Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction
Abstract We investigate the applicability of deep learning (DL) methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three‐dimensional convolutional neural network. Our approach was developed iteratively by evaluating seven...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000299 |
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| _version_ | 1849388045842776064 |
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| author | Yannis Schmutz Noemi Imfeld Stefan Brönnimann Erik Graf |
| author_facet | Yannis Schmutz Noemi Imfeld Stefan Brönnimann Erik Graf |
| author_sort | Yannis Schmutz |
| collection | DOAJ |
| description | Abstract We investigate the applicability of deep learning (DL) methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three‐dimensional convolutional neural network. Our approach was developed iteratively by evaluating seven modeling improvement techniques. The resulting method reduces the validation error by 67% compared to a two‐dimensional baseline, decreasing the error from RMSE = 0.4620 and MAE = 0.311 to RMSE = 0.1527 and MAE = 0.1093. Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method. WeRec3D is trained and validated in a self‐supervised manner using ERA5's surface temperature and pressure data over Europe. On a hold‐out set from 1950 to 1954, the validation results in an MAE of 1.11°C and 199 Pa. As a case study, we reconstruct the 1807 heat wave and validate it using a leave‐one‐out method in space. Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively. To the best of our knowledge, this is the first study to apply DL‐based video inpainting techniques for weather reconstruction, proposing it as a novel approach for reconstructing missing weather information. |
| format | Article |
| id | doaj-art-0b47fa3daba444feaa76739d050905a0 |
| institution | Kabale University |
| issn | 2993-5210 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-0b47fa3daba444feaa76739d050905a02025-08-20T03:42:25ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102024-12-0114n/an/a10.1029/2024JH000299Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather ReconstructionYannis Schmutz0Noemi Imfeld1Stefan Brönnimann2Erik Graf3Applied Machine Intelligence Bern University of Applied Sciences Bern SwitzerlandOeschger Center for Climate Change Research University of Bern Bern SwitzerlandOeschger Center for Climate Change Research University of Bern Bern SwitzerlandApplied Machine Intelligence Bern University of Applied Sciences Bern SwitzerlandAbstract We investigate the applicability of deep learning (DL) methods for reconstructing daily weather data. Inspired by video inpainting, we propose a novel method, WeRec3D, which utilizes a three‐dimensional convolutional neural network. Our approach was developed iteratively by evaluating seven modeling improvement techniques. The resulting method reduces the validation error by 67% compared to a two‐dimensional baseline, decreasing the error from RMSE = 0.4620 and MAE = 0.311 to RMSE = 0.1527 and MAE = 0.1093. Additionally, we demonstrate the impact of the spatial distribution of observations on reconstruction accuracy and propose a potential integration with the analogue resampling method. WeRec3D is trained and validated in a self‐supervised manner using ERA5's surface temperature and pressure data over Europe. On a hold‐out set from 1950 to 1954, the validation results in an MAE of 1.11°C and 199 Pa. As a case study, we reconstruct the 1807 heat wave and validate it using a leave‐one‐out method in space. Compared to the original data, the reconstructed time series exhibit a correlation of at least 0.91, with a maximum normalized RMSE and standard deviation delta of 0.58 and 0.51 respectively. To the best of our knowledge, this is the first study to apply DL‐based video inpainting techniques for weather reconstruction, proposing it as a novel approach for reconstructing missing weather information.https://doi.org/10.1029/2024JH000299weather reconstructionvideo inpaintingdeep learning |
| spellingShingle | Yannis Schmutz Noemi Imfeld Stefan Brönnimann Erik Graf Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction Journal of Geophysical Research: Machine Learning and Computation weather reconstruction video inpainting deep learning |
| title | Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction |
| title_full | Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction |
| title_fullStr | Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction |
| title_full_unstemmed | Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction |
| title_short | Enhanced Video Inpainting: A Deep Learning Approach for Historical Weather Reconstruction |
| title_sort | enhanced video inpainting a deep learning approach for historical weather reconstruction |
| topic | weather reconstruction video inpainting deep learning |
| url | https://doi.org/10.1029/2024JH000299 |
| work_keys_str_mv | AT yannisschmutz enhancedvideoinpaintingadeeplearningapproachforhistoricalweatherreconstruction AT noemiimfeld enhancedvideoinpaintingadeeplearningapproachforhistoricalweatherreconstruction AT stefanbronnimann enhancedvideoinpaintingadeeplearningapproachforhistoricalweatherreconstruction AT erikgraf enhancedvideoinpaintingadeeplearningapproachforhistoricalweatherreconstruction |