arxiv: 2605.00972 · v1 · submitted 2026-05-01 · ⚛️ physics.data-an · cs.AI· cs.CV· cs.IR

Recognition: unknown

Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration

Charlie Becker, David John Gagne, John Clyne, John Schreck, Kirsten J. Mayer, Matt Rehme, Nihanth W. Cherukuru

Authors on Pith no claims yet

Pith reviewed 2026-05-09 14:52 UTC · model grok-4.3

classification ⚛️ physics.data-an cs.AIcs.CVcs.IR

keywords visual analyticsembedding spacesweather data explorationclimate data retrievalsimilarity searchtropical cyclone analogslatent space inspectionout-of-core retrieval

0 comments

The pith

A visual analytics workbench links embedding spaces of weather data back to physical observations so researchers can identify and retrieve similar meteorological events across large archives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces an open-source visual analytics workbench that connects embedding representations of Earth system data to their source observations, metadata, spatial context, and model settings. This linkage lets users inspect how different models organize meteorological information, issue similarity queries, and check results against familiar physical views. The system supports a workflow in which experts first characterize a known phenomenon in a well-understood dataset, locate its signature in latent space, and then search much larger unlabeled collections for matching events. A demonstration with tropical-cyclone retrieval from ERA5 embeddings and IBTrACS metadata shows the approach in practice, while an out-of-core backend confirms that searches remain feasible on ordinary workstation hardware even when collections exceed memory limits.

Core claim

The workbench integrates embedding experiments with source meteorological data, metadata, spatial views, and model configurations so that nearest neighbors in latent space can be traced to their physical origins. Users can compare representation models, develop retrieval strategies, and validate analogs against real weather evidence. In the tropical-cyclone case, a signature identified in a labeled subset is used to probe broader archives for similar events.

What carries the argument

The visual analytics workbench that links embedding vectors to source data, metadata, and interactive meteorological views for inspection, comparison, and retrieval.

If this is right

Side-by-side comparison of multiple embedding models becomes possible within the same interface.
Global and localized similarity queries can be issued and inspected through standard meteorological visualizations.
Retrieval strategies can be tested and refined before scaling to unlabeled archives or ensembles.
Large embedding collections can be searched on commodity hardware without loading everything into memory.
The workflow allows characterization of events in small well-labeled sets followed by discovery in much larger climate archives.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same linking approach could be applied to other high-dimensional scientific domains such as ocean or atmospheric chemistry data.
Visual validation might lower the barrier for using embeddings in settings where full quantitative labels are scarce.
The workbench could be extended with automated candidate ranking before human review to speed up large-scale searches.
Tracing results back to source physics may improve the reproducibility of analog-based studies in climate research.

Load-bearing premise

Visual inspection together with metadata links will let domain scientists reliably separate physically meaningful structures in the embedding space from preprocessing artifacts or model biases.

What would settle it

Domain experts using the workbench on a controlled dataset with injected known biases consistently label artifact clusters as physically meaningful events when asked to characterize phenomena.

Figures

Figures reproduced from arXiv: 2605.00972 by Charlie Becker, David John Gagne, John Clyne, John Schreck, Kirsten J. Mayer, Matt Rehme, Nihanth W. Cherukuru.

**Figure 1.** Figure 1: Overview of the workbench and example retrieval workflow. (a) The workbench augments conventional weather and view at source ↗

**Figure 2.** Figure 2: Latent-space inspection linked to input composites. The view at source ↗

**Figure 3.** Figure 3: Scalability of out-of-core embedding retrieval. (a) As the view at source ↗

read the original abstract

Earth system science is producing increasingly large, high-dimensional datasets from physics based Earth system models to AI-based weather and climate models. Embedding-based representations can make these data searchable through similarity search and analog retrieval, but nearest neighbors in latent space are not automatically scientifically meaningful: it may reflect real weather structure, or preprocessing, geography, or model bias. Researchers therefore need ways to inspect how embeddings organize meteorological data, compare representation models, develop retrieval strategies, and verify results against physical evidence. We present an open-source visual analytics workbench for each of these steps. The system links embedding experiments to source data, metadata, spatial context, and model configurations, so latent-space results can be traced back to the physics. Users can explore latent spaces for different models, issue global or localized queries, and inspect analogs through familiar meteorological views. This enables a discovery workflow in which scientists characterize a phenomenon of interest in a well-understood dataset, identifying its signature in latent space, and then use that signature to probe larger, less-labeled archives or ensembles for similar events. We demonstrate the workbench through tropical-cyclone retrieval using ERA5-derived embeddings and IBTrACS metadata, and evaluate its out-of-core retrieval backend to show that large embedding collections can be searched beyond in-memory limits on commodity workstation hardware.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This paper ships a practical visual analytics workbench that links climate embeddings back to physical metadata and spatial views, demonstrated on tropical cyclone retrieval, but the discovery workflow claim leans heavily on unvalidated visual inspection.

