2 Basic visualization of radar scans

2.1 The structure of polar volumes

# Let's first download the NEXRAD polar volume files for the KHGX radar (Houston)
# for a 15 minute period in 2017:
download_pvolfiles(date_min=as.POSIXct("2017-05-04 01:25:00"), date_max=as.POSIXct("2017-05-04 01:40:00"), radar="KHGX", directory="./data_pvol")
# store the filenames in my_pvolfiles
my_pvolfiles <- list.files("./data_pvol", recursive = TRUE, full.names = TRUE, pattern="KHGX")
# print to console our files:
# let's load the first of our downloaded files:
my_pvol <- read_pvolfile(my_pvolfiles[1])

Exercise 1: What is the minimum and maximum scan elevation contained in the volume? And which scan parameters are available? (See manual page of the read_pvolfile() function for the nomenclature of various available quantities).

2.2 Plotting radar scans

# let's extract the scan collected at 1.5 degree elevation from our polar volume:
my_scan <- get_scan(my_pvol, 0.5)
# print some information about this scan:
# let's plot the reflectivity factor parameter of the scan in a range - azimuth coordinate system:
plot(my_scan, param = "DBZH")

Usually it is easier to visually explore radar scans as a PPI (plan position indicator), which is a projection of the scan on a Cartesian (X,Y) or (lat,lon) grid:

# before we can plot the scan, we need to project it on a Cartesian grid,
# i.e. we need to make a Plan Position Indicator (PPI)
my_ppi <- project_as_ppi(my_scan)
# print some information about this ppi:
# you can see we projected it on a 500 meter grid
# (check the manual of the project_as_ppi function to see how you can
# change the grid size (argument grid_size) and the maximum distance
# from the radar up to where to plot data (argument range_max))
# Now we are ready to plot the ppi, for example let's plot reflectivity factor DBZH:
plot(my_ppi, param = "DBZH")

Exercise 2: This case shows an incoming precipitation front, characterized by localized but intense thunderstorms, as well as biological scattering. Make also a ppi plot of the correlation coefficient (RHOHV) and radial velocity (VRADH). Verify which regions are precipitation, and the approximate direction of movement of biology and precipitation.

Exercise 3: Based on the radial velocity image, are the biological scatterers birds or insects? Why?

2.3 Overlaying radar scans on maps

# It is often informative to plot radar data on a base layer.
# first download the background image:
basemap <- download_basemap(my_ppi)
# plot the basemap:
# then overlay the PPI on the basemap, restricting the color scale from -20 to 40 dBZ:
map(my_ppi, map = basemap, param = "DBZH", zlim = c(-20, 40))