A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting

Heather Elliott; Kimberly Moreland; Maher A Dayeh; Michael J Starkey; Samuel Hart; Subhamoy Chatterjee

REVIEW 4 major objections 4 minor 13 references

A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting

T0 review · 4 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper introduces a three-module tool that turns multi-source near-real-time space weather data into machine-learning-ready datasets.

desk verdict A plausible design sketch for an ML-ready NRT space weather pipeline, but with no code or validation, the central existence claim is unverifiable. read the letter →

arxiv 2502.08555 v1 pith:U4S5WNKE submitted 2025-02-12 astro-ph.SR astro-ph.IMcs.LG

Maher A Dayeh , Michael J Starkey , Subhamoy Chatterjee , Heather Elliott , Samuel Hart , Kimberly Moreland This is my paper · ORCID

classification astro-ph.SRastro-ph.IMcs.LG

keywords spaceweatherforecastingmachinelearningsolarenergeticparticlesnearreal-timedataprocessingpipelinemulti-instrumentdatasetstime-serieseventdetection

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Space weather forecasting with machine learning is held back less by model design than by data preparation: near-real-time observations arrive from many spacecraft and instruments in incompatible formats, with gaps, outliers, and instrument degradation. This paper presents a Python-based tool that packages the preparation workflow into three modules—a data downloader, a data processor, and a data splitter—producing standardized CSV and FITS data ready for ML models. The processor normalizes timestamps, flags missing data, corrects known degradation, and removes outliers, while the splitter divides the result into training, validation, and test sets. If the tool works as described, it would let researchers train and run forecast models on a far broader set of solar, heliospheric, and geomagnetic observations without building their own data pipelines.

What carries the argument

The load-bearing piece is the three-module pipeline: a data downloader, a data processor, and a data splitter. The data processor does the real work: it converts all timestamps to one standard format, flags missing points with consistent fill values, applies degradation-correction routines for instruments whose sensitivity drifts with age, and detects outliers using two independent statistical methods—the interquartile range and the z-score, each computed within a sliding time window. A workflow management system orchestrates the entire chain so the pipeline is reproducible and scalable. The output is standardized CSV tables for time-series quantities and FITS files for imagery, which are what the ML models consume.

What would settle it

Run the tool over a fixed historical time window and compare its processed output, file by file, against the raw archives of each listed source: if the timestamp-normalized series contains points that do not appear in any source archive, or if the same run on the same inputs yields different training and test sets, the pipeline's claims would be refuted. Until a public release or executable demonstration exists, an independent reader cannot perform this check.

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Extended reading notes

Core claim

The paper's central claim is that a data processing tool now exists that merges near-real-time data from more than twenty space- and ground-based sources—coronagraph and EUV imagery, magnetograms, solar wind plasma and magnetic field readings, energetic particle fluxes, X-ray flux, and geomagnetic indices—into one unified, ML-ready format. The tool standardizes timestamps to a single time system, applies fill values and degradation corrections, flags outliers by interquartile-range and z-score statistics, and splits the prepared dataset into training, validation, hold-out, and test partitions. It operates in two modes: historical mode for building training sets, and streaming mode with a user-defined buffer for continuous near-real-time forecasting. In the authors' framing, this fills a central gap: the data required to predict extreme solar events are already collected, but they are not organized in a form machine learning models can consume directly.

Load-bearing premise

The load-bearing premise is that the three modules are actually implemented and run as described, but the paper supplies no code, repository, or test output and says that packaging the tool into a Python-ready library is still future work.

