AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

• URL: http://arxiv.org/abs/2309.10808v2
• Date: Wed, 20 Sep 2023 03:03:16 GMT
• Title: AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
• Authors: S. Karthik Mukkavilli, Daniel Salles Civitarese, Johannes Schmude, Johannes Jakubik, Anne Jones, Nam Nguyen, Christopher Phillips, Sujit Roy, Shraddha Singh, Campbell Watson, Raghu Ganti, Hendrik Hamann, Udaysankar Nair, Rahul Ramachandran, Kommy Weldemariam
• Abstract summary: Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow scales and specific tasks.
• Score: 3.3929630603919394
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, and meteorological agencies in building digital twins of the Earth. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow spatiotemporal scales and specific tasks. With the recent success of generative artificial intelligence (AI) using pre-trained transformers for language modeling and vision with prompt engineering and fine-tuning, we are now moving towards generalizable AI. In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks. Despite this progress, we are still in the nascent stages of a generalizable AI model for global Earth system models, regional climate models, and mesoscale weather models. Here, we review current state-of-the-art AI approaches, primarily from transformer and operator learning literature in the context of meteorology. We provide our perspective on criteria for success towards a family of foundation models for nowcasting and forecasting weather and climate predictions. We also discuss how such models can perform competitively on downstream tasks such as downscaling (super-resolution), identifying conditions conducive to the occurrence of wildfires, and predicting consequential meteorological phenomena across various spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, we examine current AI methodologies and contend they have matured enough to design and implement a weather foundation model.

Related papers

• WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning [69.82211470647349]
  We introduce the first generalist weather foundation model (WeatherGFM) It addresses a wide spectrum of weather understanding tasks in a unified manner. Our model can effectively handle up to ten weather understanding tasks, including weather forecasting, super-resolution, weather image translation, and post-processing.
  arXiv  Detail & Related papers  (2024-11-08T09:14:19Z)
• Advancing Meteorological Forecasting: AI-based Approach to Synoptic Weather Map Analysis [3.686808512438363]
  Our study proposes a novel preprocessing method and convolutional autoencoder model to improve the interpretation of synoptic weather maps. This model could recognize historical synoptic weather maps that nearly match current atmospheric conditions.
  arXiv  Detail & Related papers  (2024-11-08T07:46:50Z)
• Robustness of AI-based weather forecasts in a changing climate [1.4779266690741741]
  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.
  arXiv  Detail & Related papers  (2024-09-27T08:11:49Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Generalizing Weather Forecast to Fine-grained Temporal Scales via Physics-AI Hybrid Modeling [55.13352174687475]
  This paper proposes a physics-AI hybrid model (i.e., WeatherGFT) which Generalizes weather forecasts to Finer-grained Temporal scales. Specifically, we employ a carefully designed PDE kernel to simulate physical evolution on a small time scale. We introduce a lead time-aware training framework to promote the generalization of the model at different lead times.
  arXiv  Detail & Related papers  (2024-05-22T16:21:02Z)
• Forecasting the Future with Future Technologies: Advancements in Large Meteorological Models [3.332582598089642]
  The field of meteorological forecasting has undergone a significant transformation with the integration of large models. Models like FourCastNet, Pangu-Weather, GraphCast, ClimaX, and FengWu have made notable contributions by providing accurate, high-resolution forecasts.
  arXiv  Detail & Related papers  (2024-04-10T00:52:54Z)
• Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
  We present an AI-based data assimilation model, i.e., Adas, for global weather variables. We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term. We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
  arXiv  Detail & Related papers  (2023-12-18T09:05:28Z)
• Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey [39.08108001903514]
  We offer an exhaustive, timely overview of state-of-the-art AI methodologies specifically engineered for weather and climate data. Our primary coverage encompasses four critical aspects: types of weather and climate data, principal model, model scopes and applications, and datasets for weather and climate.
  arXiv  Detail & Related papers  (2023-12-05T01:10:54Z)
• 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)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)

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.