bioRad is an R package for the biological analysis and visualization of weather radar data. The package is described in our paper:
Dokter AM, Desmet P, Spaaks JH, Van Hoey S, Veen L, Verlinden L, Nilsson C, Haase G, Leijnse H, Farnsworth A, Bouten W, Shamoun-Baranes S (2019) bioRad: biological analysis and visualization of weather radar data. Ecography 42: 852-860. https://doi.org/10.1111/ecog.04028
The paper also defines weather radar equivalents for familiar measures used in the field of migration ecology and is thus a good start if you are new to radar aeroecology. Below we included the sections and figure from the paper describing the package and typical analysis workflow, with links to the functions used.
The functionality of bioRad is summarized in Fig. 2. Essentially, the package allows users to:
In bioRad, class objects are used for storing low-level data and data products, shown as blue/green boxes in Fig. 2. R has multiple class object systems, and bioRad uses the S3 object system (Chambers 2016). Most of these class objects have an associated plot method for making quick visualizations. The right-hand side of Fig. 2 shows examples of the output of these plot methods, for two migration events of similar intensity, one in Europe and one in the US. bioRad is able to extract vertical profiles of speed, direction, and density at different flight altitudes from low-level radar data, while offering standardized tools for post-processing and further analysis. Spatial variation in the horizontal plane is averaged out in profiles, and data is usually processed up to 25–35 km from the radar. Vertical profiles are generated in bioRad with the vol2bird algorithm, originally developed for single and dual-polarization C-band radars (Dokter et al. 2011).
The underlying C-code for the algorithm has been refactored for compatibility with European and US radar formats, and for improved structure and readability of the code base. Additional support has been added for dual-polarization S-band radars, like the US WSR-88D/NEXRAD radars, as well for dealiasing radial velocities. The package does not yet support automated removal of precipitation signals for single-polarization S-band radar. For these radars the generated profiles should be manually screened for precipitation contamination (cf. step 4 analysis workflow).
The figure shows the structure and interrelation of bioRad’s main
class objects, functions, and plotting methods. (A)
objects (rounded box), functions
fixed width font) and their relation
(arrows). (B–K) output of the default plot methods for a European radar
(left row, Offenthal radar, Germany, 2016-10-04 15:15 UTC–2016-10-05
08:45 UTC) and US radar (right row, KBRO radar, Texas, 2017-05-14 00:09
UTC–2017-05-14 13:25 UTC). The dotted line in (H) and (K) indicates the
time slice of (B), (D), (F) and (C), (E), (J) respectively. Grey shading
indicates night time (time on the x-axis is in UTC). Altitudes are
relative to mean sea level.
dens) versus altitude (RCS = 11 cm2) for a single vertical profile.
dens) and speed and direction (
ff) for a time series of vertical profiles.
The low-level radar data with which bioRad interacts are so-called
polar volume data. A polar volume is a collection of
full-circle azimuthal scans (also referred to as sweeps) at various
elevations of the radar antenna, which together provide a sampling of
the atmosphere at all altitudes of interest. bioRad reads polar volumes
read_pvolfile() function, which returns the polar
volume as an object of class
pvol. bioRad currently
supports HDF5 files (Michelson
2014) that are compliant with the European OPERA Data Information
Model (ODIM) (OPERA: Operational Program for Exchange of Weather Radar
Information; see Huuskonen et
al. 2014), and level-2 data generated by the US Next Generation
Weather Radar (NEXRAD) network.
A polar volume (class
pvol) contains a list of
scan), each of which consists
of a list of scan parameters (class
param). A scan
parameter is one of the radar’s basic observed quantities, such
as reflectivity factor and radial velocity, and for dual-polarization
radars additional quantities such as correlation coefficient,
differential phase, and differential reflectivity.
Scan parameters can be projected on a georeferenced Cartesian grid in
the form of a plan position indicator (PPI) objects
ppi) using the function
project_as_ppi(). These can either be plotted directly
using the function
plot.ppi() (Fig. 2B
and 2C) or overlayed on a customizable basemap using the function
map() (Fig. 2D and 2E), which makes
use of the ggplot2 (Wickham 2016) and
and Wickham 2013) R libraries.
