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bioRad provides standardized methods for extracting and reportingbiological signals from weather radars. It includes functionality toinspect low-level radar data, process these data into meaningfulbiological information on animal speeds and directions at differentaltitudes in the atmosphere, visualize these biological extractions, andcalculate further summary statistics.

To get started, see:

• Dokter et al. (2019): a paperdescribing the package.
• bioRadvignette: anintroduction to bioRad’s main functionalities.
• Functionreference: anoverview of all bioRad functions.
• Introductoryexercises:a tutorial with code examples and exercises.

More vignettes:

• Rangecorrection:estimate spatial images of vertically integrated density corrected forrange effects.

Documentation for the latest development version can be foundhere.

For OS X and Linux the GNU Scientific Library (GSL), PROJ and HDF5libraries need to be installed as system libraries prior toinstallation, which are required by dependency packagevol2birdR. On linux,additionally, CURL, SSL and GDAL are required by other dependencypackages of bioRad:

You can install the released version of bioRad fromCRAN with:

install.packages("bioRad")

Alternatively, you can install the latest development version fromGitHub with:

# install.packages("devtools")devtools::install_github("adokter/bioRad")

Then load the package with:

library(bioRad)#> Welcome to bioRad version 0.10.0.9000#> using vol2birdR version 1.1.1.9000 (MistNet installed)

To enable MistNet, the following vol2birdR commands should be executed:

vol2birdR::install_mistnet()vol2birdR::install_mistnet_model()

Read thevol2birdRdocumentationfor more details.

bioRad can read weather radar data (= polar volumes) in theODIMformat and formats supported by theRSLlibrary, such asNEXRAD data. NEXRAD data (US) areavailable as opendata and onAWS.

Here we read an example polar volume data file withread_pvolfile(),extract the scan/sweep at elevation angle 3 withget_scan(), projectthe data to a plan position indicator withproject_as_ppi() and plottheradial velocity of detected targets withplot():

library(tidyverse)# To pipe %>% the steps belowsystem.file("extdata","volume.h5",package="bioRad") %>%  read_pvolfile() %>%  get_scan(3) %>%  project_as_ppi() %>%  plot(param="VRADH")# VRADH = radial velocity in m/s

Radial velocities towards the radar are negative, while radialvelocities away from the radar are positive, so in this plot there ismovement from the top right to the bottom left.

Weather radar data can be processed into vertical profiles of biologicaltargets usingcalculate_vp(). This type of data isavailable as opendata for over 100 European weather radars.

Once vertical profile data are loaded into bioRad, these can be boundinto time series usingbind_into_vpts(). Here we read an example timeseries, project it on a regular time grid withregularize_vpts() andplot it withplot():

example_vpts %>%  regularize_vpts() %>%  plot()

The gray bars in the plot indicate gaps in the data.

The altitudes in the profile can be integrated withintegrate_profile() resulting in a dataframe with rows for datetimesand columns for quantities. Here we plot the quantitymigration trafficrate (columnmtr) withplot():

my_vpi<- integrate_profile(example_vpts)plot(my_vpi,quantity="mtr")# mtr = migration traffic rate

To know the total number of birds passing over the radar during the fulltime series, we use the last value of thecumulative migration traffic(columnmt):

my_vpi %>%  pull(mt) %>%# Extract column mt as a vector  last()#> [1] 129491.5

For more exercises, seethistutorial.

• We welcomecontributionsincluding bug reports.
• License: MIT
• Get citation information forbioRad in R doingcitation("bioRad").
• Please note that this project is released with aContributor Code ofConduct. Byparticipating in this project you agree to abide by its terms.