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A convective-scale static background-error covariance (BEC) matrix is further developed to include the capability of direct reflectivity assimilation and evaluated within the GSI-based three-dimensional variational (3DVar) and hybrid ensemble–variational (EnVar) methods. Specific developments are summarized as follows: 1) Control variables (CVs) are extended to include reflectivity, vertical velocity, and all hydrometeor types. Various horizontal momentum and moisture CV options are included. 2) Cross correlations between all CVs are established. 3) A storm intensity-dependent binning method is adopted to separately calculate static error matrices for clear-air and storms with varying intensities. The resultant static BEC matrices are simultaneously applied at proper locations guided by the observed reflectivity. 4) The EnVar is extended to adaptively incorporate static BECs based on the quality of ensemble covariances. Evaluation and examination of the new static BECs are first performed on the 8 May 2003 Oklahoma City supercell. Detailed diagnostics and 3DVar examinations suggest zonal/meridional winds and pseudo–relative humidity are selected as horizontal momentum and moisture CVs for direct reflectivity assimilation, respectively; inclusion of cross correlations favors spin up and maintains the analyzed storms; application of binning improves characteristics and persistence of the simulated storm. Relative to an experiment using the full ensemble BECs (Exp-PureEnVar), incorporating static BECs in hybrid EnVar reduces spinup time and better analyzes reflectivity distributions while the background ensemble is deficient in sampling errors. Compared to both pure 3DVar and Exp-PureEnVar, hybrid EnVar better predicts reflectivity distributions and better maintains a strong mesocyclone. Further examination through the 20 May 2013 Oklahoma supercells confirms these results and additionally demonstrates the effectiveness of adaptive hybridization. [ABSTRACT FROM AUTHOR]

With the launch of GOES-16 in November 2016, effective utilization of its data in convective-scale numerical weather prediction (NWP) has the potential to improve high-impact weather (HIWeather) forecasts. In this study, the impact of satellite-derived layered precipitable water (LPW) and cloud water path (CWP) in addition to NEXRAD observations on short-term convective-scale NWP forecasts are examined using three severe weather cases that occurred in May 2017. In each case, satellite-derived CWP and LPW products and radar observations are assimilated into the Advanced Research Weather Research and Forecasting (WRF-ARW) Model using the NSSL hybrid Warn-on-Forecast (WoF) analysis and forecast system. The system includes two components: the GSI-EnKF system and a deterministic 3DEnVAR system. This study examines deterministic 0-6-h forecasts launched from the hybrid 3DEnVAR analyses for the three severe weather events. Three types of experiments are conducted and compared: (i) the control experiment (CTRL) without assimilating any data, (ii) the radar experiment (RAD) with the assimilation of radar and surface observations, and (iii) the satellite experiment (RADSAT) with the assimilation of all observations including surface-, radar-, and satellite-derived CWP and LPW. The results show that assimilating additional GOES products improves short-range forecasts by providing more accurate initial conditions, especially for moisture and temperature variables. [ABSTRACT FROM AUTHOR]

Assessments of spatiotemporal severe hailfall characteristics using hail reports are plagued by serious limitations in report databases, including biases in reported sizes, occurrence time, and location. Multiple studies have used Next Generation Weather Radar (NEXRAD) network observations or environmental hail proxies from reanalyses. Previous work has specifically utilized the single-polarization radar parameter maximum expected size of hail (MESH). In addition to previous work being temporally limited, updates are needed to include recent improvements that have been made to MESH. This study aims to quantify severe hailfall characteristics during a 23-yr period, markedly longer than previous studies, using both radar observations and reanalysis data. First, the improved MESH configuration is applied to the full archive of gridded hourly radar observations known as GridRad (1995–2017). Next, environmental constraints from the Modern-Era Retrospective Analysis for Research and Applications, version 2, are applied to the MESH distributions to produce a corrected hailfall climatology that accounts for the reduced likelihood of hail reaching the ground. Spatial, diurnal, and seasonal patterns show that in contrast to the report climatology indicating one high-frequency hail maximum centered on the Great Plains, the MESH-only method characterizes two regions: the Great Plains and the Gulf Coast. The environmentally filtered MESH climatology reveals improved agreement between report characteristics (frequency, location, and timing) and the recently improved MESH calculation methods, and it reveals an overall increase in diagnosed hail days and westward broadening in the spatial maximum in the Great Plains than that seen in reports. [ABSTRACT FROM AUTHOR]

Cloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by the authors revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband's general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud–environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0°C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops. [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]

