Efficient Subseasonal Weather Forecast using Teleconnection-informed
Transformers
- URL: http://arxiv.org/abs/2401.17870v2
- Date: Mon, 5 Feb 2024 12:43:24 GMT
- Title: Efficient Subseasonal Weather Forecast using Teleconnection-informed
Transformers
- Authors: Shan Zhao, Zhitong Xiong, Xiao Xiang Zhu
- Abstract summary: Subseasonal forecasting is pivotal for agriculture, water resource management, and early warning of disasters.
Recent advances in machine learning have revolutionized weather forecasting by achieving competitive predictive skills to numerical models.
However, training such foundation models requires thousands of GPU days, which causes substantial carbon emissions.
- Score: 29.33938664834226
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Subseasonal forecasting, which is pivotal for agriculture, water resource
management, and early warning of disasters, faces challenges due to the chaotic
nature of the atmosphere. Recent advances in machine learning (ML) have
revolutionized weather forecasting by achieving competitive predictive skills
to numerical models. However, training such foundation models requires
thousands of GPU days, which causes substantial carbon emissions and limits
their broader applicability. Moreover, ML models tend to fool the pixel-wise
error scores by producing smoothed results which lack physical consistency and
meteorological meaning. To deal with the aforementioned problems, we propose a
teleconnection-informed transformer. Our architecture leverages the pretrained
Pangu model to achieve good initial weights and integrates a
teleconnection-informed temporal module to improve predictability in an
extended temporal range. Remarkably, by adjusting 1.1% of the Pangu model's
parameters, our method enhances predictability on four surface and five
upper-level atmospheric variables at a two-week lead time. Furthermore, the
teleconnection-filtered features improve the spatial granularity of outputs
significantly, indicating their potential physical consistency. Our research
underscores the importance of atmospheric and oceanic teleconnections in
driving future weather conditions. Besides, it presents a resource-efficient
pathway for researchers to leverage existing foundation models on versatile
downstream tasks.
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