mPing

Name: mPing
Author: University of Oklahoma

University of Oklahoma

VALIDATED

Initial evidence from research studies

For:Researchers & AcademicsGeneral Public & Enthusiasts

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App Summary

mPing is a citizen science app that enables the public to submit ground-level observations of precipitation type, providing crucial data for NOAA researchers working to refine weather radar algorithms and forecasting models. The associated research demonstrates that these public reports provide consistent, accurate data that can be used to validate numerical weather models, revealing systemic biases such as over-forecasting snow and under-forecasting ice pellets. The authors conclude that these citizen science observations are vital for assessing and improving the performance of forecast models, particularly in areas lacking traditional automated sensors.

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Detailed Description

Functionality & Mechanism

Developed by the NOAA National Severe Storms Laboratory, mPing is a citizen science platform for collecting ground-level precipitation data. The application leverages a device's GPS to geolocate user reports. The interface facilitates the rapid submission of observations by prompting users to select the current precipitation type from a predefined list, including rain, snow, freezing rain, and hail. For hail, the system also captures an estimated size. These geolocated reports are transmitted for meteorological research.

Evidence & Research Context

The platform was developed to provide human-observed, ground-truth data for validating and refining dual-polarization NEXRAD radar precipitation-type detection algorithms.
The associated research describes the crowd-sourced data as consistent and accurate, filling observational gaps where automated surface sensors are unavailable or insufficient.
Data collected via mPing has been utilized to evaluate the accuracy of numerical weather prediction models, including the North American Mesoscale Forecast System.
An analysis using these citizen science reports facilitated the identification of model biases, specifically an over-forecasting of snow/rain and an under-forecasting of freezing rain/ice pellets.

Intended Use & Scope

mPing is intended for the general public, educators, and students acting as citizen scientists to contribute to meteorological research. Its sole function is to collect user-submitted observations of ground-level precipitation. The application does not provide weather forecasts, warnings, or personal safety alerts. Users must consult official sources for operational weather information and guidance.

Studies & Publications

2 publications

Peer-reviewed research associated with this app.

Validation Study

Verifying Forecast Precipitation Type with mPING

Elmore et al. (2015) · Weather and Forecasting

Successfully validated crowd-sourced observations for assessing precipitation type forecast accuracy.

In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

Development/Design Paper

mPING: Crowd-sourcing weather reports for research

Elmore et al. (2014) · Bulletin of the American Meteorological Society

Describes the research-driven development of this app

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among 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. Unfortunately, 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 because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple "app" called mobile Precipitation Identification Near the Ground (mPING) was developed to run on "smart" phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smartphone 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 providing consistent, accurate observational data.

In the Media

mPING awarded for help in operational meteorology

NOAA's National Severe Storms Laboratory and the University of Oklahoma developed mPING to improve weather forecasting by significantly increasing ground-truth weather observations from citizen scientists. The National Weather Association awarded the mPING team the Larry R. Johnson Special Award "for creating the mPING applications which improved forecast operations by significantly increasing the number, quality, and type of ground-truth weather observations." The free app was featured in Scientific American's list of 8 Apps That Turn Citizens into Scientists.

NoaaRead article

Weather Reports from Citizens Provide Research Input

NOAA's National Severe Storms Laboratory developed mPing to gather ground-level weather reports from citizen scientists, using a free smartphone app that allows users to submit precipitation observations every 30 seconds. Research scientist Kim Elmore found that "people can clearly discriminate between freezing rain and rain and freezing rain and ice pellets," with studies showing only 17 percent of disagreeing observations were actually rain while 60 percent were ice pellets. The app helps researchers develop new radar technologies and enables the National Weather Service to fine-tune forecasts since weather radars cannot "see" conditions at ground level.

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NSSL Projects: mPING

The NOAA National Severe Storms Laboratory developed mPING to collect public weather reports for research, using a free smartphone app that allows users to report meteorological phenomena occurring at ground level. The app was created through a partnership between NSSL, the University of Oklahoma and the Cooperative Institute for Severe & High-Impact Weather Research & Operations, and was included in Scientific American's list of 8 Apps That Turn Citizens into Scientists. Weather radars cannot "see" at the ground, so NOAA uses mPING reports to fine-tune forecasts and develop new radar technologies.

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mPING Weather App Goes Global

Developers from NOAA's National Severe Storms Laboratory and the University of Oklahoma's Cooperative Institute for Mesoscale Meteorological Studies expanded mPING globally to allow citizen scientists worldwide to submit weather observations, announcing the upgrade during the American Meteorological Society's annual meeting in New Orleans. Since its December 2012 launch, mPING has received nearly a million weather reports on U.S.-based events, and "these are exciting times" as the improvements make the app "even more useful for researchers and forecasters as well as anyone who wants to know about the weather," said Kim Elmore, CIMMS research scientist leading the project. The updated interface now features multi-language support with 11 languages available and allows NOAA National Weather Service forecasters to access mPING observations directly on their office workstations.

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NOAA, University of Oklahoma introduce mPING app to help track weather as it happens

NOAA and the University of Oklahoma developed mPING to collect public weather reports through a smartphone app that helps meteorologists track precipitation as it happens at ground level. "It's nice to have an extra set of eyes in the field, and this app and the observations that come in can be really beneficial to meteorologists," said WNCT chief meteorologist Jerry Jackson. Scientists compare these field reports to radar detections to improve forecasting technologies since radars cannot "see" at ground level.

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NSSL Launches iPhone and Android Apps to collect precipitation reports from the public

The NOAA National Severe Storms Laboratory (NSSL), in partnership with the University of Oklahoma, launched mPing to collect anonymous precipitation reports from the public using iPhone and Android devices. NSSL researchers compare these reports with radar data to develop new radar and forecasting technologies that determine whether snow, rain, ice pellets, mixtures or hail is falling. NSSL hopes to build a valuable database of tens of thousands of observations from across the U.S.

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