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Robert A. Maddox and Kenneth W. Howard

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Steven V. Vasiloff and Kenneth W. Howard

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A Shared Mobile Atmospheric Research and Teaching Radar (SMART-R) was deployed near Phoenix, Arizona, during the summer of 2004. The goal was to capture a severe microburst at close range to understand the low-altitude wind structure and evolution. During the evening of 27 July, a severe storm formed along the Estrella Mountains south of Phoenix and moved south of the SMART-R as well as the National Weather Service’s (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) in Phoenix (KIWA). Several microburst–downburst pulses were observed by radar and a surface wind gust of 67 mi h−1 was reported. The radar data illustrate the finescale structure of the microburst pulses, with the SMART-R’s higher-resolution data showing Doppler velocities 3–4 m s−1 greater than the KIWA radar. SMART-R wind shear values were 2–3 times greater with the finer resolution of the SMART-R revealing smaller features in the surface outflow wind structure. Asymmetric outflow may have been a factor as well in the different divergence values. The evolution of the outflow was very rapid with the 5-min KIWA scan intervals being too course to sample the detailed evolution. The SMART-R scans were at 3–5-min intervals and also had difficulty resolving the event. The storm environment displayed characteristics of both moderate-to-high-reflectivity microbursts, typical of the high plains of Colorado.

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Xiaoyong Xu, Kenneth Howard, and Jian Zhang

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A radar-based automated technique for the identification of tropical precipitation was developed to improve quantitative precipitation estimation during extreme rainfall events. The technique uses vertical profiles of reflectivity to identify the potential presence of warm rain (i.e., tropical rainfall) microphysics and delineates the tropical rainfall region to which the tropical ZR relationship is applied. The performance of the algorithm is examined based on case studies of five storms that produced extreme precipitation in the United States. Results demonstrate relative improvements in radar-based quantitative precipitation estimation through the automated identification of tropical rainfall and the subsequent adaptation of the tropical ZR relation to account for the potential warm rain processes.

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Carrie Langston, Jian Zhang, and Kenneth Howard

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Communities and many industries are affected by severe weather and have a need for real-time accurate Weather Surveillance Radar-1988 Doppler (WSR-88D) data spanning several regions. To fulfill this need the National Severe Storms Laboratory has developed a Four-Dimensional Dynamic Grid (4DDG) to accurately represent discontinuous radar reflectivity data over a continuous 4D domain. The objective is to create a seamless, rapidly updating radar mosaic that is well suited for use by forecasters in addition to advance radar applications such as qualitative precipitation estimates. Several challenges are associated with creating a 3D radar mosaic given the nature of radar data and the spherical coordinates of radar observations. The 4DDG uses spatial and temporal weighting schemes to overcome these challenges, with the intention of applying minimal smoothing to the radar data. Previous multiple radar mosaics functioned in two or three dimensions using a variety of established weighting schemes. The 4DDG has the advantage of temporal weighting to smooth radar observations over time. Using an exponentially decaying weighting scheme, this paper will examine different weather scenarios and show the effects of temporal smoothing using different time scales. Specifically, case examples of the 4DDG approach involving a rapidly evolving convective event and a slowly developing stratiform weather regime are considered.

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Andrew A. Rosenow, Kenneth Howard, and José Meitín

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On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1.4 km from unlimited in less than 10 min. This snow squall was largely undetected by the operational WSR-88D network because of the Sangre de Cristo Range of the Rocky Mountains lying between the valley and the nearest WSR-88D in Pueblo, Colorado. This study presents observations of the snow squall from the X-band NOAA X-Pol radar, which was deployed in the San Luis Valley during the event. These observations document the squall developing from individual convective cells and growing upscale into a linear squall, with peak radial velocities of 15 m s−1. The environment conducive to the development of this snow squall is examined using data from the High-Resolution Rapid Refresh model, which shows an environment unstable to ascending surface-based parcels, with surface-based convective available potential energy (SBCAPE) values up to 600 J kg−1 in the San Luis Valley. The mobile radar data are integrated into the Multi-Radar Multi-Sensor (MRMS) mosaic to illustrate both the large improvement in detectability of this event gained from a gap-filling radar as well as the capability of MRMS to incorporate data from new radars designed to fill gaps in the current radar network.

