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.

Pressure observations from smartphones have the potential to provide millions of observations per hour that could revolutionize high-resolution weather prediction.

During the past few years, tens of millions of smartphones with relatively accurate pressure sensors have been sold throughout the world, with the goal of providing information for internal navigation within buildings and better altimetry, among other uses. A smartphone is defined here as a mobile phone with substantial computational ability, a high-resolution screen, and wifi and GPS capabilities, in addition to the phone and text capabilities of standard cellular phones. Smartphones are capable of running a wide variety of applications (apps) and are available with a number of operating systems (e.g., Apple iOS, Google Android, Windows mobile). By 2016, industry sources (IHS Technology; https://technology.ihs.com/) expect that between 500 million and one billion smartphones and tablets will have the capacity to measure pressure as well as parameters such as position, humidity, and temperature. Ultra-dense networks of pressure observations provided by smartphones and other portable platforms could contribute detailed information describing mesoscale phenomena such as convective cold pools, mountain waves, fronts, and others. This paper will examine the potential of such massive numbers of surface observations to greatly improve our ability to describe and forecast the three-dimensional structure at the atmosphere, potentially leading to revolutionary improvements in high-resolution numerical weather prediction.

WHY IS SURFACE PRESSURE SO SPECIAL?

Pressure is perhaps the most valuable surface meteorological variable observed regularly. Unlike surface air temperature and humidity, surface pressure reflects the deep structure of the overlying atmosphere. Surface pressure has fewer of the observational problems that plague surface wind, temperature, and humidity; unlike wind and temperature, pressure can be measured inside or outside of a building, in or out of the shade, and is not seriously impacted by nearby obstacles or urbanization. Surface pressure is not influenced by the characteristics of the underlying surface, as are temperature and wind. Although surface pressure measurements can have systematic biases like other surface variables, pressure biases for a static sensor are generally unchanging (perhaps owing to poor elevation information or calibration) and thus can be easily removed by straightforward quality control algorithms.

Several recent studies, most using ensemble-based data assimilation systems, have demonstrated that surface pressure provides considerable information about three-dimensional atmospheric structures. Ensemble-based data assimilation systems are particularly adept in getting maximum value from surface pressure information; such systems produce flow-dependent background error covariances, build covariances based on the natural atmospheric structures in the model, and allow impacts for pressure on all other model variables throughout the atmospheric volume. On the synoptic scale, Whitaker et al. (2004) showed that a limited number of global surface pressure observations could produce a highly realistic twentieth-century reanalysis that closely resembled the analysis produced by the full collection of observing assets during a comparison period encompassing the later part of the century. Using regional assimilation of pressure observations from airport locations, Dirren et al. (2007) was able to reproduce synoptic-scale upper-air patterns over western North America and the eastern Pacific.

Although less work has been completed on the assimilation of surface pressure observations on the mesoscale, early investigations have been promising. Wheatley and Stensrud (2010) investigated the impacts of assimilating both surface pressure and 1-h pressure change for two convective events over the U.S. Midwest. Using a relatively coarse model resolution (30 km) and only assimilating airport Automated Surface Observing System (ASOS) observations, they found that surface pressure observations facilitated accurate depictions of the mesoscale pressure patterns associated with convective systems. More recently, Madaus et al. (2014) found that ensemble-based data assimilation of dense pressure observations can produce improved high-resolution (4 km) analyses and short-term forecasts that better resolve features such as fronts and convection. Considering the apparent promise of surface pressure observations for improving analyses and forecasts, the next step is to evaluate this potential by applying state-of-the-art data assimilation approaches to a pressure observation network encompassing conventional observations and enhanced with pressure data available from new observing platforms such as smartphones.

INCREASING AVAILABILITY OF FIXED SURFACE PRESSURE OBSERVATIONS.

