填充图

本节介绍如何使用 cedarkit-maps 绘制 850 hPa 风速填充图。

首先导入需要使用的包。包括：

• 数据结构：numpy, pandas

• cedarkit 工具套件

  • 数据准备：reki

    • 数据查找函数： find_local_file

    • GRIB2文件要素加载函数：load_field_from_file

  • 气象可视化：cedarkit.maps

    • 绘图板：Panel

    • 底图布局：EastAsiaMapDomain

    • 绘图样式：ContourStyle

    • 颜色表加载函数：get_ncl_colormap

import numpy as np
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 EastAsiaMapTemplate
from cedarkit.maps.colormap import get_ncl_colormap

设置绘图的数据参数，使用 CMA-GFS 2024 年 11 月 14 日 00 时次 024 数据。

system_name = "CMA-GFS"
data_type = "cma_gfs_gmf/grib2/orig"
start_time = pd.to_datetime("2024-11-14 00:00:00")
forecast_time = pd.to_timedelta("24h")

加载数据

获取 GRIB2 数据路径

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

PosixPath('/g3/COMMONDATA/OPER/CEMC/GFS_GMF/Prod-grib/2024111400/ORIG/gmf.gra.2024111400024.grb2')

加载 850 hPa 风场。风场在 ecCodes 的内置要素名为 u 和 v

u_850_field = load_field_from_file(
    file_path,
    parameter="u",
    level_type="pl",
    level=850
)
v_850_field = load_field_from_file(
    file_path,
    parameter="v",
    level_type="pl",
    level=850
)
u_850_field

<xarray.DataArray 'u' (latitude: 1440, longitude: 2880)> Size: 33MB
array([[-3.42031738, -3.42031738, -3.42031738, ..., -3.42031738,
        -3.42031738, -3.42031738],
       [-1.61031738, -1.61031738, -1.61031738, ..., -1.61031738,
        -1.61031738, -1.61031738],
       [-1.77031738, -1.77031738, -1.77031738, ..., -1.77031738,
        -1.77031738, -1.77031738],
       ...,
       [-2.60031738, -2.59031738, -2.58031738, ..., -2.63031738,
        -2.62031738, -2.61031738],
       [-2.63031738, -2.62031738, -2.62031738, ..., -2.69031738,
        -2.67031738, -2.65031738],
       [-2.77031738, -2.79031738, -2.81031738, ..., -2.86031738,
        -2.85031738, -2.81031738]])
Coordinates:
    time        datetime64[ns] 8B 2024-11-14
    step        timedelta64[ns] 8B 1 days
    valid_time  datetime64[ns] 8B 2024-11-15
    pl          float64 8B 850.0
  * latitude    (latitude) float64 12kB 89.94 89.81 89.69 ... -89.81 -89.94
  * longitude   (longitude) float64 23kB 0.0 0.125 0.25 ... 359.6 359.8 359.9
Attributes: (12/17)
    GRIB_edition:             2
    GRIB_centre:              babj
    GRIB_subCentre:           0
    GRIB_tablesVersion:       4
    GRIB_localTablesVersion:  0
    GRIB_dataType:            fc
    ...                       ...
    GRIB_stepType:            instant
    GRIB_stepUnits:           1
    GRIB_stepRange:           24
    GRIB_endStep:int:         24
    GRIB_count:               149
    long_name:                discipline=0 parmcat=2 parm=2

xarray.DataArray

'u'

• latitude: 1440
• longitude: 2880

• -3.42 -3.42 -3.42 -3.42 -3.42 -3.42 ... -2.87 -2.86 -2.86 -2.85 -2.81

  array([[-3.42031738, -3.42031738, -3.42031738, ..., -3.42031738,
          -3.42031738, -3.42031738],
         [-1.61031738, -1.61031738, -1.61031738, ..., -1.61031738,
          -1.61031738, -1.61031738],
         [-1.77031738, -1.77031738, -1.77031738, ..., -1.77031738,
          -1.77031738, -1.77031738],
         ...,
         [-2.60031738, -2.59031738, -2.58031738, ..., -2.63031738,
          -2.62031738, -2.61031738],
         [-2.63031738, -2.62031738, -2.62031738, ..., -2.69031738,
          -2.67031738, -2.65031738],
         [-2.77031738, -2.79031738, -2.81031738, ..., -2.86031738,
          -2.85031738, -2.81031738]])

