Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature
Abstract Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly in developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely developed urban areas. The dataset comprises da...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05032-6 |
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| author | Mengqi Sun Qingyan Meng Linlin Zhang Xinli Hu Xuewen Lei Shize Chen Junyan Hou |
| author_facet | Mengqi Sun Qingyan Meng Linlin Zhang Xinli Hu Xuewen Lei Shize Chen Junyan Hou |
| author_sort | Mengqi Sun |
| collection | DOAJ |
| description | Abstract Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly in developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely developed urban areas. The dataset comprises daily maximum, minimum, and mean temperatures for the summer months (June to August) from 2019 to 2023, at a spatial resolution of 100 m, across the Jiangbei climate zone in China. We applied the Convolutional Long Short-Term Memory (ConvLSTM) deep learning model with multi-source data, including ERA5 temperature data, topography, landcover and vegetation fraction cover. Model evaluation indicates high accuracy, with mean absolute errors (MAE) ranging from 0.564 to 0.784 °C, root mean square errors (RMSE) from 0.733 to 1.027 °C, and coefficients of determination (R2) from 0.892 to 0.943. Our dataset is distinguished by the 100 m spatial resolution and the inclusion of recent summer data from 2023 at a daily scale. This work is valuable for urban or inner-urban climate studies on heatwave mitigation policies and adaptation strategies. |
| format | Article |
| id | doaj-art-ac8bd98453cd406f941085de1d0686aa |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-ac8bd98453cd406f941085de1d0686aa2025-08-20T03:10:18ZengNature PortfolioScientific Data2052-44632025-05-0112111610.1038/s41597-025-05032-6Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperatureMengqi Sun0Qingyan Meng1Linlin Zhang2Xinli Hu3Xuewen Lei4Shize Chen5Junyan Hou6Aerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesAerospace Information Research Institute, Chinese Academy of SciencesBeijing Institute of Remote Sensing InformationAbstract Global warming and urbanization serve as critical research themes in fine-scale climate studies, particularly in developed cities. This study aims to provide a high spatiotemporal resolution dataset of near-surface air temperatures for densely developed urban areas. The dataset comprises daily maximum, minimum, and mean temperatures for the summer months (June to August) from 2019 to 2023, at a spatial resolution of 100 m, across the Jiangbei climate zone in China. We applied the Convolutional Long Short-Term Memory (ConvLSTM) deep learning model with multi-source data, including ERA5 temperature data, topography, landcover and vegetation fraction cover. Model evaluation indicates high accuracy, with mean absolute errors (MAE) ranging from 0.564 to 0.784 °C, root mean square errors (RMSE) from 0.733 to 1.027 °C, and coefficients of determination (R2) from 0.892 to 0.943. Our dataset is distinguished by the 100 m spatial resolution and the inclusion of recent summer data from 2023 at a daily scale. This work is valuable for urban or inner-urban climate studies on heatwave mitigation policies and adaptation strategies.https://doi.org/10.1038/s41597-025-05032-6 |
| spellingShingle | Mengqi Sun Qingyan Meng Linlin Zhang Xinli Hu Xuewen Lei Shize Chen Junyan Hou Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature Scientific Data |
| title | Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature |
| title_full | Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature |
| title_fullStr | Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature |
| title_full_unstemmed | Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature |
| title_short | Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature |
| title_sort | convolutional long short term memory network for generating 100 m daily near surface air temperature |
| url | https://doi.org/10.1038/s41597-025-05032-6 |
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