This paper describes how operational radar, satellite and lightning data may be used in conjunction with numerical weather model data to provide remote detection and diagnosis of atmospheric turbulence in and around thunderstorms. In-cloud turbulence is measured with the NEXRAD Turbulence Detection Algorithm (NTDA) using extensively qualitycontrolled, ground-based Doppler radar data. A real-time demonstration of the NTDA includes generation of a 3-D turbulence mosaic covering the CONUS east of the Rocky Mountains, a web-based display, and experimental uplinks of turbulence maps to en-route commercial aircraft. Near-cloud turbulence is inferred from thunderstorm morphology, intensity, growth rate and environment data provided by (1) satellite radiance measurements, rates of change, winds, and other derived features, (2) lightning strike measurements, (3) radar reflectivity measurements and (4) weather model data. These are combined via a machine learning technique trained using a database of in situ turbulence measurements from commercial aircraft to create a predictive model. This new capability is being developed under FAA and NASA funding to enhance current U.S. and international turbulence decision support systems, allowing rapid-update, highresolution, comprehensive assessments of atmospheric turbulence hazards for use by pilots, dispatchers, and air traffic controllers. It will also contribute to the comprehensive 4-D weather information database for NextGen.
Remote detection and diagnosis of thunderstorm turbulence
Remote Sensing Applications for Aviation Weather Hazard Detection and Decision Support ; 2008 ; San Diego,California,United States
Proc. SPIE ; 7088
2008-08-22
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Remote detection and diagnosis of thunderstorm turbulence [7088-03]
British Library Conference Proceedings | 2008
|Thunderstorm Detection Systems
Online Contents | 1994
Engineering Index Backfile | 1947
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