mPING awarded for help in operational meteorology

The National Weather Association recently awarded the  mPING  development team for help the application has provided in meteorology.

The Meteorological Phenomena Identification Near the Ground – or mPING – team was awarded the Larry R. Johnson Special Award for significantly contributing to operational meteorology.

“For creating the mPING applications which improved forecast operations by significantly increasing the number, quality, and type of ground-truth weather observations.​”

NWA will present the award during its annual meeting in late August. Individuals or groups must be nominated for the Larry R. Johnson Special Award.

MPING is a project by NOAA’s National Severe Storms Laboratory and the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies. The free application was listed in Scientific American’s list of 8 Apps That Turn Citizens into Scientists.

View live reports and help NSSL and OU CIMMS researchers improve understanding of how different types of precipitation can be identified by radar.

mPING reports
mPING reports submitted by users.
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Weather Reports from Citizens Provide Research Input

Is it raining, snowing or hailing where you are? Tell us about it! Report the weather at your location any time on the mPING app to help NOAA researchers and forecasters, and join citizen scientists all around the world participating in Citizen Science Days through May 20.

Downloadable to your smartphone, mPING (Meteorological Phenomena Identification Near the Ground) is a free application that allows users to submit information about the weather to NOAA’s National Severe Storms Laboratory. Reports are immediately archived into a database at NSSL, and are displayed on a map accessible to anyone.

An mPING report. See more at

To use the app, reporters select the type of weather that is occurring, and tap “submit.” The anonymous reports can be submitted as often as every 30 seconds.

The main goal of mPING is to provide more information to researchers and forecasters about the weather affecting the public. As a bonus, that very same public can see these reports! Weather radars cannot “see” at the ground, so mPING reports are used by the NOAA National Weather Service to fine-tune their forecasts.

Reports from mPING are also helping NOAA researchers in a variety of ways, including to develop new radar and forecasting technologies and techniques, said Kim Elmore, research scientist with The University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies working at the NOAA NSSL.

The first goal of mPING was verifying the type of precipitation detected by new radar technology. But, data from mPING proved useful for things not originally envisioned. While checking the accuracy of reports, Elmore and his team learned more about the scale and variability of freezing rain.

One mistake people could reasonably make is calling freezing rain just rain, since freezing rain is rain until it freezes onto something, Elmore explained. But with disagreeing observations, Elmore said they found only about 17 percent of the observations were rain; but about 60 percent of the observations that disagreed with freezing rain were really ice pellets.

“What that tells us is people can clearly discriminate between freezing rain and rain and freezing rain and ice pellets, since they all agree on what ice pellets are,” Elmore said. This shows that people know what they are seeing and mPING reports are mostly accurate.

Digging a little deeper, we know that freezing rain can exist in only a very narrow set of environmental conditions. If the air near the ground gets only a little colder, the raindrops will freeze before they reach the surface and be reported as ice pellets. If conditions warm up only a degree or two near the ground, the temperature is no longer below freezing and there can be no freezing rain.

Reports from mPING also helped the team learn about one of the newest numerical weather prediction models called the Rapid Update model, or RAP, which is used for short term weather prediction. Data from mPING showed the old version of RAP did not properly identify ice pellets. Once the model developers learned about this, they immediately made changes to better forecast ice pellets.

For one winter season, NOAA’s Earth System Research Lab and the

 operated the old version (but with the ice pellet fix) and new RAP models at the same time. This was a perfect perfect opportunity to see how much better the new RAP system handled ice pellets. To test this, mPING reports were compared to RAP model forecasts.

“It turns out, ice pellets are reported far more often than the model forecasts them,” he said. “We reported on the fact the new version of RAP helps some — it is a small but statistically significant improvement.”

The mPING app was developed through a partnership between NSSL, the University of Oklahoma and the Cooperative Institute for Mesoscale Meteorological Studies and was included in Scientific American’s list of 8 Apps That Turn Citizens into Scientists. For more information on the application, or to watch a short video about it, visit

Scientists will continue to look for new ways to use the mPING data in their research, Elmore said. So keep those reports coming!

