Portrait de Paula Harder

Paula Harder

Visiteur de recherche indépendant
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Apprentissage automatique dans la modélisation climatique
Apprentissage automatique et changement climatique
Apprentissage profond
IA pour le changement climatique
Modélisation climatique
Réduction d'échelle des variables climatiques
Science du climat
Site web
Google Scholar
GitHub

Publications

Benchmarking the geographic generalization of deep learning models for precipitation downscaling
Paula Harder
Luca Schmidt
Francis Pelletier
Nicole Ludwig
Matthew Chantry
Christian Lessig
Alex Hernández-García
David Rolnick
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (voir plus)tion for local-scale risk-assessments is not computationally feasible. Deep learning-based super-resolution models offer a promising solution to downscale ESM outputs to higher resolutions by learning from data. Yet, due to regional variations in climatic processes, these models typically require retraining for each geographical area–demanding high-resolution observational data, which is unevenly available across the globe. This highlights the need to assess how well these models generalize across geographic regions. To address this, we introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts. We evaluate state-of-the-art downscaling approaches including GANs and diffusion models in generalizing across data gaps between the Global North and Global South. Our findings reveal substantial performance drops in out-of-distribution regions, depending on model and geographic area. While expanding the training domain generally improves generalization, it is insufficient to overcome shifts between geographically distinct regions. We show that addressing these shifts through, for example, domain adaptation can improve spatial generalization. Our work advances the global applicability of downscaling methods and represents a step toward reducing inequities in access to high-resolution climate information.
2026-01-26
Scientific Reports (publié)
doi.org
On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Paula Harder
Christian Lessig
Matthew Chantry
Francis Pelletier
David Rolnick
2025-11-30
ArXiv (prépublication)
doi.org
arxiv.org
RainShift: A Benchmark for Precipitation Downscaling Across Geographies
Paula Harder
Luca Schmidt
Francis Pelletier
Nicole Ludwig
Matthew Chantry
Christian Lessig
Alex Hernández-García
David Rolnick
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolu… (voir plus)tion for local-scale risk-assessments is not computationally feasible. Deep learning-based super-resolution models offer a promising solution to downscale ESM outputs to higher resolutions by learning from data. Yet, due to regional variations in climatic processes, these models typically require retraining for each geographical area-demanding high-resolution observational data, which is unevenly available across the globe. This highlights the need to assess how well these models generalize across geographic regions. To address this, we introduce RainShift, a dataset and benchmark for evaluating downscaling under geographic distribution shifts. We evaluate state-of-the-art downscaling approaches including GANs and diffusion models in generalizing across data gaps between the Global North and Global South. Our findings reveal substantial performance drops in out-of-distribution regions, depending on model and geographic area. While expanding the training domain generally improves generalization, it is insufficient to overcome shifts between geographically distinct regions. We show that addressing these shifts through, for example, data alignment can improve spatial generalization. Our work advances the global applicability of downscaling methods and represents a step toward reducing inequities in access to high-resolution climate information.
2025-07-06
ArXiv (prépublication)
doi.org
arxiv.org
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad
Paula Harder
Qidong Yang
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding … (voir plus)and adapting to climate change at regional and local scales. Deep learning approaches have proven useful in tackling this problem. However, existing studies usually focus on training models for one specific task, location and variable, which are therefore limited in their generalizability and transferability. In this paper, we evaluate the efficacy of training deep learning downscaling models on multiple diverse climate datasets to learn more robust and transferable representations. We evaluate the effectiveness of architectures zero-shot transferability using CNNs, Fourier Neural Operators (FNOs), and vision Transformers (ViTs). We assess the spatial, variable, and product transferability of downscaling models experimentally, to understand the generalizability of these different architecture types.
2024-07-16
ArXiv (prépublication)
doi.org
arxiv.org
Climate Variable Downscaling with Conditional Normalizing Flows
Christina Winkler
Paula Harder
David Rolnick
Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations.… (voir plus) This has led to a considerable interest in methods for statistical downscaling, a similar process to super-resolution in the computer vision context, to provide more local and regional climate information. In this work, we apply conditional normalizing flows to the task of climate variable downscaling. We showcase its successful performance on an ERA5 water content dataset for different upsampling factors. Additionally, we show that the method allows us to assess the predictive uncertainty in terms of standard deviation from the fitted conditional distribution mean.
