Forecasting the lower atmospheric drivers of ionosphere variability

Thursday, 14 February 2019: 09:00
Fountain I/II (Westin Pasadena)
Nicholas M Pedatella, Hanli Liu, Daniel Robert Marsh, Kevin Raeder and Jeffrey L Anderson, National Center for Atmospheric Research, Boulder, CO, United States
Abstract:
Waves propagating upwards from the lower atmosphere, including gravity waves, tides, and planetary waves, are a significant source of the day-to-day variability in the ionosphere, especially during geomagnetically quiet time periods. The synoptic (global) scale sources of these waves in the troposphere-stratosphere are often predictable days to weeks in advance. As these waves drive a significant amount of ionospheric variability, their predictability presents the opportunity to extend the skillful range of ionospheric forecasts. That is, if the lower atmospheric drivers can be forecast days to weeks in advance, it is possible to forecast the ionospheric variability on similar time scales, at least during solar quiescent periods. The recent advancements in whole atmospheric modeling have made such predictions possible. As an example, it has been demonstrated that the ionospheric variability during the 2009 sudden stratospheric warming (SSW) can be predicted ~10 days in advance in both the NOAA Whole Atmosphere Model (WAM) and NCAR Whole Atmosphere Community Climate Model eXtended (WACCMX). SSWs are, however, an extreme event in terms of both their predictability as well as their impact on the middle and upper atmosphere. The predictability during SSWs may thus not be representative of more typical conditions. In order to gain an understanding of the predictability during more typical conditions, we have preformed an extensive set of hindcast experiments, consisting of 30-day hindcasts initialized on the 1st and 15th of each month in 2009 and 2010. Results of the hindcast experiments will be presented to demonstrate the current capabilities for predicting the dynamical variability in the mesosphere and lower thermosphere (MLT), including the ability to predict variations on different scale sizes. The implications of the ability to predict the dynamics in the MLT on space weather forecasts will be discussed.