Researchers study lower atmosphere to answer remaining questions

While scientists have learned a lot about our planet, questions remain about the lowest part of the atmosphere where we live. Researchers at the NOAA National Severe Storms Laboratory are looking for answers. Utilizing a series of instruments located in a mobile research unit, researchers are analyzing data gathered by those tools to improve severe weather forecasts.

The lowest mile or so of the atmosphere, known as the planetary boundary layer, is where several elements mix — from pollution to moisture — and how those elements mix and change during the day impact events in the atmosphere.

“Understanding the boundary layer can improve forecasts of severe weather, pollution, and several other things impacting the surface,” said Elizabeth Smith, NOAA National Severe Storms Laboratory researcher.

In an effort to improve understanding, weather researchers with the Cooperative Institute for Mesoscale Meteorological Studies at the University of Oklahoma and NOAA NSSL deployed two trailers decked out with a collection of weather instruments known as the Collaborative Lower Atmospheric Mobile Profiling System in fall 2020.

A research trailer known as CLAMPS parked in a grassy field near a power connector. In the background is an operational radar. The sky is overcast, cloudy and gray.
The Collaborative Lower Atmospheric Mobile Profiling System, or CLAMPS, in Norman, Oklahoma. CLAMPS was deployed near a weather radar and weather station as part of an experiment to better understanding the depth of the boundary layer. (Photo by James Murnan/NOAA)

The CLAMPS platforms were deployed near a weather radar and a weather station in Oklahoma as well as the National Weather Service Forecast Office in Shreveport, Louisiana. The fast-updating, high-resolution data collected provides a more detailed view of the atmosphere and its processes for researchers to analyze.

In addition, the Shreveport NWS Office utilized CLAMPS to monitor both fog and fire weather forecasts during CLAMPS deployment in the area. That office also noted interesting and surprising boundary layer behavior when smoke from fires raging in the western part of the United States infiltrated into the area.

NWS Shreveport Science and Operations Officer Brad Bryant said output from CLAMPS was particularly useful for refining fog and fire weather forecasts because both sets of parameters are closely tied to specifics of the boundary layer CLAMPS is tuned to monitor.

Research equipment parked on the green grass. Behind it is a building and tall operational weather radar.
The CLAMPS trailer in Shreveport, Louisiana at the NWS Forecast Office as part of an OU CIMMS and NOAA NSSL experiment. (Photo by Matthew Carney/OU)

CIMMS Researcher and Project Lead Jacob Carlin said the CLAMPS platform collects information more frequently than weather balloons launched daily by NWS forecasters across the nation. Although both methods gather similar information about the atmosphere, weather balloons are typically launched twice a day while CLAMPS gather data every couple of minutes.

More data can result in a more accurate representation of atmospheric processes at any moment. Data from the CLAMPS systems is combined with data from the NEXRAD radar, further enhancing researchers’ view of the atmosphere and what is happening.

This project is an extension of a recently published study that compared the twice-a-day balloon launch data with data from a nearby NEXRAD radar. Carlin’s team is going further, comparing CLAMPS minute data with a nearby NEXRAD radar and weather station.

“We want to understand how well this method performs with CLAMPS, because if it is able to reliably observe boundary layer height and development, then this method can improve forecasts and forecasting tools,” Carlin said.

With this new dataset, the researchers hope to learn more about how well NEXRAD radar can detect the boundary layer, expanding the capability of existing infrastructure at no additional cost.

Funding for this study was provided by the Cooperative Institute for Mesoscale Meteorological Studies’ Director’s Discretionary Research Fund, which supports the piloting of small-scale innovative and experimental projects.

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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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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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Significant Paper: A Qualitative Analysis of NWS Forecasters’ Use of Phased-Array Radar Data during Severe Hail and Wind Event

A Qualitative Analysis of NWS Forecasters’ Use of Phased-Array Radar Data during Severe Hail and Wind Event
Authors: Katie A. Bowden, Pamela L. Heinselman
Journal: Weather and Forecasting
Publication Date: In Print 2/2016

Important Conclusions:
Forecasters using 1-minute radar updates perceived significantly more information than forecasters using 5-minute radar updates and demonstrated improved projections of storm activity in the hail and wind cases worked, owing to earlier perception of severe weather precursor signatures and the ability to more easily observe strengthening and diminishing trends in storms. Such improvements in situational awareness from the use of 1-minute radar updates resulted in superior severe warning lead times and supported correct rejections of unverified threats.

