Weather Surveillance Radar-1988 Doppler Research Articles

Analyses of the temporal and spatial structures of heavy rainfall from a catalog of high-resolution radar rainfall fields

In this paper, we develop a storm catalog of heavy rainfall events for a region centered on the Milwaukee, Wisconsin WSR-88D (Weather Surveillance Radar — 1988 Doppler) radar. The study region includes portions of southern Wisconsin, northern Illinois and Lake Michigan. The long-term objective of this study is to develop rainfall frequency analysis methods based on a storm catalog of major rain events. The specific objectives of this study are to develop a long-term catalog of high-resolution radar rainfall fields and characterize key features of the space–time variability of rainfall. The research questions that underlie these objectives are: 1) What are the spatial heterogeneities of rainfall over the study region for major flood-producing storm systems? 2) What are the key elements of storm evolution that control the scale-dependent properties of extreme rainfall? The storm catalog contains a record of the 50 “largest” storm days during the 1996–2011 observation period. We show that mean rainfall for the 50 largest storm days exhibits pronounced spatial heterogeneity with a broad maximum in western Wisconsin and a minimum in the eastern portion of the study region over Lake Michigan. We also show that there is a narrow line of maximum mean rainfall extending from west to east along the Wisconsin–Illinois border. This feature is tied to a maximum in the probability of daily rainfall exceeding 100mm. There are characteristic elements to the storm life cycle of heavy rainfall days that relate to size, structure and evolution of heavy rainfall. Extreme rainfall is also linked with severe weather (tornados, large hail and damaging wind). The diurnal cycle of rainfall for heavy rain days is characterized by an early peak in the largest rainfall rates, an afternoon–evening peak in rain area exceeding 25mmh−1 and development of a large stratiform rain area during the night and early morning.

Potential Utilization of Specific Attenuation for Rainfall Estimation, Mitigation of Partial Beam Blockage, and Radar Networking

Abstract The potential utilization of specific attenuation A for rainfall estimation, mitigation of partial beam blockage, and radar networking is investigated. The R(A) relation is less susceptible to the variability of drop size distributions than traditional rainfall algorithms based on radar reflectivity Z, differential reflectivity ZDR, and specific differential phase KDP in a wide range of rain intensity. Specific attenuation is estimated from the radial profile of the measured Z and the total span of the differential phase using the ZPHI method. Since the estimated A is immune to reflectivity biases caused by radar miscalibration, attenuation, partial beam blockage, and wet radomes, rain retrieval from R(A) is also immune to the listed factors. The R(A) method was tested at X band using data collected by closely located radars in Germany and at S band for polarimetrically upgraded Weather Surveillance Radar-1988 Doppler (WSR-88D) radars in the United States. It is demonstrated that the two adjacent X-band radars—one of which is miscalibrated and another which is affected by partial beam blockage—produce almost indistinguishable fields of rain rate. It is also shown that the R(A) method yields robust estimates of rain rates and rain totals at S band, where specific attenuation is vanishingly small. The X- and S-band estimates of rainfall obtained from R(A) are in good agreement with gauges.

Numerical Prediction of the 8 May 2003 Oklahoma City Tornadic Supercell and Embedded Tornado Using ARPS with the Assimilation of WSR-88D Data

Abstract The 8 May 2003 Oklahoma City, Oklahoma, tornadic supercell is predicted with the Advanced Regional Prediction System (ARPS) model using four nested grids with 9-km, 1-km, 100-m, and 50-m grid spacings. The Oklahoma City Weather Surveillance Radar-1988 Doppler (WSR-88D) radial velocity and reflectivity data are assimilated through the ARPS three-dimensional variational data assimilation (3DVAR) and cloud analysis on the 1-km grid to generate a set of initial conditions that includes a well-analyzed supercell and associated low-level mesocyclone. Additional 1-km experiments show that the use of radial velocity and the proper use of a divergence constraint in the 3DVAR play an important role in the establishment of the low-level mesocyclone during the assimilation and forecast. Assimilating reflectivity data alone failed to predict the mesocyclone intensification. The 100-m grid starts from the interpolated 1-km control initial conditions, while the further nested 50-m grid starts from the 20-min forecast on the 100-m grid. The forecasts on both grids cover the entire period of the observed tornado outbreak and successfully capture the development of tornadic vortices. The intensity of a tornado on the 50-m grid reaches the high end of category 3 on the Fujita scale (F3), while the corresponding simulated tornado on the 100-m grid reaches F2 intensity. The timing of the tornadogenesis on both grids agrees with the observations very well, although the predicted tornado was slightly weaker and somewhat shorter lived. The predicted tornado track parallels the observed damage track although it is displaced northward by about 8 km. The predicted tornado vortices have realistic structures similar to those documented in previous theoretical, idealized modeling and some observational studies. The prediction of an observed tornado in a supercell with a similar degree of realism has not been achieved before.

