Amirpasha - Podcast Details

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Earthly Machine LearningEarly Warning of Complex Climate Risk with Integrated Artificial Intelligence🧠 Abstract:Climate change is increasing the frequency and severity of disasters, demanding more effective Early Warning Systems (EWS). While current systems face hurdles in forecasting, communication, and decision-making, this episode examines how integrated Artificial Intelligence (AI) can revolutionize risk detection and response.📌 Bullet points summary:Current EWS struggle with forecasting accuracy, impact prediction across diverse contexts, and effective communication with affected communities.Integrated AI and Foundation Models (FMs) enhance EWS by improving forecast precision, offering impact-specific alerts, and utilizing diverse data sources—from weather to social media.Foundation Models for geospati...2025-07-0416 min

Earthly Machine LearningOn Some Limitations of Current Machine Learning Weather Prediction Models🧠 Abstract:Machine Learning (ML) is increasingly influential in weather and climate prediction. Recent advances have led to fully data-driven ML models that often claim to outperform traditional physics-based systems. This episode evaluates forecasts from three leading ML models—Pangu-Weather, FourCastNet, and GraphCast—focusing on their accuracy and physical realism.📌 Bullet points summary:ML models like Pangu-Weather, FourCastNet, and GraphCast fail to capture sub-synoptic and mesoscale phenomena with adequate fidelity, producing forecasts that become overly smooth over time.Their energy spectra diverge significantly from traditional models and reanalysis data, leading to poor representation of feature...2025-06-2720 min

Earthly Machine LearningArtificial intelligence for modeling and understanding extreme weather and climate events🌍 Abstract:Artificial intelligence (AI) is transforming Earth system science, especially in modeling and understanding extreme weather and climate events. This episode explores how AI tackles the challenges of analyzing rare, high-impact phenomena using limited, noisy data—and the push to make AI models more transparent, interpretable, and actionable.📌 Bullet points summary:🌪️ AI is revolutionizing how we model, detect, and forecast extreme climate events like floods, droughts, wildfires, and heatwaves, and plays a growing role in attribution and risk assessment.⚠️ Key challenges include limited data, lack of annotations, and the complexity of defining extremes...2025-06-1520 min

Earthly Machine LearningFixing the Double Penalty in Data-Driven Weather Forecasting Through a Modified Spherical Harmonic Loss Function🎙️ 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.🌪️ Data-driven weather models like GraphCast often produce overly smooth outputs due to MSE loss, limiting resolution and underestimating extremes.⚙️ The proposed Adjusted Mean Squared Error (AMSE) loss function addresses this by separating decorrelation and amplitude errors, improving spectrum fidelity.📈 Fine-tuning Gr...2025-06-0816 min

Earthly Machine LearningClimate-invariant machine learning🌍 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 improving these process representations but often extrapolate poorly outside their training climates. To bridge this gap, the authors propose a “climate-invariant” ML framework, incorporating knowledge of climate processes into ML algorithms, and show that this approach enhances generalization across different climate regimes.📌 Key...2025-05-0912 min

Earthly Machine LearningClimaX: A foundation model for weather and climate🎙️ Episode 25: ClimaX: A foundation model for weather and climateDOI: 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. In contrast, recent machine learning methods seek to learn data-driven mappings directly from curated climate datasets but often lack flexibility and generalization. ClimaX introduces a versatile and generalizable deep learning model for weather and climate science, capable of learning from diverse, heterogeneous datasets that cover va...2025-05-0213 min

Earthly Machine LearningAI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation🎙️ Episode 24: AI-empowered Next-Generation Multiscale Climate Modelling for Mitigation and Adaptation🔗 DOI: https://doi.org/10.1038/s41561-024-01527-w🌐 AbstractDespite 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 transformative vision for next-generation climate modeling—one that embeds AI across multiple scales to enhance resolution, improve model fidelity, and better inform climate mitigation and adaptation strategies.📌 Bullet points summaryExisting ESMs struggle with inaccuracies in climate projections due to subgrid-scale and unknown proc...2025-04-2517 min

