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 11 月 14 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("2024-11-14 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网站的数据条目。

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 '' (latitude: 1671, longitude: 2501)> Size: 33MB
array([[ 27.04400024,  27.05800781,  27.05098877, ...,  27.54900513,
         27.57198486,  27.66100464],
       [ 27.0960022 ,  27.05098877,  27.06798706, ...,  27.57601318,
         27.57198486,  27.66100464],
       [ 27.06200562,  27.07299194,  27.06798706, ...,  27.54299316,
         27.55001221,  27.65899048],
       ...,
       [ -6.43500366,  -6.69699707,  -5.9329895 , ..., -21.05498352,
        -21.48097839, -20.02599182],
       [ -6.62601318,  -6.78101196,  -6.38400879, ..., -21.55898132,
        -21.51698914, -20.27397766],
       [-19.79797974, -19.87097778, -19.94298401, ..., -20.50598755,
        -19.98699036, -19.9599823 ]])
Coordinates:
  * latitude   (latitude) float64 13kB 10.0 10.03 10.06 ... 60.04 60.07 60.1
  * longitude  (longitude) float64 20kB 70.0 70.03 70.06 ... 144.9 145.0 145.0

xarray.DataArray

• latitude: 1671
• longitude: 2501

• 27.04 27.06 27.05 27.06 27.05 ... -19.88 -20.15 -20.51 -19.99 -19.96

  array([[ 27.04400024,  27.05800781,  27.05098877, ...,  27.54900513,
           27.57198486,  27.66100464],
         [ 27.0960022 ,  27.05098877,  27.06798706, ...,  27.57601318,
           27.57198486,  27.66100464],
         [ 27.06200562,  27.07299194,  27.06798706, ...,  27.54299316,
           27.55001221,  27.65899048],
         ...,
         [ -6.43500366,  -6.69699707,  -5.9329895 , ..., -21.05498352,
          -21.48097839, -20.02599182],
         [ -6.62601318,  -6.78101196,  -6.38400879, ..., -21.55898132,
          -21.51698914, -20.27397766],
         [-19.79797974, -19.87097778, -19.94298401, ..., -20.50598755,
          -19.98699036, -19.9599823 ]])

• Coordinates: (2)
  • latitude
    (latitude)
    float64
    10.0 10.03 10.06 ... 60.07 60.1

    units :

    degrees_north

    standard_name :

    latitude

    long_name :

    latitude

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

  • 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([              10.0,   10.0299999990863,   10.0599999981726,
             10.0899999972589,   10.1199999963452,   10.1499999954315,
             10.1799999945178,   10.2099999936041,   10.2399999926904,
             10.2699999917767,
           ...
            59.82999848234439, 59.859998481430694, 59.889998480516994,
           59.919998479603294, 59.949998478689594, 59.979998477775894,
           60.009998476862194, 60.039998475948494, 60.069998475034794,
           60.099998474121094],
          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 EastAsiaMapTemplate
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 = EastAsiaMapTemplate()
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()