Data Flow¶
This page explains how information moves through the system.
Step 1: External providers¶
The fetcher reaches out to multiple free sources:
- Open-Meteo
- National Weather Service
- MET Norway
- 7Timer
Each provider uses its own response shape, units, time conventions, and field names.
Step 2: Normalization¶
The Python fetcher converts all providers into one shared record structure with these fields:
- Provider name
- State name
- Location name
- Latitude and longitude
- Forecast hour offset
- Forecast timestamp
- Temperature
- Humidity
- Wind speed
- Precipitation probability
- Surface pressure
- Cloud cover
Step 3: Numerical combination¶
The Fortran engine reads all normalized rows and groups them by location and hour. It then:
- Applies provider weights.
- Ignores missing values using a sentinel number.
- Computes weighted averages.
- Applies gentle smoothing to avoid unrealistic jumps.
- Calculates a basic confidence score from provider coverage.
Step 4: Visualization¶
The Angular application reads the generated JSON and turns it into:
- A regional summary panel
- Per-location cards
- A temperature sparkline
- A beginner-friendly hourly table