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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3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Upgrades to WeatherGFT PCNNs with WENO-5 solver, unified autoregressive block, and two new neural backbones yield 8-22% lower RMSE at 1-12 h leads on WeatherBench South Pacific data while improving physical consistency.
An XGBoost model reproduces convective cell frequency near cold fronts with high skill but underestimates counts at the surface front, depending most on CAPE and time of day.
citing papers explorer
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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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Physics-Constrained Neural Networks for Improved Short-Term Weather Forecasting: A Case Study over the South Pacific
Upgrades to WeatherGFT PCNNs with WENO-5 solver, unified autoregressive block, and two new neural backbones yield 8-22% lower RMSE at 1-12 h leads on WeatherBench South Pacific data while improving physical consistency.
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Modelling convective cell occurrence in proximity to cold fronts using extreme gradient boosting
An XGBoost model reproduces convective cell frequency near cold fronts with high skill but underestimates counts at the surface front, depending most on CAPE and time of day.