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This article describes a concept whereby future operational polarimetric phased array radars (PPAR) routinely monitor ice crystal alignment regions caused by thundercloud electric fields with volume scan updates (∼12 min−1) sufficient to resolve the temporal variation due to lightning and subsequent rapid electric field regeneration in nonsevere thunderstorms. Routine observations of crystal alignment regions may enhance thunderstorm nowcasting through comparison of their temporal and spatial structure with other polarimetric signatures, integration with lightning detection data, and assimilation into convection resolving numerical weather prediction models. If crystal alignment observations indicate strong electrification well in advance of the first lightning strike and likewise reliably indicate the decay of strong electric fields at the end of a storm, this capability may improve warning for lightning-sensitive activities such as airport ramp operations and space launch. Experimental observations of crystal alignment volumes in central Oklahoma severe storms and their relation to those storms' structures are presented and used to motivate discussion of possible PPAR architectures. In one case—a tornadic supercell—these observations illustrate an important limitation. Even the hypothesized 12 min−1 volume scan update rate would not resolve the temporal variation of the crystal alignment regions in such storms, suggesting that special, adaptive scanning methods may be appropriate for such storms. We describe how future operational phased array radars could support a crystal alignment measurement mode via parallel, time-multiplexed processing and discuss potential impacts on the radar's primary weather observation mission. We conclude by discussing research needed to better understand technical challenges and operational benefits. [ABSTRACT FROM AUTHOR]

This study uses a new, unique dataset created by combining multi-Doppler radar wind and reflectivity analysis, diabatic Lagrangian analysis (DLA) retrievals of temperature and water substance, and a complex hail trajectory model to create millions of numerically simulated hail trajectories in the Kingfisher, Oklahoma, supercell on 29 May 2012. The DLA output variables are used to obtain a realistic, 4D depiction of the storm's thermal and hydrometeor structure as required input to the detailed hail growth trajectory model. Hail embryos are initialized in the hail growth module every 3 min of the radar analysis period (2251–0000 UTC) to produce over 2.7 million hail trajectories. A spatial integration technique considering all trajectories is used to identify locations within the supercell where melted particles and subsevere and severe hailstones reside in their lowest and highest concentrations. It is found that hailstones are more likely to reside for longer periods closer to the downshear updraft within the midlevel mesocyclone in a region of decelerated midlevel mesocyclonic horizontal flow, termed the downshear deceleration zone (DDZ). Additionally, clusters of trajectories are analyzed using a trajectory clustering method. Trajectory clusters show there are many trajectory pathways that result in hailstones ≥ 4.5 cm, including trajectories that begin upshear of the updraft away from ideal growth conditions and trajectories that grow within the DDZ. There are also trajectory clusters with similar shapes that experience widely different environmental and hailstone characteristics along the trajectory. Significance Statement: The purpose of this study is to understand how hail grew in a thunderstorm that was observed by numerous instruments. The observations were input into a hail trajectory model to simulate hail growth. We found a part of the storm near the updraft where hailstones could remain aloft longer and therefore grow larger. Most modeled severe hailstones were found in the storm in this region. However, we also found that there are many different pathways hailstones can take to become large, although there are still some common characteristics among the pathways. [ABSTRACT FROM AUTHOR]

The assimilation of reflectivity (Z) within 3DVar or hybrid ensemble-3DVar (En3DVar) requires the adjoint of the Z observation operator. With the 3DVar or En3DVar method, previous studies often use Z operators consistent with a single-moment microphysics scheme even when the forecast model uses a double-moment scheme. As such, only the mixing ratios of hydrometeors are directly updated by the data assimilation (DA) system, leading to inconsistency between the analyzed microphysics state variables and the microphysics scheme in the prediction model. In this study, we formulated a Z operator consistent with the double-moment Thompson microphysics used in the numerical integrations; in the operator the snow and graupel reflectivity components are simplified using functions fitted to T-matrix simulation results. This operator and its adjoint are implemented within the GSI hybrid En3DVar DA system to enable direct assimilation of Z with a consistent operator. The impacts of this new operator on convective storm analysis through DA cycles, and on the ensuing 3-h forecasts are first examined in detail for a tornado outbreak case of 16 May 2017 in Texas and Oklahoma, and then for five additional thunderstorm cases. Forecast reflectivity, hourly precipitation, and updraft helicity tracks are subjectively evaluated, while neighborhood ETSs and performance diagrams are examined for reflectivity and/or precipitation. Compared to experiments using a Z operator consistent with a single-moment microphysics scheme, the Z operator consistent with doublemoment Thompson microphysics used in the forecast model produces better forecasts of reflectivity, hourly precipitation, and updraft helicity tracks with smaller biases, and the improvement is somewhat larger for a higher Z threshold. [ABSTRACT FROM AUTHOR]

