ILLINOI S UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN PRODUCTION NOTE University of Illinois at Urbana-Champaign Library Large-scale Digitization Project, 2007. ILL Natural History Survey LibratM ILLINO.\ IS N.\T-IR.AL HISTORY --Is r SLRV\ ~EY~ I CENTER FOR WILDLIFE ECOLOGY NEXRAD Algorithm for Bird Hazard Warning Contract No. 14-16-009-87-1221 Final Report to U.S. Fish and Wildlife Service 1 June 1990 - 31 December 1992 Prepared by: Ronald P. Larkin, Principal Investigator Illinois Natural History Survey 3 November 1994 I - Executive Summary The NEXRAD (WSR-88D) Doppler weather radar system is being installed throughout the United States, replacing the current generation of weather radars. Extensive computerization of the WSR-88D permits it to perform sophisticated and sometimes automated processing of the echoes it receives. Although designed to detect and warn of dangerous weather, the WSR-88D also receives echoes from flying birds, as determined by a preliminary study completed in 1983. This final report describes the results of research carried out at the Illinois Natural History Survey in 1984-1991 to develop a capability for the WSR-88D to process bird echoes. Three algorithms (or computer programs), described here, are the product of this research. They would permit the WSR-88D to process, quantify, and issue real-time information on bird echoes received by the radar, without human intervention. Such capability is desired by military aviation safety authorities such as the U.S. Air Force Bird-Aircraft Strike Hazard (BASH) Team, because of continuing annual loss of lives and heavy dollar losses due to collisions between high- performance military aircraft and flying birds. Real-time warning of concentrations of flying birds, supplied by WSR-88D radars, would reduce this annual loss. In addition, use of these powerful instruments, which together nearly blanket the nation, as research tools will be beneficial to wildlife and agriculture as well as to aviation. Research was carried out before quantitatively-accurate WSR-88D data became available; therefore, data from research weather radars were adapted to resemble WSR-88D data as closely as possible. Algorithm capability and skill were assessed by comparing algorithm results with separate visual and tracking radar observations and with various simulations and trial runs. Parameters in the algorithms that can be adjusted to suit new species or local conditions are defined in an Appendix. The Migrating Birds Algorithm concentrates on broad-front migration of mixed species of birds, most of which takes place at night in North America. Internally, it uses a knowledge matrix (expert system) approach to sort bird echoes from weather, insects, and clutter in widespread echo regions moving at night. The operation of this algorithm, which could not be verified to the degree that the other two algorithms were verified due to unavailability of data, is largely described in earlier interim reports. The Roosting Birds Algorithm finds locations where large numbers of various species of birds, especially "blackbirds", gather nightly and marks the area surrounding such roosts as hazardous for low-level operations, takeoffs, and landings. Internally, the algorithm uses a computer vision technique called the Hough Transform to recognize specific patterns generated by waves of birds departing a roost in the morning. The algorithm located about 75% of test roosts accurate to 2 km of their actual location and yielded a function relating echo strength to numbers of birds that was significant at p = 0.03. Larger roosts were located more accurately. The Flocks of Waterfowl Algorithm focuses on particular days of the year when spatially-extensive flocks of large birds migrate en masse both day and night. Internally, a multidimensional track-while-scan operates on successive radar sweeps, taking advantage of known properties of the species .of birds. Algorithm-generated paths of individual flocks were biologically accurate, corresponded to spot-observations in the field and to results of a limited hand- analysis, and were highly consistent among three migration events in three years. An extensive discussion of the kinds of errors to which such an algorithm is subject is presented. Executive summary I. Introduction and general methods II. Migrating birds II. Roosting birds IV. Following flocks of waterfowl Acknowledgements Literature Cited Appendix I: Glossary and acronyms Appendix II. English and scientific names of animals Appendix I. The radar equation Appendix IV. EMULATE_NEXRAD subprogram Appendix V. Adaptable parameters I. Introduction and general methods Birds pose to military aviation a risk that is costly in dollars and in human lives (Blokpoel 1976, Gauthreaux 1974, United States General Accounting Office 1989). One way to reduce these costs is to reduce or avoid operations where hazardous birds are in the air. This can be accomplished on an actuarial basis, by forecasting the annual or daily statistical likelihood of the presence of birds and avoiding scheduling flight operations at those locations, times, and