iris

本节介绍如何使用 iris 加载 GRIB2 要素场，并使用 cedarkit-maps 绘图。

安装

使用 conda 安装 iris 和 iris-grib

conda install -c conda-forge iris iris-grib

准备

导入需要的包

import xarray as xr
import pandas as pd
import iris

设置绘图的数据参数，使用 CMA-MESO 2024 年 4 月 1 日 00 时次 024 时效数据。

system_name = "CMA-MESO"
data_type = "cma_meso_3km/grib2/orig"
start_time = pd.to_datetime("2024-04-01 00:00:00")
forecast_time = pd.to_timedelta("24h")

加载数据

设置 GRIB2 数据文件路径

注：可以使用 reki 库查找本地文件路径

file_path = '/g3/COMMONDATA/OPER/CEMC/MESO_3KM/Prod-grib/2024040100/ORIG/rmf.hgra.2024040100024.grb2'
file_path

'/g3/COMMONDATA/OPER/CEMC/MESO_3KM/Prod-grib/2024040100/ORIG/rmf.hgra.2024040100024.grb2'

注：可以使用 reki 库查找本地文件路径

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from reki.data_finder import find_local_file

file_path_use_reki = find_local_file(
    data_type,
    start_time=start_time,
    forecast_time=forecast_time,
)
file_path_use_reki

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PosixPath('/g3/COMMONDATA/OPER/CEMC/MESO_3KM/Prod-grib/2024040100/ORIG/rmf.hgra.2024040100024.grb2')

使用 iris 加载温度要素场

cubes = iris.load(file_path, "air_temperature")
print(cubes)

0: air_temperature / (K)               (latitude: 1671; longitude: 2501)
1: air_temperature / (K)               (latitude: 1671; longitude: 2501)
2: air_temperature / (K)               (pressure: 21; latitude: 1671; longitude: 2501)
3: air_temperature / (K)               (unknown: 4; latitude: 1671; longitude: 2501)

选择 2 米温度要素场

t_2m_cube = cubes[1]
t_2m_cube

Air Temperature (K)	latitude	longitude
Shape	1671	2501
Dimension coordinates
latitude	x	-
longitude	-	x
Scalar coordinates
forecast_period	24.0 hours
forecast_reference_time	2024-04-01 00:00:00
height	2.0 m
time	2024-04-02 00:00:00
Attributes
GRIB_PARAM	GRIB2:d000c000n000

将 iris.cube.Cube 转为 xarray.DataArray

t_2m_field = xr.DataArray.from_iris(t_2m_cube) - 273.15
t_2m_field

<xarray.DataArray 'air_temperature' (latitude: 1671, longitude: 2501)> Size: 33MB
dask.array<sub, shape=(1671, 2501), dtype=float64, chunksize=(1671, 2501), chunktype=numpy.ndarray>
Coordinates:
  * latitude                 (latitude) float64 13kB 60.1 60.07 ... 10.03 10.0
  * longitude                (longitude) float64 20kB 70.0 70.03 ... 145.0 145.0
    forecast_period          timedelta64[ns] 8B ...
    forecast_reference_time  datetime64[ns] 8B ...
    height                   float64 8B ...
    time                     datetime64[ns] 8B ...

xarray.DataArray

'air_temperature'

• latitude: 1671
• longitude: 2501

• dask.array<chunksize=(1671, 2501), meta=np.ndarray>

  Array Chunk Bytes 31.88 MiB 31.88 MiB Shape (1671, 2501) (1671, 2501) Dask graph 1 chunks in 4 graph layers Data type float64 numpy.ndarray

• Coordinates: (6)
  • latitude
    (latitude)
    float64
    60.1 60.07 60.04 ... 10.03 10.0

    standard_name :

    latitude

    units :

    degrees

    array([60.099998, 60.069998, 60.039998, ..., 10.059998, 10.029998,  9.999998])

  • longitude
    (longitude)
    float64
    70.0 70.03 70.06 ... 145.0 145.0

    standard_name :

    longitude

    units :

    degrees

    array([ 70.  ,  70.03,  70.06, ..., 144.94, 144.97, 145.  ])

  • forecast_period
    ()
    timedelta64[ns]
    ...

    standard_name :

    forecast_period

    [1 values with dtype=timedelta64[ns]]

  • forecast_reference_time
    ()
    datetime64[ns]
    ...

    standard_name :

    forecast_reference_time

    [1 values with dtype=datetime64[ns]]

  • height
    ()
    float64
    ...

    long_name :

    height

    units :

    m

    [1 values with dtype=float64]

  • time
    ()
    datetime64[ns]
    ...

    standard_name :

    time

    [1 values with dtype=datetime64[ns]]

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([         60.099998,          60.069998,          60.039998,
           60.009997999999996,          59.979998,          59.949998,
                    59.919998,          59.889998,          59.859998,
           59.829997999999996,
           ...
           10.269998000000001,          10.239998, 10.209997999999999,
           10.179997999999998, 10.149998000000004, 10.119998000000002,
           10.089998000000001,          10.059998, 10.029997999999999,
            9.999997999999998],
          dtype='float64', name='latitude', length=1671))

  • longitude
    PandasIndex

    PandasIndex(Index([              70.0,              70.03,              70.06,
                        70.09,              70.12,              70.15,
                        70.18,              70.21,              70.24,
                        70.27,
           ...
           144.73000000000002,             144.76,             144.79,
                       144.82,             144.85,             144.88,
                       144.91,             144.94,             144.97,
                        145.0],
          dtype='float64', name='longitude', length=2501))

• Attributes: (0)

绘图

使用 cedarkit-maps 绘制 2 米温度填充图

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from cedarkit.maps.style import ContourStyle
from cedarkit.maps.chart import Panel
from cedarkit.maps.domains import EastAsiaMapDomain
from cedarkit.maps.colormap import get_ncl_colormap

t_2m_level = [-24, -20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20, 24, 28, 32]
color_index = [2, 12, 22, 32, 42, 52, 62, 72, 82, 92, 102, 112, 122, 132, 142, 152]
t_2m_color_map = get_ncl_colormap("BlAqGrYeOrReVi200", index=color_index)
t_2m_style = ContourStyle(
    colors=t_2m_color_map,
    levels=t_2m_level,
    fill=True,
)
domain = EastAsiaMapDomain()
panel = Panel(domain=domain)
panel.plot(t_2m_field, style=t_2m_style)
domain.set_title(
    panel=panel,
    graph_name="2m Temperature (C)",
    system_name=system_name,
    start_time=start_time,
    forecast_time=forecast_time,
)
domain.add_colorbar(panel=panel, style=t_2m_style)
panel.show()