During the past decades, there has been an explosion in the availability of surface pressure observations across the United States. A quarter century ago, surface pressure observations were limited to approximately 1000 airport locations across the country. Today, these ASOS sites are joined by hundreds of networks run by utilities, air quality agencies, departments of transportation and others, plus public volunteer networks such as the Weather Underground (www.wunderground.com/) and the Citizen Weather Observer Program (CWOP; http://wxqa.com/). By combining these networks, tens of thousands of surface pressure observations are collected each hour across the United States. Over the Pacific Northwest region, encompassing mainly Washington, Oregon, and Idaho, roughly, 1800 pressure observations are currently collected each hour from approximately 70 networks (Fig. 1), compared to approximately 100 ASOS locations. As shown in that figure, even when large numbers of networks are combined, substantial areas, particularly in rural locations, have few pressure observations, and many observation locations only report once an hour. Fortunately, an approach for increasing radically the number and temporal frequency of surface pressure observations exists: the use of pressures from smartphones and other portable digital devices.

Fig. 1.

Surface pressure locations for a typical contemporary period (from 0000 UTC 10 Nov to 2100 UTC 10 Dec 2012) from roughly 70 networks over the Pacific Northwest. Figure from Madaus et al. (2014).

Fig. 1.

Surface pressure locations for a typical contemporary period (from 0000 UTC 10 Nov to 2100 UTC 10 Dec 2012) from roughly 70 networks over the Pacific Northwest. Figure from Madaus et al. (2014).

SMARTPHONE PRESSURE OBSERVATIONS.

During the past two years a number of smartphone vendors have added pressure sensors, predominantly to Android-based phones and tablets/pads. The main reason for installing these pressure sensors was to identify the floor on which the device is located or to aid in vertical altimetry. Samsung began using pressure sensors in its popular Galaxy S III smartphone in 2012 and such sensors have remained in the Galaxy S IV released in 2013 (Fig. 2) and the Galaxy S V (2014). Pressure sensors are also available in other Android phones and pads, including the Galaxy Nexus 4 and 10, Galaxy Note, Xoom, RAZR MAXX HD, Xiaomi MI-2, and Droid Ultra. According to industry analyst IHS Electronics and Media (https://technology.ihs.com/), approximately 80 million pressure-capable Android devices were sold in 2012, with expectations of 160 and 325 million units for 2013 and 2014, respectively. By 2015, IHS estimates that well over a half-billion portable devices worldwide will have the capability for real-time pressure observation, including over 200 million in North America. There is the strong expectation that non-Android device vendors such as Apple will include pressure sensors in upcoming smartphones and tablets. Thus, the potential may exist to increase the number of hourly pressure observations over the United States by roughly 10,000 times over the current availability from current networks.

Fig. 2.

The Samsung Galaxy S4 is one of several Android phones with high-quality pressure sensors. (Source: www.imgreview.info/samsung-galaxy-s4-active-orange/)

Fig. 2.

The Samsung Galaxy S4 is one of several Android phones with high-quality pressure sensors. (Source: www.imgreview.info/samsung-galaxy-s4-active-orange/)

Some insight into the potential availability and distribution of smartphone pressures is available from a map of the current U.S. coverage for the largest American cell phone network, Verizon (Fig. 3). Nearly all of the eastern two-thirds of the lower 48 states is covered, encompassing nearly the entire range of U.S. severe convective storms. Coverage over the western United States has gaps over the highest terrain and sparely populated desert areas, but is still extensive (covering perhaps 65% of the land area) and includes all the major West Coast population centers from Seattle to San Diego. Coverage over the Interstate Highway System is particularly good, even over less populated rural areas. The number of smartphone observations will undoubtedly be dependent on population density, with the largest over the eastern United States and the West Coast.

Fig. 3.

Verizon cell phone coverage map on 4 Oct 2013. Darker red areas indicate enhanced digital coverage. White areas are without coverage.

Fig. 3.

Verizon cell phone coverage map on 4 Oct 2013. Darker red areas indicate enhanced digital coverage. White areas are without coverage.

