Reading FURUNO Data

FURUNO data are HDF5 or SCN/SCNX files. In order to access the functions of WRaINfo, you have to import wrainfo in your Python environment.

import wrainfo as wrf

Note

For the examples, data of the WR2120 from the Weather Radar FURUNO are used.

Import packages

For reading the SCN/SCNX files xarray backend was implemented in \(\omega radlib\). To begin with the following packages have to be imported.

[2]:

import wradlib as wrl
import xarray as xr
import datetime as dt

Load FURUNO HDF5 Data

Furuno HDF5 single data can be loaded as xarray dataset. If the keyword argument “reindex_angle” is False, then the original azimuth angles are loaded. Here is an example with original azimuth angles.

[3]:

file_path="/tests/data/raw/2022/02/16/2006_20220216_134500.h5"

[4]:

file_hdf5=xr.open_dataset(file_path,
                          engine="odim",
                          group="dataset1",
                          backend_kwargs=dict(reindex_angle=False))
display(file_hdf5)

Note

Due to the jitter of the azimuth angles we read the data with a reindex angle of 1.0.

The group argument sets the elevation angle that is read.

We can also load multiple HDF5 datasets as one xarray dataset. For this we create a file list with WRaINfo. Then we load all files by linking them over time.

[5]:

flist=wrf.create_filelist(starttime=dt.datetime(2022,2,16,13,40),
                          endtime=dt.datetime(2022,2,16,14,45),
                          path="/tests/data/test_settings_wrainfo.json",
                          pattern='_000.scnx.gz')

[6]:

ds_hdf5 = xr.open_mfdataset(flist,
                            engine="odim",
                            combine ="nested",
                            concat_dim="time",
                            group="dataset1",
                            backend_kwargs=dict(reindex_angle=1.0))
display(ds_hdf5)

<xarray.Dataset>
Dimensions:     (azimuth: 720, time: 3, range: 936)
Coordinates:
  * azimuth     (azimuth) float64 0.25 0.75 1.25 1.75 ... 358.8 359.2 359.8
    elevation   (azimuth) float64 dask.array<chunksize=(720,), meta=np.ndarray>
    rtime       (time, azimuth) datetime64[ns] dask.array<chunksize=(1, 720), meta=np.ndarray>
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
  * time        (time) datetime64[ns] 2022-02-16T13:45:01 ... 2022-02-16T13:5...
    sweep_mode  <U20 'azimuth_surveillance'
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
Data variables:
    RATE        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    VRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    KDP         (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    WRAD        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    QC_INFO     (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
Attributes:
    fixed_angle:  0.5

xarray.Dataset

Dimensions:
- azimuth: 720
- time: 3
- range: 936

Coordinates: (9)

azimuth
(azimuth)
float64
0.25 0.75 1.25 ... 359.2 359.8
standard_name :
ray_azimuth_angle
long_name :
azimuth_angle_from_true_north
units :
degrees
axis :
radial_azimuth_coordinate
a1gate :
1
angle_res :
0.5
```
array([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,
       3.5975e+02])
```

elevation

(azimuth)

float64

dask.array<chunksize=(720,), meta=np.ndarray>

standard_name :: ray_elevation_angle
long_name :: elevation_angle_from_horizontal_plane
units :: degrees
axis :: radial_elevation_coordinate

	Array	Chunk
Bytes	5.62 kiB	5.62 kiB
Shape	(720,)	(720,)
Count	10 Tasks	1 Chunks
Type	float64	numpy.ndarray

rtime

(time, azimuth)

datetime64[ns]

dask.array<chunksize=(1, 720), meta=np.ndarray>

standard_name :: time

	Array	Chunk
Bytes	16.88 kiB	5.62 kiB
Shape	(3, 720)	(1, 720)
Count	8 Tasks	3 Chunks
Type	datetime64[ns]	numpy.ndarray

range
(range)
float32
37.5 112.5 ... 7.009e+04 7.016e+04
units :
meters
standard_name :
projection_range_coordinate
long_name :
range_to_measurement_volume
spacing_is_constant :
true
axis :
radial_range_coordinate
meters_to_center_of_first_gate :
37.5
meters_between_gates :
75.0
```
array([3.75000e+01, 1.12500e+02, 1.87500e+02, ..., 7.00125e+04, 7.00875e+04,
       7.01625e+04], dtype=float32)
```

time

(time)

datetime64[ns]

2022-02-16T13:45:01 ... 2022-02-...

standard_name :: time

array(['2022-02-16T13:45:01.000000000', '2022-02-16T13:50:01.000000000',
       '2022-02-16T13:55:01.000000000'], dtype='datetime64[ns]')

sweep_mode

()

