bioRad provides standardized methods for extracting and reporting biological signals from weather radars. It includes functionality to inspect low-level radar data, process these data into meaningful biological information on animal speeds and directions at different altitudes in the atmosphere, visualize these biological extractions, and calculate further summary statistics.
To get started, see:
Documentation for the latest development version can be found here.
You can install the released version of bioRad from CRAN with:
Alternatively, you can install the latest development version from GitHub with:
Then load the package with:
Google has recently changed its API requirements, and ggmap - the package used by bioRad to overlay radar scans on maps - now requires users to provide an API key and enable billing in order to use Google imagery. bioRad switched to using stamen maps by default, which do not require special credentials.
ggmap itself is outdated on CRAN; its developers hope to have the new version up on CRAN soon, but until then, see ggmap Github page for how to install the latest development version.
You only need to install Docker to:
read_pvolfile(). Docker is not required for reading ODIM radar data.
Shared drives> Select the drive(s) where you will be processing radar files > Click
File sharing> Add the drive(s) where you will be processing radar files > Click
Apply & Restart.
Known issues with Docker
Here we read an example polar volume data file with
read_pvolfile(), extract the scan/sweep at elevation angle 3 with
get_scan(), project the data to a plan position indicator with
project_as_ppi() and plot the radial velocity of detected targets with
Radial velocities towards the radar are negative, while radial velocities away from the radar are positive, so in this plot there is movement from the top right to the bottom left.
Once vertical profile data are loaded into bioRad, these can be bound into time series using
bind_into_vpts(). Here we read an example time series, project it on a regular time grid with
regularize_vpts() and plot it with
The gray bars in the plot indicate gaps in the data.
The altitudes in the profile can be integrated with
integrate_profile() resulting in a dataframe with rows for datetimes and columns for quantities. Here we plot the quantity migration traffic rate (column
To know the total number of birds passing over the radar during the full time series, we use the last value of the cumulative migration traffic (column
For more exercises, see this tutorial.