The accuracy and resolution of the pressure sensors in smartphones and tablets are surprisingly good. Many of the current Android devices use the ST Microelectronics LPS331 MEMS pressure sensor, which has a relative accuracy of ±0.2 hPa, an absolute accuracy of ±2.6 hPa, and includes temperature compensation (details at www.st.com/st-web-ui/static/active/en/resource/technical/document/datasheet/DM00036196.pdf). Such relative accuracy allows accurate determination of pressure change, the use of which is discussed later in this paper.

The potential for large numbers of smartphone pressure observations has attracted several application developers that have created Android apps that col lect smar tphone pressures and positions (through GPS or cell tower triangulation). One firm, Cumulonimbus, has developed the pressureNet app for Android phones and tablets (www.cumulonimbus.ca/). Smartphone owners mus t download the pressureNet app to allow their pressures to be reported; however, with the insertion of the pressureNet code into popular apps, it is expected that the number of smartphone pressures collected by Cumulonimbus will increase by one or two orders of magnitude during the next year. Currently, they are collecting tens of thousands of surface pressure observations globally each hour and have made them available to the research community and others. Another group collecting pressure observations on Android phones is OpenSignal (http://opensignal.com/), whose application of the same name collects smartphone pressure observations, other meteorological parameters (temperature, humidity, and light levels), and wifi/cell phone signal levels. They have also developed an app, called WeatherSignal, that displays the meteorological observations provided by a phone. A plot of the pressureNet and OpenSignal observations at one time (0100 UTC 18 July 2014) over the U.S. and adjacent areas of Canada and Mexico is shown in Fig. 4. Although only about 100,000 hourly smartphone pressure observations are available today (July 2014) across the United States through the pressureNet app and OpenSignal apps, a small number compared to the millions of phones with pressure capabilities, there are still regions, such as the Northeast United States, with substantial smartphone observation densities that greatly enhance current observation networks.

Fig. 4.

Smartphone pressure observations for the hour ending 0100 UTC 18 Jul 2014. A total of 102,191 pressure observations were available at this time. Data are provided by two commercial firms: Cumulonimbus and OpenSignal.

Fig. 4.

Smartphone pressure observations for the hour ending 0100 UTC 18 Jul 2014. A total of 102,191 pressure observations were available at this time. Data are provided by two commercial firms: Cumulonimbus and OpenSignal.

Motor vehicles offer another potential platform for acquiring high-density pressure observations. Solid-state atmospheric pressure sensors are found in most cars and trucks, which also possess ambient temperature sensors for use in engine management computers (Mahoney and O'Sullivan 2013). The main challenges for use of vehicle pressure observations are position determination (easily dealt with by GPS), real-time communication, and privacy issues. A number of auto industry analysts (e.g., https://m2m.telefonica.com/m2m-media/m2m-downloads/detail/doc_details/530-connected-car-report-2013#530-Connected%20Car%20Report%202013-english) predict that most cars will have Internet connectivity by 2020.

OTHER SMARTPHONE WEATHER OBSERVING CAPABILITIES.

Some smartphones, such as the Samsung Galaxy IV, have the capability to measure other environmental parameters such a battery temperature, humidity, magnetic field, and lighting intensity. Temperature and humidity measurements from smartphones are of far less value than pressure, since the dominant influence of the immediate environment (inside of a pocket or a building) produces readings that are unrepresentative of the conditions in the free air. However, a recent study found that with statistical training and correction using observed temperatures, large numbers of smartphone temperatures can be calibrated to provide useful measures of daily average air temperatures over major cities (Overeem et al. 2013b). Related work has shown that the attenuation of the microwave signals between cell towers is sensitive to precipitation intensity and that such information can be used to create precipitation maps that closely resemble radar reflectivitiy (Overeem et al. 2013a).

CHALLENGES IN USING SMARTPHONE PRESSURE OBSERVATIONS.

