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Found 12 papers, 4 papers with code
Evaluating the transferability potential of deep learning models for climate downscaling
Climate downscaling, the process of generating high-resolution climate data from low-resolution simulations, is essential for understanding and adapting to climate change at regional and local scales.
Climate Variable Downscaling with Conditional Normalizing Flows
Predictions of global climate models typically operate on coarse spatial scales due to the large computational costs of climate simulations.
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change.
Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling
In this work, we propose a downscaling method based on the Fourier neural operator.
Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds
The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented.
Identifying the Causes of Pyrocumulonimbus (PyroCb)
A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.
Hard-Constrained Deep Learning for Climate Downscaling
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.
Physics-Informed Learning of Aerosol Microphysics
The original M7 model is used to generate data of input-output pairs to train a neural network on it.
Detecting AutoAttack Perturbations in the Frequency Domain
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention.
Emulating Aerosol Microphysics with Machine Learning
This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time.
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
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, crafted perturbations in the input images can lead to false predictions.
NightVision: Generating Nighttime Satellite Imagery from Infra-Red Observations
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.
