Junjie Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Junjie Ma has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Peng Wang
School of Mathematics, North University of China, Taiyuan 030051, China
1 shared publication Qiyu Zhang
Key Laboratory of Core Tech on Numerical Model-AI Integrated Forecast for Hazardous Precipitation, Chongqing Institute of Meteorological Sciences
1 shared publication- Xiaoyun Su
School of Mathematics, North University of China
1 shared publication Xinyu Liu
School of Mathematics, North University of China
1 shared publicationXuan Tong
Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)/Jiangsu Key Laboratory of Intelligent Weather Forecasting and Applications Based on Big Data, Nanjing University of Information Science and Technology
1 shared publication
Latest Publications
- Open Access
- 1 August 2026
- Published by IOP Publishing in Environmental Research Communications
- Vol. 8 (8) , 085024
Enhancing subseasonal 2 m temperature forecast correction and downscaling over north China based on terrain-informed deep learning
Accurate high-resolution temperature predictions at the subseasonal-to-seasonal (S2S) timescale are vital for applications in agriculture, energy management, and disaster mitigation. However, current S2S forecasting capacity is hindered by declining predictive skill with increasing lead time and limited capability in capturing extreme events and fine-scale spatial variability, particularly over regions with complex terrain. To bridge this gap, this study develops a deep learning (DL)-based framework, termed Terrain-ResNet-U-Net (TRU-Net), for the downscaling (from 0.5 ∘ to 0.1 ∘ resolution) and bias correction of 2 m air temperature forecasts over North China. Unlike existing approaches that treat topographic information as auxiliary input, TRU-Net integrates terrain information through a dedicated feature extraction module and incorporates it into a U-Net architecture with a ResNet34 backbone and a spatial attention mechanism, enabling enhanced nonlinear feature learning and improved representation of terrain-induced spatial heterogeneity. Comparative experiments show that TRU-Net consistently outperforms the original forecasts for daily 2 m temperature prediction at the S2S timescale. Averaged across the six lead times, TRU-Net reduces the root mean squared error by 22.97% and increases the Pearson correlation coefficient and Accuracy by 25.16% and 24.48%, respectively, relative to the original forecasts. It also outperforms representative baseline methods, including Linear Regression, U-Net, and several DL baselines. The largest overall improvements are observed at lead times of 3–5 weeks, while the ablation results confirm the contributions of terrain information and the proposed multi-scale terrain feature extraction and fusion strategy. Similar advantages are maintained for representative high-impact events, where the proposed framework also provides positive correction and downscaling effects under these challenging conditions. These results indicate that an effective terrain-aware framework can substantially improve S2S temperature bias correction and downscaling over North China.
- 1 August 2026
- Published by IOP Publishing in Environmental Research Communications
- Vol. 8 (8) , 085024
Open AccessEnhancing subseasonal 2 m temperature forecast correction and downscaling over north China based on terrain-informed deep learning
Accurate high-resolution temperature predictions at the subseasonal-to-seasonal (S2S) timescale are vital for applications in agriculture, energy management, and disaster mitigation. However, current S2S forecasting capacity is hindered by declining predictive skill with increasing lead time and limited capability in capturing extreme events and fine-scale spatial variability, particularly over regions with complex terrain. To bridge this gap, this study develops a deep learning (DL)-based framework, termed Terrain-ResNet-U-Net (TRU-Net), for the downscaling (from 0.5 ∘ to 0.1 ∘ resolution) and bias correction of 2 m air temperature forecasts over North China. Unlike existing approaches that treat topographic information as auxiliary input, TRU-Net integrates terrain information through a dedicated feature extraction module and incorporates it into a U-Net architecture with a ResNet34 backbone and a spatial attention mechanism, enabling enhanced nonlinear feature learning and improved representation of terrain-induced spatial heterogeneity. Comparative experiments show that TRU-Net consistently outperforms the original forecasts for daily 2 m temperature prediction at the S2S timescale. Averaged across the six lead times, TRU-Net reduces the root mean squared error by 22.97% and increases the Pearson correlation coefficient and Accuracy by 25.16% and 24.48%, respectively, relative to the original forecasts. It also outperforms representative baseline methods, including Linear Regression, U-Net, and several DL baselines. The largest overall improvements are observed at lead times of 3–5 weeks, while the ablation results confirm the contributions of terrain information and the proposed multi-scale terrain feature extraction and fusion strategy. Similar advantages are maintained for representative high-impact events, where the proposed framework also provides positive correction and downscaling effects under these challenging conditions. These results indicate that an effective terrain-aware framework can substantially improve S2S temperature bias correction and downscaling over North China.