Problems associated with uncertain parameters and missing physics for long-term ionosphere-thermosphere forecasting



[1] Data assimilation models like the Utah State University (USU) Global Assimilation of Ionospheric Measurements (GAIM) models use physics-based models of the ionosphere, ionosphere-plasmasphere, or thermosphere and a Kalman filter as a basis for assimilating a diverse set of measurements. With a sufficient amount of data and with multiple data types, the data assimilation models can provide reliable specifications and near-term forecasts. However, for long-term forecasts (5 days or longer) stand-alone or coupled physics-based models are needed. Unfortunately, the various physics-based models contain several uncertain parameters and processes as well as missing physics. Further complications arise for coupled physics-based models because of coupling issues and error propagation from model to model. Some of the problems are associated with the magnetosphere and lower atmosphere drivers, the adopted set of physics-based equations, the parameterization of physical processes, the values adopted for the transport coefficients, the numerical techniques used, the spatial and temporal resolutions adopted, and the uncertainties in the initial and boundary conditions. Examples of the type of problems the space weather community faces in its attempt at long-term ionosphere-thermosphere forecasting are given.