Supporting Information
Appendix S1. Complete description of the methods used to prepare the weather surveillance
radar data for analysis.
Weather surveillance radar data acquisition and preparation
We obtained raw Level II NEXRAD Doppler radar data (WSR-88D) for 12 weather surveillance
radar (WSR) stations located in the northeastern United States (Fig. 1a) from the National
Climatic Data Center archive. For each WSR station, we compiled 2010 and 2011 radar scans
between sunset and sunrise during the period from the 1st of August to the 30th of November.
Each WSR station conducts a sequence of volume scans, where the radar is tilted at a variety of
angles from the horizon, every six to ten minutes (Crum & Alberty 1993). For our analysis, we
retained one radar scan per hour, starting with the scan occurring closest to sunset and then
selecting scans as close as possible to one-hour increments after sunset. This procedure resulted
in 39,840 hourly radar scans for our study period. We previewed the hourly radar scans using a
custom-designed application to eliminate scans contaminated by non-biological targets (e.g.,
precipitation or ground clutter) within 37.5 km radius of each WSR station and up to a maximum
altitude of 3,000 m above ground level (AGL). We retained a total of 27,850 scans for analysis
after excluding scans that contained various types of targets contaminating the volume or showed
ambiguous patterns indicative of atmospheric instabilities.
We used nearest neighbor interpolation to align each radar scan onto a fixed polar grid
with angular resolution of 0.5° and range bins of 250 m. Radial velocity and reflectivity of scans
may be collected at different spatial resolutions, so alignment facilitates direct comparison. We
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built static clutter maps from these scans to exclude pulse volumes that had consistently high
returns, which indicate contamination by targets on or near the ground, or consistently low
returns, which indicate blockage of the radar beam by objects closer to the radar. Static clutter
maps used the fixed polar grid at tilt elevations of 0.5°, 1.5°, 2.5°, 3.5°, and 4.5° above the
horizon. For each pulse volume in the static clutter maps, we analyzed its probability of detection
and mean reflectivity over the entire set of scans (Buler & Diehl 2009). Probability of detection
is defined as the fraction of reflectivity measurements that exceeded a fixed threshold, which we
set to 10 dBZ. We classified pulse volumes with probabilities >0.7 and mean reflectivity >15
dBZ as ground clutter. We also considered beam blockage, which occurs when the scan is
blocked from reaching certain pulse volumes by ground targets or landforms near the WSR
station. In visual inspection of the imagery, the impact of beam blockage was limited to one
WSR station (KCXX). The blockage was only significant in the lowest tilt elevation (30-50%),
supporting the retention of the WSR station in the analysis based on the quality of the data in the
four higher tilt elevations. To exclude additional clutter from stationary targets on or near the
ground, all pulse volumes with radial velocity measurements in the range ±1 m sec-1 were
classified as “dynamic clutter” and excluded from analysis (Doviak & Zrnić 2006).
We estimated target velocities within the radar scans using an algorithm developed by
Sheldon et al. (2013). The procedure builds on a set of techniques known as velocity-azimuthal
display or velocity volume profiling (Doviak & Zrnić 2006) by incorporating a model for
aliasing ambiguity in the radial velocity measurements directly into the velocity model. With this
procedure, we binned pulse volumes based on height above ground level and extracted the
following summary measurements for each 250 m height bin: (1) mean target velocity, (2) the
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root mean squared error of the radial velocity measurements relative to the fitted uniform
velocity model, and (3) the number of pulse volumes.
To distinguish birds from wind-borne targets such as dust, smoke, and insects, we
compared the radial velocity of each pulse volume to the radial wind velocity at the same
location. Wind velocity estimates were taken from the North American Regional Reanalysis
(NARR; dataset number ds608.0; Mesinger et al. 2006). We classified pulse volumes as birds if
the target radial velocity differed from the wind radial velocity by at least 6 m s-1 (Gauthreaux &
Belser 2003) and wind-borne targets if they did not meet this threshold. Next, we computed the
average reflectivity in each elevation bin. We considered pulse volumes with no data (i.e., where
the signal-to-noise ratio was below the minimum threshold determined by WSR-88D) and pulse
volumes classified as wind-borne targets as having zero reflectivity. We discarded pulse volumes
that were classified as static or dynamic clutter. Of the remaining pulse volumes, we discarded
the largest and smallest 25% of all values as well as any values exceeding 35 dBZ to minimize
the potential impact of false absences and very high reflectivity values due to undetected clutter
(Buler & Diehl 2009). Finally, we averaged the remaining values to get the reflectivity value of
the height bin. We converted the WSR-88D units of reflectivity factor (mm6 m-3) to reflectivity
(cm2 km-3), which gives a quantity that is easier to interpret biologically: it is the total effective
scattering area of targets (in square centimeters) per unit volume (cubic km) (Chilson et al.
2012). To produce the overall density value for the scan, we averaged the reflectivity values
from each non-empty elevation bin. By using an unweighted average, we effectively computed
the density of scatters within a cylinder (of height 3km and radius 37.5km) centered at the WSR
station. When computing average velocities we excluded elevation bins where the velocity
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measurements were based on fewer than 10 pulse volumes or the root-mean-square error was
more than 5 m sec-1.
References
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