weather-correlation

To correlate weather data with Strava activity performance, the Skill processes recent activities within a user-specified window, fetching historical temperature data using activity coordinates and timestamps from the Open-Meteo Archive API. It then computes the correlation between weather factors and pace or heart rate.

Ambient conditions like temperature, humidity, and wind affect running pace and heart rate. The Skill cross-references your recent runs or rides with historical weather data to quantify these weather-related effects and presents an overall correlation verdict to guide training decisions.

Yes, you need a local Strava data store to analyze weather impact on activities. The Skill requires Python 3.10+, the requests library, and this local data store to process activity coordinates and timestamps, utilizing built-in error handling and memory persistence during the correlation computation.

To fetch historical temperature data for specific run coordinates and timestamps, the Skill queries the Open-Meteo Archive API using the location and time data from your local Strava activity store. It retrieves per-activity weather conditions to cross-reference against your performance metrics.

Yes, you can use Open-Meteo API data to plan upcoming workouts based on heat and humidity. By analyzing the computed correlations between historical temperature, humidity, wind, and your past effort, the Skill helps you anticipate how similar ambient conditions might influence future training sessions.