9.7    Use of Small Unmanned Airplanes to Improve On-Demand Local Forecasts

Ršgnvaldsson, īlafur, Institute for Meteorological Research and the University of Bergen, Norway and ēgœstsson, H‡lfd‡n, Institute for Meteorological Research and University of Iceland and Icelandic Meteorological Office and Jonassen, Marius, Institute for Meteorological Research and the University of Bergen and īlafsson, Haraldur, University of Iceland, University of Bergen and the Icelandic Meteorological Office

An on-demand weather forecasting system, named SARWeather, has been developed. The system is tailored to meet the demanding needs of Search And Rescue operators world-wide. SARWeather uses the AR-WRF model, initialized and forced on the boundaries with data from the GFS global forecasting system. One of the unique features of the system is that it is run on the Amazon Elastic Compute Cloud (Amazon EC2). This ensures that twenty individual forecasts can be run simultaneously for any region in the world. Increasing the number of potential forecasts is straight forward, and can be done at a short notice. A second unique feature of SARWeather is that the system does not require any prior knowledge on behalf of the user regarding atmospheric modeling and/or high performance computing. Thirdly, output from SARWeather can be easily ingested into other decision support software, such as ArcGIS.

Data from a UAS system named SUMO (Small Unmanned Meteorological Observer) have been shown to improve local weather forecasts. Ongoing research aims at combining the SUMO with SARWeather by transmitting atmospheric observations from vertical proŽles, made by the SUMO observer, directly from the Želd to the SARWeather system via 3G mobile transmissions.