Impacts of Assimilating Smartphone Pressure Observations on Forecast Skill during Two Case Studies in the Pacific Northwest

Abstract

Abstract Over a half-billion smartphones are now capable of measuring atmospheric pressure, potentially providing a global surface observing network of unprecedented density and coverage. An earlier study by the authors described an Android app, uWx, that served as a test bed for advanced quality control and bias correction strategies. To evaluate the utility and quality of the resulting smartphone pressure observations, ensemble data assimilation experiments were performed for two case studies over the Pacific Northwest. In both case studies, smartphone pressures improved the analyses and forecasts of assimilated and nonassimilated variables. In case I, which considered the passage of a front across the region, cycled smartphone pressure assimilation consistently improved 1-h forecasts of the altimeter setting, 2-m temperature, and 2-m dewpoint. During a postfrontal period, cycled smartphone pressure assimilation improved mesoscale forecasts of hourly precipitation accumulation. In case II, which considered a major coastal windstorm, cycling experiments assimilating smartphone pressures improved 10-m wind forecasts as well as the predicted track and intensity. For both cases, free-forecast experiments initialized with smartphone data produced forecast improvements extending several hours, suggesting the utility of crowdsourced smartphone pressures for short-term numerical weather prediction.