Reconstruction of missing radiosonde observations for Indian region using conditional GAN framework
(2026) In Journal of Atmospheric and Solar-Terrestrial Physics 284.- Abstract
Radiosonde observations represent one of the most reliable and relevant methods of vertical profile measurements of the upper air metrological observations. Despite its importance in Numerical weather prediction (NWP), radiosonde observations suffer from persistent data gaps resulting from sensor failure, critical environment conditions, and signal interference. Therefore, there is a growing need for data reconstruction that may be able to fill the missing records and extend the data usability in NWP models for enhanced short range weather forecasts. Advanced machine learning methods offer a promising path to overcome the problem and increase the reliability of radiosonde observations. Present study utilized a robust approach to... (More)
Radiosonde observations represent one of the most reliable and relevant methods of vertical profile measurements of the upper air metrological observations. Despite its importance in Numerical weather prediction (NWP), radiosonde observations suffer from persistent data gaps resulting from sensor failure, critical environment conditions, and signal interference. Therefore, there is a growing need for data reconstruction that may be able to fill the missing records and extend the data usability in NWP models for enhanced short range weather forecasts. Advanced machine learning methods offer a promising path to overcome the problem and increase the reliability of radiosonde observations. Present study utilized a robust approach to reconstruct missing radiosonde observations across six distinct sites in Indian region during the year 2024 (Delhi, Lucknow, Karaikal, Patna, Pune and Srinagar) using Conditional Generative Adversarial Networks (c-GAN) framework. For robustness and generalization capabilities of AI driven model performance validation metrics (Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Error (MSE)) have been considered. From results, it is observed that the coefficient of determination ranges from 0.98 to 0.99 demonstrates that the model performs very well for synthesizing the missing data across all stations. The cGAN model achieves a Mean Absolute Error (MAE) range from 1.5 to 3.0 °C, and RMSE lies between 2.5 °C and 4.5 °C, indicating high reconstruction precision. Present research provides a promising pathway for atmospheric data synthesis based on physics integrated AI method with direct application in a meteorology.
(Less)
- author
- Singh, Gaganpreet ; Durbha, Surya S. ; Budakoti, Sachin LU and C R, Shreelakshmi
- organization
- publishing date
- 2026-07
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- c-GAN, Machine learning, Meteorology, Radiosonde, Validation metrics
- in
- Journal of Atmospheric and Solar-Terrestrial Physics
- volume
- 284
- article number
- 106840
- publisher
- Elsevier
- external identifiers
-
- scopus:105039956524
- ISSN
- 1364-6826
- DOI
- 10.1016/j.jastp.2026.106840
- language
- English
- LU publication?
- yes
- id
- 9e7481e5-20e1-4b95-877b-6faf02bfebef
- date added to LUP
- 2026-08-11 11:15:28
- date last changed
- 2026-08-12 08:50:13
@article{9e7481e5-20e1-4b95-877b-6faf02bfebef,
abstract = {{<p>Radiosonde observations represent one of the most reliable and relevant methods of vertical profile measurements of the upper air metrological observations. Despite its importance in Numerical weather prediction (NWP), radiosonde observations suffer from persistent data gaps resulting from sensor failure, critical environment conditions, and signal interference. Therefore, there is a growing need for data reconstruction that may be able to fill the missing records and extend the data usability in NWP models for enhanced short range weather forecasts. Advanced machine learning methods offer a promising path to overcome the problem and increase the reliability of radiosonde observations. Present study utilized a robust approach to reconstruct missing radiosonde observations across six distinct sites in Indian region during the year 2024 (Delhi, Lucknow, Karaikal, Patna, Pune and Srinagar) using Conditional Generative Adversarial Networks (c-GAN) framework. For robustness and generalization capabilities of AI driven model performance validation metrics (Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Square Error (MSE)) have been considered. From results, it is observed that the coefficient of determination ranges from 0.98 to 0.99 demonstrates that the model performs very well for synthesizing the missing data across all stations. The cGAN model achieves a Mean Absolute Error (MAE) range from 1.5 to 3.0 °C, and RMSE lies between 2.5 °C and 4.5 °C, indicating high reconstruction precision. Present research provides a promising pathway for atmospheric data synthesis based on physics integrated AI method with direct application in a meteorology.</p>}},
author = {{Singh, Gaganpreet and Durbha, Surya S. and Budakoti, Sachin and C R, Shreelakshmi}},
issn = {{1364-6826}},
keywords = {{c-GAN; Machine learning; Meteorology; Radiosonde; Validation metrics}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Journal of Atmospheric and Solar-Terrestrial Physics}},
title = {{Reconstruction of missing radiosonde observations for Indian region using conditional GAN framework}},
url = {{http://dx.doi.org/10.1016/j.jastp.2026.106840}},
doi = {{10.1016/j.jastp.2026.106840}},
volume = {{284}},
year = {{2026}},
}