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Surface Pressure Observations from Smartphones:
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A Potential Revolution for High-Resolution Weather Prediction?
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Clifford F. Mass1 and Luke E. Madaus
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Department of Atmospheric Sciences
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University of Washington
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Submitted to
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Bulletin of the American Meteorological Society
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September 2013
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Capsule Description: Pressure observations from smartphones have the potential to provide
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millions of observations per hour that could revolutionize high-resolution weather prediction.
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Corresponding author
Professor Clifford F. Mass
Department of Atmospheric Sciences
Box 351640
University of Washington
Seattle, Washington 98195
cmass.uw.edu
(206) 685-0910
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Abstract
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Millions of smartphones now possess relatively accurate pressure sensors and the
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expectation is that these numbers will grow into the hundreds of millions during the next few
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years. The availability of tens or hundreds of millions of pressure observations each hour over
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the U.S. has major implications for high-resolution numerical weather prediction. This paper
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reviews smartphone pressure sensor technology, describes commercial efforts to collect the data
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in real time, examines the implications for mesoscale weather prediction, and provides some
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examples of assimilating smartphone pressure observations for a strong convective event over
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eastern Washington State.
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Introduction
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During the past few years, tens of millions of smartphones with relatively accurate
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pressure sensors have been sold throughout the world, with the goal of providing information for
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internal navigation within buildings, among other uses.
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isuppli.com) expect that between 500 million and one billion smartphones and tablets will have
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the capacity to measure pressure as well as parameters such as position, humidity, and
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temperature. Ultra-dense networks of pressure observations provided by smartphones and other
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portable platforms could provide critical information describing mesoscale phenomena such as
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convective cold pools, mountain waves, fronts, and others.
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potential of such massive numbers of surface observations to greatly improve our ability to
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describe and forecast the three-dimensional structure at the atmosphere, potentially leading to
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revolutionary improvements in high-resolution numerical weather prediction.
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Why is surface pressure so special?
By 2016, industry sources (HIS,
This paper will examine the
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Pressure is perhaps the most valuable surface meteorological variable observed regularly.
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Unlike temperature and humidity, surface pressure reflects the vertical structure of the overlying
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atmosphere.
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representativeness, that plague surface wind, temperature and humidity; pressure can be
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measured inside or outside of a building, in or out of the shade, and is not seriously impacted by
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downstream obstacles or urbanization.
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systematic biases like other surface variables, pressure biases are generally unchanging (perhaps
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from poor elevation information or calibration) and thus can be easily removed by
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straightforward quality control algorithms.
Surface pressure has fewer of the observational problems, including
Although surface pressure measurements can have
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Several recent studies, many using ensemble-based data assimilation systems, have
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demonstrated that surface pressure provides substantial information about three-dimensional
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atmospheric structures.
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getting maximum value from surface pressure information, since such systems produce flow-
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dependent background error covariances, build covariances based on the natural atmospheric
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structures in the model, and allow impacts on all other model variables. On the synoptic scale,
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Whitaker et al. (2004) showed that a limited number of global surface pressure observations
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could produce a highly realistic 20th-century reanalysis that closely resembled the analysis
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produced by the full collection of observing assets during the later part of the century.
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regional assimilation of pressure observations from airport locations, Dirren et al. (2007) was
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able to capture synoptic-scale upper-air patterns over western North America and the eastern
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Pacific.
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Ensemble-based data assimilation systems are particularly adept in
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Although less work has been completed on the assimilation of surface pressure
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observations on the mesoscale, early investigations have been promising.
Wheatley and
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Stensrud (2010) investigated the impacts of assimilating both surface pressure and one-hour
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pressure change for two convective events over the U.S. Midwest. Using a relatively coarse
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model resolution (30 km) and only assimilating airport ASOS (Automated Surface Observing
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System) observations, they found that surface pressure observations facilitated accurate
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depictions of the mesoscale pressure patterns associated with convective systems.
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recently, Madaus et al. (2013) found that ensemble-based data assimilation of dense pressure
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observations can produce improved high-resolution (4-km) analyses and short-term forecasts that
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better resolve features such as fronts and convection. Considering the apparent promise of
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surface pressure observations for improving analyses and forecasts, the next step is to evaluate
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this potential by applying state-of-the-art data assimilation approaches to a pressure observation
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network enhanced with conventional observations and pressure data available from new
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observing platforms such as smartphones.