Polarimetric radar observations from the NEXRAD WSR-88D operational radar network in the contiguous United States, routinely available since 2013, are used to reveal three prominent microphysical signatures in landfalling tropical cyclones: 1) hydrometeor size sorting within the eyewall convection, 2) vertical displacement of the melting layer within the inner core, and 3) dendritic growth layers within stratiform regions of the inner core. Size sorting signatures within eyewall convection are observed with greater frequency and prominence in more intense hurricanes, and are observed predominantly within the deep-layer environmental wind shear vector-relative quadrants that harbor the greatest frequency of deep convection (i.e., downshear and left-of-shear). Melting-layer displacements are shown that exceed 1 km in altitude compared to melting-layer altitudes in outer rainbands and are complemented by analyses of archived dropsonde data. Dendritic growth and attendant snow aggregation signatures in the inner core are found to occur more often when echo-top altitudes are low (≤10 km MSL), nearer the −15°C isotherm commonly associated with dendritic growth. These signatures, uniquely observed by polarimetric radar, provide greater insight into the physical structure and thermodynamic characteristics of tropical cyclones, which are important for improving rainfall estimation and the representation of tropical cyclones in numerical models. [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]

This paper examines nowcasts of precipitation from the High-Resolution Rapid Refresh (HRRRv2) model from the summer of 2017 along the Colorado Front Range. It was found that model nowcasts (2 h or less) of precipitation amount were less skillful than extrapolation of the KFTG WSR-88-D data at a spatial scale of 120 km. It was also found that local-scale (mesoscale) influences on rainfall intensity and amount have a much greater impact on rainfall intensity than large-scale (synoptic) influences. Thus, large-scale trends are not useful for modifying extrapolation nowcasts on the local scale. Errors in the HRRR nowcasts are attributed to an inability of the model and data assimilation to resolve convergence along outflow boundaries and other terrain-influenced mesogamma-scale flows that contribute to storm formation and evolution. While the HRRRv2 1-h nowcasts were strongly correlated with observed precipitation events, the nowcast precipitation amounts were in error by more than a factor of 2 about 50% of the time, with half of the cases being overestimates and half being underestimates. A large fraction of the HRRRv2 overestimates were associated with stratiform rain events. It is speculated that this was a result of misinterpretation of the radar bright band as more intense precipitation aloft by the data assimilation scheme. A large fraction of the HRRRv2 underestimates occurred when the data assimilation and model were unable to fully resolve the low-level convergence along small-scale, narrow boundaries that led to new storm initiation and/or storm growth. [ABSTRACT FROM AUTHOR]

An improved understanding of common differences between tornadic and nontornadic supercells is sought using a large set of observations from the operational NEXRAD WSR-88D polarimetric radar network in the contiguous United States. In particular, data from 478 nontornadic and 294 tornadic supercells during a 7-yr period (2011–17) are used to produce probability-matched composite means of microphysical and kinematic variables. Means, which are centered on echo-top maxima and in a horizontal coordinate system rotated such that storm motion points in the positive x dimension, are created in altitude relative to ground level at times of peak echo-top altitude and peak midlevel rotation for nontornadic supercells and times at and prior to the first tornado in tornadic supercells. Robust differences between supercell types are found, with consistent characteristics at and preceding tornadogenesis in tornadic storms. In particular, the mesocyclone is found to be vertically aligned in tornadic supercells and misaligned in nontornadic supercells. Microphysical differences found include a low-level radar reflectivity hook echo aligned with and ~10 km right of storm center in tornadic supercells and displaced 5–10 km down-motion in nontornadic supercells, a low-to-midlevel differential radar reflectivity dipole that is oriented more parallel to storm motion in tornadic supercells and more perpendicular in nontornadic supercells, and a separation between enhanced differential radar reflectivity and specific differential phase (with unique displacement-relative correlation coefficient reductions) at low levels that is more perpendicular to storm motion in tornadic supercells and more parallel in nontornadic supercells. [ABSTRACT FROM AUTHOR]

Observations of the air vertical velocities (wair) in supercell updrafts are presented, including uncertainty estimates, from radiosonde GPS measurements in two supercells. These in situ observations were collected during the Colorado State University Convective Cloud Outflows and Updrafts Experiment (C³LOUD-Ex) in moderately unstable environments in Colorado and Wyoming. Based on the radiosonde accelerations, instances when the radiosonde balloon likely bursts within the updraft are determined, and adjustments are made to account for the subsequent reduction in radiosonde buoyancy. Before and after these adjustments, the maximum estimated wair values are 36.2 and 49.9ms-1, respectively. Radar data are used to contextualize the in situ observations and suggest that most of the radiosonde observations were located several kilometers away from the most intense vertical motions. Therefore, the radiosonde-based wair values presented likely underestimate the maximum values within these storms due to these sampling biases, as well as the impacts from hydrometeors, which are not accounted for. When possible, radiosonde-based wair values were compared to estimates from dual-Doppler methods and from parcel theory. When the radiosondes observed their highest wair values, dual-Doppler methods generally produced 15–20ms-1 lower wair for the same location, which could be related to the differences in the observing systems’ resolutions. In situ observations within supercell updrafts, which have been limited in recent decades, can be used to improve our understanding and modeling of storm dynamics. This study provides new in situ observations, as well as methods and lessons that could be applied to future field campaigns. [ABSTRACT FROM AUTHOR]

