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Atmosphere
Special Issues
Aviation Meteorology: Current Status and Perspective
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Aviation Meteorology: Current Status and Perspective

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3849

Special Issue Editors

Dr. Pak-Wai Chan

E-Mail Website
Guest Editor
Aviation Weather Services, Hong Kong Observatory, Hong Kong 999077, China
Interests: aviation meteorology; aviation safety; mountain meteorology; meteorological instrumentation
Special Issues, Collections and Topics in MDPI journals
Dr. Feng Chen

E-Mail Website
Guest Editor
Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, Shanghai 1239, China
Interests: transportation safety, aviation meteorology; civil aviation safety; statistical modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Afaq Khattak

E-Mail Website
Guest Editor
Key Laboratory of Infrastructure Durability and Operation Safety in Airfield of CAAC, College of Transportation Engineering, Tongji University, Shanghai 1239, China
Interests: civil aviation safety, aviation meteorology; ai in aviation; transportation system optimization
Dr. Kaijun Wu

E-Mail Website
Guest Editor
Department of Aviation Meteorology, Civil Aviation University of China, Tianjin, China
Interests: aviation meteorology; turbulence; statistical modeling; low visibility

Special Issue Information

Dear Colleagues,

Aviation safety and efficiency are both impacted by the weather. It is a significant factor in flight disruptions, delays, and, in the worst instances, crashes. Wind shear is an important aspect of aviation safety because it can drastically alter aircraft lift during takeoff and landing, putting everyone on board in danger. It is caused by microbursts from thunderstorms, temperature inversions, and surface obstructions, and has a negative impact on the efficiency of airport operations. Analysis of such weather conditions is of the utmost importance from a safety standpoint. Articles in this Special Issue cover a wide range of topics related to aviation meteorology and aviation safety. The latest developments in numerical simulation as well as statistical and AI modeling of various aspects concerning aviation meteorology will be included in this section. We also hope that the Special Issue will serve as an important reference point for researchers investigating airport operational aspects and civil aviation safety.

Dr. Pak-Wai Chan
Dr. Feng Chen
Dr. Afaq Khattak
Dr. Kaijun Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • aviation meteorology
  • aviation safety
  • wind shear
  • turbulence
  • ai in aviation
  • statistical modeling
  • numerical simulation
  • missed-approach
  • wind tunnel
  • microburst

Published Papers (3 papers)