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Increasing availability of fixed surface pressure observations
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During the past decades there has been an explosion in the availability of surface pressure
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observations across the U.S. A quarter century ago, surface pressure observations were limited
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to approximately 1000 airport locations across the country. Today, these ASOS sites are joined
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by hundreds of networks run by utilities, air quality agencies, departments of transportation and
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others, plus public volunteer networks such as the Weather Underground and the Citizen
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Weather Observer Program (CWOP).
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surface pressure observations are collected each hour across the U.S.
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Northwest region, encompassing mainly Washington, Oregon, and Idaho, roughly 1800 pressure
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observations are collected each hour from approximately 70 networks (Figure 1 from Madaus et
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al., 1013), compared to approximately 100 ASOS locations. As shown in that figure, even when
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large numbers of networks are combined, substantial areas, particularly in rural locations, have
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few pressure observations, and many observation locations only report once an hour.
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Fortunately, an approach for increasing radically the number and temporal frequency of surface
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pressure observations exists: the use of pressures from smartphones and other portable digital
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devices.
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Smartphone pressure observations
By combining these networks, tens of thousands of
Over the Pacific
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During the past two years a number of smartphone vendors have added pressure sensors,
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predominantly to Android-based phones and tablets/pads. The main reason for installing these
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pressure sensors was to identify the building floor on which the device is located.
Samsung
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began using pressure sensors in its popular Galaxy S III smartphone in 2012 and the sensor has
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remained in the Galaxy S IV released in 2013 (Figure 2). Pressure sensors are also available in
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other Android phones and pads, including the Galaxy Nexus 4 and 10, Galaxy Note, Xoom,
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RAZR MAXX HD, Xiaomi MI-2 and the Droid Ultra. According to industry analyst IHS
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Electronics and Media (isuppli.com), approximately 80 million pressure-capable Android
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devices were sold in 2012, with expectations of 160 and 325 million units for 2013 and 2014,
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respectively.
By 2015, isuppli.com estimates that well over a half-billion portable devices
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worldwide will have the capability for real-time pressure observation, including over 200 million
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in North America. There is the strong expectation that non-Android device vendors such as
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Apple will include pressure sensors in upcoming smartphones and tablets. Thus, the potential
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may well exist to increase the number of hourly pressure observations over the United States by
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roughly 10,000 times over the current availability from all accessible networks.
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Some insight into the potential availability and distribution of smartphone pressures is
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available from a map of the current U.S. coverage for the largest American cell phone network,
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Verizon (Figure 3).
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encompassing nearly the entire range of U.S. severe convective storms. Coverage over the
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western U.S. has gaps over the highest terrain and sparely populated desert areas, but is still
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extensive (covering perhaps 65% of the land area) and includes all the major West Coast
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population centers from Seattle to San Diego. The number of smartphone observations will also
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be dependent on population density, which is obviously greatest over the eastern U.S. and the
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West Coast.
Nearly all of the eastern two-thirds of the lower 48-states is covered,
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The accuracy and resolution of the pressure sensors in smartphones and tablets are
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surprisingly good.
Many of the current Android devices use the ST Microelectronics LPS331
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MEMS pressure sensor, which has a relative accuracy of +-.2 hPa and an absolute accuracy of
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+-2.6 hPa2. Such relative accuracy allows accurate determination of pressure change, the use of
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which is discussed later in this paper.
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The potential for large numbers of smartphone pressure observations has attracted several
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application developers that have created Android apps that collect smartphone pressures and
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positions (through GPS or cell tower triangulation).
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developed the pressureNet app for Android phones and tablets (http://www.cumulonimbus.ca/.
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Currently, they are collecting tens of thousands of surface pressure observations globally each
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hour and have made them available to the research community and others. A plot of the
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pressureNet observations at one time (2300 UTC August 13, 2013) over North America is shown
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in Figure 4. Although only about 10,000 smartphone pressure observations are available today
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through the pressureNet app, a small number compared to the millions of phones with pressure
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capabilities, there are still regions, such as the northeast U.S., with substantial observation
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densities.
