Utilising Benford's Law in the Validation of Precipitation Datasets

Gollop, Amee, Wilson Kemsley, Sarah, Osborn, Tim, Joshi, Manoj, Stevens, David and Harris, Ian (2026) Utilising Benford's Law in the Validation of Precipitation Datasets. International Journal of Climatology, 46 (3). ISSN 0899-8418

PDF (Gollop_etal_accepted) - Accepted Version Restricted to Repository staff only until 31 December 2099. Request a copy

Abstract

The increasing magnitude and complexity of precipitation datasets necessitate robust and efficient data integrity assessment. This study systematically applies Benford's Law, a mathematical theorem describing leading digit frequencies, as a novel diagnostic tool for precipitation data in the environmental and hydroclimate sciences. We present a reproducible and robust methodology, demonstrating that global monthly precipitation consistently conforms to Benford's Law across diverse data types, including raw observations, gridded products, reanalysis and synthetic simulations. This key finding fundamentally challenges traditional assumptions regarding the influence of data origin on Benford's Law adherence, significantly broadening its applicability. Our findings underscore the importance of underlying quantitative characteristics for successful application: while regional analyses reveal that monthly precipitation data in the United Kingdom and Ireland do not conform to Benford's Law-based principles, a shift to daily temporal granularity successfully restores conformance, highlighting how temporal resolution can introduce the necessary data properties. This research uniquely positions Benford's Law as a powerful, complementary diagnostic tool capable of detecting subtle data corruptions, as demonstrated through an artificial experiment. Ultimately, this work advances the utility of Benford's Law in climate research, providing a scalable method to enhance the reliability of foundational datasets critical for climate modelling, forecasting and a wide array of hydroclimatological applications.

Item Type:	Article
Uncontrolled Keywords:	benford's law,data pipeline,data quality,data validation,error detection,hydroclimatology,precipitation data,atmospheric science ,/dk/atira/pure/subjectarea/asjc/1900/1902
Faculty \ School:	Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA Faculty of Science > School of Engineering, Mathematics and Physics
UEA Research Groups:	Faculty of Science > Research Groups > Climatic Research Unit Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences Faculty of Social Sciences > Research Centres > Water Security Research Centre University of East Anglia Schools > Faculty of Science > Tyndall Centre for Climate Change Research Faculty of Science > Research Centres > Tyndall Centre for Climate Change Research Faculty of Science > Research Groups > Fluids & Structures
Related URLs:	https://www.scopus.com/pages/publication...
Depositing User:	LivePure Connector
Date Deposited:	16 Dec 2025 16:30
Last Modified:	14 May 2026 15:04
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101435
DOI:	10.1002/joc.70221

Actions (login required)

View Item