Predictability and Ensemble Modeling of the Space-Atmosphere Interaction Region NASA

Credit: UCAR

UCAR

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, solar wind and magnetosphere interactions. The predictability of the SAIR is a fundamental question in space science, and calls for a paradigm shift from a deterministic to a probabilistic modeling framework.

By using an ensemble forecasting and data assimilation system that extends from the ground to edge of space, which is being built on the National Weather Service's operational numerical weather prediction systems, the project aims to:

Determine the intrinsic predictability of the SAIR system and its nonlinear sensitivity of internal dynamical and chemical processes to initial conditions, as well as to space and terrestrial weather drivers; and

Quantify the limits of practical predictability set by the uncertainty associated with both SAIR initial states and
forcing specified by observations through the use of data assimilation.

  • Funding sources: $1.2M (2014-2017) from NASA Heliophysics Division
  • PI: Tomoko Matsuo (CU-Boulder)
  • Collaborators: NOAA, University of Maryland at College Park, NCAR, UCAR, University of California at Berkeley, John Hopkins University Applied Physics Laboratory, and National Central University, Taiwan
  • Societal relevance: The region's geophysical conditions affect orbit determination, re-entry, descent, and landing of sub-orbital and orbital vehicles, which are highly relevant to interests of the growing commercial space transportation industry.
  • For more information: Project Webpage

Assimilative Mapping of Polar Ionospheric Electrodynamics NSF

FAC

The polar ionosphere plays an important role in the Sun-Earth connection chain, hosting the most dynamic electromagnetic energy and momentum exchange processes between the upper atmosphere and the magnetosphere. Specification of the electrodynamic state of the polar ionosphere is of paramount interest to the space science community. It defines one of the major driving forces of the thermosphere and ionosphere system and provides us with a means to probe physical processes in the distant magnetosphere.

Inspired by the availability of global monitoring of high-latitude ionospheric currents by the Iridium Satellite constellation, we are extending the capabilities of NCAR's community data assimilative procedure called Assimilative Mapping of Ionospheric Electrodynamics (AMIE) under the NSF-funded AMIE Nextgen project. The project aims to:

Analyze multiple types of space-based and ground-based observations simultaneously and self-consistently with rigorous consideration of uncertainty associated with observations and prior information; and

Obtain a coherent inter-hemispheric picture of global ionospheric electrodynamics including ionospheric convective electric fields, field-aligned currents, ionospheric currents and conductivity, Poynting flux and Joule heating.

Assimilative Modeling of Thermospheric Mass Density and Its Impact on Satellite and Debris AFOSR

Credit:swpc

SWPC

The amount of space debris has increased steadily since the launch of Sputnik in 1957, and about 22,000 man-made objects that are 10 cm or larger in diameter are currently cataloged and tracked in low Earth orbit (LEO) by the DoD's Joint Space Operations Center. The largest uncertainty in LEO orbit prediction is aerodynamic drag estimation. We are developing data assimilative approaches for specification and forecasting of thermospheric mass density using an ensemble Kalman filter in collaboration with NCAR. A promising research direction is the inference of thermospheric mass density from abundant plasma density observations through feedback between plasma and neutral variables incorporated in coupled thermosphere-ionosphere data assimilation. All the software to reproduce these research results has been available for community use.

  • Funding sources: $160K (2013-2016) from Air Force Office of Scientific Research
  • PI: Tomoko Matsuo (CU-Boulder)
  • Collaborators: NCAR and National Central University, Taiwan
  • Societal relevance: The Earth's upper atmosphere mass density variations are, among other factors such as neutral winds and drag coefficients, the major source of drag estimation errors at altitudes below about 700 km.

Assimilation of GNSS Data for Specification and Forecasting of the Ionosphere AFOSR

Credit:NICT

NICT

The recent availability of global observations of ionospheric parameters, especially from GNSS receivers on LEO platforms, has motivated a number of attempts at assimilating ionospheric data. Our research strategy is to take advantage of the tight coupling between neutral and plasma species in the upper atmosphere in assimilation methods, to facilitate inference of under-observed thermospheric states from better observed ionospheric states. Because the ionospheric state is strongly affected by thermospheric winds, temperature, and compositions, and the thermosphere has a relatively long-term memory, this strategy can enhance the predictive capability of a coupled thermosphere and ionosphere general circulation model. Analogous approaches are found in ocean-atmosphere coupled data assimilation. We have examined the impact of assimilating GNSS-based plasma density observations, from radio occultation experiments as well as ground-based stations, into a coupled thermosphere-ionosphere model for ionospheric specification and forecasting.

  • Funding sources: $160K (2013-2016) from Air Force Office of Scientific Research
  • PI: Tomoko Matsuo (CU-Boulder)
  • Collaborators: NCAR, National Cheng Kung University, Taiwan, National Central University, Taiwan, and John Hopkins University Applied Physics Laboratory
  • Societal relevance: Ionospheric specification and forecasting is highly relevant to communication, navigation and positioning systems.
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Multi-resolution Spatio-Temporal Random Fields Modeling NSF

Geophysical processes such as wind and oceanic currents, gravity and magnetic fields are well-studied examples of vector fields on the sphere. Through an interdisciplinary collaboration with the Department of Statistics at the University of California at Davis, we are developing a novel statistical inferential framework for multi-resolution modeling of random vector fields on the sphere. The use of multi-resolution needlet frames on the sphere for analysis of scalar random fields is extended to construct localized and parsimonious representation of vector fields that satisfy natural physical constraints such as being curl-free or divergence-free, thereby enabling a flexible approach to approximating physical processes.

  • Funding sources: $397K (2015-2018) from NSF and $25K (2015-2016) from CU's innovative research program
  • PI: Tomoko Matsuo (CU-Boulder)
  • Collaborators: NCAR and University of California at Davis
  • Broader impacts: The innovative statistical methodology developed by our multidisciplinary collaboration is expected to influence model building and data analysis in diverse disciplines such as oceanic and atmospheric sciences, space and astrophysics, geophysics, and neuroscience since spatiotemporal data with complex dependency structures are ubiquitous in such fields.
needletgrid needlet3D

Magnetospheric Data AssimilationAFOSR

Credit:Wikipedia

en.wikipedia

The interaction between solar wind and the magnetosphere results in the deposition of electromagnetic energy at the polar ionosphere, triggering dramatic global upper atmosphere responses and disturbing the LEO environmental conditions that impact satellite and debris drag. In collaboration with the Physics Department at the University of New Hampshire and NCAR, we are investigating the potential of ensemble data assimilation to improve the global magnetospheric model's predictive capability. Comprehensive global magneto-hydrodynamics magnetosphere models developed since the mid 1980's are now being coupled to ionosphere-thermosphere models. The idea is to calibrate relevant model parameters by using remote sensing and in-situ observations of the ionosphere and thermosphere. Even though observations of the magnetospheric state are extremely sparse, imprints of magnetospheric processes are amply found in thermospheric and ionospheric observations.

  • Funding sources: Subcontract $80K (2015-2018) from AFOSR
  • PI: Jimmy Raeder (University of New Hampshire)
  • Collaborators: University of New Hampshire and NCAR
  • Societal relevance: During space weather events, elevated electric currents in the magnetosphere and ionosphere experience induce currents (GIC) in conductors operated on the Earth's surface. GIC can cause problems, such as increased corrosion of pipeline steel and damaged to high-voltage power transformers. GIC are one possible consequence of geomagnetic storms, which may also affect geophysical exploration surveys and oil and gas drilling operations.