Sampling error stems from the use of ensemble-based data assimilation (EDA) with a limited ensemble size and can result in spurious background error covariances, leading to false analysis corrections. The WRF-LETKF radar assimilation system (WLRAS) is performed separately with 256 and 40 members to investigate the characteristics of convective-scale sampling errors in the EDA and its impact on precipitation prediction based on a heavy rainfall event on 16 June 2008. The results suggest that the sampling errors for this event are sensitive to the relationships between the simulated observations and model variables, the intensity of reflectivity, and how the prevailing wind projects to the radial wind in the areas that the radar cannot resolve U or V wind. The sampling errors lead to an underprediction of heavy rainfall when the horizontal localization radius is inadequately large, but this can be mitigated when a more accurate moisture condition is provided. In addition, being able to use a larger vertical localization plays an important role in providing necessary adjustments for representing the vertical thermodynamical structure of convection, which further improves precipitation prediction. A strategy mitigating the impact of sampling errors associated with the limitation of radial wind measurement by inflating the observation error over sensitive areas can bring benefits to precipitation prediction. [ABSTRACT FROM AUTHOR]

On 10 November 2018, during the RELAMPAGO field campaign in Argentina, South America, a thunderstorm with supercell characteristics was observed by an array of mobile observing instruments, including three Doppler on Wheels radars. In contrast to the archetypal supercell described in the Glossary of Meteorology, the updraft rotation in this storm was rather short lived (;25 min), causing some initial doubt as to whether this indeed was a supercell. However, retrieved 3D winds from dual-Doppler radar scans were used to document a high spatial correspondence between midlevel vertical velocity and vertical vorticity in this storm, thus providing evidence to support the supercell categorization. Additional data collected within the RELAMPAGO domain revealed other storms with this behavior, which appears to be attributable in part to effects of the local terrain. Specifically, the IOP4 supercell and other short-duration supercell cases presented had storm motions that were nearly perpendicular to the long axis of the Sierras de Córdoba Mountains; a long-duration supercell case, on the other hand, had a storm motion nearly parallel to these mountains. Sounding observations as well as model simulations indicate that a mountain-perpendicular storm motion results in a relatively short storm residence time within the narrow zone of terrain-enhanced vertical wind shear. Such a motion and short residence time would limit the upward tilting, by the left-moving supercell updraft, of the storm-relative, antistreamwise horizontal vorticity associated with anabatic flow near complex terrain. [ABSTRACT FROM AUTHOR]

Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently. [ABSTRACT FROM AUTHOR]

Radar and surface rainfall observations are two sources of operational data crucial for heavy rainfall prediction. Their individual values on improving convective forecasting through data assimilation have been examined in the past using convection-permitting numerical models. However, the benefit of their simultaneous assimilations has not yet been evaluated. The objective of this study is to demonstrate that, using a 4D-Var data assimilation system with a microphysical scheme, these two data sources can be assimilated simultaneously and the combined assimilation of radar data and estimated rainfall data from radar reflectivity and surface network can lead to improved short-term heavy rainfall prediction. In our study, a combined data assimilation experiment is compared with a rainfall-only and a radar-only (with or without reflectivity) experiments for a heavy rainfall event occurring in Taiwan during the passage of a mei-yu system. These experiments are conducted by applying the Weather Research and Forecasting (WRF) 4D-Var data assimilation system with a 20-min time window aiming to improve 6-h convective heavy rainfall prediction. Our results indicate that the rainfall data assimilation contributes significantly to the analyses of humidity and temperature whereas the radar data assimilation plays a crucial role in wind analysis, and further, combining the two data sources results in reasonable analyses of all three fields by eliminating large, unphysical analysis increments from the experiments of assimilating individual data only. The results also show that the combined assimilation improves forecasts of heavy rainfall location and intensity of 6-h accumulated rainfall for the case studied. [ABSTRACT FROM AUTHOR]

