open-meteo-advanced

name	open-meteo-advanced
description	Use when the user wants a specific weather model, needs ensemble uncertainty ranges, requests seasonal outlooks, or asks for long-term climate projections.

Open-Meteo MCP — Advanced

Overview

Use this skill when:

• The user asks for a specific weather model (ECMWF, GFS, DWD ICON, Météo-France, JMA, MET Norway, GEM)
• The user wants to compare models (make parallel calls, one per model tool)
• Forecasts beyond 16 days are needed
• Ensemble uncertainty / confidence intervals are requested
• A seasonal outlook (1–9 months) or climate projection (to 2050) is needed

For everyday weather questions, use open-meteo instead.

Model Selection Guide

Tool	Provider	Geographic Focus	Horizon	Best For
ecmwf_forecast	ECMWF IFS	Global	15 days	Highest global accuracy
gfs_forecast	NOAA GFS	Global	16 days	Global, especially Americas
dwd_icon_forecast	DWD ICON	Europe only	2–7.5 days	High-res Europe
meteofrance_forecast	Météo-France AROME/ARPEGE	France, DOM-TOM, Mediterranean/Europe	2–4 days	France + nearby regions
jma_forecast	JMA	Asia-Pacific	4–11 days	Japan and Asia-Pacific
metno_forecast	MET Norway	Nordic-primary (global extension via blending)	2.5 days	Nordic region precision
gem_forecast	Environment Canada GEM	North America	2–10 days	Canada
ensemble_forecast	Multi-model	Global	35 days	Forecast uncertainty ranges
seasonal_forecast	ECMWF SEAS5	Global	45–274 days	1–9 month outlook
climate_projection	CMIP6	Global	1950–2050	Multi-decade scenarios

Geographic constraints:

• dwd_icon_forecast: Europe only — do not use for other regions.
• metno_forecast with metno_nordic: Nordic region only. metno_seamless is Nordic-primary with global extension via blending, not a general-purpose global model.
• meteofrance_forecast: France + DOM-TOM + Mediterranean/Europe coverage.

Key Parameters

Model-specific forecast tools

Applies to: dwd_icon_forecast, gfs_forecast, meteofrance_forecast, ecmwf_forecast, jma_forecast, metno_forecast, gem_forecast

All share the same parameters as weather_forecast (see open-meteo skill), plus:

Parameter	Required	Notes
latitude, longitude	Yes
models	Yes*	Exactly one model per request
hourly / daily / current	No**	Same variables as weather_forecast

*metno_forecast can omit models to use the default Met.no model. **At least one variable group required.

Multi-model comparison: Make parallel tool calls, one per model.

Model key examples:

Tool	Example model keys
ecmwf_forecast	ecmwf_ifs, ecmwf_ifs025, best_match (only these 3 are valid)
dwd_icon_forecast	dwd_icon_seamless, dwd_icon_global, dwd_icon_eu, dwd_icon_d2
gfs_forecast	ncep_gfs_global, ncep_gfs_seamless, ncep_hrrr_us_conus
meteofrance_forecast	meteofrance_seamless, meteofrance_arome_france, meteofrance_arpege_europe
jma_forecast	jma_seamless, jma_msm, jma_gsm
metno_forecast	metno_nordic, metno_seamless
gem_forecast	gem_global, gem_regional, gem_seamless

ECMWF warning: ecmwf_ifs_025, ecmwf_ifs_hres_9km, and ecmwf_aifs_025_single are NOT valid on ecmwf_forecast and will return HTTP 400.

ensemble_forecast

Parameter	Required	Notes
latitude, longitude	Yes
models	Yes	One ensemble model per request (required)
hourly	No*	Same variables as weather_forecast
forecast_days	No	1–35, default 7

Response format: Each variable is returned as one array per ensemble member:

