Research Collaborations for Better Predictions of Aviation Weather Hazards

Kim, Jung-Hoon; Lee, Danbi; Kim, Soohyun; Strahan, Matt; Pettegrew, Brian P.; Gill, Philip G.; Williams, Paul D.; Schumann, U.; Tenenbaum, J.; Lee, Young-Gon; Choi, Hee-Wook; Song, In-Sul; Park, Ye-Ji; Sharman, Robert

doi:10.1175/bams-d-17-0010.1

Cited by 13 publications

(5 citation statements)

References 18 publications

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“…These results will aid in understanding the global turbulence characteristics that have not yet been obtained due to high costs and observational limits. These results can also be used as additional turbulence data in aviation turbulence studies (Chun et al, ), which can ultimately contribute to providing safe and economical aviation services. In addition to turbulence studies, HVRRD can be invaluable data sources in studies of gravity waves, planetary boundary layers, tropopause structures, and numerical forecasting models (Geller et al, ).…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Chun

Wilson

et al. 2019

JGR Atmospheres

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In this study, we estimate atmospheric turbulence in the free atmosphere in terms of the Thorpe scale (LT) and eddy dissipation rate (ε) using U.S. high vertical‐resolution radiosonde data over 4 years (September 2012 to August 2016) at 68 operational stations. In addition, same calculations are conducted for 12 years (October 2005 to September 2017) at four stations among the 68 stations. These high vertical‐resolution radiosonde data have a vertical resolution of approximately 5 m and extend to an altitude of approximately 33 km, and thus, turbulence can be retrieved in the entire troposphere and lower stratosphere. There are thicker and stronger turbulent layers in the troposphere than in the stratosphere, with mean ε values of 1.84 × 10−4 and 1.37 × 10−4 m2/s3 in the troposphere and stratosphere, respectively. The vertical structure of ε exhibits strong seasonal variations, especially in the upper troposphere and lower stratosphere, with the largest ε values in summer and the smallest in winter. In the horizontal distribution of ε, large ε is seen mainly above the mountainous region in the troposphere, but this pattern is not seen in the stratosphere. Although ε is estimated by the square of LT multiplied by the cube of the Brunt‐Väisälä frequency (N), the regions of large ε are matched with large LT regions where N is relatively small. For the time series of ε near the tropopause for 12 years at four stations, an annual variation is prominent at all stations without significant interannual variations. There is, however, a slightly increasing trend of ε at two stations.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Chun

Wilson

et al. 2019

JGR Atmospheres

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“…A 2001 study found 46% of turbulence-related injuries among flight attendants resulted in serious injury [38]. Research efforts to better forecast turbulence are ongoing [39][40][41]. Enhanced communication and coordination with flight crew and staying seated with seatbelts fastened are effective measures to limit injuries to flight attendants during encounters with turbulence [42,43].…”

Section: Unique Circumstancesmentioning

confidence: 99%

Workers’ compensation injury claims of aviation industry worker injuries in Alaska, 2014–2015

Moller

O'Connor

Lee

et al. 2020

International Journal of Circumpolar Health

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Aviation operations in Alaska often occur in remote locations and during inclement weather. Limited infrastructure and staff in some locations often requires aviation workers to perform tasks outside of their specific job descriptions. Researchers identified workers' compensation claims as a valuable data source to characterise nonfatal injuries among Alaskan aviation workers. Keyword searches of injury claim narrative fields and industry codes were used to identify potentially aviation-related workers' compensation claims during 2014-2015. These claims were manually reviewed to verify whether aviation related and manually coded according to the US Bureau of Labour Statistics' Occupational Injury and Illness Classification System. There were 875 aviationrelated injury claims accepted during 2014-2015. Ramp/baggage/cargo agents incurred the most injuries (35%), followed by mechanics/maintenance workers (15%). Among all workers, Overexertion and Bodily Reaction (40%) was most often cited as the injury event, followed by Contact with Objects and Equipment (28%), and Falls, Slips, Trips (22%). Sprains, strains, tears were the most frequent nature of injury (55%). Cargo/freight/luggage was the most frequent source of injury (24%). The 3 most frequently identified injury event types were responsible for over 90% of all injuries, which indicates that preventive interventions should be directed towards tasks rather than occupational groups.

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“…For everyone concerned with assuring aircraft and human safety, accurate weather forecasting has been a top priority [2]. The aviation industry strives for effective systems for precise forecasting since the issue has not yet been fully resolved [3]. In addition to other reasons, the weather is the biggest hazard to both aircraft and people on board since it may harm both the aircraft and the passengers [4].…”

Section: Introductionmentioning

confidence: 99%

Satellite Imagery-Based Cloud Classification Using Deep Learning

Yousaf,

Rehman,

Khan

et al. 2023

Remote Sensing

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A significant amount of satellite imaging data is now easily available due to the continued development of remote sensing (RS) technology. Enabling the successful application of RS in real-world settings requires efficient and scalable solutions to extend their use in multidisciplinary areas. The goal of quick analysis and precise classification in Remote Sensing Imaging (RSI) is often accomplished by utilizing approaches based on deep Convolution Neural Networks (CNNs). This research offers a unique snapshot-based residual network (SnapResNet) that consists of fully connected layers (FC-1024), batch normalization (BN), L2 regularization, dropout layers, dense layer, and data augmentation. Architectural changes overcome the inter-class similarity problem while data augmentation resolves the problem of imbalanced classes. Moreover, the snapshot ensemble technique is utilized to prevent over-fitting, thereby further improving the network’s performance. The proposed SnapResNet152 model employs the most challenging Large-Scale Cloud Images Dataset for Meteorology Research (LSCIDMR), having 10 classes with thousands of high-resolution images and classifying them into respective classes. The developed model outperforms the existing deep learning-based algorithms (e.g., AlexNet, VGG-19, ResNet101, and EfficientNet) and achieves an overall accuracy of 97.25%.

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Research Collaborations for Better Predictions of Aviation Weather Hazards

Cited by 13 publications

References 18 publications

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Characteristics of Atmospheric Turbulence Retrieved From High Vertical‐Resolution Radiosonde Data in the United States

Workers’ compensation injury claims of aviation industry worker injuries in Alaska, 2014–2015

Satellite Imagery-Based Cloud Classification Using Deep Learning

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