The intrinsic uncertainty of radar-based retrievals in snow originates from a large diversity of snow growth habits, densities, and particle size distributions, all of which can make interpreting radar measurements of snow very challenging. The application of polarimetric radar for snow measurements can mitigate some of these issues. In this study, a novel polarimetric method for quantification of the extinction coefficient and visibility in snow, based on the joint use of radar reflectivity at horizontal polarization Z and specific differential phase KDP, is introduced. A large 2D-video-disdrometer snow dataset from central Oklahoma is used to derive a polarimetric bivariate power-law relation for the extinction coefficient, σ e ⁡ (K DP , Z) = γ K DP α Z β . The relation is derived for particle aspect ratios ranging from 0.5 to 0.8 and the width of the canting angle distribution ranging from 0° to 40°, values typical of aggregated snow, and validated via theoretical and analytical derivations/simulations. The multiplier of the relation is sensitive to variations in particles' densities, the width of the canting angle distribution, and particles' aspect ratios, whereas the relation's exponents are practically invariant to changes in the latter two parameters. This novel approach is applied to polarimetric S-band WSR-88D data and verified against previous studies and in situ measurements of the extinction coefficient for four snow events in the eastern United States. The polarimetric radar estimates of the extinction coefficient exhibit smaller biases in comparison to previous studies concerning the ground measurements. The results indicate that there is good potential for reliable radar estimates of visibility from polarimetric weather radars, a parameter inversely proportional to the extinction coefficient. [ABSTRACT FROM AUTHOR]

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 CVlogq for the analyses and forecasts of five convective storm cases from the spring of 2017. Results show that CVpq with p 5 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 CVlogq. Compared to CVq and CVlogq, 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. [ABSTRACT FROM AUTHOR]

In this study, a hybrid En3DVar data assimilation (DA) scheme is compared with 3DVar, EnKF, and pure En3DVar for the assimilation of radar data in a real tornadic storm case. Results using hydrometeor mixing ratios (CVq) or logarithmic mixing ratios (CVlogq) as the control variables are compared in the variational DA framework. To address the lack of radial velocity impact issues when using CVq, a procedure that assimilates reflectivity and radial velocity data in two separate analysis passes is adopted. Comparisons are made in terms of the root-mean-square innovations (RMSIs) as well as the intensity and structure of the analyzed and forecast storms. For pure En3DVar that uses 100% ensemble covariance, CVlogq and CVq have similar RMSIs in the velocity analyses, but errors grow faster during forecasts when using CVlogq. Introducing static background error covariance B at 5% in hybrid En3DVar (with CVlogq) significantly reduces the forecast error growth. Pure En3DVar produces more intense reflectivity analyses than EnKF that more closely match the observations. Hybrid En3DVar with 50% B outperforms other weights in terms of the RMSIs and forecasts of updraft helicity and is thus used in the final comparison with 3DVar and EnKF. The hybrid En3DVar is found to outperform EnKF in better capturing the intensity and structure of the analyzed and forecast storms and outperform 3DVAR in better capturing the intensity and evolution of the rotating updraft. [ABSTRACT FROM AUTHOR]

