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ARXIV:2605.13197 · WEATHER FORECASTING · SUBMITTED 14 MAY · 20:10 UTC · FRESHNESS FRESH
ARXIV:2605.13197WEATHER FORECASTINGSUBMITTED 14 MAY · 20:10 UTCFRESHNESS FRESHPenghui Wen · Yu Luo · Lintao Wang · Mengwei He · Patrick Filippi · Thomas Francis Bishop · +1 at arXiv
A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts.
Opportunity summary
Pain A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution trajectories.
Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on two widely used benchmarks, SEVIR and MeteoNet, show that McCast achieves state-of-the-art performance, particularly in challenging long-horizon forecasting scenarios. Code availability is…
Weather Forecasting moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
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A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts.
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Paper Pack
10.48550/arXiv.2605.13197A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts.
Abstract
Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution trajectories. Although various studies have attempted to alleviate this problem by improving step-wise prediction accuracy, they largely neglect the global temporal evolution of meteorological systems and lack mechanisms to actively correct drift during rollouts. To address this issue, we propose McCast, a memory-guided latent drift correction method for precipitation nowcasting. Rather than treating memory as an unordered dictionary of latent states for passive conditioning, McCast leverages temporally organized memory to actively correct autoregressive latent evolution. Specifically, McCast introduces a Drift-Corrective Memory Bank (DCBank) that explicitly estimates the temporally consistent drift corrections to calibrate the divergent trajectory. DCBank performs drift correction in two stages: a Corrective Latent Extractor first predicts an initial correction from the current prediction and a reference latent state, and a Correction-Aware Memory Retrieval module then refines the initial correction using temporally organized historical memory. By explicitly correcting latent evolution, instead of improving step-wise prediction accuracy only, McCast produces more temporally coherent and reliable long-horizon forecasts. Experiments on two widely used benchmarks, SEVIR and MeteoNet, show that McCast achieves state-of-the-art performance, particularly in challenging long-horizon forecasting scenarios.
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Proof status
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Dimensions overall score 7.0
PROBLEM
A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution trajectories.
METHOD
Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on two widely used benchmarks, SEVIR and MeteoNet, show that McCast achieves state-of-the-art performance, particularly in challenging long-horizon forecasting scenarios. Code availability is...
WHY NOW
Weather Forecasting moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution trajectories.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Existing precipitation nowcasting methods typically adopt an autoregressive formulation, where future states are predicted from previous outputs. However, such an approach accumulates errors over long rollouts, causing forecasts to drift away from physically plausible evolution trajectories.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Experiments on two widely used benchmarks, SEVIR and MeteoNet, show that McCast achieves state-of-the-art performance, particularly in challenging long-horizon forecasting scenarios. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Weather Forecasting moved forward this cycle; last verified May 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A memory-guided system that corrects latent drift for more reliable long-horizon precipitation forecasts.
Segment
Weather Forecasting
Adoption evidence
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Commercial read
7.0/10 public viability
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Build Passport
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status
missing
reason
passport_row_missing
proof status
unverified
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confidence low
next verification path
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Source missing: Build Passport payload.
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Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
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fresh
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Build readiness
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passport absent
fresh
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Artifact maturity
GitHub and Hugging Face maturity payloads
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fresh
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
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Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
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0 references, 0 sources, 0% evidence coverage.
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missing
Current read
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Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
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Defensibility signals are missing.
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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WATCHTOWER
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