Quantitative Assessment of Operational Weather Radar Rainfall Estimates over California’s Northern Sonoma County Using HMT-West Data

Abstract An evaluation of Weather Surveillance Radar-1988 Doppler (WSR-88D) KMUX and KDAX radar quantitative precipitation estimation (QPE) over a site in California’s northern Sonoma County is performed and rain type climatology is presented. This site is next to the flood-prone Russian River basin and, because of the mountainous terrain and remoteness from operational radars, is generally believed to lack adequate coverage. QPE comparisons were conducted for multiyear observations with concurrent classification of rainfall structure using measurements from a gauge and an S-band profiler deployed at the location of interest. The radars were able to detect most of the brightband (BB) rain, which contributed over half of the total precipitation. For this rain type hourly radar-based QPE obtained with a default vertical profile of reflectivity correction provided results with errors of about 50%–60%. The operational radars did not detect precipitation during about 30% of the total rainy hours with mostly shallow nonbrightband (NBB) rain, which, depending on the radar, provided ~(12%–15%) of the total precipitation. The accuracy of radar-based QPE for the detected fraction of NBB rain was rather poor with large negative biases and characteristic errors of around 80%. On some occasions, radars falsely detected precipitation when observing high clouds, which did not precipitate or coexisted with shallow rain (less than 10% of total accumulation). For heavier rain with a significant fraction of BB hourly periods, radar QPE for event totals showed relatively good agreement with gauge data. Cancelation of errors of opposite signs contributed, in part, to such agreement. On average, KDAX-based QPE was biased low compared to KMUX.

A Comparison of Precipitation Occurrence from the NCEP Stage IV QPE Product and the CloudSat Cloud Profiling Radar

Abstract Because of its extensive quality control procedures and uniform space–time grid, the NCEP Stage IV merged Weather Surveillance Radar-1988 Doppler (WSR-88D) radar and surface rain gauge dataset is often considered to be the best long-term gridded dataset of precipitation observations covering the contiguous United States. Stage IV accumulations are employed in a variety of applications, and while the WSR-88D systems are well suited for observing heavy rain events that are likely to affect flooding, limitations in surface radar and gauge measurements can result in missed precipitation, especially near topography and in the western United States. This paper compares hourly Stage IV observations of precipitation occurrence to collocated observations from the 94-GHz CloudSat Cloud Profiling Radar, which provides excellent sensitivity to light and frozen precipitation. Statistics from 4 yr of comparisons show that the CloudSat observes precipitation considerably more frequently than the Stage IV dataset, especially in northern states where frozen precipitation is prevalent in the cold season. The skill of Stage IV for precipitation detection is found to decline rapidly when the near-surface air temperature falls below 0°C. As a result, agreement between Stage IV and CloudSat tends to be best in the southeast, where radar coverage is good and moderate-to-heavy liquid precipitation dominates. Stage IV and CloudSat precipitation detection characteristics are documented for each of the individual river forecast centers that contribute to the Stage IV dataset to provide guidance regarding potential sampling biases that may impact hydrologic applications.

