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.
Roberts and Humphrey W
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Two new global-domain smoothing methods enable spatial verification scores like FSS on high-resolution global precipitation forecasts while handling grid area variability and missing data.
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.
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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Smoothing and spatial verification of global fields
Two new global-domain smoothing methods enable spatial verification scores like FSS on high-resolution global precipitation forecasts while handling grid area variability and missing data.
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Pointwise is Pointless? A Multimodal Ablation Study for Precipitation Nowcasting with Graph Neural Networks
A multimodal GNN ablation for Nordic precipitation nowcasting shows sparse point observations improve station and onset scores while NWP and CRPS losses improve radar-grid performance, indicating local and field skills are distinct targets.
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Flow Matching for Convective-Scale Precipitation Downscaling
Flow matching produces better spatial structure than diffusion models for convective precipitation downscaling but underestimates heavy rainfall amounts.