[HTML][HTML] Assessing the fiscal implications of changes in critical minerals' demand in the low-carbon energy transition
P D'Orazio - Applied Energy, 2024 - Elsevier
This study introduces the Fiscal Revenue Risk Index (FRRI), a novel metric designed to
assess the impact of market fluctuations in critical minerals due to the materialization of …
assess the impact of market fluctuations in critical minerals due to the materialization of …
Data imbalance, uncertainty quantification, and transfer learning in data‐driven parameterizations: Lessons from the emulation of gravity wave momentum transport in …
Neural networks (NNs) are increasingly used for data‐driven subgrid‐scale
parameterizations in weather and climate models. While NNs are powerful tools for learning …
parameterizations in weather and climate models. While NNs are powerful tools for learning …
Predicting rare events using neural networks and short-trajectory data
Estimating the likelihood, timing, and nature of events is a major goal of modeling stochastic
dynamical systems. When the event is rare in comparison with the timescales of simulation …
dynamical systems. When the event is rare in comparison with the timescales of simulation …
Inexact iterative numerical linear algebra for neural network-based spectral estimation and rare-event prediction
Understanding dynamics in complex systems is challenging because there are many
degrees of freedom, and those that are most important for describing events of interest are …
degrees of freedom, and those that are most important for describing events of interest are …
Statistics of sudden stratospheric warmings using a large model ensemble
S Ineson, NJ Dunstone, AA Scaife… - Atmospheric Science …, 2024 - Wiley Online Library
Using a large ensemble of initialised retrospective forecasts (hindcasts) from a seasonal
prediction system, we explore various statistics relating to sudden stratospheric warmings …
prediction system, we explore various statistics relating to sudden stratospheric warmings …
Data-driven transition path analysis yields a statistical understanding of sudden stratospheric warming events in an idealized model
Atmospheric regime transitions are highly impactful as drivers of extreme weather events,
but pose two formidable modeling challenges: predicting the next event (weather …
but pose two formidable modeling challenges: predicting the next event (weather …
Extreme value analysis of ground magnetometer observations at Valentia observatory, Ireland
AR Fogg, CM Jackman, J Malone‐Leigh… - Space …, 2023 - Wiley Online Library
Understanding global space weather effects is of great importance to the international
scientific community, but more localized space weather predictions are important on a …
scientific community, but more localized space weather predictions are important on a …
Can AI weather models predict out-of-distribution gray swan tropical cyclones?
Predicting gray swan weather extremes, which are possible but so rare that they are absent
from the training dataset, is a major concern for AI weather/climate models. An important …
from the training dataset, is a major concern for AI weather/climate models. An important …
[HTML][HTML] Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori–Zwanzig formulation
D Aristoff, M Johnson, D Perez - AIP Advances, 2023 - pubs.aip.org
Stochastic dynamics, such as molecular dynamics, are important in many scientific
applications. However, summarizing and analyzing the results of such simulations is often …
applications. However, summarizing and analyzing the results of such simulations is often …
Extreme heat wave sampling and prediction with analog Markov chain and comparisons with deep learning
We present a data-driven emulator, a stochastic weather generator (SWG), suitable for
estimating probabilities of prolonged heat waves in France and Scandinavia. This emulator …
estimating probabilities of prolonged heat waves in France and Scandinavia. This emulator …