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Aviation Meteorology: Developments and Latest Achievements

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

Deadline for manuscript submissions: closed (1 January 2026) | Viewed by 8437

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Special Issue Editor

Dr. Afaq Khattak

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Guest Editor

Department of Civil, Structural and Environmental Engineering, Trinity College Dublin, D02 PN40 Dublin, Ireland
Interests: sustainable transportation; AI in transportation; transport policy; transportation systems modeling; aviation meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue explores the transformative advancements in aviation meteorology, with a particular emphasis on the integration of artificial intelligence (AI) alongside other novel methods and technologies in weather forecasting. Aviation safety and operational efficiency heavily depend on precise and timely weather data, and recent developments in both AI and traditional meteorological techniques are opening new frontiers in this area.

The issue will feature articles that highlight cutting-edge research and practical applications of AI in turbulence forecasting, wind shear detection, and real-time monitoring of hazardous weather conditions, as well as achievements in traditional aviation meteorology. These advances help optimize flight paths, minimize weather-related delays, and improve decision-making processes. In doing so, we aim to foster discussions around enhancing weather prediction accuracy, mitigating weather-related risks, and contributing to safer skies.

By bridging AI-driven innovations and traditional meteorological methods, this Special Issue outlines the future of aviation, focusing on how both AI and other advancements are shaping a safer and more efficient aviation ecosystem.

Dr. Afaq Khattak
Guest Editor

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 250 words) can be sent to the Editorial Office for assessment.

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
artificial intelligence in weather forecasting
turbulence prediction
wind shear detection
real-time weather monitoring
aviation safety
AI-driven meteorological analysis
weather-related risk mitigation
predictive analytics in aviation
traditional weather forecasting methods

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Published Papers (6 papers)

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Research

15 pages, 3475 KB

Open AccessArticle

An Optimal Selection Method for Object-Based Thunderstorms Using Numerical Models

by Kan Li, Chongyu Zhang, Wei Zhang, Chen Wang and Wei Chen

Atmosphere 2026, 17(3), 260; https://doi.org/10.3390/atmos17030260 - 28 Feb 2026

Viewed by 355

Abstract

To address the challenge of rapidly selecting optimal numerical model products for weather forecasting in critical applications such as aviation route planning, this study proposes an enhanced object-based methodology comprising individual object scoring matching and a regional overall forecast selection scheme, building upon previous research. The method focuses on radar reflectivity forecasts within critical areas along air routes. Individual thunderstorm cells are evaluated using weighted scores for multiple parameters, including the Threat Score (TS), center-of-mass position, maximum radar reflectivity intensity, and shape forecasting accuracy. The regional overall score is then calculated by applying different weights to each convective cell within the area. After examining case studies of various convection types and bulk tests from June to September of 2024 and 2025, the results demonstrate that this method effectively selects the optimal convective forecasts from among the numerical models initiated at different times. The methodology shows promising applications in aviation weather forecasting. Different optimal selection schemes yield varying results: for large-scale convective weather, various test schemes generally align with TS score selection; for small-scale convective weather, schemes emphasizing radar reflectivity intensity show better performance; for scattered convection, schemes prioritizing center-of-mass position forecasting demonstrate superior results. These findings provide valuable insights for precision weather forecasting in both aviation and the agricultural–ecological sectors, in which accurate convective weather prediction is crucial for operational safety and resource management. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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16 pages, 6100 KB

Open AccessArticle

An Observational Study of Low-Level Windshear at Hong Kong International Airport in Late Autumn and Early Winter

by P. W. Chan, P. Cheung and Y. Y. Leung

Atmosphere 2026, 17(2), 202; https://doi.org/10.3390/atmos17020202 - 13 Feb 2026

Viewed by 748

Abstract

Case studies of low-level windshear and turbulence encountered by aircraft with actual pilot reports under all sky conditions are conducted for the first time at Hong Kong International Airport (HKIA) for late autumn to early winter (October to December of the year). These observational analyses follow from the recent enhancements of meteorological instrumentation at HKIA, namely, range–height indicator (RHI) scans of the light detection and ranging system (LIDAR) and the installation of Doppler microwave cloud radar in July 2025. Some novel features of windshear and turbulence are revealed in the present paper. In particular, windshear due to a low-level jet in the presence of liquid clouds shows up clearly in the cloud radar picture. The elevated and elongated mountain wake is well captured by the LIDAR’s RHI scans. And terrain-induced windshear is analyzed for light northerly wind conditions at HKIA. The combination of LIDAR and microwave cloud radar has the potential to provide a truly all-weather windshear and turbulence alerting service for aircraft. However, further research still needs to be performed, and this paper also discusses the future algorithm developments that would be required for this all-weather protection to be realized. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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17 pages, 17543 KB

Open AccessEditor’s ChoiceArticle

Characteristics and Synoptic-Scale Background of Low-Level Wind Shear Induced by Downward Momentum Transport: A Case Study at Xining Airport, China

by Yuqi Wang, Dongbei Xu, Ziyi Xiao, Xuan Huang, Wenjie Zhou and Hongyu Liao

Atmosphere 2026, 17(1), 75; https://doi.org/10.3390/atmos17010075 - 13 Jan 2026

Viewed by 655

Abstract

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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16 pages, 3242 KB

