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Conor McNicholas and Clifford F. Mass

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.

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Conor McNicholas and Clifford F. Mass

Abstract

Over half a billion smartphones worldwide are now capable of measuring atmospheric pressure, providing a pressure network of unprecedented density and coverage. This paper describes novel approaches for the collection, quality control, and bias correction of such smartphone pressures. An Android app was developed and distributed to several thousand users, serving as a test bed for onboard pressure collection and quality-control strategies. New methods of pressure collection were evaluated, with a focus on reducing and quantifying sources of observation error and uncertainty. Using a machine learning approach, complex relationships between pressure bias and ancillary sensor data were used to predict and correct future pressure biases over a 4-week period from 10 November to 5 December 2016. This approach, in combination with simple quality-control checks, produced an 82% reduction in the average smartphone pressure bias, substantially improving the quality of smartphone pressures and facilitating their use in numerical weather prediction.

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