Characterizing the severe turbulence environments associated with commercial aviation accidents. Part 1: A 44-case study synoptic observational analyses

Kaplan, Michael L.; Huffman, Allan; Lux, Kevin M.; Charney, Joseph J.; Riordan, Allen J.; Lin, Yuh-Lang

doi:10.1007/s00703-004-0080-0

Cited by 57 publications

(36 citation statements)

References 16 publications

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“…), deep convective clouds are one of the most important. For example, Kaplan et al (2005) examined 44 cases of severe turbulence and found that 86% of these were within 100 km of active deep convection. Wolff and Sharman (2008) have also illustrated the frequent occurrence of turbulence reports over regions known for convective activity ( Fig.…”

mentioning

confidence: 99%

Recent Advances in the Understanding of Near-Cloud Turbulence

Lane

Sharman

Trier

et al. 2012

Bulletin of the American Meteorological Society

101

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Anyone who has flown in a commercial aircraft is familiar with turbulence. Unexpected encounters with turbulence pose a safety risk to airline passengers and crew, can occasionally damage aircraft, and indirectly increase the cost of air travel. Deep convective clouds are one of the most important sources of turbulence. Cloud-induced turbulence can occur both within clouds and in the surrounding clear air. Turbulence associated with but outside of clouds is of particular concern because it is more difficult to discern using standard hazard identification technologies (e.g., satellite and radar) and thus is often the source of unexpected turbulence encounters. Although operational guidelines for avoiding near-cloud turbulence exist, they are in many ways inadequate because they were developed before the governing dynamical processes were understood. Recently, there have been significant advances in the understanding of the dynamics of near-cloud turbulence. Using examples, this article demonstrates how these advances have stemmed from improved turbulence observing and reporting systems, the establishment of archives of turbulence encounters, detailed case studies, and high-resolution numerical simulations. Some of the important phenomena that have recently been identified as contributing to near-cloud turbulence include atmospheric wave breaking, unstable upper-level thunderstorm outflows, shearing instabilities, and cirrus cloud bands. The consequences of these phenomena for developing new en route turbulence avoidance guidelines and forecasting methods are discussed, along with outstanding research questions.

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mentioning

confidence: 99%

Recent Advances in the Understanding of Near-Cloud Turbulence

Lane

Sharman

Trier

et al. 2012

Bulletin of the American Meteorological Society

101

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“…Shapiro (1978) related gradients of potential vorticity to CAT; Kaplan et al (2005) created a CAT predictor related to gradients of relative vorticity. A long-standing rule-of-thumb CAT forecasting technique in the aviation meteorology community has been to identify regions of strong negative absolute vorticity advection (e.g., Sharman et al 2006, their appendix A, item p).…”

Section: Discussionmentioning

confidence: 99%

Application of the Lighthill–Ford Theory of Spontaneous Imbalance to Clear-Air Turbulence Forecasting

2008

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A new method of clear-air turbulence (CAT) forecasting based on the Lighthill-Ford theory of spontaneous imbalance and emission of inertia-gravity waves has been derived and applied on episodic and seasonal time scales. A scale analysis of this shallow-water theory for midlatitude synoptic-scale flows identifies advection of relative vorticity as the leading-order source term. Examination of leading-and second-order terms elucidates previous, more empirically inspired CAT forecast diagnostics. Application of the Lighthill-Ford theory to the Upper Mississippi and Ohio Valleys CAT outbreak of 9 March 2006 results in good agreement with pilot reports of turbulence. Application of Lighthill-Ford theory to CAT forecasting for the 3 November 2005-26 March 2006 period using 1-h forecasts of the Rapid Update Cycle (RUC) 2 1500 UTC model run leads to superior forecasts compared to the current operational version of the Graphical Turbulence Guidance (GTG1) algorithm, the most skillful operational CAT forecasting method in existence. The results suggest that major improvements in CAT forecasting could result if the methods presented herein become operational.

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“…About 75% of turbulence encounters were found to occur in proximity of significant convective activity, even though the aircraft may have been "out of cloud." 40 For altitudes above 2000 ft AGL, existing airborne wind shear radars possess significant signal processing capabilities that can be utilized to enable turbulence detection. From this it was concluded that the reflectivity detection capability of current airborne radars, while setting a floor for convectively-induced turbulence, could provide a greater than 50% increase in look-ahead turbulence prediction capabilities.…”

Section: A Enhanced Turbulence Radarmentioning

confidence: 99%

New Technologies for Weather Accident Prevention

Stough¹,

Watson²,

Daniels³

et al. 2005

AIAA 5th ATIO And16th Lighter-Than-Air Sys Tech. And Balloon Systems Conferences

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Weather is a causal factor in thirty percent of all aviation accidents. Many of these accidents are due to a lack of weather situation awareness by pilots in flight. Improving the strategic and tactical weather information available and its presentation to pilots in flight can enhance weather situation awareness and enable avoidance of adverse conditions. This paper presents technologies for airborne detection, dissemination and display of weather information developed by the National Aeronautics and Space Administration (NASA) in partnership with the Federal Aviation Administration (FAA), National Oceanic and Atmospheric Administration (NOAA), industry and the research community. These technologies, currently in the initial stages of implementation by industry, will provide more precise and timely knowledge of the weather and enable pilots in flight to make decisions that result in safer and more efficient operations.

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Characterizing the severe turbulence environments associated with commercial aviation accidents. Part 1: A 44-case study synoptic observational analyses

Cited by 57 publications

References 16 publications

Recent Advances in the Understanding of Near-Cloud Turbulence

Recent Advances in the Understanding of Near-Cloud Turbulence

Application of the Lighthill–Ford Theory of Spontaneous Imbalance to Clear-Air Turbulence Forecasting

New Technologies for Weather Accident Prevention

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