NOAA Hazardous Weather Testbed starts with HS-PHI experiment

The first week of the Hazard Services – Probabilistic Hazard Information experiment in the NOAA Hazardous Weather Testbed at the National Weather Center in Norman, OK in 2018. This is the third year of the HS-PHI experiment in the HWT. (Photo by James Murnan/NOAA NSSL)

The NOAA Hazardous Weather Testbed is once again busy buzzing with activity as researchers kick off the year’s first research activities. Located in the National Weather Center in Norman, Oklahoma, the testbed is operated by the NOAA National Severe Storms Laboratory and the NOAA National Weather Service.

Starting this week, participants will assess a new tool using rapid-updating, high-resolution Probabilistic Hazard Information, known as PHI. From March 12-16,  April 2-6 and April 9-13 the Hazard Services – Probabilistic Hazard Information Experiment is testing an experimental concept for delivering information to the public in a way that simulates how National Weather Service forecasters would use it within their software.

“PHI will bring the public more specific weather information, but most importantly it will deliver severe weather information hours, rather than minutes before severe weather could become a threat,” said Alyssa Bates, University of Oklahoma cooperative institute and NWS Warning Decision Training Division researcher. “That will allow ample time for businesses, outdoor venues, and healthcare facilities to execute their severe weather preparedness plan.”

This experiment is one of many under the umbrella of  NSSL’s FACETs, Forecasting a Continuum of Environmental Threats project. FACETs is an initiative aimed at improving the communication of hail, wind, and tornado hazards to save lives and property.  Instead of a creating a warning area, in the FACETs paradigm forecasters would create probabilistic hazard information “plumes.” New types of severe weather warnings can be derived from the plumes. These include the traditional warnings 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.

“Thanks to thoughtful forecaster feedback in a well-constructed test environment, the software has become more stable over the last three years to the point that we can now focus on the more fundamental questions of the meaning of the probabilistic hazard information and how best to communicate it to our partners and the public” said Tracy Hansen, lead software engineer at NOAA’s  Earth System Research Laboratory Global Systems Division.

Participants in the first week include NWS forecasters from Albany, New York, and Tulsa, Oklahoma, as well as researchers from NOAA’s  ESRL and the University of Akron.

Second week participants from April 2-6 include NWS forecasters from Texas and Guam, as well as researchers from NOAA’s ESRL GSD, University of Akron, OU CIMMS and NOAA NWS Warning Decision Training Division.

Third week participants from April 9-13 include human factors scientists,  NWS forecasters from 9-13 April Peachtree City / Atlanta and  Spokane, Washington , as well as researchers from NOAA’s ESRL GSD, University of Akron, OU CIMMS and NOAA NWS Warning Decision Training Division, as well as the FACETs Working Group.

HS-PHI was developed by the National Severe Storms Laboratory with the National Weather Service and ESRL, and is in its third year of evaluation.

PHI is one of six different experiments taking place in the NOAA HWT this spring.

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Researchers evaluating lightning data in Hazardous Weather Testbed

For the first time ever, lightning data from a weather satellite is available and being evaluated in the NOAA Hazardous Weather Testbed.

Forecasters, researchers, product developers and broadcast journalists are analyzing recently available experimental data from an instrument on GOES-16, the newly launched NOAA satellite as part of the HWT Experimental Warning Program.

GOES-16, launched by NASA last November, scans the skies five times faster than NOAA’s current geostationary weather satellites and provides images at four times greater resolution.

The higher resolution allows forecasters to see more details in storm systems, particularly during periods of rapid strengthening or weakening. GOES-16 is also the first to carry a lightning detector in geostationary orbit.

The Geostationary Lightning Mapper observes total lightning, meaning in-cloud and cloud-to-ground lightning. GLM can help increase the accuracy of forecasts and warning times when combined with other forecaster tools.

The HWT EWP GOES-16 experiment just wrapped up its second of four weeks. Kristin Calhoun, CIMMS research scientist working at NSSL, said this is the first time forecasters have seen GLM data from GOES-16.

“We are here to test it and to contribute anything from ideas for data integration to training needs,” Calhoun said. “We want people to identify as many training gaps as possible.”

Bill Line, a meteorologist with the NOAA National Weather Service Pueblo forecast office, said if people like him learn to use GLM’s data, it will better his forecasts.

“These are new tools and we want to make sure forecasters are ready to use them,” he said. “There are many combinations of data and probabilities they haven’t looked at before.”

David Stark, a meteorologist with the NOAA National Weather Service New York forecast office, in the Hazardous Weather Testbed working with GLM data. (Photo by James Murnan/NOAA)

That is the purpose of the HWT – the facility allows end users to test new, experimental products before they are released to the NWS or other NOAA entities and partners.

“We’ve held similar experiments in the past but with proxy data,” Calhoun said. “This is the first year we are able to use real data. Ideally we will continue experiments like this, using real GOES-16 data, for years to come.”

David Stark, a meteorologist with the NWS New York forecast office, participated in the first week’s experiment. He described the experience as outstanding.

“Testing out some new products and helping fine tune them so they just aren’t thrown into the NWS is great,” he said. “To be able to see these tools and see the new research, while acting like I’m issuing warnings in an area gives me a good idea and feel of what I could be doing with this in real life and how it would enhance our current products.”

Stark said the product helps better show storm formations, providing the forecaster with a better idea of when and where a storm may form.

“This would add more confidence to my forecasts and allow me to focus more on increasing warning on possible life-threatening storms,” Stark said.

The GOES-16 experiment continues in the NOAA Hazardous Weather Testbed through July 21.

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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 https://hwt.nssl.noaa.gov/ and http://www.nssl.noaa.gov/projects/facets/.

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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2014 NOAA Hazardous Weather Testbed experiments kick off this week

NOAA Hazardous Weather Testbed: During a break in operations, Kristin Calhoun led an impromptu discussion of the complexities of interpreting PGLM lightning data.
NOAA Hazardous Weather Testbed:
During a break in operations, Kristin Calhoun led an impromptu discussion of the complexities of interpreting PGLM lightning data.

The NOAA Hazardous Weather Testbed (HWT) annual spring experiments kick off this week, and will run weekdays through June 6, 2014. During the experiments, researchers, modelers, and forecasters from around the world work together to improve severe weather forecasts and warnings in a simulated operational environment. NSSL, the NOAA Storm Prediction Center, and the NOAA National Weather Service Forecast Office in Norman sponsor the experiments each year.

The NOAA HWT has two branches, the Experimental Forecast Program (EFP) and the Experimental Warning program (EWP). They each have independent but complementary and goals.

The 2014 NSSL-NWS Experimental Warning Program will focus on applications geared toward National Weather Service Forecast Office (NWSFO) severe thunderstorm warning operations. Participants will test a prototype tool that provides Probabilistic Hazards Information (PHI), as part of the new Forecasting A Continuum of Environmental Threats (FACETs) program. They will also evaluate multiple GOES-R applications, including lightning mapper products; look at the performance and usefulness of two experimental short-term forecast models; and assess a new tool that tracks thunderstorm features.

Participants in the 2014 Spring Forecasting Experiment will evaluate a suite of new and improved experimental high-resolution models that can depict the probability of potential thunderstorms, their hazards, and their trends and transitions over time. This is an important step toward the NWS strategy of providing nearly continuous probabilistic hazard forecasts.  Participants will be dividing between either an “SPC desk” team, to examine products and techniques closer to operational implementation, or a “developmental desk” team, to explore experimental products and techniques.
The spring experiments have been the cornerstone of the HWT for more than a decade, and accelerate the transition of promising technology into forecast operations.

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