A detailed damage survey is combined with high-resolution mobile, rapid-scanning X-band polarimetric radar data collected on the Shawnee, Oklahoma, tornado of 19 May 2013. The focus of this study is the radar data collected during a period when the tornado was producing damage rated EF3. Vertical profiles of mobile radar data, centered on the tornado, revealed that the radar reflectivity was approximately uniform with height and increased in magnitude as more debris was lofted. There was a large decrease in both the cross-correlation coefficient (ρhv) and differential radar reflectivity (ZDR) immediately after the tornado exited the damaged area rated EF3. Low ρhv and ZDR occurred near the surface where debris loading was the greatest. The 10th percentile of ρhv decreased markedly after large amounts of debris were lofted after the tornado leveled a number of structures. Subsequently, ρhv quickly recovered to higher values. This recovery suggests that the largest debris had been centrifuged or fallen out whereas light debris remained or continued to be lofted. Range–height profiles of the dual-Doppler analyses that were azimuthally averaged around the tornado revealed a zone of maximum radial convergence at a smaller radius relative to the leading edge of lofted debris. Low-level inflow into the tornado encountering a positive bias in the tornado-relative radial velocities could explain the existence of the zone. The vertical structure of the convergence zone was shown for the first time. [ABSTRACT FROM AUTHOR]

Supercell thunderstorms produce a variety of hazards, including tornadoes. A supercell will often exist for some time prior to producing a tornado, while other supercells never become tornadic. In this study, a series of hypotheses is tested regarding the ability of S-band polarimetric radar fields to distinguish pretornadic from nontornadic supercell storms. Several quantified polarimetric radar metrics are examined that are related to storm inflow, updraft, and hailfall characteristics in samples of 19–30 pretornadic and 18–31 nontornadic supercells. The results indicate that pretornadic supercells are characterized by smaller hail extent and echo appendages with larger mean drop size. Additionally, differential reflectivity ZDR column size is larger and less variable in the pretornadic storms in the 25–30 min prior to initial tornadogenesis. Many of the results indicate relatively small polarimetric differences that will likely be difficult to translate to operational use. Hail extent and ZDR column size, however, may exhibit operationally useful differences between pretornadic and nontornadic supercells. [ABSTRACT FROM AUTHOR]

Radar reflectivity (Z) data are either directly assimilated using 3DVar, 4DVar, or ensemble Kalman filter, or indirectly assimilated using, for example, cloud analysis that preretrieves hydrometeors from Z. When directly assimilating radar data variationally, issues related to the highly nonlinear Z operator arise that can cause nonconvergence and bad analyses. To alleviate the issues, treatments are proposed in this study and their performances are examined via observing system simulation experiments. They include the following: 1) When using hydrometeor mixing ratios as control variables (CVq), small background Z can cause extremely large cost function gradient. Lower limits are imposed on the mixing ratios (qLim treatment) or the equivalent reflectivity (ZeLim treatment) in Z observation operator. ZeLim is found to work better than qLim in terms of analysis accuracy and convergence speed. 2) With CVq, the assimilation of radial velocity (Vr) is ineffective when assimilated together with Z data due to the much smaller cost function gradient associated with Vr. A procedure (VrPass) that assimilates Vr data in a separate pass is found very helpful. 3) Using logarithmic hydrometeor mixing ratios as control variables (CVlogq) can also avoid extremely large cost function gradient, and has much faster convergence. However, spurious analysis increments can be created when transforming the analysis increments back to mixing ratios. A background smoothing and a lower limit are applied to the background mixing ratios, and are shown to be effective. Using CVlogq with associated treatments produces better reflectivity analysis that is much closer to the observation without resorting to multiple analysis passes, and the cost function minimization also converges faster. CVlogq is therefore recommended for variational radar data assimilation. [ABSTRACT FROM AUTHOR]

The potential future installation of a multifunction phased-array radar (MPAR) network will provide capabilities of case-specific adaptive scanning. Knowing the impacts adaptive scanning may have on short-term forecasts will influence scanning strategy decision-making in hopes to produce the most optimal ensemble forecast while also benefiting human severe weather warning decision-making. An ensemble-based targeted observation algorithm is applied to an observing system simulation experiment (OSSE) where the impacts of synthetic idealized supercell radial velocity observations are estimated before the observations are "collected" and assimilated. The forecast metric of interest is the low-level rotation forecast metric (0–1-km updraft helicity), a surrogate for tornado prediction. It is found that the ensemble-based targeted observation approach can reasonably estimate the true error variance reduction when an effective method that treats sampling error is applied, the period of model forecast is associated with less degrees of nonlinearity, and the observation information content relative to the background forecast is larger. In some scenarios, a subset of a full-volume scan assimilation produces better forecasts than all observations within the full volume. Assimilating the full-volume scan increases the number of potential spurious correlations arising between the forecast metric and radial velocity observation induced state perturbations, which may degrade the forecast metric accuracy. [ABSTRACT FROM AUTHOR]

