MeteoSaver v1.0: a machine-learning based software for the transcription of historical weather data

Muheki, D. ORCID: https://orcid.org/0000-0001-9390-2836, Vercruysse, B., Chandrasekar, K. K. T., Verbruggen, C., Birkholz, J. M., Hufkens, K., Verbeeck, H. ORCID: https://orcid.org/0000-0003-1490-0168, Boeckx, P., Lampe, S. ORCID: https://orcid.org/0000-0002-7907-4496, Hawkins, E. ORCID: https://orcid.org/0000-0001-9477-3677, Thorne, P. ORCID: https://orcid.org/0000-0003-0485-9798, Ntumba, D. K., Moulasa, O. K. and Thiery, W. ORCID: https://orcid.org/0000-0002-5183-6145 (2026) MeteoSaver v1.0: a machine-learning based software for the transcription of historical weather data. Geoscientific Model Development, 19 (8). pp. 3213-3255. ISSN 1991-9603 doi: 10.5194/gmd-19-3213-2026

Abstract/Summary

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 and precipitation observations from the Democratic Republic of the Congo. The results show that 95 %–100 % of the daily temperature values 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.

Item Type	Article
URI	https://centaur.reading.ac.uk/id/eprint/129494
Identification Number/DOI	10.5194/gmd-19-3213-2026
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > NCAS Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	European Geosciences Union
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