Editorial extensions

If this is right

Researchers can train forecast models on a much wider set of near-real-time observables without writing their own downloaders, cleaners, and time-alignment code.
The splitter's options—random, time-interval, or cluster-based partitions—let modelers avoid temporal leakage and evaluate generalization more honestly.
The streaming mode with a sliding buffer makes it possible to retrain or update a forecast model hourly on freshly downloaded data.
A common timestamp standard and uniform file formats enable direct fusion of imagery, in-situ solar wind, and particle data into a single multivariate time series.
Event detection and classification models for solar flares, coronal mass ejections, and solar energetic particle events can be trained and tested on a consistent, reproducible corpus.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The download-clean-split pattern is general enough that the same architecture could be applied to other multi-instrument forecasting problems, such as geomagnetic storm indices or radiation-belt dynamics, without redesign.
A natural validation of the tool would be to reproduce the authors' prior ML-oriented solar energetic particle event dataset from the raw near-real-time streams; if the tool cannot regenerate that catalog, its readiness claim is weakened.
If the tool becomes a public library, the most informative benchmark would compare models trained on its unified output against the same models trained on individually cleaned raw datasets, quantifying the added value of the merge.
The paper offers a design and a pipeline diagram but no executable demonstration; whether the tool is usable today hinges on a release that the paper itself lists as future work.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a 4-page conference paper that describes a three-module Python/Snakemake pipeline for downloading, cleaning, splitting, and packaging NRT space weather data into ML-ready form. It reads like a design document, not a working tool. The central assertion—that the tool exists and does these things—is unsupported: no code, no repository, no DOI, no sample outputs, and no end-to-end run are anywhere in the manuscript. Section 6 explicitly defers "packaging it into a Python-ready library" to future work, which undercuts the claim that the tool is operational now. Section 5's "Validation" paragraph contains no validation—only validation flags and a concept of operation. That is the load-bearing problem, and the stress test has it right.

To give credit where it is due: the paper is clearly written, the architecture is sensible, and the choice of standard methods (IQR and Z-score outlier detection, timestamp normalization, train/validation/test splitting with time-aware options) is reasonable. The source list is broad and relevant to SEP and geomagnetic storm forecasting. The authors cite the main prior work (Angryk et al. 2020 and their own MEMPSEP) and acknowledge the packaging gap. If the code were released, this would be a convenient workflow contribution for a definite audience of space weather ML practitioners.

Soft spots, in proportion: the lack of artifact is the big one; everything else is minor. The MDI row in Table 1 is questionable—SOHO/MDI was retired years ago, so calling it an NRT source suggests the table was assembled without end-to-end checking. The validation section is really just a plan, not a demonstration. The novelty is modest: combining known data sources with standard cleaning techniques is engineering, not science, and the paper correctly does not overclaim otherwise.

Bottom line: this is a paper for people who want a checklist for building their own NRT ML dataset pipeline, not for people who need a tool they can run. It does not deserve a full peer-review cycle in current form; a serious referee would reject it for lack of evidence. If the authors release the code and show it working on real data—even one processed event—it becomes a legitimate software paper worth reviewing.

My recommendation: desk-reject as is, but invite a resubmission with the artifact.

Referee Report

4 major / 4 minor

Summary. The paper describes a Python-based tool that is claimed to download, process, and label near-real-time (NRT) space weather data from multiple sources into a unified, machine-learning-ready format. The tool is organized into three modules (Data Downloader, Data Processor, Data Splitter), uses Snakemake for workflow management, and is intended to support both historical training and NRT streaming applications. The manuscript presents the architecture and intended behavior of these modules but reports no quantitative results, provides no code or repository link, and gives no validation outputs.

Significance. If the tool is actually implemented and operational, it would be a useful contribution to the space weather ML community by addressing a real bottleneck in preparing multivariate NRT datasets. The proposed architecture is sensible and the choice of Snakemake is appropriate for reproducible pipelines. However, the paper as written supplies only a design description, and the central claim that a working tool exists is unverified: no code, data, or end-to-end outputs are provided, and Section 6 defers packaging the tool into a Python library to future work. The significance is therefore conditional on evidence that is missing from the manuscript.

major comments (4)

[§2 and §5] The central claim that the tool currently exists and performs the described download/process/split functions is unsupported: no repository, DOI, URL, or any executable artifact is provided, and Section 5 reports no actual run, output file, log, or quantitative check. As written, a reader cannot distinguish an implemented system from a design sketch. Please provide a public repository link and at least one concrete example of end-to-end execution, such as a sample processed file, a Snakemake run log, or a small validation report.
[§5 (Implementation and Validation)] The validation discussion is limited to stating that 'validation flags are used' and to showing a pipeline 'concept of operation' in Figure 1, with no test results. Please report at least one specific execution: which data sources were downloaded, the time range, the number of files or samples produced, processing time, and what checks (e.g., outlier-removal statistics, timestamp continuity) were performed and passed.
[Table 1] Row 4 lists 'SOHO/MDI **' as an NRT data source, but the Michelson Doppler Imager was decommissioned in 2011 and is not a current real-time stream; the '**' marker is also dangling, with no footnote explaining it. This suggests the source list has not been validated against actual data availability and should be corrected or clarified.
[§6 (Summary)] Section 6 states that 'packaging it into a Python-ready library' is future work, yet the abstract and Section 1 imply the tool is already available to users. Please clarify the current readiness level: is the tool usable by external researchers today, or is it an internal prototype? This distinction directly affects the central existence claim and should be stated explicitly.