Significance:
This paper summarizes qualitative findings from the 2013 Phased Array Radar Innovative Sensing Experiment. It builds on results presented in the published Impacts of Phased-Array Radar Data on Forecaster Performance during Severe Hail and Wind Events. This paper demonstrates efforts that are being made to learn about the forecaster warning decision process through the use of social science techniques.

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NSSL researchers lead project to evaluate experimental flash flood products

DSC_0052During July, NOAA National Weather Service forecasters from forecast offices and river forecast centers will assess emerging hydrometeorological concepts and products in the Multi-Radar / Multi-Sensor (MRMS) Hydro Experiment 2015. Their goal is to improve the accuracy, timing, and specificity of flash flood watches and warnings.

MRMS-Hydro is led by NSSL and is part of the 2015 United States Weather Research Program (USWRP) Hydrometeorological Testbed (HMT). Operational activities will take place Monday through Friday for three weeks (July 6 to 24).

During the experiment, participating forecasters will evaluate short-term predictive tools derived from MRMS quantitative precipitation estimates (QPE) and Flooded Locations and Simulated Hydrographs (FLASH) hydrologic modeling framework. Forecasters will also explore the utility of experimental watch and warning products conveying uncertainty and magnitude issued through the Hazard Services software from the Earth Systems Research Lab/Global Systems Division (GSD). Research scientists will investigate human factors to determine operationally relevant best practices for the warning decision making process and the system usability of the Hazard Services platform.

HMT-Hydro will coordinate operations with the third annual Flash Flood and Intense Rainfall experiment (FFaIR) at the NOAA/NWS Weather Prediction Center (WPC) to simulate the collaboration that occurs between the National Weather Service’s national centers, river forecast centers, and local forecast offices during flash flood events.

HMT-Hydro and FFaIR will simulate the real-time workflow from WPC 6-24 hour forecast and guidance products to experimental flash flood watches and warnings issued in the 0-6 hour period. The HMT-hydro team will shift its area of responsibility on a daily basis to where heavy precipitation events and associated flash flooding is anticipated.

Researchers will collect feedback from NWS operational forecasters through comments during their shifts, electronic surveys, de-briefings, and a webinar at the end of each week. NWS feedback is critical for future development and eventual implementation of new applications, displays, and product concepts into AWIPS2 and other operational systems.
HMT-Hydro 2015 provides a real-time environment to rapidly test the latest observational and modeling capabilities so they may be improved and optimized for transition to operational decision-making in the National Weather Service to support a Weather-Ready Nation.

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2015 Spring Warning Project will look at new severe weather warning guidance

Researchers and forecasters work side by side in the Hazardous Weather Testbed.

Several experiments to improve National Weather Service severe weather warnings will be conducted this spring in the NOAA Hazardous Weather Testbed (HWT) as part of the annual Experimental Warning Program, a joint project of the National Weather Service and NSSL/CIMMS to support NOAA’s goal to evolve the National Weather Service and build a Weather-Ready Nation. The EWP’s Spring Warning Project will run from May 4 through June 12, and provides a conceptual framework and a physical space to foster collaboration between research and operations to test and evaluate emerging technologies and science.

Forecasters will evaluate an updated Lightning Jump Algorithm (LJA), based on the GOES-R Geostationary Lightning Mapper, that was enhanced based on feedback from forecasters participating in the 2014 program. In severe storms, rapid increases in lightning flash rate, or “lightning jumps,” typically precede severe weather such as tornadoes, hail, and straight line winds at the surface by tens of minutes.  These evaluations will help prepare for possible operational implementation in 2016 following the launch of GOES-R.