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Significant Paper: Using Citizen Science Reports to Evaluate Estimates of Surface Precipitation Type

mPING overlaid on MRMS

Using Citizen Science Reports to Evaluate Estimates of Surface Precipitation Type
Authors: Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, Zac Flamig
Journal: Bulletin of the American Meteorological Society
Publication Date: In Print 2/2016

Important Conclusions: Consistency in results from city to city give an indication that the citizen science reports of rain and snow from the meteorological Phenomena Identification Near the Ground app (mPING) provide useful information about the quality of the MRMS precipitation type algorithm. The MRMS surface precipitation type algorithm has a slight propensity to produce too much rain where there is snow; this suggests some modifications are needed to the temperature thresholds and motivates probabilistic approaches.

Significance: This is the first paper to comprehensively evaluate the MRMS rain-snow product using mPING crowd-sourced observations.

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mPING Weather App Goes Global

mPING app 2016
Available on any smart phone, the mPING app allows users to quickly and anonymously report weather at their current location.

Citizen scientists around the world, not just those in the United States, can now submit weather observations and view reports on the go using the newly upgraded mPING smart phone application. Developers from NOAA’s National Severe Storms Laboratory and the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies announced the app’s expanded reach and utility Monday during the American Meteorological Society’s annual meeting in New Orleans.

Since its launch in December 2012, mPING (meteorological Phenomena Identification Near the Ground) has received nearly a million weather reports on U.S.-based weather events including rain, snow, ice, wind, hail, tornadoes, floods, landslides, fog and dust storms. These reports are used to improve forecasts related to road maintenance, aviation operations and public warnings.

Now, users around the world and outside the continental U.S. can participate in mPING and see their reports. The updated interface is user-friendly and available globally. New features include multi-language support, with 11 languages currently available. Additionally, the app design has been refined for both iOS and Android devices, allowing for greater consistency and precision.

MPING 2016 display
The mPING app allows users to share weather observations and view reports from anywhere in the world.

Use of mPING data is expanding as well. NOAA National Weather Service forecasters now have access to mPING observations on their office workstations. This means NWS forecasters will be able to overlay mPING reports with other data such as radar and satellite observations to aid them in their decision-making.

The ability to submit and display in other, independent applications is now possible as well. Television stations and private weather companies have the opportunity to build the ability to submit and display mPING submissions in their own branded applications, making the information available to the public in new ways.

“These are exciting times! 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 working at NSSL, who leads the project with CIMMS scientists Jeff Brogden and Zac Flamig.

The mPING app has been cited as a successful example of citizen science. It was included in Scientific American’s list of “8 Apps That Turn Citizens into Scientists,” and the White House’s “Federal Citizen Science and Crowdsourcing Toolkit.” The official web page for mPING can be found here.

CONTACT: Keli Pirtle,, 405-203-4839

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Gab at the Lab: Kim Elmore

Kim Elmore, Research Scientist (OU CIMMS)





Background:B.S. Meteorology, University of Oklahoma
M.S., Ph.D. Meteorology, University of Oklahoma
Experience:Kim worked for the National Center for Atmospheric Research before coming to NSSL in 1995. During his 20+ years as a research scientist here, he has developed new radar techniques, statistical verification and extraction methods, and performed field work on various projects. Most recently, he has played an instrumental role in developing the mPING app for NSSL, which has received wide acclaim.
What He Does:The mPING app was developed as a crowd-sourcing tool for collecting weather reports. mPING stands for Meteorological Phenomenon Identification Near the Ground. The app provides an opportunity for individuals to immediately share their weather observations, which then become archived and publically accessible. This information helps NOAA’s National Weather Service to fine-tune their forecasts, and assists NSSL in developing new radar and forecasting technologies and techniques.
For More: Download mPING -

Check out mPING in the White House blog -
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NSSL/CIMMS researchers to present at AMS annual meeting

AMSlogo-compact-final.ashxNSSL and CIMMS staff are preparing to receive honors and present recent research at the 2014 American Meteorological Society Annual Meeting in Atlanta, Ga., February 1-6.

NSSL’s Doug Forsyth, retired Chief of the Radar Research and Development Division, has been elected an AMS Fellow and will be honored at the meeting.

Presentations and poster topics include the first real-data demonstration of the potential impact from an MPAR observing capability for storm-scale numerical weather prediction, using cloud top temperatures in numerical weather prediction models to forecast when thunderstorms will form, and crowdsourcing public observations of weather.  Real-time flash flood modeling, understanding forecasters’ needs to improve radar observations using adaptive scanning, and aircraft detection and tracking on the National Weather Radar Testbed Phased Array Radar will also be presented.

