天擎大数据云平台

天擎大数据云平台 CMADaaS 使用 MUSIC 接口用于检索数据，提供包括 Python 在内的多种编程语言 SDK，详情请访问天擎门户网站 开发SDK 栏目。

因天擎 MUSIC SDK 仅内网使用，本文介绍仿照 MUSIC SDK 重新实现的 MUSIC Python 接口库 nuwe-cmadaas-python。

安装

nuwe-cmadaas-python 库尚未发布到 PYPI 中，请从 GitHub 项目库中下载最新代码并使用 pip 安装。 仓库地址: perillaroc/nuwe-cmadaas-python

git clone https://github.com/perillaroc/nuwe-cmadaas-python.git
cd nuwe-cmadaas-python
pip install .

配置

天擎平台检索数据需要准备账户，并申请资料的访问权限。

创建配置文件 ~/.config/cedarkit.yaml，配置访问天擎的服务器信息、账户名和密码，类似：

cmadaas:
  auth:
    user: user name
    password: user password
  server:
    music_server: music server ip
    music_port: music server port
    music_connTimeout: 3  # connection time out, seconds
    music_readTimeout: 3000  # read time out, seconds
    music_ServiceId: music service id

详细配置参数信息请查看天擎 MUSIC SDK 示例文件。

准备

导入需要的包

import xarray as xr
import pandas as pd
from nuwe_cmadaas.model import retrieve_model_grid

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。

天擎数据条目使用“中国气象局区域模式CMA-MESO原始分辨率预报产品（0.03×0.03）”，数据编码“NAFP_GRAPES_MESO_FOR_3KM”。

system_name = "CMA-MESO"
start_time = pd.to_datetime("2024-04-01 00:00:00")
forecast_time = pd.to_timedelta("24h")

CMADAAS_DATA_ID = "NAFP_GRAPES_MESO_FOR_3KM"

加载数据

加载 2 米温度场

参数解释：

• paramter="TEM"：气温

• level_type=103：特定高度层（地面以上），单位米

• level=2：2 米

更详细的数据说明请访问天擎门户万战的数据条目。

t_2m_field = retrieve_model_grid(
    CMADAAS_DATA_ID,
    start_time=start_time,
    forecast_time=forecast_time,
    parameter="TEM",
    level_type=103,
    level=2,
) - 273.15
t_2m_field

<xarray.DataArray 'TEM_103' (latitude: 1671, longitude: 2501)> Size: 33MB
array([[-0.7265686 , -0.76657715, -0.80655518, ..., -4.17655029,
        -2.8765625 , -2.95654907],
       [-3.64655151, -3.65656128, -2.26657715, ..., -6.91657104,
        -6.31656494, -3.7265686 ],
       [-3.53656616, -3.57657471, -0.85657349, ..., -7.61655273,
        -7.4765686 , -3.43656006],
       ...,
       [26.59343872, 26.60344849, 26.59343872, ..., 25.80343018,
        25.79345093, 25.9734436 ],
       [26.59343872, 26.60344849, 26.59343872, ..., 25.7734314 ,
        25.78344116, 26.00344238],
       [26.58342896, 26.59343872, 26.60344849, ..., 25.78344116,
        25.78344116, 25.9734436 ]])
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

'TEM_103'

• latitude: 1671
• longitude: 2501

• -0.7266 -0.7666 -0.8066 -0.8366 -0.8766 ... 25.78 25.78 25.78 25.97

  array([[-0.7265686 , -0.76657715, -0.80655518, ..., -4.17655029,
          -2.8765625 , -2.95654907],
         [-3.64655151, -3.65656128, -2.26657715, ..., -6.91657104,
          -6.31656494, -3.7265686 ],
         [-3.53656616, -3.57657471, -0.85657349, ..., -7.61655273,
          -7.4765686 , -3.43656006],
         ...,
         [26.59343872, 26.60344849, 26.59343872, ..., 25.80343018,
          25.79345093, 25.9734436 ],
         [26.59343872, 26.60344849, 26.59343872, ..., 25.7734314 ,
          25.78344116, 26.00344238],
         [26.58342896, 26.59343872, 26.60344849, ..., 25.78344116,
          25.78344116, 25.9734436 ]])

• Coordinates: (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([60.099998474121094, 60.069998475034794, 60.039998475948494,
           60.009998476862194, 59.979998477775894, 59.949998478689594,
           59.919998479603294, 59.889998480516994, 59.859998481430694,
            59.82999848234439,
           ...
             10.2699999917767,   10.2399999926904,   10.2099999936041,
             10.1799999945178,   10.1499999954315,   10.1199999963452,
             10.0899999972589,   10.0599999981726,   10.0299999990863,
                         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)

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()