A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning detects cloud changes due to human aerosols.
Develops a method to disaggregate aerosol optical depth into vertical extinction profiles.
Study shows reducing anthropogenic emissions significantly lowers levels but has little effect on in Delhi.
Reanalysis datasets combining numerical physics models and limited observations to generate a synthesised estimate of variables in an Earth system, are prone to biases against ground truth. Biases identified with the NASA Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) aerosol optic…
Modeling wildfire aerosols using satellite data to predict solar radiation reduction.
ESN model helps understand climate event impacts.
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.
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.
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.
Though machine learning has achieved notable success in modeling sequential and spatial data for speech recognition and in computer vision, applications to remote sensing and climate science problems are seldom considered. In this paper, we demonstrate techniques from unsupervised learning of future video frame predict…
MOAI evaluates indoor airflow's impact on COVID-19 transmission.
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.
Paper develops a new model for forecasting ocean currents.
Framework improves marine mammal monitoring in noisy underwater environments.
SPF uses a hierarchical approach to efficiently emulate climate changes.
Combining forecasts of 16 ED causes improves accuracy and stability.