<U20

'azimuth_surveillance'

array('azimuth_surveillance', dtype='<U20')

longitude
()
float64
13.24
long_name :
longitude
units :
degrees_east
standard_name :
longitude
```
array(13.24397)
```
latitude
()
float64
53.55
long_name :
latitude
units :
degrees_north
positive :
up
standard_name :
latitude
```
array(53.55478)
```
altitude
()
float64
38.0
long_name :
altitude
units :
meters
standard_name :
altitude
```
array(38.)
```

Data variables: (9)

RATE

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: rainfall_rate
units :: mm h-1
long_name :: rainfall_rate

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

DBZH

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_equivalent_reflectivity_factor_h
units :: dBZ
long_name :: Equivalent reflectivity factor H

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

VRAD

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radial_velocity_of_scatterers_away_from_instrument
units :: meters per seconds
long_name :: Radial velocity of scatterers away from instrument

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

ZDR

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_differential_reflectivity_hv
units :: dB
long_name :: Log differential reflectivity H/V

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

KDP

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_specific_differential_phase_hv
units :: degrees per kilometer
long_name :: Specific differential phase HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

PHIDP

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_differential_phase_hv
units :: degrees
long_name :: Differential phase HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

RHOHV

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_correlation_coefficient_hv
units :: unitless
long_name :: Correlation coefficient HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

WRAD

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0
standard_name :: radar_doppler_spectrum_width
units :: meters per second
long_name :: Doppler spectrum width

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

QC_INFO

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

_Undetect :: 0

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

Attributes: (1)
fixed_angle :
0.5

Load FURUNO SCN/SCNX raw data

When we load SCN/SCNX data, there are small differences. First we load a single file again.

[7]:

file_path="/tests/data/raw/2022/04/05/2006_20220405_123000_000.scnx.gz"

[8]:

file_scnx = xr.open_dataset(file_path,
                            engine="furuno",
                            group="group1",
                            backend_kwargs=dict(reindex_angle=1.0))
display(file_scnx)

Note

For each elevation angle there exists an SCN/SCNX file, reason why we do not choose the “group”. The “group” argument is default to “group1”.

We can load multiple files as follows. At first we have to create a file list again.

[9]:

flist=wrf.create_filelist(starttime=dt.datetime(2022,4,5,12,30),
                          endtime=dt.datetime(2022,4,5,12,50),
                          path="/tests/data/test_settings_wrainfo.json",
                          pattern='_000.scnx.gz')

[10]:

ds_scnx = xr.open_mfdataset(flist,
                            engine="furuno",
                            combine ="nested",
                            concat_dim="time",
                            group="group1",
                            backend_kwargs=dict(reindex_angle=1.0))
display(ds_scnx)

<xarray.Dataset>
Dimensions:     (azimuth: 720, time: 3, range: 936)
Coordinates:
  * azimuth     (azimuth) float32 0.25 0.75 1.25 1.75 ... 358.8 359.2 359.8
    elevation   (azimuth) float32 dask.array<chunksize=(720,), meta=np.ndarray>
  * range       (range) float32 37.5 112.5 187.5 ... 7.009e+04 7.016e+04
  * time        (time) datetime64[ns] 2022-04-05T12:30:01 ... 2022-04-05T12:4...
    rtime       (time, azimuth) datetime64[ns] dask.array<chunksize=(1, 720), meta=np.ndarray>
    longitude   float64 13.24
    latitude    float64 53.55
    altitude    float64 38.0
    sweep_mode  <U20 'azimuth_surveillance'
Data variables:
    RATE        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    DBZH        (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    VRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    ZDR         (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    KDP         (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    PHIDP       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    RHOHV       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    WRADH       (time, azimuth, range) float32 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
    QUAL        (time, azimuth, range) uint16 dask.array<chunksize=(1, 720, 936), meta=np.ndarray>
Attributes:
    fixed_angle:  0.5

xarray.Dataset

Dimensions:
- azimuth: 720
- time: 3
- range: 936

Coordinates: (9)

azimuth
(azimuth)
float32
0.25 0.75 1.25 ... 359.2 359.8
standard_name :
ray_azimuth_angle
long_name :
azimuth_angle_from_true_north
units :
degrees
axis :
radial_azimuth_coordinate
a1gate :
499
angle_res :
0.5
```
array([2.5000e-01, 7.5000e-01, 1.2500e+00, ..., 3.5875e+02, 3.5925e+02,
       3.5975e+02], dtype=float32)
```

elevation

(azimuth)

float32

dask.array<chunksize=(720,), meta=np.ndarray>

standard_name :: ray_elevation_angle
long_name :: elevation_angle_from_horizontal_plane
units :: degrees
axis :: radial_elevation_coordinate

