Abstract
Turbulence in and around clouds can pose a significant hazard to aviation, with convective turbulence identified as being responsible for a majority of all turbulence-related aircraft accidents. Regions of convective turbulence may be small (~1 km) and highly transient (~few minutes). Graphical Turbulence Guidance, an operational NWP-based turbulence forecast, produces hourly forecasts at a relatively coarse spatial resolution and does not explicitly forecast convective turbulence. High-resolution storm data from radar reflectivity or satellites may provide some indication of the likelihood of convective turbulence development, but cannot pinpoint its location or severity. The NCAR/NEXRAD Turbulence Detection Algorithm (NTDA) uses ground-based Doppler weather radar data to measure in-cloud turbulence, with a focus on identifying convective turbulence hazards. NTDA utilizes Level II data from the U.S. network of WSR-88Ds (NEXRADs) to produce real-time, rapid-update, three-dimensional mosaics of in-cloud turbulence. An NTDA product is also produced operationally in the Taiwan Advanced Operational Aviation Weather System using NEXRAD and Gematronik radar data. NTDA turbulence maps are suitable for tactical use by pilots and airline dispatchers and for providing input to comprehensive turbulence nowcasts. They also provide information about storm evolution useful for studying the relationship of turbulence production to thunderstorm dynamics and kinematics. This chapter motivates and describes the NTDA and discusses its performance.
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Acknowledgements
The authors wish to thank NASA Langley Research Center for providing aircraft data from the spring, 2002 Boeing 757 flight tests. Many colleagues assisted with the NTDA project: Jason Craig, Steve Carson, Jaimi Yee, Gary Blackburn, Seth Linden, Andy Cotter, and Shelly Knight helped design, implement, and optimize the real-time NTDA, 3-D mosaic and operational demonstration software system; Larry Cornman and Kent Goodrich provided the turbulence theory on which the SW to EDR scaling function computation was based; and Frank McDonough, Tina Kalb, and Wiebke Deierling performed numerous case studies. We greatly appreciate their contributions.
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Williams, J.K., Meymaris, G. (2016). Remote Turbulence Detection Using Ground-Based Doppler Weather Radar. In: Sharman, R., Lane, T. (eds) Aviation Turbulence. Springer, Cham. https://doi.org/10.1007/978-3-319-23630-8_7
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DOI: https://doi.org/10.1007/978-3-319-23630-8_7
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