Extending Precipitation Nowcasting Horizons via Spectral Fusion of Radar Observations and Foundation Model Priors

Dimensions overall score 8.0

• PW-FouCast achieves state-of-the-art performance on the SEVIR and MeteoNet benchmarks for precipitation nowcasting.result 90%
• The proposed method effectively extends the reliable forecast horizon for precipitation nowcasting while maintaining structural fidelity.result 85%
• Radar-only models frequently suffer from performance degradation at longer lead times due to lack of large-scale atmospheric context.limitation 90%
• Existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data.limitation 85%
• PW-FouCast introduces Pangu-Weather-guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors.method 90%
• The system's accuracy might dwindle in regions with sparse radar coverage or unusual weather patterns not covered by training data.limitation 80%
• PW-FouCast introduces Frequency Memory to correct phase discrepancies and preserve temporal evolution in precipitation nowcasting.method 90%
• The method was validated using historical weather data, showing improvements in nowcasting accuracy beyond traditional radar-only methods.result 85%

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