read the letter

The core contribution is a workbench that ties embedding spaces for weather data to source fields, metadata like IBTrACS tracks, and model configs so users can check whether nearest neighbors reflect real structure or artifacts from preprocessing or geography. They walk through a tropical cyclone example on ERA5 embeddings and add an out-of-core retrieval test showing the backend scales past memory limits on ordinary hardware. That integration of comparison tools, localized queries, and traceable views is the new piece; separate components exist elsewhere, but the climate-specific workflow with explicit physics linking is a concrete extension. The demo is straightforward and addresses the stated problem that latent neighbors are not automatically meaningful. The main soft spot is that the central workflow—characterize a phenomenon in a labeled set, pull its signature, then search unlabeled archives—still assumes domain scientists can reliably separate signal from bias using the visual and metadata layers alone. The abstract notes the artifact risk but provides no user study, no quantitative false-positive rates on the retrievals, and no comparison against simpler baselines for how often the inspection step actually works. That leaves the discovery claim more aspirational than demonstrated. Minor gaps include missing details on exact embedding models and preprocessing choices, which would help reproducibility. This is aimed at climate researchers already experimenting with embeddings who need an inspection layer, and at visual analytics groups building domain tools. The artifact is solid enough on its own terms to deserve referee time rather than a desk reject, even if revisions will likely focus on validation and code release.

Referee Report

1 major / 2 minor

Summary. The manuscript presents an open-source visual analytics workbench for exploring and validating embedding-based representations of large weather and climate datasets. The system links latent-space embeddings to source data, metadata (e.g., IBTrACS), spatial context, and model configurations, enabling users to inspect organization of meteorological data, perform similarity queries, and trace results to physical evidence. It supports a discovery workflow of characterizing phenomena in well-understood datasets and retrieving analogs from larger archives, demonstrated via tropical-cyclone retrieval on ERA5-derived embeddings and evaluated for out-of-core scalability on commodity hardware.

Significance. If the linked visual and metadata views prove effective at distinguishing physically meaningful structures from artifacts, the workbench would be a significant contribution to Earth system data analysis by making embedding-based similarity search interpretable and usable for scientific discovery. The open-source release, concrete demonstration on ERA5/IBTrACS data, and evidence that large collections can be searched beyond in-memory limits on standard hardware are clear strengths that could support community adoption and extension.

major comments (1)

[Abstract] The central claim that the workbench enables a verifiable discovery workflow (abstract) rests on the assumption that visual inspection and metadata linking will reliably allow domain scientists to identify physically meaningful latent-space structures rather than preprocessing or bias artifacts. The tropical-cyclone demonstration illustrates the workflow but provides no quantitative metrics (e.g., precision against IBTrACS labels or controlled comparison to non-visual baselines) or user evaluation to substantiate this reliability.

minor comments (2)

The out-of-core retrieval evaluation would be strengthened by reporting specific dataset sizes, embedding dimensions, and hardware specifications used in the scalability test.
Adding interface screenshots or a system architecture diagram would improve clarity on how the linked views (embedding space, spatial context, metadata) operate together.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive comments and positive assessment of the manuscript's significance. We address the major comment below.

read point-by-point responses

Referee: [Abstract] The central claim that the workbench enables a verifiable discovery workflow (abstract) rests on the assumption that visual inspection and metadata linking will reliably allow domain scientists to identify physically meaningful latent-space structures rather than preprocessing or bias artifacts. The tropical-cyclone demonstration illustrates the workflow but provides no quantitative metrics (e.g., precision against IBTrACS labels or controlled comparison to non-visual baselines) or user evaluation to substantiate this reliability.