Tornadic supercells moved across parts of Oklahoma on the afternoon and evening of 9 May 2016. One such supercell, while producing a long-lived tornado, was observed by nearby WSR-88D radars to contain a strong anticyclonic velocity couplet on the lowest elevation angle. This couplet was located in a very atypical position relative to the ongoing cyclonic tornado and to the supercell’s updraft. A storm survey team identified damage near where this couplet occurred, and, in the absence of evidence refuting otherwise, the damage was thought to have been produced by an anticyclonic tornado. However, such a tornado was not seen in near-ground, high-resolution radar data from a much closer, rapid-scan, mobile radar. Rather, an elongated velocity couplet was observed only at higher elevation angles at altitudes similar to those at which the WSR-88D radars observed the strong couplet. This paper examines observations from two WSR-88D radars and a mobile radar from which it is argued that the anticyclonic couplet (and a similar one; 10 min later)were actually quasi-horizontal vortices centered ;1–1.5 km AGL. The benefits of having data from a radar much closer to the convective storm being sampled (e.g., better spatial resolution and near-ground data coverage) and providing more rapid volume updates are readily apparent. An analysis of these additional radar data provides strong, but not irrefutable, evidence that the anticyclonic tornado that may be inferred from WSR-88D data did not exist; consequently, upon discussions with the National Weather Service, it was not included in Storm Data. SIGNIFICANCE STATEMENT The official nationwide radar network in the United States is capable of detecting some tornadoes, but this capability generally decreases with increasing distance from the radar. In this study, we examined two peculiar radar signatures observed in a tornado-producing thunderstorm in Oklahoma in May 2016. These signatures looked similar to those seen in anticyclonic (i.e., clockwise-rotating in the Northern Hemisphere) tornadoes, but the location within the storm would have been highly unusual for a tornado. Data from a rapid-scan mobile radar closer to the storm revealed that the signatures were not from anticyclonic tornadoes but, rather, may have been quasi-horizontal vortices centered;1 km above ground. This case serves as an example of the benefits expected from a denser radar network of next-generation, rapid-scan weather radars. [ABSTRACT FROM AUTHOR]

Raindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (ZH) and differential reflectivity (ZDR). This study presents a new µ-Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radar ZH and ZDR were calculated using the self-consistency constraint and vertical pointing measurements. The derived µ-Λ relationship was verified using the mass-weighted mean diameter (Dm) and standard deviation of the size distribution (σm), calculated from the 2DVD, for comparison with existing µ-Λ relationships for Florida and Oklahoma. The Dm-σm relationship derived from the 2DVD corresponded well with the µ-Λ relationship. The µ-Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had larger µ values than Oklahoma for the same Λ. The derived µ-Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derived µ-Λ relationship performed the best at retrieving rain microphysical parameters. [ABSTRACT FROM AUTHOR]

In a 2018 paper by Bukovčić et al., polarimetric bivariate power-law relations for estimating snowfall rate S and ice water content (IWC), S ⁡ (K DP , Z) = γ K DP α Z β and IWC ⁡ (K DP , Z) = γ 2 K DP α 2 Z β 2 , were developed utilizing 2D video disdrometer snow measurements in Oklahoma. Herein, these disdrometer-based relations are generalized for the range of particle aspect ratios from 0.5 to 0.8 and the width of the canting angle distribution from 0° to 40° and are validated via analytical/theoretical derivations and simulations. In addition, a novel S(KDP, Zdr) polarimetric relation utilizing the ratio between specific differential phase KDP and differential reflectivity Zdr, K DP / ⁡ (1 − Z dr − 1 ) , is derived. Both KDP and (1 − Z dr − 1 ) are proportionally affected by the ice particles' aspect ratio and width of the canting angle distribution; therefore, the variables' ratio tends to be almost invariant to the changes in these parameters. The S(KDP, Z) and S(KDP, Zdr) relations are applied to the polarimetric S-band WSR-88D data obtained from three geographical locations in Virginia, Oklahoma, and Colorado, and their performance is compared with estimations from the standard S(Z) relations and ground snow measurements. The polarimetric estimates of snow accumulations from the three cases exhibit smaller bias in comparison with the S(Z), indicating good potential for more reliable radar snow measurements. [ABSTRACT FROM AUTHOR]

