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PODCAST

Earthly Machine Learning

“Earthly Machine Learning (EML)” offers AI-generated insights into cutting-edge machine learning research in weather and climate sciences. Powered by Google NotebookLM, each episode distils the essence of a standout paper, helping you decide if it’s worth a deeper look. Stay updated on the ML innovations shaping our understanding of Earth. It may contain hallucinations.

science earth sciences

All Episodes

00:15:08

GRAPHDOP — Towards Skillful Data-Driven Medium-Range...

Earthly Machine Learning ·

2025/03/14

DOI:https://doi.org/10.48550/arXiv.2412.15687Abstract:We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between observed quantities - such as brightness temperatures from polar orbiters and geostationary satellites - and geophysical quantities

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00:14:20

DiffDA — A Diffusion Model for Weather-Scale Data...

Earthly Machine Learning ·

2025/03/07

DOI: https://doi.org/10.48550/arXiv.2401.059327Abstract: The generation of initial conditions via accurate data assimilation is crucial for weather forecasting and climate modeling. We propose DiffDA as a denoising diffusion model capable of assimilating atmospheric variables using predicted states and sparse observations. Acknowledging the similarity between a weather forecast model and a denoising diffusion model dedicated to weather applications, we adapt the pretrained GraphCast neural network

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00:16:34

ARCHESWEATHER — An Efficient AI Weather Forecasting...

Earthly Machine Learning ·

2025/02/28

DOI: https://doi.org/10.48550/arXiv.2405.14527Abstract: One of the guiding principles for designing AI-based weather forecasting systems is to embed physical constraints as inductive priors in the neural network architecture. A popular prior is locality, where the atmospheric data is processed with local neural interactions, like 3D convolutions or 3D local attention windows as in Pangu-Weather...Citation:Mukkavilli, S. Karthik, et al. "Ai foundation models for weather and climate: Applications,

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00:16:07

Advances in Land Surface Model-Based Forecasting

Earthly Machine Learning ·

2025/02/21

DOI:https://doi.org/10.5194/egusphere-2024-2081Abstract:Most useful weather prediction for the public is near the surface. The processes that are most relevant for near-surface weather prediction are also those that are most interactive and exhibit positive feedback or have key role in energy partitioning. Land surface models (LSMs) consider these processes together with surface heterogeneity and forecast water, carbon and energy fluxes, and coupled with an atmospheric model provide boundary and

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00:11:35

ACE2 - Accurately learning subseasonal to decadal...

Earthly Machine Learning ·

2025/02/14

DOI:https://doi.org/10.48550/arXiv.2411.11268Abstract: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 on timescales from days to decades. ACE2 is a 450M-parameter autoregressive machine learning emulator,

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00:15:33

AURORA — A Foundation Model of the Atmosphere

Earthly Machine Learning ·

2025/02/07

DOI:https://doi.org/10.48550/arXiv.2405.13063Abstract: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 the Earth system trained on over a million hours of diverse data... Citation: Bodnar, Cristian, et al

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00:13:24

ACE - A Fast, Skillful Learned Global Atmospheric...

Earthly Machine Learning ·

2025/02/03

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 conservation of mass and moisture... Citation: Watt-Meyer, Oliver, et al. "ACE: A fast, skillful learned global

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00:25:34

WeatherBench 2 - A benchmark for the next generation...

Earthly Machine Learning ·

2025/01/29

DOI:https://doi.org/10.48550/arXiv.2308.15560Abstract: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 continuously updated website with the latest metrics and state-of-the-art model... Citation: Rasp,

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00:12:33

FuXi-ENS - A machine learning model for medium-range...

Earthly Machine Learning ·

2025/01/28

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 surpassing the forecast performance of traditional NWP models. However, challenges arise when applying

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00:13:06

SFNO - Spherical Fourier Neural Operators: Learning...

Earthly Machine Learning ·

2025/01/23

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 manner. To this end, FNOs rely on the discrete Fourier transform (DFT), however, DFTs cause visual and

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00:17:16

Identifying and Categorizing Bias in AI/ML for Earth...

Earthly Machine Learning ·

2025/01/20

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 categorization of bias for AI in the Earth sciences. This categorization can assist AI developers to

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00:15:05

Aardvark weather: end-to-end data-driven weather...

Earthly Machine Learning ·

2025/01/16

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 demonstrate that a machine learning model can replace the entire operational NWP pipeline. Aardvark Weather, an

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00:10:27

Prithvi WxC: Foundation Model for Weather and Climate

Earthly Machine Learning ·

2025/01/14

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 tuned to address multiple, different use cases -- the developments on the weather and climate side largely

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00:12:39

Neural general circulation models for weather and...

Earthly Machine Learning ·

2025/01/13

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 weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or

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00:11:45

Deep learning for predicting rate-induced tipping

Earthly Machine Learning ·

2025/01/12

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 transitions can be triggered because the forcing exceeds a critical rate. For instance, the rapid pace of

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00:12:47

AIFS -- ECMWF's data-driven forecasting system

Earthly Machine Learning ·

2025/01/11

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 decoder, and a sliding window transformer processor, and is trained on ECMWF's ERA5 re-analysis and

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00:17:37

Robustness of AI-based weather forecasts in a...

Earthly Machine Learning ·

2025/01/07

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

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00:22:18

Probabilistic weather forecasting with machine learning

Earthly Machine Learning ·

2025/01/02

Abstract : Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast

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