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Bryan J. Putnam, Ming Xue, Youngsun Jung, Nathan A. Snook, and Guifu Zhang

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 (Z DR) and specific differential phase (K DP) fields, the double-moment ensemble compares more realistically to the observations and better distinguishes the stratiform and convective precipitation regions. The Z DR 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 K DP values in the single-moment ensemble.

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Jeffrey C. Snyder, Howard B. Bluestein, Daniel T. Dawson II, and Youngsun Jung

Abstract

With the development of multimoment bulk microphysical schemes and polarimetric radar forward operators, one can better examine convective storms simulated in high-resolution numerical models from a simulated polarimetric radar perspective. Subsequently, relationships between observable and unobservable quantities can be examined that may provide useful information about storm intensity and organization that otherwise would be difficult to obtain. This paper, Part I of a two-part sequence, describes the bulk microphysics scheme, polarimetric radar forward operator, and numerical model configuration used to simulate supercells in eight idealized, horizontally homogenous environments with different wind profiles. The microphysical structure and evolution of copolar cross-correlation coefficient (ρhv) rings associated with simulated supercells are examined in Part I, whereas Part II examines Z DR columns, Z DR rings, and K DP columns. In both papers, some systematic differences between the signature seen at X and S bands are discussed. The presence of hail is found to affect ρhv much more at X band than at S band (and is found to affect Z DR more at S band than at X band), which corroborates observations. The ρhv half ring is found to be associated with the presence of large, sometimes wet, hail aloft, with an ~20-min time lag between increases in the size of the ρhv ring aloft and the occurrence of a large amount of hail near the ground in some simulations.

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Jeffrey C. Snyder, Howard B. Bluestein, Daniel T. Dawson II, and Youngsun Jung

Abstract

A high-resolution numerical model and polarimetric forward operator allow one to examine simulated convective storms from the perspective of observable polarimetric radar quantities, enabling a better comparison of modeled and observed deep moist convection. Part I of this two-part study described the model and forward operator used for all simulations and examined the structure and evolution of rings of reduced copolar cross-correlation coefficient (i.e., ρ hv rings). The microphysical structure of upward extensions of enhanced differential reflectivity (Z DR columns and Z DR rings) and enhanced specific differential phase (K DP columns) near and within the updrafts of convective storms serve as the focus of this paper. In general, simulated Z DR columns are located immediately west of the midlevel updraft maximum and are associated with rainwater lofted above the 0°C level and wet hail/graupel, whereas Z DR rings are associated with wet hail located near and immediately east of the midlevel updraft maximum. The deepest areas of Z DR > 1 dB aloft are associated with supercells in the highest shear environments and those that have the most intense updrafts; the upper extent of the Z DR signatures is found to be positively correlated with the amount and mean-mass diameter of large hail aloft likely as a by-product of the shared correlations with updraft intensity and wind shear. Large quantities of rain compose the K DP columns, with the size and intensity of the updrafts directly proportional to the size and depth of the K DP columns.

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Bryan J. Putnam, Ming Xue, Youngsun Jung, Guifu Zhang, and Fanyou Kong

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.

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Timothy A. Supinie, Nusrat Yussouf, Youngsun Jung, Ming Xue, Jing Cheng, and Shizhang Wang

