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mPING: Crowd-Sourcing Weather Reports for Research
Kimberly L. Elmore1
Cooperative Institute for Mesoscale Meteorological Studies, University of OklahomaNOAA/National Severe Storms Laboratory
Norman, Oklahoma
Z. L. Flamig
University of Oklahoma School of MeteorologyNorman, Oklahoma
V. Lakshmanan1
Cooperative Institute for Mesoscale Meteorological Studies, University of OklahomaNOAA/National Severe Storms Laboratory
Norman, Oklahoma
B. T. Kaney1
Cooperative Institute for Mesoscale Meteorological Studies, University of OklahomaNOAA/National Severe Storms Laboratory
Norman, Oklahoma
V. Farmer
INDUS Corp.Norman, OK
and
Lans P. Rothfusz
NOAA National Severe Storms LaboratoryNorman, OK
Submitted to:
Bulletin of the American Meteorological Society
April 24, 2013
1. Also affiliated with the NOAA/National Severe Storms LaboratoryCorresponding author address: Dr. Kimberly L. Elmore, NSSL, 120 David L Boren Blvd, Norman, OK 73072. Email: [email protected]
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ABSTRACT
The Weather Service Radar 88 Doppler (WSR-88D) network within the United States has
recently been upgraded to include dual-polarization capability. One of the expectations that have
resulted from the upgrade is the ability to discriminate between different precipitation types in
winter precipitation events. To know how well any such algorithm performs, and whether new
algorithms are an improvement, observations of winter precipitation type are needed. Unfortu-
nately, the automated observing systems cannot discriminate between some of the more important
types. Thus human observers are needed. Yet, to deploy dedicated human observers is impractical
and unnecessary because the knowledge needed to identify the various precipitation types is com-
mon among the public. To most efficiently gather such observations would require the public to
be engaged as Citizen Scientists using a very simple, convenient, non-intrusive method. To
achieve this, a simple “app” called mPING (meteorological Phenomena Identification Near the
Ground) was developed to run on “smart” phones or, more generically, web-enabled devices with
GPS location capabilities. Using mPING, anyone with a smart phone can pass observations to
researchers at no additional cost to their phone service or to the research project. Deployed in
mid-December 2012, mPING has proven to be not only very popular, but also capable of provid-
ing consistent, accurate observational data.
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1. Introduction or Why is this such a great idea?
Late in 2011 a planned upgrade of the Weather Service Radar 1988 Doppler (WSR-88D)
radar network began in earnest (http://www.roc.noaa.gov/WSR88D/PublicDocs/DualPol/DPsta-
tus.pdf). This upgrade adds vertical polarization moments to the already-available horizontal
polarization moments. The over-arching focus for the dual-pol upgrade remains improvement in
quantitative precipitation estimation (QPE) and, in this, there has been some success (Cocks et al.
2011; Berkowitz et al. 2013). But dual-pol radar also offers far more capabilities, especially when
merged with environmental data.
Among these capabilities is the ability to help discriminate between different precipitation
species or types in winter weather. Precipitation type information is useful for various reasons.
For example, forecasters need knowledge of winter precipitation type because it helps inform
them whether or not the thermodynamic profiles are developing as expected. Winter weather pre-
cipitation type affects surface transportation support and road maintenance since precipitation
type affects decisions about whether to treat roads and, if treatment is needed, what process to use.
Aviation ground deicing operations are heavily affected by precipitation type, but certain types of
precipitation, e.g., ice pellets, also indicates freezing rain aloft and so flight conditions that should
be avoided. Electric utility infrastructure suffers during freezing precipitation events, which
means that knowledge of where freezing precipitation is occurring helps utilities plan how best to
maintain the power grid.
Within the suite of dual-pol algorithms fielded with the upgraded radars is the hydrome-
teor classification algorithm (HCA), which is intended primarily for QPE enlacement (Straka et
al., 2000; Berkowitz et al. 2013). Because the HCA is fundamentally intended to provide hydrom-
eteor type within the radar pulse volume, applying it to precipitation type at the surface has pre-
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dictably unsatisfactory results (Elmore, 2011). These results have also been noted by operational
meteorologists of the NWS and the broadcast media, with the strong desire for improved surface
HCA output expressed by both groups. To address the specific need for surface hydrometeor type
information in winter weather, the winter surface hydrometeor classification algorithm
(WiSHCA) is being developed. In order to both develop and also validate such algorithms, and
other dual-pol algorithms such as Lakshmanan et al. (2013), high-quality surface observations of
precipitation type are needed. The current automated observing systems do not provide informa-
tion about some types, such as ice pellets. Yet, these types have important operational ramifica-
tions. So, a better source of precipitation type data is needed.
People are ideal observers and observing precipitation requires no advanced education in
meteorology. The general public can distinguish between different forms of frozen precipitation
(e.g., snow vs. ice pellets) and the difference between non-freezing and freezing precipitation.
