1984NASA Technical Reports Server (NASA)Requires access

Precipitation and kinematic structure of microburst producing storms

Rita D. Roberts, John Wilson

Open publisher page 4 citations

Abstract

Single Doppler radar techniques are used to study the precipitation and kinematic structure of microburst-producing storms. Radar data collected by NCAR radars during the Joint Airport Weather Studies (JAWS) experiment are presented along with rawinsonde data taken at Denver, Colorado near the times of microburst occurrence. The radar reflectivity and velocity structure of the storms exhibited great variability, with no unique signature indicating a microburst was imminent. Detection of descending divergent flow is probably not a microburst forecasting tool, nor can the presence of rotation be used as a precursor at present. Convergent flow aloft was a prominent feature in all events. Its occurrence with a descending precipitation shaft and/or at high altitudes is a good indicator of a downdraft. It is concluded that convergent flow is a very important microburst forecasting clue, particularly when coupled with the entrainment of Theta(e) air and a dry-adiabatic lapse rate below cloud base.

About this research paper

What this paper is about

Single Doppler radar techniques are used to study the precipitation and kinematic structure of microburst-producing storms. Radar data collected by NCAR radars during the Joint Airport Weather Studies (JAWS) experiment are presented along with rawinsonde data taken at Denver, Colorado near the times of microburst occurrence. The radar reflectivity and velocity structure of the storms exhibited great variability, with no unique signature indicating a microburst was imminent. Detection of descending divergent flow is probably not a microburst forecasting tool, nor can the presence of rotation be used as a precursor at present. Convergent flow aloft was a prominent feature in all events. Its occurrence with a descending precipitation shaft and/or at high altitudes is a good indicator of a downdraft. It is concluded that convergent flow is a very important microburst forecasting clue, particularly when coupled with the entrainment of Theta(e) air and a dry-adiabatic lapse rate below cloud base.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Single Doppler radar techniques are used to study the precipitation and kinematic structure of microburst-producing storms. Radar data collected by NCAR radars during the Joint Airport Weather Studies (JAWS) experiment are presented along with rawinsonde data taken at Denver, Colorado near the times of microburst occurrence. The radar reflectivity and velocity structure of the storms exhibited great variability, with no unique signature indicating a microburst was imminent. Detection of descending divergent flow is probably not a microburst forecasting tool, nor can the presence of rotation be used as a precursor at present. Convergent flow aloft was a prominent feature in all events. Its occurrence with a descending precipitation shaft and/or at high altitudes is a good indicator of a downdraft. It is concluded that convergent flow is a very important microburst forecasting clue, particularly when coupled with the entrainment of Theta(e) air and a dry-adiabatic lapse rate below cloud base.

Key concepts: Microburst, Storm, Meteorology, Radar, Lapse rate, Geology, Doppler radar, Precipitation

Related papers

Back to paper searchBrowse research topicsOriginal source
Precipitation and kinematic structure of microburst producing storms — Research Paper | ScholarLens