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Found 19 papers, 8 papers with code
Discovering Latent Structural Causal Models from Spatio-Temporal Data
Many important phenomena in scientific fields such as climate, neuroscience, and epidemiology are naturally represented as spatiotemporal gridded data with complex interactions.
Adapting While Learning: Grounding LLMs for Scientific Problems with Intelligent Tool Usage Adaptation
On average, our models demonstrate a 28. 18% improvement in answer accuracy and a 13. 89% increase in tool usage precision across all datasets, surpassing state-of-the-art models including GPT-4o and Claude-3. 5.
ClimaQA: An Automated Evaluation Framework for Climate Foundation Models
However, a critical issue remains: the lack of a comprehensive evaluation framework capable of assessing the quality and scientific validity of model outputs.
Harnessing AI data-driven global weather models for climate attribution: An analysis of the 2017 Oroville Dam extreme atmospheric river
The analysis is framed on the extreme atmospheric river episode of February 2017 that contributed to the Oroville dam spillway incident in Northern California.
The impact of internal variability on benchmarking deep learning climate emulators
We identify that this outcome is a result of high levels of internal variability in the benchmark targets.
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling
To address these limitations, we propose Multi-fidelity Residual Neural Processes (MFRNP), a novel multi-fidelity surrogate modeling framework.
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
Anthropogenic emissions of aerosols can alter the albedo of clouds, but the extent of this effect, and its consequent impact on temperature change, remains uncertain.
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
The result is an emulator that \textit{(i)} enjoys the flexibility of statistical machine learning models and can learn from data, and \textit{(ii)} has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system.
Exploring Randomly Wired Neural Networks for Climate Model Emulation
We also find no significant difference in prediction speed between networks with standard feedforward dense layers and those with randomly wired layers.
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.
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.
AODisaggregation: toward global aerosol vertical profiles
In this work, we develop a framework for the vertical disaggregation of AOD into extinction profiles, i. e. the measure of light extinction throughout an atmospheric column, using readily available vertically resolved meteorological predictors such as temperature, pressure or relative humidity.
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.
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world.
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.
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.
Cumulo: A Dataset for Learning Cloud Classes
One of the greatest sources of uncertainty in future climate projections comes from limitations in modelling clouds and in understanding how different cloud types interact with the climate system.
