ARXIV:2603.06153 · CLIMATE MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

arXiv

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries. This work investigates ensemble learning strategies for sea surface temperature…

METHOD

Full abstract

Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation. We adapt a GNN architecture to the Canary Islands region in the North Atlantic and implement a homogeneous ensemble approach inspired by bagging, where diversity is introduced during inference by perturbing initial ocean states rather than retraining multiple models. Several noise-based ensemble generation strategies are evaluated, including Gaussian noise, Perlin noise, and fractal Perlin noise, with systematic variation of noise intensity and spatial structure. Ensemble forecasts are assessed over a 15-day horizon using deterministic metrics (RMSE and bias) and probabilistic metrics, including the Continuous Ranked Probability Score (CRPS) and the Spread-skill ratio. Results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations strongly influence uncertainty representation, particularly at longer lead times. Ensembles generated with spatially coherent perturbations, such as low-resolution Perlin noise, achieve better calibration and lower CRPS than purely random Gaussian perturbations. These findings highlight the critical role of noise structure and scale in ensemble GNN design and demonstrate that carefully constructed input perturbations can yield well-calibrated probabilistic forecasts without additional training cost, supporting the feasibility of ensemble GNNs for operational regional ocean prediction.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations strongly influence uncertainty representation, particularly…

WHY NOW

Climate Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainImprove sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

ARXIV:2603.06153 · CLIMATE MODELING · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

arXiv

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Climate Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score7.0

PainImprove sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Paper Pack

10.48550/arXiv.2603.06153

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback38 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Climate Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with a focus on how input perturbation design affects forecast skill and uncertainty representation.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that, while deterministic skill remains comparable to the single-model forecast, the type and structure of input perturbations strongly influence uncertainty representation, particularly at longer lead times.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Climate Modeling moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Climate Modeling

Competitors

not indexed

Competitive landscape

Improve sea surface temperature forecasting by using graph neural networks and input perturbations to generate probabilistic forecasts, enabling better risk management for maritime industries.

Segment

Climate Modeling

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.06153 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(38)

Voronoi-Induced Artifacts from Grid-to-Mesh Coupling and Bathymetry-Aware Meshes in Graph Neural Networks for Sea Surface Temperature Forecasting

2025Giovanny A. Cuervo-Londoño, José G. Reyes et al.

Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting

2025Victor E. Medina, Giovanny A. Cuervo-Londoño et al.

Accurate Mediterranean Sea forecasting via graph-based deep learning

2025Daniel Holmberg, Emanuela Clementi et al.

Deep Learning Weather Models for Subregional Ocean Forecasting: A Case Study on the Canary Current Upwelling System

2025Giovanny C.-Londoño, Javier Sánchez et al.

A foundation model for the Earth system

2025Cristian Bodnar, W. Bruinsma et al.

Forecasting Sea Surface Temperature from Satellite Images with Graph Neural Networks

2025Giovanny C.-Londoño, Javier Sánchez et al.

Adaptive Meshes in Graph Neural Networks for Predicting Sea Surface Temperature Through Remote Sensing

2025José G. Reyes, Giovanny A. Cuervo-Londoño et al.

ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting

2024Guillaume Couairon, Renu Singh et al.

Probabilistic weather forecasting with machine learning

2024Ilan Price, Alvaro Sanchez-Gonzalez et al.

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks

2024Joel Oskarsson, Tomas Landelius et al.

Learning skillful medium-range global weather forecasting

2023Remi Lam, Alvaro Sanchez-Gonzalez et al.

Neural general circulation models for weather and climate

2023Dmitrii Kochkov, J. Yuval et al.

Ensemble Learning for Graph Neural Networks

2023Zhen Hao Wong, Ling Yue et al.

Graph-based Neural Weather Prediction for Limited Area Modeling

2023Joel Oskarsson, Tomas Landelius et al.

WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

2023S. Rasp, Stephan Hoyer et al.

Accurate medium-range global weather forecasting with 3D neural networks

2023Kaifeng Bi, Lingxi Xie et al.

FuXi: a cascade machine learning forecasting system for 15-day global weather forecast

2023Lei Chen, Xiaohui Zhong et al.

GNN-Ensemble: Towards Random Decision Graph Neural Networks

2023Wenqi Wei, Mu Qiao et al.

FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

2022Jaideep Pathak, Shashank Subramanian et al.

Forecasting Global Weather with Graph Neural Networks

2022R. Keisler

Showing 20 of 38 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

Prior WorkFreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting

7.0

Prior WorkGraph-Conditioned Mixture of Graph Neural Network Experts for Traffic Forecasting

7.0

Prior WorkEmpowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learning

7.0

Prior WorkSkillful Global Ocean Emulation and the Role of Correlation-Aware Loss

7.0

Prior WorkGCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables

7.0

Prior WorkDecoupling Distance and Networks: Hybrid Graph Attention-Geostatistical Methods for Spatio-temporal Risk Mapping

7.0

Extension

Builds On ThisRandom-Set Graph Neural Networks

4.0

Builds On ThisDemystifying Data-Driven Probabilistic Medium-Range Weather Forecasting

4.0

Builds On ThisGNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation

5.0

Builds On ThisSpatio-temporal probabilistic forecast using MMAF-guided learning

4.0

Commercially relevant

none indexed

Conflicting

none indexed

Related Resources

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Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2603.06153
Route: /paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations
Active tab: read
Artifact: ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations/paper-pack
REST build passport API/api/v1/paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

{
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  "arxiv_id": "2603.06153",
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  "active_tab": "synced from current hash by the drawer client",
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  "endpoints": {
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}

Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

Canonical ID ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations | Route /paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

MCP example

{
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  "arguments": {
    "arxiv_id": "2603.06153"
  }
}

source_context

{
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  "paper_ref": "ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

/buildability/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

Watchwatch

Subject: Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

Paper ref

ensemble-graph-neural-networks-for-probabilistic-sea-surface-temperature-forecasting-via-input-perturbations

arXiv id

2603.06153

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

e5316f8e64e009fe77a8ef00847b45e674db19dca1bf45207fe40e9a287c971c

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations

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