📦 光伏设计NASA日均气象数据获取

MetData-NASA-访问

Fetch NASA POWER meteoro记录ical data for wind and solar energy analysis, 输出 structured Excel files.

Trigger

User provides a location name or coordinates, e.g.:

"上海市闵行区申虹路虹桥天地3号楼" "31.1932, 121.3111" "西安" 工作流 Step 1: Resolve Coordinates

If user provides a location name, resolve to lat/lon using Nominatim geocoding:

curl -s -G "https://nominatim.openstreetmap.org/搜索" \ --data-urlencode "q=" \ --data-urlencode "格式化=json" \ --data-urlencode "limit=1" \ -H "User-代理: MetData-NASA-访问/1.0"

解析 JSON 响应 to 获取 lat and lon. If user already provides coordinates, skip this step.

Step 2: 运行 the Script

Use the Python script at scripts/fetch_metdata.py:

python3 scripts/fetch_metdata.py \ --lat \ --lon \ --启动 \ --end \ --输出 <输出_PATH>

Parameters:

--lat, --lon: Coordinates (required) --启动, --end: Year range (default: 2016, 2017) --输出: Excel 输出 path (default: ~/.OpenClaw/workspace/输出/metdata/) --params: Comma-separated custom parameter 列出 (default: wind+ solar defaults) --granularity: monthly, dAIly, 命令行工具mato记录y, or all (default: all) Step 3: 报告 to User

Confirm the file path and summarize key findings (e.g., average wind speed, average solar irradiance).

Default Parameters

Solar (PV): ALLSKY_SFC_SW_DWN, CLRSKY_SFC_SW_DWN, ALLSKY_TOA_SW_DWN, ALLSKY_SFC_LW_DWN, KT, KT_CLEAR

Temperature: T2M, T2M_MAX, T2M_MIN, T10M, T10M_MAX, T10M_MIN, TS, TS_MAX, TS_MIN

Humidity: RH2M, QV2M, T2MDEW

Wind: WSC, WS50M, WS50M_MAX, WS50M_MIN, WS10M, WS10M_MAX, WS10M_MIN, WD50M, WD10M

Pressure: PSC, PS

Other: PRECTOT, TQV, FROST_DAYS

输出 格式化

Excel with sheets:

月度数据: Monthly averages 日均数据: DAIly values 气候平均数据: 20-year 命令行工具mato记录ical means (2001-2020) Notes NASA POWER data uses MERRA-2 reanalysis, ~0.5° resolution community=RE = Renewable Energy 应用 Temperature: °C, Wind: m/s, Radiation: kWh/m²/day Time standard: LST (Local Standard Time) For monthly data, row "201613" = annual mean within that year For 命令行工具mato记录y, months are JAN-DEC + ANN (annual mean) Fetching all 3 granularities with 32 params takes ~5-8 minutes Use --granularity monthly for faster 结果s