- name
- metdata-nasa-access
- description
- Fetch NASA POWER meteorological data for wind and solar energy, output Excel files.
- homepage
- https://power.larc.nasa.gov
- metadata
- {"clawdbot":{"emoji":"⚡","requires":{"bins":["curl"],"python":["openpyxl"]}}}
MetData-NASA-Access
Fetch NASA POWER meteorological data for wind and solar energy analysis, output 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"
Workflow
Step 1: Resolve Coordinates
If user provides a location name, resolve to lat/lon using Nominatim geocoding:
curl -s -G "https://nominatim.openstreetmap.org/search" \
--data-urlencode "q=<LOCATION>" \
--data-urlencode "format=json" \
--data-urlencode "limit=1" \
-H "User-Agent: MetData-NASA-Access/1.0"Parse JSON response to get lat and lon. If user already provides coordinates, skip this step.
Step 2: Run the Script
Use the Python script at scripts/fetch_metdata.py:
python3 scripts/fetch_metdata.py \
--lat <LAT> \
--lon <LON> \
--start <YEAR> \
--end <YEAR> \
--output <OUTPUT_PATH>Parameters:
--lat,--lon: Coordinates (required)--start,--end: Year range (default: 2016, 2017)--output: Excel output path (default:~/.openclaw/workspace/output/metdata/)--params: Comma-separated custom parameter list (default: wind+ solar defaults)--granularity:monthly,daily,climatology, orall(default:all)
Step 3: Report 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
Output Format
Excel with sheets:
- 月度数据: Monthly averages
- 日均数据: Daily values
- 气候平均数据: 20-year climatological means (2001-2020)
Notes
- NASA POWER data uses MERRA-2 reanalysis, ~0.5° resolution
community=RE= Renewable Energy application- 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 climatology, months are JAN-DEC + ANN (annual mean)
- Fetching all 3 granularities with 32 params takes ~5-8 minutes
- Use
--granularity monthlyfor faster results