Evaluating the Integration of High-Resolution Datasets for Convective Warning Decision Making at the National Weather Service Operations Proving Ground

In May and June of 2017, the National Weather Service (NWS) Operations Proving Ground (OPG) facilitated an evaluation of integrating high-resolution datasets for convective warning decision making. The primary objective of the evaluation was for twelve NWS forecasters (three forecasters a week) to assess the operational readiness of a new NWS field-developed Near-Storm Environment Awareness (NSEA) decision aid. Feedback from participants was gathered about the readiness of the NSEA tools, an interactive sounding and hodograph application and an Advanced Weather Interactive Processing System functionality that provides convective parameter cursor readout, as they completed four historical convective exercises in the role of warning forecaster. A secondary objective of the evaluation was to gather feedback on whether integrating the NSEA tools with high-resolution datasets adds significant insight or workload to the convective warning decision process. Here, NWS forecasters used the NSEA tools in real time with datasets such as WSR-88D, Geostationary Operational Environmental Satellite (GOES)-16 beta validation mature imagery, products from the Multi-Radar/Multi-Sensor System, and total lightning data from Earth Networks and GOES-16 (pre-beta validation maturity). Eleven of the twelve participating NWS forecasters said it was extremely important for the warning forecaster to understand and learn how to effectively integrate the high-resolution datasets or at the minimum, for particularly complex convective environments, ensure a dedicated mesoscale analyst is actively monitoring the datasets and communicating observations to the warning forecasters. However, many NWS Weather Forecast Offices do not have the infrastructure and hardware to efficiently communicate this information during convective warning operations. This presentation will discuss the complete list of evaluation findings and recommendations for integrating high-resolution datasets for convective warning decision making.