Authors: We appreciate the referee's point on the strength of the claims. The abstract states that the workbench 'enables a discovery workflow' by linking embeddings to source data, metadata, and physical context so that 'latent-space results can be traced back to the physics.' It does not assert that visual inspection and metadata linking will reliably or automatically distinguish meaningful structures from artifacts; the system supplies the inspection mechanisms, while domain expertise supplies the judgment. The tropical-cyclone demonstration shows one concrete use of these mechanisms (retrieval against IBTrACS labels and ERA5 fields) but is presented as an illustration rather than a controlled validation. We agree that quantitative metrics (e.g., retrieval precision against IBTrACS) and a user study would strengthen the paper. In revision we will (1) tighten the abstract wording to emphasize 'support for' rather than implicit reliability of verification, (2) add a limitations paragraph discussing the illustrative nature of the case study, and (3) include a brief quantitative retrieval analysis against the IBTrACS labels already present in the dataset. A full user study lies outside the current scope but is noted as future work. revision: partial

Circularity Check

0 steps flagged

No significant circularity; software tool description without derivations or self-referential predictions

full rationale

The paper describes the design and demonstration of an open-source visual analytics workbench for embedding-based exploration of weather and climate data. The central claim is that linked views (embedding space, source data, metadata, spatial context) enable a discovery workflow of characterizing phenomena in known datasets and retrieving analogs from larger archives. This is presented as a system architecture and usage example (tropical cyclone retrieval on ERA5/IBTrACS), not as a mathematical derivation or fitted prediction. No equations, parameter fits, uniqueness theorems, or ansatzes appear in the provided text. Any self-citations are incidental and non-load-bearing for the contribution, which rests on the software implementation itself rather than reducing to prior author results by construction. The work is self-contained as a tool report.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The contribution is a software system rather than new physical theory; the only background assumptions are standard properties of learned embeddings and the availability of labeled meteorological metadata.

axioms (1)

domain assumption Embedding models produce latent spaces in which proximity can correspond to meteorological similarity when properly inspected
Invoked throughout the motivation and demonstration sections as the premise for the workbench utility.

pith-pipeline@v0.9.0 · 5562 in / 1215 out tokens · 61698 ms · 2026-05-09T14:52:20.303610+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

27 extracted references · 21 canonical work pages · 3 internal anchors

[1]

R. P. Abernathey, T. Augspurger, A. Banihirwe, C. C. Blackmon-Luca, T. J. Crone, C. L. Gentemann, J. J. Hamman, N. Henderson, C. Lep- ore, T. A. McCaie, N. H. Robinson, and R. P. Signell. Cloud-Native Repositories for Big Scientific Data.Computing in Science & Engi- neering, 23(2):26–35, Mar. 2021. doi: 10.1109/MCSE.2021.3059437 2

work page doi:10.1109/mcse.2021.3059437 2021
[2]

Z. B. Bouall `egue, M. C. A. Clare, L. Magnusson, E. Gasc ´on, M. Maier-Gerber, M. Janou ˇsek, M. Rodwell, F. Pinault, J. S. Dram- sch, S. T. K. Lang, B. Raoult, F. Rabier, M. Chevallier, I. Sandu, P. Dueben, M. Chantry, and F. Pappenberger. The Rise of Data- Driven Weather Forecasting: A First Statistical Assessment of Ma- chine Learning–Based Weather Fo...

work page doi:10.1175/bams-d-23-0162.1 2024
[3]

C. F. Brown, M. R. Kazmierski, V . J. Pasquarella, W. J. Ruck- lidge, M. Samsikova, C. Zhang, E. Shelhamer, E. Lahera, O. Wiles, S. Ilyushchenko, N. Gorelick, L. L. Zhang, S. Alj, E. Schechter, S. Askay, O. Guinan, R. Moore, A. Boukouvalas, and P. Kohli. AlphaEarth Foundations: An embedding field model for accurate and efficient global mapping from sparse...

work page doi:10.48550/arxiv.2507.22291 2025
[4]

Eyring, W

V . Eyring, W. D. Collins, P. Gentine, E. A. Barnes, M. Bar- reiro, T. Beucler, M. Bocquet, C. S. Bretherton, H. M. Christensen, K. Dagon, D. J. Gagne, D. Hall, D. Hammerling, S. Hoyer, F. Iglesias- Suarez, I. Lopez-Gomez, M. C. McGraw, G. A. Meehl, M. J. Molina, C. Monteleoni, J. Mueller, M. S. Pritchard, D. Rolnick, J. Runge, P. Stier, O. Watt-Meyer, K....

