286754 Forecasting winter storms with Global Hawk UAS observations

Wednesday, 13 January 2016
Lidia Cucurull, NOAA/OAR/ESRL, Boulder, CO; and J. M. English, H. Wang, T. Peevey, and A. Kren

The NOAA UAS Program designed a project focused on “Sensing Hazards with Operational Unmanned Technology” (SHOUT) to quantify the influence of UAS environmental data to high impact weather prediction and assess the operational effectiveness of UAS environmental data to high impact weather prediction, and assess the operational effectiveness of UAS to help mitigate the risk of satellite observing gaps. NASA and NOAA have demonstrated that Global Hawk UAS can overfly storms in the Atlantic, Pacific, and Arctic Oceans and deliver real-time vertical atmospheric profiles and environmental data. Although environmental data collected by UAS cannot replace the global coverage of satellite data, the long endurance and long range of a Global Hawk UAS does provide new capabilities to reach and stay with high impact oceanic weather events.

We have conducted Observing System Simulations Experiments (OSSEs) to evaluate the impact of observations from the Global Hawk on improving the prediction of winter storms. While Observing System Experiments (OSEs) allow the evaluation of real data, they cannot be used to analyze the impact of future observing systems. On the other hand, OSSEs provide a rigorous, cost-effective approach to evaluate the potential impact of new observing systems and alternate deployments of existing systems, and to optimize observing strategies. They are also used to prepare for the assimilation of new types of data, and to optimize the assimilation of existing data. Atmospheric OSSEs determine the impact of new systems by performing data denial experiments that assimilate synthetic observations simulated from a realistic Nature Run (NR) stipulated to represent the “true” atmosphere.

During this presentation, status of current OSSEs and OSEs with Global Hawk and a discussion on future activities within SHOUT will be presented.

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