Publications

Sun, MQ; Meng, QY; Zhang, LL; Hu, XL; Lei, XW; Chen, SZ; Hou, JY (2025). Convolutional Long Short-Term Memory network for generating 100 m daily near-surface air temperature. SCIENTIFIC DATA, 12(1), 749.

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 degrees C, root mean square errors (RMSE) from 0.733 to 1.027 degrees 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.

DOI:
10.1038/s41597-025-05032-6

ISSN:
2052-4463