The recent dual-polarization upgrade to the National Weather Service radar network provides forecasters with new information to use during operations, yet currently this information is not routinely used to explicitly make warning decisions. One potential way to increase operational use is to link new radar signatures and products to existing forecaster conceptual models and the warning decision process. Over the past several years, a unique dataset consisting of rapid-update (<2-min volumes) radar data of storms over central Oklahoma has been collected to examine possible links between ZDR columns and forecaster conceptual models. In total, over 1400 volume scans from 42 storms—ranging from tornadic supercells to nonsevere multicells—are used to relate ZDR column depth to storm reports and radar signatures typically used to issue warnings, such as −20°C reflectivity core and low-level mesocyclone evolution. After completing the analysis, the following key operational findings emerged: 1) no clear differences exist between the ZDR column depth of tornadic and nontornadic mesocyclones, but statistically significant differences do exist between severe and nonsevere storms, 2) the lead time in advance of severe hail and wind reports provided by peaks in ZDR column depth is greater than that provided by peaks in −20°C reflectivity cores, 3) increases in ZDR column size precede increases in −20°C reflectivity core size by about 3.5–9.0 min, and 4) rapid-update volumetric data captures signature evolution several minutes earlier than conventional-update data therefore providing forecasters more time to anticipate hazards and issue warnings. [ABSTRACT FROM AUTHOR]

To improve severe thunderstorm prediction, a novel pseudo-observation and assimilation approach involving water vapor mass mixing ratio is proposed to better initialize NWP forecasts at convection-resolving scales. The first step of the algorithm identifies areas of deep moist convection by utilizing the vertically integrated liquid water (VIL) derived from three-dimensional radar reflectivity fields. Once VIL is obtained, pseudo–water vapor observations are derived based on reflectivity thresholds within columns characterized by deep moist convection. Areas of spurious convection also are identified by the algorithm to help reduce their detrimental impact on the forecast. The third step is to assimilate the derived pseudo–water vapor observations into a convection-resolving-scale NWP model along with radar radial velocity and reflectivity fields in a 3DVAR framework during 4-h data assimilation cycles. Finally, 3-h forecasts are launched every hour during that period. The performance of this method is examined for two selected high-impact severe thunderstorm events: namely, the 24 May 2011 Oklahoma and 16 May 2017 Texas and Oklahoma tornado outbreaks. Relative to a control simulation that only assimilated radar data, the analyses and forecasts of these supercells (reflectivity patterns, tracks, and updraft helicity tracks) are qualitatively and quantitatively improved in both cases when the water vapor information is added into the analysis. [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]

This study documents the formation and evolution of secondary vortices associated within a large, violent tornado in Oklahoma based on data from a close-range, mobile, polarimetric, rapid-scan, X-band Doppler radar. Secondary vortices were tracked relative to the parent circulation using data collected every 2 s. It was found that most long-lived vortices (those that could be tracked for ≥15 s) formed within the radius of maximum wind (RMW), mainly in the left-rear quadrant (with respect to parent tornado motion), passing around the center of the parent tornado and dissipating closer to the center in the right-forward and left-forward quadrants. Some secondary vortices persisted for at least 1 min. When a Burgers–Rott vortex is fit to the Doppler radar data, and the vortex is assumed to be axisymmetric, the secondary vortices propagated slowly against the mean azimuthal flow; if the vortex is not assumed to be axisymmetric as a result of a strong rear-flank gust front on one side of it, then the secondary vortices moved along approximately with the wind. [ABSTRACT FROM AUTHOR]

On 2 April 2010, a developing quasi-linear convective system (QLCS) moved rapidly northeastward through central Oklahoma spawning at least three intense, mesoscale vortices. At least two of these vortices caused damage rated as category 0 to 1 on the enhanced Fujita scale (EF0-EF1) in and near the town of Rush Springs. Two radar networks--the National Weather Service Weather Surveillance Radar-1988 Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network--collected high spatial and temporal resolution data of the event. This study is an in-depth polarimetric analysis of mesovortices within a QLCS. In this case study, the storm development and evolution of the QLCS mesovortices are examined. Significant findings include the following: 1) The damage in Rush Springs was caused by a combination of the fast translation speed and the embedded circulations associated with QLCS vortices. The vortices' relative winds nearly negated the storm motion to the left of the vortex, but doubled the ground-relative wind to the right of the vortex. 2) A significant differential reflectivity (ZDR) arc developed along the forward flank of the first vortex. The ZDR arc propagated northeastward along the QLCS with the development of each new vortex. 3) A minimum in the copolar correlation coefficient (phv) in the center of the strongest vortex was observed, indicating the likely existence of a polarimetric tornado debris signature (TDS). A secondary Phv minimum also was found just to the right of the vortex center, possibly associated with lofted debris from straight-line winds. [ABSTRACT FROM AUTHOR]