{
  "hourly": {
    "time": ["2024-01-01T00:00", "2024-01-01T01:00", "..."],
    "temperature_2m_member01": [2.1, 2.3, "..."],
    "temperature_2m_member02": [1.8, 2.0, "..."],
    "temperature_2m_member03": [2.4, 2.6, "..."]
  }
}

To derive uncertainty ranges, calculate min/max/percentiles across all _memberNN arrays for each timestep.

seasonal_forecast

Parameter	Required	Notes
latitude, longitude	Yes
hourly	No*	6-hourly variables: temperature_2m, precipitation, wind_speed_10m, relative_humidity_2m, cloud_cover, pressure_msl, soil_moisture_0_to_10cm
daily	No*	temperature_2m_max, temperature_2m_min, precipitation_sum, wind_speed_10m_max
forecast_days	No	45, 92 (default), 183, or 274

Output represents ensemble anomalies relative to climatology, not absolute forecasts.

climate_projection

Parameter	Required	Notes
latitude, longitude	Yes
start_date	Yes	YYYY-MM-DD (supported range: 1950-01-01 to 2050-12-31)
end_date	Yes	YYYY-MM-DD
daily	Yes	One or more of: temperature_2m_max, temperature_2m_min, temperature_2m_mean, precipitation_sum, wind_speed_10m_mean, wind_speed_10m_max, cloud_cover_mean, relative_humidity_2m_mean, shortwave_radiation_sum, soil_moisture_0_to_10cm_mean, pressure_msl_mean
models	Yes	CMIP6 models (array, at least one): CMCC_CM2_VHR4, MRI_AGCM3_2_S, EC_Earth3P_HR, MPI_ESM1_2_XR, NICAM16_8S, FGOALS_f3_H, HiRAM_SIT_HR

Data note: Dates before the current year represent CMIP6 model simulation output for validation purposes, not observed historical measurements. For real historical weather data, use weather_archive.

Examples

"Get the ECMWF forecast for Bordeaux tomorrow"

1. geocoding with name: "Bordeaux" → coordinates
2. ecmwf_forecast with coordinates + models: "ecmwf_ifs", daily: ["temperature_2m_max", "temperature_2m_min", "precipitation_sum", "weather_code"], forecast_days: 2, timezone: "auto"

"Compare DWD ICON and GFS forecasts for Berlin this week"

1. geocoding with name: "Berlin" → coordinates
2. Two parallel calls:
   • dwd_icon_forecast with models: "dwd_icon_seamless", daily: ["temperature_2m_max", "precipitation_sum"], forecast_days: 7, timezone: "auto"
   • gfs_forecast with models: "ncep_gfs_global", daily: ["temperature_2m_max", "precipitation_sum"], forecast_days: 7, timezone: "auto"

"Show me forecast uncertainty for Paris next week"

1. geocoding with name: "Paris" → coordinates
2. ensemble_forecast with coordinates + hourly: ["temperature_2m"], forecast_days: 10 The response will contain temperature_2m_member01, temperature_2m_member02, etc. — calculate spread across members for uncertainty.

"What will the climate be like in Lyon in 2040?"

1. geocoding with name: "Lyon" → coordinates
2. climate_projection with coordinates + start_date: "2040-01-01", end_date: "2040-12-31", models: ["MRI_AGCM3_2_S"], daily: ["temperature_2m_max", "temperature_2m_min", "precipitation_sum"]

Best Practices

• Don't use regional model tools outside their geographic coverage — dwd_icon_forecast for Asia will return empty or incorrect data.
• ecmwf_forecast accepts only 3 model IDs — ecmwf_ifs, ecmwf_ifs025, or best_match. Any other ECMWF key causes HTTP 400.
• Ensemble output is member arrays, not scalar values — process all _memberNN keys to derive uncertainty ranges.
• Climate data before the current year is CMIP6 simulation, not observed data — use weather_archive for real historical measurements.
• seasonal_forecast outputs anomalies, not absolute values — it answers "warmer than usual?" not "what temperature exactly?".
• Geocode first if you only have a city name.