STRATA is the first autoregressive transformer emulator for global 4.9-km storm-resolving atmospheric dynamics, achieving 50x better energy efficiency than the underlying physics model while producing realistic km-scale features in 24-hour forecasts.
Pedruzo-Bagazgoitia, X
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
physics.ao-ph 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
The explicit-convection km-scale simulation produces fewer and weaker Atlantic hurricanes than parameterized coarser runs because seed vortices fail to amplify after crossing the West African coast due to weaker top-heavy mass flux profiles and underestimated MCS stratiform components.
citing papers explorer
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Scaling Storm-Resolving Atmospheric AI Simulation to the Entire Planet
STRATA is the first autoregressive transformer emulator for global 4.9-km storm-resolving atmospheric dynamics, achieving 50x better energy efficiency than the underlying physics model while producing realistic km-scale features in 24-hour forecasts.
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PRecover 1.0: Process Rate Recovery with Machine Learning
Machine learning models recover most warm-rain and ice microphysical process rates from standard ICON model outputs for accumulation intervals of 10 minutes or less using a two-step classification-regression approach with calibrated uncertainty.
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Dynamics of East Atlantic seed vortex populations in global km-scale models
The explicit-convection km-scale simulation produces fewer and weaker Atlantic hurricanes than parameterized coarser runs because seed vortices fail to amplify after crossing the West African coast due to weaker top-heavy mass flux profiles and underestimated MCS stratiform components.