Because this knowledge is common, it seems only natural to use it. But to do so effectively
requires something that is available essentially anywhere. The new generation of web-enabled
portable devices (“smart” devices) offers the ideal platform.
To employ these devices requires an application, or “app” that reports back only the
required data. Because there are so many devices and potentially so many available observations,
the observations must be compact – free-form comments and photographs of precipitation fail in
this regard because of their sheer volume but also because photographs, in particular, cause an
enormous increase in required bandwidth. Another requirement is to use the device’s intrinsic
GPS location and time for tagging observations. Finally the app should keep the reporter anony-
mous to ensure privacy.
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2. Architecture
The meteorological Phenomena Identification Near the Ground (mPING) project origi-
nated in 2006 as a way to gather validation information to assess the performance of the HCA as a
surface precipitation type classifier (Elmore 2011). In its initial form, observations were entered
through a web page interface (Fig. 1). Observations were requested within a 150 km radius of the
KOUN testbed radar because, at the time, it was the sole WSR-88D-based dual pol prototype.
Users provided their latitude and longitude, based on either their own knowledge or through any
of a number of web-based geolocation services, the time of the observation and, through radio
buttons, the precipitation type. The resulting data were added to a large database system main-
tained at NSSL. While data collection through the web form continues, it has become clear that
with the nationwide dual pol upgrade to the WSR-88D, a more effective data gathering means is
needed.
This led to a program based on the Severe Hazards and Verification Experiment (SHAVE,
Ortega et al. 2009) wherein students actively probe areas of winter weather via telephone calls,
seeking observations of precipitation type. While successful, it became clear that targeting areas
of transitional precipitation types, such as mixes, freezing precipitation and ice pellets is not
straightforward; standard surface observations are inadequate; radar clues are ambiguous; and
such regions are relatively small and transient in nature.
One of us (Flamig) has substantial experience developing weather-based apps for iOS
devices and offered to help develop one that would support widespread, easy submission of pre-
cipitation type observations. The iOS development of mPING and the Android version are func-
tionally identical, but follow different operating system guidelines and so look very different. So
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far, apps exist only for the iOS and Android platforms, as these make up about 80% of the devices
currently in use. Versions for other platforms may be developed in the future.
Among the key features of mPING are immediate feedback to users that their submission
has been accepted and the ability to display and even download all submissions using a web-
based display (viewable from within the apps). Up to 24 hours of reports from across the conti-
nental United States and for any day back to November 2006 can be displayed. While users
remain anonymous, the report density and frequency is such that when the display is centered on
the users location and magnified (zoomed in), individual reports are easily seen when they appear.
The display can be seen using a desktop browser at http://www.nssl.noaa.gov/projects/ping/dis-
play/ (Fig. 2). A simplified display is used for mobile devices (http://www.nssl.noaa.gov/projects/
ping/display/phone.php).
3. Considerations
We paid particular attention to simplicity. The user interface had to be very simple (Fig.
3), and data entry had to also be simple and intuitive, not because users lack sophistication, but
because the app must remain unobstrusive. Users are extremely concerned about battery life, so
the app has to be “smart” about the way it uses the GPS engine, which is a significant power
drain. To both avoid confusion and to standardize the various types that can be reported, users
choose from a limited number of precipitation types with a pull-down menu (Fig. 4). These types
are: Test, None, Hail, Rain, Drizzle, Freezing Rain, Freezing Drizzle, Snow, Wet Snow, Mixed
Rain and Snow, Mixed Rain and Ice Pellets, Mixed Ice Pellets and Snow, Ice Pellets/Sleet and
Graupel/Snow Grains. For Hail only, an additional parameter (size to the nearest 0.6 cm) is also
required. Location and observation time (in UTC) are gathered from the device’s internal GPS
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engine. Thus, only the precipitation type is provided by the user; all else is automatic. The
WiSHCA research at NSSL is focused exclusively on precipitation type so no intensity estimates
are requested.
To avoid rapid, inadvertent data submission while the device is being carried in a pocket
or purse, a 5 minute lockout timer is enforced so that observations can be entered at most every 5
min. The 5 minute lockout timer also suppresses malicious attempts to rapidly enter misleading
data.
Both the mobile apps and the web page submit information via HTTP to a common data-
base that validates user input (to prevent malicious attacks, but not to quality-control the observa-
tions) and provides persistent storage of the public reports. All quality control is done in post
processing. We have so far found that these crowdsourced data are very high quality. It is clear to
us that the vast majority of entries are made with the best intentions. Even so, mistakes occur and
the occasional misleading report appears. Fortunately, misleading reports in particular are very
obvious, e.g., 20 cm hail reports in the absent any convection, rain in midst of large-scale snow,
reports of precipitation in areas known to be clear, etc.
