EWP Projects

Several experiments to improve National Weather Service severe weather warnings are conducted in the spring in the NOAA Hazardous Weather Testbed as part of the annual Experimental Warning Program.

Spring Warning Project

The Spring Warning Project (SWP) is a joint project conducted by scientists representing the Geostationary Operational Environmental Satellite – R Series (GOES-R), Joint Polar Satellite System (JPSS), Lightning Jump Algorithm (LJA), and Earth Networks Total Lightning Network (ENTLN) groups. In this experiment, National Weather Service forecasters and broadcast meteorologists participate as evaluators. Feedback includes live blogging, experimental warnings, daily debriefs and surveys, weekly debriefs and surveys, conversations, and the “Tales from the Testbed” webinar. The following sub-projects were a part of the SWP.

GOES-R and JPSS Convective Applications

During the SWP, the GOES-R Proving Ground conducts a pre-operational demonstration of recently developed products and capabilities. These products are associated with the next generation GOES-R series of geostationary satellites, subject to the constraints of existing data sources to emulate the satellite sensors. This early exposure was designed to increase forecaster familiarity with future GOES-R capabilities. In this way, SWP forecasters are readied for receipt and use of the GOES-R data prior to the launch. Additionally, feedback received from participants is utilized in the continued development of GOES-R algorithms. The first of the GOES-R series of satellites is scheduled to launch in October 2016. Additional demonstration of JPSS products introduces and familiarizes users with advanced satellite data that are already available.

GOES-R HWT Blog

Lightning Jump Algorithm

The lightning jump algorithm is also evaluated during the SWP. In severe storms, rapid increases in lightning flash rate, or “lightning jumps,” are coincident with pulses in the storm updraft and 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) provides a general path to operations for the use of continuous total lightning observations and the lightning jump concept over a hemispheric domain. SWP forecasters evaluate a gridded sigma-level based lightning jump product on a CONUS scale in real time.

Earth Networks Total Lightning Network

Earth Networks Incorporated’s (ENI) total lightning and total lightning derived products are evaluated in real-time as part of the SWP. In 2015, this experiment built upon the initial evaluation in 2014, including enhancements following forecaster feedback of product use and incorporation into the warning-decision process. ENI-derived products that were evaluated included storm-based flash rates tracks, and time-series as well as three levels of thunderstorm alerts. The 2015 evaluation tested the feasibility of use and performance under the stress of real-time warning operations. Forecasters evaluated an updated Lightning Jump Algorithm (LJA), based on the GOES-R Geostationary Lightning Mapper, which was enhanced based on feedback from forecasters participating in the 2014 program. 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 were evaluated in real time, building upon the initial evaluation in 2014. The 2015 evaluation tested the feasibility of use and performance under the stress of real-time warning operations.

Report: Earth Networks Total Lightning Data and Dangerous Thunderstorm Alerts Evaluation in the NOAA Hazardous Weather Testbed (PDF, March 2016)

Probabilistic Hazards Information Experiment (PHI)

During the weeks of the Spring Experiment, forecasters 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 is 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 move 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.

2015 marked the inaugural HWT Experiment with emergency managers (EMs). The EMs provided feedback on their interpretation of experimental probabilistic forecasts generated in the HWT from the PHI experiment and the Experimental Forecast Program (EFP). This feedback was used in conjunction with feedback from forecasters to refine how the uncertainty information is generated and disseminated.

Hydrology Experiment

The Multi-Radar / Multi-Sensor (MRMS) Hydro Experiment (hereafter, “Hydro”), was a part of the 2015 United States Weather Research Program (USWRP) Hydrometeorological Testbed (HMT). The HMT-Hydro experiment was conducted in conjunction with the Flash Flood and Intense Rainfall (FFaIR) Experiment at the Weather Prediction Center (WPC) from 6 July to 24 July. During the experiment, National Weather Service and River Forecast Center forecasters worked with research scientists to assess emerging hydrometeorological concepts and products to improve the accuracy, timing, and specificity of flash flood watches and warnings. In particular, forecasters evaluated short-term predictive tools derived from MRMS quantitative precipitation estimates (QPE) and Flooded Locations and Simulated Hydrographs (FLASH) hydrologic modeling framework. The Hydro Experiment also explored the utility of experimental watch and warning products conveying uncertainty and magnitude issued through the Hazard Services software. This allowed research scientists to investigate human factors to determine operationally relevant best practices for the warning decision making process and the system usability of the Hazard Services platform.

Phased Array Innovative Sensing Experiment (PARISE)

The 2015 Phased Array Innovative Sensing Experiment (PARISE) ran for six weeks during August and September. During the experiment, National Weather Service forecasters from the Great Plains utilized phased array radar data in displaced real-time warning operations. The primary goal of the experiment was to assess the impacts of higher-temporal resolution radar data on the warning decision processes and performance of the forecasters. Similar studies—conducted in 2010, 2012, and 2013—showed encouraging results, but the sample size was too small for generalization. Thus, to improve the reliability of previous findings, this study increased the sample by increasing the number of forecasters to 30 and the number of cases worked to 9.

The 2015 PARISE featured three parts: 1) the traditional experiment, 2) an eye-tracking experiment, and 3) a focus group. While the traditional experiment built on knowledge obtained from previous experiments, the eye-tracking experiment brought a new and exciting avenue to the work of PARISE. Forecasters' eye gaze data was collected as they worked a case in simulated real-time. This data provided new insight into impacts of higher-temporal resolution on the forecaster warning decision process and allowed PARISE scientists to analyze and compare forecasters’ cognitive processes objectively. Finally, the focus group session drew on participants’ experiences throughout the whole week, and generated insightful feedback and ideas important to the development of a future PAR network. The traditional experiment took place in the Hazardous Weather Testbed, and the eye-tracking experiment took place in RRDD space on the 4th floor of the National Weather Center.