Team title: Data Assimilation for Thermosphere Neutral Density
Team ID: G2A-05
Team Leads:
Shaylah Mutschler (Space Environment Technologies, USA), smutschler@spacewx.com
Nick Dietrich (NRL - Naval Research Laboratory, USA) Nicholas.Dietrich@colorado.edu
Keywords (impact): Satellite/debris drag
Keywords (activity type): Modeling, Forecasting, Assessment
Introduction:
Data assimilation (DA) models or frameworks have demonstrated superior skill in predicting thermosphere densities over semi-empirical climatological models or first-principles models. Presently there is only one operational model with DA for thermosphere density prediction, HASDM, but several models are in development. This team will address the different methods of DA specifically for improving thermosphere density predictions including realistic uncertainty. The main difficulty to run a model with DA operationally is the (non-) availability of adequate data. The different data types available for DA, neutral densities or another type of observation that improves the thermosphere state via coupling in the model, will also be investigated and evaluated.
Assessment of the performance of the models will be done by means of comparison with thermosphere density data on a daily basis for predicted densities of day n (nowcast), and n+1, N+2 and n+3. CCMC is solicited to develop and run a scoreboard for density forecasts for all types of thermosphere models, i.e. without or with DA.
Objectives:
- Development of data assimilative models with improved predicted thermosphere state
- Provide realistic uncertainty estimates of the predictions
- Investigate the consistency of the available near real time density data
- Assess model performance and quantify respective contributions made by the DA and the forecasted drivers to the quality of the density prediction
Action topics:
- Advance thermosphere modeling capability
Cluster with overlapping topics:
- G2A: Atmosphere variability
Link to external website:
- TBD
Publications
- TBD