4. What we have learned and where we may go next
We have become convinced that immediate feedback to the user is very important and fig-
ures largely in the success of the mPING app. Not only are users rewarded by seeing that their
data are actually being ingested, but feedback from users expresses an overall increased interest in
weather and the project by simply watching the reports as they come in and change with time. In
addition, the data are open and publicly available for text download in 24 h increments via the
main display web page.
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When these apps were initially released, the announcement was limited to only social
media, i.e., Facebook™, Twitter™, etc. The formal press release occurred much later, on 6 Feb
2013. Yet, we found that once mention was made within social networks, word spread rapidly
about both the apps and the mPING project among those who are interested in weather but are not
necessarily professional meteorologists; evidence is apparent in the download history of both the
iOS and Android versions of the app (Fig. 5) and in the ~209,000 reports recieved between 19
December 2102 and 23 April 2013 (Fig. 6, Table 1). During this time, we have occasionally seen
occasional locations around cities become very active within the span of about an hour following
mention of the app and project, often in cooperation with a local National Weather Service Fore-
cast Office. Even before the formal media announcement, several media articles were published
about mPING and at least one favorable editorial (http://idealab.talkingpointsmemo.com/2013/
02/mping-noaa-storm-app.php, http://www.npr.org/blogs/alltechconsidered/2013/02/25/
171715999/this-app-uses-the-power-of-you-to-report-the-weather, http://www.bostonglobe.com/
editorials/2013/02/08/the-folks-behind-national-weather-service-are-now-crowdsourcing-nemo/
5MD65k88EfDUA30iV8DY3K/story.html).
Among the informal comments made on various social networks and in e-mails to the
authors, users find two favorable characteristics that stand out. In no particular order, the first is
the simplicity of the interface. Users appreciate the simplicity and how quickly they can enter
observations and then be about their business. The second is immediate, uncluttered feedback,
which is is typically mentioned as being very important to users because it both satisfies their
basic curiosity and helps retain their interest, even when weather is not occurring in their immedi-
ate vicinity. Both of these characteristics, taken together, may constitute a fundamental require-
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ment for future efforts like mPING. The simple observation entry interface avoids tedium and
immediate feedback keeps user’s interest.
We suspect that allowing users to submit a “test” report and then see the report appear on
the real-time display satisfies a reasonable desire to use and test the app immediately upon instal-
lation. Test report submission also strengthens users’ confidence that the app does what is
claimed. While we have no proof, we also suspect that the ability to submit “test” reports helps
users resist the temptation to falsely report precipitation to test the app and see a report when no
precipitation is occurring.
Finally, even though the vast majority of observers are not trained in meteorological
observations, we find that the observations appear to of remarkably consistent and of high quality.
In several instances, professors of meteorology in regions experiencing complex winter precipita-
tion, such as ice pellets, freezing precipitation, or mixed precipitation have been polled. In every
case, these trained meteorologists validate the reports that are nearest to them in both time and
space (Reeves, personal communication).
The app itself is not static: enhancements are forthcoming and additional platforms will be
added. These data will prove to be invaluable for the development of precipitation type algorithms
that work with the upgraded dual-pol WSR-88D radars and also for hail-size algorithms planned
for the WSR-88D dual pol radars. These data will also no doubt be useful for additional studies
and works, including (but not limited to) precipitation type algorithms for numerical models,
ground icing for road maintenance and aviation operations, and even aviation in-flight icing work.
Precipitation type and hail size reports received via mPING will also be relayed electronically and
in real-time to NWS field offices so as to provide data that otherwise can be challenging to collect
via standard communication methods and difficult to discern using remote sensing technologies.
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5. Acknowledgements
This work was supported by the NEXRAD Product Improvement Program, by NOAA/
Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Cooperative
Agreement NA17RJ1227, U.S. Department of Commerce and the University of Oklahoma Coop-
erative Institute for Mesoscale Meteorological Studies. The statements, findings, conclusions, and
recommendations are those of the authors and do not necessarily reflect the views of NOAA, the
U.S. DOC, or the University of Oklahoma.
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References
Berkowitz, D. S., J. A. Schultz, S. Vasiloff, K. L. Elmore, C. D. Payne, and J. B. Boettcher, 2013:
Status of dual pol QPE in the WSR-88D network. 27th Conf. on Hydrology, Austin, TX.,
6 pp.
Cocks, S. B., D. S. Berkowitz, R. Murnan, J. A. Schultz, S. Castleberry, K. Howard, K. L. Elmore,
and S. Vasiloff, 2012: Initial assessment of the dual-polarization quantitative precipitation
estimate algorithm's performance for two dual-polarization WSR-88Ds. 28th Conf. on
Interactive Information Processing Systems (IIPS), New Orleans, LA, 15 pp.
