AI-empowered next-generation multiscale climate modelling for mitigation and adaptation
Earth system models have been continously improved over the past decades, but systematic
errors compared with observations and uncertainties in climate projections remain. This is …
errors compared with observations and uncertainties in climate projections remain. This is …
Machine learning for clouds and climate
Machine learning (ML) algorithms are powerful tools to build models of clouds and climate
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …
that are more faithful to the rapidly increasing volumes of Earth system data than commonly …
A review of geospatial exposure models and approaches for health data integration
Background Geospatial methods are common in environmental exposure assessments and
increasingly integrated with health data to generate comprehensive models of …
increasingly integrated with health data to generate comprehensive models of …
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
[PDF][PDF] Systematic sampling and validation of machine Learning-Parameterizations in climate models
Progress in hybrid physics-machine learning (ML) climate simulations has been limited by
the difficulty of obtaining performant coupled (ie online) simulations. While evaluating …
the difficulty of obtaining performant coupled (ie online) simulations. While evaluating …
ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators
Modern climate projections lack adequate spatial and temporal resolution due to
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
computational constraints. A consequence is inaccurate and imprecise predictions of critical …
An Intercomparison of deep-learning methods for super-resolution bias-correction (SRBC) of Indian summer monsoon rainfall (ISMR) using CORDEX-SA simulations
Abstract The Indian Summer Monsoon Rainfall (ISMR) plays a significant role in India's
agriculture and economy. Our understanding of the climate dynamics of the Indian summer …
agriculture and economy. Our understanding of the climate dynamics of the Indian summer …
[HTML][HTML] Recent Applications of Explainable AI (XAI): A Systematic Literature Review
M Saarela, V Podgorelec - Applied Sciences, 2024 - mdpi.com
This systematic literature review employs the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
Reviews and Meta-Analyses (PRISMA) methodology to investigate recent applications of …
Interpretable multiscale machine learning‐based parameterizations of convection for ICON
Abstract Machine learning (ML)‐based parameterizations have been developed for Earth
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …
System Models (ESMs) with the goal to better represent subgrid‐scale processes or to …
[HTML][HTML] Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
AC Billault-Roux, G Ghiggi, L Jaffeux… - Atmospheric …, 2023 - amt.copernicus.org
The use of meteorological radars to study snowfall microphysical properties and processes
is well established, in particular via a few distinct techniques: the use of radar polarimetry, of …
is well established, in particular via a few distinct techniques: the use of radar polarimetry, of …