Severe weather nowcasting is a crucial mission of atmospheric science for the betterment of society to save life, limb, and property. In this study, composite radar data from the Central Weather Bureau of 16 typhoons are collected to examine the statistical performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan, an extrapolation algorithm that predicts future precipitation based on current radar echoes. In addition, instead of mixing the precipitation between radar extrapolation and numerical model forecast as in previous studies, a blending system is formed by synthesizing the wind information from model forecast with the echo extrapolation motion field via a variational algorithm to improve the nowcasting system. The statistical results of the radar echo extrapolation for 16 typhoon cases show that while the quantitative precipitation nowcasting skill can persist for up to 2 h, significant distortion for the rotational system is found after 2 h. On the other hand, the blending system helps to capture and maintain the rotation of typhoon rainband structures. The blending system extends the nowcasting skill by 1 h to a total of 3 h. Furthermore, the blending scheme performs especially well after the typhoon makes landfall in Taiwan. For disaster prevention and mitigation, this blending nowcasting technique may provide effective weather information immediately. [ABSTRACT FROM AUTHOR]

The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case. [ABSTRACT FROM AUTHOR]

This study utilizes data collected by the University of Oklahoma Advanced Radar Research Center's Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) C-band radar as well as the federal KTLX and KOUN WSR-88D S-band radars to study a supercell that simultaneously produced a long-track EF-4 tornado and an EF-2 landspout tornado (EF indicates the enhanced Fujita scale) near Norman, Oklahoma, on 10 May 2010. Contrasting polarimetric characteristics of two tornadoes over similar land cover but with different intensities are documented. Also, the storm-scale sedimentation of debris within the supercell is investigated, which includes observations of rotation and elongation of a tornadic debris signature with height. A dual-wavelength comparison of debris at S and C bands is performed. These analyses indicate that lofted debris within the tornado was larger than debris located outside the damage path of the tornado and that debris size outside the tornado increased with height, likely as the result of centrifuging. Profiles of polarimetric variables were observed to become more vertically homogeneous with time. [ABSTRACT FROM AUTHOR]

We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDAsimulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale. [ABSTRACT FROM AUTHOR]

This case study analyzes a nocturnal mesoscale convective system (MCS) that was observed on 25-26 June 2015 in northeastern Kansas during the Plains Elevated Convection At Night (PECAN) project. Over the course of the observational period, a broken line of elevated nocturnal convective cells initiated around 0230UTCon the cool side of a stationary front and subsequently merged to form a quasi-linearMCSthat later developed strong, surface-based outflow and a trailing stratiform region. This study combines radar observations with mobile and fixed mesonet and sounding data taken during PECAN to analyze the kinematics and thermodynamics of the MCS from 0300 to 0630 UTC. This study is unique in that 38 consecutive multi- Doppler wind analyses are examined over the 3.5 h observation period, facilitating a long-duration analysis of the kinematic evolution of the nocturnal MCS. Radar analyses reveal that the initial convective cells and linear MCS are elevated and sustained by an elevated residual layer formed via weak ascent over the stationary front. During upscale growth, individual convective cells develop storm-scale cold pools due to pockets of descending rear-to-front flow that are measured by mobile mesonets. By 0500 UTC, kinematic analysis and mesonet observations show that the MCS has a surface-based cold pool and that convective line updrafts are ingesting parcels from below the stable layer. In this environment, the elevated system has become surface based since the cold pool lifting is sufficient for surface-based parcels to overcome the CIN associated with the frontal stable layer. [ABSTRACT FROM AUTHOR]

This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when instorm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones. [ABSTRACT FROM AUTHOR]

This is a study of a tornadic supercell in Kansas on 14 May 2018 in which data of relatively high spatiotemporal resolution from a mobile, polarimetric, X-band, Doppler radar were integrated with GOES-16 geosynchronous satellite imagery, and with fixed-site, surveillance, S-band polarimetric Doppler radar data. The data-collection period spanned the early life of the storm from when it was just a series of ordinary cells, with relatively low cloud tops, through its evolution into a supercell with much higher cloud tops, continuing through the formation and dissipation of a brief tornado, and ending after the supercell came to a stop and reversed direction, produced another tornado, and collided with a quasi-linear convective system. The main goal of this study was to examine the relationship between the overshooting tops and radar observed features prior to and during tornadogenesis. The highest radar echo top was displaced about 10 km, mainly to the north or northeast of the main updraft and cloud top, from the supercell phase through the first tornado phase of the supercell phase, after which the updraft and the cloud top became more closely located and then jumped ahead; this behavior is consistent with what would be expected during cyclic mesocyclogenesis. The change in direction of the supercell later on occurred while the nocturnal low-level jet was intensifying. No relationship was apparent between changes in the highest cloud-top height and tornadogenesis, but changes in cloud-top heights (rapid increases and rapid decreases) were related to two phases in multicell evolution and to supercell formation. [ABSTRACT FROM AUTHOR]