The Advantages of a Mixed-Band Radar Network for Severe Weather Operations: A Case Study of 13 May 2009

Abstract Adding a mix of X- or C-band radars to the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network could address several limitations of the network, including improvements to spatial gaps in low-level coverage and temporal sampling of volume scans. These limitations can result in missing critical information in highly dynamic events, such as tornadoes and severe straight-line wind episodes. To evaluate the potential value of a mixed-band radar network for severe weather operations, a case study is examined using data from X- and S-band radars. On 13 May 2009, a thunderstorm complex associated with a cold front moved southward into southwest Oklahoma. A tornado rapidly developed from an embedded supercell within the complex. The life cycle of the tornado and subsequent wind event was sampled by the experimental Collaborative Adaptive Sensing of the Atmosphere (CASA) radar testbed of four X-band radars as well as two operational WSR-88Ds. In this study, the advantages of a mixed-band radar network are demonstrated through a chronological analysis of the event. The two radar networks provided enhanced overall situational awareness. Data from the WSR-88Ds provided 1) clear-air sensitivity, 2) a broad overview of the storm complex, 3) a large maximum unambiguous range, and 4) upper-level scans up to 19.5°. Data from the CASA radars provided 1) high-temporal, 1-min updates; 2) overlapping coverage for dual-Doppler analysis; and 3) dense low-level coverage. The combined system allowed for detailed, dual- and single-Doppler observations of a wind surge, a mesocyclone contraction, and a downburst.

The Analysis and Prediction of Microphysical States and Polarimetric Radar Variables in a Mesoscale Convective System Using Double-Moment Microphysics, Multinetwork Radar Data, and the Ensemble Kalman Filter

AbstractDoppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a double-moment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8–9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SM or DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation.

Investigation of PR and TMI Version 6 and Version 7 Rainfall Algorithms in Landfalling Tropical Cyclones Relative to the NEXRAD Stage-IV Multisensor Precipitation Estimate Dataset

AbstractRainfall estimates from versions 6 (V6) and 7 (V7) of the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) 2A25 and Microwave Imager (TMI) 2A12 algorithms are compared relative to the Next Generation Weather Radar (NEXRAD) Multisensor Precipitation Estimate stage-IV hourly rainfall product. The dataset consists of 252 TRMM overpasses of tropical cyclones from 2002 to 2010 within a 230-km range of southeastern U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) sites. All rainfall estimates are averaged to a uniform 1/7° square grid. The grid boxes are also divided by their TMI surface designation (land, ocean, or coast). A detailed statistical analysis is undertaken to determine how changes to the TRMM rainfall algorithms in the latest version (V7) are influencing the rainfall retrievals relative to ground reference data. Version 7 of the PR 2A25 is the best-performing algorithm over all three surface types. Over ocean, TMI 2A12 V7 is improved relative to V6 at high rain rates. At low rain rates, the new ocean TMI V7 probability-of-rain parameter creates ambiguity in differentiating light rain (≤0.5 mm h−1) and nonraining areas. Over land, TMI V7 underestimates stage IV more than V6 does at a wide range of rain rates, resulting in an increased negative bias. Both versions of the TMI coastal algorithm are also negatively biased at both moderate and heavy rain rates. Some of the TMI biases can be explained by uncertain relationships between rain rate and 85-GHz ice scattering.

Polarimetric Radar Characteristics of Melting Hail. Part II: Practical Implications

AbstractThe results of theoretical modeling in Part I are utilized to develop practical recommendations for developing the algorithms for hail detection and determination of its size as well as attenuation correction and rainfall estimation in the presence of hail. A new algorithm for discrimination between small hail (with maximal size of less than 2.5 cm), large hail (with diameters between 2.5 and 5.0 cm), and giant hail with size exceeding 5.0 cm is proposed and implemented for applications with the S-band dual-polarization Weather Surveillance Radar-1988 Doppler (WSR-88D) systems. The fuzzy-logic algorithm is based on the combined use of radar reflectivity Z, differential reflectivity ZDR, and cross-correlation coefficient ρhv. The parameters of the membership functions depend on the height of the radar resolution volume with respect to the freezing level, exploiting the size-dependent melting characteristics of hailstones. The attenuation effects in melting hail are quantified in this study, and a novel technique for polarimetric attenuation correction in the presence of hail is suggested. The use of a rainfall estimator that is based on specific differential phase KDP is justified on the basis of the results of theoretical simulations and comparison of actual radar retrievals at S band with gauge measurements for storms containing large hail with diameters exceeding 2.5 cm.