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Research

13 pages, 3880 KiB  
Article
Analysis of Density Altitude Characteristics at Chinese Airports
by Xianbiao Kang, Guoqing Zhao, Haijun Song and Xianfeng Zeng
Atmosphere 2023, 14(12), 1784; https://doi.org/10.3390/atmos14121784 - 03 Dec 2023
Viewed by 687
Abstract
This study conducts a detailed 23-year analysis of Density Altitude (DA) at 34 major airports across China, utilizing Meteorological Aviation Routine Weather Report (METAR) datasets, and discovers significant regional DA variations due to the country’s diverse topography and climate. Central and eastern regions [...] Read more.
This study conducts a detailed 23-year analysis of Density Altitude (DA) at 34 major airports across China, utilizing Meteorological Aviation Routine Weather Report (METAR) datasets, and discovers significant regional DA variations due to the country’s diverse topography and climate. Central and eastern regions exhibit higher DA values because of lower atmospheric pressures at higher altitudes, while northeastern airports have lower DA values, attributed to colder temperatures and lower elevations. A crucial finding is the substantial impact of humidity on DA, particularly in the southern coastal regions, a factor often neglected in pilot training, highlighting the necessity to revise aviation education to include humidity’s impact on DA. The study advocates for a region-specific approach to Chinese aviation operations, tailored to local DA influences, and suggests strategic adjustments in flight planning and risk assessment to address these regional differences, enhancing aviation safety and efficiency. Full article
(This article belongs to the Special Issue Aviation Meteorology: Current Status and Perspective)
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22 pages, 1828 KiB  
Article
Modelling the Impact of Adverse Weather on Airport Peak Service Rate with Machine Learning
by Ramon Dalmau, Jonathan Attia and Gilles Gawinowski
Atmosphere 2023, 14(10), 1476; https://doi.org/10.3390/atmos14101476 - 24 Sep 2023
Cited by 2 | Viewed by 1271
Abstract
Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive [...] Read more.
Accurate prediction of traffic demand and airport capacity plays a crucial role in minimising ground delays and airborne holdings. This paper focuses on the latter aspect. Adverse weather conditions present significant challenges to airport operations and can substantially reduce capacity. Consequently, any predictive model, regardless of its complexity, should account for weather conditions when estimating the airport capacity. At present, the sole shared platform for airport capacity information in Europe is the EUROCONTROL Public Airport Corner, where airports have the option to voluntarily report their capacities. These capacities are presented in tabular form, indicating the maximum number of hourly arrivals and departures for each possible runway configuration. Additionally, major airports often provide a supplementary table showing the impact of adverse weather in a somewhat approximate manner (e.g., if the visibility is lower than 100 m, then arrival capacity decreases by 30%). However, these tables only cover a subset of airports, and their generation is not harmonised, as different airports may use different methodologies. Moreover, these tables may not account for all weather conditions, such as snow, strong winds, or thunderstorms. This paper presents a machine learning approach to learn mapping from weather conditions and runway configurations to the 99th percentile of the delivered throughput from historical data. This percentile serves as a capacity proxy for airports operating at or near capacity. Unlike previous attempts, this paper takes a novel approach, where a single model is trained for several airports, leveraging the generalisation capabilities of cutting-edge machine learning algorithms. The results of an experiment conducted using 2 years of historical traffic and weather data for the top 45 busiest airports in Europe demonstrate better alignment in terms of mean pinball error with the observed departure and arrival throughput when compared to the operational capacities reported in the EUROCONTROL Public Airport Corner. While there is still room for improvement, this system has the potential to assist airports in defining more reasonable capacity values, as well as aiding airlines in assessing the impact of adverse weather on their flights. Full article
(This article belongs to the Special Issue Aviation Meteorology: Current Status and Perspective)
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17 pages, 6898 KiB  
Article
Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach
by Afaq Khattak, Jianping Zhang, Pak-Wai Chan and Feng Chen
Atmosphere 2023, 14(6), 920; https://doi.org/10.3390/atmos14060920 - 24 May 2023
Cited by 2 | Viewed by 1010
Abstract
Aircraft landings can be dangerous near airport runways due to wind variability. As a result, an aircraft could potentially miss an approach or divert off its flight path. In this study, turbulence intensity along the runway glide path was investigated using a scaled-down [...] Read more.
Aircraft landings can be dangerous near airport runways due to wind variability. As a result, an aircraft could potentially miss an approach or divert off its flight path. In this study, turbulence intensity along the runway glide path was investigated using a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain nearby built in a TJ-3 atmospheric boundary layer wind tunnel. Different factors, including the effect of terrain, distance from the runway threshold, assigned approach runway, wind direction, and wind speed, were taken into consideration. Next, based on the experimental results, we trained and tested a novel tree-structured Parzen estimator (TPE)-optimized kernel and tree-boosting (KTBoost) model. The results obtained by the TPE-optimized KTBoost model outperformed other advanced machine learning models in terms of MAE (0.83), MSE (1.44), RMSE (1.20), and R2 (0.89). The permutation-based importance analysis using the TPE-optimized KTBoost model also revealed that the top three factors that contributed to the high turbulence intensity were the effect of terrain, distance from the runway threshold, and wind direction. The presence of terrain, the shorter distance from the runway, and the wind direction from 90 degrees to 165 degrees all contributed to high turbulence intensity. Full article
(This article belongs to the Special Issue Aviation Meteorology: Current Status and Perspective)
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