Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity_best_estimate
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Best-estimate reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
reflectivity
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Reflectivity
units :
dBZ
ancillary_variables :
qc_reflectivity
valid_min :
-90.0
valid_max :
50.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mean_doppler_velocity
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Mean Doppler velocity
units :
m/s
ancillary_variables :
qc_mean_doppler_velocity
valid_min :
-25.0
valid_max :
25.0
resolution :
0.001
positive :
up
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mean_doppler_velocity
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Mean Doppler velocity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
spectral_width
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Spectral width
units :
m/s
ancillary_variables :
qc_spectral_width
valid_min :
0.0
valid_max :
10.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_spectral_width
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Spectral width
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
linear_depolarization_ratio
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Linear depolarization ratio
units :
dBZ
ancillary_variables :
qc_linear_depolarization_ratio
valid_min :
-50.0
valid_max :
50.0
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_linear_depolarization_ratio
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Linear depolarization ratio
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.49 GiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
signal_to_noise_ratio
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Signal-to-noise ratio
units :
dB
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
No detection due to missing radar and micropulse lidar data
flag_1_description :
Clear according to radar and lidar
flag_2_description :
Cloud detected by radar and lidar
flag_3_description :
Cloud detected by radar only
flag_4_description :
Cloud detected by lidar only
flag_5_description :
Cloud detected by radar but lidar data missing
flag_6_description :
Cloud detected by lidar but radar data missing
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
761.19 MiB
\n",
+ "
1.02 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int16 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
precip_mean
(time)
float32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
Precipitation mean from rain gauge
units :
mm/hr
ancillary_variables :
qc_precip_mean
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_precip_mean
(time)
int32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
Quality check results on field: Precipitation mean from rain gauge
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mwr_lwp
(time)
float32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
Liquid water path best-estimate from microwave radiometer
units :
g/m^2
ancillary_variables :
qc_mwr_lwp
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mwr_lwp
(time)
int32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
Quality check results on field: Liquid water path best-estimate from microwave radiometer
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
radar_first_top
(time)
float32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
KAZR top height of lowest significant detection layer, before clutter removal
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_base_best_estimate
(time)
float32
dask.array<chunksize=(900,), meta=np.ndarray>
long_name :
Cloud base best estimate, based on ceilometer and micropulse lidar
units :
m
valid_range :
[ 0. 25000.]
flag_values :
[-2. -1.]
flag_meanings :
possible_clear_sky clear_sky
comment :
-2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
2.55 MiB
\n",
+ "
3.52 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600,)
\n",
+ "
(900,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_base_height
(time, layer)
float32
dask.array<chunksize=(900, 10), meta=np.ndarray>
long_name :
Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
25.54 MiB
\n",
+ "
35.16 kiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 10)
\n",
+ "
(900, 10)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
744 chunks in 63 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_top_height
(time, layer)
float32
dask.array<chunksize=(900, 10), meta=np.ndarray>
long_name :
Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
5.95 GiB
\n",
+ "
196.44 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(669600, 4, 596)
\n",
+ "
(21600, 4, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
31 chunks in 94 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
minimum_detectable_reflectivity_flag
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Flag identifies points with reflectivity below radar expected sensitivity level
Only two radar modes are in use simultaneously. See global attribute radar_modes_in_use for list of modes used
array([b'hi', b'md', b'ge', b'pr'], dtype='|S2')
base_time
(time)
datetime64[ns]
string :
2020-03-01 00:00:00 0:00
long_name :
Base time in Epoch
ancillary_variables :
time_offset
array([], dtype='datetime64[ns]')
time_offset
(time)
datetime64[ns]
long_name :
Time offset from base_time
ancillary_variables :
base_time
array([], dtype='datetime64[ns]')
reflectivity_best_estimate
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Best-estimate reflectivity
units :
dBZ
ancillary_variables :
qc_reflectivity_best_estimate
valid_min :
-90.0
valid_max :
50.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity_best_estimate
(time, height)
int32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Quality check results on field: Best-estimate reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
reflectivity
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Reflectivity
units :
dBZ
ancillary_variables :
qc_reflectivity
valid_min :
-90.0
valid_max :
50.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity
(time, height)
int32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Quality check results on field: Reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mean_doppler_velocity
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Mean Doppler velocity
units :
m/s
ancillary_variables :
qc_mean_doppler_velocity
valid_min :
-25.0
valid_max :
25.0
resolution :
0.001
positive :
up
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mean_doppler_velocity
(time, height)
int32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Quality check results on field: Mean Doppler velocity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
spectral_width
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Spectral width
units :
m/s
ancillary_variables :
qc_spectral_width
valid_min :
0.0
valid_max :
10.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_spectral_width
(time, height)
int32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Quality check results on field: Spectral width
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
linear_depolarization_ratio
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Linear depolarization ratio
units :
dBZ
ancillary_variables :
qc_linear_depolarization_ratio
valid_min :
-50.0
valid_max :
50.0
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_linear_depolarization_ratio
(time, height)
int32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Quality check results on field: Linear depolarization ratio
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
signal_to_noise_ratio