minor comments (4)

[Table 1] The row numbering in Table 1 contains a duplicate: the SOHO/EIT row and the SOHO/MDI row are both numbered '3'.
[§4 (Modes of Operation)] The Data Splitter is described in §2 as producing 'training, validation, hold-out, and test sets', but §4 lists 'training, validation, and test sets'; please reconcile the terminology.
[Acronyms/Abbreviations] Several acronyms listed (e.g., SWAVES, EPAM, SHARP) are not expanded or used in the main text, and the table itself would benefit from a proper caption and footnote definitions.
[References] Reference [7] is an arXiv preprint without a DOI; if the MEMPSEP paper has been published or updated, please provide a more permanent citation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper reports a data-processing tool design, not a derived prediction or fitted result.

full rationale

The manuscript's central claim is that a Python-based tool exists for downloading, processing, and splitting near-real-time space weather data into ML-ready form. This claim is not derived from any equation, fit, or prior result; it is a software-engineering report. No quantity is defined in terms of another predicted quantity, no parameter is fitted and then renamed as a prediction, and no scientific conclusion is forced by a self-citation chain. The only self-citation is reference [7] (MEMPSEP III), whose authors overlap with the present paper, but it is cited merely as part of a general statement about efforts to mitigate space-weather risks and does not carry the tool's central claim. The tool's described modules are presented as intended capabilities, with packaging into a Python-ready library deferred to future work; this is a verifiability or completeness concern, not a circularity concern. There is no self-referential derivation to expose, so the appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

No free parameters, axioms, or invented entities are introduced. The paper describes software architecture and standard statistical methods, not a physical or mathematical derivation.

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Cite this review

Pith. "Pith review of A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting." pith.science (2026). https://pith.science/paper/U4S5WNKE

@misc{pith2026250208555,
  author       = {Pith},
  title        = {Pith review of: A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U4S5WNKE}},
  note         = {Machine review of arXiv:2502.08555}
}

read the original abstract

Space weather forecasting is critical for mitigating radiation risks in space exploration and protecting Earth-based technologies from geomagnetic disturbances. This paper presents the development of a Machine Learning (ML)- ready data processing tool for Near Real-Time (NRT) space weather forecasting. By merging data from diverse NRT sources such as solar imagery, magnetic field measurements, and energetic particle fluxes, the tool addresses key gaps in current space weather prediction capabilities. The tool processes and structures the data for machine learning models, focusing on time-series forecasting and event detection for extreme solar events. It provides users with a framework to download, process, and label data for ML applications, streamlining the workflow for improved NRT space weather forecasting and scientific research.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

13 extracted references · 9 canonical work pages

[1]

G., Allison, C., Quinn, P., Barzilla, J., et al

Whitman, K., Egeland, R., Richardson, I. G., Allison, C., Quinn, P., Barzilla, J., et al. (2022). Review of Solar Energetic Particle Models. Advances in Space Research. doi: 10.1016/j.asr.2022.08.006

doi:10.1016/j.asr.2022.08.006 2022
[2]

All rights reserved

Copyright ©2024 by the International Astronautical Federation (IAF). All rights reserved. IAC-24,D5,IP,12,x89662 Page 2 of 4 that integrates NRT data streams from multiple spacecraft and instruments into a unified, ML-ready format. The tool will significantly advance space weather forecasting by offering streamlined access to a diverse array of near-real-...

work page 2024
[3]

All rights reserved

Copyright ©2024 by the International Astronautical Federation (IAF). All rights reserved. IAC-24,D5,IP,12,x89662 Page 3 of 4 most recent space weather data is always available for real-time forecasting

work page 2024
[4]

M., Steenburgh, R

Bain, H. M., Steenburgh, R. A., Onsager, T. G., & Stitely, E. M. (2021). A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill. Space Weather, 19(7). doi: 10.1029/2020sw002670

work page doi:10.1029/2020sw002670 2021
[5]

Camporeale, E. (2019). The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting. Space Weather, 17(8), 1166–1207. doi: 10.1029/2018sw002061

doi:10.1029/2018sw002061 2019
[6]

