# 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 <- list.files("./data_pvol", recursive = TRUE, full.names = TRUE, pattern="KHGX") my_pvolfiles # print to console our files: my_pvolfiles# let's load the first of our downloaded files: <- read_pvolfile(my_pvolfiles)my_pvol
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).
# let's extract the scan collected at 1.5 degree elevation from our polar volume: <- get_scan(my_pvol, 0.5) my_scan # print some information about this scan: my_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) <- project_as_ppi(my_scan) my_ppi # print some information about this ppi: my_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?
# It is often informative to plot radar data on a base layer. # first download the background image: <- download_basemap(my_ppi) basemap # plot the basemap: plot(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))