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Found 6 papers, 1 papers with code
Lossy Neural Compression for Geospatial Analytics: A Review
Data compression has gained relevance over the past decade, with neural compression (NC) emerging from deep learning and information theory, making EO data and ESM outputs ideal candidates due to their abundance of unlabeled data.
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.
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions
Estimating the effects of continuous-valued interventions from observational data is a critically important task for climate science, healthcare, and economics.
Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific
Aerosol-cloud interactions include a myriad of effects that all begin when aerosol enters a cloud and acts as cloud condensation nuclei (CCN).
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.
Detecting anthropogenic cloud perturbations with deep learning
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance.
