in terms of predicted radar reflectivity), and are important steps toward an operational EnKF-based ensemble analysis and probabilistic forecast system to support convective-scale warn-on-forecast operations.

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 Simulation of Polarimetric Radar Variables from 2013 CAPS Spring Experiment Storm-Scale Ensemble Forecasts and Evaluation of Microphysics Schemes

2016 ◽  
Vol 145 (1) ◽  
pp. 49-73 ◽  
Author(s):  
Bryan J. Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Guifu Zhang ◽  
Fanyou Kong
Keyword(s):  
Mesoscale Convective System ◽  
Liquid Water Content ◽  
Convective System ◽  
Polarimetric Radar ◽  
Ensemble Forecasts ◽  
Direct Evaluation ◽  
Mesoscale Convective ◽  
Double Moment ◽  
Size Sorting ◽  
Radar Simulator

Abstract Polarimetric radar variables are simulated from members of the 2013 Center for Analysis and Prediction of Storms (CAPS) Storm-Scale Ensemble Forecasts (SSEF) with varying microphysics (MP) schemes and compared with observations. The polarimetric variables provide information on hydrometeor types and particle size distributions (PSDs), neither of which can be obtained through reflectivity (Z) alone. The polarimetric radar simulator pays close attention to how each MP scheme [including single- (SM) and double-moment (DM) schemes] treats hydrometeor types and PSDs. The recent dual-polarization upgrade to the entire WSR-88D network provides nationwide polarimetric observations, allowing for direct evaluation of the simulated polarimetric variables. Simulations for a mesoscale convective system (MCS) and supercell cases are examined. Five different MP schemes—Thompson, DM Milbrandt and Yau (MY), DM Morrison, WRF DM 6-category (WDM6), and WRF SM 6-category (WSM6)—are used in the ensemble forecasts. Forecasts using the partially DM Thompson and fully DM MY and Morrison schemes better replicate the MCS structure and stratiform precipitation coverage, as well as supercell structure compared to WDM6 and WSM6. Forecasts using the MY and Morrison schemes better replicate observed polarimetric signatures associated with size sorting than those using the Thompson, WDM6, and WSM6 schemes, in which such signatures are either absent or occur at abnormal locations. Several biases are suggested in these schemes, including too much wet graupel in MY, Morrison, and WDM6; a small raindrop bias in WDM6 and WSM6; and the underforecast of liquid water content in regions of pure rain for all schemes.

scholarly journals Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

2015 ◽  
Vol 143 (4) ◽  
pp. 1035-1057 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Surface Wind ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Lateral Boundary Condition ◽  
Convective System ◽  
Near Surface ◽  
Radar Observations ◽  
Probabilistic Forecasts ◽  
Mesoscale Convective

Abstract In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated. The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.

scholarly journals Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

2017 ◽  
Vol 145 (12) ◽  
pp. 4911-4936 ◽  
Author(s):  
Jonathan Labriola ◽  
Nathan Snook ◽  
Youngsun Jung ◽  
Bryan Putnam ◽  
Ming Xue
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
South Central ◽  
Single Moment ◽  
Double Moment ◽  
Horizontal Grid ◽  
Severe Weather Event ◽  
Numerical Weather Prediction Models

Explicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size of hail (MESH), hydrometeor classification algorithm (HCA) output, and hail size discrimination algorithm (HSDA) output. Resulting hail forecasts show at most marginal skill, with the level of skill dependent on the forecast initialization time and microphysical scheme used. Forecasts using the double-moment scheme predict many small hailstones aloft, while the single-moment members predict larger hailstones. Near the surface, double-moment members predict larger hailstone sizes than their single-member counterparts. Hail in the forecasts is found to melt too quickly near the surface for members using either of the microphysics schemes examined. Analysis of microphysical budgets in both schemes indicates that both schemes suboptimally represent hail processes, adversely impacting the skill of surface hail forecasts.

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.

Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM

2015 ◽  
Vol 72 (2) ◽  
pp. 623-640 ◽  
Author(s):  
Weixin Xu ◽  
Steven A. Rutledge
Keyword(s):  
Mesoscale Convective System ◽  
Tropical Rainfall Measuring Mission ◽  
Radar Data ◽  
Convective System ◽  
Precipitation Radar ◽  
Convective Systems ◽  
Trmm Pr ◽  
Mesoscale Convective ◽  
Trmm Precipitation

Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.

scholarly journals Genesis of Hurricane Julia (2010) within an African Easterly Wave: Developing and Nondeveloping Members from WRF–LETKF Ensemble Forecasts

2014 ◽  
Vol 71 (7) ◽  
pp. 2763-2781 ◽  
Author(s):  
Stefan F. Cecelski ◽  
Da-Lin Zhang ◽  
Takemasa Miyoshi
Keyword(s):  
Surface Pressure ◽  
Mesoscale Convective System ◽  
Convective System ◽  
Ensemble Forecasts ◽  
Upper Troposphere ◽  
African Easterly Wave ◽  
Easterly Wave ◽  
Mesoscale Convective ◽  
Rossby Radius Of Deformation ◽  
Faster Development

Abstract In this study, the predictability of and parametric differences in the genesis of Hurricane Julia (2010) are investigated using 20 mesoscale ensemble forecasts with the finest resolution of 1 km. Results show that the genesis of Julia is highly predictable, with all but two members undergoing genesis. Despite the high predictability, substantial parametric differences exist between the stronger and weaker members. Notably, the strongest-developing member exhibits large upper-tropospheric warming within a storm-scale outflow during genesis. In contrast, the nondeveloping member has weak and more localized warming due to inhibited convective development and a lack of a storm-scale outflow. A reduction in the Rossby radius of deformation in the strongest member aids in the accumulation of the warmth, while little contraction takes place in the nondeveloping member. The warming in the upper troposphere is responsible for the development of meso-α-scale surface pressure falls and a meso-β surface low in the strongest-developing member. Such features fail to form in the nondeveloping member as weak upper-tropospheric warming is unable to induce meaningful surface pressure falls. Cloud ice content is nearly doubled in the strongest member as compared to its nondeveloping counterparts, suggesting the importance of depositional heating of the upper troposphere. It is found that the stronger member undergoes genesis faster due to the lack of convective inhibition near the African easterly wave (AEW) pouch center prior to genesis. This allows for the faster development of a mesoscale convective system and storm-scale outflow, given the already favorable larger-scale conditions.

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