	Array	Chunk
Bytes	2.81 kiB	2.81 kiB
Shape	(720,)	(720,)
Count	10 Tasks	1 Chunks
Type	float32	numpy.ndarray

range
(range)
float32
37.5 112.5 ... 7.009e+04 7.016e+04
units :
meters
standard_name :
projection_range_coordinate
long_name :
range_to_measurement_volume
spacing_is_constant :
true
axis :
radial_range_coordinate
meters_to_center_of_first_gate :
37.5
meters_between_gates :
75
```
array([3.75000e+01, 1.12500e+02, 1.87500e+02, ..., 7.00125e+04, 7.00875e+04,
       7.01625e+04], dtype=float32)
```

time

(time)

datetime64[ns]

2022-04-05T12:30:01 ... 2022-04-...

standard_name :: time

array(['2022-04-05T12:30:01.000000000', '2022-04-05T12:35:01.000000000',
       '2022-04-05T12:45:01.000000000'], dtype='datetime64[ns]')

rtime

(time, azimuth)

datetime64[ns]

dask.array<chunksize=(1, 720), meta=np.ndarray>

standard_name :: time

	Array	Chunk
Bytes	16.88 kiB	5.62 kiB
Shape	(3, 720)	(1, 720)
Count	8 Tasks	3 Chunks
Type	datetime64[ns]	numpy.ndarray

longitude
()
float64
13.24
long_name :
longitude
units :
degrees_east
standard_name :
longitude
```
array(13.24397)
```
latitude
()
float64
53.55
long_name :
latitude
units :
degrees_north
positive :
up
standard_name :
latitude
```
array(53.55478)
```
altitude
()
float64
38.0
long_name :
altitude
units :
meters
standard_name :
altitude
```
array(38.)
```

sweep_mode

()

<U20

'azimuth_surveillance'

array('azimuth_surveillance', dtype='<U20')

Data variables: (9)

RATE

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: rainfall_rate
units :: mm h-1
long_name :: rainfall_rate

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

DBZH

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_equivalent_reflectivity_factor_h
units :: dBZ
long_name :: Equivalent reflectivity factor H

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

VRADH

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radial_velocity_of_scatterers_away_from_instrument_h
units :: meters per seconds
long_name :: Radial velocity of scatterers away from instrument H

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

ZDR

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_differential_reflectivity_hv
units :: dB
long_name :: Log differential reflectivity H/V

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

KDP

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_specific_differential_phase_hv
units :: degrees per kilometer
long_name :: Specific differential phase HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

PHIDP

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_differential_phase_hv
units :: degrees
long_name :: Differential phase HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

RHOHV

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_correlation_coefficient_hv
units :: unitless
long_name :: Correlation coefficient HV

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

WRADH

(time, azimuth, range)

float32

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

standard_name :: radar_doppler_spectrum_width_h
units :: meters per seconds
long_name :: Doppler spectrum width H

	Array	Chunk
Bytes	7.71 MiB	2.57 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	float32	numpy.ndarray

QUAL

(time, azimuth, range)

uint16

dask.array<chunksize=(1, 720, 936), meta=np.ndarray>

	Array	Chunk
Bytes	3.86 MiB	1.29 MiB
Shape	(3, 720, 936)	(1, 720, 936)
Count	12 Tasks	3 Chunks
Type	uint16	numpy.ndarray

Attributes: (1)
fixed_angle :
0.5

Fix bugs of elevation angles

In the example dataset above there exists maybe an incorrect value in the data array “elevation”. We fix this error as follows:

[12]:

# median over the elevation dataset
elev  = file_scnx_new.elevation.median(dim="azimuth")

# set the median to the dataset
file_scnx_new["elevation"][:] = elev

Plot FURUNO raw data

If you want to plot some raw data, you can do it as under. Here a plot of the raw reflectivity “DBZH”.

[13]:

import matplotlib.pyplot as plt

[14]:

file_scnx_new.DBZH.plot(cmap="jet")

[14]:

<matplotlib.collections.QuadMesh at 0x1475317a9af0>

Note

In this plot you see the raw reflectivity of the weather radar FURUNO. In the reflectivity there are some artefacts, which we will correct with the WRaINfo package on the following pages.

With the \(\omega radlib\) package it is possible to georeference the dataset for a plot. For this we use:

[15]:

# georeferencing the dataset
ds_georef = file_scnx_new.pipe(wrl.georef.georeference_dataset)

# plot the data with x and y coordinates
file_scnx_new.DBZH.plot(x="x", y="y", cmap="jet")

[15]:

<matplotlib.collections.QuadMesh at 0x147531956910>

../_images/jupyter_notebooks_read_furuno_data_34_1.png

Reading FURUNO Data

Import packages

Load FURUNO HDF5 Data

Load FURUNO SCN/SCNX raw data

Rename a variable

Fix bugs of elevation angles

Plot FURUNO raw data