Mapping raw radar data to 1km resolution reflectivity grid

[Adam Conway]

Hello,

I have a large amount of historic radar data which I'm wanting to map to a 2D-grid centered around the radar station (say 250x250km at 1km resolution). The data is originally in SIGMET-Iris format and I've managed to import it into Radar objects and convert it using grid_from_radars().

The main field I'm interested in is the reflectivity and I was wanting to export this in a (50,50) numpy array.

The data is from radar stations in New Zealand so I'm also trying to grid it using the New Zealand Transverse Mercator 2000 projection (http://epsg.io/2193).

What is the best (and most efficent) way for me todo this? I'm also unsure what z-value I should be using in the grid_shape and grid_limits.

My current code is:

import cartopy.crs as ccrs

import matplotlib.pyplot as plt

import pyart

from pyproj import CRS

# Use NZTM2000 projection

crs = CRS.from_proj4("+proj=tmerc +lat_0=0 +lon_0=173 +k=0.9996 +x_0=1600000 +y_0=10000000 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs")

# Read from raw data

radar_a = pyart.io.read('AKL170101000003.RAWVOLA.bz2')    

radar_b = pyart.io.read('AKL170101000342.RAWVOLB.bz2')    

# Convert to grid using NZTM2000

grid = pyart.map.grid_from_radars((radar_a, radar_b), 

                                    grid_shape=(4, 250,250), 

                                    grid_limits=((0.,8000.,),(-250000.,250000.), (-250000.,250000.)),

                                    grid_projection=crs.to_dict(),

                                    weighting_function = 'Barnes2',

                                    fields=['reflectivity'])

# create the plot

fig = plt.figure()

ax = fig.add_subplot(111)

ax.imshow(grid.fields['reflectivity']['data'][0], origin='lower')

plt.show()

This results in an empty plot. When I comment out grid_projection line it results in a plot with data. Both of these are attached.

Thanks,

Adam

[Zach Sherman]

Hi Adam,

I'm not too sure on the projection issue, I'm wondering if there is some format issue, but the dictionary structure looks fine, maybe someone else has insight on that. For z you could set shape to 1, and index that data when plugging into an array. And shape, for equal spacing, add a 1 to your shapes for 251. But everything else looks fine, the use of fields for just reflectivity etc. Adding a gatefilter would probably help with speed.

Zach S.

[Adam Conway]

Hi Zach,

Thanks for the help. Which gates would you recommend filtering for speed?

Could anyone recommend methods/changes to remove clutter/noise (or improve the data) before saving it to a grid?

I've been doing some research but I am still a beginner in the field.

Adam

[Collis, Scott M.]

For one any gate that has a FillValue (eg -999 et al)

You only want to be gridding valid returns. Our experience with NEXRAD, depending on the scene, this can speed things up over a factor of two.

 

I -think- it was gatefilter.exclude_invalid

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