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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Project using unmanned aerial systems starts May 8

Meteorologists are always looking for better ways to measure the lower atmosphere. This spring, researchers from NOAA’s National Severe Storms Laboratory (NSSL) will join with others to test the value of airborne, mobile observing systems for observing important changes in the local environment that can spawn severe thunderstorms in a new way. EPIC, the Environmental Profiling and Initiation of Convection Field Project, will deploy fixed-wing and rotary small Unmanned Aircraft Systems (UAS) May 8 through 20 at and near the Department of Energy’s Southern Great Plains (SGP) site in Lamont, Oklahoma.

During rapidly evolving severe weather conditions, the instruments will provide detailed profiles of temperature, moisture and winds to determine the potential for severe weather development. Such information has the potential to improve the accuracy of short-term weather forecasts three to six hours before weather impacts a community.

During the project, scientists will test miniaturized, high-precision, and fast-response atmospheric sensors adapted for use on the UAS. These are expected to have high accuracy in the strong winds they expect to encounter in north central Oklahoma.

The data provided by the instruments we’re testing is different from anything available, including satellites, radars, manned aircraft, and ground observing stations. We don’t yet know the value of UASs to monitor the atmosphere.

Colorado University’s TTwistor will be used in EPIC. (Photo provided)


At the SGP site, researchers will conduct short-duration experiments and a second site will be chosen in “real-time” from the Oklahoma Mesonet. Timing and location of activities will be coordinated with the National Weather Service Norman Forecast Office, which will be receiving data from the instruments in real time for evaluation.

EPIC is a collaborative effort funded by NOAA’s UAS Program Office.  NSSL’s partners in EPIC consist of the University of Colorado, The University of Oklahoma, and Meteomatics.

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April 27 Reddit AMA: Tornado! Severe Weather Research & Prediction with NOAA

Spring has arrived and with it come efforts to study and learn to better predict severe weather like tornadoes. Join NOAA for a Reddit Ask Me Anything (AMA) on severe weather research and prediction on April 27, 2017.

Patrick Marsh, Adam Clark, Kim Klockow and Harold Brooks will take your questions during Thursday’s #Reddit AMA.

Severe weather touches every state in the U.S. Tornadoes, severe thunderstorms, hail, strong winds, and floods are real threats to our property and our lives. The NOAA Hazardous Weather Testbed and VORTEX-SE (Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast) are designed to learn more about storms, helping to improve our prediction abilities and bring you better forecasts.

At the National Weather Center, which houses NOAA’s National Severe Storm Laboratory (NSSL) and Storm Prediction Center, as well as the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), our scientists work to better understand and predict severe weather to help everyone be prepared.

Reddit AMA Details


     Harold Brooks, NOAA NSSL research meteorologist

     Kim Klockow, UCAR scientist at CIMMS

     Adam Clark, NOAA NSSL research meteorologist

     Patrick Marsh, NOAA SPC warning coordination meteorologist

When: Thursday, April 27, 2017, from 9:00 a.m. to 11:00 a.m. CT

Where: Reddit Science AMA series

About the Scientists

Harold Brooks, a senior scientist in the Forecast Research and Development Division of NOAA NSSL, is originally from St. Louis, Missouri. He received a Ph.D. in atmospheric science in 1990 from the University of Illinois at Urbana-Champaign. He joined NSSL in 1991 as a research meteorologist specializing in tornado climatology.

Adam Clark is a meteorologist with NOAA NSSL and a 2014 Presidential Early Career Award for Scientists and Engineers (PECASE) winner. Originally from Des Moine, Iowa, Clark received his Ph.D. in meteorology and started working at NSSL in 2009. Clark is active in the NOAA Hazardous Weather Testbed, which conducts experiments mainly late March and April.

Kim Klockow is a University Corporation for Atmospheric Research (UCAR) project scientist at NOAA’s Cooperative Institute for Mesoscale Meteorological Studies at The University of Oklahoma who earned her Ph.D. in Human Geography. Working with the NOAA National Severe Storms Laboratory, her research involves behavioral science focused on weather and climate risk, and explores the effects of risk visualization on judgment and perceptions of severe weather risk from a combination of place-based and cognitive perspectives.

Patrick Marsh is a warning coordination meteorologist at the NOAA National Weather Service’s Storm Prediction Center, which provides forecasts and watches for severe thunderstorms and tornadoes over the contiguous United States. He was born in Georgia but grew up in Arkansas and received his Ph.D. at the University of Oklahoma.

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Collaboration improves UK and US radar techniques to improve forecasts

The national weather radar system used throughout the United States by NOAA National Weather Service  forecasters to “see” weather across the country is unique because it can be upgraded and modified with the newest capabilities, unlike other systems worldwide.

