Robustness of AI-based weather forecasts in a changing climate

• URL: http://arxiv.org/abs/2409.18529v1
• Date: Fri, 27 Sep 2024 08:11:49 GMT
• Title: Robustness of AI-based weather forecasts in a changing climate
• Authors: Thomas Rackow, Nikolay Koldunov, Christian Lessig, Irina Sandu, Mihai Alexe, Matthew Chantry, Mariana Clare, Jesper Dramsch, Florian Pappenberger, Xabier Pedruzo-Bagazgoitia, Steffen Tietsche, Thomas Jung,
• Abstract summary: We show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states. Despite current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science.
• Score: 1.4779266690741741
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links between weather and climate modelling, this raises the question whether machine learning models could also revolutionize climate science, for example by informing mitigation and adaptation to climate change or to generate larger ensembles for more robust uncertainty estimates. Here, we show that current state-of-the-art machine learning models trained for weather forecasting in present-day climate produce skillful forecasts across different climate states corresponding to pre-industrial, present-day, and future 2.9K warmer climates. This indicates that the dynamics shaping the weather on short timescales may not differ fundamentally in a changing climate. It also demonstrates out-of-distribution generalization capabilities of the machine learning models that are a critical prerequisite for climate applications. Nonetheless, two of the models show a global-mean cold bias in the forecasts for the future warmer climate state, i.e. they drift towards the colder present-day climate they have been trained for. A similar result is obtained for the pre-industrial case where two out of three models show a warming. We discuss possible remedies for these biases and analyze their spatial distribution, revealing complex warming and cooling patterns that are partly related to missing ocean-sea ice and land surface information in the training data. Despite these current limitations, our results suggest that data-driven machine learning models will provide powerful tools for climate science and transform established approaches by complementing conventional physics-based models.

Related papers

• Modeling chaotic Lorenz ODE System using Scientific Machine Learning [1.4633779950109127]
  In this paper, we have integrated Scientific Machine Learning (SciML) methods into foundational weather models. By combining the interpretability of physical climate models with the computational power of neural networks, SciML models can prove to be a reliable tool for modeling climate.
  arXiv  Detail & Related papers  (2024-10-09T01:17:06Z)
• Super Resolution On Global Weather Forecasts [0.1747623282473278]
  Group seeks to improve upon existing deep learning based forecasting methods by increasing spatial resolutions of global weather predictions. Specifically, we are interested in performing super resolution (SR) on GraphCast temperature predictions by increasing the global precision from 1 degree of accuracy to 0.5 degrees.
  arXiv  Detail & Related papers  (2024-09-17T19:07:13Z)
• Building a temperature forecasting model for the city with the regression neural network (RNN) [0.0]
  Research on weather forecast models is a recent development, having only begun around 2000. along with advancements in computer science, mathematical models are being built and applied with machine learning techniques to create more accurate and reliable predictive models. This article will summarize the research and solutions for applying recurrent neural networks to forecast urban temperatures.
  arXiv  Detail & Related papers  (2024-05-27T18:32:36Z)
• FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting [56.73502043159699]
  This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution. It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model. The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
  arXiv  Detail & Related papers  (2024-01-28T13:23:25Z)
• Comparing Data-Driven and Mechanistic Models for Predicting Phenology in Deciduous Broadleaf Forests [47.285748922842444]
  We train a deep neural network to predict a phenological index from meteorological time series. We find that this approach outperforms traditional process-based models.
  arXiv  Detail & Related papers  (2024-01-08T15:29:23Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• ClimaX: A foundation model for weather and climate [51.208269971019504]
  ClimaX is a deep learning model for weather and climate science. It can be pre-trained with a self-supervised learning objective on climate datasets. It can be fine-tuned to address a breadth of climate and weather tasks.
  arXiv  Detail & Related papers  (2023-01-24T23:19:01Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Dynamical Landscape and Multistability of a Climate Model [64.467612647225]
  We find a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production drastically change the topography of Earth's climate.
  arXiv  Detail & Related papers  (2020-10-20T15:31:38Z)
• Smart Weather Forecasting Using Machine Learning:A Case Study in Tennessee [2.9477900773805032]
  We present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models. The accuracy of the models is good enough to be used alongside the current state-of-the-art techniques.
  arXiv  Detail & Related papers  (2020-08-25T02:41:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.