8 citations · 39 across the 15 of their papers we have counts for
16 papers
On Global Applicability and Location Transferability of Generative Deep Learning Models for Precipitation Downscaling
Paula Harder, Christian Lessig, Matthew Chantry +2
Deep learning offers promising capabilities for the statistical downscaling of climate and weather forecasts, with generative approaches showing particular success in capturing fin…
RainShift: A Benchmark for Precipitation Downscaling Across Geographies
Paula Harder, Luca Schmidt, Francis Pelletier +5
Earth System Models (ESM) are our main tool for projecting the impacts of climate change. However, running these models at sufficient resolution for local-scale risk-assessments is…
Evaluating the transferability potential of deep learning models for climate downscaling
Ayush Prasad, Paula Harder, Qidong Yang +4
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at region…
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. This has led to a considerable interest…
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Jose González-Abad, Álex Hernández-García, Paula Harder +2
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change. However, they often operate at a coarse spatial resolution…
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
Qidong Yang, Alex Hernandez-Garcia, Paula Harder +5
Climate simulations are essential in guiding our understanding of climate change and responding to its effects. However, it is computationally expensive to resolve complex climate…
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
Kenza Tazi, Emiliano Díaz Salas-Porras, Ashwin Braude +5
Pyrocumulonimbus (pyroCb) clouds are storm clouds generated by extreme wildfires. PyroCbs are associated with unpredictable, and therefore dangerous, wildfire spread. They can also…
Identifying the Causes of Pyrocumulonimbus (PyroCb)
Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude +5
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented. Invariant Causal Prediction was used to develop to…
Abstract error analysis for Cahn--Hilliard type equations with dynamic boundary conditions
Paula Harder
This work addresses the problem of solving the Cahn-Hilliard equation numerically. For that we introduce an abstract formulation for Cahn-Hilliard type equations with dynamic bound…
Hard-Constrained Deep Learning for Climate Downscaling
Paula Harder, Alex Hernandez-Garcia, Venkatesh Ramesh +5
The availability of reliable, high-resolution climate and weather data is important to inform long-term decisions on climate adaptation and mitigation and to guide rapid responses…
Physics-Informed Learning of Aerosol Microphysics
Paula Harder, Duncan Watson-Parris, Philip Stier +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…
Detecting AutoAttack Perturbations in the Frequency Domain
Peter Lorenz, Paula Harder, Dominik Strassel +2
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very…
Emulating Aerosol Microphysics with Machine Learning
Paula Harder, Duncan Watson-Parris, Dominik Strassel +3
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of u…
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
Paula Harder, Franz-Josef Pfreundt, Margret Keuper +1
Despite the success of convolutional neural networks (CNNs) in many computer vision and image analysis tasks, they remain vulnerable against so-called adversarial attacks: Small, c…
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
Paula Harder, William Jones, Redouane Lguensat +8
The recent explosion in applications of machine learning to satellite imagery often rely on visible images and therefore suffer from a lack of data during the night. The gap can be…
Error estimates for the Cahn--Hilliard equation with dynamic boundary conditions
Paula Harder, Balázs Kovács
A proof of convergence is given for bulk--surface finite element semi-discretisation of the Cahn--Hilliard equation with Cahn--Hilliard-type dynamic boundary conditions in a smooth…