arXiv subjects

Jeff Adie

Publications and source records attributed to Jeff Adie.

5 recordsLinked to original sources

arXiv2025

An intercomparison of generative machine learning methods for downscaling precipitation at fine spatial scales

Machine learning (ML) offers a computationally efficient approach for generating large ensembles of high-resolution climate projections, but deterministic ML methods often smooth fine-scale structures and underestimate extremes. While stochastic generative models show promise, few studies have compared their skill under both present-day and future climates. This study compares Generative Adversarial Networks (GANs), flow matching and diffusion models across multiple configurations for downscaling daily precipitation from a regional climate model (RCM) over New Zealand. Model skill is assessed across spatial structure, distributional metrics, climatological means, extremes, ensemble calibration, and climate change signals. Unlike GANs, diffusion and flow matching models generate predictions through many sequential steps. Here we show that using higher-order differential equation solvers, the number of steps required can be reduced with only a minor reduction in skill, heavily reducing the computational burden for downscaling large ensembles, which may have otherwise prevented their use in operational settings. Overall, GANs, flow matching and diffusion perform competitively across most metrics, except that diffusion and flow matching produce higher-fidelity predictions and better-calibrated ensembles compared to GANs -which are under-dispersive. Most approaches capture mean precipitation signals reasonably well, but underestimate end-of-century climate change signals of extreme precipitation, despite being trained on RCM simulations spanning the future period. Only one GAN and one flow matching configuration can reproduce this change signal reliably. These results highlight the importance of evaluating model performance across a comprehensive set of metrics, and that neither visual realism nor good skill on standard metrics guarantee skill in predicting climate change signals.

physics.ao-ph↗

arXiv2025

XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change

Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction skills are increasing with advances in numerical weather prediction and artificial intelligence tools, extreme weather still present challenges. More specifically, identifying the precursors of such extreme weather events and how these precursors may evolve under climate change remain unclear. In this paper, we propose to use post-hoc interpretability methods to construct relevance weather maps that show the key extreme-weather precursors identified by deep learning models. We then compare this machine view with existing domain knowledge to understand whether deep learning models identified patterns in data that may enrich our understanding of extreme-weather precursors. We finally bin these relevant maps into different multi-year time periods to understand the role that climate change is having on these precursors. The experiments are carried out on Indochina heatwaves, but the methodology can be readily extended to other extreme weather events worldwide.

cs.LG↗

arXiv2025

CondensNet: Enabling stable long-term climate simulations via hybrid deep learning models with adaptive physical constraints

Accurate and efficient climate simulations are crucial for understanding Earth's evolving climate. However, current general circulation models (GCMs) face challenges in capturing unresolved physical processes, such as cloud and convection. A common solution is to adopt cloud resolving models, that provide more accurate results than the standard subgrid parametrisation schemes typically used in GCMs. However, cloud resolving models, also referred to as super paramtetrizations, remain computationally prohibitive. Hybrid modeling, which integrates deep learning with equation-based GCMs, offers a promising alternative but often struggles with long-term stability and accuracy issues. In this work, we find that water vapor oversaturation during condensation is a key factor compromising the stability of hybrid models. To address this, we introduce CondensNet, a novel neural network architecture that embeds a self-adaptive physical constraint to correct unphysical condensation processes. CondensNet effectively mitigates water vapor oversaturation, enhancing simulation stability while maintaining accuracy and improving computational efficiency compared to super parameterization schemes. We integrate CondensNet into a GCM to form PCNN-GCM (Physics-Constrained Neural Network GCM), a hybrid deep learning framework designed for long-term stable climate simulations in real-world conditions, including ocean and land. PCNN-GCM represents a significant milestone in hybrid climate modeling, as it shows a novel way to incorporate physical constraints adaptively, paving the way for accurate, lightweight, and stable long-term climate simulations.

physics.ao-ph↗

arXiv2024

Data-driven Surface Solar Irradiance Estimation using Neural Operators at Global Scale

Accurate surface solar irradiance (SSI) forecasting is essential for optimizing renewable energy systems, particularly in the context of long-term energy planning on a global scale. This paper presents a pioneering approach to solar radiation forecasting that leverages recent advancements in numerical weather prediction (NWP) and data-driven machine learning weather models. These advances facilitate long, stable rollouts and enable large ensemble forecasts, enhancing the reliability of predictions. Our flexible model utilizes variables forecast by these NWP and AI weather models to estimate 6-hourly SSI at global scale. Developed using NVIDIA Modulus, our model represents the first adaptive global framework capable of providing long-term SSI forecasts. Furthermore, it can be fine-tuned using satellite data, which significantly enhances its performance in the fine-tuned regions, while maintaining accuracy elsewhere. The improved accuracy of these forecasts has substantial implications for the integration of solar energy into power grids, enabling more efficient energy management and contributing to the global transition to renewable energy sources.

physics.ao-ph↗

arXiv2023

A Hybrid Two-level MCMC Framework to Accelerate Posterior Mean Estimation with Deep Learning Surrogates for Bayesian Inverse Problems

Bayesian inverse problems arise in various scientific and engineering domains, and solving them can be computationally demanding. This is especially the case for problems governed by partial differential equations, where the repeated evaluation of the forward operator is extremely expensive. Recent advances in Deep Learning (DL)-based surrogate models have shown promising potential to accelerate the solution of such problems. However, despite their ability to learn from complex data, DL-based surrogate models generally cannot match the accuracy of high-fidelity numerical models, which limits their practical applicability. We propose a novel hybrid two-level Markov Chain Monte Carlo (MCMC) method that combines the strengths of DL-based surrogate models and high-fidelity numerical solvers to {compute the posterior mean of Quantities of Interest (QoI) in} Bayesian inverse problems governed by partial differential equations. The intuition is to leverage the evaluation speed of a DL-based surrogate model as the base chain, and correct its errors using a limited number of high-fidelity numerical model evaluations in a correction chain; hence its name hybrid two-level MCMC method. Through a detailed theoretical analysis, we show that our approach can achieve the same accuracy as a pure numerical MCMC method while requiring only a small fraction of the computational cost. The theoretical analysis is further supported by several numerical experiments, namely a Poisson, a non-linear reaction-diffusion, and a Navier-Stokes equation. The proposed hybrid framework can be generalized to other approaches such as the ensemble Kalman filter and sequential Monte Carlo methods.

math.NA↗