Bias Correction, Anonymization, and Analysis of Smartphone Pressure Observations Using Machine Learning and Multi-Resolution Kriging

Abstract

AbstractWith over a billion smartphones capable of measuring atmospheric pressure, a global mesoscale surface pressure network based on smartphone pressure sensors may be possible if key technical issues are solved, including collection technology, privacy and bias correction. To overcome these challenges, a novel framework was developed for the anonymization and bias correction of smartphone pressure observations (SPOs) and was applied to billions of SPOs from The Weather Company (IBM). Bias correction using machine learning reduced the errors of anonymous (ANON) SPOs and uniquely identifiable (UID) SPOs by 43% and 57%, respectively. Applying multi-resolution kriging, gridded analyses of bias-corrected smartphone pressure observations were made for an entire year (2018), using both anonymized (ANON) and non-anonymized (UID) observations. Pressure analyses were also generated using conventional (MADIS) surface pressure networks. Relative to MADIS analyses, ANON and UID smartphone analyses reduced domain-average pressure errors by 21% and 31%. The performance of smartphone and MADIS pressure analyses was evaluated for two high-impact weather events: the landfall of Hurricane Michael and a long-lived mesoscale convective system. For these two events, both anonymized and non-anonymized smartphone pressure analyses better captured the spatial structure and temporal evolution of mesoscale pressure features than the MADIS analyses.