Reexamining the Vertical Development of Tornadic Vortex Signatures in Supercells

Abstract Observations from a hybrid phased-array Doppler radar, the Mobile Weather Radar, 2005 X-band, Phased Array (MWR-05XP), were used to investigate the vertical development of tornadic vortex signatures (TVSs) during supercell tornadogenesis. Data with volumetric update times of ∼10 s, an order of magnitude better than that of most other mobile Doppler radars, were obtained up to storm midlevels during the formation of three tornadoes. It is found that TVSs formed upward with time during tornadogenesis for two cases. In a third case, missing low-level data prevented a complete time–height analysis of TVS development; however, TVS formation occurred first near the ground and then at storm midlevels several minutes later. These results are consistent with the small number of volumetric mobile Doppler radar tornadogenesis cases from the past ∼10 years, but counter to studies prior to that, in which a descending TVS was observed in roughly half of tornado cases utilizing Weather Surveillance Radar-1988 Doppler (WSR-88D) data. A comparative example is used to examine the possible effects relatively long WSR-88D volumetric update times have on determining the mode of tornadogenesis.

Correction of Radar QPE Errors Associated with Low and Partially Observed Brightband Layers

AbstractThe melting of aggregated snow/crystals often results in an enhancement of the reflectivity observed by weather radars, and this is commonly referenced as the bright band (BB). The locally high reflectivity often causes overestimation in radar quantitative precipitation estimates (QPE) if no appropriate correction is applied. When the melting layer is high, a complete BB layer profile (including top, peak, and bottom) can be observed by the ground radar, and a vertical profile of reflectivity (VPR) correction can be made to reduce the BB impact. When a melting layer is near the ground and the bottom part of the bright band cannot be observed by the ground radar, a VPR correction cannot be made directly from the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar observations. This paper presents a new VPR correction method under this situation. From high-resolution precipitation profiler data, an empirical relationship between BB peak and BB bottom is developed. The empirical relationship is combined with the apparent BB peak observed by volume scan radars and the BB bottom is found. Radar QPEs are then corrected based on the estimated BB bottom. The new method was tested on 13 radars during seven low brightband events over different areas in the United States. It is shown to be effective in reducing the radar QPE overestimation under low brightband situations.

Principles and applications of dual-polarization weather radar. Part I: Description of the polarimetric radar variables

The United States Weather Surveillance Radar-1988 Doppler (WSR-88D) radar network has been upgraded to dual-polarization capabilities, providing operational and research meteorologists with a wealth of new information regarding the types and distributions of hydrometeors within precipitating storms, as well as a means for improved radar data quality. In addition to the conventional moments of reflectivity factor at horizontal polarization (ZH), Doppler velocity (Vr), and Doppler spectrum width (W), the new variables available from upgraded radars are the differential reflectivity (ZDR), differential propagation phase shift (ΦDP), specific differential phase (KDP), and the co-polar correlation coefficient (ρhv or CC). In the first part of this review series, a description of the polarimetric radar variables available from the newly polarimetric WSR-88D radars is provided. An emphasis is made on their physical meaning and interpretation in the context of operational meteorology.

The Improvement to the Environmental Wind and Tropical Cyclone Circulation Retrievals with the Modified GBVTD (MGBVTD) Technique