Open AccessEditor’s ChoiceArticle

A Comparative Study of Pilot Reports and In Situ EDR Measurements of Aircraft Turbulence

by Jingyuan Shao, Yi Li, Yan Yu Leung, Zhenyu Yu, Kaijun Wu, Wenhan Gu, Yiqin Bai, Pak-Wai Chan and Zibo Zhuang

Atmosphere 2025, 16(12), 1414; https://doi.org/10.3390/atmos16121414 - 18 Dec 2025

Cited by 2 | Viewed by 1499

Abstract

Accurate characterization of aircraft turbulence is vital for aviation safety and efficiency. This study leverages 2021 data from nationwide Pilot Reports (PIREPs) and China Eastern Airlines’ in situ Eddy Dissipation Rate (EDR) measurements to systematically compare these two primary turbulence monitoring sources. We quantify their consistencies and discrepancies in capturing turbulence intensity and spatiotemporal patterns to assess their respective value and limitations. The findings indicate that while the diurnal and monthly variation trends of turbulence distributions are generally consistent between the two datasets, significant differences exist in intensity distribution, vertical profiles, and spatial patterns. By examining 242 turbulence events concurrently recorded by both China Eastern Airlines’ EDR and pilot reports, the study identifies a spatial discrepancy within 40 km and an average reporting delay of approximately two minutes in PIREPs, with the delay becoming more pronounced as turbulence intensity increases. Furthermore, pilot-reported “severe” turbulence corresponds to EDR values notably lower than the ICAO standard, revealing a systematic overestimation bias. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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24 pages, 5555 KB

Open AccessArticle

A Signal Processing-Guided Deep Learning Framework for Wind Shear Prediction on Airport Runways

by Afaq Khattak, Pak-wai Chan, Feng Chen, Hashem Alyami and Masoud Alajmi

Atmosphere 2025, 16(7), 802; https://doi.org/10.3390/atmos16070802 - 1 Jul 2025

Cited by 1 | Viewed by 2055

Abstract

Wind shear at the Hong Kong International Airport (HKIA) poses a significant safety risk due to terrain-induced airflow disruptions near the runways. Accurate assessment is essential for safeguarding aircraft during take-off and landing, as abrupt changes in wind speed or direction can compromise flight stability. This study introduces a hybrid framework for short-term wind shear prediction based on data collected from Doppler LiDAR systems positioned near the central and south runways of the HKIA. These systems provide high-resolution measurements of wind shear magnitude along critical flight paths. To predict wind shear more effectively, the proposed framework integrates a signal processing technique with a deep learning strategy. It begins with optimized variational mode decomposition (OVMD), which decomposes the wind shear time series into intrinsic mode functions (IMFs), each capturing distinct temporal characteristics. These IMFs are then modeled using bidirectional gated recurrent units (BiGRU), with hyperparameters optimized via the Tree-structured Parzen Estimator (TPE). To further enhance prediction accuracy, residual errors are corrected using Extreme Gradient Boosting (XGBoost), which captures discrepancies between the reconstructed signal and actual observations. The resulting OVMD–BiGRU–XGBoost framework exhibits strong predictive performance on testing data, achieving R² values of 0.729 and 0.926, RMSE values of 0.931 and 0.709, and MAE values of 0.624 and 0.521 for the central and south runways, respectively. Compared with GRUs, LSTM, BiLSTM, and ResNet-based baselines, the proposed framework achieves higher accuracy and a more effective representation of multi-scale temporal dynamics. It contributes to improving short-term wind shear prediction and supports operational planning and safety management in airport environments. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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15 pages, 3497 KB

Open AccessArticle

Climate Change Impacts on Maximum Aviation Payloads of Chinese Airports

by Haijun Song, Tinglong Zhang, Jian Zou and Xianbiao Kang

Atmosphere 2025, 16(5), 597; https://doi.org/10.3390/atmos16050597 - 15 May 2025

Cited by 1 | Viewed by 1481

Abstract

This research investigates climate change impacts on the maximum aviation payload capacity across China’s airport network. Through analysis of projections from 30 Coupled Model Intercomparison Project Phase 6 (CMIP6) models under the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5) scenario, we quantify the temperature and the pressure effects on maximum take-off weight (MTOW) at 184 Chinese airports. The results reveal that all airports experience MTOW reductions by 2081–2100, with high-plateau airports (>2438 m) facing more moderate decreases (−1.25%) than plain airports (<1500 m) (−1.72%). This counterintuitive pattern stems from elevation-dependent pressure compensation: high-altitude regions benefit from significant pressure increases (4.6 hPa) that partially offset temperature-induced density reductions, while lowland areas receive minimal pressure compensation (0.9 hPa). For commercial aircraft, these changes translate to 1.3–2.9 tons of payload reduction for narrow-body aircraft at plain airports. Our findings demonstrate how topography modulates climate impacts on aviation operations, highlighting the need for regionally tailored adaptation strategies with a focus on economically vital lowland hubs. Full article

(This article belongs to the Special Issue Aviation Meteorology: Developments and Latest Achievements)

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