Data-Driven Predictive Sporadic-E Model (D2PSEM)

Civilian and military communications, navigation, and radar applications depend on predictable propagation of radio waves through the ionosphere. These applications are severely impacted by irregular ionospheric structures that can suddenly alter the propagation properties, including scintillation of transmitted waves. Spectral Sciences, Inc. proposes to develop a forecasting capability of ionospheric irregularities. The Data-Driven Predictive Sporadic E Model (D2PSEM) will be an algorithm which provides a two-week to one-month forecast of the time-dependent probability of occurrence of sporadic E layer of the ionosphere above a user-defined density threshold at user-specified locations on the globe.D2PSEM will operate on measured data to establish a set of correlations between relevant physical quantities in the temporal and spatial domains. Once developed, this function will be used to identify the key physical quantities of interest from a known set of correlative properties. The comprehensive correlation model will then be the kernel of a Data Assimilation Predictive Model that propagates occurrences while updating the correlation coefficients based on the latest ionospheric data.