Millions of smartphones possess relatively accurate pressure sensors and the expectation is that these numbers will grow into the hundreds of millions globally during the next few years. The availability of millions of pressure observations each hour from smartphones has major implications for high-resolution numerical weather prediction. This paper reviews smartphone pressure-sensor technology, describes commercial efforts to collect the data in real time, examines the implications for mesoscale weather prediction, and provides an example of assimilating smartphone pressure observations for a strong convective event over eastern Washington State.
The use of dense pressure observations is investigated for creating mesoscale ensemble analyses and improving short-term mesoscale forecasts. By exploiting additional observation platforms, the number of pressure observations over the Pacific Northwest region is increased by an order of magnitude over standard airport observations. Quality control and bias correction methods for these observations are discussed, including the use of pressure tendency as an alternative observation type with fewer bias concerns. The enhanced station density provided by these observations contributes to localized adjustments for a variety of mesoscale phenomena. These adjusted analyses yield improved forecasts, including more accurate forecasts of frontal passages and convective bands. Assimilating dense 3-h pressure tendency observations also reduces the error in some forecast surface fields similarly to raw pressure observations, suggesting further investigation into pressure tendency as a mesoscale observation type.
Smartphone pressure observations have the potential to greatly increase surface observation density on convection-resolving scales. Currently available smartphone pressure observations are tested through assimilation in a mesoscale ensemble for a 3-day, convectively active period in the eastern United States. Both raw pressure (altimeter) observations and 1-h pressure (altimeter) tendency observations are considered. The available observation density closely follows population density, but observations are also available in rural areas. The smartphone observations are found to contain significant noise, which can limit their effectiveness. The assimilated smartphone observations contribute to small improvements in 1-h forecasts of surface pressure and 10-m wind, but produce larger errors in 2-m temperature forecasts. Short-term (0–4 h) precipitation forecasts are improved when smartphone pressure and pressure tendency observations are assimilated as compared with an ensemble that assimilates no observations. However, these improvements are limited to broad, mesoscale features with minimal skill provided at convective scales using the current smartphone observation density. A specific mesoscale convective system (MCS) is examined in detail, and smartphone pressure observations captured the expected dynamic structures associated with this feature. Possibilities for further development of smartphone observations are discussed.
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