数据获取 | ECMWF教你如何下载 ECMWF 实时预报数据

!pip install ecmwf-opendata  -i https://pypi.mirrors.ustc.edu.cn/simple/

Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Collecting ecmwf-opendata
  Downloading https://mirrors.ustc.edu.cn/pypi/packages/6a/2a/b2a925b4e2830901f6e9064b90892eb3aba4b4ca8564bbaa5acc18666727/ecmwf_opendata-0.3.29-py3-none-any.whl (22 kB)
Collecting multiurl>=0.2.1 (from ecmwf-opendata)
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Installing collected packages: multiurl, ecmwf-opendata
Successfully installed ecmwf-opendata-0.3.29 multiurl-0.3.7

from ecmwf.opendata import Client

client = Client(source="aws")
client.retrieve(
    type="fc",
    param="msl",
    step=24,
    target="data.grib2",
)

20260603180000-24h-oper-fc.grib2:   0%|          | 0.00/515k [00:00<?, ?B/s]

By downloading data from the ECMWF open data dataset, you agree to the terms: Attribution 4.0 International (CC BY 4.0). Please attribute ECMWF when downloading this data.

<ecmwf.opendata.client.Result at 0x7f4ba55bbbd0>

import xarray as xr

ds = xr.open_dataset("data.grib2", engine="cfgrib")
print(ds)

<xarray.Dataset> Size: 4MB
Dimensions:     (latitude: 721, longitude: 1440)
Coordinates:
    time        datetime64[ns] 8B ...
    step        timedelta64[ns] 8B ...
    meanSea     float64 8B ...
  * latitude    (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * longitude   (longitude) float64 12kB -180.0 -179.8 -179.5 ... 179.5 179.8
    valid_time  datetime64[ns] 8B ...
Data variables:
    msl         (latitude, longitude) float32 4MB ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2026-06-04T13:26 GRIB to CDM+CF via cfgrib-0.9.1...

client = Client(source="aws")
result = client.retrieve(
    type="fc",
    param=["gh", "t"],
    levelist=[500, 850],
    step=[24, 48],
    target="gh_t_500_850.grib2",
)
print("起报时间:", result.datetime)

<multiple>:   0%|          | 0.00/4.01M [00:00<?, ?B/s]

起报时间: 2026-06-03 18:00:00

ds_multi = xr.open_dataset("gh_t_500_850.grib2", engine="cfgrib")
print(ds_multi)

<xarray.Dataset> Size: 33MB
Dimensions:        (step: 2, isobaricInhPa: 2, latitude: 721, longitude: 1440)
Coordinates:
    time           datetime64[ns] 8B ...
  * step           (step) timedelta64[ns] 16B 1 days 2 days
  * isobaricInhPa  (isobaricInhPa) float64 16B 850.0 500.0
  * latitude       (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0
  * longitude      (longitude) float64 12kB -180.0 -179.8 -179.5 ... 179.5 179.8
    valid_time     (step) datetime64[ns] 16B ...
Data variables:
    gh             (step, isobaricInhPa, latitude, longitude) float32 17MB ...
    t              (step, isobaricInhPa, latitude, longitude) float32 17MB ...
Attributes:
    GRIB_edition:            2
    GRIB_centre:             ecmf
    GRIB_centreDescription:  European Centre for Medium-Range Weather Forecasts
    GRIB_subCentre:          0
    Conventions:             CF-1.7
    institution:             European Centre for Medium-Range Weather Forecasts
    history:                 2026-06-04T13:30 GRIB to CDM+CF via cfgrib-0.9.1...

# IFS
client_ifs = Client(source="aws", model="ifs")
client_ifs.retrieve(
    type="fc", param="msl",
    step=24,
    target="ifs_msl.grib2",
)

# AIFS
client_aifs = Client(source="aws", model="aifs-single")
client_aifs.retrieve(
    type="fc", param="msl",
    step=24,
    target="aifs_msl.grib2",
)

20260603180000-24h-oper-fc.grib2:   0%|          | 0.00/515k [00:00<?, ?B/s]

20260604000000-24h-oper-fc.grib2:   0%|          | 0.00/471k [00:00<?, ?B/s]

<ecmwf.opendata.client.Result at 0x7f4acc9bff90>

import os

ifs_size = os.path.getsize("ifs_msl.grib2")
aifs_size = os.path.getsize("aifs_msl.grib2")
print(f"IFS: {ifs_size / 1024:.1f} KB")
print(f"AIFS: {aifs_size / 1024:.1f} KB")

IFS: 514.8 KB
AIFS: 470.9 KB

import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import numpy as np
import xarray as xr
# # 下载数据
client = Client(source="aws", model="aifs-single")
client.retrieve(type="fc", param="gh", levelist=500, step=24, target="aifs_gh500.grib2")
client.retrieve(type="fc", param="msl", step=24, target="aifs_msl.grib2")

# 读取
gh = xr.open_dataset("aifs_gh500.grib2", engine="cfgrib")["gh"]
msl = xr.open_dataset("aifs_msl.grib2", engine="cfgrib")["msl"]

# 画图
proj = ccrs.PlateCarree(central_longitude=110)
fig, ax = plt.subplots(figsize=(12, 8), subplot_kw={"projection": proj})

ax.set_extent([70, 140, 15, 55], crs=ccrs.PlateCarree())
ax.add_feature(cfeature.COASTLINE, linewidth=0.8)
# ax.add_feature(cfeature.BORDERS, linewidth=0.4, alpha=0.5)

# 位势高度等值线
levels = np.arange(5000, 6000, 40)
cs = ax.contour(
    gh.longitude, gh.latitude, gh.squeeze() / 10,
    levels=levels, colors="black", linewidths=1.0,
    transform=ccrs.PlateCarree(),
)
ax.clabel(cs, inline=True, fontsize=8, fmt="%d")

# 海平面气压填色
msl_hpa = msl.squeeze() / 100
cf = ax.contourf(
    msl.longitude, msl.latitude, msl_hpa,
    levels=np.arange(960, 1050, 5),
    cmap="RdBu_r", alpha=0.4,
    transform=ccrs.PlateCarree(),
)
plt.colorbar(cf, ax=ax, label="MSL (hPa)", shrink=0.7)

ax.set_title(
    "AIFS 500hPa Geopotential + MSL\n"
    f"Init: {gh.time.values.astype('datetime64[h]').tolist()} "
    f"Step: +24h"
)
plt.tight_layout()
plt.savefig("aifs_500hpa_forecast.png", dpi=150)
plt.show()

from ecmwf.opendata import Client

client = Client(source="aws")
latest = client.latest(type="fc", param="msl", step=24)
print("最新可用起报时间:", latest)

client = Client(source="aws")
# 仅按 date/time/step/type 定位文件，忽略 param 等子集参数
client.download(
    type="fc",
    step=24,
    target="full_file.grib2",
)

# 下载控制成员
client = Client(source="aws")
client.retrieve(
    stream="enfo",
    type="cf",
    step=24,
    param="msl",
    target="ens_cf_msl.grib2",
)

# 下载第 1、3、5… 奇数成员
client.retrieve(
    stream="enfo",
    type="pf",
    step=24,
    param="msl",
    number=[num for num in range(1, 51, 2)],
    target="ens_pf_msl.grib2",
)

client = Client(source="aws")
request = {
    "type": "fc",
    "step": [24, 48, 72],
    "param": ["2t", "msl"],
}
client.retrieve(request, target="dict_request.grib2")