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ARXIV:2603.21768 · WEATHER FORECASTING · SUBMITTED 24 MAR · 21:26 UTC · FRESHNESS STALE
ARXIV:2603.21768WEATHER FORECASTINGSUBMITTED 24 MAR · 21:26 UTCFRESHNESS STALEYuze Qin · Qingyong Li · Zhiqing Guo · Wen Wang · Yan Liu · Yangli-ao Geng · arXiv
A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons.
Opportunity summary
Pain A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons.
Evidence 0 refs | 0 sources | 50% coverage
Blocker Evidence unverified
A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to…
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times.
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance, effectively extending the reliable forecast horizon while maintaining structural fidelity.…
Weather Forecasting moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score8.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons.
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Paper Pack
10.48550/arXiv.2603.21768A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons.
Abstract
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times. While integrating meteorological variables predicted by weather foundation models offers a potential remedy, existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data. To bridge this gap, we propose PW-FouCast, a novel frequency-domain fusion framework that leverages Pangu-Weather forecasts as spectral priors within a Fourier-based backbone. Our architecture introduces three key innovations: (i) Pangu-Weather-guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors; (ii) Frequency Memory to correct phase discrepancies and preserve temporal evolution; and (iii) Inverted Frequency Attention to reconstruct high-frequency details typically lost in spectral filtering. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance, effectively extending the reliable forecast horizon while maintaining structural fidelity. Our code is available at https://github.com/Onemissed/PW-FouCast.
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Dimensions overall score 8.0
PROBLEM
A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to...
METHOD
Precipitation nowcasting is critical for disaster mitigation and aviation safety. However, radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times.
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance, effectively extending the reliable forecast horizon while maintaining structur...
WHY NOW
Weather Forecasting moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Extensive experiments on the SEVIR and MeteoNet benchmarks demonstrate that PW-FouCast achieves state-of-the-art performance
Explicitly stated in the abstract with reference to benchmark validation
partial
effectively extending the reliable forecast horizon while maintaining structural fidelity
Directly stated in abstract as a key outcome of the research
partial
radar-only models frequently suffer from a lack of large-scale atmospheric context, leading to performance degradation at longer lead times
Explicitly stated as a problem in the abstract
partial
existing architectures fail to reconcile the profound representational heterogeneities between radar imagery and meteorological data
Directly stated as a limitation of current approaches in the abstract
partial
Pangu-Weather-guided Frequency Modulation to align spectral magnitudes and phases with meteorological priors
Explicitly described as a key innovation in the abstract
partial
The system's accuracy might dwindle in regions with sparse radar coverage or unusual weather patterns not covered by training data
Explicitly stated in the analysis section as a caveat
partial
Frequency Memory to correct phase discrepancies and preserve temporal evolution
Explicitly described as a key innovation in the abstract
partial
The method was validated using historical weather data, showing improvements in nowcasting accuracy beyond traditional radar-only methods
Directly stated in the analysis section with reference to validation
partial
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A novel frequency-domain fusion framework that integrates radar observations with weather foundation model forecasts to significantly extend precipitation nowcasting horizons.
Segment
Weather Forecasting
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Commercial read
8.0/10 public viability
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