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pressures to be reported; however, with the insertion of the pressureNet code into popular apps, it
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is expected that the number of smartphone pressures collected by pressureNet will increase by
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one or two orders of magnitude during the next year.
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pressure observations is OpenSignal Inc., which has created an application called WeatherSignal
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(http://weathersignal.com/).
One firm, Cumulonimbus Inc., has
Currently, smartphone owners must download the pressureNet app to allow their
Another group collecting Android
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details at http://www.st.com/st-webui/static/active/en/resource/technical/document/datasheet/DM00036196.pdf
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Motor vehicles offer another potential platform for acquiring high-density pressure
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observations. Solid-state atmospheric pressure sensors are found in most cars and trucks, which
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also measure ambient temperature for use in engine management computers (Mahoney et al.,
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2013). The main challenges for use of vehicle pressure observations are position determination
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(easily dealt with by GPS), real-time communication, and privacy issues. A number of auto
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industry analysts (e.g., Machina 20133) predict that most cars will have Internet connectivity by
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2020.
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Other smartphone weather observing capabilities
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Some smartphones, such as the Samsung Galaxy IV, have the capability to measure other
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environmental parameters such a battery temperature, humidity, magnetic field, and lighting
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intensity. Temperature and humidity measurements from smartphones are of far less value than
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pressure, since the dominant influence of the immediate environment (inside of a pocket or a
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building) produces readings that are unrepresentative of the conditions in the free air. However,
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a recent study found that with statistical training and correction using observed temperatures,
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large numbers of smartphone temperatures can be calibrated to provide useful measures of daily
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average air temperatures over major cities (Overeem et al., 2013). Related work has shown that
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the attenuation of the microwave signals between cell towers is sensitive to precipitation
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intensity, and that such information can be used to create precipitation maps that closely
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resemble radar reflectivitiy (Overeem et al., 2013b).
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Challenges in using smartphone pressure observations
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https://m2m.telefonica.com/m2m-media/m2m-downloads/detail/doc_details/530-connectedcar-report-2013#530-Connected%20Car%20Report%202013-english
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The value of smartphone pressures in support of numerical weather prediction can be
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greatly enhanced with proper calibration, pre-processing, and preselection. Gross range checks
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can reject clearly erroneous observations. Either pressure or pressure change can be assimilated
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by modern data assimilation systems. For pressure-change assimilation, only smartphones that
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are not moving should be used, something that can be determined from the GPS position and
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recorded pressures of the phones (vertical movement will generally produce far more rapid
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pressure changes than from meteorological changes).
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required to assimilate either pressure or pressure change. GPS elevations are available, but can
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have modest errors (typically +-10 meters, roughly equivalent to a 1 hPa pressure error, the
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typical error variance used in most operational data assimilation systems).
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collection of pressures in an area, it might reasonable to assume that the highest pressures reflect
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values on the first floor of a residence or in a vehicle, representing observations roughly 1-m
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above ground elevation.
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regions where models lack sufficient resolution to duplicate the observed pressure features,
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pressure observations in such areas should be rejected when model and actual terrain are
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substantially different (Madaus et al. 2013). Clearly, some experimentation will be required for
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developing algorithms that derive maximum value from smartphone pressures.
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What kind of weather forecasts could smartphone pressures help the most?
The elevation of the smartphone is
If one had a
Since it makes little sense to assimilate pressure observations in
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Although an ultra-dense network of smartphone pressure observations would
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undoubtedly positively impact general weather prediction, there are several phenomena for
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which they might be particularly useful. One major problem is forecasting the initiation of
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severe convection, with models needing to be initialized before any precipitation or radar echo is
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apparent. At such an early stage of development, subtle troughs, dry lines, convergence lines,
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and remnants of past cold pools can supply major clues about potential convective development,
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information that dense collections of smartphone pressures might well be able to provide. The
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example in the next section of this paper illustrates the great value of even a modest density of
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smartphone pressures for simulating a strong convective event. Forecasting the positions of
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fronts and major troughs, even a few hours in advance, can have large value for wind energy
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prediction since such features often are associated with sudden rapid ramp ups and ramp downs
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in wind energy generation.
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pressure observations can shift fronts in a realistic way that substantially improves short-term
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wind forecasts. High-resolution pressure observations might also aid in the initialization and
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monitoring of mesoscale troughing associated with downslope winds and leeside convergence
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zones.
