The next generation of air-transportation systems will face challenges from a broad spectrum of external hazards, including turbulence-related air motion, heavy rain, snow, hail, and an increased amount of air-traffic. Traditionally, a different sensor and/or processor must be used to detect each different type of external hazard. Airborne weather radars rely on a simple signal-intensity or image-analysis based approach to detect external hazards, resulting in limited capability and accuracy. Airborne Doppler radars have evolved over time from early Moving Target Indication (MTI) to today's subspace tracking radar, such as in Ground Moving Target Indication (GMTI); however, these technologies are generally designed for individual point target, but distributed weather targets are treated as clutters. The application of the latest airborne, pulsed-Doppler radar technologies to weather radar systems at a minimum cost would be meaningful with the continued development of newer signal processors, especially for distributed targets.

The concept of comprehensive External Hazard Monitoring Radar (EHMR) is being studied as an integrated solution for detecting multiple types of hazards. In parallel to the ground-based Multi-functional Phased Array Radar (MPAR), this radar system is being developed by the Radar Innovations Laboratory at the University of Oklahoma. Forward-looking, dual-polarized, circular-array antenna architecture is suggested. With the aide of a distributed target scattering model, the external hazards are categorized as dynamic hazards, static hazards, and point targets. Dynamic hazard detection and classification are achieved by analyzing signals from a simple space-time correlation receiver structure, which is considered to be a variation of Space-Time Adaptive Processing (STAP). Wind-field in each resolution cell can be estimated by further analyzing the time-spectral signatures. Based on hazard statistical characteristics, a hazard classification approach allows the radar to ‘focus' on a specific type of hazard while suppressing other kinds of hazards. Static hazard classification, on the other hand, is achieved by analyzing dual-polarized information related to hazard microphysics. A knowledge-base regarding different types of hazards is established from both field and in-door emulated data, which provides basic support to adaptive hazard detection and classification algorithms.