arXiv subjects

Anboyu Guo

Publications and source records attributed to Anboyu Guo.

4 recordsLinked to original sources

arXiv2026

Data-driven ensemble prediction of the global ocean

Data-driven models have advanced deterministic ocean forecasting, but extending machine learning to probabilistic global ocean prediction remains an open challenge. Here we introduce FuXi-ONS, the first machine-learning ensemble forecasting system for the global ocean, providing 5-day forecasts on a global 1{\deg} grid up to 365 days for sea-surface temperature, sea-surface height, subsurface temperature, salinity and ocean currents. Rather than relying on repeated integration of computationally expensive numerical models, FuXi-ONS learns physically structured perturbations and incorporates an atmospheric encoding module to stabilize long-range forecasts. Evaluated against GLORYS12 reanalysis, FuXi-ONS improves both ensemble-mean skill and probabilistic forecast quality relative to deterministic and noise-perturbed baselines, and shows competitive performance against established seasonal forecast references for SST and Ni\~no3.4 variability, while running orders of magnitude faster than conventional ensemble systems. These results provide a strong example of machine learning advancing a core problem in ocean science, and establish a practical path toward efficient probabilistic ocean forecasting and climate risk assessment.

physics.ao-ph↗

arXiv2026

AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts

Current AI weather forecasting models predict conventional atmospheric variables but cannot distinguish between cloud microphysical species critical for aviation safety. We introduce AviaSafe, a hierarchical, physics-informed neural forecaster that produces global, six-hourly predictions of these four hydrometeor species for lead times up to 7 days. Our approach addresses the unique challenges of cloud prediction: extreme sparsity, discontinuous distributions, and complex microphysical interactions between species. We integrate the Icing Condition (IC) index from aviation meteorology as a physics-based constraint that identifies regions where supercooled water fuels explosive ice crystal growth. The model employs a hierarchical architecture that first predicts cloud spatial distribution through masked attention, then quantifies species concentrations within identified regions. Training on ERA5 reanalysis data, our model achieves lower RMSE for cloud species compared to baseline and outperforms operational numerical models on certain key variables at 7-day lead times. The ability to forecast individual cloud species enables new applications in aviation route optimization where distinguishing between ice and liquid water determines engine icing risk.

cs.LG↗

arXiv2025

A data-driven global ocean forecasting model with sub-daily and eddy-resolving resolution

High-fidelity ocean forecasting at high spatial and temporal resolution is essential for capturing fine-scale dynamical features, with profound implications for hazard prediction, maritime navigation, and sustainable ocean management. While conventional numerical models can generate sub-daily, eddy-resolving forecasts, they demand substantial computational resources and often struggle to maintain predictive skill at such fine scales. Data-driven models offer a promising alternative with significantly higher computational efficiency; however, most are constrained to daily outputs and show a rapid decay in accuracy when extended to sub-daily timescales. Here, we introduce TianHai, the first-of-its-kind global data-driven 6-hour forecasting model, which delivers predictions at 1/12{\deg} eddy-resolving resolution with a vertical extent down to 1,500 m. A key feature of TianHai is the integration of atmospheric forcings through FuXi-Atmosphere, a data-driven atmospheric forecasting system, which enables the explicit representation of air-sea coupling effects. Unlike conventional approaches, TianHai does not rely on numerical atmospheric models or external meteorological forecasts, making it a fully data-driven framework for coupled prediction. Benchmark experiments demonstrate that TianHai delivers state-of-the-art performance in forecasting temperature and salinity profiles, zonal and meridional currents, sea surface temperature, and sea level anomalies for lead times ranging from 1 to 10 days.

physics.ao-ph↗

arXiv2025

FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution

Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy at fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time. We introduce FuXi-Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12{\deg} spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The core innovation is the Mixture-of-Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-specific reliability , mitigating cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths.

cs.LG↗