风场图

本节介绍如何使用 cedarkit-maps 绘制 850 hPa 风场图。

首先导入需要使用的包。包括：

• 数据结构：numpy, pandas

• 可视化：matplotlib

• 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：BarbStyle

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 BarbStyle
from cedarkit.maps.chart import Panel
from cedarkit.maps.domains import EastAsiaMapTemplate

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

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
)
v_850_field

<xarray.DataArray 'v' (latitude: 1440, longitude: 2880)> Size: 33MB
array([[ 0.71418457,  0.71418457,  0.71418457, ...,  0.69418457,
         0.70418457,  0.70418457],
       [ 0.68418457,  0.68418457,  0.69418457, ...,  0.67418457,
         0.68418457,  0.68418457],
       [ 0.44418457,  0.44418457,  0.44418457, ...,  0.44418457,
         0.44418457,  0.44418457],
       ...,
       [-5.38581543, -5.45581543, -5.45581543, ..., -5.45581543,
        -5.43581543, -5.43581543],
       [-5.13581543, -5.30581543, -5.29581543, ..., -5.34581543,
        -5.32581543, -5.32581543],
       [-3.73581543, -3.84581543, -3.83581543, ..., -3.86581543,
        -3.86581543, -3.86581543]])
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:               189
    long_name:                discipline=0 parmcat=2 parm=3

xarray.DataArray

'v'

• latitude: 1440
• longitude: 2880

• 0.7142 0.7142 0.7142 0.7242 0.7242 ... -3.856 -3.866 -3.866 -3.866

  array([[ 0.71418457,  0.71418457,  0.71418457, ...,  0.69418457,
           0.70418457,  0.70418457],
         [ 0.68418457,  0.68418457,  0.69418457, ...,  0.67418457,
           0.68418457,  0.68418457],
         [ 0.44418457,  0.44418457,  0.44418457, ...,  0.44418457,
           0.44418457,  0.44418457],
         ...,
         [-5.38581543, -5.45581543, -5.45581543, ..., -5.45581543,
          -5.43581543, -5.43581543],
         [-5.13581543, -5.30581543, -5.29581543, ..., -5.34581543,
          -5.32581543, -5.32581543],
         [-3.73581543, -3.84581543, -3.83581543, ..., -3.86581543,
          -3.86581543, -3.86581543]])

• 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 :

  189

  long_name :

  discipline=0 parmcat=2 parm=3

配置

定义风场图样式 (BarbStyle)

wind_barb_style = BarbStyle(
    barbcolor="red",
    flagcolor="red",
    linewidth=0.3,
)

绘制

创建中国区域底图布局

domain = EastAsiaMapTemplate()

绘图风场图，设置标题。

注：这里将风场稀疏化后再绘图，并只在主区域（第 1 层）绘图，即 layer=[0]

panel = Panel(domain=domain)
panel.plot([[u_850_field[::14, ::14], v_850_field[::14, ::14]]], style=wind_barb_style, layer=[0])

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