cartome.org

05 March 2001

Source: http://www.srh.noaa.gov/ftproot/topics/attach/html/ssd96-36.htm

SR/SSD 96-36 8-15-96

Technical Attachment

A NEW ALGORITHM TO IDENTIFY ERRORS CAUSED BY MIGRATING BIRDS IN PROFILER WINDS

Doug van de Kamp NOAA, Forecast Systems Laboratory

(Ed. note: The 404 MHz Wind Profiler Network operated by NOAA's Forecast Systems Laboratory in the central United States is implementing a new quality control algorithm effective 0000 UTC on August 15, 1996. The algorithm and details of the changes are described below. This information was provided by Rick Decker (NWSH Office of Meteorology, NWS program leader for the profiler program.)

Introduction

A new quality control (QC) algorithm will be implemented on the hourly winds from the NOAA Profiler Network (formerly called the Wind Profiler Demonstration Network). The algorithm is designed to flag winds that are suspected of being biased in wind velocity or direction by migrating birds. The algorithm is applied only at night, only below 4.5 km above mean sea level (below about 570 mb or 15,000 ft), and only during the spring and fall migration periods. No change to the AFOS display software for profiler data will be needed. Winds suspected of being biased by migrating birds will be identified with a "1" (QC check failed or not performed) flag on the AFOS time-height wind display. A more detailed description follows covering bird migration patterns, the new QC algorithm, and the results of an experiment conducted to determine the impact of birds to the wind profiler data and to evaluate the performance of the algorithm (Barth et al., 1996).

Bird Migration Patterns

Different species of birds migrate at different times of the year. Definite peak migration periods occur at night during the spring and fall when low-level winds are favorable for their direction of travel. Specifically, the birds migrate northward during the spring, using the low-level southerly jet, and southward in the fall.

Large migrating geese are not a major problem to the profiler, as might be expected. The birds that cause the greatest impact to the profilers are small songbirds (passerines) which fly in a dispersed pattern rather than a tight flock. They typically start flying about 30 minutes after sunset and continue until about 30 minutes before sunrise, flying at an altitude of most favorable tailwinds (or very weak headwinds), generally less than 2-4 km above the ground. Their maximum height appears to be limited by the ambient air temperature (warmer than -2 C). They typically fly at a lower maximum altitude in the spring (using the nocturnal low-level southerly jet) compared to the fall when they fly at greater heights in the warmer, deeper mixed layer. The birds typically fly with an airspeed of about 8-15 m/s (15-30 kt) (Wilczak et al., 1995).

Contamination of Wind Profiler Data Caused by Birds

Wind profiling radars are designed to measure extremely weak signals from the clear air throughout the troposphere and lower stratosphere. These weak signals require a long averaging time (one minute) in each of the three fixed-beam positions used by the profilers (generally pointing toward the east, north, and vertical). The reflected energy from a single bird flying through the radar beam even for just a few seconds is much greater than the clear-air signal averaged over the one-minute dwell time. Therefore, the profiler reports a velocity equal to the flying speed of the bird(s) toward or away from the profiler. This is most noted in the northward pointing beam.

The existing hourly consensus averaging algorithm that has been used for the past ten years effectively removes isolated (a few per hour) erroneous velocity measurements before averaging the remaining "good" points. Problems occur when consistent "bad" data (in time) are competing with, or overwhelming, the "good" wind data. In addition, because the birds produce higher reflectivity than clear-air, they can also be detected as they fly through the profiler's antenna sidelobes. This causes differing velocity and spectral width components to be measured and averaged together. However, the erroneous positive and negative velocity measurements may average out to nearly the correct background wind, while the wider than normal spectral widths remain (a key element in the quality control algorithm).

In order to measure the extent of the contamination, data from four profilers in Kansas and Oklahoma were compared with data from collocated rawinsondes that were launched every three hours during the spring and fall of 1994. The average errors caused by the bird migrations were 8-9 m/s, with larger errors possible up to the airspeed of the birds (15 m/s). In total, it was found that during the spring and fall migration periods only about two percent of all the profiler wind data were being contaminated by birds (when considering all wind measurements in time and height).

The Bird Detection Algorithm

The algorithm checks for the following conditions at each measurement height (range gate) in the hourly averaged profiler data:

Springtime (February 15 - May 15) and 0200 - 1200 UTC or Fall (August 15 - November 30) and 0000 - 1300 UTC
Height less than 4.5 km above msl
Wind direction from the south during springtime or from the north during the fall
North beam velocity variance greater than 1 (m/s)2
Vertical velocity greater than 3 m/s (wider spectral widths naturally occur during precipitation)
The wind is flagged as bad (identified with a "1" on the AFOS display) at each height where all of the above conditions are met.

The rawinsonde data were also used to statistically evaluate the algorithm. The Probability of Detection (POD) was generally good, successfully flagging over 60 percent of the bird-contaminated wind data. (Bird contamination was defined by computing the standard deviation of wind component velocity differences (approximately 2 m/s) during non-bird migration periods, and using two times this standard deviation as the threshold for identifying velocity contamination in the spring and fall.)

A rather high False Alarm Rate (FAR) of 44 percent was noted. This appears to be due to the positive and negative velocity errors (caused by birds in the sidelobes) averaging out to near the true wind velocity, while the spectral width average remains high and trips the bird detection algorithm. Therefore, the FAR appears to be somewhat fictitiously high. Further evaluation and adjustments to the algorithm are expected.

An obvious change after the algorithm becomes operational on August 15 will be an increase in the number of winds below 4.5 km being flagged as failing the quality control. Users of the data should employ their best judgment to interpret these winds. For further information, contact the author at (303) 497-6309 or vandekamp@fsl.noaa.gov, or the Profiler Control Center at (303) 497-6033. For a copy of the report documenting the experiment, contact Mike Barth at (303) 497-6589 or barth@fsl.noaa.gov.

References

Barth, M. F., P. A. Miller, J. R. Smart, L. A. Benjamin, 1996: Quality Control of Hourly Profiler Winds for Bird Contamination. Memorandum for The Record (NOAA/FSL).

Wilczak, J. M., R. G. Strauch, F. M. Ralph, B. L. Weber, D. A. Merritt, J. R. Jordan, D. E. Wolfe, L. K. Lewis, D. B. Wuertz, J. E. Gaynor, S. A. McLaughlin, R. R. Rogers, A. C. Riddle, and T. S. Dye, 1995: Contamination of Wind Profiler Data by Migrating Birds: Characteristics of Corrupted Data and Potential Solutions. J. Atmos. Oceanic Technol., 12, 449-469.