批量下载网格数据(nc格式)

需求：下载指定经纬度范围内 2025 年 8 月逐小时露点温度网格数据，输出为 NetCDF（.nc）格式。

历史数据模型：era5_seamless
支持的逐小时要素：era5历史数据
全部支持的逐日数据要素查看：逐日要素支持
全部支持的逐月数据要素查看：逐月要素支持
以 Python 代码为例，分四步完成：

---

第一步：配置请求参数

网格数据使用 area.type = "grid"，通过经纬度范围框定下载区域。

import time
import zipfile
import requests
from pathlib import Path

API_KEY = "your_api_key"
BASE_URL = "https://api.mirror-earth.com"

headers = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json",
}

payload = {
    "domain": "era5_seamless",
    "hourly": ["dew_point_2m"],
    "daily": [],
    "monthly": [],
    "time_range": {
        "type": "date_range",
        "start": "2025-08-01",
        "end": "2025-08-10",
    },
    "area": {
        "type": "grid",
        "lat_min": 30.0,
        "lat_max": 40.0,
        "lon_min": 110.0,
        "lon_max": 120.0,
    },
    "timezone": "Asia/Shanghai",
    "area_average": False,
}

---

第二步：创建批量下载任务

res = requests.post(f"{BASE_URL}/api/tasks/export", json=payload, headers=headers)
res.raise_for_status()
task_id = res.json()["data"]["task_id"]
print(f"任务已创建，task_id: {task_id}")

---

第三步：轮询等待任务完成

output_file = None

while True:
    res = requests.get(f"{BASE_URL}/api/tasks/{task_id}/status", headers=headers)
    res.raise_for_status()
    data = res.json()["data"]

    status = data["status"]
    progress = data.get("progress_percent", 0)
    print(f"任务状态：{status}，进度：{progress}%")

    if status == "completed":
        output_file = data["output_file"]
        break
    elif status in ("failed", "cancelled"):
        raise RuntimeError(f"任务异常，状态：{status}")

    time.sleep(10)

---

第四步：下载并解压任务结果

压缩包内包含 .nc 格式的 NetCDF 文件，可直接用 xarray 等库读取。

download_url = f"{BASE_URL}/user_downloads/{output_file}"
res = requests.get(download_url, headers=headers, stream=True)
res.raise_for_status()

zip_path = "grid_2025_08.zip"
with open(zip_path, "wb") as f:
    for chunk in res.iter_content(chunk_size=8192):
        f.write(chunk)

output_dir = Path("grid_2025_08")
with zipfile.ZipFile(zip_path, "r") as z:
    z.extractall(output_dir)

print(f"下载完成，nc文件已解压到 {output_dir}/")

用 xarray 读取结果：

import xarray as xr

nc_files = list(output_dir.glob("*.nc"))
ds = xr.open_dataset(nc_files[0])
print(ds)

---

完整代码

import time
import zipfile
import requests
from pathlib import Path

API_KEY = "your_api_key"
BASE_URL = "https://api.mirror-earth.com"

headers = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json",
}

# ── 1. 配置请求参数 ──────────────────────────────────────────────
payload = {
    "domain": "era5_seamless",
    "hourly": ["dew_point_2m"],
    "daily": [],
    "monthly": [],
    "time_range": {
        "type": "date_range",
        "start": "2025-08-01",
        "end": "2025-08-10",
    },
    "area": {
        "type": "grid",
        "lat_min": 30.0,
        "lat_max": 40.0,
        "lon_min": 110.0,
        "lon_max": 120.0,
    },
    "timezone": "Asia/Shanghai",
    "area_average": False,
}

# ── 2. 创建任务 ──────────────────────────────────────────────────
res = requests.post(f"{BASE_URL}/api/tasks/export", json=payload, headers=headers)
res.raise_for_status()
task_id = res.json()["data"]["task_id"]
print(f"任务已创建，task_id: {task_id}")

# ── 3. 轮询任务进度 ──────────────────────────────────────────────
output_file = None
while True:
    res = requests.get(f"{BASE_URL}/api/tasks/{task_id}/status", headers=headers)
    res.raise_for_status()
    data = res.json()["data"]
    status = data["status"]
    progress = data.get("progress_percent", 0)
    print(f"任务状态：{status}，进度：{progress}%")

    if status == "completed":
        output_file = data["output_file"]
        break
    elif status in ("failed", "cancelled"):
        raise RuntimeError(f"任务异常，状态：{status}")
    time.sleep(10)

# ── 4. 下载并解压结果 ────────────────────────────────────────────
download_url = f"{BASE_URL}/user_downloads/{output_file}"
res = requests.get(download_url, headers=headers, stream=True)
res.raise_for_status()

zip_path = "grid_2025_08.zip"
with open(zip_path, "wb") as f:
    for chunk in res.iter_content(chunk_size=8192):
        f.write(chunk)

output_dir = Path("grid_2025_08")
with zipfile.ZipFile(zip_path, "r") as z:
    z.extractall(output_dir)

print(f"下载完成，nc文件已解压到 {output_dir}/")

# ── 5. 用 xarray 读取 nc 文件 ────────────────────────────────────
import xarray as xr

nc_files = list(output_dir.glob("*.nc"))
ds = xr.open_dataset(nc_files[0])
print(ds)