2024-05-30
ArXiv (prépublication)
doi.org
arxiv.org
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose González-Abad
Alex Hernández-García
Paula Harder
David Rolnick
José Manuel Gutiérrez
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they ofte… (voir plus)n operate at a coarse spatial resolution that limits their accuracy in reproducing local-scale phenomena. Statistical downscaling methods leveraging deep learning offer a solution to this problem by approximating local-scale climate fields from coarse variables, thus enabling regional GCM projections. Typically, climate fields of different variables of interest are downscaled independently, resulting in violations of fundamental physical properties across interconnected variables. This study investigates the scope of this problem and, through an application on temperature, lays the foundation for a framework introducing multi-variable hard constraints that guarantees physical relationships between groups of downscaled climate variables.
2024-01-21
AAAI Symposium Series (publié)
doi.org
arxiv.org
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose Gonz'alez-Abad
'Alex Hern'andez-Garc'ia
Paula Harder
David Rolnick
Jos'e Manuel Guti'errez
2023-08-01
ArXiv (prépublication)
doi.org
arxiv.org
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang
Alex Hernández-García
Paula Harder
Venkatesh Ramesh
Prasanna Sattegeri
Daniela Szwarcman
Campbell D. Watson
David Rolnick
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computational… (voir plus)ly expensive to resolve complex climate processes at high spatial resolution. As one way to speed up climate simulations, neural networks have been used to downscale climate variables from fast-running low-resolution simulations, but high-resolution training data are often unobtainable or scarce, greatly limiting accuracy. In this work, we propose a downscaling method based on the Fourier neural operator. It trains with data of a small upsampling factor and then can zero-shot downscale its input to arbitrary unseen high resolution. Evaluated both on ERA5 climate model data and on the Navier-Stokes equation solution data, our downscaling model significantly outperforms state-of-the-art convolutional and generative adversarial downscaling models, both in standard single-resolution downscaling and in zero-shot generalization to higher upsampling factors. Furthermore, we show that our method also outperforms state-of-the-art data-driven partial differential equation solvers on Navier-Stokes equations. Overall, our work bridges the gap between simulation of a physical process and interpolation of low-resolution output, showing that it is possible to combine both approaches and significantly improve upon each other.
2023-05-22
arXiv (prépublication)
doi.org
arxiv.org
Hard-Constrained Deep Learning for Climate Downscaling
Paula Harder
Venkatesh Ramesh
Alex Hernández-García
Qidong Yang
Prasanna Sattegeri
D. Szwarcman
Campbell Watson
David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and, therefore, often generate coarse-resolution predictions. Statistical downscaling, including super-resolution methods from deep learning, can provide an efficient method of upsampling low-resolution data. However, despite achieving visually compelling results in some cases, such models frequently violate conservation laws when predicting physical variables. In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics. We compare different constraining approaches and demonstrate their applicability across different neural architectures as well as a variety of climate and weather data sets. Besides enabling faster and more accurate climate predictions through downscaling, we also show that our novel methodologies can improve super-resolution for satellite data and natural images data sets.
2022-12-31
Journal of Machine Learning Research (publié)
arxiv.org
arxiv.org
Generating physically-consistent high-resolution climate data with hard-constrained neural networks
Paula Harder
Qidong Yang
Venkatesh Ramesh
Prasanna Sattegeri
Alex Hernández-García
Campbell Watson
D. Szwarcman
David Rolnick
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and … (voir plus)mitigation and to guide rapid responses to extreme events. Forecasting models are limited by computational costs and therefore often can only make coarse resolution predictions. Statistical downscaling can provide an efficient method of upsampling low-resolution data. In this field, deep learning has been applied successfully, often us-ing image super-resolution methods from computer vision. Despite achieving visually compelling results in some cases, such models often violate conservation laws when predicting physical variables. In order to conserve important physical quantities, we develop methods that guarantee physical constraints are satisfied by a deep downscaling model while also increasing their performance according to traditional metrics. We introduce two ways of constraining the network: A renor-malization layer added to the end of the neural network and a successive approach that scales with increasing upsampling factors. We show the applicability of our methods across different popular architectures and upsampling factors using ERA5 reanalysis data.
2021-12-31
arXiv.org (prépublication)
doi.org