AbstractThe ground-based velocity track display (GBVTD) was developed to deduce a three-dimensional primary circulation of landfalling tropical cyclones from single-Doppler radar data. However, the cross-beam component of the mean wind cannot be resolved and is consequently aliased into the retrieved axisymmetric tangential wind . Recently, the development of the hurricane volume velocity processing method (HVVP) enabled the independent estimation of ; however, HVVP is potentially limited by the unknown accuracy of empirical assumptions used to deduce the modified Rankine-combined vortex exponent . By combing the GBVTD with HVVP techniques, this study proposes a modified GBVTD method (MGBVTD) to objectively deduce from the GBVTD technique and provide a more accurate estimation of and via an iterative procedure to reach converged and cross-beam component of solutions. MGBVTD retains the strength of both algorithms but avoids their weaknesses. The results from idealized experiments demonstrate that the MGBVTD-retrieved cross-beam component of is within 2 m s−1 of reality. MGBVTD was applied to Hurricane Bret (1999) whose inner core was captured simultaneously by two Weather Surveillance Radar-1988 Doppler (WSR-88D) instruments. The MGBVTD-retrieved cross-beam component of from single-Doppler radar data is very close to that from dual-Doppler radar synthesis using extended GBVTD (EGBVTD); their difference is less than 2 m s−1. The mean difference in the MGBVTD-retrieved from the two radars is ~2 m s−1, which is significantly smaller than that resolved in GBVTD retrievals (~5 m s−1).

Wind Profiles in Tropical Cyclone Stratiform Rainbands over Land

Abstract Observations of 14 stratiform periods in outer tropical cyclone rainbands are used to evaluate wind structure using a velocity–azimuth display (VAD) technique applied to KAMX (Miami) Weather Surveillance Radar-1988 Doppler (WSR-88D) data. These 14 cases occurred over land in southern Florida from Tropical Storm Fay and Hurricanes Gustav and Ike during 2008. Profiles show a maximum horizontal wind speed between 1000 and 1500 m in height, with occasional evidence of a secondary horizontal wind maximum near 3500–5000 m. Storm-relative wind components are calculated, and radial wind profiles show a mean transition from radial inflow at low levels to radial outflow around 2500–3000-m altitude. The radial inflow maximum is around 500 m, while maximum outflow is more variable. These profile characteristics are consistent with previous wind observations in rainbands over land and water. Changes in wind structure within one 4-h period are examined, with changes seen linked to the environmental influence on the rainband. All rainbands show a logarithmic wind speed decrease below 200 m. This layer is studied in detail using a log-wind fit method and a ratio method to calculate aerodynamic roughness length. Much lower ratios of surface to higher-level winds were found than in previous studies over open oceans. Another significant finding of this work is the lack of a constant aerodynamic roughness length despite similar storm wind profiles. These results are useful in broadening the understanding of low-level impacts of landfalling rainbands far from the storm center.

The Impact of Covariance Localization for Radar Data on EnKF Analyses of a Developing MCS: Observing System Simulation Experiments

Abstract Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

Spatial and temporal resolution considerations in evaluating and utilizing radar quantitative precipitation estimates

In recent years, Weather Surveillance Radar-1988 Doppler base data have become available at substantially finer spatial resolution—i.e., 0.25 km  0.5o (0.25 km  1o for use in precipitation processing) versus the legacy 1 km  1o. Quantitative precipitation estimation (QPE) at this higher resolution may yield greater accuracy in the discretization of precipitation to delineated stream and river basins, in turn resulting in operational benefit in improved performance of hydrologic models and forecasting tools. To assess this potential, 1-h radar QPEs were determined from two experimental, S-band radar systems across several spatial resolutions—starting with base reflectivity data at or near the newly available, fine resolution, and recombining them up to, and including, the legacy resolution. Next, 1-h QPE was calculated at these various resolutions (based on the traditional Z–R relationship) and matched against 1-h rain gauge accumulations from dense gauge networks. After performing several steps of manual quality control, the dataset contained over 9500 warm-season gauge–radar pairs; these were assessed in various sub-groups and configurations (including stratification by precipitation intensity and range from the radar) and in simulated small stream basins. In various statistical analyses, however, the error differences between the fine- and legacy-resolutions were not statistically significant. Several possible causes for this supposed counterintuitive result were investigated, including (1) increased sampling error/noisiness in smaller versus larger sample bins, (2) sub-beam factors acting on falling hydrometeors, and (3) the matter that temporal sampling frequencies employed operationally were not increased commensurately with the increase in spatial sampling resolution.