• Coordinates: (6)
  • time
    ()
    datetime64[ns]
    2024-11-14

    array('2024-11-14T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    1 days

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

  • valid_time
    ()
    datetime64[ns]
    2024-11-15

    array('2024-11-15T00:00:00.000000000', dtype='datetime64[ns]')

  • pl
    ()
    float64
    850.0

    array(850.)

  • latitude
    (latitude)
    float64
    89.94 89.81 89.69 ... -89.81 -89.94

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([ 89.9375,  89.8125,  89.6875, ..., -89.6875, -89.8125, -89.9375])

  • longitude
    (longitude)
    float64
    0.0 0.125 0.25 ... 359.8 359.9

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([0.00000e+00, 1.25000e-01, 2.50000e-01, ..., 3.59625e+02, 3.59750e+02,
           3.59875e+02])

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([ 89.9375,  89.8125,  89.6875,  89.5625,  89.4375,  89.3125,  89.1875,
            89.0625,  88.9375,  88.8125,
           ...
           -88.8125, -88.9375, -89.0625, -89.1875, -89.3125, -89.4375, -89.5625,
           -89.6875, -89.8125, -89.9375],
          dtype='float64', name='latitude', length=1440))

  • longitude
    PandasIndex

    PandasIndex(Index([    0.0,   0.125,    0.25,   0.375,     0.5,   0.625,    0.75,   0.875,
               1.0,   1.125,
           ...
            358.75, 358.875,   359.0, 359.125,  359.25, 359.375,   359.5, 359.625,
            359.75, 359.875],
          dtype='float64', name='longitude', length=2880))

• Attributes: (17)

  GRIB_edition :

  2

  GRIB_centre :

  babj

  GRIB_subCentre :

  0

  GRIB_tablesVersion :

  4

  GRIB_localTablesVersion :

  0

  GRIB_dataType :

  fc

  GRIB_dataDate :

  20241114

  GRIB_dataTime :

  0

  GRIB_validityDate :

  20241115

  GRIB_validityTime :

  0

  GRIB_step :

  24

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_stepRange :

  24

  GRIB_endStep:int :

  24

  GRIB_count :

  149

  long_name :

  discipline=0 parmcat=2 parm=2

计算得到风速场

wind_speed_850_field = np.sqrt(u_850_field ** 2 + v_850_field ** 2)
wind_speed_850_field

<xarray.DataArray (latitude: 1440, longitude: 2880)> Size: 33MB
array([[3.49408509, 3.49408509, 3.49408509, ..., 3.49005204, 3.49205483,
        3.49205483],
       [1.74963728, 1.74963728, 1.75357187, ..., 1.7457511 , 1.74963728,
        1.74963728],
       [1.82519138, 1.82519138, 1.82519138, ..., 1.82519138, 1.82519138,
        1.82519138],
       ...,
       [5.98069046, 6.03950877, 6.03522657, ..., 6.05677237, 6.03441402,
        6.03007846],
       [5.77019667, 5.91757894, 5.90861441, ..., 5.98460945, 5.95776006,
        5.94882276],
       [4.65091124, 4.75143846, 4.75514078, ..., 4.80894422, 4.80300303,
        4.77937368]])
Coordinates:
    time        datetime64[ns] 8B 2024-11-14
    step        timedelta64[ns] 8B 1 days
    valid_time  datetime64[ns] 8B 2024-11-15
    pl          float64 8B 850.0
  * latitude    (latitude) float64 12kB 89.94 89.81 89.69 ... -89.81 -89.94
  * longitude   (longitude) float64 23kB 0.0 0.125 0.25 ... 359.6 359.8 359.9