Earth Networks’ total lightning and total lightning derived products, including storm-based flash rates tracks, time-series, and three levels of thunderstorm alerts will be evaluated in real time, building upon the initial evaluation in 2014. The 2015 evaluation will test the feasibility of use and performance under the stress of real-time warning operations.

A new set of high-resolution Weather Research and Forecasting (WRF) models will serve as a prototype for developing the “Warn-on-Forecast” warning paradigm. Feedback from this project will go into developing new model tools capable of managing the large amounts of model information associated with future forecast systems.

During three weeks of the experiment, forecasters will assess a new tool using rapidly-updating high-resolution gridded Probabilistic Hazard Information (PHI) as the basis for next-generation severe weather warnings. This experiment is part of a broad effort to revitalize the NWS watch/warning paradigm known as Forecasting a Continuum of Environmental Threats (FACETs). The major emphasis of the HWT PHI experiment will be on initial testing of concepts related to human-computer interaction while generating short-fused high-impact Probabilistic Hazard Information for severe weather. The long-term goal of this effort is to migrate the refined concepts and methodologies that result from this experiment into Hazard Services, the next generation warning tool for the NWS, for further testing and evaluation in the HWT prior to operational deployment.

This year will mark the inaugural HWT Experiment with Emergency Managers. The EMs will be provide feedback on their interpretation of experimental probabilistic forecasts generated in the HWT from the PHI experiment and the Experimental Forecast Program (EFP). This feedback will be used in conjunction with feedback from forecasters to refine how the uncertainty information is generated and disseminated.

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CIMMS/NSSL researchers work to get West Texas lightning data in AWIPS in time for severe weather

For the past few weeks, Darrel Kingfield and Dr. Kristin Calhoun from CIMMS/NSSL and Dr. Eric Bruning from Texas Tech University have been working alongside forecasters from the National Weather Service (NWS) Forecast Office in Lubbock, Texas, to generate and integrate real-time 1km lightning products into the Advanced Weather Interactive Processing System-2 (AWIPS-2). AWIPS-2 is the weather forecasting, display and analysis package currently being used by the NWS. The team pushed hard to get these products functional for potential severe weather on April 16 and were successful.

Two products — Flash Extent Density and Flash Initiation Density — are gridded visualizations of total lightning data from Lightning Mapping Arrays (LMA). There are several arrays across the U.S. that are able to map the three-dimensional shape, extent and development of branched lightning channels. These data are an essential component of modern lightning detection and physics studies, because they reliably map the extent of the in-cloud charge reservoirs tapped by each lightning flash. Both of these products have been successfully evaluated in the Hazardous Weather Testbed.

A comparison of the 2000 UTC 1 min. flash extent density (left) and mean flash area (right) for a supercell over Kingfisher County, Oklahoma on 16 May 2010. This storm produced a wide swath of giant hail (>2" in diameter), causing severe damage to buildings and vehicles in it path.
An example of the lightning products installed at the NWSFO in Lubbock, TX. This is a comparison of the 2000 UTC 1 min. flash extent density (left) and mean flash area (right) for a supercell over Kingfisher County, Oklahoma on 16 May 2010. This storm produced a wide swath of giant hail (>2″ in diameter), causing severe damage to buildings and vehicles in its path.

A third product, the experimental Flash Area product from Texas Tech University, was also integrated into AWIPS-2. Forecasters have seen in the LMA data that small, compact flashes are mostly associated with robust or developing convection. As thunderstorms mature and reach the subsequent dissipation stage, the flash area starts to increase. The transfer of these products to operations will provide the developers, researchers and forecasters with the opportunity to learn more about how total lightning products can be utilized in the forecast and warning decision process.

This work benefits operational forecasters, highlights research to operations transitions, and supports NOAA’s work to evolve the National Weather Service. It also supports NSSL’s Grand Scientific Challenge to predict useful warnings of lightning activity one hour in advance.