Preliminary analyses of research data collected during the 2013 May tornado outbreaks in Oklahoma will be a special focus at the meeting.

NSSL staff will also serve as session chairs.

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Latest weather radar research on display this week

Weather radar research is a key part of NSSL’s mission in support of the NOAA National Weather Service (NWS). This week, NSSL/CIMMS scientists will share the latest in weather radar research at the American Meteorological Society’s 2013 Conference on Radar Meteorology in Breckenridge, Colo.

Phased array radar research presentations include:

  • An overview of the latest improvements to the National Weather Radar Testbed
  • Phased Array Radar (NWRT PAR) capabilities to demonstrate Multi-function
  • Phased Array Radar (MPAR) program weather and aviation requirements
  • How NWS forecasters’ responded to rapid, adaptive phased array radar sampling and if it increased their ability to effectively cope with tough tornado
  • warning cases
  • New techniques to increase the NWRT PAR scan rate and reduce observation
  • times
  • NWRT PAR observations of microburst events
  • A method to detect and characterize storm merges and splits using rapidly updating NWRT PAR observations in thunderstorm models

NSSL/CIMMS researchers also work with current weather radars in operation and will present:

  • A new algorithm that combines output from a forecast model with dual-polarized radar data to more accurately estimate what winter weather is occurring between the lowest scan of the radar and the ground.
  • A study of how NSSL’s products that estimate precipitation amounts improved using dual-polarized radar data
  • Evaluation of existing hail size estimation algorithms
  • Crowdsourced reports precipitation types at the ground using the “meteorological Phenomena Identification Near the Ground” (mPING) smart phone app
  • Development of a database of U.S. flash flood events using NSSL’s Severe Hazards Analysis and Verification Experiment, and mPING reports
  • Improvements in radar wind data quality control

Other presentations include mobile radar observations of a tornadic supercell and rainfall in the Mediterranean region and airborne radar observations of precipitation in the Indian Ocean.

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FLOCAST: Flood Observations – Citizens As Scientists using Technology project

mPING mPING floodNSSL, CIMMS and University of Oklahoma researchers have launched a new project to collect public observations of flooding that will help improve flash-flood prediction and warning tools in the US.

The Flood Observations – Citizens As Scientists using Technology project (FLOCAST) will first use crowdsourced data about flooding and its severity collected through the already successful mPING (meteorological Phenomena Identification Near the Ground) app available on smart phones. Crowdsourced reports have the potential to provide a large and independent database flood events at fine spatial resolution.

The FLOCAST team will then target the local emergency management community, who tend to provide the most accurate and detailed reports of flooding, and ask them to respond to a 5-minute web-based questionnaire. As time permits, participants will provide details of the timing and location of flash flooding impacts in their areas of responsibility shortly following the event. They will also be able to submit a photo documenting the flooding event.

This same group of expert witnesses will be asked to identify victims, those directly impacted by the flooding, to volunteer their participation in a telephone interview. Researchers will use the information to better understand how society perceives, behaves and responds during flash-flood events, and improve the design, utility, and communication of information about impending flash floods to reduce loss of life.


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NSSL leverages new technologies in winter weather experiment

21Jan 2012
Dual-pol radar data of a winter precipitation event in New York.

NSSL and collaborators will leverage new technology including dual-polarized radar observations and a precipitation reporting mobile device app to improve forecasts of winter weather during February and March.

The experiment will evaluate the performance of new algorithms that use dual-polarized radar data and determine what new tools could be developed to improve detection of precipitation type and amount in winter storms.

The group will assess a new technique that is a “first-guess” of precipitation type using dual-pol data and compare it to observations collected from the Precipitation Identification Near the Ground mobile app and the Severe Hazards Analysis and Verification Experiment phone calls. They plan to identify potential biases and regions of poor performance.

They will also look at quantitative precipitation estimation products that include dual-polarized information and compare them to current products to see if dual-polarized data improves the result.

The experiment is a collaboration between NSSL, the Storm Prediction Center, the Norman Weather Forecast Office, the National Weather Service Warning Decision Training Branch and the Radar Operations Center.

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