The assimilation of water vapor mass mixing ratio derived from total lightning data from the Geostationary Lightning Mapper (GLM) within a three-dimensional variational (3DVAR) system is evaluated for the analysis and short-term forecast (≤6 h) of a high-impact convective event over the northern Great Plains in the United States. Building on recent work, the lightning data assimilation (LDA) method adjusts water vapor mass mixing ratio within a fixed layer depth above the lifted condensation level by assuming nearly water-saturated conditions at observed lightning locations. In this algorithm, the total water vapor mass added by the LDA is balanced by an equal removal outside observed lightning locations. Additional refinements were also devised to partially alleviate the seasonal and geographical dependence of the original scheme. To gauge the added value of lightning, radar data (radial velocity and reflectivity) were also assimilated with or without lightning. Although the method was evaluated in quasi–real time for several high-impact weather events throughout 2018, this work will focus on one specific, illustrative severe weather case wherein the control simulation—which did not assimilate any data—was eventually able to initiate and forecast the majority of the observed storms. Given a relatively reasonable forecast in the control experiment, the GLM and radar assimilation experiments were still able to improve the short-term forecast of accumulated rainfall and composite radar reflectivity further, as measured by neighborhood-based metrics. These results held whether the simulations made use of one single 3DVAR analysis or high-frequency (10 min) successive cycling over a 1-h period. [ABSTRACT FROM AUTHOR]

A new thermodynamic retrieval scheme is developed by which one can use the wind fields synthesized from multiple-Doppler radars to derive the three-dimensional thermodynamic fields over complex terrain. A cost function consisting of momentum equations and a simplified thermodynamic equation is formulated. By categorizing the analysis domain into flow and terrain regions, the variational technique is applied to minimize this cost function only within the flow region, leading to the solutions for the three-dimensional pressure and temperature perturbations immediately over terrain. Using idealized datasets generated by a numerical model, an experiment is first conducted to assess the accuracy of the proposed algorithm. The retrieval scheme is then applied to a real case that occurred during the 2008 Southwestern Monsoon Experiment (SoWMEX) conducted in Taiwan. The retrieved thermodynamic fields, verified by radiosonde data, reveal the structure of a prefrontal squall line as it approaches a mountain. The retrieved three-dimensional high-resolution pressure and temperature along with the wind fields not only allow us to better understand the thermodynamic and kinematic structure of a heavy rainfall system, but can also be assimilated into a numerical model to improve the forecast. [ABSTRACT FROM AUTHOR]

This work describes developments to improve the Doppler radial wind data assimilation scheme used in the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) data assimilation system with a focus on convection-permitting, 0–18-h forecasts of a heavy precipitation single case study. This work focuses on two aspects: 1) the extension of the radial wind observation operator to include vertical velocity and 2) a refinement of the radial wind super-observation processing. The refinement includes reducing the magnitude of observation smoothing and allowing observations from higher scan angles into the analysis with the intent to improve the assimilation of the radar data for operational, convection-permitting models. The results of this study demonstrate that there is sensitivity to the refinement in super-observation settings. The inclusion of vertical velocity in the observation operator is shown to have a neutral to slightly positive impact on the forecast. Results from this study are suggested to be used as a foundation to prioritize future research into the effective assimilation of radial winds in an operational setting. [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]

Heavy rainfall that occurred at the south coast of China on 10–11 May 2014 was associated with a synoptic-system-related low-level jet (SLLJ) and a boundary layer jet (BLJ). To clarify the role of the double low-level jets in convection initiation (CI), we perform convective-permitting simulations using a nonhydrostatic mesoscale model. The simulations reproduce the occurrence location and mesoscale evolution of new convective cells as well as their small-scale wavelike structures at the elevated layers, which are generally consistent with radar observations despite some differences in their orientation. The nighttime BLJ over the northern South China Sea strengthens the convergence at ~950 hPa near the coast where the BLJ's northern terminus reaches the coastal terrain. Meanwhile, the SLLJ to the south of the inland cold front provides divergence at ~700 hPa near the SLLJ's entrance region. Such low-level convergence and midlevel divergence collectively produce strong mesoscale lifting for CI at the coast. In addition to the enhanced mesoscale lifting, the double low-level jets also provide favorable conditions for the superimposed small-scale disturbances that can serve as effective moistening mechanisms of the lower troposphere during CI. In a sensitivity experiment with coastal terrain removed, CI still occurs near the coast but is delayed and weaker compared to the control run. This latter experiment suggests that double low-level jets and their coupling indeed exert key effects on CI, while the BLJ colliding with terrain may enhance coastal convergence for amplifying CI. These findings provide new insights into the occurrence of coastal heavy rainfall in the warm sector far ahead of the fronts. [ABSTRACT FROM AUTHOR]