Correction of Radar QPE Errors for Nonuniform VPRs in Mesoscale Convective Systems Using TRMM Observations

Abstract Mesoscale convective systems (MCSs) contain both regions of convective and stratiform precipitation, and a bright band (BB) is often found in the stratiform region. Inflated reflectivity intensities in the BB often cause positive biases in radar quantitative precipitation estimation (QPE). A vertical profile of reflectivity (VPR) correction is necessary to reduce such biases. However, existing VPR correction methods for ground-based radars often perform poorly for MCSs owing to their coarse resolution and poor coverage in the vertical direction, especially at far ranges. Spaceborne radars such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), on the other hand, can provide high resolution VPRs. The current study explores a new approach of incorporating the TRMM VPRs into the VPR correction for the Weather Surveillance Radar-1988 Doppler (WSR-88D) radar QPE. High-resolution VPRs derived from the Ku-band TRMM PR data are converted into equivalent S-band VPRs using an empirical technique. The equivalent S-band TRMM VPRs are resampled according to the WSR-88D beam resolution, and the resampled (apparent) VPRs are then used to correct for BB effects in the WSR-88D QPE when the ground radar VPR cannot accurately capture the BB bottom. The new scheme was tested on six MCSs from different regions in the United States and it was shown to provide effective mitigation of the radar QPE errors due to BB contamination.

Prognostic Equation for Radar Radial Velocity Derived by Considering Atmospheric Refraction and Earth Curvature

Abstract The prognostic equation for the radial velocity field observed with a Doppler radar is derived to include the effects of atmospheric refraction and earth curvature on radar-beam height and slope angle. The derived equation, called the radial velocity equation, contains a high-order small term that can be truncated. The truncated radial velocity equation is shown to be much more accurate than its counterpart radial velocity equation derived without considering the effects of atmospheric refraction and earth curvature. The truncated equation has the same concise form as its counterpart radial velocity equation but remains to be sufficiently accurate as a useful dynamic constraint for radar wind analysis and assimilation (in normal situations) even up to the farthest 300-km radial range of operational Weather Surveillance Radar-1988 Doppler (WSR-88D) scans where its counterpart radial velocity equation becomes erroneous.

Operational recognition of high precipitation efficiency and low-echo-centroid convection

A rarely studied cause of flash flooding—the leading cause of weather-related fatalities in the United States—is from a type of convective cell called a low-echo centroid (LEC), where most of the reflectivity is within the warm portion of the cloud-bearing layer. Since the inception of the LEC classification, the definition has remained ambiguous and difficult to quantify. Operational identification of LEC convection is critical to anticipating flash flooding, because rainfall estimates from the Weather Surveillance Radar-1988 Doppler often are too low. Four LEC convective cases are examined in order to refine the definition and to provide the characteristic features of these storms. From the sample events, two types of LEC storms were identified that shared features that would assist in their operational identification. Analyses indicated that radar reflectivity 60 dBZ should exist within the cell, the cell should possess a long-lived steady-state reflectivity of 45–55 dBZ, and the LEC cell should have increasing reflectivity as height decreases. The development of a system to assist operational forecasters in identification of the atmospheric parameters favoring LEC convection, and the real-time recognition of LEC events to support flash-flood-warning decision making, is an important effort to reducing unwarned and/or unexpected flooding events.

Axis Ratios and Flutter Angles of Cloud Ice Particles: Retrievals from Radar Data

Abstract A novel method of retrieving the mean axis ratio (width/length) and standard deviation of orientation angles (σθ, which is called herein the intensity of fluttering) of ice cloud particles from polarimetric radar data is described. The method is based on measurements of differential reflectivity ZDR and the copolar correlation coefficient in cloud areas with ZDR > 4 dB. In three analyzed cases, the values of the retrieved axis ratio were in an interval from 0.15 to 0.4 and σθ found in an interval from 2° to 20°. The latter values indicate that the particles experienced light to moderate fluttering. Ambiguities in the retrievals because of uncertainties in the bulk ice density of the particles and possible presence of columnar crystals are considered. The retrieval method is applicable for centimeter-wavelength radars; the analyzed data were collected with the dual-polarization S-band Weather Surveillance Radar-1988 Doppler (WSR-88D).