Development of Scanning Strategies to Meet Operational Needs of the Multimission Phased Array Radar

In order to improve the quality of radar coverage across the contiguous United States, a new nationwide network of radars has been under investigation. This radar network must meet certain requirements for data quality, spatial resolution, and temporal resolution. Additionally, it is a goal of the United States government for this new radar program to be spectrum-efficient, allowing for sale of spectrum to private companies. Thus, tasks which were previously split between separate radar systems such as WSR-88D (NEXRAD), TDWR, ASR-9/11, and ARSR-4 will now be combined into one Multimission Phased Array Radar (MPAR) system. One major difficulty of this undertaking is combining tasks which were previously spread across multiple radar frequency bands into one frequency band. Another difficulty is balancing the disparate requirements, both legacy and newly imposed, of each radar functionality. For example, terminal-area aircraft scanning must be updated every 4.8 seconds, while the volume coverage pattern (VCP) for weather must meet a 60-second update time. One proposed method of meeting these goals is via use of an all-digital phased array radar, which can leverage electronic beam steering and beam multiplexing on transmit for faster update times. In this study, several VCPs and time-slot allocations are developed which would meet or exceed the program requirements for meteorological surveillance, as well as for tracking both cooperative and non-cooperative aircraft. The scanning strategies proposed herein achieve the desired goals via techniques such as radar imaging, multiple-beam technique (MBT), and adaptive scanning. The advantages and tradeoffs of each scanning strategy are compared both quantitatively and qualitatively. For a quantitative analysis of the advantages gained via MPAR over NEXRAD, the new scanning strategies will be tested in a radar simulator (SimRadar) with high-temporal resolution weather data input from the Weather Research and Forecasting (WRF) model. By comparing simulated radar data obtained by MPAR with simulated lower-resolution ‘legacy’ data, we can quantify the benefits from the higher temporal resolution of MPAR for weather observations. Eventually, the effects of higher temporal resolution on data assimilation into numerical weather prediction (NWP) models will be explored.