Polar volumes can be processed into vertical profiles of biological
targets using the
calculate_vp() function, which is a
release of the algorithm vol2bird (Dokter et al. 2011),
available independently on Github. The function
takes in a polar volume file and outputs a vertical profile file and/or
a vertical profile (
vp) class object. The function has an
autoconf, which when set to TRUE will select
default settings automatically (depending on radar wavelength and
availability of polarimetric data).
We describe the most important algorithm parameters and their preferred settings:
range_max: sets the minimum and maximum range (distance from the radar) of data to include. We recommend a minimum range of 5 km, to exclude the closest ranges that typically contain a lot of ground clutter. We recommend a maximum range of 35 km, which for most radars allows coverage up to 3 to 4 km a.s.l., which is the altitude band in which most migration occurs (Bruderer et al. 2018). At longer ranges, the radar beam gets very wide, hampering the radar’s ability to resolve altitudinal distributions.
layer_height: sets the number of altitude layers and their thickness, respectively. Altitudes are defined relative to mean sea level, taking into account the antenna height as stored in the original polar volume file. We recommend a thickness of 200 m. Profiles with narrower altitude bin spacings can be extracted (Buler and Diehl 2009), but the finite size of the radar beam precludes resolving altitudinal features smaller than approximately 100–200 m. Profile quantities are estimated based on resolution samples centered within the altitudinal spacing of each layer.
rho_hv: the logical
dual_polenables polarimetric filtering of precipitation, which discards contiguous areas with correlation coefficient (ρHV) above a threshold
rho_hv. We recommend
rho_hv= 0.95, since precipitation typically has higher correlation coefficient values (Stepanian et al. 2016) (but note that lower ρHV is possible in mixed precipitation, like a combination of snow and rain, cf. Ryzhkov and Zrnic 1998). Single polarization mode is currently only available for C-band radars.
nyquist_min: the logical
dealiasenables radial velocity dealiasing following the method by Haase and Landelius (2004) when scans are present with a Nyquist velocity smaller than threshold
nyquist_min(default 25 m s–1). The Nyquist velocity is stored in the
attributes$how$NIslot of scan class objects. Some radars dealias velocities at acquisition time, e.g. using the dual-PRF technique (Holleman 2005). For such radars we recommend no dealiasing for scans on which this is applied. For data acquired with a single PRF we recommend dealiasing when the Nyquist velocity of a scan is below 25 m s–1, i.e. if there is a high probability that animal movements will be faster than the Nyquist velocity.
sd_vvp_threshold: animal speed and direction are estimated using the Volume Velocity Profiling (VVP) technique (Waldteufel and Corbin 1978, Holleman 2005). VVP also provides the standard deviation of the fit residuals (quantity
sd_vvpin a profile). The
sd_vvp_thresholdparameter sets the threshold for discarding data based on this standard deviation measure. Animal density will be set to zero in altitude layers with a VVP standard deviation
sd_vvp_threshold. We recommend applying this thresholding as a way of removing residual rain contaminations and insects in bird studies using C-band radars, where
sd_vvp_threshold= 2 m s–1 was shown a suitable value (Dokter et al. 2011). We note that
sd_vvpmay become large in relatively rare cases where the velocity field is highly nonlinear (e.g. strong shear), causing this thresholding criterion to break down. For S-band radars VVP standard deviation thresholding has not been thoroughly evaluated, but radial velocity variability during bird migration may be lower than at C-band in certain cases. We currently recommend a conservative threshold of 1 m s–1 to retain more biological scatter.
rcs: value for the radar cross section (RCS) of an individual. We recommend 11 cm2 as a starting point, which was the seasonal average for C-band radars in western Europe during nocturnal passerine migration, according to a calibration experiment (Dokter et al. 2011). Note that radar cross sections depend on target size, body orientation, and radar wavelength (Vaughn 1985).
The various quantities in a vertical profile
dens: animal density,
ff: ground speed,
dd: ground speed direction,
can be visualized with
plot.vp(), as shown in Fig. 2F and 2G for density. These profile plots and
Fig. 2D and 2E are for the same moment in time.