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Valliappa Lakshmanan, Jian Zhang, and Kenneth Howard

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Existing techniques of quality control of radar reflectivity data rely on local texture and vertical profiles to discriminate between precipitating echoes and nonprecipitating echoes. Nonprecipitating echoes may be due to artifacts such as anomalous propagation, ground clutter, electronic interference, sun strobe, and biological contaminants (i.e., birds, bats, and insects). The local texture of reflectivity fields suffices to remove most artifacts, except for biological echoes. Biological echoes, also called “bloom” echoes because of their circular shape and expanding size during the nighttime, have proven difficult to remove, especially in peak migration seasons of various biological species, because they can have local and vertical characteristics that are similar to those of stratiform rain or snow. In this paper, a technique is described that identifies candidate bloom echoes based on the range variance of reflectivity in areas of bloom and uses the global, rather than local, characteristic of the echo to discriminate between bloom and rain. Every range gate is assigned a probability that it corresponds to bloom using morphological (shape based) operations, and a neural network is trained using this probability as one of the input features. It is demonstrated that this technique is capable of identifying and removing echoes due to biological targets and other types of artifacts while retaining echoes that correspond to precipitation.

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David O. Blanchard and Kenneth W. Howard

A brief overview of the 13 June 1984 Denver hailstorm is presented. This storm produced very large hail in a few locations and copious amounts of small hail over a large area. Documentation of the storm includes data from a surface mesonetwork, cooperative observers and storm spotters, dual Doppler radar, profiler winds, radiosonde, lightning detectors, and photographs of smoke tracers. Integration of these data sets provides an interesting and informative look at this destructive storm.

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John A. Augustine and Kenneth W. Howard

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Infrared imagery from GOES was used to document mesoscale convective complexes (MCCs) over the United States during 1986 and 1987. A near-record 58 MCCs occurred in 1986, and 44 occurred in 1987. Although these totals were above average relative to MCC numbers of the 7 years prior to 1985, seasonal distributions for both years were atypical. Particularly, each had an extended period (∼3 weeks) when no MCCs occurred in late spring and early summer, a time when the mean MCC seasonal distribution peaks. This peculiarity was investigated by comparing mean large-scale surface and upper-air environments of null- and active-MCC periods of both years. Results confirmed the primary importance to MCC development of strong low-level thermal forcing, as well as proper vertical phasing of favorable lower- and midtropospheric environments.

A cursory survey of MCCs documented outside of the United States reveals that MCCs, or MCC-type storms, are a warm-season phenomenon of midlatitude, subtropical, and low-latitude regions around the globe. They have been documented in South America, Mexico, Europe, West Africa, and China. These storm systems are similar to United States MCCs in that they are nocturnal, persist for over 10 h, tend to develop within weak synoptic-scale dynamics in response to strong low-level thermal forcing and conditional instability, and occur typically downwind (midlevel) of elevated terrain. It is surmised that MCCs probably occur over other parts of the midlatitudes, subtropics, and low latitudes that have yet to be surveyed.

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John A. Augustine and Kenneth W. Howard

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Digital GOES infrared imagery is used to document mesoscale convective complexes (MCCs) over the United States during 1985. The introduction of digital imagery to this process, which has been carried out since 1978, has made possible a partial automation of the MCC documentation procedure and subsequently expanded opportunities for research. In conjunction with these improvements, the definition of an MCC has been slightly modified from that proposed by Maddox in 1980. The warmer threshold area measurement (⩽−32°C) of Maddox's original criteria has been dropped from consideration because its measurement was too subjective, and also was determined to be unnecessary. In 1985, 59 MCCs were identified; this total is approximately 20 to 40 more than in any year since 1978, when these annual summaries began. The monthly distribution and seasonal progression of MCCs in 1985 are similar to those of prior years. The enhanced MCC activity in June 1985 is associated with a persistent favorable quasi-geostrophic forcing during that period. Significant MCC research conducted in 1985 included a prototype large-scale field program (0.-K. PRE-STORM) in May and June dedicated solely to the investigation of middle-latitude mesoscale convective systems.

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Jian Zhang, Carrie Langston, and Kenneth Howard

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The occurrence of a bright band, a layer of enhanced reflectivity due to melting of aggregated snow, increases uncertainties in radar-based quantitative precipitation estimation (QPE). The height of the brightband layer is an indication of 0°C isotherm and can be useful in identifying areas of potential icing for aviation and in the data assimilation for numerical weather prediction (NWP). Extensive analysis of vertical profiles of reflectivity (VPRs) derived from the Weather Surveillance Radar-1988 Doppler (WSR-88D) base level data showed that the brightband signature could be easily identified from the VPRs. As a result, an automated brightband identification (BBID) scheme has been developed. The BBID algorithm can determine from a volume scan mean VPR and a background freezing level height from a numerical weather prediction model whether a bright band exists and the height of the brightband layer. The paper presents a description of the BBID scheme and evaluation results from a large dataset from WSR-88D radars in different geographical regions and seasons.

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