Pam Heinselman came to the NOAA National Severe Storms Laboratory in 1995 as a scientist with the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies. She grew up in Westminster, Maryland, and completed her meteorological studies at the University of Saint Louis. After joining CIMMS and living in Norman for several years, she made the decision to continue her education and pursue a Ph.D. at OU. Pam was awarded her Ph.D. in May of 2004. Five years later, in 2009, she became a full-time NOAA employee, and she has continued to make important contributions to the Lab in this role.
Heinselman is the leader of the phased array radar and meteorological studies team. She coordinates the Phased Array Radar Innovative Sensing Experiment with OU PhD student Katie Bowden, who is funded through the OU Cooperative Institute for Mesoscale Meteorological Studies. PARISE is conducted through the NOAA Hazardous Weather Testbed, and investigates scientific and operational applications of rapid-scan data sampled by NSSL’s phased array radar. This research improves understanding of hazardous weather and develops methods to use rapid-scan radar data in forecasting operations
In her personal time, Pam enjoys exercising outdoors and especially looks forward to trips home to the East Coast, where she can dine on her favorite Maryland crab cakes. She also enjoys the company of her dog, Rolli.
This week, researchers from NOAA’s National Severe Storms Laboratory will launch the 2015 Phased Array Radar Innovative Sensing Experiment to assess the impacts of rapidly updating radar data on forecasters’ warning decision performance. The project will be carried out over the course of six weeks, and will conclude on September 25.
As in previous years, NSSL Research Scientist Dr. Pam Heinselman and CIMMS Researcher Katie Bowden will take the lead on the experiment. They will be working with NOAA National Weather Service forecasters to produce timelines of the warning decision process. Later they will analyze these timelines to determine the situational awareness attained from phased array radar data and how that information was used in warning decisions. The experiment will be conducted in three parts.
The first segment of 2015 PARISE will be conducted like a traditional experiment, according to Heinselman. Thirty National Weather Service forecasters from across the Great Plains region will be assembled to study nine archived cases. These cases will be worked in simulated real-time, using one-, two-, or five- minute phased array radar updates. The forecasters will determine whether or not to warn, based on the situational awareness gained from the radar data. Upon completion of each study, they will provide a detailed account of their warning decision process and overall workload. With more participants and additional case studies this year, the results are expected to be an improvement over previous experiments.
New this year will be the use of eye-tracking technology to better understand the decision-making processes of the forecasters. Eye-tracking technology has been successfully used for analysis in healthcare, air traffic control, and other human-computer interactions. Data pertaining to eye gaze will be gathered from each of the 30 forecasters while they are working on PAR case studies. Analysis of this data is expected to illustrate how update timelines impact forecasters’ decisions.
On the final day of PARISE, researchers will conduct a focus group aimed at generating insightful feedback. Forecasters will have the opportunity to share new ideas that will help shape the future of the PAR network. As radar continues to develop and forecasting resources are enhanced, National Weather Service meteorologists will be better equipped to warn the public of impending severe weather. This, in turn, will support the NWS objective to protect life and property and will help to build a Weather Ready Nation.
One of the highest awards presented within NOAA will be awarded to the NSSL team that developed Multi-Radar Multi-Sensor, a system that helps forecasters manage the flood of weather data available to them. Under Secretary of Commerce Kathryn D. Sullivan announced the award of a NOAA silver medal for science/engineering achievement. Their work was a collaborative effort with the University of Oklahoma’s Cooperative Institute for Mesoscale Meteorological Studies.
The MRMS system, which became operational throughout the National Weather Service in October 2014, quickly harnesses the tremendous amount of weather data from multiple sources, intelligently integrates the information, and provides a detailed picture of the current weather. MRMS uses a holistic approach to merging multiple data sources, allowing forecasters to better analyze data and potentially make better predictions.
The new MRMS products, generated every two minutes, combine multiple radars, along with satellites, surface observations, upper air observations, lightning reports, rain gauges, and numerical weather prediction models. With this data, forecasters are able to better visualize high-impact weather threats like heavy rain, snow, hail, and tornadoes. This, in turn, leads to better forecasting techniques and improves lead time.
