AI Extreme Weather and Climate

In this episode of AI Extreme Weather and Climate , Allen and Sydney explore a major breakthrough in meteorological AI: predicting rare but high-impact events like typhoons. While foundation models like Pangu-Weather excel at everyday forecasts, they often stumble during extreme anomalies due to severe data imbalance. We dive into a newly proposed framework called Target Concept Tuning (TaCT) whic...

This paper introduces NeuralGCM , a hybrid atmospheric model that integrates machine learning with traditional differentiable physics to improve global precipitation simulations. Unlike older models that rely on high-resolution simulations for training, this framework is trained directly on satellite observations , specifically the IMERG dataset . By leveraging this observational data, the model e...

The episode describes the Continuous Flow Operator (CFO) , a novel neural framework for learning the continuous-time dynamics of Partial Differential Equations (PDEs) , aimed at overcoming limitations found in conventional models like autoregressive schemes and Neural Ordinary Differential Equations (ODEs). CFO's key innovation is the use of a flow matching objective to directly learn the right-ha...

This episode provides a comprehensive monograph on diffusion models, detailing their foundational principles through three unifying perspectives: the Variational View (related to VAEs and DDPMs), the Score-Based View (rooted in EBMs and Score SDEs), and the Flow-Based View (connecting to Normalizing Flows and Flow Matching). The core concept involves defining a continuous forward process that adds...

This episode introduces RainSeer , a novel, structure-aware framework for reconstructing high-resolution rainfall fields by treating radar reflectivity as a physically grounded structural prior . The authors argue that existing interpolation methods fail to capture localized extremes and sharp transitions crucial for applications like flood forecasting. RainSeer addresses two main challenges: the...

This episode dives into FlowCast-ODE , a novel deep learning framework designed to achieve accurate and continuous hourly weather forecasting . The model tackles critical challenges in high-frequency prediction, such as the rapid accumulation of errors in autoregressive rollouts and temporal discontinuities inherent in the ERA5 dataset stemming from its 12-hour assimilation cycle. FlowCast-ODE mod...

Welcome to a new episode where we dive into AQUAH, the Automatic Quantification and Unified Agent in Hydrology ! This groundbreaking system is the first end-to-end language-based agent specifically designed for hydrologic modeling . In this episode, we'll explore how AQUAH tackles the persistent challenges in water resource management, such as fragmented workflows, steep technical requirements, an...

cBottle, developed by NVIDIA, is a generative diffusion-based framework that acts as a generative foundation model for the global atmosphere . It directly tackles the challenge of petabyte-scale climate simulation data, which is currently almost impossible to access and interact with easily due to immense storage and data movement issues1.... This revolutionary system works in two stages: a coarse...

This research evaluates the performance of the Aurora weather foundation model by using lightweight decoders to predict hydrological and energy variables not included in its original training. The study highlights that this decoder-based approach significantly reduces training time and memory requirements compared to fine-tuning the entire model, while still achieving strong accuracy. A key findin...

When we talk about foundation models, what are we talking about? This is a reflection piece on foundation models by drawing an analogy from numerical solutions in fluid dynamics. This paper explore the challenges in building these models for science and engineering and introduce a promising framework called the Data-Driven Finite Element Method (DD-FEM) , which aims to bridge traditional numerical...

IBM recently released the first-of-its-kind geospatial intelligence any-to-any model TerraMind. In this podcast, we feature this new generative model and learn its capability of multi-modality. I believe there is a lot of potential with such a model. Jakubik, J., Yang, F., Blumenstiel, B., Scheurer, E., Sedona, R., Maurogiovanni, S., Bosmans, J., Dionelis, N., Marsocci, V., Kopp, N., Ramachandran,...

Today, we are featuring a geospatial foundation model Prithvi, produced by NASA and IBM, one of the first foundation model in this space. Trained on a large global dataset of NASA’s Harmonized Landsat and Sentinel-2 data, Prithvi-EO-2.0 demonstrates significant improvements over its predecessor by incorporating temporal and location embeddings. Through extensive benchmarking using GEO-Bench, it ou...

This research article introduces HydroGraphNet , a novel physics-informed graph neural network for improved flood forecasting. Traditional hydrodynamic models are computationally expensive, while machine learning alternatives often lack physical accuracy and interpretability. HydroGraphNet integrates the Kolmogorov–Arnold Network (KAN) to enhance model interpretability within an unstructured mesh...

We are featuring three papers: Mardani, M., Brenowitz, N., Cohen, Y., Pathak, J., Chen, C., Liu, C., Vahdat, A., Nabian, M. A., Ge, T., Subramaniam, A., Kashinath, K., Kautz, J., & Pritchard, M. (2025). Residual corrective diffusion modeling for km-scale atmospheric downscaling. Communications Earth & Environment , 6 (1), 1-10. https://doi.org/10.1038/s43247-025-02042-5 Price, I., Alet, F....