Earthly Machine LearningFourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators🎙️ Episode 23: FourCastNet – Accelerating Global High-Resolution Weather Forecasting Using Adaptive Fourier Neural Operators🔗 DOI: https://doi.org/10.1145/3592979.3593412🌍 AbstractAs 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 system emulator that delivers high-resolution, medium-range global forecasts at unprecedented speed—up to five orders of magnitude faster than NWP—while maintaining near state-of-the-art accuracy.📌 Bullet points summaryFourCastNet outpaces traditional NWP with forecasts that are not only faster by several magnitudes but also comparably accurate, thanks to its data-d...2025-04-1811 min

Earthly Machine LearningKnowledge-guided machine learning can improve carbon cycle quantification in agroecosystems🎙️ Episode 22: Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems🔗 DOI: https://doi.org/10.1038/s41467-023-43860-5🧠 AbstractImproving 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) framework that integrates process-based models, high-resolution remote sensing, and machine learning to address key limitations in conventional approaches.📌 Bullet points summaryIntroduces KGML-ag-Carbon, a hybrid model combining process-based simulation (ecosys), remote sensing, and ML to improve carbon cycle modeling in agroecosystems.Outperforms tr...2025-04-1116 min

Earthly Machine LearningAtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning🎙️ Episode 21 — AtmoRep: A Stochastic Model of Atmospheric Dynamics Using Large-Scale Representation LearningThis 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.🔍 Highlights from the episode:Introduction to AtmoRep, a stochastic computer model leveraging AI to simulate the atmosphere.Zero-shot capabilities for nowcasting, temporal interpolation, model correction, and generating counterfactuals.Outperforms or matches state-of-the-art models like Pangu-Weather and even ECMWF's IFS at short for...2025-04-0418 min

Earthly Machine LearningFinding the Right XAI Method—A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate Science🎙️ Episode 20: Finding the Right XAI Method—Evaluating Explainable AI in Climate Science🔗 DOI: https://doi.org/10.48550/arXiv.2303.00652🧩 AbstractExplainable 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 methods based on key properties tailored to climate research needs.📌 Bullet points summaryIntroduces XAI evaluation for climate science, offering a structured approach to assess and compare explanation methods using key desirable properties.Identifies five critical pr...2025-03-2716 min

Earthly Machine LearningPangu-Weather — Accurate medium-range global weather forecasting with 3D neural networks🎧 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 and reduce error accumulation over time.📌 Bullet points summary:Pangu-Weather applies 3D deep learning with Earth-specific priors for accurate medium-range global weather forecasts.It utilizes a hierarchical temporal aggregation strategy to minimize accumulation errors.Outperforms ECMWF’s operational Integrated Forecasting System (IFS) in...2025-03-2116 min

Earthly Machine LearningGRAPHDOP — Towards Skillful Data-Driven Medium-Range Weather Forecasts🎧 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 weather forecasts up to five days ahead.📌 Bullet points summary:GraphDOP is developed by ECMWF and operates purely on observational data, without physics-based (re)analysis or feedback.Produces skillful forecasts for surface and upper-air parameters up to five days into the future...2025-03-1415 min

Earthly Machine LearningDiffDA — A Diffusion Model for Weather-Scale Data Assimilation🎧 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 forecasts.📌 Bullet points summary:Introduces DiffDA, a machine learning-based data assimilation method that leverages predicted states and sparse observations.Utilizes the pretrained GraphCast weather model, repurposed as a denoising diffusion model.Employs a two-phase conditioning strategy: on predicte...2025-03-0714 min

Earthly Machine LearningARCHESWEATHER — An Efficient AI Weather Forecasting Model at 1.5º Resolution🎙️ 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 AI model trained at 1.5º resolution with minimal compute (a few GPU-days), offering low-cost inference and strong performance.The Cross-Level Attention (CLA) mechanism enables vertical atmospheric feature interactions, replacing 3D local attention with 2D horizontal attention and column-wise CLA in a 3D Swin U-Net with Ea...2025-02-2816 min

Earthly Machine LearningAdvances in Land Surface Model-Based Forecasting🌍 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 scheme.⚡ Bullet points summary:Three machine learning models—LSTM, XGB, and MLP—were evaluated as statistical emulators for ECLand to enable faster experimentation in land surface forecasting.All models showed strong accuracy, but LSTM excelled in long-range co...2025-02-2116 min