The value of smartphone pressures in support of numerical weather prediction can be greatly enhanced with proper calibration, preprocessing, and preselection. Gross range checks can reject clearly erroneous pressures. Either pressure or pressure change can be assimilated by modern data assimilation systems. For pressure-change assimilation, only smartphones that are not moving should be used—something that can be determined from the GPS position and observed pressures from the phones (vertical movement will generally produce far more rapid pressure variations than meteorological changes).

The elevation of the smartphone is required to assimilate either pressure or pressure change. GPS elevations are available, but can have modest errors (typically ±10 m, roughly equivalent to a 1-hPa pressure error, the typical error variance used in most operational data assimilation systems; see http://gpsinformation.net/main/altitude.htm for a discussion on the vertical errors in GPS-based elevation). If one has a collection of pressures in an area, it might be reasonable to assume that the highest pressures reflect values on the first floor of residences or in a vehicle, representing pressure at roughly 1 m above ground elevation. Since it makes little sense to assimilate pressure observations in regions where models lack sufficient resolution to duplicate observed pressure features, pressure observations in such areas should be rejected when model and actual terrain are substantially different (Madaus et al. 2014). Clearly, some experimentation will be required for developing algorithms that derive maximum value from smartphone pressures.

WHAT KIND OF WEATHER FORECASTS COULD SMARTPHONE PRESSURES HELP THE MOST?

Although an ultra-dense network of smartphone pressure observations would undoubtedly positively impact general weather prediction, there are several phenomena for which they might be particularly useful. One major problem is forecasting the initiation of severe convection, with models being initialized before any precipitation or radar echo is apparent. At such an early stage of development, subtle troughs, drylines, convergence lines, and remnants of past cold pools can supply major clues about potential convective development—information that dense collections of smartphone pressures might well be able to provide. The example in the next section of this paper illustrates the value of even a modest density of smartphone pressures for simulating a strong convective event. Forecasting the positions of fronts and major troughs, even a few hours in advance, can have large value for wind energy prediction since such features often are associated with sudden rapid ramp ups and ramp downs in wind energy generation. As shown by Madaus et al. (2014) the assimilation of dense pressure observations can shift fronts in a realistic way that substantially improves short-term wind forecasts. High-resolution pressure observations from smartphones might also aid in the initialization and monitoring of mesoscale troughing associated with downslope winds and leeside convergence zones. Dense pressure observations along coastlines could provide significant information regarding approaching weather features, including the positions of offshore low centers and fronts.

Even the densest portions of the U.S. surface observation network are generally too coarse to observe and initialize features on the meso-gamma (2–20 km) and smaller scales. Smartphone pressure observations may offer sufficient data to do so, particularly over the smartphone-rich regions of the eastern United States and West Coast. An interesting advantage of smartphone pressure observations is that they could be easily added in any location where power and cell phone coverage is available.

AN EXAMPLE OF ASSIMILATING SMARTPHONE PRESSURES.

Although the smartphone pressure acquisition is still at an early stage, with observation densities orders of magnitude less than what will be available in a few years, it is of interest to try some initial assimilation experiments to judge the impacts of even modest numbers of smartphone pressures. To complete such a test, smartphone observations made available by Cumulonimbus's PressureNet app (PNET) were used to simulate an active convective event over the eastern slopes of the Washington Cascades that brought heavy showers and several lightning-initiated wildfires. For this experiment, an ensemble Kalman filter (EnKF) data assimilation system, adapted from one provided by the University Corporation for Atmospheric Research (UCAR) Data Assimilation and Research Testbed (DART) program (Anderson et al., 2009), was applied at 4-km grid spacing and used the Weather Research and Forecasting (WRF) model, V3.1. The ensembles (64 members) for these experiments were cycled every 3 h from 1200 UTC 29 June through 1200 UTC 30 June 2013. The impacts of smartphone pressures were examined for a 3-h period ending on 0300 UTC 30 June 2013. During that period there were 110 aviation routine weather report (METAR) observation sites and 350 smartphone pressure locations available.