On 2 April 2010, a quasi-linear convective system (QLCS) moved eastward through Oklahoma during the early morning hours. Wind damage in Rush Springs, Oklahoma approached EF1-scale intensity and was likely associated with a mesovortex along the leading edge of the QLCS. The evolution of the QLCS as it produced its first bow echo was captured by the National Weather Radar Testbed Phased Array Radar (NWRT PAR) in Norman, Oklahoma. The NWRT PAR is an S-band radar with an electronically steered beam, allowing for rapid volumetric updates (~1 min) and user-defined scanning strategies. The rapid temporal updates and dense vertical sampling of the PAR created a detailed depiction of the damaging wind mechanisms associated with the QLCS. Key features sampled by the PAR include microbursts, an intensifying midlevel jet, and rotation associated with the mesovortex. In this work, PAR data are analyzed and compared to data from nearby operational radars, highlighting the advantages of using high-temporal-resolution data to monitor storm evolution. The PAR sampled the events preceding the Rush Springs circulation in great detail. Based on PAR data, the midlevel jet in the QLCS strengthened as it approached Rush Springs, creating an area of strong midlevel convergence where it impinged on the system-relative front-to-rear-flow. As this convergence extended to the lower levels of the storm, a preexisting azimuthal shear maximum increased in magnitude and vertical extent, and EF1-scale damage occurred in Rush Springs. The depiction of these events in the PAR data demonstrates the complex and rapidly changing nature of QLCSs. [ABSTRACT FROM AUTHOR]

Rapid-scan weather radars, such as the S-band phased array radar at the National Weather Radar Testbed in Norman, Oklahoma, improve precision in the depiction of severe storm processes. To explore potential impacts of such data on forecaster warning decision making, 12 National Weather Service forecasters participated in a preliminary study with two control conditions: 1) when radar scan time was similar to volume coverage pattern 12 (4.5 min) and 2) when radar scan time was faster (43 s). Under these control conditions, forecasters were paired and worked a tropical tornadic supercell case. Their decision processes were observed and audio was recorded, interactions with data displays were video recorded, and the products were archived. A debriefing was conducted with each of the six teams independently and jointly, to ascertain the forecaster decision-making process. Analysis of these data revealed that teams examining the same data sometimes came to different conclusions about whether and when to warn. Six factors contributing toward these differences were identified: 1) experience, 2) conceptual models, 3) confidence, 4) tolerance of possibly missing a tornado occurrence, 5) perceived threats, and 6) software issues. The three 43-s teams issued six warnings: three verified, two did not verify, and one event was missed. Warning lead times were the following: tornado, 18.6 and 11.5 min, and severe, 6 min. The three tornado warnings issued by the three 4.5-min teams verified, though warning lead times were shorter: 4.6 and 0 min (two teams). In this case, use of rapid-scan data showed the potential to extend warning lead time and improve forecasters' confidence, compared to standard operations. [ABSTRACT FROM AUTHOR]

The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere's (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8-9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso- γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data. Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development. [ABSTRACT FROM AUTHOR]

This study evaluates rainfall estimates from the Next Generation Weather Radar (NEXRAD), operational rain gauges, Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) in the context as inputs to a calibrated, distributed hydrologic model. A high-density Micronet of rain gauges on the 342-km2 Ft. Cobb basin in Oklahoma was used as reference rainfall to calibrate the National Weather Service's (NWS) Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) at 4-km/l-h and 0.25°/3-h resolutions. The unadjusted radar product was the overall worst product, while the stage IV radar product with hourly rain gauge adjustment had the best hydrologic skill with a Micronet relative efficiency score of −0.5, only slightly worse than the reference simulation forced by Micronet rainfall. Simulations from TRMM-3B42RT were better than PERSIANN-CCS-RT (a real-time version of PERSIANN-CSS) and equivalent to those from the operational rain gauge network. The high degree of hydrologic skill with TRMM-3B42RT forcing was only achievable when the model was calibrated at TRMM's 0.25°/3-h resolution, thus highlighting the importance of considering rainfall product resolution during model calibration. [ABSTRACT FROM AUTHOR]

