选取数据¶
fields属性存放了雷达体扫数据:
>>> from pycwr.io import read_auto
>>> PRD = read_auto("./data/NUIST.20150627.002438.AR2.bz2")
通过[层数]方法索引对应产品, 层数从0开始:
>>> print(PRD.fields[0])
<xarray.Dataset>
Dimensions: (range: 1933, time: 668)
Coordinates:
azimuth (time) float64 9.149 9.69 10.22 10.77 ... 8.094 8.611 9.168 9.693
elevation (time) float64 0.5933 0.5933 0.5933 ... 0.5054 0.5054 0.5054
x (time, range) float64 11.92 23.85 35.77 ... 2.439e+04 2.44e+04
y (time, range) float64 74.04 148.1 222.1 ... 1.428e+05 1.429e+05
z (time, range) float64 174.8 175.6 176.3 ... 2.688e+03 2.689e+03
lat (time, range) float64 32.21 32.21 32.21 ... 33.49 33.49 33.49
lon (time, range) float64 118.7 118.7 118.7 ... 119.0 119.0 119.0
* range (range) float64 75.0 150.0 225.0 ... 1.448e+05 1.449e+05 1.45e+05
* time (time) datetime64[ns] 2015-06-27T00:24:38.640231 ... 2015-06-2...
Data variables:
dBZ (time, range) float64 -13.6 17.34 24.1 ... 18.62 18.24 19.32
dBT (time, range) float64 -13.6 17.34 24.1 ... 18.62 18.24 19.32
V (time, range) float64 0.0 -0.08 -1.12 -1.55 ... -1.76 -3.22 -3.41
W (time, range) float64 0.01 0.01 0.01 1.12 ... 1.63 1.43 1.5 1.68
ZDR (time, range) float64 -0.81 13.73 0.41 -0.39 ... 1.1 0.67 0.43
KDP (time, range) float64 7.03 6.13 5.44 4.78 ... 0.5 0.47 0.48 0.51
PhiDP (time, range) float64 100.0 33.3 1.02 2.86 ... 103.2 104.5 102.6
CC (time, range) float64 1.0 0.57 0.938 0.994 ... 0.923 0.938 0.96
SQI (time, range) float64 1.0 1.0 1.0 0.966 ... 0.913 0.905 0.898
SNRH (time, range) float64 67.77 81.21 81.95 ... 14.46 14.08 15.16
通过[层数][关键词]方法索引对应产品,关键词详见表1:
>>> print(PRD.fields[0]["dBZ"])
<xarray.DataArray 'dBZ' (time: 668, range: 1933)>
array([[-13.60000038, 17.34000015, 24.10000038, ..., 16.87999916,
16.76000023, 17.52000046],
[-13.60000038, 16.65999985, 24.09000015, ..., 18.37000084,
16.42000008, 17.86000061],
[-13.60000038, 18.03000069, 23.57999992, ..., 16.98999977,
20.04000092, 20.85000038],
...,
[-13.60000038, 12.61999989, 24. , ..., 15.63000011,
14.63000011, 13.81000042],
[-13.60000038, 15.75 , 25.35000038, ..., 12.85999966,
17.29999924, 14.15999985],
[-13.60000038, 14.97999954, 24.55999947, ..., 18.62000084,
18.23999977, 19.31999969]])
Coordinates:
azimuth (time) float64 9.149 9.69 10.22 10.77 ... 8.094 8.611 9.168 9.693
elevation (time) float64 0.5933 0.5933 0.5933 ... 0.5054 0.5054 0.5054
x (time, range) float64 11.92 23.85 35.77 ... 2.439e+04 2.44e+04
y (time, range) float64 74.04 148.1 222.1 ... 1.428e+05 1.429e+05
z (time, range) float64 174.8 175.6 176.3 ... 2.688e+03 2.689e+03
lat (time, range) float64 32.21 32.21 32.21 ... 33.49 33.49 33.49
lon (time, range) float64 118.7 118.7 118.7 ... 119.0 119.0 119.0
* range (range) float64 75.0 150.0 225.0 ... 1.448e+05 1.449e+05 1.45e+05
* time (time) datetime64[ns] 2015-06-27T00:24:38.640231 ... 2015-06-2...
Attributes:
units: dBZ
standard_name: equivalent_reflectivity_factor
long_name: Reflectivity
valid_max: 80.0
valid_min: -30.0
coordinates: elevation azimuth range
scan_info存放了雷达位置信息以及扫描方式:
>>> print(PRD.scan_info)
<xarray.Dataset>
Dimensions: (sweep: 15)
Coordinates:
* sweep (sweep) int64 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Data variables:
latitude float64 32.21
longitude float64 118.7
altitude int64 87
scan_type <U3 'ppi'
frequency float64 5.592
start_time datetime64[ns] 2015-06-27T00:24:38.640231
end_time datetime64[ns] 2015-06-27T00:31:47.207645
nyquist_velocity (sweep) float32 13.4 13.4 13.4 13.4 ... 26.81 26.81 26.81
unambiguous_range (sweep) int32 145000 145000 145000 ... 70000 70000 10000
rays_per_sweep (sweep) int64 668 668 668 668 668 ... 669 668 668 668 668
fixed_angle (sweep) float32 0.5 1.5 2.4 3.4 ... 14.0 16.7 19.5 90.0
beam_width (sweep) float64 0.5389 0.5389 0.5389 ... 0.5389 0.5389
表1，不同变量对应索引的关键词:
key |
variable |
|---|---|
dBT |
total_power |
dBZ |
reflectivity |
V |
velocity |
W |
spectrum_width |
SQI |
normalized_coherent_power |
CPA |
clutter_phase_alignment |
ZDR |
differential_reflectivity |
LDR |
linear_depolarization_ratio |
CC |
cross_correlation_ratio |
PhiDP |
differential_phase |
KDP |
specific_differential_phase |
CP |
clutter_probability |
Flag |
flag_of_rpv_data |
HCL |
hydro_class |
CF |
clutter_flag |
Zc |
corrected_reflectivity |
Vc |
corrected_velocity |
Wc |
spectrum_width_corrected |
SNRH |
horizontal_signal_noise_ratio |
SNRV |
vertical_signal_noise_ratio |