Fiscal Year	2019
Division	Graduate School of Engineering & Applied Science
Department	Applied Mathematics
Investigator(s)	Giraldo, Francis X.
Sponsor	Defense Advanced Research Projects Agency (DoD)
Summary	We propose a new high-resolution thermospheric forecast and data assimilation (DA) capability, coupled to a physics-based assimilative ionospheric model, offering transformative advances in thermosphere-ionosphere prediction. Revolutionary new features include: (a) solutions to the nonhydrostatic deep-atmosphere Euler equations on the sphere to ~600 km altitude; (b) use of spectral element (SE) methods with low interprocessor communication footprints, coded for a new era of exascale computing (>1018 Flops) using MIC-, GPU- and TPU-based accelerators, allowing scalability to >10^6 processor cores; (c) unified modeling framework allowing arbitrary global or regional forecast configurations; (d) unstructured grids providing static mesh refinement over areas of operational responsibility (AORs); (e) a new local ensemble transform Kalman filter (LETKF) DA framework for the deep atmosphere; (f) improved forecasts of solar irradiance based on machine-learning algorithms; (g) general infrastructure for two-way coupling of the prognostic thermosphere to any physics-based ionospheric model, and; (h) full forecast specification of all drivers relevant to 0-72 hour thermospheric prediction (solar, geomagnetic, multiscale atmospheric waves, and electrodynamics). We will assess how new high-resolution thermospheric forecast drivers can unlock new ionospheric prediction capabilities via two-way coupling to a physics-based assimilative model NIMO (Navy Ionospheric Model for Operations). We will focus on fine-scale phenomena of high relevance to SEE objectives, such as traveling ionospheric disturbances (TIDs), equatorial spread F (ESF), and, with the inclusion of new metallic ion chemistry and physics into NIMO, sporadic E. Impacts of new forecasting capabilities created by this system can be transformative. It vaults space weather prediction out of the present stasis, in which ionospheric prediction down to ~100 km scales remains impossible despite many physics-based models with sufficient resolution to resolve these scales, into a new era in which thermospheric forecast drivers unlock fine-scale ionospheric predictability within AORs. Game-changing possibilities include, for example, covert ionospheric information asymmetry for electromagnetic maneuver warfare (EMW) in contested/denied AORs. Concentrating R&D resources on this large capability gap in thermospheric prediction, while leveraging state-of-the-art DoD operational modeling and DA components, ensures not just efficient use of DARPA resources and a new system owned and maintained by the government, but provides a credible in-place DoD transition pathway for this new technology, substantially mitigating the traditional research-to-operations (R2O) “valley of death” risk that dooms most technological innovation in this field (Merceret et al. 2013).
Keywords	high-altitude weather model space weather
Publications	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal
Data	Publications, theses (not shown) and data repositories will be added to the portal record when information is available in FAIRS and brought back to the portal