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 Ensemble Kalman Filter Assimilation of Polarimetric Radar Observations for the 20 May 2013 Oklahoma Tornadic Supercell Case

2019 ◽  
Vol 147 (7) ◽  
pp. 2511-2533 ◽  
Author(s):  
Bryan Putnam ◽  
Ming Xue ◽  
Youngsun Jung ◽  
Nathan Snook ◽  
Guifu Zhang
Keyword(s):  
Kalman Filter ◽  
Scattering Amplitudes ◽  
Ensemble Kalman Filter ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Microphysics Scheme ◽  
Radar Observations ◽  
Rayleigh Approximation ◽  
Double Moment ◽  
T Matrix

Abstract Real polarimetric radar observations are directly assimilated for the first time using the ensemble Kalman filter (EnKF) for a supercell case from 20 May 2013 in Oklahoma. A double-moment microphysics scheme and advanced polarimetric radar observation operators are used together to estimate the model states. Lookup tables for the observation operators are developed based on T-matrix scattering amplitudes for all hydrometeor categories, which improve upon previous curved-fitted approximations of T-matrix scattering amplitudes or the Rayleigh approximation. Two experiments are conducted: one assimilates reflectivity (Z) and radial velocity (Vr) (EXPZ), and one assimilates in addition differential reflectivity (ZDR) below the observed melting level at ~2-km height (EXPZZDR). In the EnKF analyses, EXPZZDR exhibits a ZDR arc that better matches observations than EXPZ. EXPZZDR also has higher ZDR above 2 km, consistent with the observed ZDR column. Additionally, EXPZZDR has an improved estimate of the model microphysical states. Specifically, the rain mean mass diameter (Dnr) in EXPZZDR is higher in the ZDR arc region and the total rain number concentration (Ntr) is lower downshear in the forward flank than EXPZ when compared to values retrieved from the polarimetric observations. Finally, a negative gradient of hail mean mass diameter (Dnh) is found in the right-forward flank of the EXPZZDR analysis, which supports previous findings indicating that size sorting of hail, as opposed to rain, has a more significant impact on low-level polarimetric signatures. This paper represents a proof-of-concept study demonstrating the value of assimilating polarimetric radar data in improving the analysis of features and states related to microphysics in supercell storms.

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.

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.

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.

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 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.

The Use of a Modified Ebert–McBride Technique to Evaluate Mesoscale Model QPF as a Function of Convective System Morphology during IHOP 2002

2006 ◽  
Vol 21 (3) ◽  
pp. 288-306 ◽  
Author(s):  
Jeremy S. Grams ◽  
Willam A. Gallus ◽  
Steven E. Koch ◽  
Linda S. Wharton ◽  
Andrew Loughe ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Cold Pool ◽  
Convective System ◽  
Convective Systems ◽  
Mesoscale Convective ◽  
Almost All ◽  
Rain Rates ◽  
Average Rain

Abstract The Ebert–McBride technique (EMT) is an entity-oriented method useful for quantitative precipitation verification. The EMT was modified to optimize its ability to identify contiguous rain areas (CRAs) during the 2002 International H2O Project (IHOP). This technique was then used to identify systematic sources of error as a function of observed convective system morphology in three 12-km model simulations run over the IHOP domain: Eta, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), and the Weather Research and Forecasting (WRF). The EMT was fine-tuned to optimize the pattern matching of forecasts to observations for the scales of precipitation systems observed during IHOP. To investigate several error measures provided by the EMT, a detailed morphological analysis of observed systems was performed using radar data for all CRAs identified in the IHOP domain. The modified EMT suggests that the Eta Model produced average rain rates, peak rainfall amounts, and total rain volumes that were lower than observed for almost all types of convective systems, likely because of its production of overly smoothed and low-variability quantitative precipitation forecasts. The MM5 and WRF typically produced average rain rates and peak rainfall amounts that were larger than observed in most linear convective systems. However, the rain volume for these models was too low for almost all types of convective systems, implying a sizeable underestimate in areal coverage. All three models forecast rainfall too far northwest for linear systems. The results for the WRF and MM5 are consistent with previous observations of mesoscale models run with explicit microphysics and no convective parameterization scheme, suggesting systematic problems with the prediction of mesoscale convective system cold pool dynamics.

scholarly journals Ensemble Kalman Filter Analyses and Forecasts of a Severe Mesoscale Convective System Using Different Choices of Microphysics Schemes

2014 ◽  
Vol 142 (9) ◽  
pp. 3243-3263 ◽  
Author(s):  
Dustan M. Wheatley ◽  
Nusrat Yussouf ◽  
David J. Stensrud
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Higher Order ◽  
Convective System ◽  
Forecast Performance ◽  
Stratiform Precipitation ◽  
Mesoscale Convective

A Weather Research and Forecasting Model (WRF)-based ensemble data assimilation system is used to produce storm-scale analyses and forecasts of the 4–5 July 2003 severe mesoscale convective system (MCS) over Indiana and Ohio, which produced numerous high wind reports across the two states. Single-Doppler observations are assimilated into a 36-member, storm-scale ensemble during the developing stage of the MCS with the ensemble Kalman filter (EnKF) approach encoded in the Data Assimilation Research Testbed (DART). The storm-scale ensemble is constructed from mesoscale EnKF analyses produced from the assimilation of routinely available observations from land and marine stations, rawinsondes, and aircraft, in an attempt to better represent the complex mesoscale environment for this event. Three EnKF simulations were performed using the National Severe Storms Laboratory (NSSL) one- and two-moment and Thompson microphysical schemes. All three experiments produce a linear convective segment at the final analysis time, similar to the observed system at 2300 UTC 4 July 2003. The higher-order schemes—in particular, the Thompson scheme—are better able to produce short-range forecasts of both the convective and stratiform components of the observed bowing MCS, and produce the smallest temperature errors when comparing surface observations and dropsonde data to corresponding model data. Only the higher-order microphysical schemes produce any appreciable rear-to-front flow in the stratiform precipitation region that trailed the simulated systems. Forecast performance by the three microphysics schemes is discussed in context of differences in microphysical composition produced in the stratiform precipitation regions of the rearward expanding MCSs.

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

2009 ◽  
Vol 24 (3) ◽  
pp. 730-748 ◽  
Author(s):  
Hyang Suk Park ◽  
A. V. Ryzhkov ◽  
D. S. Zrnić ◽  
Kyung-Eak Kim
Keyword(s):  
Fuzzy Logic ◽  
Classification Scheme ◽  
Mesoscale Convective System ◽  
Radar Data ◽  
Classification Algorithm ◽  
Convective System ◽  
Polarimetric Radar ◽  
The Matrix ◽  
Mesoscale Convective ◽  
Radar Measurements

Abstract This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric variables and discerns 10 different classes of radar echoes. Furthermore, a methodology for the new fuzzy logic classification scheme is discussed and the results are illustrated using polarimetric radar data collected with the Norman, Oklahoma (KOUN), WSR-88D prototype radar during a mesoscale convective system event on 13 May 2005.

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