开始使用

本文介绍如何使用 cedarkit 工具栈为 CMA-MESO 数据绘制中国区域 2 米温度填充图。

首先加载需要使用的包，包括：

• 数据结构相关：pandas

• 数据准备工具库：reki

• 绘图工具库：cedarkit.maps

  • 绘图板：Panel

  • 底图布局：EastAsiaMapDomain

  • 填充图样式：ContourStyle

  • 颜色表：get_ncl_colormap

  • 地图包：set_default_map_package

import pandas as pd

from reki.data_finder import find_local_file
from reki.format.grib.eccodes import load_field_from_file

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
from cedarkit.maps.map import set_default_map_package

设置绘图的数据参数。绘制 CMA-MESO 2024 年 4 月 1 日 00 时次 024 时效的 2 米温度场。

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

获取 GRIB2 文件目录

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

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

加载 2 米温度场。这里直接使用 ecCodes 内置的要素名 2t

t_2m_field = load_field_from_file(
    file_path,
    parameter="2t",
) - 273.15
t_2m_field

<xarray.DataArray '2t' (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:
    time               datetime64[ns] 8B 2024-04-01
    step               timedelta64[ns] 8B 1 days
    valid_time         datetime64[ns] 8B 2024-04-02
    heightAboveGround  int64 8B 2
  * latitude           (latitude) float64 13kB 60.1 60.07 60.04 ... 10.03 10.0
  * longitude          (longitude) float64 20kB 70.0 70.03 70.06 ... 145.0 145.0

xarray.DataArray

'2t'

• 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: (6)
  • time
    ()
    datetime64[ns]
    2024-04-01

    array('2024-04-01T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    1 days

    array(86400000000000, dtype='timedelta64[ns]')

  • valid_time
    ()
    datetime64[ns]
    2024-04-02

    array('2024-04-02T00:00:00.000000000', dtype='datetime64[ns]')

  • heightAboveGround
    ()
    int64
    2

    array(2)

  • latitude
    (latitude)
    float64
    60.1 60.07 60.04 ... 10.03 10.0

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([60.099998, 60.069998, 60.039998, ..., 10.06    , 10.03    , 10.      ])

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

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

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

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([         60.099998, 60.069998001197604,  60.03999800239521,
            60.00999800359281,  59.97999800479042,  59.94999800598802,
           59.919998007185626,  59.88999800838323, 59.859998009580835,
            59.82999801077844,
           ...
            10.26999998922156, 10.239999990419165, 10.209999991616769,
           10.179999992814373, 10.149999994011978, 10.119999995209582,
           10.089999996407187, 10.059999997604791, 10.029999998802396,
                         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,              70.21,              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)

配置

定义填充图层次

t_2m_level = [-24, -20, -16, -12, -8, -4, 0, 4, 8, 12, 16, 20, 24, 28, 32]

定义填充图颜色表。色表来自 cedarkit-maps 内置的 NCL 色表文件 BlAqGrYeOrReVi200.rgb。

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)

定义填充图样式

t_2m_style = ContourStyle(
    colors=t_2m_color_map,
    levels=t_2m_level,
    fill=True,
)

使用 CEMC 地图包

注：如果没有安装 cemc-meda-data 包，可以注释改行代码，直接使用默认地图包

set_default_map_package("cedarkit.maps.map.cemc")

'cedarkit.maps.map.cemc'

绘图

创建中国区域底图布局

domain = EastAsiaMapDomain()

创建绘图板，绘制等值线，设置标题，添加颜色条

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()