10 Jun 2025
| 10 Jun 2025
Abstract. Archives of observed weather data present unique opportunities for scientists to obtain long time series of the historical climate for many regions of the world. Unfortunately, most of these observational records are to-date available only on paper, and thus require digitization and transcription to facilitate analysis of climatic trends. Here we present a new open-source software, MeteoSaver, that uses machine learning (ML) algorithms to transcribe handwritten records of historical weather data. MeteoSaver version 1.0 processes images of tabular sheets alongside user-defined configuration settings, performing transcription through five sequential steps: (i) image pre-processing, (ii) table and cell detection, (iii) transcription, (iv) quality assessment and quality control, and (v) data formatting and upload. As an illustration and evaluation of the software, we apply MeteoSaver to ten pictured sheets of handwritten temperature observations from the Democratic Republic of the Congo. The results show that 95–100 % of the records can be transcribed, of which a median of 74.4 % reached the highest internal quality flag and 74 % matches with the manually transcribed record, yielding a median mean absolute error of 0.3 °C. These results illustrate that MeteoSaver can be applied to a range of handwriting styles and varying tabular dimensions, paper sizes, and maintenance conditions, highlighting its potential for transcribing tabular meteorological observations from multiple regions, especially if the sheets have a consistent format. Overall, our open-source software can help address the challenges of limited available hydroclimatic data within many regions of the world, by helping to save millions of handwritten records of historical weather data presently stored in archives, and expedite research on the climate and environmental changes in data scarce regions.
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Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Journal article(s) based on this preprint
23 Apr 2026
MeteoSaver v1.0: a machine-learning based software for the transcription of historical weather data
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
Geosci. Model Dev., 19, 3213–3255,
https://doi.org/10.5194/gmd-19-3213-2026, 2026
Short summary
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor |
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
ED: Publish as is (27 Mar 2026) by Taesam Lee
AR by Derrick Muheki on behalf of the Authors (02 Apr 2026) Manuscript
Journal article(s) based on this preprint
23 Apr 2026
MeteoSaver v1.0: a machine-learning based software for the transcription of historical weather data
Derrick Muheki, Bas Vercruysse, Krishna Kumar Thirukokaranam Chandrasekar, Christophe Verbruggen, Julie M. Birkholz, Koen Hufkens, Hans Verbeeck, Pascal Boeckx, Seppe Lampe, Ed Hawkins, Peter Thorne, Dominique Kankonde Ntumba, Olivier Kapalay Moulasa, and Wim Thiery
Geosci. Model Dev., 19, 3213–3255,
https://doi.org/10.5194/gmd-19-3213-2026, 2026
Short summary
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