A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.
This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable o...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0316548 |
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| author | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Diogo Freitas João Pestana Dinarte Vieira Marko Radeta Fernando Morgado-Dias |
| author_facet | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Diogo Freitas João Pestana Dinarte Vieira Marko Radeta Fernando Morgado-Dias |
| author_sort | Décio Alves |
| collection | DOAJ |
| description | This study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model's efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety. |
| format | Article |
| id | doaj-art-0b32257fb6b0482f8b0df1b2aefd25cc |
| institution | OA Journals |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-0b32257fb6b0482f8b0df1b2aefd25cc2025-08-20T02:15:50ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031654810.1371/journal.pone.0316548A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach.Décio AlvesFábio MendonçaSheikh Shanawaz MostafaDiogo FreitasJoão PestanaDinarte VieiraMarko RadetaFernando Morgado-DiasThis study introduces a high-resolution wind nowcasting model designed for aviation applications at Madeira International Airport, a location known for its complex wind patterns. By using data from a network of six meteorological stations and deep learning techniques, the produced model is capable of predicting wind speed and direction up to 30-minute ahead with 1-minute temporal resolution. The optimized architecture demonstrated robust predictive performance across all forecast horizons. For the most challenging task, the 30-minute ahead forecasts, the model achieved a wind speed Mean Absolute Error (MAE) of 0.78 m/s and a wind direction MAE of 33.06°. Furthermore, the use of Gaussian noise concatenation to both input and label training data yielded the most consistent results. A case study further validated the model's efficacy, with MAE values below 0.43 m/s for wind speed and between 33.93° and 35.03° for wind direction across different forecast horizons. This approach shows that combining strategically deployed sensor networks with machine learning techniques offers improvements in wind nowcasting for airports in complex environments, possibly enhancing operational efficiency and safety.https://doi.org/10.1371/journal.pone.0316548 |
| spellingShingle | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Diogo Freitas João Pestana Dinarte Vieira Marko Radeta Fernando Morgado-Dias A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. PLoS ONE |
| title | A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. |
| title_full | A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. |
| title_fullStr | A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. |
| title_full_unstemmed | A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. |
| title_short | A networked station system for high-resolution wind nowcasting in air traffic operations: A data-augmented deep learning approach. |
| title_sort | networked station system for high resolution wind nowcasting in air traffic operations a data augmented deep learning approach |
| url | https://doi.org/10.1371/journal.pone.0316548 |
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