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Weather radar
Published in Mike Tooley, David Wyatt, Aircraft Communications and Navigation Systems, 2017
The basic display used for primary radar systems is the plan-position-indicator (PPI). As the beam sweeps from side to side, a radial image on the display (synchronised with each sweep) moves across the display. The image on the display depends on the amount of energy returned from the target. Original weather radar systems had dedicated monochrome displays based on a cathode ray tube (CRT); these have evolved over the years into full colour displays, often integrated with other electronic flight instruments. The full benefits of a weather radar system can be appreciated when the system is used on an aircraft with an electronic flight instrument system (EFIS) display, Figure 20.7. A symbol generator is used to provide specific weather radar images as determined by the transceiver. An electronic display control panel allows each pilot to select the range of weather radar in increments of 10, 20, 40, 80, 160 and 320 miles.
Introduction
Published in Graham V. Weinberg, Radar Detection Theory of Sliding Window Processes, 2017
Sliding window detectors (SWDs) have been in use since the 1960s and provide a somewhat simpler alternative to the Neyman-Pearson based decision rules. These arose out of analysis of plan position indicator (PPI) displays used to show the radar view of the scanned region, as well as plots of intensity measurements of radar returns as a function of range, Doppler or both. The popularity of such ad hoc detection processes was due to the fact that, in lower resolution X-band maritime surveillance radar clutter returns, the Gaussian model was appropriate. When viewed from an amplitude squared or intensity perspective, the clutter is exponential in distribution. Hence it became somewhat simple to propose sliding window detection schemes whose probability of false alarm (Pfa) could be set independently of the clutter power, and so allowing for CFAR control. These detectors could be implemented regardless of the underlying target model, thus producing a useful alternative to optimal detectors which require some assumptions to be made regarding the target model, or at least an approximation for it.
Introduction
Published in Habibur Rahman, Fundamental Principles of Radar, 2019
Plan-position-indicator (PPI): It is a circular or polar display with echo signals from reflecting objects indicated by the plan position, and range and azimuth angle indicated by the polar coordinate, forming a map-like display. In surveillance and color weather radar applications the PPI displays a full coverage of 360°.
Application of weather Radar for operational hydrology in Canada – a review
Published in Canadian Water Resources Journal / Revue canadienne des ressources hydriques, 2021
Dayal Wijayarathne, Paulin Coulibaly
The precipitation input for numerical forecasting models mainly comes from conventional rain gauges. There is significant attention to real-time precipitation information derived from Radar to complement traditional rainfall gauges as it provides real-time, spatially, and temporally continuous data that can enhance operational forecasting. Different aspects were used in the past to produce operational forecasts using weather Radars in Canada. Using McGill S-band weather Radar, the Short-term Automated Radar Predictions (SHARP) method was developed to generate 0 to 3-hour interval precipitation forecasts (Bellon and Austin 1984, 1978). The mean absolute deviation between Radar and gauge precipitation calculated over the catchment varies from 49% to 60% for 0.5 to 3-hr forecasts, respectively. Nearly a decade after the verification against the rain gauges, the MRO began providing real-time rainfall estimates using the linear extrapolation of the Radar image (Bellon and Zawadzki 1994). The linear extrapolation method was frequently used by researchers to make nowcasts in Canada (Bellon and Austin 1984; Bellon and Zawadzki 1994). In this method, the Radar echoes were translated by the same amount proportional to a calculated past motion. One-hour forecast rainfall accumulation was transformed at a spatial and temporal resolution of 1 km and 5 minutes based on the linear extrapolation of the latest Constant Altitude Plan Position Indicator (CAPPI) data. An attempt to use spatial and temporal averaging (smoothing) was taken to improve the skill of this technique by researchers at MRO (Bellon and Austin 1984). A 10% reduction of Root Mean Square Error (RMSE) was observed after smoothing for 1 hour ahead precipitation forecasts compared to the unsmoothed Radar data. Another study conducted in 1980 developed an algorithm that yields the likelihood of rain from combining satellite imagery and McGill S-band Radar data obtained in eastern Canada (Bellon, Lovejoy, and Austin 1980). The algorithm acquired two bivariate frequency distributions from infrared and visible satellite images and then collocated with Radar, which was used to discriminate rain and non-rain clouds. An operational version called Rainsat was developed after this study and was used in real-time operational forecasting since 1981. Damant, Austin, Bellon, Osseyrane, et al. (1983) examined the errors involved in Radar QPEs using linear extrapolation forecasting technique to generate precipitation forecast. The unadjusted radar-gauge comparison showed an average bias of 3%, RMSE of 87%, and an absolute difference of 50%. After adjusting Radar QPEs to the storm-bias, bias, RMSE, and absolute differences were reduced to −2%, 67%, and 33%, respectively.