Deep learning detects cloud changes due to human aerosols.
problem Uncertainty in the effect of anthropogenic aerosols on cloud properties and Earth's energy balance.
method Deep convolutional neural networks to analyze cloud images.
result Identified and characterized specific cloud perturbations due to human aerosols.
Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.
problem Uncertainty in measuring aerosol vertical distributions due to limited observations.
method Bayesian nonparametric Gaussian process modeling using meteorological predictors.
result Model reconstructs realistic extinction profiles with well-calibrated uncertainty, outperforming idealized baselines.
Deep learning improves aerosol optical depth predictions from reanalysis data.
problem Biases in reanalysis datasets like MERRA-2 against ground truth AOD measurements.
method A hybrid CNN model combining MERRA-2 reanalysis with deep learning.
result The CNN-based model provides better AOD estimates than MERRA-2 alone.
Study shows reducing anthropogenic emissions significantly lowers PM2.5 levels but has little effect on O3 in Delhi.
problem Understanding and mitigating the effects of anthropogenic emissions on air pollution in Delhi.
method Predictive modeling, causal inference, Gaussian Process modeling, Granger causality analysis.
result Reductions in anthropogenic emissions lead to significant decreases in PM2.5 levels but have little effect on O3. Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
problem Accurately estimate and predict AOD propagation from wildfires using multi-source satellite data.
method Physics-informed statistical modeling integrating multi-source satellite data with an advection-diffusion equation.
result The proposed approach accurately predicts AOD propagation and demonstrates model interpretability.
ESN model helps understand climate event impacts.
problem Understanding complex climate event impacts.
method Feature importance methods for ESNs on spatio-temporal climate data.
result Characterized relationships between Mount Pinatubo eruption variables.
Measurements made by satellite remote sensing, Moderate Resolution Imaging Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network (AERONET) are compared. Comparison of the two datasets measurements for aerosol optical depth values show that there are biases between the two data products. In this pa…
As global greenhouse gas emissions continue to rise, the use of stratospheric aerosol injection (SAI), a form of solar geoengineering, is increasingly considered in order to artificially mitigate climate change effects. However, initial research in simulation suggests that naive SAI can have catastrophic regional conse…
In this survey, we present and compare different approaches to estimate Mutual Information (MI) from data to analyse general dependencies between variables of interest in a system. We demonstrate the performance difference of MI versus correlation analysis, which is only optimal in case of linear dependencies. First, w…
Robustly detects and attributes climate change impacts under interventions.
problem Detect and attribute climate change impacts from observations robustly.
method Supervised learning with anchor regression for robust predictions under interventions.
result CO2 forcing can be robustly predicted from temperature patterns under strong solar forcing interventions.
In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, …
Deep learning reconstructs pressure fields and classifies leakage rates in CCS storage sites.
problem Monitoring CO2 leakage in CCS storage sites.
method Variational auto-encoder tailored for pressure field reconstruction and leakage rate classification.
result Uncertainty estimates of predictions illustrated on synthetic data.
To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy. Both could seriously limit applicability of deep learning in some domains particul…
Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.
problem Characterize spatio-temporal variability of PM2.5 in Bujumbura.
method Used low-cost sensors to collect data from August 2022 to August 2023. Analyzed hourly, daily, and seasonal PM2.5 concentrations using RNN with LSTM.
result Hourly and annual PM2.5 concentrations in Bujumbura exceed WHO standards.
Machine learning improves ice flow tracking in satellite images.
problem Improving accuracy of ice flow tracking in multi-spectral satellite images.
method Adversarial learning method to predict future ice flow.
result Adversarial learning improves ice flow tracking accuracy.
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
problem Understanding indoor airflow's role in COVID-19 transmission.
method Developed a privacy-preserving app and model to evaluate risk exposure.
result Quantified factors contributing to higher or lower contamination in settings.
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" …
AI methods are energy-intensive, but efficiency alone isn't enough for sustainability.
problem AI methods are energy-intensive and contribute to climate change.
method Critically examines the limitations of efficiency in improving environmental sustainability of AI.
result Efficiency alone is insufficient to address the environmental impacts of AI.
Paper develops a new model for forecasting ocean currents.
problem Forecasting the Loop Current and its eddies for the Gulf of Mexico.
method Physics-informed Tensor-train ConvLSTM, incorporating prior physical knowledge.
result PITT-ConvLSTM outperforms state-of-the-art methods in volumetric velocity forecasting.
Framework improves marine mammal monitoring in noisy underwater environments.
problem Underwater bioacoustic monitoring challenges due to overlapping calls and variable noise.
method Multi-step attention-guided framework with segmentation and mid-level fusion.
result Improved signal discrimination, reduced false positives, reliable representations.
SPF uses a hierarchical approach to efficiently emulate climate changes.
problem Slow and unstable climate emulation for long horizons.
method Spatiotemporal Pyramid Flows (SPF) model data hierarchically across spatial and temporal scales.
result SPF outperforms flow matching baselines and pre-trained models on ClimateBench.
Combining forecasts of 16 ED causes improves accuracy and stability.
problem Forecasting accuracy and stability for ED admissions is poor due to model uncertainty and limited data.
method High-dimensional forecast combinations of 16 cause-specific ED forecasts using extensive covariates.
result Forecast combinations yield forecast accuracies of 3.81%-23.54% across causes, outperforming individual models in 50% of scenarios.