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 EastAsiaMapDomain
from cedarkit.maps.colormap import get_ncl_colormap

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

system_name = "CMA-GFS"
data_type = "cma_gfs_gmf/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 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/2024040100/ORIG/gmf.gra.2024040100024.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([[ 4.8503418,  4.8703418,  4.8903418, ...,  4.7703418,  4.7803418,
         4.8103418],
       [ 4.6903418,  4.7103418,  4.7303418, ...,  4.6403418,  4.6603418,
         4.6803418],
       [ 4.5903418,  4.6103418,  4.6203418, ...,  4.5403418,  4.5503418,
         4.5703418],
       ...,
       [-0.7196582, -0.6996582, -0.6896582, ..., -0.7596582, -0.7396582,
        -0.7296582],
       [-0.2396582, -0.2296582, -0.2096582, ..., -0.2796582, -0.2696582,
        -0.2596582],
       [-0.5796582, -0.5696582, -0.5596582, ..., -0.6096582, -0.6096582,
        -0.5896582]])
Coordinates:
    time        datetime64[ns] 8B 2024-04-01
    step        timedelta64[ns] 8B 1 days
    valid_time  datetime64[ns] 8B 2024-04-02
    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:             24
    GRIB_count:               149
    long_name:                discipline=0 parmcat=2 parm=2

xarray.DataArray

'u'

• latitude: 1440
• longitude: 2880

• 4.85 4.87 4.89 4.91 4.92 ... -0.6297 -0.6197 -0.6097 -0.6097 -0.5897

  array([[ 4.8503418,  4.8703418,  4.8903418, ...,  4.7703418,  4.7803418,
           4.8103418],
         [ 4.6903418,  4.7103418,  4.7303418, ...,  4.6403418,  4.6603418,
           4.6803418],
         [ 4.5903418,  4.6103418,  4.6203418, ...,  4.5403418,  4.5503418,
           4.5703418],
         ...,
         [-0.7196582, -0.6996582, -0.6896582, ..., -0.7596582, -0.7396582,
          -0.7296582],
         [-0.2396582, -0.2296582, -0.2096582, ..., -0.2796582, -0.2696582,
          -0.2596582],
         [-0.5796582, -0.5696582, -0.5596582, ..., -0.6096582, -0.6096582,
          -0.5896582]])

• 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]')

  • 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 :

  20240401

  GRIB_dataTime :

  0

  GRIB_validityDate :

  20240402

  GRIB_validityTime :

  0

  GRIB_step :

  24

  GRIB_stepType :

  instant

  GRIB_stepUnits :

  1

  GRIB_stepRange :

  24

  GRIB_endStep :

  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([[10.05180549, 10.05272224, 10.04495258, ..., 10.03983504,
        10.03579651, 10.041342  ],
       [ 9.25990743,  9.26144246,  9.25443484, ...,  9.26928709,
         9.26202629,  9.2634746 ],
       [ 8.89937259,  8.90114955,  8.88924093, ...,  8.90807667,
         8.9045805 ,  8.89765148],
       ...,
       [ 5.73121708,  5.73866535,  5.74738235, ...,  5.74628915,
         5.73376278,  5.73248135],
       [ 5.26131566,  5.27086012,  5.27002657, ...,  5.28326124,
         5.26276752,  5.25227681],
       [ 4.29514855,  4.29381042,  4.29249517, ...,  4.29930001,
         4.2894013 ,  4.28660439]])
Coordinates:
    time        datetime64[ns] 8B 2024-04-01
    step        timedelta64[ns] 8B 1 days
    valid_time  datetime64[ns] 8B 2024-04-02
    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

• 10.05 10.05 10.04 10.05 10.04 10.04 ... 4.302 4.301 4.299 4.289 4.287

  array([[10.05180549, 10.05272224, 10.04495258, ..., 10.03983504,
          10.03579651, 10.041342  ],
         [ 9.25990743,  9.26144246,  9.25443484, ...,  9.26928709,
           9.26202629,  9.2634746 ],
         [ 8.89937259,  8.90114955,  8.88924093, ...,  8.90807667,
           8.9045805 ,  8.89765148],
         ...,
         [ 5.73121708,  5.73866535,  5.74738235, ...,  5.74628915,
           5.73376278,  5.73248135],
         [ 5.26131566,  5.27086012,  5.27002657, ...,  5.28326124,
           5.26276752,  5.25227681],
         [ 4.29514855,  4.29381042,  4.29249517, ...,  4.29930001,
           4.2894013 ,  4.28660439]])

• 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]')

  • 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 = EastAsiaMapDomain()

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()