Improving high-impact numerical weather prediction with lidar and drone observations

Abstract

The current atmospheric observing systems fail to provide a satisfactory amount of spatially and temporally resolved observations of temperature and humidity in the planetary boundary layer (PBL) despite their potential positive impact on numerical weather prediction (NWP). This is particularly critical for humidity, which exhibits a very high variability in space and time or for the vertical distribution of temperature, determining the atmosphere's stability. Novel ground-based lidar remote sensing technologies and in situ measurements from unmanned aerial vehicles can fill this observational gap, but operational maturity was so far lacking. Only recently, commercial lidar systems for temperature and humidity profiling in the lower troposphere and automated observations on board of drones have become available. Raman lidar can provide profiles of temperature and humidity with high temporal and vertical resolution in the troposphere. Drones can provide high-quality in situ observations of various meteorological variables with high temporal and vertical resolution, but flights are complicated in high-wind situations, icing conditions, and can be restricted by aviation activity. Both observation systems have shown to considerably improve analyses and forecasts of high-impact weather, such as thunderstorms and fog in an operational, convective-scale NWP framework. The results of this study demonstrate the necessity for and the value of additional, high-frequency PBL observations for NWP and how lidar and drone observations can fill the gap in the current operational observing system.