Understanding Forecasters' Needs to Improve Radar Observations using Adaptive Scanning

The Phased Array Radar Innovative Sensing Experiment (PARISE) of 2013 was held during six weeks in May, June, and July at the National Severe Storms Laboratory (NSSL) in Norman, OK. During PARISE 2013, NSSL scientists worked with twelve National Weather Service (NWS) forecasters to assess how the use of rapid-scan phased-array radar (PAR) data assists situational awareness and warning decisions during simulated severe-weather events. Another goal of this experiment and the focus of this paper is to understand the strengths and limitations of weather-radar scan strategies used by forecasters in their warning-decision process. The experiment was conducted in three phases over three days: orientation, case studies, and debriefing. Prior to working with the cases selected for this study, participants were briefed on the basic trade-offs that need to be considered when designing weather-radar scan strategies, and on the potential impacts of different scan-strategy characteristics to the quality, spatial resolution, and temporal resolution of the data. After working each case, participants were asked to assess the strengths and limitations of the associated scan strategy. At the end of each week, participants discussed the impacts of these scan strategies on their warning decision process. They also participated in interactive exercises conducted to assess the value of different scanning-strategy characteristics to their situational awareness and warning decisions. Questions were focused on two weather scenarios: supercells and microbursts. As the latter matched the phenomena observed in the simulated weather events, participants were able to draw from their experience with previous case studies. Forecasters' scan strategy choices and reasoning in response to these questionnaires and interviews were recorded, and the main common themes were identified from the analysis of this information. This is the first in a series of experiments with the overarching goal of understanding forecasters' needs to improve radar observations using adaptive scanning algorithms, which are best suited for the next generation of weather-surveillance radars based on PAR technology.