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Signal-to-noise ratio
units :
dB
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
No detection due to missing radar and micropulse lidar data
flag_1_description :
Clear according to radar and lidar
flag_2_description :
Cloud detected by radar and lidar
flag_3_description :
Cloud detected by radar only
flag_4_description :
Cloud detected by lidar only
flag_5_description :
Cloud detected by radar but lidar data missing
flag_6_description :
Cloud detected by lidar but radar data missing
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 596)
\n",
+ "
(0, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int16 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
precip_mean
(time)
float32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
Precipitation mean from rain gauge
units :
mm/hr
ancillary_variables :
qc_precip_mean
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_precip_mean
(time)
int32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
Quality check results on field: Precipitation mean from rain gauge
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mwr_lwp
(time)
float32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
Liquid water path best-estimate from microwave radiometer
units :
g/m^2
ancillary_variables :
qc_mwr_lwp
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mwr_lwp
(time)
int32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
Quality check results on field: Liquid water path best-estimate from microwave radiometer
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
radar_first_top
(time)
float32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
KAZR top height of lowest significant detection layer, before clutter removal
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_base_best_estimate
(time)
float32
dask.array<chunksize=(0,), meta=np.ndarray>
long_name :
Cloud base best estimate, based on ceilometer and micropulse lidar
units :
m
valid_range :
[ 0. 25000.]
flag_values :
[-2. -1.]
flag_meanings :
possible_clear_sky clear_sky
comment :
-2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0,)
\n",
+ "
(0,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_base_height
(time, layer)
float32
dask.array<chunksize=(0, 10), meta=np.ndarray>
long_name :
Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 10)
\n",
+ "
(0, 10)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 64 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_top_height
(time, layer)
float32
dask.array<chunksize=(0, 10), meta=np.ndarray>
long_name :
Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
0 B
\n",
+ "
0 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(0, 4, 596)
\n",
+ "
(0, 4, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
1 chunks in 95 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
minimum_detectable_reflectivity_flag
(time, height)
float32
dask.array<chunksize=(0, 596), meta=np.ndarray>
long_name :
Flag identifies points with reflectivity below radar expected sensitivity level
Contains data from best radar operating mode (see radar_mode_flag); data points with non-missing values contain hydrometeors or both hydrometeors and clutter (see reflectivity_clutter_flag)
comment2 :
Reflectivity values have not yet been calibrated. Use with caution.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity_best_estimate
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Best-estimate reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
reflectivity
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Reflectivity
units :
dBZ
ancillary_variables :
qc_reflectivity
valid_min :
-90.0
valid_max :
50.0
resolution :
0.001
comment1 :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
comment2 :
Reflectivity values have not yet been calibrated. Use with caution.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_reflectivity
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Reflectivity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mean_doppler_velocity
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Mean Doppler velocity
units :
m/s
ancillary_variables :
qc_mean_doppler_velocity
valid_min :
-25.0
valid_max :
25.0
resolution :
0.001
positive :
up
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mean_doppler_velocity
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Mean Doppler velocity
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
spectral_width
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Spectral width
units :
m/s
ancillary_variables :
qc_spectral_width
valid_min :
0.0
valid_max :
10.0
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_spectral_width
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Spectral width
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
linear_depolarization_ratio
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Linear depolarization ratio
units :
dBZ
ancillary_variables :
qc_linear_depolarization_ratio
valid_min :
-50.0
valid_max :
50.0
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_linear_depolarization_ratio
(time, height)
int32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Quality check results on field: Linear depolarization ratio
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Value is less than the valid_min.
bit_1_assessment :
Bad
bit_2_description :
Value is greater than the valid_max.
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
98.22 MiB
\n",
+ "
2.05 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
signal_to_noise_ratio
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Signal-to-noise ratio
units :
dB
resolution :
0.001
comment :
Contains data from best radar operating mode (see radar_mode_flag); Data points with non-missing values had significant power detections from hydrometeors and/or clutter
No detection due to missing radar and micropulse lidar data
flag_1_description :
Clear according to radar and lidar
flag_2_description :
Cloud detected by radar and lidar
flag_3_description :
Cloud detected by radar only
flag_4_description :
Cloud detected by lidar only
flag_5_description :
Cloud detected by radar but lidar data missing
flag_6_description :
Cloud detected by lidar but radar data missing
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
49.11 MiB
\n",
+ "
1.02 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 596)
\n",
+ "
(901, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
48 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int16 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
precip_mean
(time)
float32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
Precipitation mean from rain gauge
units :
mm/hr
ancillary_variables :
qc_precip_mean
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_precip_mean
(time)
int32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
Quality check results on field: Precipitation mean from rain gauge
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
mwr_lwp
(time)
float32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
Liquid water path best-estimate from microwave radiometer
units :
g/m^2
ancillary_variables :
qc_mwr_lwp
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
qc_mwr_lwp
(time)
int32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
Quality check results on field: Liquid water path best-estimate from microwave radiometer
units :
unitless
description :
This field contains bit packed integer values, where each bit represents a QC test on the data. Non-zero bits indicate the QC condition given in the description for those bits; a value of 0 (no bits set) indicates the data has not failed any QC tests.
