Rapid Regional Ionospheric Modeling System (R-RIPS)

A key requirement for Air Force theater battle management is the ability to rapidly and accurately determine regional profiles of the upper atmosphere and ionosphere. To meet this requirement, we propose to develop the Rapid Regional Ionospheric ProfilingSystem (R-RIPS). Current models are limited in their ability to meet global requirements for remote and rapid generation of upper atmospheric and ionospheric properties to aid the theater command. They are also limited in their ability to assimilatedisparate data to yield real-time updates for regional applications. We propose an innovative application of current models and both existing and evolving data sources combined with learning algorithms (neural nets) to develop a Fully EquivalentOperational Model (FEOM). The proposed methodology for R-RIPS is built on innovative application of proven techniques. We will use data from existing ionosonde/digisonde stations, satellite sounding data from SSUSI & SSULI, DMSP particle detectors andtwo-frequency GPS signals combined with current ionospheric modeling capabilities to establish sub-global scale ionospheric characterization. Sub-gridding rules will then be established for mesoscale modeling. Learning algorithms will be used to developcritical correlation rules for data assimilation. The combination of real-world data and first-principles models will, in turn, be used to generate a FEOM, which is a model input-output response surface generating a rapidly converging series oflow-dimensional correlation functions linking critical input parameters (e.g. F10.7, Ap, Lat/Lon, time, date, particle fluxes) with output density profiles. The FEOM can be initialized using a local two-frequency GPS receiver which generates line-of-sighttotal electron content. Updated data sets and first-principles modeling will be used to