National Weather Service Data Needs for Short-Term Forecasts and the Role of Unmanned Aircraft in Filling the Gap: Results from a Nationwide Survey

Abstract

AbstractInclusion of unmanned aircraft systems (UAS) into the weather surveillance network has the potential to improve short-term (<1 day) weather forecasts through direct integration of UAS-collected data into the forecast process and assimilation into numerical weather prediction models. However, one of the primary means by which the value of any new sensing platform can be assessed is through consultation with principal stakeholders. National Weather Service (NWS) forecasters are principal stakeholders responsible for the issuance of short-term forecasts. The purpose of the work presented here is to use results from a survey of 630 NWS forecasters to assess critical data gaps that impact short-term forecast accuracy and explore the potential role of UAS in filling these gaps. NWS forecasters view winter precipitation, icing, flood, lake-effect/lake-enhanced snow, turbulence, and waves as the phenomena principally impacted by data gaps. Of the 10 high-priority weather-related characteristics that need to be observed to fill critical data gaps, 7 are either measures of precipitation or related to precipitation-producing phenomena. The three most important UAS capabilities/characteristics required for useful data for weather forecasting are real-time or near-real-time data, the ability to integrate UAS data with additional data gathered by other systems, and UASs equipped with cameras to verify forecasts and monitor weather. Of the three operation modes offered for forecasters to consider, targeted surveillance is considered to be the most important compared to fixed site profiling or transects between fixed sites.