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Xiaoyong Xu
,
Kenneth Howard
, and
Jian Zhang

Abstract

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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John A. Augustine
and
Kenneth W. Howard

Abstract

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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John A. Augustine
and
Kenneth W. Howard

Abstract

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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Valliappa Lakshmanan
,
Jian Zhang
, and
Kenneth Howard

Abstract

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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Steven V. Vasiloff
and
Kenneth W. Howard

Abstract

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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Jian Zhang
,
Youcun Qi
,
David Kingsmill
, and
Kenneth Howard

Abstract

This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive ZR relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.

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Andrew A. Rosenow
,
Kenneth Howard
, and
José Meitín

Abstract

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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Karl D. Moore
,
Kenneth J. Voss
, and
Howard R. Gordon

Abstract

A measurement system for determining the spectral reflectance of whitecaps in the open ocean is described. The upwelling radiance is obtained from a ship by observing a small region of the water surface over time using a six-channel radiometer (410, 440, 510, 550, 670, and 860 nm) extended from the bow of the ship. Downwelling irradiance is simultaneously measured and used to provide surface reflectance. The system includes a TV camera mounted beside the radiometer that provides a visual reference of surface events. Air/water temperature and wind speed/direction are also measured along with global positioning system data. Calibration procedures and radiometric characterization of the system for operation under different sky conditions and solar zenith angles are emphasized so that full advantage is taken of ship time whenever whitecap events occur. The radiometer was operated at sea and examples of the spectral reflectance of different foam types (thick foam layers to thin residual patches) generated by the ship’s bow in coastal waters are presented and found to vary spectrally. The presence of submerged bubbles in the foam measurement results in a lower reflectance at the longer wavelengths. For wavebands in the visible region, the spectral reflectance values tend to equalize with higher reflecting foam from thicker foam layers.

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Robert A. Maddox
,
Kenneth W. Howard
, and
Charles L. Dempsey

Abstract

On 20/21 August 1993, deep convective storms occurred across much of Arizona, except for the southwestern quarter of the state. Several storms were quite severe, producing downbursts and extensive wind damage in the greater Phoenix area during the late afternoon and evening. The most severe convective storms occurred from 0000 to 0230 UTC 21 August and were noteworthy in that, except for the first reported severe thunderstorm, there was almost no cloud-to-ground (CG) lightning observed during their life cycles. Other intense storms on this day, particularly early storms to the south of Phoenix and those occurring over mountainous terrain to the north and east of Phoenix, were prolific producers of CG lightning. Radar data for an 8-h period (2000 UTC 20 August–0400 UTC 21 August) indicated that 88 convective cells having maximum reflectivities greater than 55 dBZ and persisting longer than 25 min occurred within a 200-km range of Phoenix. Of these cells, 30 were identified as “low-lightning” storms, that is, cells having three or fewer detected CG strikes during their entire radar-detected life cycle. The region within which the low-lightning storms were occurring spread to the north and east during the analysis period.

Examination of the reflectivity structure of the storms using operational Doppler radar data from Phoenix, and of the supportive environment using upper-air sounding data taken at Luke Air Force Base just northwest of Phoenix, revealed no apparent physical reasons for the distinct difference in observed cloud-to-ground lightning character between the storms in and to the west of the immediate Phoenix area versus those to the north, east, and south. However, the radar data do reveal that several extensive clouds of chaff initiated over flight-restricted military ranges to the southwest of Phoenix. The prevailing flow advected the chaff clouds to the north and east. Convective storms that occurred in the area likely affected by the dispersing chaff clouds were characterized by little or no CG lightning.

Field studies in the 1970s demonstrated that chaff injected into building thunderstorms markedly decreased CG lightning strikes. There are no data available regarding either the in-cloud lightning character of storms on this day or the technical specifications of the chaff being used in military aircraft anti–electronic warfare systems. However, it is hypothesized that this case of severe, but low-lightning, convective storms resulted from inadvertent lightning suppression over south-central Arizona due to an extended period of numerous chaff releases over the military ranges.

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Jian Zhang
,
Youcun Qi
,
Carrie Langston
,
Brian Kaney
, and
Kenneth Howard

Abstract

High-resolution, accurate quantitative precipitation estimation (QPE) is critical for monitoring and prediction of flash floods and is one of the most important drivers for hydrological forecasts. Rain gauges provide a direct measure of precipitation at a point, which is generally more accurate than remotely sensed observations from radar and satellite. However, high-quality, accurate precipitation gauges are expensive to maintain, and their distributions are too sparse to capture gradients of convective precipitation that may produce flash floods. Weather radars provide precipitation observations with significantly higher resolutions than rain gauge networks, although the radar reflectivity is an indirect measure of precipitation and radar-derived QPEs are subject to errors in reflectivity–rain rate (ZR) relationships. Further, radar observations are prone to blockages in complex terrain, which often result in a poor sampling of orographically enhanced precipitation. The current study aims at a synergistic approach to QPE by combining radar, rain gauge, and an orographic precipitation climatology. In the merged QPE, radar data depict high-resolution spatial distributions of the precipitation and rain gauges provide accurate precipitation measurements that correct potential biases in the radar QPE. The climatology provides a high-resolution background of the spatial precipitation distribution in the complex terrain where radar coverage is limited or nonexistent. The merging algorithm was tested on heavy precipitation events in different areas of the United States and provided a superior QPE to the individual components. The new QPE algorithm is fully automated and can be easily implemented in an operational system.

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