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vol2birdR

‘vol2birdR’ is an ‘R’ package for the ‘vol2bird’ algorithm for calculating vertical profiles of birds and other biological scatterers from weather radar data.

It also provides an ‘R’ interface to the ‘MistNet’ convolutional neural network for precipitation segmentation, installing PyTorch libraries and model.

‘vol2birdR’ can be used as a stand-alone package, but we recommend bioRad as the primary user interface, with ‘vol2birdR’ acting as a dependency of bioRad.

Install

‘vol2birdR’ is available for all major platforms (Linux, OS X and Windows).

For OS X and Linux the GNU Scientific Library (GSL), PROJ and HDF5 libraries need to be installed as system libraries prior to installation of ‘vol2birdR’:

Additional information when installing the dependencies on macOS

Since the installation process requires the Homebrew package manager you will have to install it. Open a terminal and issue the following command:

  /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

When the installation has completed it will print out some additional information that is essential to follow.

==> Next steps:
- Run these two commands in your terminal to add Homebrew to your PATH:
    echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> /Users/anders/.zprofile
    eval "$(/opt/homebrew/bin/brew shellenv)"
- Run brew help to get started
- Further documentation:
    https://docs.brew.sh

You need to ensure that you follow the above two commands. The first one will add the necessary environment variables to your user

    echo 'eval "$(/opt/homebrew/bin/brew shellenv)"' >> /Users/anders/.zprofile

The second command will ensure that you get the necessary environment variables into the terminal where you ran the installation process of Homebrew.

    eval "$(/opt/homebrew/bin/brew shellenv)"

System	Command
OS X (using Homebrew)	`brew install hdf5 proj gsl pkg-config`
Debian-based systems (including Ubuntu)	`sudo apt-get install libhdf5-dev libproj-dev gsl-bin libgsl-dev pkg-config libbz2-dev`
Systems supporting yum and RPMs	`sudo yum install hdf5-devel proj-devel gsl gsl-devel pkgconfig`

Next, you can install the released version of ‘vol2birdR’ from CRAN with:

install.packages("vol2birdR")

Alternatively, you can install the latest development version from GitHub with:

# install.packages("devtools")
devtools::install_github("adokter/vol2birdR")

Then load the package with:

library(vol2birdR)

MistNet installation

MistNet is a deep convolution neural net for segmenting out precipitation from radar data, see Lin et al. 2019. To use MistNet, follow the following additional installation steps in R:

# STEP 1: install additional libraries for using MistNet:
library(vol2birdR)
install_mistnet()

After completing this step, the following command should evaluate to TRUE:

mistnet_exists()

Next, download the mistnet model. Note that the model file is large, over 500Mb.

# STEP 2: download mistnet model:
install_mistnet_model()

See vignette for additional installation information

References:

Citation for ‘vol2bird’ algorithm:

Bird migration flight altitudes studied by a network of operational weather radars Dokter AM, Liechti F, Stark H, Delobbe L, Tabary P, Holleman I J. R. Soc. Interface, 8, 30–43, 2011, DOI 10.1098/rsif.2010.0116

Paper describing recent algorithm extensions and the bioRad package:

bioRad: biological analysis and visualization of weather radar data 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 J. Ecography, 42, 852-860, 2019, DOI 10.1111/ecog.04028

‘vol2bird’ implements dealiasing using the torus mapping method by Haase and Landelius:

Dealiasing of Doppler radar velocities using a torus mapping Haase G, Landelius T. Journal of Atmospheric and Oceanic Technology 21, 1566-1573, 2004, DOI 10.1175/1520-0426(2004)021<1566:DODRVU>2.0.CO;2

Use the following citation for the ‘MistNet’ rain segmentation model:

MistNet: Measuring historical bird migration in the US using archived weather radar data and convolutional neural networks. Lin T-Y, Winner K, Bernstein G, Mittal A, Dokter AM, Horton KG, Nilsson C, Van Doren BM, Farnsworth A, La Sorte FA, Maji S, Sheldon D. Methods in Ecology and Evolution, 10, 1908-1922, 2019, DOI 10.1111/2041-210X.13280

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.