Furuno#
[1]:
import xarray as xr
from open_radar_data import DATASETS
import xradar as xd
Download#
Fetching Furuno radar data file from open-radar-data repository.
[2]:
filename_scnx = DATASETS.fetch("2006_20220324_000000_000.scnx.gz")
filename_scn = DATASETS.fetch("0080_20210730_160000_01_02.scn.gz")
Downloading file '2006_20220324_000000_000.scnx.gz' from 'https://github.com/openradar/open-radar-data/raw/main/data/2006_20220324_000000_000.scnx.gz' to '/home/docs/.cache/open-radar-data'.
Downloading file '0080_20210730_160000_01_02.scn.gz' from 'https://github.com/openradar/open-radar-data/raw/main/data/0080_20210730_160000_01_02.scn.gz' to '/home/docs/.cache/open-radar-data'.
xr.open_dataset#
Making use of the xarray furuno
backend.
scn format#
[3]:
ds = xr.open_dataset(filename_scn, engine="furuno")
display(ds)
<xarray.Dataset> Size: 55MB Dimensions: (azimuth: 1376, range: 602) Coordinates: elevation (azimuth) float64 11kB ... * range (range) float32 2kB 25.0 75.0 ... 3.002e+04 3.008e+04 time (azimuth) datetime64[ns] 11kB ... longitude float64 8B ... latitude float64 8B ... altitude float64 8B ... * azimuth (azimuth) float64 11kB 0.21 0.47 0.74 ... 359.7 359.9 Data variables: (12/14) RATE (azimuth, range) float64 7MB ... DBZH (azimuth, range) float64 7MB ... VRADH (azimuth, range) float64 7MB ... ZDR (azimuth, range) float64 7MB ... KDP (azimuth, range) float64 7MB ... PHIDP (azimuth, range) float64 7MB ... ... ... QUAL (azimuth, range) uint16 2MB ... sweep_mode <U20 80B ... sweep_number int64 8B ... prt_mode <U7 28B ... follow_mode <U7 28B ... sweep_fixed_angle float64 8B ...
Plot Time vs. Azimuth#
[4]:
ds.azimuth.plot(y="time")
[4]:
[<matplotlib.lines.Line2D at 0x7f5c0cd30f10>]
Plot Range vs. Time#
We need to sort by time
and specify the y-coordinate.
[5]:
ds.DBZH.sortby("time").plot(y="time")
[5]:
<matplotlib.collections.QuadMesh at 0x7f5c0982c350>
Plot Range vs. Azimuth#
[6]:
ds.DBZH.sortby("azimuth").plot(y="azimuth")
[6]:
<matplotlib.collections.QuadMesh at 0x7f5c016fc350>
scnx format#
[7]:
ds = xr.open_dataset(filename_scnx, engine="furuno")
display(ds)
<xarray.Dataset> Size: 45MB Dimensions: (azimuth: 722, range: 936) Coordinates: elevation (azimuth) float64 6kB ... * range (range) float32 4kB 37.5 112.5 ... 7.009e+04 7.016e+04 time (azimuth) datetime64[ns] 6kB ... longitude float64 8B ... latitude float64 8B ... altitude float64 8B ... * azimuth (azimuth) float64 6kB 0.19 0.68 1.16 ... 359.2 359.7 Data variables: (12/14) RATE (azimuth, range) float64 5MB ... DBZH (azimuth, range) float64 5MB ... VRADH (azimuth, range) float64 5MB ... ZDR (azimuth, range) float64 5MB ... KDP (azimuth, range) float64 5MB ... PHIDP (azimuth, range) float64 5MB ... ... ... QUAL (azimuth, range) uint16 1MB ... sweep_mode <U20 80B ... sweep_number int64 8B ... prt_mode <U7 28B ... follow_mode <U7 28B ... sweep_fixed_angle float64 8B ...
Plot Time vs. Azimuth#
[8]:
ds.azimuth.plot(y="time")
[8]:
[<matplotlib.lines.Line2D at 0x7f5c0a86c290>]
Plot Range vs. Time#
We need to sort by time
and specify the y-coordinate.
[9]:
ds.DBZH.sortby("time").plot(y="time")
[9]:
<matplotlib.collections.QuadMesh at 0x7f5c0069bb90>
Plot Range vs. Azimuth#
[10]:
ds.DBZH.sortby("azimuth").plot(y="azimuth")
[10]:
<matplotlib.collections.QuadMesh at 0x7f5c0a733cd0>
open_furuno_datatree#
Furuno scn/scnx files consist only of one sweep. But we might load and combine several sweeps into one DataTree.
[11]:
dtree = xd.io.open_furuno_datatree(filename_scn)
display(dtree)
<xarray.DatasetView> Size: 232B Dimensions: () Data variables: volume_number int64 8B 0 platform_type <U5 20B 'fixed' instrument_type <U5 20B 'radar' time_coverage_start <U20 80B '2021-07-30T16:00:00Z' time_coverage_end <U20 80B '2021-07-30T16:00:14Z' longitude float64 8B 15.45 altitude float64 8B 407.9 latitude float64 8B 47.08 Attributes: Conventions: None instrument_name: None version: None title: None institution: None references: None source: None history: None comment: im/exported using xradar
Plot Sweep Range vs. Time#
[12]:
dtree["sweep_0"].ds.DBZH.plot()
[12]:
<matplotlib.collections.QuadMesh at 0x7f5c0a683cd0>
Plot Sweep Range vs. Azimuth#
[13]:
dtree["sweep_0"].ds.DBZH.sortby("azimuth").plot(y="azimuth")
[13]:
<matplotlib.collections.QuadMesh at 0x7f5c0a5b18d0>