Figure 5 shows both the surface pressures provided by the conventional ASOS network (METAR, blue squares) and the smartphone pressures (PNET, red dots) available at 0000 UTC 30 June 2013. A number of smartphone pressures were available over the eastern slopes of the Cascades, the region of strongest convection. The accumulated rainfall estimated using the Pendleton, Oregon, National Weather Service radar (PDT) for the 3 h ending at 0300 UTC 30 June (Fig. 6) shows substantial accumulation (up to approximately 32 mm) from intense convective cells. The University of Washington runs a real-time ensemble Kalman filter data assimilation system (RTENKF) that uses conventional surface observations, radiosondes, Aircraft Communications Addressing and Reporting System (ACARS) observations, and satellite-based cloud/water vapor track winds (Torn and Hakim 2008). This system, run on a 3-h update cycle, produced 3-h precipitation totals shown in Fig. 6. This modeling system did produce some convective showers over and to the east of the Cascades, but failed to duplicate the intensity of the leeside showers and had considerable spread in convective locations. Figure 6 shows the result of adding the smartphone pressure observations (Fig. 5) to the mix of observations used in the RTENKF system. With the added pressure observations, the ensemble system produced far more intense convective cells east of the Cascade crest, with some with orientations and magnitudes more reminiscent of the observed than provided by the RTENKF system. In addition, more ensemble members were near the observed location of the most intense convection (Fig. 7). This, of course, represents only one case, but suggests that assimilating smartphone pressures can both change and enhance short-term mesoscale forecasts. It is reasonable to expect that further increases in the number of pressure observations would provide additional improvements in convective and other forecasts.

Fig. 5.

Smartphone pressure observations (PNET) and pressure measurement sites from ASOS observation locations (METAR) at 0000 UTC 30 Jun 2013.

Fig. 5.

Smartphone pressure observations (PNET) and pressure measurement sites from ASOS observation locations (METAR) at 0000 UTC 30 Jun 2013.

Fig. 6.

3-h precipitation from the Pendleton (PDT) radar, as well as ensemble means from the University of Washington real-time ensemble Kalman filter system (RTENKF) and the same system using pressures from smartphones, for a 3-h period ending at 0000 UTC 30 Jun 2013.

Fig. 6.

3-h precipitation from the Pendleton (PDT) radar, as well as ensemble means from the University of Washington real-time ensemble Kalman filter system (RTENKF) and the same system using pressures from smartphones, for a 3-h period ending at 0000 UTC 30 Jun 2013.

Fig. 7.

The number of ensemble members with a local maxima in 3-h precipitation of at least 20 mm at each grid point ending at 0000 UTC 30 Jun 2013 for the operational University of Washington EnKF data assimilation system (RTENKF) and a similar system that also assimilates smartphone observations (PNET). An exclusion radius of 40 km was used to isolate independent maxima. The 10-mm 3-h precipitation derived from the PDT radar is also outlined. More ensemble members indicated a maximum of precipitation near an observed convective location when smartphone pressures were assimilated.

Fig. 7.

The number of ensemble members with a local maxima in 3-h precipitation of at least 20 mm at each grid point ending at 0000 UTC 30 Jun 2013 for the operational University of Washington EnKF data assimilation system (RTENKF) and a similar system that also assimilates smartphone observations (PNET). An exclusion radius of 40 km was used to isolate independent maxima. The 10-mm 3-h precipitation derived from the PDT radar is also outlined. More ensemble members indicated a maximum of precipitation near an observed convective location when smartphone pressures were assimilated.

LOOKING TOWARD THE FUTURE.