The impact of stochastically perturbed parameterizations on short-term tornadic supercell ensemble forecasts (EFs) was evaluated using two tornado cases that occurred in eastern China. The initial condition (IC) perturbations of EFs were generated by a three-dimensional variational data assimilation system with perturbed radar data. The parameterization perturbations of EFs were produced by a stochastic procedure that was applied to diffusion and microphysics parameterizations. This procedure perturbed tendencies from both parameterizations and intercept parameters (INTCPs) of the microphysics parameterizations. In addition to individually perturbing these quantities, a combination of perturbations of diffusion and INTCPs was also examined. A resampling method was proposed to handle perturbations that vary substantially, and a vertical localization was applied to the microphysics tendency perturbations. The results indicated that combining perturbations of diffusion and INTCPs produced the intensity and path forecasts of the low-level vortex (LLV) that better match observations for a weak tornado case; this combination also had a positive impact on the LLV intensity forecast for a much stronger tornado case. This combination outperformed the stochastic procedures that perturbed only diffusion or INTCPs, which indicated that it is better to use both error representations. The vertical localization prevented the temperature tendency perturbations of microphysics from always suppressing storms in negative perturbation (<0.0) areas. The negative INTCP and diffusion perturbations benefited the strong LLV, which is consistent with that of the idealized case. The current stochastic procedure could not address the LLV displacement error that is caused by the IC error. [ABSTRACT FROM AUTHOR]

Despite the well-known importance of background error covariance in data assimilation, not much study has been focused on its impact on the assimilation of radar reflectivity within a three-dimensional variational (3DVar) framework. In this study, it is shown that unphysical analysis increments of hydrometeors are produced when using vertically homogeneous background error variance. This issue cannot be fully solved by using the so-called hydrometeor classification in the reflectivity observation operator. Alternatively, temperature-dependent background error profiles for hydrometeor control variables are proposed. With such a treatment, the vertical background error profiles are specified to be temperature dependent, allowing for more physical partitioning of radar-observed precipitation information among the liquid and ice hydrometeors. The 3DVar analyses using our treatment are compared with those using constant background error or "hydrometeor classification" through observing system simulation experiments with a simulated supercell storm. Results show that 1) 3DVar with constant hydrometeor background errors produces unphysical rainwater at the high levels and unphysical snow at the low levels; 2) the hydrometeor classification approach reduces unphysical rainwater and snow at those levels, but the analysis increments are still unphysically spread in the vertical by the background error covariance when the vertically invariant background errors are used; and 3) the temperature-dependent background error profiles enable physically more reasonable analyses of liquid and ice hydrometeors from reflectivity assimilation. [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]

The factors affecting the inland and orographic enhancement of lake-effect precipitation are poorly understood, yet critical for operational forecasting. Here we use nine cool seasons (16 November-15 April) of radar data from the Montague/Ft. Drum, New York (KTYX), WSR-88D, the North American Regional Reanalysis (NARR), and observations from the Ontario Winter Lake-effect Systems (OWLeS) field campaign to examine variations in lake-effect precipitation enhancement east of Lake Ontario and over the Tug Hill Plateau (hereafter Tug Hill). Key factors affecting the inland and orographic enhancement in this region include the strength of the incident boundary layer flow, the intensity of the lake-induced convective available potential energy (LCAPE), and the mode of the lake-effect system. Stronger flow favors higher precipitation rates, a precipitation maximumdisplaced farther downwind, and greater inland and orographic enhancement. The effects of LCAPE depend upon the strength of the flow. During periods of weak flow, higher LCAPE favors lower precipitation rates, a maximum closer to the shoreline, and lesser inland and orographic enhancement. During periods of strong flow, higher LCAPE favors higher precipitation rates, a maximum displaced farther downwind, and greater inland and orographic enhancement. Banded (nonbanded) modes favor higher (lower) precipitation rates, lesser (greater) inland and orographic enhancement, and a maximum closer to the shoreline (over Tug Hill). These results, for both manually measured and radar-estimated precipitation, are robust when many lake-effect events are considered, but substantial variability exists during individual events. [ABSTRACT FROM AUTHOR]

High-resolution data of the tornadic debris signature (TDS) and weak-echo reflectivity band (WRB) associated with a large, violent tornado on 24 May 2011 in central Oklahoma are examined using a rapid-scan, X-band, polarimetric, mobile Doppler radar. Various characteristics of these features and their evolution are examined over time intervals of 20 s or less. The formation of the TDS, debris fallout, and inhomogeneities in the TDS structure, are analyzed from volumetric and single-elevation observations. Constant-radius vertical cross sections of Doppler velocity, reflectivity, and copolar cross-correlation coefficient are compared at various times during the tornado's life cycle; from them it is found that the weak echo column (WEC) is considerably narrower than the TDS and the WEC is confined to the strong gradient of Doppler velocities in the tornado's core. The TDS of the mature tornado extends radially outward, bound approximately by the 40 m s−1 radial isodop. Rapid-scan, near-surface data were collected for a period of 6 min, during which 2-s single-elevation PPI updates at 1° were available at heights below 100 m above radar level. During this period, a WRB associated with a visually observed horizontal vortex developed east of the tornado, along the leading edge of the secondary rear-flank gust front, as the tornado was rapidly intensifying. A relationship was noted between reduced radar-observed reflectivity and increased radar-observed radial convergence/divergence in the vicinity of the horizontal vortex as it strengthened. This feature is qualitatively analyzed and hypotheses explaining its generation and structure are discussed. [ABSTRACT FROM AUTHOR]