Abstract

NOAA’s National Severe Storms Laboratory is actively developing phased-array radar (PAR) technology, a potential next-generation weather radar, to replace the current operational WSR-88D radars. One unique feature of PAR is its rapid scanning capability, which is at least 4–5 times faster than the scanning rate of WSR-88D. To explore the impact of such high-frequency PAR observations compared with traditional WSR-88D on severe weather forecasting, several storm-scale data assimilation and forecast experiments are conducted. Reflectivity and radial velocity observations from the 22 May 2011 Ada, Oklahoma, tornadic supercell storm are assimilated over a 45-min period using observations from the experimental PAR located in Norman, Oklahoma, and the operational WSR-88D radar at Oklahoma City, Oklahoma. The radar observations are assimilated into the ARPS model within a heterogeneous mesoscale environment and 1-h ensemble forecasts are generated from analyses every 15 min. With a 30-min assimilation period, the PAR experiment is able to analyze more realistic storm structures, resulting in higher skill scores and higher probabilities of low-level vorticity that align better with the locations of radar-derived rotation compared with the WSR-88D experiment. Assimilation of PAR observations for a longer 45-min time period generates similar forecasts compared to assimilating WSR-88D observations, indicating that the advantage of rapid-scan PAR is more noticeable over a shorter 30-min assimilation period. An additional experiment reveals that the improved accuracy from the PAR experiment over a shorter assimilation period is mainly due to its high-temporal-frequency sampling capability. These results highlight the benefit of PAR’s rapid-scan capability in storm-scale modeling that can potentially extend severe weather warning lead times.

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Dasol Kim, Chang-Hoi Ho, Doo-Sun R. Park, Johnny C. L. Chan, and Youngsun Jung

Abstract

In this study, the variation of tropical cyclone (TC) rainfall area over the subtropical oceans is investigated using the Tropical Rainfall Measuring Mission precipitation data collected from 1998 to 2014, with a focus on its relationship with environmental conditions. In the subtropics, higher moving speed and larger vertical wind shear significantly contribute to an increase in TC rainfall area by making horizontal rainfall distribution more asymmetric, while sea surface temperature rarely affects the fluctuation of TC rainfall area. This relationship between TC rainfall area and environmental conditions in the subtropics is almost opposite to that in the tropics. It is suggested that, in the subtropics, unlike the tropics, dynamic environmental conditions are likely more crucial to varying TC rainfall area than thermodynamic environmental ones.

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Marcus Johnson, Youngsun Jung, Jason A. Milbrandt, Hugh Morrison, and Ming Xue

Abstract

Many flavors of multicategory, multimoment bulk microphysics schemes (BMPs) have various treatments of rimed ice. In this study, we compare three two-moment schemes available in the WRF Model—Milbrandt–Yau (MY2), National Severe Storms Laboratory (NSSL), and the two-category configuration of the Predicted Particle Properties (P3) scheme—focusing on differences in rimed-ice representation and their impacts on surface rain and ice. Idealized supercell simulations are performed. A polarimetric radar data simulator is used to evaluate their ability to reproduce the Z DR arc and hail signature in the forward-flank downdraft, well-known supercell polarimetric signatures that are potentially sensitive to rimed-ice parameterization. Both the MY2 and NSSL schemes simulate enhanced surface Z DR bands, but neither scheme simulates a Z DR arc commonly identified in observation-based studies. Surface Z DR in the default P3 scheme is homogeneous in the supercell’s forward flank, and is due to the scheme’s restrictive minimum rain particle size distribution (PSD) slope bound preventing the presence of larger drops creating a Z DR arc. The NSSL scheme simulates the location of the hail signature in the forward-flank downdraft more consistent with observations than the other two schemes. Large hail in MY2 sediments well downstream of the updraft (atypically compared to observations) near the surface. The sedimentation of large ice in the default P3 scheme is limited by a restrictive maximum ice number-weighted mean diameter limit within the scheme, precluding the scheme’s ability to reduce Z DR (and ρ HV compared to the MY2 and NSSL schemes) near the surface.

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Lianglyu Chen, Chengsi Liu, Ming Xue, Gang Zhao, Rong Kong, and Youngsun Jung

