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:

bash

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:

bash

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, or all (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 monthly for faster results