The distribution of radar-estimated precipitation from lake-effect snowbands over and downwind of Lake Ontario shows more snowfall in downwind areas than over the lake itself. Here, two nonexclusive processes contributing to this are examined: the collapse of convection that lofts hydrometeors over the lake and allows them to settle downwind; and stratiform ascent over land, due to the development of a stable boundary layer, frictional convergence, and terrain, leading to widespread precipitation there. The main data sources for this study are vertical profiles of radar reflectivity and hydrometeor vertical velocity in a well-defined, deep long-lake-axis-parallel band, observed on 11 December 2013 during the Ontario Winter Lake-effect Systems (OWLeS) project. The profiles are derived from an airborne W-band Doppler radar, as well as an array of four K-band radars, an X-band profiling radar, a scanning X-band radar, and a scanning S-band radar. The presence of convection offshore is evident from deep, strong (up to 10 m s−1) updrafts producing bounded weak-echo regions and locally heavily rimed snow particles. The decrease of the standard deviation, skewness, and peak values of Doppler vertical velocity during the downwind shore crossing is consistent with the convection collapse hypothesis. Consistent with the stratiform ascent hypothesis are (i) an increase in mean vertical velocity over land; and (ii) an increasing abundance of large snowflakes at low levels and over land, due to depositional growth and aggregation, evident from flight-level and surface particle size distribution data, and from differences in reflectivity values from S-, X-, K-, and W-band radars at nearly the same time and location. [ABSTRACT FROM AUTHOR]

On 8 June 2003, an expansive squall line along a surface cold frontal boundary was sampled during the Bow Echo and Mesoscale Convective Vortex Experiment. The Naval Research Laboratory P-3 aircraft and the National Oceanic and Atmospheric Administration P-3 aircraft simultaneously sampled the leading and trailing edge of this squall line, respectively, with X-band Doppler radars. Data from these two airborne radar systems have been synthesized to produce a pseudo-quad-Doppler analysis of the squall line, yielding a detailed three-dimensional kinematic analysis of its structure. A simulation of the squall line was carried out using the Weather Research and Forecasting Model to complement the pseudo-quad-Doppler analysis. The simulation employed a 3-km, convection-allowing, nested domain centered over the pseudo-quad-Doppler domain, along with a 9-km parent domain to capture the larger synoptic-scale cyclone. The pseudo-quad-Doppler analysis reveals that the convective line was embedded within the upper-tropospheric jet stream, causing local decelerations and deviations in the jet-level flow. The vertical transport of low momentum air from the boundary layer via convective updrafts is shown to significantly decelerate jet-level flow. Pressure perturbations associated with the intrusion of low momentum air into the jet stream-level flow led to deviation of the jet stream flow around the squall line that resulted in counter-rotating ribbons of vertical vorticity parallel to the squall line. Model results indicate that disturbances in the jet stream structure persisted downwind of the squall line for several hours. [ABSTRACT FROM AUTHOR]

Previous work has shown that vorticity mixing in the tropical cyclone (TC) inner core can promote mesovortex (MV) formation and impact storm intensity. Observations of MVs have largely been serendipitous but are necessary to improve understanding of these features and their role in TC dynamics. This study presents nearly 10 h of ground-based dual-Doppler analysis of MVs in the eyewall of Hurricane Ike (2008) near and during landfall. Derived 3D winds, vertical vorticity, horizontal divergence, and perturbation pressures are analyzed. Results indicate persistent kinematic field arrangements and evolving vertical structures. Perturbation pressure retrievals suggest local pressure minima associated with the MVs. Preferential updraft locations appear to transition cyclonically about the local vorticity maximum as the MVs progress around the eye. Based on published observational datasets, the dual-Doppler updraft magnitudes in Ike's MVs are within the top 5%-10% of TC vertical velocities. The MVs are marked by peak vorticity in the lowest 2 km and contain vertically coherent vorticity structures extending to 8 km AGL. After prolonged land interaction, the MV structures deteriorate. First, the vertical extent of localized vorticity diminishes, followed by a deterioration in the prelandfall characteristic kinematic arrangements. This supports the notion that the replenishment of a high vorticity annulus contributes to MV production and maintenance, and when the elevated vorticity aloft is not maintained, MV kinematic patterns become less consistent. It is unclear whether the decay of the vertically coherent vorticity structures occurs in response to land interaction, TC inner core processes, or some combination of both. [ABSTRACT FROM AUTHOR]