Ensemble Kalman filter (EnKF) techniques have been proposed for obtaining atmospheric state estimates on the scale of individual convective storms from radar and other observations, but tests of these methods with observations of real convective storms are still very limited. In the current study, radar observations of the 8 May 2003 Oklahoma City tornadic supercell thunderstorm were assimilated into the National Severe Storms Laboratory (NSSL) Collaborative Model for Multiscale Atmospheric Simulation (NCOMMAS) with an EnKF method. The cloud model employed 1-km horizontal grid spacing, a single-moment bulk precipitation-microphysics scheme, and a base state initialized with sounding data. A 50-member ensemble was produced by randomly perturbing base-state wind profiles and by regularly adding random local perturbations to the horizontal wind, temperature, and water vapor fields in and near observed precipitation. In a reference experiment, only Doppler-velocity observations were assimilated into the NCOMMAS ensemble. Then, radar-reflectivity observations were assimilated together with Doppler-velocity observations in subsequent experiments. Influences that reflectivity observations have on storm-scale analyses were revealed through parameter-space experiments by varying observation availability, observation errors, ensemble spread, and choices for what model variables were updated when a reflectivity observation was assimilated. All experiments produced realistic storm-scale analyses that compared favorably with independent radar observations. Convective storms in the NCOMMAS ensemble developed more quickly when reflectivity observations and velocity observations were both assimilated rather than only velocity, presumably because the EnKF utilized covariances between reflectivity and unobserved model fields such as cloud water and vertical velocity in efficiently developing realistic storm features. Recurring spatial patterns in the differences between predicted and observed reflectivity were noted particularly at low levels, downshear of the supercell''s updraft, in the anvil of moderate-to-light precipitation, where reflectivity in the model was typically lower than observed. Bias errors in the predicted rain mixing ratios and/or the size distributions that the bulk scheme associates with these mixing ratios are likely responsible for this reflectivity underprediction. When a reflectivity observation is assimilated, bias errors in the model fields associated with reflectivity (rain, snow, and hail--graupel) can be projected into other model variables through the ensemble covariances. In the current study, temperature analyses in the downshear anvil at low levels, where reflectivity was underpredicted, were very sensitive both to details of the assimilation algorithm and to ensemble spread in temperature. This strong sensitivity suggests low confidence in analyses of low-level cold pools obtained through reflectivity-data assimilation. [ABSTRACT FROM AUTHOR]

The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution numerical simulations of a mesoscale convective system and associated cyclonic line-end vortex (LEV) that spawned several tornadoes in central Oklahoma on 8--9 May 2007. The simulation uses a 1000 km ×× 1000 km domain with 2-km horizontal grid spacing. The ARPS three-dimensional variational data assimilation (3DVAR) is used to assimilate a variety of data types. All experiments assimilate routine surface and upper-air observations as well as wind profiler and Oklahoma Mesonet data over a 1-h assimilation window. A subset of experiments assimilates radar data. Cloud and hydrometeor fields as well as in-cloud temperature are adjusted based on radar reflectivity data through the ARPS complex cloud analysis procedure. Radar data are assimilated from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network as well as from the Engineering Research Center for Collaborative and Adaptive Sensing of the Atmosphere (CASA) network of four X-band Doppler radars. Three-hour forecasts are launched at the end of the assimilation window. The structure and evolution of the forecast MCS and LEV are markedly better throughout the forecast period in experiments in which radar data are assimilated. The assimilation of CASA radar data in addition to WSR-88D data increases the structural detail of the modeled squall line and MCS at the end of the assimilation window, which appears to yield a slightly better forecast track of the LEV. [ABSTRACT FROM AUTHOR]