During the next few years, the number of smartphones/tablets with pressure sensors should increase into the tens of millions over North America and the hundreds of millions globally. If private sector firms or other organizations can develop the infrastructure to “harvest” and share these pressure observations in real time, there could be a substantial improvements in the quality of the initializations of high-resolution numerical weather prediction models and their subsequent forecasts for a wide range of important weather features such as severe convection. Initial research on the impacts of networks of surface pressure observations on mesoscale prediction (e.g., Wheatley and Stensrud 2010; Madaus et al. 2014) suggest that ensemble-based mesoscale data assimilation may offer an attractive approach to securing maximum benefit from smartphone and other pressure observations, but considerably more testing and experimentation is needed, including understanding the relative value of pressure and pressure change assimilation. Furthermore, better approaches for quality control and bias correction of smartphone pressures can enhance the value of these new observation sources. During the next decade a large number of pressure observations from vehicles will likely join the current smartphone collection as transportation platforms gain Internet connectivity. The combination of smartphone and vehicle surface pressure observations may well contribute to a substantial increase in our ability to describe and forecast the atmosphere at high resolution, with substantial economic benefits and the potential to save lives and property.

ACKNOWLEDGMENTS

This research has been supported by the National Science Foundation under Award AGS-1041879 and a NOAA CSTAR Grant Award NA10OAR4320148AM63. Professor Greg Hakim has been a major contributor to the UW effort in assimilating surface pressure observations. The pressure data for this work has been provided by Jacob Sheehy of Cumulonimbus and Samuel Johnson of OpenSignal.

REFERENCES

REFERENCES
Anderson
,
J.
,
T.
Hoar
,
K.
Raeder
,
H.
Liu
,
N.
Collins
,
R.
Torn
, and
A.
Avellano
,
2009
:
The data assimilation research testbed: a community facility
.
Bull. Amer. Meteor. Soc.
,
90
,
1283
1296
.
Dirren
,
S.
,
R.
Torn
, and
G.
Hakim
,
2007
:
A data assimilation case study using a limited-area ensemble Kalman filter
.
Mon. Wea. Rev.
,
135
,
1455
1473
,
doi:10.1175/MWR3358.1
.
Madaus
,
L. E.
,
G. J.
Hakim
, and
C. F.
Mass
,
2014
:
Utility of dense pressure observations for improving mesoscale analyses and forecasts
.
Mon. Wea. Rev.
,
142
,
2398
2413
,
doi:10.1175/MWR-D-13-00269.1
.
Mahoney
,
W. P.
, and
J.M.
O'Sullivan
,
2013
:
Realizing the Potential of Vehicle-Based Observations
.
Bull. Amer. Meteor. Soc.
,
94
,
1007
1018
.
Overeem
,
A.
,
H.
Leijnse
, and
R.
Uijlenhoet
,
2013a
:
Country-wide rainfall maps from cellular communication networks
.
Proc. Natl. Acad. Sci. USA
,
110
,
2741
2745
,
doi:10.1073/pnas.1217961110
.
Overeem
,
A.
,
J. C. R.
Robinson
,
H.
Leijnse
,
G. J.
Steeneveld
,
B. K. P.
Horn
, and
R.
Uijlenhoet
,
2013b
:
Crowdsourcing urban air temperatures from smartphone battery temperatures
.
Geophys. Res. Lett.
,
40
,
4081
4085
,
doi:10.1002/grl.50786
.
Torn
,
R. D.
, and
G. J.
Hakim
,
2008
:
Performance characteristics of a pseudo-operational ensemble Kalman filter
.
Mon. Wea. Rev.
,
136
,
3947
3963
,
doi:10.1175/2008MWR2443.1
.
Wheatley
,
D.
, and
D.
Stensrud
,
2010
:
The impact of assimilating surface pressure observations on severe weather events in a WRF mesoscale ensemble system
.
Mon. Wea. Rev.
,
138
,
1673
1694
,
doi:10.1175/2009MWR3042.1
.
Whitaker
,
J.
,
G.
Compo
,
X.
Wei
, and
T.
Hamill
,
2004
:
Reanalysis without radiosondes using ensemble data assimilation
.
Mon. Wea. Rev.
,
132
,
1190
1200
,
doi:10.1175/1520-0493(2004)1322.0.CO;2
.