Refractivity From Radio (RFR) Inversions During the 2015 CASPER-East Experiment

Accurate characterization of low-altitude atmospheric refractivity is required by navies to assess the capabilities of electromagnetic (EM) sensors and communications systems. It provides essential information for mission planning, as well as situational awareness as it relates to the EM spectrum. Mesoscale numerical weather prediction (NWP) is widely used to forecast sensor and communications performance, however coarse vertical resolution and lack of local/current observations restrict the ability to accurately predict low-altitude ducts.

In previous works, methods have been demonstrated which infer refractivity from real-time sensor observations of the EM field, namely Refractivity From Radio (RFR) and Refractivity From Clutter (RFC) methods. Here we use a RFR method to infer atmospheric refractivity from data collected during the CASPER-East experiment, part of a Multi University Research Initiative (MURI) under funding from the Office of Naval Research.

One of the CASPER datasets consists of long-term power measurements aboard a moving ship received from signals of opportunity – digital television broadcasts and commercial FM radio. The spatial variation in received power is used to infer refractivity, first by reducing the problem space to a set of diagnostic parameter vectors, then by using an error-minimization approach to determine the best-fit from a library of precomputed RF loss functions. The performance of this RFR algorithm is evaluated for this dataset, and is validated against radiosonde refractivity observations during the same timeframe.