Articles | Volume 15, issue 10
https://doi.org/10.5194/gmd-15-4105-2022
© 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-15-4105-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulation, precursor analysis and targeted observation sensitive area identification for two types of ENSO using ENSO-MC v1.0
Bin Mu
School of Software Engineering, Tongji University, Shanghai, China
Yuehan Cui
School of Software Engineering, Tongji University, Shanghai, China
Shijin Yuan
CORRESPONDING AUTHOR
School of Software Engineering, Tongji University, Shanghai, China
School of Software Engineering, Tongji University, Shanghai, China
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Many weather models rely on decades-old Fortran code that is hard to maintain and makes gradients difficult to compute. We rewrote a widely used regional weather model in Python so that gradients can be computed automatically, providing a framework for gradient-based weather research. In 27 ten-day forecasts across nine regions, it was as accurate as the original model, and a single graphics card ran it at least as fast as the original on 128 processors.
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
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Short summary
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea surface temperature, analyse the precursor and identify the sensitive area. The results reveal the pronounced subsurface features before different types of events and indicate that oceanic thermal anomaly in the central and western Pacific provides a key long-term memory for predictions, demonstrating the potential usage of the ENSO-MC model in simulation, understanding and observations of ENSO.
An ENSO deep learning forecast model (ENSO-MC) is built to simulate the spatial evolution of sea...
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