Earthly Machine Learning

🎙️ Abstract: Recent progress in data-driven weather forecasting has surpassed traditional physics-based systems. Yet, the common use of mean squared error (MSE) loss functions introduces a “double penalty,” smoothing out fine-scale structures. This episode discusses a simple, parameter-free fix to this issue by modifying the loss to disentangle decorrelation errors from spectral amplitude errors....

🌍 Abstract: Projecting climate change is a generalization problem: we extrapolate the recent past using physical models across past, present, and future climates. Current climate models require representations of processes that occur at scales smaller than the model grid size, which remain the main source of projection uncertainty. Recent machine learning (ML) algorithms offer promise for improvi...

🎙️ Episode 25: ClimaX: A foundation model for weather and climate DOI: https://doi.org/10.48550/arXiv.2301.10343 🌀 Abstract: Most cutting-edge approaches for weather and climate modeling rely on physics-informed numerical models to simulate the atmosphere's complex dynamics. These methods, while accurate, are often computationally demanding, especially at high spatial and temporal resolutions...

🎙️ Episode 24: AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation 🔗 DOI: https://doi.org/10.1038/s41561-024-01527-w 🌐 Abstract Despite decades of progress, Earth system models (ESMs) still face significant gaps in accuracy and uncertainty, largely due to challenges in representing small-scale or poorly understood processes. This episode explores a transformat...

🎙️ Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators 🔗 DOI: https://doi.org/10.1145/3592979.3593412 🌍 Abstract As climate change intensifies extreme weather events, traditional numerical weather prediction (NWP) struggles to keep pace due to computational limits. This episode explores FourCastNet, a deep learning Earth syste...

🎙️ Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems 🔗 DOI: https://doi.org/10.1038/s41467-023-43860-5 🧠 Abstract Improving the accuracy and scalability of carbon cycle quantification in agroecosystems is essential for climate mitigation and sustainable agriculture. This episode discusses a new Knowledge-Guided Machine Learning (KGML) framewo...

🎙️ Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation Learning This week, we explore AtmoRep , a novel task-independent AI model for simulating atmospheric dynamics. Built on large-scale representation learning and trained on ERA5 reanalysis data, AtmoRep delivers strong performance across a variety of tasks—without needing task-specific training. 🔍...

🎙️ Episode 20: Finding the Right XAI Method—Evaluating Explainable AI in Climate Science 🔗 DOI: https://doi.org/10.48550/arXiv.2303.00652 🧩 Abstract Explainable AI (XAI) methods are increasingly used in climate science, but the lack of ground truth explanations makes it difficult to evaluate and compare them effectively. This episode dives into a new framework for systematically evaluating XAI m...

🎧 Abstract: Weather forecasting is essential for both science and society. This episode explores a breakthrough in medium-range global weather forecasting using artificial intelligence. The researchers introduce Pangu-Weather , an AI-powered system that leverages 3D deep networks with Earth-specific priors and a hierarchical temporal aggregation strategy to significantly enhance forecast accuracy...

🎧 Abstract: In this episode, we dive into GraphDOP , a novel data-driven forecasting system developed by ECMWF. Unlike traditional models, GraphDOP learns directly from Earth System observations—without relying on physics-based reanalysis. By capturing relationships between satellite and conventional observations, it builds a latent representation of Earth’s dynamic systems and delivers accurate...

🎧 Abstract: In this episode, we explore DiffDA , a novel data assimilation approach for weather forecasting and climate modeling. Built on the foundations of denoising diffusion models, DiffDA uses the pretrained GraphCast neural network to assimilate atmospheric variables from predicted states and sparse observations—providing a data-driven pathway to generate accurate initial conditions for for...

🎙️ Abstract: Embedding physical constraints as inductive priors is key in AI weather forecasting models. Locality—a common prior—relies on local neural interactions like 3D convolutions or attention. ARCHESWEATHER challenges this norm by introducing global vertical interactions, improving efficiency without sacrificing accuracy. 📌 Bullet points summary: ARCHESWEATHER is a lightweight, efficient A...

🌍 Abstract: Surface-level weather is what matters most to the public—but it's also where feedback loops and complex interactions dominate. Land Surface Models (LSMs) capture these dynamics. Coupled with atmospheric models, they help forecast water, carbon, and energy fluxes. This study explores machine learning emulators as fast, accurate alternatives for ecLand, the ECMWF’s land surface sche...

DOI: https://doi.org/10.48550/arXiv.2411.11268 Abstract: Existing machine learning models of weather variability are not formulated to enable assessment of their response to varying external boundary conditions such as sea surface temperature and greenhouse gases. Here we present ACE2 (Ai2 Climate Emulator version 2) and its application to reproducing atmospheric variability over the past 80 years...

DOI:https://doi.org/10.48550/arXiv.2405.13063 Abstract:Reliable forecasts of the Earth system are crucial for human progress and safety from natural disasters. Artificial intelligence offers substantial potential to improve prediction accuracy and computational efficiency in this field, however this remains underexplored in many domains. Here we introduce Aurora, a large-scale foundation model for...

Abstract: Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing comprehensive 100-km resolution global atmospheric model. The formulation of ACE allows evaluation of physical laws such as the conservati...

DOI:https://doi.org/10.48550/arXiv.2308.15560 Abstract: WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source evaluation framework, publicly available training, ground truth and baseline data as well as a...

Abstract: Ensemble forecasting is crucial for improving weather predictions, especially for forecasts of extreme events. Constructing an ensemble prediction system (EPS) based on conventional NWP models is highly computationally expensive. ML models have emerged as valuable tools for deterministic weather forecasts, providing forecasts with significantly reduced computational requirements and even...

Abstract : Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their ability to accurately model long-range dependencies in spatio-temporal data by learning global convolutions in a computationally efficient manne...

Abstract: Artificial intelligence (AI) can be used to improve performance across a wide range of Earth system prediction tasks. As with any application of AI, it is important for AI to be developed in an ethical and responsible manner to minimize bias and other effects. In this work, we extend our previous work demonstrating how AI can go wrong with weather and climate applications by presenting a...

Abstract : Weather forecasting is critical for a range of human activities including transportation, agriculture, industry, as well as the safety of the general public. Machine learning models have the potential to transform the complex weather prediction pipeline, but current approaches still rely on numerical weather prediction (NWP) systems, limiting forecast speed and accuracy. Here we demonst...

Abstract : Triggered by the realization that AI emulators can rival the performance of traditional numerical weather prediction models running on HPC systems, there is now an increasing number of large AI models that address use cases such as forecasting, downscaling, or nowcasting. While the parallel developments in the AI literature focus on foundation models -- models that can be effectively tu...

Abstract : General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic...

Abstract : Nonlinear dynamical systems exposed to changing forcing values can exhibit catastrophic transitions between distinct states. The phenomenon of critical slowing down can help anticipate such transitions if caused by a bifurcation and if the change in forcing is slow compared with the system’s internal timescale. However, in many real-world situations, these assumptions are not met and tr...

Abstract : Machine learning-based weather forecasting models have quickly emerged as a promising methodology for accurate medium-range global weather forecasting. Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS is based on a graph neural network (GNN) encoder and...