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regarding approaching weather features, including the positions of offshore low centers and
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fronts.
As shown by Madaus et al. (2013) the assimilation of dense
Dense pressure observations along coastlines could provide significant information
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Even the densest portions of the U.S. surface observation network are generally too
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coarse to observe and initialize features on the meso-gamma (2-20 km) and smaller scales.
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Smartphone pressure observations may offer sufficient data to do so, particularly over the
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smartphone-rich regions of the eastern U.S. and West Coast.
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smartphone pressure observations is that they could be easily added in any location where power
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and cell-phone coverage is available.
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An example of assimilating smartphone pressures
An interesting advantage of
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Although the smartphone pressure acquisition is still at an early stage, with observation
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densities orders of magnitude less than what will be available in a few years, it is of interest to
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try some initial assimilation experiments to judge the impacts of even modest numbers of
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smartphone pressures.
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Cumulonimbus, Inc. were used to simulate an active convective event on the eastern slopes of
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the Washington Cascades that brought heavy showers and several lightning-initiated wildfires.
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For this experiment, an ensemble-Kalman filter (EnKF) data assimilation system, adapted from
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one provided by the UCAR Data Assimilation and Research Testbed (DART) program, was
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applied at 4-km grid spacing and used the Weather Research and Forecasting (WRF) model,
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V3.1). The ensembles (64 members) for these experiments were cycled every three hours from
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1200 UTC 29 June through 1200 UTC 30 June 2013. The impacts of smartphone pressures were
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examined for a three-hour period ending on 0300 UTC 30 June 2013.
To complete such a test, smartphone observations made available by
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Figure 5 shows both the surface pressures provided by the conventional ASOS network
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(blue squares) and the smartphone pressures (red dots) available at 0000 UTC 30 June 2013. A
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number of smartphone pressures were available over the eastern slopes of the Cascades, the
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region of strongest convection. The accumulated rainfall from the Pendleton, Oregon National
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Weatther Service radar (PDT) for the three hours ending at 0300 UTC 30 June (Figure 6a) shows
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substantial accumulation (up to approximately 32 mm) from intense convective cells. The
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University of Washington runs a real-time ensemble Kalman filter data assimilation system
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(RTENKF) that uses convention surface observations, radiosondes, ACARS aircraft
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observations, and satellite-based cloud/water vapor track winds (Torn and Hakim 2008).
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system, run on a three-hour update cycle, produced three-hour precipitation totals shown in
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Figure 6b. This modeling system did produce some convective showers over and to the east of
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the Cascades, but failed to duplicate the intensity of the lee-side showers and had considerable
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spread in convective locations. Figure 6c shows the result of adding the smartphone pressure
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observations (Figure 5) to the mix of observations used in the RTENKF system. With the added
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pressure observations, the ensemble system produced far more intense convective cells east of
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the Cascade crest, some with orientations and magnitudes more reminiscent of the observed than
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provided by the RTENKF system. In addition, more ensemble members agreed on the location
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of the most intense convection. This, of course, represents only one case, but suggests that
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assimilating smartphone pressures can both change and enhance short-term mesoscale forecasts.
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It is reasonable to expect that further increases in the number of pressure observations would
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provide additional improvements in convective and other forecasts.
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Looking towards the future
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During the next few years the number of smartphones with pressure sensors should
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increase into the tens of millions over North America and the hundreds of millions globally. If
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private sector firms or other organizations can develop the infrastructure to “harvest” these
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pressure observations in real time, there could be a substantial improvements in the quality of
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the initializations of high-resolution numerical weather prediction models and their subsequent
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forecasts for a wide range of important weather features, such as severe convection.
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research on the impacts of networks of surface pressure observations on mesoscale prediction
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(e.g., Wheatley and Stensrud 2010, Madaus et al. 2013) suggest that ensemble-based mesoscale
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data assimilation may offer an attractive approach to securing maximum benefit from
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smartphone and other pressure observations, but considerably more testing and experimentation
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is needed, including understanding the relative value of pressure and pressure change
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assimilation.