Because of this, and the need to work with experts in radar signal processing for improving the quality of radar data, international partners from the United Kingdom Met Office are collaborating with researchers from The University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies at the NOAA National Severe Storms Laboratory to develop new techniques for U.K.-based radars.

The U.K. Met Office operated a radar system that did not allow changes and was considered a “commercial off-the-shelf solution.”

“Most weather services in the world purchase radar systems from companies and in those systems, the signal processor is typically a black box,” said Sebastian Torres, senior research scientist with OU CIMMS and NSSL. “The signal processor is a key component in all weather radar systems. Its job is to convert echoes received by the radar into weather images. It’s something most weather services don’t really have access to. They know how it works but they can’t change or improve anything.”

The U.K. Met Office decided to build its own signal processor for their radar systems. This allows a similar degree of flexibility to that of the NEXRAD radars, also known as the WSR-88D (weather surveillance radar-88 Doppler), operated in the United States. NOAA offered some of its tested techniques to the U.K. Met Office and in return received access to valuable data it could use for future research and operations.

Inside every NEXRAD radar is a rotating parabolic antenna. As the antenna rotates, it travels up and around while sending out pulses of electromagnetic energy. When radars send and receive these pulses, buildings and other structures may obstruct the radar’s view, contaminating the storm data.

To help keep unwanted objects from impacting storm data, Torres and fellow CIMMS Researcher David Warde developed two complementary signal-processing techniques for the WSR-88D. One technique, called CLEAN-AP, or Clutter Environment Analysis using Adaptive Processing filter, removes unwanted radar echoes from objects on the ground. The other one, called WET or Weather Environment Thresholding, intelligently decides when the CLEAN-AP filter should be applied. This prevents slow-moving storms from being confused with stationary objects.

NSSL and CIMMS researchers Sebastian Torres and David Warde (second and third person from the left) visited the UK Met Office in Exeter from February 22-26, 2016 to support implementation of CLEAN-AP on the UK weather radar network.


“The goal of CLEAN-AP and WET is to clean the data as much as possible so the forecasters have the best data available to make warnings and forecasts,” Torres said.

Through collaboration with the U.K. Met Office, who implemented CLEAN-AP and WET, the techniques were fine-tuned and improved. Both techniques are being transferred to the NOAA National Weather Service, and CLEAN-AP is licensed by OU to U.S. weather radar manufacturer Baron.

CLEAN-AP before and after


Another CIMMS Researcher, Igor Ivic, developed a third product transferred to the U.K. called the Radial-by-Radial Noise Estimator. RBRN  improves the quality of radar data by removing “noise,” the radar equivalent of radio static or television static. It was implemented on the U.S. NEXRAD network as part of ongoing research-to-operations efforts at NSSL and CIMMS.

“If you have noise and you can remove it from the radar returns, then you get just the signal, and that can be used to get better quality data,” Torres said.

Torres called the collaboration a “win-win” situation because the information exchange, as well as the new technologies and techniques that have been developed are good for both the U.S. and U.K.

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Gab at the Lab: April 14, 2017

Gab at the Lab is Friday, April 14, 2017.

Speakers include Junjun Hu with FRDD, Swapan Mallick with FRDD, Amanda Murphy with RRDD, Humerto Vergara with WRDD, and Sean Waugh with WRDD

Gab at the Lab is from 10 to 11 a.m. in Room 3910.

Learn about your coworkers and who they are, not just what they do!


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Significant papers

Significant Papers reported to HQ for the week ending April 7. The links to each are in blue.

1. MacGorman, D. R., M. S. Elliott, E. DiGangi (2017). Electrical discharges in the overshooting tops of thunderstorms. Journal of Geophysical ResearchMarch, 2017.

Significance: Observations are consistent with the reason for the small size and continual occurrence of discharges in overshooting tops being that small distinct concentrations of charge of one polarity are continually brought near concentrations of charge of the opposite polarity throughout much of the overshooting top. Having opposite polarities of particle charging across a relatively short horizontal span of strong gradients in the updraft may also contribute to the very large rate of small flashes typically observed around the updraft of supercell storms.”

2. Argyle, E. M., J. J. Gourley, C. Ling, R. L. Shehab, Z. Kang (2017). Effects of display design on signal detection in flash flood forecasting. International Journal of Human-Computer Studies, March 2017.

Significance: “A sample of 30 participants viewed a series of stimuli created from FLASH images and were asked to judge whether or not they predicted significant or insignificant amounts of flash flooding. Analyses revealed that choice of aggregation method did affect probability of detection. Additional visual indicators such as geographic scale of the stimuli and threat level affected the odds of interpreting the model predictions correctly as well as congruence in responses between national and local scale model outputs.”


3. Alappattu, D. P., Q. Wang, J. Kalogiros, N. Guy, D. P. Jorgensen (2017). Variability of upper ocean thermohaline structure during a MJO event from DYNAMO aircraft observations. Journal of Geophysical Research: Oceans, Feb. 2017.

