pygrib

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

安装

使用 conda 安装 pygrib

conda install -c conda-forge pygrib

准备

导入需要的包

import xarray as xr
import pandas as pd
import pygrib

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 pygrib 加载 2 米温度要素场。

grbs = pygrib.open(file_path)
selected_grbs = grbs.select(shortName="2t")
selected_grb = selected_grbs[0]
selected_grb

10:2 metre temperature:K (instant):regular_ll:heightAboveGround:level 2 m:fcst time 24 mins:from 202404010000

获取经纬度坐标

lats, lons = selected_grb.latlons()
lats[:, 0], lons[0, :]

(array([60.099998, 60.069998, 60.039998, ..., 10.059998, 10.029998,
        10.      ]),
 array([ 70.  ,  70.03,  70.06, ..., 144.94, 144.97, 145.  ]))

获取要素场二维数组

values = selected_grb.values
values.shape

(1671, 2501)

将要素场包装为 xarray.DataArray 对象

t_2m_field = xr.DataArray(
    values,
    coords={
        "latitude": lats[:, 0],
        "longitude": lons[0, :],
    },
    dims=["latitude", "longitude"]
) - 273.15
t_2m_field

<xarray.DataArray (latitude: 1671, longitude: 2501)> Size: 33MB
array([[-0.71931641, -0.75931641, -0.80931641, ..., -4.16931641,
        -2.87931641, -2.95931641],
       [-3.65931641, -3.66931641, -2.27931641, ..., -6.91931641,
        -6.31931641, -3.72931641],
       [-3.54931641, -3.58931641, -0.85931641, ..., -7.60931641,
        -7.47931641, -3.43931641],
       ...,
       [26.60068359, 26.60068359, 26.59068359, ..., 25.80068359,
        25.79068359, 25.97068359],
       [26.59068359, 26.60068359, 26.59068359, ..., 25.78068359,
        25.78068359, 26.00068359],
       [26.59068359, 26.59068359, 26.60068359, ..., 25.78068359,
        25.79068359, 25.97068359]])
Coordinates:
  * latitude   (latitude) float64 13kB 60.1 60.07 60.04 ... 10.06 10.03 10.0
  * longitude  (longitude) float64 20kB 70.0 70.03 70.06 ... 144.9 145.0 145.0

xarray.DataArray

• latitude: 1671
• longitude: 2501

• -0.7193 -0.7593 -0.8093 -0.8393 -0.8793 ... 25.78 25.78 25.79 25.97

  array([[-0.71931641, -0.75931641, -0.80931641, ..., -4.16931641,
          -2.87931641, -2.95931641],
         [-3.65931641, -3.66931641, -2.27931641, ..., -6.91931641,
          -6.31931641, -3.72931641],
         [-3.54931641, -3.58931641, -0.85931641, ..., -7.60931641,
          -7.47931641, -3.43931641],
         ...,
         [26.60068359, 26.60068359, 26.59068359, ..., 25.80068359,
          25.79068359, 25.97068359],
         [26.59068359, 26.60068359, 26.59068359, ..., 25.78068359,
          25.78068359, 26.00068359],
         [26.59068359, 26.59068359, 26.60068359, ..., 25.78068359,
          25.79068359, 25.97068359]])

• Coordinates: (2)
  • latitude
    (latitude)
    float64
    60.1 60.07 60.04 ... 10.03 10.0

    array([60.099998, 60.069998, 60.039998, ..., 10.059998, 10.029998, 10.      ])

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

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

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([         60.099998,          60.069998,          60.039998,
           60.009997999999996, 59.979997999999995, 59.949997999999994,
            59.91999799999999,  59.88999799999999,  59.85999799999999,
            59.82999799999999,
           ...
           10.269997999998452, 10.239997999998453, 10.209997999998453,
           10.179997999998454, 10.149997999998455, 10.119997999998455,
           10.089997999998456, 10.059997999998457, 10.029997999998457,
                         10.0],
          dtype='float64', name='latitude', length=1671))

  • longitude
    PandasIndex

    PandasIndex(Index([              70.0,              70.03,              70.06,
                        70.09,              70.12,              70.15,
                        70.18,  70.21000000000001,  70.24000000000001,
            70.27000000000001,
           ...
           144.73000000000283, 144.76000000000283, 144.79000000000283,
           144.82000000000284, 144.85000000000284, 144.88000000000284,
           144.91000000000284, 144.94000000000284, 144.97000000000284,
                        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()