xarray.DataArray

• latitude: 1440
• longitude: 2880

• 3.494 3.494 3.494 3.496 3.496 3.498 ... 4.799 4.801 4.809 4.803 4.779

  array([[3.49408509, 3.49408509, 3.49408509, ..., 3.49005204, 3.49205483,
          3.49205483],
         [1.74963728, 1.74963728, 1.75357187, ..., 1.7457511 , 1.74963728,
          1.74963728],
         [1.82519138, 1.82519138, 1.82519138, ..., 1.82519138, 1.82519138,
          1.82519138],
         ...,
         [5.98069046, 6.03950877, 6.03522657, ..., 6.05677237, 6.03441402,
          6.03007846],
         [5.77019667, 5.91757894, 5.90861441, ..., 5.98460945, 5.95776006,
          5.94882276],
         [4.65091124, 4.75143846, 4.75514078, ..., 4.80894422, 4.80300303,
          4.77937368]])

• Coordinates: (6)
  • time
    ()
    datetime64[ns]
    2024-11-14

    array('2024-11-14T00:00:00.000000000', dtype='datetime64[ns]')

  • step
    ()
    timedelta64[ns]
    1 days

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

  • valid_time
    ()
    datetime64[ns]
    2024-11-15

    array('2024-11-15T00:00:00.000000000', dtype='datetime64[ns]')

  • pl
    ()
    float64
    850.0

    array(850.)

  • latitude
    (latitude)
    float64
    89.94 89.81 89.69 ... -89.81 -89.94

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

    array([ 89.9375,  89.8125,  89.6875, ..., -89.6875, -89.8125, -89.9375])

  • longitude
    (longitude)
    float64
    0.0 0.125 0.25 ... 359.8 359.9

    units :

    degrees_east

    standard_name :

    longitude

    long_name :

    longitude

    array([0.00000e+00, 1.25000e-01, 2.50000e-01, ..., 3.59625e+02, 3.59750e+02,
           3.59875e+02])

• Indexes: (2)
  • latitude
    PandasIndex

    PandasIndex(Index([ 89.9375,  89.8125,  89.6875,  89.5625,  89.4375,  89.3125,  89.1875,
            89.0625,  88.9375,  88.8125,
           ...
           -88.8125, -88.9375, -89.0625, -89.1875, -89.3125, -89.4375, -89.5625,
           -89.6875, -89.8125, -89.9375],
          dtype='float64', name='latitude', length=1440))

  • longitude
    PandasIndex

    PandasIndex(Index([    0.0,   0.125,    0.25,   0.375,     0.5,   0.625,    0.75,   0.875,
               1.0,   1.125,
           ...
            358.75, 358.875,   359.0, 359.125,  359.25, 359.375,   359.5, 359.625,
            359.75, 359.875],
          dtype='float64', name='longitude', length=2880))

• Attributes: (0)

配置

定义填充图层次

wind_speed_contour_lev = np.arange(8, 26, 2)

定义填充图色表

color_index = np.arange(90, 236, 15) - 2
color_index[0] = -1
wind_speed_color_map = get_ncl_colormap(
    "rainbow+white+gray", index=color_index
)

定义填充图样式 (ContourStyle)

wind_speed_style = ContourStyle(
    colors=wind_speed_color_map,
    levels=wind_speed_contour_lev,
    fill=True,
)

绘制

创建中国区域底图布局

domain = EastAsiaMapTemplate()

绘制填充图，设置标题和颜色条

panel = Panel(domain=domain)
panel.plot(wind_speed_850_field, style=wind_speed_style)

domain.set_title(
    panel=panel,
    graph_name="850 hPa WIND (m/s) shadow",
    system_name=system_name,
    start_time=start_time,
    forecast_time=forecast_time,
)
domain.add_colorbar(panel=panel, style=wind_speed_style)
panel.show()