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NSSL/CIMMS scientist to brief NWS forecasters

Screen Shot 2014-05-15 at 10.48.39 AMKristin Calhoun (NSSL/CIMMS) will give an invited webinar to National Weather Service meteorologists and hydrologists on April 1, 2015, about current lightning prediction products in research and development at NSSL. Calhoun will also discuss the NOAA Hazardous Weather Testbed (HWT) and how forecaster feedback is used to evaluate and refine the products.The webinar is part of a monthly series for NWS Science Operations Officers and Development and Operations Hydrologists that benefits operational forecasters, highlights research to operations transitions, and supports NOAA’s work to evolve the National Weather Service.

It has long been theorized, and in limited studies demonstrated, that the use of total lightning detections and associated derivative products could have positive impacts on the warning process for thunderstorm events. Two total lightning algorithms to potentially improve short-term prediction and warnings of severe storms have been evaluated in the Hazardous Weather Testbed (HWT) in Norman, Oklahoma.

In severe storms, rapid increases in lightning flash rate, or “lightning jumps,” typically precede severe weather, such as tornadoes, hail and straight line winds, at the surface by tens of minutes. The GOES-R Geostationary Lightning Mapper (GLM) will allow the use of continuous total lightning observations and the lightning jump concept operationally throughout the United States. A total lightning jump algorithm (LJA) that can be used by NWS forecasters to enhance situational awareness and diagnose convective trends was evaluated in the HWT as part of the experimental warning program in 2014 and will be evaluated again in 2015.

Earth Networks (ENI), a private company that provides lightning data and products, has indicated the potential for their total lightning data and “Dangerous Thunderstorm Alerts” to increase lead-time over current National Weather Service (NWS) severe weather and tornado warnings, while maintaining a similar probability of detection and false alarm ratio. This project integrates the ENI total lightning data and products into the NWS operational software and tests the feasibility of use and performance under the stress of real time warning operations.

In 2014, 18 NWS forecasters visited the HWT during a period of six weeks, 21 July-29 August, for a full product evaluation. The forecasters completed a series of six two hour weather-warning simulations of marginally severe storms to high-impact tornadic events throughout the United States. The 2015 HWT experiment will build upon the initial evaluation in 2014, including enhancements based on forecaster feedback.

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Impacts of Phased Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

Early online release 1/13/15
Journal: Weather and Forecasting
Impacts of Phased Array Radar Data on Forecaster Performance during Severe Hail and Wind Events

Katie A. Bowden, Pamela L. Heinselman, Darrel M. Kingfield, and Rick P. Thomas

Summary:  Twelve National Weather Service (NWS) forecasters participated in the Phased Array Innovative Sensing Experiment (PARISE) 2013 and were assigned to either a control (5-min radar data updates) or experimental (1-min radar data updates) group. Each group worked a marginally severe hail event and a severe hail and wind event in simulated real time. While working each event, participants made warning decisions regarding the detection, identification, and re-indentification of severe weather, now known as “the compound warning decision process.”

Important conclusions:  The experimental group’s performance exceeded that of the control group’s, as demonstrated through their significantly longer median warning lead time, as well as superior probability of detection and false alarm ratio scores. The experimental group also had a larger proportion of mastery decisions (i.e., confident and correct) than the control group, possibly because of their enhanced ability to observe and track individual storm characteristics through the use of 1-min updates.

Significance:  This work furthers efforts that have already been made to understand the impact of higher-temporal resolution radar data, as provided by PAR, on the warning decision process of NWS forecasters. The research questions, methodology, and analysis presented in this paper build upon the findings presented from earlier PARISE work, while also sharing findings that are of a new nature.

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Tornado Warning Decisions Using Phased Array Radar Data

Weather and Forecasting: Early Online Release

Tornado Warning Decisions Using Phased Array Radar Data

Authors:  Pamela Heinselman, Daphne LaDue, Darrel M. Kingfield, and Robert Hoffman

The 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned full-volumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters’ use of PAR data provided a median tornado-warning lead time (TLT) of 20 min.  Precursors that triggered forecasters’ decisions to warn occurred within one or two typical WSR-88D scans, indicating PAR’s temporal sampling better matches the time-scale at which these precursors evolve.

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