Elmore, K. L. 2011: The NSSL hydrometeor classification algorithm in winter surface precipita-
tion: Evaluation and future development. Wea. and Forecasting, 26, 756–765.
Lakshmanan, V., C. Karstens, J. Krause, and L. Tang, 2013: Quality control of weather radar data
using polarimetric variables, J. Atm. Ocea. Tech., In Review.
Ortega, K. L., T. M. Smith, K. L. Manross, K. A. Scharfenberg, A. Witt, A. G. Kolodziej, J. J.
Gorley, 2009: The severe hazards analysis and verification experiment. Bull. Amer.
Meteor. Soc., 90, 1519–1530.
Straka, J. M., D. S. Zrnic, A. V Ryzhkov, 2000: Bulk hydrometeor classification and quantifica-
tion using polarimetric radar data: synthesis of relations. J. of Appl. Meteorology, 39,
1341–1372.
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Tables
Table 1. mPING reports received starting 19 Dec 2012 and ending 23 April 2013.
Table 1.
Type Number
Test 13188
None 45348
Rain 38713
Drizzle 17485
Freezing Rain 3234
Freezing Drizzle 1909
Snow 50470
Wet Snow 13474
Ice Pellets 5574
Graupel 4091
Rain and Snow mixed 6533
Rain and Ice Pellets mixed
4644
Snow and Ice Pellets mixed
4123
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Figure Captions
FIGURE 1. The original web page interface used to enter mPING observations. Submissions had to
include the observer’s location in decimal latitude and longitude, as well as time and time zone.
Precipitation type is selected via radio buttons.
FIGURE 2. Display of all observations submitted in a single hour spanning 0000 UTC to 0100
UTC on 22 Feb 2013. The display can loop over selected periods showing the spatiotemporal pro-
gression of precipitation and precipitation type. In addition, a rectangle can be created by a mouse
click-and-drag operation such that any subregion can be zoomed and displayed. Text versions of
reports, accurate to two decimal places, can be displayed in a new tab using the “Text Reports”
button and saved with a cut-and-paste operation.
FIGURE 3. The mPING interface is shown for the iOS™ systems on the left and the Android™
systems on the right. The interfaces are kept intentionally simple and relatively uncluttered.
FIGURE 4. Precipitation type choice is made via a drop-down list or menu. Users simply select the
observed precipitation type, at which point the app returns to the submit page. Two taps of the
screen are all that is needed to submit an observation once the app is opened,. An extra tap is
needed for hail because the user must select the (preferably measured) hail size using a slider bar.
FIGURE 5. Download history for the combined iPhone and Android mPING versions. Increases in
download rates are typically the result of media attention. In particular, the increased download
rates spanning 6 Feb 2013 through 13 Feb 2013 are due to the NOAA press release followed on
12 Feb 2013 by a brief feature on the National Public Radio “All Things Considered” newscast.
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FIGURE 6. Spatial distribution of mPING observations submitted between 19 Dec 2012 and 23
April 2013. Dueto poulation distribution, coverage over the eastern half of the CONUS is much
better thna the western half. Reports that appear outside of the CONUS are legitimate.
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FIGURE 1. The original web page interface used to enter PING observations. Submissions had to include theobserver’s location in decimal latitude and longitude, as well as time and time zone. Precipitation type is selected viaradio buttons.
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FIGURE 2. Display of all observations submitted in a single hour spanning 0000 UTC to 0100 UTC on 22 Feb 2013.The display can loop over selected periods showing the spatiotemporal progression of precipitation and precipitationtype. In addition, a rectangle can be created by a mouse click-and-drag operation such that any subregion can bezoomed and displayed. Text versions of reports, accurate to two decimal places, can be displayed in a new tab usingthe “Text Reports” button and saved with a cut-and-paste operation.
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FIGURE 3. The mPING interface is shown for the iOS™ systems on the left and the Android™ systems on the right.The interfaces are kept intentionally simple and relatively uncluttered.
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FIGURE 4. Precipitation type choice is made via a drop-down list or menu. Users simply select the observed precipita-tion type, at which point the app returns to the submit page. Two taps of the screen are all that is needed to submit anobservation once the app is opened,. An extra tap is needed for hail because the user must select the (preferably mea-sured) hail size using a slider bar.
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FIGURE 5. Download history for the combined iPhone and Android mPING versions. Increases in download rates aretypically the result of media attention. In particular, the increased download rates spanning 6 Feb 2013 through 13Feb 2013 are due to the NOAA press release followed on 12 Feb 2013 by a brief feature on the National Public Radio“All Things Considered” newscast.
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FIGURE 6. Spatial distribution of mPING observations submitted between 19 Dec 2012 and 23 April 2013. Coverageover the eastern half of the CONUS is mucg better thna the wetsren half, a result of population distribution.