Significance: Atmospheric perturbations associated with MJO have profound impact on the upper ocean thermohaline structure and variability. Reduction in incoming solar radiation, precipitation produced freshwater influx into the ocean surface, and strong surface winds during the active phase of MJO modify ocean mixed layer dynamics. This analysis, first of its kind from the data sparse and climatically important southern tropical Indian Ocean, provides a broad and clear picture of upper ocean response as well as the variability of thermohaline structure in different phases of MJO.”

4. Torres, S. M., D. A. Warde (2017). Staggered-PRT Sequences for Doppler Weather Radars. Part II: Ground Clutter Mitigation on the NEXRAD Network Using the CLEAN-AP Filter. Journal of Atmospheric and Oceanic Technology, March 2017.

Significance:Using simulations, the CLEAN-AP filter for staggered-pulse repetition time (SPRT) sequences was shown to meet NEXRAD requirements for clutter suppression. The performance of the filter was illustrated using two cases collected with the KOUN radar in Norman, by comparing the SPRT acquisition mode to two standards NEXRAD acquisition modes: split cut and batch. Performance observed for the data cases confirmed improvements can be realized operationally when the batch acquisition mode is replaced with the SPRT acquisition mode.”

5. Kerr, C. A., D. J. Stensrud, X. Wang (2017). Verification of Convection-Allowing Model Ensemble Analyses of Near-Storm Environments Using MPEX Upsonde Observations. Monthly Weather ReviewMarch, 2017.

Significance: Convective-scale model analyses are a suitable tool to assess the mesoscale feedbacks due to convective storms, which could affect further convection evolution. However, even more extensive near-storm observations are required to truly evaluate the impact of convection on its surrounding environment, particularly within inflow and outflow regions.”

6. Kingfield, D. M., K. M. de Beurs (2017). Landsat Identification of Tornado Damage by Land Cover and an Evaluation of Damage Recovery in Forests. Journal of Applied Meteorology and Climatology, April 2017.

Significance: During these spring and summer, these spectral signatures correspond to many tornado-damaged regions having higher Tasseled Cap brightness values as a result of a general increase in reflectance across most Landsat bands, lower Tasseled Cap greenness values driven by the decline in NIR reflectance, and lower Tasseled Cap wetness values due to the larger increases in SWIR reflectance compared to visible reflectance. While NDVI is beneficial at providing a cursory look at localized change caused by natural hazards, analyses of recovery using NDVI is limited to the acquisition of cloud-free, intraseason imagery.



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Probabilistic Hazard Information experiment completes second week

The NOAA Hazardous Weather Testbed,  a joint project of the National Weather Service and the National Severe Storms Laboratory, is buzzing with activity again as the Experimental Warning Program focuses on its second of three years of testing probabilistic hazards with Hazard Services software. The objective is to improve severe weather warnings.

During severe storms, NOAA National Weather Service forecasters draw what is commonly referred to as a polygon around the storm to create warnings, said Gabe Garfield, National Weather Service HWT liaison and researcher with the University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies.

“When we issue warnings at the NWS it shows up as a polygon box,” Garfield said. “When a tornado passes, you’re still in the box and you may be unsure if you can leave your shelter. That’s an issue. Everywhere within the polygon has equal warning even though it’s not equally probable everyone is getting that severe weather.”

One focus of the EWP’s annual Spring Experiment is the Hazard Services – Probabilistic Hazard Information experiment, HS-PHI. The testbed allows users to test technologies and methods while providing input to see if it is ready to be implemented in forecast offices. The HS-PHI experiment is designed to test forecasters’ ability to provide probabilistic information about severe storms utilizing new software that is being designed to  be used in the future by the NWS. 

This experiment is part of the National Severe Storm Laboratory’s Forecasting a Continuum of Environmental Threats, FACETs, an initiative aimed at improving the communication of hail, wind, and tornado hazards to save lives and property.  Instead of a single polygon, in the FACETs paradigm forecasters would create probabilistic hazard information “plumes.” New types of severe weather warnings can be derived from the plumes. which new types of severe weather warnings can be derived.  These include the traditional warnings that the public receives today, to special warnings for specific users that have a lower tolerance to severe weather and require longer lead times to take action.

“Imagine you’re in a storm and the probability of severe weather at the center of the storm is 100 percent,” Garfield said. “There’s a circle around that 100 percent area but right outside there, it is 90 percent likely in the direction the storm is moving and then further out it may go down to 50 percent as it gets further away because we don’t know if it will weaken.”

As the storm moves, the circle around it moves, too. When that circle moves away from a location,  to know when the storm has passed over their location and they are able to come out of shelter.

