Data-Driven Predictive Sporadic-E Model (D2PSEM)

Civilian and military communications, navigation, and radar applications depend on reliable propagation of radio waves through and in the ionosphere. These applications are severely impacted by irregular ionospheric structures that can suddenly alter the propagation properties or produce 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 (Es) of the ionosphere above a user-defined density threshold at arbitrary locations on the globe.  D2PSEM will operate on measured data to establish a set of correlations between relevant physical quantities in the temporal domain followed by a relationship between those correlations and the spatial domain.  Once developed, this function will be used to derive 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 foreword propagates occurrences while updating the correlation coefficients based on the latest ionospheric data.