Smartphones equipped with multiple sensors have great potential to form high-resolution meteorological observation fields. In this study, we focused on smartphone pressure observations in tropical cyclone environments. We developed a machine-learning-based quality control program that greatly reduced errors and found that smartphone data led to significant improvements in analysis fields. Some traditional best tracks were found to consistently underestimate the minimum sea-level pressure.