He’s been around the National Weather Center since 2007, but you might not know everything he’s been up to! Darrel earned his bachelor’s degree in 2006 from Purdue University. After college, he was outsourced to India (seriously!) before making his way to Norman and and the NOAA National Weather Service’s Warning Decision Training Branch (now Division) as a CIMMS employee in 2007. At WDTB, he was responsible for warning decision training and simulations in AWIPS. It was during this time he decided to pursue a Master’s degree in Geospatial Science, which he earned from The University of Oklahoma in 2010.
In 2012, he joined us here at the NOAA National Severe Storms Laboratory, becoming a CIMMS researcher in the Warning Research and Development Division. He worked on research to operations projects, algorithm development, and technology transfer, leading to his eventual decision to become a Ph.D. candidate at OU. His field of study is “terrestrial and spaceborne applications to thunderstorm and attendant hazard identification.”
These days, Darrel does his best to prepare National Weather Service forecasters for the future. He evaluates existing algorithms for strengths and weaknesses using large-scale radar and satellite climatologies. He develops new algorithms and builds displays for evaluation. And he evaluates new applications in NOAA’s Hazardous Weather Testbed.
And, just in case you were wondering, his three biggest fears are: spike strips, sporting events with flying objects (think hockey, baseball, etc.), and haunted houses!
During the week of July 20-24, six forecasters from NWS offices nationwide joined NSSL and CIMMS researchers for the final week of the Hydrometeorological Testbed. This project was supported by JJ Gourley, Steve Martinaitis, Race Clark and Zac Flamig.
During the week, the forecasters issued experimental watches and warnings for hydrologic extremes in real-time, with the objective of improving flash flood guidance. This project leveraged opportunities for collaboration with two other NSSL research programs, the Severe Hazards Analysis and Verification Experiment (SHAVE) and the Meteorological Phenomenon Identification Near the Ground (mPING).
The purpose of the HMT was to evaluate the skills of the NSSL-designed FLASH suite of products in flash flood forecasting and, ultimately, to enhance understanding of short-term flash flood forecasting challenges. The meteorologists shared their findings in a “Tales from the Testbed” teleconference held at the end of the week, highlighting the difficulties and successes they encountered when applying FLASH products in various weather scenarios. Notably, they found that it is beneficial to have soil moisture products available when considering flash flood watch and warning issuance. Overall, they determined the new FLASH products to be an improvement in operational capabilities that will lead to more accurate and timely decision-making.
31 May 2013 El Reno Tornadoes: Advantages of Rapid-scan Phased Array Radar Data from a Warning Forecaster’s Perspective. Authors: Kuster, C.M., Heinselman, P.L., Austin, M.
Journal: Weather and Forecasting
Publication Date: Online 6/12/15
Important conclusions: Researchers collaborated with the forecaster who issued real-time tornado warnings for the 31 May 2013 supercell near El Reno Oklahoma. The forecaster compared critical radar signatures frequently assessed during warning operations from rapid-scan PAR data and the Weather Surveillance Radar 1988-Doppler. The study identified potential benefits of rapid-update radar data especially in terms of better understanding rapid storm evolutions and the resulting ability to better communicate threat information to the general public during high-impact weather events.
Rapid-scan volumetric radar data in cases like this would augment a forecaster’s ability to observe rapidly evolving storm features and deliver timely, life-saving information to the general public.
Automated Detection of Polarimeteric Tornadic Debris Signatures using A Hydrometeor Classification Algorithm
Journal: Journal of Applied Meteorology and Climatology
Publication Date: Online 7/2/2015
Authors:Snyder, Jeffrey C., Ryzhkov, Alexander. Important conclusions:
Since debris lofted by tornadoes has scattering characteristics that are distinct from those of hydrometeors, the additional information provided by polarimetric weather radars can aid in identifying debris from tornadoes. The polarimetric tornadic debris signature (TDS) provides what is nearly “ground truth” that a tornado is ongoing (or had recently occurred).This paper presents a modification to the existing hydrometeor classification algorithm used with the Weather Surveillance Radar – 1988 Doppler network in the U.S. to include a Tornado Debris Signature (TDS) category. Examples of automated TDS classification are provided for several recent cases observed in the United States.
As we continue to refine the algorithm, we think that it my prove very useful to operational meteorologists. Emergency responders may also find value in the product given the association between TDSs and damaging tornadoes.