In recent years, there has been widespread interest in collecting and analyzing rapid updates of radar data in severe convective storms. To this end, conventional single-polarization rapid-scan radars and phased array radar systems have been employed in numerous studies. However, rapid updates of dual-polarization radar data in storms are not widely available. For this study, a rapid scanning strategy is developed for the polarimetric prototype research Weather Surveillance Radar-1988 Doppler (WSR-88D) radar in Norman, Oklahoma (KOUN), which emulates the future capabilities of a polarimetric multifunction phased array radar (MPAR). With this strategy, data are collected over an 80° sector with 0.5° azimuthal spacing and 250-m radial resolution ('super resolution'), with 12 elevation angles. Thus, full volume scans over a limited area are collected every 71-73 s. The scanning strategy was employed on a cyclic nontornadic supercell storm in western Oklahoma on 1 June 2008. The evolution of the polarimetric signatures in the supercell is analyzed. The repetitive pattern of evolution of these polarimetric features is found to be directly tied to the cyclic occlusion process of the low-level mesocyclone. The cycle for each of the polarimetric signatures is presented and described in detail, complete with a microphysical interpretation. In doing so, for the first time the bulk microphysical properties of the storm on small time scales (inferred from polarimetric data) are analyzed. The documented evolution of the polarimetric signatures could be used operationally to aid in the detection and determination of various stages of the low-level mesocyclone occlusion. [ABSTRACT FROM AUTHOR]

Radar measurements are useful for determining rainfall rates because of their ability to cover large areas. Unfortunately, estimating rainfall rates from radar reflectivity data alone is prone to errors resulting from variations in drop size distributions, precipitation types, and other physics that cannot be represented in a simple, one-dimensional Z- R relationship. However, improving estimates is possible by utilizing additional inputs, thereby increasing the dimensionality of the model. The main purpose of this study is to determine the value of surface observations for improving rainfall-rate estimation. This work carefully designed an artificial neural network to fit a model that would relate radar reflectivity, surface temperature, humidity, pressure, and wind to observed rainfall rates. Observations taken over 13 years from the Oklahoma Mesonet and the KTLX WSR-88D radar near Oklahoma City, Oklahoma, were used for the training dataset. While the artificial neural network underestimated rainfall rates for higher reflectivities, it did have an overall better performance than the best-fit Z- R relation. Most importantly, it is shown that the surface data contributed significant value to an unaugmented radar-based rainfall-rate estimation model. [ABSTRACT FROM AUTHOR]

Rainfall products from radar, satellite, rain gauges, and combinations have been evaluated for a season of record rainfall in a heavily instrumented study domain in Oklahoma. Algorithm performance is evaluated in terms of spatial scale, temporal scale, and rainfall intensity. Results from this study will help users of rainfall products to understand their errors. Moreover, it is intended that developers of rainfall algorithms will use the results presented herein to optimize the contribution from available sensors to yield the most skillful multisensor rainfall products. [ABSTRACT FROM AUTHOR]

It is well acknowledged that there are large uncertainties associated with the operational quantitative precipitation estimates produced by the U.S. national network of the Weather Surveillance Radar-1988 Doppler (WSR-88D). These errors result from the measurement principles, parameter estimation, and the not fully understood physical processes. Even though comprehensive quantitative evaluation of the total radar-rainfall uncertainties has been the object of earlier studies, an open question remains concerning how the error model results are affected by parameter values and correction setups in the radar-rainfall algorithms. This study focuses on the effects of different exponents in the reflectivity–rainfall (Z–R) relation [Marshall–Palmer, default Next Generation Weather Radar (NEXRAD), and tropical] and the impact of an anomalous propagation removal algorithm. To address this issue, the authors apply an empirically based model in which the relation between true rainfall and radar rainfall could be described as the product of a systematic distortion function and a random component. Additionally, they extend the error model to describe the radar-rainfall uncertainties in an additive form. This approach is fully empirically based, and rain gauge measurements are considered as an approximation of the true rainfall. The proposed results are based on a large sample (6 yr) of data from the Oklahoma City radar (KTLX) and processed through the Hydro-NEXRAD software system. The radar data are complemented with the corresponding rain gauge observations from the Oklahoma Mesonet and the Agricultural Research Service Micronet. [ABSTRACT FROM AUTHOR]

