Introduction to vol2birdR
In vol2birdR: Vertical Profiles of Biological Signals in Weather Radar Data

vol2birdR is an R package for calculating vertical profiles and other biological scatterers from weather radar data. The original vol2bird is written as a C-package and has been migrated to also work as an R package.

Introduction

The vol2birdR package provides necessary functions to process polar volume data of C-band and S-band radars into vertical profiles of biological scatterers. This package also enables libtorch and the MistNet model for segmentation of meteorological and biological signals.

Calculating vertical profiles

First, define a configuration instance, and modify configuration settings according to needs.

# load the library
library(vol2birdR)

# create a configuration instance
config<-vol2bird_config()

# modify the configuration instance as needed
# in this example we set the maximum range to 25 km:
config$rangeMax <- 25000

The configuration object can be modified heavily. Learn more about the available options in the documentation of vol2bird_config()

Note that configuration objects are copied by reference by default, and true copies that can be used independently should be assigned using:

config <- vol2bird_config()
config_copy <- vol2bird_config(config)

Finally, the vertical profile can be calculated using function vol2bird():

vol2bird(file="/your/input/pvolfile",config=config, vpfile="/your/output/vpfile")

The input pvolfile needs to be in ODIM HDF5 format, IRIS RAW format, or NEXRAD format. The output vpfile containing the profile will be in ODIM HDF5 format.

MistNet

Installation

MistNet is a deep convolution neural net for segmenting out rain in S-band radar data, see the publication at https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13280. To use MistNet, follow the following additional installation steps:

After installing and loading vol2birdR, run install_mistnet() from R. This will download libtorch from the download section of https://pytorch.org as well as a wrapper library from AWS that enables the mistnet functionality:

# STEP 1: install mistnet libraries
library(vol2birdR)
install_mistnet()

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

mistnet_exists()

Next, the pytorch mistnet model needs to be downloaded, which is hosted at http://mistnet.s3.amazonaws.com/mistnet_nexrad.pt. Note that this file is large, over 500Mb. It can be downloaded directly from R using install_mistnet_model():

# STEP 2: download mistnet model:
# install mistnet model into vol2birdR package:
install_mistnet_model()

install_mistnet_model() installs the model by default into the vol2birdR package directory. As a result, when reinstalling vol2birdR the model file will have to be re-downloaded as well.

Alternatively, you can store the model in an alternative location outside the vol2birdR package directory. This has the advantage that you don't have to re-download the model when reinstalling vol2birdR. Simply store the path of the mistnet_nexrad.pt file in the mistNetPath element of your configuration object (see vol2bird_config())

vol2birdR will automatically locate the file if it is located at /opt/vol2bird/etc/mistnet_nexrad.pt, which can be done as follows:

# create the directory
# (in case of a permission-denied error, create the directory manually)
dir.create("/opt/vol2bird/etc", recursive=TRUE)

# download the model
install_mistnet_model(path="/opt/vol2bird/etc/mistnet_nexrad.pt")

Using MistNet

After installing the MistNet libraries and model file, a profile can be calculated as follows:

# define configuration object:
config <- vol2bird_config()

# enable MistNet:
config$useMistNet <- TRUE

# calculate the profile:
vol2bird(file="/your/input/pvolfile", config=config, vpfile="/your/output/vpfile")