Posner, A., Hesse, M., & Cyr, O. C. St. (2014). The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations. Space Weather, 12(4), 257–276. doi: 10.1002/2013sw001007

work page doi:10.1002/2013sw001007 2014
[7]

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

Moreland, K., Dayeh, M. A., Bain, H. M., Chatterjee, S., Munoz-Jaramillo, A., & Hart S., MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach, doi: 10.48550/arXiv.2310.15390

work page Pith review arXiv doi:10.48550/arxiv.2310.15390
[8]

A., Martens, P

Angryk, R. A., Martens, P. C., Aydin, B., Kempton, D., Mahajan, S. S., Basodi, S., et al. (2020). Multivariate time series dataset for space weather data analytics. Scientific Data, 7(1),

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[10]

M., Kitiashvili, I

Sadykov, V. M., Kitiashvili, I. N., Tobiska, W. K., & Guhathakurta, M. (2021). Radiation Data Portal: Integration of Radiation Measurements at the Aviation Altitudes and Solar‐Terrestrial Environment Observations. Space Weather, 19(1). doi: 10.1029/2020sw002653

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P., Letcher, B., et al

Mölder F., Jablonski, K. P., Letcher, B., et al. Sustainable data analysis with Snakemake [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2021, 10:33 (doi: 10.12688/f1000research.29032.1 75th International Astronautical Congress (IAC), Milan, ...

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doi
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All rights reserved

Copyright ©2024 by the International Astronautical Federation (IAF). All rights reserved. IAC-24,D5,IP,12,x89662 Page 1 of 4 IAC-24,D5,IP,12,x89662 A Machine Learning-Ready Data Processing Tool for Near Real-Time Forecasting Maher A Dayeha,b *, Michael J Starkeya, Subhamoy Cha...

2024

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.

[1] [1]

G., Allison, C., Quinn, P., Barzilla, J., et al

Whitman, K., Egeland, R., Richardson, I. G., Allison, C., Quinn, P., Barzilla, J., et al. (2022). Review of Solar Energetic Particle Models. Advances in Space Research. doi: 10.1016/j.asr.2022.08.006

doi:10.1016/j.asr.2022.08.006 2022

[2] [2]

All rights reserved

Copyright ©2024 by the International Astronautical Federation (IAF). All rights reserved. IAC-24,D5,IP,12,x89662 Page 2 of 4 that integrates NRT data streams from multiple spacecraft and instruments into a unified, ML-ready format. The tool will significantly advance space weather forecasting by offering streamlined access to a diverse array of near-real-...

work page 2024

[3] [3]

All rights reserved

Copyright ©2024 by the International Astronautical Federation (IAF). All rights reserved. IAC-24,D5,IP,12,x89662 Page 3 of 4 most recent space weather data is always available for real-time forecasting

work page 2024

[4] [4]

M., Steenburgh, R

Bain, H. M., Steenburgh, R. A., Onsager, T. G., & Stitely, E. M. (2021). A Summary of National Oceanic and Atmospheric Administration Space Weather Prediction Center Proton Event Forecast Performance and Skill. Space Weather, 19(7). doi: 10.1029/2020sw002670

work page doi:10.1029/2020sw002670 2021

[5] [5]

Camporeale, E. (2019). The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting. Space Weather, 17(8), 1166–1207. doi: 10.1029/2018sw002061

doi:10.1029/2018sw002061 2019

[6] [6]

Posner, A., Hesse, M., & Cyr, O. C. St. (2014). The main pillar: Assessment of space weather observational asset performance supporting nowcasting, forecasting, and research to operations. Space Weather, 12(4), 257–276. doi: 10.1002/2013sw001007

work page doi:10.1002/2013sw001007 2014

[7] [7]

MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach

Moreland, K., Dayeh, M. A., Bain, H. M., Chatterjee, S., Munoz-Jaramillo, A., & Hart S., MEMPSEP III. A machine learning-oriented multivariate data set for forecasting the Occurrence and Properties of Solar Energetic Particle Events using a Multivariate Ensemble Approach, doi: 10.48550/arXiv.2310.15390

work page Pith review arXiv doi:10.48550/arxiv.2310.15390

[8] [8]

A., Martens, P

Angryk, R. A., Martens, P. C., Aydin, B., Kempton, D., Mahajan, S. S., Basodi, S., et al. (2020). Multivariate time series dataset for space weather data analytics. Scientific Data, 7(1),

work page 2020