Predictability and Ensemble Modeling of the Space-Atmosphere Interaction Region

Tuesday, 16 December 2014
Tomoko Matsuo1,2, Timothy J Fuller-Rowell1,2, Rashid A Akmaev2, Houjun Wang1,2, Tzu-Wei Fang1,2, Kayo Ide3, Daryl T Kleist3, Jeffrey S Whitaker4, Xinan Yue5, Mihail Codrescu2, Arthur D Richmond6, Thomas J Immel7, Brian J Anderson8, Larry J Paxton8 and J. Y. Liu9, (1)University of Colorado, Boulder, CO, United States, (2)NOAA, Space Weather Prediction Center, Boulder, CO, United States, (3)University of Maryland, College Park, MD, United States, (4)NOAA Earth System Research Laboratory, Physical Sciences Division, Boulder, CO, United States, (5)UCAR, Boulder, CO, United States, (6)National Center for Atmospheric Research, High Altitude Observatory, Boulder, CO, United States, (7)Univ of California, Berkeley, CA, United States, (8)The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States, (9)NCU National Central University of Taiwan, Institute of Space Science, Jhongli, Taiwan
The Space-Atmosphere Interaction Region (SAIR), encompassing the mesosphere, thermosphere and ionosphere, is an intersection between geospace and the Earth’s atmosphere, and is exposed to vacillating conditions of both space and terrestrial weather. Recent observational and modeling studies have revealed clear reaches of terrestrial weather far beyond the mesosphere lower-thermosphere region into the topside ionosphere. At the same time, the region lends itself to forcing originating from the Sun and solar-wind magnetosphere interactions. The predictability of the SAIR is a fundamental question in Heliophysics, and calls for a paradigm shift from a deterministic to a probabilistic modeling framework. To meet with this contemporary modeling and simulation challenge, we will systematically compare and combine ensemble simulations of a comprehensive whole atmosphere model, coupled with an ionosphere and plasmasphere model called the Integrated Dynamics in Earth’s Atmosphere (IDEA) with global Earth and geospace observations. Building on the National Weather Service's operational ensemble forecasting and data assimilation systems as well as our earlier efforts, we will construct an ensemble forecasting and data assimilation system that will ultimately be capable of assimilating observations from the ground to SAIR. We will present the project overview along with some initial results from our new interdisciplinary initiatives.