“In the past, when the forecaster issued a polygon, the storm may change direction and the forecaster did not have any mechanism to update that without creating some confusion,” Garfield said. “But, with the probabilistic hazard information plumes, we can actually have that information and modify the plumes in real-time.”

For officials with responsibility for weather sensitive populations, such as festival organizers and hospitals, this means more lead time when severe storms threaten and better information to use in planning for severe storms.

“The whole idea is to provide as much information as possible,” Garfield said. The purpose of HS-PHI is to develop and test a tool forecasters can use to convey the threat. The first version of this prototype was evaluated in the HWT in 2014.  The features of the prototype have been incorporated into the NWS version, and testing of this operational version, HS-PHI, is in its second year.

“As you might imagine, if you have one of the plumes, it would be really hard to always manually draw those probabilities in real-time, particularly if you have several severe storms going on at once,” Garfield said. “You won’t have enough time to manually resize all of these swaths. They’ve been trying to figure out the best machine automation versus human interaction. What’s the best algorithm used along with how much should humans actually tweak in the system?”

The EWP is testing experimental methods for improving the communication of severe weather threats, such as hail, wind, and tornadoes, using new software designed for the computer workstations that NWS meteorologists use today to forecast the weather.

This year, the HS-PHI Experiment is testing the probabilistic hazard information concept in a way that simulates how NWS forecasters would actually use it, within the Advanced Weather Interactive Processing System (AWIPS) software used by the NWS. NWS forecasters and human factors experts will evaluate the software design using several severe weather scenarios.

HS-PHI was developed by the National Severe Storms Laboratory with the National Weather Service and OAR’s Earth System Research Lab, and is in its second year of evaluation.

For more information, visit and

HWT EWP spring experiment HS-PHI in April 2017


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Award winner to present NOAA Hazardous Weather Testbed work on improving severe weather forecasts

NSSL research meteorologist Adam Clark will present his work on improving severe weather forecasts during NOAA Science Days in June.

Clark, a Presidential Early Career Award for Scientists and Engineers 2014 winner, is presenting on the 2016 Spring Forecasting Experiment, which included a new framework for evaluating  convection-allowing models known as the Community Leveraged Unified Ensemble.

Improved technology has allowed faster and more detailed experimental weather forecast models to be used in the NOAA Hazardous Weather Testbed, leading  to model improvements and increased collaboration with other academic and government research agencies. One by product of the increased collaboration was that an increasing number of convection-allowing models, or CAMs, were ingested into the HWT. This was good, but also created a problem — the models designed by different agencies could not be compared because of too many independent variables.

“In 2016, we coordinated across all the different agencies that contributed to this experiment and we decided we’re going to have everyone abide by a set of rules— a bunch of criteria for how everyone will run their modeling systems,” Clark said. The rules included using the same area to run their models, the same resolution, and the same amount of detail to depict storms.

“That way we were able to control as many variables as we could so we could say more about why the different systems worked they way they did,” Clark said. CLUE collaborators then designed experiments testing different aspects of the model configurations.

Collaboration with forecasters is key to the CLUE experiments.

“What drives what we do is being able to work with the forecasters and get their take,” he said. “To design a system that is useful, you have to get feedback from the end user, which is forecasters.”

Last year was the first for CLUE to be used in the Spring Forecasting Experiment. With a goal to make forecasts better, Clark said there would be another this spring, with hopes of building on previous years.

Adam Clark
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Researchers Begin Second Year of Tornado Study in Southeastern United States

The second field observing campaign for the Verification of the Origins of Rotation in Tornadoes EXperiment-Southeast (VORTEX-SE) research program, coordinated by the National Severe Storms Laboratory, began March 8 and continues through May 8. A media day will be held at 10 a.m. CDT March 21 at the Signature Flight Support – Huntsville International Airport. Researchers from NSSL, Air Resources Laboratory, University of Alabama – Huntsville and other participants will discuss their operational plans and show some of the vehicles and instruments they are using, including the NOAA P-3 aircraft, mobile radars and research drones.

VORTEX-SE is a research program designed to understand how environmental factors characteristic of the southeastern United States affect the formation, intensity, structure and path of tornadoes in this region. VORTEX-SE will also determine the best methods for communicating forecast uncertainty to the public and evaluate public response related to these events.

This year’s field project will gather data to address two main research topics:

1. How cold air flowing out of a storm influences the development of tornadoes.

2. The role of terrain in tornado formation and how terrain influences wind, temperature and humidity in storm environments.

The ultimate purpose of this research is better forecasts and warnings for the public.
Erik Rasmussen, VORTEX-SE project manager, speaks during media day in 2016, kicking off the spring 2016 field research campaign. Media day for VORTEX-SE’s 2017 spring field research campaign is Tuesday, March 21.
Credit NOAA/Keli Pirtle.
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