Abstract

When directly assimilating radar data within a variational framework using hydrometeor mixing ratios (q) as control variables (CVq), the gradient of the cost function becomes extremely large when background mixing ratio is close to zero. This significantly slows down minimization convergence and makes the assimilation of radial velocity and other observations ineffective because of the dominance of the reflectivity observation term in the cost function gradient. Using logarithmic hydrometeor mixing ratios as control variables (CV logq) can alleviate the problem but the high nonlinearity of logarithmic transformation can introduce spurious analysis increments into mixing ratios. In this study, power transform of hydrometeors is proposed to form new control variables (CVpq) where the nonlinearity of transformation can be adjusted by a tuning exponent or power parameter p. The performance of assimilating radar data using CVpq is compared with those using CVq and CV logq for the analyses and forecasts of five convective storm cases from the spring of 2017. Results show that CVpq with p = 0.4 (CVpq0.4) gives the best reflectivity forecasts in terms of root-mean-square error and equitable threat score. Furthermore, CVpq0.4 has faster convergence of cost function minimization than CVq and produces less spurious analysis increment than CV logq. Compared to CVq and CV logq, CVpq0.4 has better skills of 0–3-h composite reflectivity forecasts, and the updraft helicity tracks for the 16 May 2017 Texas and Oklahoma tornado outbreak case are more consistent with observations when using CVpq0.4.

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Derek R. Stratman, Nusrat Yussouf, Youngsun Jung, Timothy A. Supinie, Ming Xue, Patrick S. Skinner, and Bryan J. Putnam

Abstract

A potential replacement candidate for the aging operational WSR-88D infrastructure currently in place is the phased array radar (PAR) system. The current WSR-88Ds take ~5 min to produce a full volumetric scan of the atmosphere, whereas PAR technology allows for full volumetric scanning of the same atmosphere every ~1 min. How this increase in temporal frequency of radar observations might affect the National Severe Storms Laboratory’s (NSSL) Warn-on-Forecast system (WoFS), which is a storm-scale ensemble data assimilation and forecast system for severe convective weather, is unclear. Since radar data assimilation is critical for the WoFS, this study explores the optimal temporal frequency of PAR observations for storm-scale data assimilation using the 31 May 2013 El Reno, Oklahoma, tornadic supercell event. The National Severe Storms Laboratory’s National Weather Radar Testbed PAR in Norman, Oklahoma, began scanning this event more than an hour before the first (and strongest) tornado developed near El Reno, and scanned most of the tornadic supercell’s evolution. Several experiments using various cycling and data frequencies to synchronously and asynchronously assimilate these PAR observations are conducted to produce analyses and very short-term forecasts of the El Reno supercell. Forecasts of low-level reflectivity and midlevel updraft helicity are subjectively evaluated and objectively verified using spatial and object-based techniques. Results indicate that assimilating more frequent PAR observations can lead to more accurate analyses and probabilistic forecasts of the El Reno supercell at longer lead times. Hence, PAR is a promising radar platform for WoFS.

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Daniel T. Dawson II, Edward R. Mansell, Youngsun Jung, Louis J. Wicker, Matthew R. Kumjian, and Ming Xue

Abstract

The low levels of supercell forward flanks commonly exhibit distinct differential reflectivity (Z DR) signatures, including the low-Z DR hail signature and the high-Z DR “arc.” The Z DR arc has been previously associated with size sorting of raindrops in the presence of vertical wind shear; here this model is extended to include size sorting of hail. Idealized simulations of a supercell storm observed by the Norman, Oklahoma (KOUN), polarimetric radar on 1 June 2008 are performed using a multimoment bulk microphysics scheme, in which size sorting is allowed or disallowed for hydrometeor species. Several velocity–diameter relationships for the hail fall speed are considered, as well as fixed or variable bulk densities that span the graupel-to-hail spectrum. A T-matrix-based emulator is used to derive polarimetric fields from the hydrometeor state variables.

Size sorting of hail is found to have a dominant impact on Z DR and can result in a Z DR arc from melting hail even when size sorting is disallowed in the rain field. The low-Z DR hail core only appears when size sorting is allowed for hail. The mean storm-relative wind in a deep layer is found to align closely with the gradient in mean mass diameter of both rain and hail, with a slight shift toward the storm-relative mean wind below the melting level in the case of rain. The best comparison with the observed 1 June 2008 supercell is obtained when both rain and hail are allowed to sort, and the bulk density and associated fall-speed curve for hail are predicted by the model microphysics.

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