Note that both profiles show layering of birds: a density concentration
at high altitude (here at approx. 1.5 km) (cf. Dokter et
al. 2013). These layers show up as concentric rings in Fig. 2D and 2E. These rings appear because at an
increasing distance from the radar, measurements are made at higher
altitudes, because of the positive beam elevation and the curvature of
Also note that the peak densities of the two cases are similar, on the order of 100 individuals km–3 (assuming RCS = 11 cm2) (Fig. 2H and 2I). The reflectivity factors (in dBZ scale, not to be confused with reflectivity η (Dokter et al. 2011, Chilson et al. 2012b) are however much higher for the US case than the European case. This is related to the difference in radar wavelength (Dokter et al. 2011), with NEXRAD radars in the US being S-band and European radars being mostly C-band.
After processing volume data into profiles, the profile data of
consecutive volume scans of a radar can be organized into a time
series of vertical profiles. The function
bind_into_vpts() binds vertical profile objects (class
vp) into time series object (class
which the default plot is shown in figure 2H and
2I. The dotted line indicates the time slice of Fig. 2B–G.
plot.vpts() method overlays one of the
reflectivity-based quantities (e.g.
dbz) with a barb indicating the animals ground speed and
direction. This follows meteorological conventions for graphically
displaying wind speed and direction (with north being up). The number of
barb flags indicate the speed (
ff) while its tip points
into the direction where animals are moving (
Another useful profile quantity to inspect as time series is DBZH. This is the reflectivity factor for all scatterers, including meteorological targets like precipitation. Time periods with rain are often clearly visible as high DBZH values over the full altitude column. We recommend making plots of DBZH as a way of screening for precipitation contaminations and quality control, which is often a useful way to check remarkable altitude patterns in the biological data (e.g. the layering of birds at 1.5 km can also be seen in Fig. 2I) or short spikes with high values that might be due to rain contamination.
bioRad provides multiple functions to further aggregate and summarize
time series data. We can integrate over the altitude dimension using
integrate_profile(), which outputs a specially classed
altitudinally integrated or averaged quantities. Figure 2J and 2K show plots of migration traffic
rate, both MTR (variable transect angle, Eq. 1) and MTR0 and
MTR90 (fixed transect angle, Eq. 2). We note that MTR is
always positive, but MTRα definitions can become negative
depending on the migratory direction in relation to α. For example, the
northward spring migration (US case, Fig. 2K)
result in a positive MTR0, while the southward autumn
migration (European case, Fig. 2J) is negative.
For the US case, migration is directed mostly northward, therefore
MTR0 is much larger than MTR90, while in the
European case, migration is mostly westward, therefore (in absolute
value) MTR0 is smaller than MTR90.
Vertically-integrated time series can be further
accumulated in time into measures summarizing migration traffic having
passed the radar station during a time period, like MT (migration
traffic) or RT (reflectivity traffic) (cf. output columns
integrate_profile()). For example, for the European case we
find MT = 55 × 103, MT0 = –28 × 103 and
MT90 = –45 × 103 for the time night-time period.
This means that – assuming a radar cross section (RCS) per individual of
11 cm2 – 55 thousand birds per 1 km transect flew over the
radar station in this night (irrespective of direction). Decomposing the
migration traffic into two perpendicularly oriented components, we find
a net 28 thousand birds flew southward per km over a west-to-east
transect (MT0), and a net 45 thousand birds per km flew
westward per km over a north-to-south transect (MT90). For
these specific definitions, MT ≤ √(MT02 +
MT902), with the left- and right-hand side being
equal when migration directions
dd all point into a sector
of at most 180 degrees wide, as is usually the case for periods confined
to a single spring or fall.
bioRad includes a number of example datasets:
bioRad::example_scan: example object of class
bioRad::example_vpts: example object of class
volume.h5: example hdf5 file containing a polar
volume. Read using:
profile.h5: example hdf5 file containing a vertical
profile generated by
calculate_vp(). Read using:
vpts.txt.zip: example standard output of
calculate_vp() piped to a text file (and zipped). Read
Conventions in data files:
nodatain the ODIM convention: value to denote areas void of data (never radiated).
undetectin the ODIM convention: denote areas below the measurement detection threshold (radiated but nothing detected). The value is also used when there are too few datapoints to calculate a quantity.
0in the ODIM convention: denote areas where the quantity has a measured value of zero (radiated and value zero detected or inferred).
It depends on a radar’s detection threshold or signal to noise ratio
whether it safe to assume an
undetect is equivalent to
zero. When dealing with close range data only (within 35 km), it is
typically safe to assume aerial densities (dens) and reflectivities
(eta) are in fact zero in case of undetects.