Earthly Machine LearningACE2 - Accurately learning subseasonal to decadal atmospheric variability and forced responsesDOI: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, operating with 6-hour temporal resolution, 1° horizontal resolution and eight vertical layers. It exactly conserves global dry air mass and moisture and can be stepped forward stably f...2025-02-1411 min

Earthly Machine LearningAURORA — A Foundation Model of the AtmosphereDOI: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 the Earth system trained on over a million hours of diverse data... Citation: Bodnar, Cristian, et al. "Aurora: A foundation model of the atmosphere." arXiv preprint arXiv:2405.13063 (2024).2025-02-0715 min

Earthly Machine LearningACE - A Fast, Skillful Learned Global Atmospheric Model for Climate PredictionAbstract: 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 atmospheric model for climate prediction." arXiv preprint arXiv:2310.02074 (2023). DOI:https://doi.org/10.48550/arXiv.2310.02074 2025-02-0313 min

Earthly Machine LearningWeatherBench 2 - A benchmark for the next generation of data-driven global weather modelsDOI: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, Stephan, et al. "WeatherBench 2: A benchmark for the next generation of data‐driven global weather models." Journal of Advances in Modeling Earth Systems 16.6 (2024): e2023MS004019.2025-01-2925 min

Earthly Machine LearningFuXi-ENS - A machine learning model for medium-range ensemble weather forecastingAbstract: 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 ML models to ensemble forecasting. Recent ML models, such as GenCast and SEEDS model, rely on the ERA5 EDA or operational NWP ensemble members for forecast generation. Their spatial resolution is also considered too...2025-01-2812 min

Earthly Machine LearningSFNO - Spherical Fourier Neural Operators: Learning Stable Dynamics on the SphereAbstract : 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 spectral artifacts as well as pronounced dissipation when learning operators in spherical coordinates since they incorrectly assume a flat geometry. To overcome this limitation, we generalize FNOs on...2025-01-2313 min

Earthly Machine LearningIdentifying and Categorizing Bias in AI/ML for Earth SciencesAbstract: 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 identify potential biases that can affect their model throughout the AI development life cycle. We highlight...2025-01-2017 min

Earthly Machine LearningAardvark weather- end-to-end data-driven weather forecastingAbstract: 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 end-to-end data-driven weather prediction system, ingests raw observations and outputs global gridded forecasts and local station forecasts. Further, it can be optimised end-to-end to maximise performance over quantities...2025-01-1615 min

Earthly Machine LearningPrithvi WxC- Foundation Model for Weather and ClimateAbstract: 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 focus on single-use cases with particular emphasis on mid-range forecasting. We close this gap by introducing Prithvi WxC, a 2.3 billion parameter foundation...2025-01-1410 min

Earthly Machine LearningNeuralGCM - Neural general circulation models for weather and climateAbstract: 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 shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather...2025-01-1312 min

Earthly Machine LearningDeep learning for predicting rate-induced tippingAbstract: 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 anthropogenic climate change compared with the internal timescales of key Earth system components, like polar ice sheets or the Atlantic Meridional Overturning Circulation, poses si...2025-01-1211 min

Earthly Machine LearningAIFS - ECMWF's data-driven forecasting systemAbstract: 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 ECMWF's operational numerical weather prediction (NWP) analyses. It has a flexible and modular design and supports several levels of parallelism to enable training on high-resolution input data. AIFS forecast skill...2025-01-1112 min

Earthly Machine LearningRobustness of AI-based weather forecasts in a changing climateAbstract : 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...2025-01-0717 min

Earthly Machine LearningGenCast - Probabilistic weather forecasting with machine learningAbstract : 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 error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than...2025-01-0222 min

Midi MagazineUne féministe radicale au temps des LumièresL’époque des Lumières fut celle des grands affrontements où s’illustrèrent des militantes farouches, telle Mary Wollstonecraft. Dans un monde reclus de préjugés et de conservatisme, elle défendit becs et ongles la cause des femmes, au nom de la toute-puissance de la Raison. Elle s’appliqua, au cours d’une vie très courte et quelque peu échevelée, à donner toute leur chance aux thèses de l’émancipation.Amirpasha Tavakkoli, Wollstoneccraft, Le féminisme des Lumières. Coll. Le bien commun, Ed. MichalonDistribué par Audiomeans. Visitez audiomeans.fr/poli...2024-10-2949 min