Fetch Weather Observations and Station Metadata

Weather Observation Fetching is a reusable skill that retrieves real-world meteorological observation data for AI assistants. It provides access to METARs (aviation weather reports), historical surface observations, radiosonde (upper-air) data, and weather station metadata, enabling assistants to answer questions about current and past weather conditions from ground-based and balloon-borne instruments.

Use this skill when you need factual observational weather data: checking current airport conditions via METAR reports, analyzing historical surface weather trends, retrieving upper-atmosphere measurements from radiosondes, or looking up weather station locations and characteristics. It is ideal for aviation briefings, climate research requiring historical records, and meteorological analysis based on actual measurements.

Do NOT use this skill for weather forecasts, model predictions, or future conditions - it excludes model output entirely. Do NOT use it for radar imagery or satellite pictures - it covers only point observations and station metadata, not volumetric radar data or satellite imagery.

Users provide queries about weather observations, which may specify station identifiers, time periods for historical data, or requests for radiosonde profiles or station information. The skill returns structured observation data: decoded METAR reports with temperature, wind, visibility, and pressure readings; historical surface observation records; radiosonde temperature, humidity, and wind profiles at various altitudes; or station metadata including location coordinates and operational details.

This skill serves pilots and flight dispatchers who need current and recent METAR data for flight planning and safety decisions. Meteorologists and atmospheric scientists use it to access radiosonde profiles for upper-air analysis and historical surface observations for climatological studies. Aviation weather briefers rely on it to provide accurate observational data rather than forecasts. Researchers analyzing past weather events benefit from the historical observation capabilities, while developers building weather-aware applications can integrate factual observation data without the complexity of forecast models or imagery processing.