Improving High-Resolution Numerical Weather Simulations by Assimilating Data from an Unmanned Aerial System

Abstract

n this study, it is demonstrated how temperature, humidity, and wind profile data from the lower troposphere obtained with a lightweight unmanned aerial system (UAS) can be used to improve high-resolution numerical weather simulations by four-dimensional data assimilation (FDDA). The combined UAS and FDDA system is applied to two case studies of northeasterly flow situations in southwest Iceland from the international Moso field campaign on 19 and 20 July 2009. Both situations were characterized by high diurnal boundary layer temperature variation leading to thermally driven flow, predominantly in the form of sea-breeze circulation along the coast. The data assimilation leads to an improvement in the simulation of the horizontal and vertical extension of the sea breeze as well as of the local background flow. Erroneously simulated fog over the Reykjanes peninsula on 19 July, which leads to a local temperature underestimation of 8 K, is also corrected by the data assimilation. Sensitivity experiments show that both the assimilation of wind data and temperature and humidity data are important for the assimilation results. UAS represents a novel instrument platform with a large potential within the atmospheric sciences. The presented method of using UAS data for assimilation into high-resolution numerical weather simulations is likely to have a wide range of future applications such as wind energy and improvements of targeted weather forecasts for search and rescue missions.