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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.

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.

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.

After decades of research and development, the WSR-88D (NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data (PRD) that have the potential to improve weather observations, quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction (NWP) models. In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.

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 minutes 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 focus placed 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 towards 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.

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.

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.

Despite recent advances in storm-scale ensemble NWP, short-term (0–90 min) explicit forecasts of severe hail remain a major challenge as a result of the fast evolution and short time scales of hail-producing convective storms and the substantial uncertainty associated with the microphysical representation of hail. In this study, 0–90-min ensemble hail forecasts for the supercell storms of 20 May 2013 over central Oklahoma are examined and verified, with the goals of 1) evaluating ensemble forecast performance, 2) comparing the advantages and limitations of different forecast fields potentially suitable for the prediction of hail and severe hail in a Warn-on-Forecast setting, and 3) evaluating the use of dual-polarization radar observations for hail forecast validation. To address the challenges of hail prediction and to produce skillful forecasts, the ensemble uses a two-moment microphysics scheme that explicitly predicts a hail-like rimed-ice category and is run with a grid spacing of 500 m. Radar reflectivity factor and radial velocity, along with surface observations, are assimilated every 5 min for 1 h as the storms were developing to maturity, followed by a 90-min ensemble forecast. Several methods of hail prediction and hail forecast verification are then examined, including the prediction of the maximum hail size compared to Storm Prediction Center (SPC) and Meteorological Phenomena Identification Near the Ground (mPING) hail observations, and verification of model data against single- and dual-polarization radar-derived fields including hydrometeor classification algorithm (HCA) output and the maximum estimated size of hail (MESH). The 0–90-min ensemble hail predictions are found to be marginally to moderately skillful depending on the verification method used.

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.

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.