ARXIV:2603.24744 · GEOSCIENCE AI · SUBMITTED 31 MAR · 20:30 UTC · FRESHNESS STALE

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Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

Nathan Bailey · arXiv

Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

Ship in 2-4 weeks›Score5.0Evidence unverified

Opportunity summary

Pain Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence unverified

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PROBLEM

Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data. Creating low-dimensional embeddings requires compressing this data into a compact, shared latent space.

METHOD

Full abstract

Weather data, comprising multiple variables, poses significant challenges due to its high dimensionality and multimodal nature. Creating low-dimensional embeddings requires compressing this data into a compact, shared latent space. This compression is required to improve the efficiency and performance of downstream tasks, such as forecasting or extreme-weather detection. Self-supervised learning, particularly contrastive learning, offers a way to generate low-dimensional, robust embeddings from unlabelled data, enabling downstream tasks when labelled data is scarce. Despite initial exploration of contrastive learning in weather data, particularly with the ERA5 dataset, the current literature does not extensively examine its benefits relative to alternative compression methods, notably autoencoders. Moreover, current work on contrastive learning does not investigate how these models can incorporate sparse data, which is more common in real-world data collection. It is critical to explore and understand how contrastive learning contributes to creating more robust embeddings for sparse weather data, thereby improving performance on downstream tasks. Our work extensively explores contrastive learning on the ERA5 dataset, aligning sparse samples with complete ones via a contrastive loss term to create SPARse-data augmented conTRAstive spatiotemporal embeddings (SPARTA). We introduce a temporally aware batch sampling strategy and a cycle-consistency loss to improve the structure of the latent space. Furthermore, we propose a novel graph neural network fusion technique to inject domain-specific physical knowledge. Ultimately, our results demonstrate that contrastive learning is a feasible and advantageous compression method for sparse geoscience data, thereby enhancing performance in downstream tasks.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. This compression is required to improve the efficiency and performance of downstream tasks, such as forecasting or extreme-weather detection. Code availability is flagged in…

WHY NOW

Geoscience AI moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.

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Opportunity summary

Score5.0

PainContrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

Evidence0 refs | 0 sources | 33% coverage

Blockerno shell-level blocker reported

Analysis summary

Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

Segment

Geoscience AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

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Unknown

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Competitive landscape

Contrastive learning for weather data compression to improve forecasting and extreme-weather detection, especially with sparse data.

Segment

Geoscience AI

Adoption evidence

No public code link in the paper record yet

Commercial read

5.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

Contrastive Learning Boosts Deterministic and Generative Models for Weather Data

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