Articles | Volume 15, issue 23
https://doi.org/10.5194/gmd-15-8931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-8931-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
13 Dec 2022
Model experiment description paper |
| 13 Dec 2022
| 13 Dec 2022
Temperature forecasting by deep learning methods
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Michael Langguth
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Amirpasha Mozaffari
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Scarlet Stadtler
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Karim Mache
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
Martin G. Schultz
Jülich Supercomputing Centre, Forschungszentrum Jülich, 52425 Jülich, Germany
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Cited
17 citations as recorded by crossref.
- Development of a Temperature Prediction Method Combining Deep Neural Networks and a Kalman Filter T. INOUE et al. 10.2151/jmsj.2024-020
- MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction F. Gao et al. 10.3390/atmos15091114
- Maximum temperature forecasting using deep learning algorithm by hyperparameter optimization P. Matlani et al. 10.1051/e3sconf/202458502006
- Incremental–decremental data transformation based ensemble deep learning model (IDT-eDL) for temperature prediction V. Kumar & R. Kumar 10.1007/s40808-024-01953-0
- Analyzing urban footprints over four coastal cities of India and the association with rainfall and temperature using deep learning models A. Mukherjee et al. 10.1016/j.uclim.2024.102123
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- Pentad-mean air temperature prediction using spatial autocorrelation and attention-based deep learning model L. Xu et al. 10.1007/s00704-023-04763-z
- Precipitation Nowcasting Based on Deep Learning over Guizhou, China D. Kong et al. 10.3390/atmos14050807
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. 10.3390/rs15184447
- Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc A. Bishnoi et al. 10.5194/gmd-17-261-2024
- Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data S. Sekhar et al. 10.1142/S2972370124500041
- Spatiotemporal change of PM2.5 concentration in Beijing-Tianjin-Hebei and its prediction based on machine learning N. Liu et al. 10.1016/j.uclim.2024.102167
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al. 10.5194/gmd-15-8913-2022
- Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
- Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources S. Eide et al. 10.3390/s22072802
- A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. 10.1029/2022MS003120
- A deep learning network for improving predictions of maximum and minimum temperatures over complex terrain L. Xu et al. 10.1007/s00704-024-04901-1
12 citations as recorded by crossref.
- Development of a Temperature Prediction Method Combining Deep Neural Networks and a Kalman Filter T. INOUE et al. 10.2151/jmsj.2024-020
- MSLKSTNet: Multi-Scale Large Kernel Spatiotemporal Prediction Neural Network for Air Temperature Prediction F. Gao et al. 10.3390/atmos15091114
- Maximum temperature forecasting using deep learning algorithm by hyperparameter optimization P. Matlani et al. 10.1051/e3sconf/202458502006
- Incremental–decremental data transformation based ensemble deep learning model (IDT-eDL) for temperature prediction V. Kumar & R. Kumar 10.1007/s40808-024-01953-0
- Analyzing urban footprints over four coastal cities of India and the association with rainfall and temperature using deep learning models A. Mukherjee et al. 10.1016/j.uclim.2024.102123
- CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting Y. Ji et al. 10.5194/gmd-16-2737-2023
- Pentad-mean air temperature prediction using spatial autocorrelation and attention-based deep learning model L. Xu et al. 10.1007/s00704-023-04763-z
- Precipitation Nowcasting Based on Deep Learning over Guizhou, China D. Kong et al. 10.3390/atmos14050807
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. 10.3390/rs15184447
- Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc A. Bishnoi et al. 10.5194/gmd-17-261-2024
- Analysis and Recommendation of Outdoor Activities for Smart City Users Based on Real-Time Contextual Data S. Sekhar et al. 10.1142/S2972370124500041
- Spatiotemporal change of PM2.5 concentration in Beijing-Tianjin-Hebei and its prediction based on machine learning N. Liu et al. 10.1016/j.uclim.2024.102167
5 citations as recorded by crossref.
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al. 10.5194/gmd-15-8913-2022
- Temperature forecasting by deep learning methods B. Gong et al. 10.5194/gmd-15-8931-2022
- Deep Tower Networks for Efficient Temperature Forecasting from Multiple Data Sources S. Eide et al. 10.3390/s22072802
- A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. 10.1029/2022MS003120
- A deep learning network for improving predictions of maximum and minimum temperatures over complex terrain L. Xu et al. 10.1007/s00704-024-04901-1
Latest update: 23 Nov 2024
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Inspired by the success of deep learning in various domains, we test the applicability of video...