2024
[5]

Billion-scale similarity search with GPUs

J. Johnson, M. Douze, and H. J ´egou. Billion-Scale Similarity Search with GPUs.IEEE Transactions on Big Data, 7(3):535–547, July 2021. doi: 10.1109/TBDATA.2019.2921572 4

work page doi:10.1109/tbdata.2019.2921572 2021
[6]

ACDC: The adverse conditions dataset with correspondences for robust semantic driving scene perception,

H. J ´egou, M. Douze, and C. Schmid. Product Quantization for Near- est Neighbor Search.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 33(1):117–128, Jan. 2011. doi: 10.1109/TPAMI. 2010.57 4

work page doi:10.1109/tpami 2011
[7]

Mehta, G

Y . Kawakami, D. Cayan, D. Liu, and K.-L. Ma. ClimateSOM: A Vi- sual Analysis Workflow for Climate Ensemble Datasets.IEEE Trans- actions on Visualization and Computer Graphics, 32(01):473–483, Jan. 2026. doi: 10.1109/TVCG.2025.3634788 2

work page doi:10.1109/tvcg.2025.3634788 2026
[8]

D. A. Keim, F. Mansmann, J. Schneidewind, J. Thomas, and H. Ziegler. Visual Analytics: Scope and Challenges. In S. J. Simoff, M. H. B ¨ohlen, and A. Mazeika, eds.,Visual Data Mining: The- ory, Techniques and Tools for Visual Analytics, pp. 76–90. Springer, Berlin, Heidelberg, 2008. doi: 10.1007/978-3-540-71080-6 6 2

work page doi:10.1007/978-3-540-71080-6 2008
[9]

K. R. Knapp, M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. The International Best Track Archive for Climate Stew- ardship (IBTrACS): Unifying Tropical Cyclone Data.Bulletin of the American Meteorological Society, 91(3):363–376, Mar. 2010. doi: 10 .1175/2009BAMS2755.1 2

2010
[10]

Y . Liu, X. Wang, Y . Wang, F. Huang, Y . Huang, Y . Li, W. Zhang, S. Gong, G. Mai, Y . Yao, Y . Yue, H. Li, and F. Zhang. Representa- tion learning for geospatial data.Annals of GIS, 31(4):557–583, Oct
[11]

doi: 10.1080/19475683.2025.2552157 2

eprint: https://doi.org/10.1080/19475683.2025.2552157. doi: 10.1080/19475683.2025.2552157 2

work page doi:10.1080/19475683.2025.2552157 2025
[12]

Mahesh, W

A. Mahesh, W. D. Collins, B. Bonev, N. Brenowitz, Y . Cohen, P. Har- rington, K. Kashinath, T. Kurth, J. North, T. A. O’Brien, M. Pritchard, D. Pruitt, M. Risser, S. Subramanian, and J. Willard. Huge ensembles – Part 2: Properties of a huge ensemble of hindcasts generated with spherical Fourier neural operators.Geoscientific Model Development, 18(17):5605...

work page doi:10.5194/gmd-18-5605-2025 2025
[13]

M. J. Molina, T. A. O’Brien, G. Anderson, M. Ashfaq, K. E. Bennett, W. D. Collins, K. Dagon, J. M. Restrepo, and P. A. Ullrich. A Re- view of Recent and Emerging Machine Learning Applications for Cli- mate Variability and Weather Phenomena.Artificial Intelligence for the Earth Systems, 2(4), Sept. 2023. doi: 10.1175/AIES-D-22-0086.1 2

work page doi:10.1175/aies-d-22-0086.1 2023
[14]

A. H. Monahan, J. C. Fyfe, M. H. P. Ambaum, D. B. Stephenson, and G. R. North. Empirical Orthogonal Functions: The Medium is the Message.Journal of Climate, 22(24):6501–6514, Dec. 2009. doi: 10. 1175/2009JCLI3062.1 2

2009
[15]

J. T. Overpeck, G. A. Meehl, S. Bony, and D. R. Easterling. Climate data challenges in the 21st century.Science, 331(6018):700–702, Feb
[16]

doi: 10.1126/science.1197869 2

work page doi:10.1126/science.1197869
[17]