heights. It can also be accomplished on a real-time basis, by detecting the presence of hazardous birds on an hour-by-hour basis and rerouting, rescheduling, or canceling flight operations based on observed hazard. A preliminary study (Larkin 1982) established that large weather radars have a potential role both in providing better data for the actuarial approach and real time to warn pilots of birds. The present study describes basic research behind the development and testing of weather radar algorithms to exploit this potential contribution of weather radars to military air safety. Many aspects of the project have been described in previous reports and papers (Defusco, et al. 1986, Larkin and Quine 1988, Larkin 1982, Larkin 1990, Larkin 1991b, Larkin and Quine 1987, Larkin and Quine 1989, Quine and Larkin 1987), whose contents will not be repeated here except where necessary e.g. to provide further data from those available earlier or to provide updated descriptions of algorithms. Weather radars include a variety of different kinds of radars designed to detect echoes from moisture, particulate matter, and refractivity gradients and to aid in studying and forecasting air motion, rain, and other meteorological phenomena. The general characteristics of and principles behind weather radars are described in several books (Doviak and Zmic 1984, Rinehart 1991, Skolnik 1970). Also, some technical radar terminology is briefly defined in Appendix I of this report. The weather radars discussed in the present report are all similar, I- 1 long-range radars sharing many characteristics: They have large antennas, resulting in great sensitivity and long-range detection capability and "narrow" beams on the order of 10. They emit short pulses of microwave frequencies corresponding to wave lengths of about 10 cm (S-band) or sometimes 5 cm (C-band). They usually rotate slowly and continuously through 3600 (a "sweep") while changing angle above the horizon (elevation) more slowly. They record the amount of echo that returns to the radar (reflectivity, see Appendix fI), the phase shift of that echo that results from motion, if any, toward or away from the radar (Doppler speed), and usually the amount of variation in Doppler speed (Doppler spectral width or sometimes variance), which is often attributable to variable motion by several scatterers at nearly the same range or to different motions by different parts of the scatterer. The heretofore-standard U.S. operational weather radar, the model WSR-57, (Gauthreaux 1992) is being replaced by more a modem Doppler radar initially known as the Next Generation Weather Radar and now officially known as the WSR-88D (Crum and Alberty 1993, Crum, et al. 1993, Klazura and Imy 1993). The WSR-88D has better electronic specifications than its predecessors but more important attributes for the present application include: * Doppler ability. * Sophisticated data delivery and display capability. * Computer-controlled operation and processing of weather echoes. Importantly, this radar system contains enough computer power at each radar unit that it will be feasible for the WSR-88D to function as a bird-warning device at those (many) times when severe weather does not exercise the full capacity of the WSR-88D. Because the operators of the WSR-88D will not be trained as radar biologists, it is desirable to build as much intelligence as possible into WSR- 88D bird-recognition capabilities. Therefore, the present research sought to 1-2 design computer algorithms that can distinguish between bird echoes and other echoes in real time or near-real-time and report the presence of birds hazardous to aviation, without human intervention. The project does not address the primarily military problem of using such information to convey warnings to pilots or other personnel. A second goal of the project, that of developing the potential of automated weather radars to help in understanding the movements of flying animals, has been partly accomplished in the course of pursuing the more encompassing goal of automated bird recognition. Initially, workers on the project envisioned a two-step process: (1) distinguishing bird echoes from other echoes and (2) processing the bird echoes to determine the nature, height, and magnitude of the hazard. As the magnitude of the overall problem became visible and as the echoes began to sort themselves out over the landscape, the investigators realized that different classes of bird echoes differ from one another as much as they differ from non-bird echoes; therefore, several different algorithms would be needed to handle several very different kinds of bird movements. Eventually there evolved a Migrating Birds Algorithm (Chapt. II) to handle broad-front, mainly nocturnal migration of mixed species of songbirds, waterfowl, shorebirds, and other birds; a Roosting Birds Algorithm (Chapt. III) to handle resident birds that congregate together every night and disperse again in the morning; and a Flocks of Waterfowl Algorithm (Chapt. IV) to handle well-oriented mass movements of spatially- extensive flocks at any time of day.