Radar data during the period 1 April-31 August 2002 were used to classify all convective storms occurring in a 10-state region of the central United States into nine predominant morphologies, and the severe weather reports associated with each morphology were then analyzed. The morphologies included three types of cellular convection (individual cells, clusters of cells, and broken squall lines), five types of linear systems (bow echoes, squall lines with trailing stratiform rain, lines with leading stratiform rain, lines with parallel stratiform rain, and lines with no stratiform rain), and nonlinear systems. Because linear systems with leading and line-parallel stratiform rainfall were relatively rare in the 2002 sample of 925 events, 24 additional cases of these morphologies from 1996 and 1997 identified by Parker and Johnson were included in the sample. All morphologies were found to pose some risk of severe weather, but substantial differences existed between the number and types of severe weather reports and the different morphologies. Normalizing results per event, nonlinear systems produced the fewest reports of hail, and were relatively inactive for all types of severe weather compared to the other morphologies. Linear systems generated large numbers of reports from all categories of severe weather. Among linear systems, the hail and tornado threat was particularly enhanced in systems having leading and line-parallel stratiform rain. Bow echoes were found to produce far more severe wind reports than any other morphology. The flooding threat was largest in broken lines and linear systems having trailing and line-parallel stratiform rain. Cellular storms, despite much smaller areal coverage, also were abundant producers of severe hail and tornadoes, particularly in broken squall lines. All morphologies were found to pose some risk of severe weather, but substantial differences existed between the number and types of severe weather reports and the different morphologies. Normalizing results per event, nonlinear systems produced the fewest reports of hail, and were relatively inactive for all types of severe weather compared to the other morphologies. Linear systems generated large numbers of reports from all categories of severe weather. Among linear systems, the hail and tornado threat was particularly enhanced in systems having leading and line-parallel stratiform rain. Bow echoes were found to produce far more severe wind reports than any other morphology. The flooding threat was largest in broken lines and linear systems having trailing and line-parallel stratiform rain. Cellular storms, despite much smaller areal coverage, also were abundant producers of severe hail and tornadoes, particularly in broken squall lines.

[1] Idealized simulations of tornadogenesis in supercell storms are performed using a grid of 100 m spacing. The cold pool intensity and low-level storm dynamics are found to be very sensitive to the intercept parameters of rain and hail drop size distributions (DSD). DSDs favoring smaller (larger) hydrometeors result in stronger (weaker) cold pools due to enhanced (reduced) evaporative cooling/melting over a larger (smaller) geographic region. Sustained tornadic circulations of EF2 intensity are produced in two of the simulations with relatively weak cold pools. When the cold pool is strong, the updraft is tilted rearward by the strong, surging gust front, causing a disconnect between low-level circulation centers near gust front and the mid-level mesocyclone. Weaker cold pool cases have strong, sustained, vertical updrafts positioned near and above the low-level circulation centers, providing strong dynamic lifting and vertical stretching to the low-level parcels and favoring tornadogenesis. Citation: Snook, N., and M. Xue

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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.

In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated. The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.