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smartphone pressures can enhance the value of these new observation sources. During the next
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decade a large number of pressure observations from vehicles will likely join the current
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Furthermore, better approaches for quality control and bias correction of
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smartphone collection as transportation platforms gain Internet connectivity. The combination of
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smartphone and vehicle surface pressure observations may well contribute to a substantial
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increase in our ability to describe and forecast the atmosphere at high resolution, with substantial
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economic benefits and the potential to save lives and property.
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Acknowledgments
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This research has been supported by the National Science Foundation under award AGS-
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1041879 and a NOAA CSTAR grant award NA10OAR4320148AM63. Professor Greg Hakim
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has been a major contributor to the UW effort in assimilating surface pressure observations.
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The pressure data for this work has been provided by Jacob Sheehy of Cumulonimbus, Inc.
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References
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Dirren, S., R. Torn, G. Hakim, 2007: A data assimilation case study using a limited-area
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ensemble filter. Mon. Wea. Rev., 135, 1455-1473.
Madaus, L. E., G. J. Hakim, and C. F. Mass, 2013: Utility of dense pressure observations for
improving mesoscale analyses and forecasts. Submitted to Wea. Forecast.
Overeem A., J. C. R. Robinson, H. Leijnse, G. J. Steeneveld, B. K. P. Horn, R. Uijlenhoet,. 2013:
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Crowdsourcing urban air temperatures from smartphone battery temperatures. J. Geo.
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Res., Accepted for publication. doi: 10.1002/grl.50786
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Overeem A., H. Leijnse, R. Uijlenhoet,. 2013: Country-wide rainfall maps from cellular
communication networks. Proc. Nat. Acad. Sci., 110, 2741–2745.
Wheatley, D. and D. Stensrud, 2010: The impact of assimilating surface pressure observations on
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severe weather events in a WRF mesoscale ensemble system. Mon. Wea. Rev., 138,
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1673-1694.
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Torn, R. D., and G. J. Hakim, 2008: Performance characteristics of a pseudo-operational
ensemble Kalman filter. Mon. Wea. Rev., 136, 3947—3963
Whitaker, J., G. Compo, X. Wei, T. Hamill, 2001: Reanalysis without radiosondes using
ensemble data assimilation. Mon. Wea. Rev., 132, 1190-1200.
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Figure Captions
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Figure 1: Surface pressure locations for a typical contemporary period (0000 UTC November 10,
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2012 through 2100 UTC December 10, 2012) from roughly 70 networks over the Pacific
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Northwest. Figure from Madaus et al., (2013).
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Figure 2: The Samsung Galaxy S4 is one of several Android phones with high-quality pressure
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sensors.
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Figure 3: Verizon cell phone coverage map on 8/23/2013. Dark areas indicate enhanced digital
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coverage
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Figure 4: Smartphone pressure observations for the hour ending 2300 UTC August 13, 2013.
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Data provided by Cumulonimbus, Inc.
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Figure 5: Smartphone pressure observations (PNET) and pressure measurement sites from
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ASOS observation locations (METAR) at 0000 UTC 30 June 2013.
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Figure 6: Three-hour precipitation from the Pendelton (PDT) radar, as well as ensemble means
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from the University of Washington real-time ensemble-Kalman filter system (RTENKF) and an
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EnKF system using only pressures from smartphones for a three-hour period ending at 0000
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UTC 30 June 2013.
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Figures
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Figure 1: Surface pressure locations for a typical contemporary period (0000 UTC November 10,
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2012 through 2100 UTC December 10, 2012) from roughly 70 networks over the Pacific
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Northwest. Figure from Madaus et al., (2013).
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Figure 2: The Samsung Galaxy S4 is one of several Android phones with high-quality pressure
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sensors.
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Figure 3: Verizon cell phone coverage map on 8/23/2013. Dark areas indicate enhanced digital
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coverage.
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Figure 4: Smartphone pressure observations for the hour ending 2300 UTC August 13, 2013.
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Data provided by Cumulonimbus, Inc.
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Figure 5: Smartphone pressure observations (PNET) and pressure measurement sites from
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ASOS observation locations (METAR) at 0000 UTC 30 June 2013.
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Figure 6: Three-hour precipitation from the Pendelton (PDT) radar, as well as ensemble means
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from the University of Washington real-time ensemble-Kalman filter system (RTENKF) and an
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EnKF system using only pressures from smartphones for a three-hour period ending at 0000
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UTC 30 June 2013.
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