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This article summarizes research and risk reduction that will inform acquisition decisions regarding NOAA's future national operational weather radar network. A key alternative being evaluated is polarimetric phased-array radar (PAR). Research indicates PAR can plausibly achieve fast, adaptive volumetric scanning, with associated benefits for severe-weather warning performance. We assess these benefits using storm observations and analyses, observing system simulation experiments, and real radar-data assimilation studies. Changes in the number and/or locations of radars in the future network could improve coverage at low altitude. Analysis of benefits that might be so realized indicates the possibility for additional improvement in severe-weather and flash-flood warning performance, with associated reduction in casualties. Simulations are used to evaluate techniques for rapid volumetric scanning and assess data quality characteristics of PAR. Finally, we describe progress in developing methods to compensate for polarimetric variable estimate biases introduced by electronic beam-steering. A research-to-operations (R2O) strategy for the PAR alternative for the WSR-88D replacement network is presented. [ABSTRACT FROM AUTHOR]

Assimilation of dual-polarization (dual-pol) observations provides more accurate storm-scale analyses to initialize forecasts of severe convective thunderstorms. This study investigates the impact assimilating experimental sector-scan dual-pol observations has on storm-scale ensemble forecasts and how this impact changes over different data assimilation (DA) windows using the ensemble Kalman filter (EnKF). Ensemble forecasts are initialized after 30, 45, and 60 min of DA for two sets of experiments that assimilate either reflectivity and radial velocity only (EXPZ) or reflectivity and radial velocity plus differential reflectivity (EXPZZDR). This study uses the 31 May 2013 Oklahoma event, which included multiple storms that produced tornadoes and severe hail, with a focus on two storms that impacted El Reno and Stillwater during the event. The earliest initialized forecast of EXPZZDR better predicts the evolution of the El Reno storm than EXPZ, but the two sets of experiments become similar at subsequent forecast times. However, the later EXPZZDR forecasts of the Stillwater storm, which organized toward the end of the DA window, produce improved results compared to EXPZ, in which the storm is less intense and weakens. Evaluation of forecast products for supercell mesocyclones [updraft helicity (UH)] and hail show similar results, with earlier EXPZZDR forecasts better predicting the UH swaths of the El Reno storm and later forecasts producing improved UH and hail swaths for the Stillwater storm. The results indicate that the assimilation of ZDR over fewer DA cycles can produce improved forecasts when DA windows sufficiently cover storms during their initial development and organization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 ZDR 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 ZDR bands, but neither scheme simulates a ZDR arc commonly identified in observation-based studies. Surface ZDR 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 ZDR 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 ZDR (and ρHV compared to the MY2 and NSSL schemes) near the surface. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 ZDR columns, ZDR rings, and KDP 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 ZDR 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. [ABSTRACT FROM AUTHOR]

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., ρhv rings). The microphysical structure of upward extensions of enhanced differential reflectivity ( ZDR columns and ZDR rings) and enhanced specific differential phase ( KDP columns) near and within the updrafts of convective storms serve as the focus of this paper. In general, simulated ZDR 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 ZDR rings are associated with wet hail located near and immediately east of the midlevel updraft maximum. The deepest areas of ZDR > 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 ZDR 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 KDP columns, with the size and intensity of the updrafts directly proportional to the size and depth of the KDP columns. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The low levels of supercell forward flanks commonly exhibit distinct differential reflectivity ( ZDR) signatures, including the low- ZDR hail signature and the high- ZDR 'arc.' The ZDR 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 ZDR and can result in a ZDR arc from melting hail even when size sorting is disallowed in the rain field. The low- ZDR 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]