A method is presented by which combined S-band polarimetric weather radar and UHF wind profiler observations of precipitation can be used to extract the properties of liquid phase hydrometeors and the vertical velocity of the air through which they are falling. Doppler spectra, which contain the air motion and/or fall speed of hydrometeors, are estimated using the vertically pointing wind profiler. Complementary to these observations, spectra of rain drop size distribution (DSD) are simulated by several parameters as related to the DSD, which are estimated through the two polarimetric parameters of radar reflectivity (ZH) and differential reflectivity (ZDR) from the scanning weather radar. These DSDs are then mapped into equivalent Doppler spectra (fall speeds) using an assumed relationship between the equivolume drop diameter and the drop’s terminal velocity. The method is applied to a set of observations collected on 11 March 2007 in central Oklahoma. In areas of stratiform precipitation, where the vertical wind motion is expected to be small, it was found that the fall speeds obtained from the spectra of the rain DSD agree well with those of the Doppler velocity estimated with the profiler. For those cases when the shapes of the Doppler spectra are found to be similar in shape but shifted in velocity, the velocity offset is attributed to vertical air motion. In convective rainfall, the Doppler spectra of the rain DSD and the Doppler velocity can exhibit significant differences owing to vertical air motions together with atmospheric turbulence. Overall, it was found that the height dependencies of Doppler spectra measured by the profiler combined with vertical profiles of Z, ZDR, and the cross correlation (ρHV) as well as the estimated spectra of raindrop physical terminal fall speeds from the polarimetric radar provide unique insight into the microphysics of precipitation. Vertical air motions (updrafts/downdrafts) can be estimated using such combined measurements. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions. [ABSTRACT FROM AUTHOR]

In this paper, data from three 2-dimensional video disdrometers (2DVDs) and an S-band polarimetric radar are used to characterize rain microphysics in Oklahoma. Sampling errors from the 2DVD measurements are quantified through side-by-side comparisons. In an attempt to minimize the sampling errors, a method of sorting and averaging based on two parameters (SATP) is proposed. The shape–slope (μ–Λ) relation of a constrained gamma (C-G) model is then refined for the retrieval of drop size distributions (DSDs) from polarimetric radar measurements. An adjustable term that is based on observed radar reflectivity and differential reflectivity is introduced to make the C-G DSD model more applicable. Radar retrievals using this improved DSD model are shown to provide good agreement with disdrometer observations and to give reasonable results, including in locations near the leading edge of convection where poorly sampled large drops are often observed. [ABSTRACT FROM AUTHOR]

On 3 May 1999, an unusually large tornado that caused F4-level damage and killed several people was intercepted by the Doppler on Wheels (DOW) mobile radar near Mulhall, Oklahoma, from a range of 4 to 9 km, resulting in high-resolution volumetric data every 55 s up to 1.5-km altitude over a period of 14 min. For the first time, the evolution and three-dimensional structure of a tornado were deduced using the ground-based velocity track display (GBVTD) technique. After the circulation center was determined, the tangential wind and radial wind were derived from the GBVTD technique at each radius and height. In addition, the axisymmetric vertical velocity, angular momentum, vorticity, and perturbation pressure were deduced from the tangential and radial wind fields. This study focuses on the axisymmetric aspects of this tornado. The primary circulation of the Mulhall tornado consisted of an 84 m s-1 peak axisymmetric tangential wind with the radius of maximum wind (RMW) ranging from 500 to 1000 m. The secondary circulation exhibited a two-cell structure characterized by a central downdraft surrounded by an annular updraft near the RMW. The calculated maximum pressure deficit from a 3-km radius to the tornado center at 50-m altitude was -80 hPa. The maximum vorticity during the first 8 min of observation was located inside the RMW away from the tornado center. This vorticity profile satisfied the necessary condition of barotropic instability. As the tornado weakened afterward, the vorticity monotonically increased toward the center. The computed swirl ratios were between 2 and 6, consistent with the observed multiple vortex radar signatures and the vorticity pattern. Swirl ratios were generally smaller during the weakening phase. [ABSTRACT FROM AUTHOR]

The article offers information on the mobile imaging radar called Atmospheric Imaging Radar (AIR) which is being developed at Atmospheric Radar Research Center (ARRC) at the University of Oklahoma. This advanced X-band phased array radar obtains radar measurements at a much faster speed. The AIR transmits a fan beam, at once collecting data over a much larger field of view. The AIR will be ready for testing by May 2010 and for deployment by 2010.