flag_method :
bit
bit_1_description :
Not used
bit_1_assessment :
Bad
bit_2_description :
Not used
bit_2_assessment :
Bad
bit_3_description :
Data value not available in input file, data value has been set to missing_value.
bit_3_assessment :
Bad
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
int32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
radar_first_top
(time)
float32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
KAZR top height of lowest significant detection layer, before clutter removal
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_base_best_estimate
(time)
float32
dask.array<chunksize=(1,), meta=np.ndarray>
long_name :
Cloud base best estimate, based on ceilometer and micropulse lidar
units :
m
valid_range :
[ 0. 25000.]
flag_values :
[-2. -1.]
flag_meanings :
possible_clear_sky clear_sky
comment :
-2. Possible clear sky (No MPL observations available, Ceilometer obscured, but no cloud detected), -1. Clear sky, >= 0. Valid cloud base height
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
168.75 kiB
\n",
+ "
4 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200,)
\n",
+ "
(1,)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_base_height
(time, layer)
float32
dask.array<chunksize=(1, 10), meta=np.ndarray>
long_name :
Base height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
units :
m
valid_range :
[ 0. 25000.]
flag_values :
-1.0
flag_meanings :
clear_sky
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
1.65 MiB
\n",
+ "
40 B
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 10)
\n",
+ "
(1, 10)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
43200 chunks in 5 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
cloud_layer_top_height
(time, layer)
float32
dask.array<chunksize=(1, 10), meta=np.ndarray>
long_name :
Top height of hydrometeor layers for up to 10 layers, based on combined radar and micropulse lidar observations
Noise values are reduced by signal processing, which is dependent upon the cloud properties. Therefore actual noise levels can fluctuate with time. Reported here are mean values for the time period.
\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
Array
\n",
+ "
Chunk
\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
Bytes
\n",
+ "
392.87 MiB
\n",
+ "
196.44 MiB
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
Shape
\n",
+ "
(43200, 4, 596)
\n",
+ "
(21600, 4, 596)
\n",
+ "
\n",
+ "
\n",
+ "
Dask graph
\n",
+ "
2 chunks in 7 graph layers
\n",
+ "
\n",
+ "
\n",
+ "
Data type
\n",
+ "
float32 numpy.ndarray
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "
minimum_detectable_reflectivity_flag
(time, height)
float32
dask.array<chunksize=(901, 596), meta=np.ndarray>
long_name :
Flag identifies points with reflectivity below radar expected sensitivity level
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Barometric pressure
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Eastward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Northward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Dewpoint temperature
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Relative humidity scaled using MWR
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Ancillary quality check results on field: Relative humidity scaled using MWR
units :
unitless
description :
This field contains integer values indicating the results of QC test on the data. Non-zero integers indicate the QC condition given in the description for those integers; a value of 0 indicates the data has not failed any QC tests.
flag_method :
integer
flag_1_description :
Scale factor less than valid min, so RH has been scaled by interpolation of nearest valid scale factors
flag_1_assessment :
Indeterminate
flag_2_description :
Scale factor greater than valid max, so RH has been scaled by interpolation of nearest valid scale factors
flag_2_assessment :
Indeterminate
flag_3_description :
Scale factor less than valid min, but interpolation of nearest scale factors failed, so RH has not been scaled
flag_3_assessment :
Indeterminate
flag_4_description :
Scale factor greater than valid max, but interpolation of nearest scale factors failed, so RH has not been scaled
flag_4_assessment :
Indeterminate
flag_5_description :
Vapor unavailable, so RH has not been scaled
flag_5_assessment :
Indeterminate
flag_6_description :
RH not available in input file, data value has been set to missing_value.
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Barometric pressure
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Barometric pressure
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Eastward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Eastward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Northward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Northward wind component
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Dewpoint temperature
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Dewpoint temperature
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Relative humidity scaled using MWR
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']
Quality check results on field: Relative humidity scaled using MWR
units :
1
flag_masks :
[1, 2, 4]
flag_meanings :
['Value is less than the fail_min.', 'Value is greater than the fail_max.', 'Data value not available in input file, data value has been set to missing_value.']