SPAce weather Research and Technology Applications (SPARTA) Center of Excellence

Numerous coupled-model development efforts have been undertaken over the past two decades,

and much progress has been made in improving the specification and forecast of the state of the

sun-earth system. However, methods to resolve the “Big 3” critical space weather impacts—

ground-induced currents (GICs), radiation effects and radio frequency (RF) scintillations—remain

incomplete. Here we propose a comprehensive program to address the scientific and technical

challenges posed by the need to specify and forecast ionospheric irregularities and associated radio

wave propagation effects (i.e., scintillation) under the auspices of the Space Weather Research and

Technology Applications (SPARTA) Center of Excellence (CoE) led by Boston College joined by

an exceptionally diverse and talented team including institutional PIs from seven other universities

and institutions, six formal collaborators, both foreign and domestic, from government and

academia, four international space weather service providers and three industry partners. The

overall objectives are: 1. Develop and establish a baseline capability for forecasting global

irregularities with an operational background model; 2. Demonstrate GNSS impact products that

meet the primary user requirements documented by NOAA; and 3. Develop a technical roadmap

that prioritizes future upgrades to data collection and background model technologies and

quantifies associated improvements in scintillation forecast skill (i.e., defines “bang for the buck”).

Our modular approach employs WAM-IPE, a global physics-based coupled ionospherethermosphere

model with operational legacy, to specify and forecast the background ionosphere

and associated physical drivers. Regional models will then be applied to perform rigorous stability

analyses used to cue algorithms charting the nonlinear development of instabilities at low, mid and

high latitudes and the resulting small-scale density irregularity spectra. Finally, an advanced

propagation algorithm will be used to determine scintillation effects on radio wave signals and

anticipated impacts on the performance of global navigation satellite systems (GNSS). Validation,

a key capability enhanced by access to extensive data sources and unique exploitation techniques

provided by our diverse team, will be performed at each step to measure performance and support

analyses aimed at optimizing the entire system for scintillation forecasting, as follows.

1. Apply physical models and machine learning to identify key parameters required to assess

irregularity strength, ranking both priority and sensitivity to determine the most critical inputs

needed and to what accuracy, resolution, latency, etc. (serves to document irregularity forecast

model input requirements).

2. Determine if the background forecast model output meets the input requirements of the

instability algorithms through extensive data-based validation and forecast skill assessment.

3. If not, determine why. Are additional observations needed? To what accuracy, resolution, and

latency? Are the physics in the background and/or stability models incomplete, and if so, what

additional physics/processes/linkages are needed?

The answers to these questions inform both the required modeling capabilities and the data

collection architecture needed to attain a desired irregularity forecast skill. The forecast system

will employ machine learning to aid in this process and, through continuous training in an

operational environment demonstration, acquire the capacity to forecast independent of the

nonlinear physics-based algorithms. This effort focuses on a limited but critically important and

challenging phenomenon in space weather, RF scintillation caused by ionospheric irregularities,

particularly as they impact GNSS applications. Results ultimately depend on the quality of the

forecasts from the background model. We do not propose to solve the entire problem, but we are

addressing a critical linkage to users that does not currently exist, despite a formal request for such

knowledge from ICAO. We cannot afford to wait until we have perfect background forecasts to

develop products for GNSS users that are needed now, and SPARTA addresses this critical need.