W. Pace, C. She, L. Xu, W. Jones, A. Lockett, J. Wang, and R. Shah. Lance: Efficient Random Access in Columnar Storage through Adap- tive Structural Encodings, Apr. 2025. arXiv:2504.15247 [cs]. doi: 10. 48550/arXiv.2504.15247 3

work page arXiv 2025
[18]

Peterka, T

A. Panta, X. Huang, N. McCurdy, D. Ellsworth, A. A. Gooch, G. Scorzelli, H. Torres, P. Klein, G. A. Ovando-Montejo, and V . Pas- cucci. Web-based Visualization and Analytics of Petascale data: Eq- uity as a Tide that Lifts All Boats. In2024 IEEE 14th Symposium on Large Data Analysis and Visualization (LDAV), pp. 1–11. IEEE, St Pete Beach, FL, USA, Oct. 20...

work page doi:10.1109/lda 2024
[19]

Kashinath, M

Prabhat, K. Kashinath, M. Mudigonda, S. Kim, L. Kapp-Schwoerer, A. Graubner, E. Karaismailoglu, L. von Kleist, T. Kurth, A. Greiner, A. Mahesh, K. Yang, C. Lewis, J. Chen, A. Lou, S. Chandran, B. Toms, W. Chapman, K. Dagon, C. A. Shields, T. O’Brien, M. Wehner, and W. Collins. ClimateNet: an expert-labeled open dataset and deep learning architecture for e...

work page doi:10.5194/gmd-14-107-2021 2021
[20]

D. Ren, F. Hohman, H. Lin, and D. Moritz. Embedding At- las: Low-Friction, Interactive Embedding Visualization, July 2025. arXiv:2505.06386 [cs]. doi: 10.48550/arXiv.2505.06386 2

work page doi:10.48550/arxiv.2505.06386 2025
[21]

Richards and P

B. Richards and P. K. Balan. Latent Representations of Land–Sea Boundaries and Extreme Temperature in Aurora’s Encoder (Student Abstract).Proceedings of the AAAI Conference on Artificial Intel- ligence, 40(48):41368–41369, Mar. 2026. doi: 10.1609/aaai.v40i48. 42272 2

work page doi:10.1609/aaai.v40i48 2026
[22]

Grand Challenges

S. L. Sellars. “Grand Challenges” in Big Data and the Earth Sciences. Bulletin of the American Meteorological Society, 99(6):ES95–ES98, June 2018. doi: 10.1175/BAMS-D-17-0304.1 2

work page doi:10.1175/bams-d-17-0304.1 2018
[23]

DINOv3

O. Sim ´eoni, H. V . V o, M. Seitzer, F. Baldassarre, M. Oquab, C. Jose, V . Khalidov, M. Szafraniec, S. Yi, M. Ramamonjisoa, F. Massa, D. Haziza, L. Wehrstedt, J. Wang, T. Darcet, T. Moutakanni, L. Sen- tana, C. Roberts, A. Vedaldi, J. Tolan, J. Brandt, C. Couprie, J. Mairal, H. J ´egou, P. Labatut, and P. Bojanowski. DINOv3, Aug. 2025. arXiv:2508.10104 ...

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2508.10104 2025
[24]

K. I. Tempest, M. Beylich, and G. C. Craig. Mechanistic Interpretabil- ity Tool for AI Weather Models, 2026. Version Number: 1. doi: 10. 48550/ARXIV.2604.20467 2

work page internal anchor Pith review Pith/arXiv arXiv 2026
[25]

T. C. Vance, T. Huang, and K. A. Butler. Big data in Earth science: Emerging practice and promise.Science, 383(6688):eadh9607, Mar
[26]

doi: 10.1126/science.adh9607 2

work page doi:10.1126/science.adh9607
[27]

S. Zhao, F. Liu, X. Zhang, H. Chen, T. Han, J. Gong, R. Tao, P. Xiao, L. Bai, and W. Ouyang. Transforming Weather Data from Pixel to Latent Space, Mar. 2025. arXiv:2503.06623 [cs]. doi: 10.48550/arXiv .2503.06623 2

work page internal anchor Pith review doi:10.48550/arxiv 2025