Patterns (Jun 2022)
Meteorological data rescue: Citizen science lessons learned from Southern Weather Discovery
Abstract
Summary: Daily weather reconstructions (called “reanalyses”) can help improve our understanding of meteorology and long-term climate changes. Adding undigitized historical weather observations to the datasets that underpin reanalyses is desirable; however, time requirements to capture those data from a range of archives is usually limited. Southern Weather Discovery is a citizen science data rescue project that recovered tabulated handwritten meteorological observations from ship log books and land-based stations spanning New Zealand, the Southern Ocean, and Antarctica. We describe the Zooniverse-hosted Southern Weather Discovery campaign, highlight promotion tactics, and replicate keying levels needed to obtain 100% complete transcribed datasets with minimal type 1 and type 2 transcription errors. Rescued weather observations can augment optical character recognition (OCR) text recognition libraries. Closer links between citizen science data rescue and OCR-based scientific data capture will accelerate weather reconstruction improvements, which can be harnessed to mitigate impacts on communities and infrastructure from weather extremes. The bigger picture: Citizen science has the potential to capture historical handwritten scientific tabulated data that are not held in digital databases. However, undertaking a citizen science campaign for that purpose is not well described, which we address here. Our citizen science data rescue approach constrained data keying targets, developed participant instructions using clear examples, established replication levels to maximize completeness and confidence of data transcription, and demonstrated common data rescue pitfalls. We highlight how an effective communications strategy helps to maintain project momentum. Collaborating with industry to enhance optical character recognition (OCR) capability has the benefit of accelerating data rescue progress that can rapidly augment scientific data repositories. The resulting improvements to comprehensive historical weather datasets with global coverage can support models and predictive capabilities that help mitigate impacts on society from extreme weather.
