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.

Ultra-Light Airborne Measurement System for Investigation of Urban Boundary Layer Dynamics

“…Atmospheric measurements by using a UAV can be applied in high-resolution research, verification of modeling results [ 58 ] and as an additional source for data assimilation in the lower troposphere [ 59 ]. Recent study indicates that UAVs that are able to accurately measure three-dimensional wind might be used as a cost-effective and flexible addition to measurement masts and LIDAR scans [ 60 ].…”

Challenges and Opportunities in Lidar Remote Sensing

“…Studies show that assimilating Doppler lidar wind measurements can improve not only short-time resource predictions of wind farmers (Würth et al, 2019; Perr-Sauer et al, 2020) but also mesoscale weather forecast (Pu et al, 2010;Kawabata et al, 2014). Assimilating Raman lidar water vapor and temperature measurements can fill current observation gaps in PBL to improve model performance (Wulfmeyer et al, 2006;Chipilski et al, 2019;Leuenberger et al, 2020).…”

Survey on Cloud Robotics Architecture and Model-Driven Reference Architecture for Decentralized Multicloud Heterogeneous-Robotics Platform

“…With respect to the benchmarks of Industry 4.0, autonomy and interoperability seemingly conflict with each other, and it is therefore challenging to provide both together. Moreover, interoperability may lead to creating new products and services, and drone information can be shared with weather forecasting, photogrammetry, and streaming [79]- [81]. DMC leads to an increase in the autonomy and helps to maintain the isles of each data and resource repos in a given platform.…”