Open-Meteo API Walkthrough: London Weather Data for Canadian Developers

TL;DR

Open-Meteo's free archive API lets you pull historical London weather data without an API key. This walkthrough shows Canadian developers how to fetch, parse, and compare precipitation and temperature data between London and Canadian cities in under 50 lines of Python.

Why London Weather Data Matters for Canadian Travellers

Most Canadians heading to London, United Kingdom pack for a monsoon. The city's rainy reputation precedes it. But query the actual data and the picture shifts fast. London records ~600mm of annual rainfall across ~109 rainy days. Toronto records ~831mm across ~138 rainy days. Vancouver logs ~1,153mm per year. London is measurably drier than most Canadian cities.

This walkthrough treats London weather as a data engineering problem. By the end, you will have a reusable script that fetches and compares climate metrics across cities.

Overview

Open-Meteo provides a historical weather archive at https://archive-api.open-meteo.com/v1/archive. The service is free, requires no authentication, and supports up to 10,000 requests per day on the free tier. The UK Met Office and BBC Weather are authoritative sources for London forecasts, but for historical bulk data Open-Meteo is the practical choice.

Setup

Key query parameters for the archive endpoint:

Core Endpoints

The script below pulls a full year of daily precipitation for London and Toronto, then aggregates monthly totals:

import requests
from collections import defaultdict

CITIES = {
    "London":  (51.5074, -0.1278),
    "Toronto": (43.6532, -79.3832),
}

def get_annual_precip(city_name, lat, lon, year=2023):
    r = requests.get(
        "https://archive-api.open-meteo.com/v1/archive",
        params={
            "latitude":   lat,
            "longitude":  lon,
            "start_date": f"{year}-01-01",
            "end_date":   f"{year}-12-31",
            "daily":      "precipitation_sum,temperature_2m_max",
            "timezone":   "auto",
        },
        timeout=10
    )
    r.raise_for_status()
    d = r.json()["daily"]
    monthly = defaultdict(float)
    for date, precip in zip(d["time"], d["precipitation_sum"]):
        monthly[date[5:7]] += precip or 0.0
    total = sum(monthly.values())
    print(f"{city_name}: {total:.1f}mm total")
    return dict(monthly)

for city, (lat, lon) in CITIES.items():
    get_annual_precip(city, lat, lon)

When run against 2023 data, London consistently comes out below Toronto in total precipitation.

Error Handling

try:
    r.raise_for_status()
except requests.HTTPError as e:
    if r.status_code == 429:
        print("Rate limit hit. Cache responses with shelve or sqlite3.")
    else:
        raise

Performance Notes

API response times average ~180ms per request. Cache historical results locally since this data does not change.

London's wettest month is October at ~68mm. February is driest at ~40mm across ~9 rainy days. July sits at ~45mm and August at ~49mm. If your API output lands near those benchmarks, the pipeline is working correctly.

Staying Online in London

Testing this from London requires connectivity. HelloRoam's UK eSIM on O2 5G costs ~C$1.92 and activates before you land. Install via QR code before departure; your terminal runs the moment you clear Heathrow customs.

FAQ

Q: Do I need an API key for Open-Meteo?
A: No. The free tier is unauthenticated and allows 10,000 requests per day.

Q: What coordinates target central London?
A: Latitude 51.5074, longitude -0.1278.

Q: How does London's rainfall compare to Vancouver?
A: Vancouver receives approximately 1,153mm annually. London receives approximately 600mm.

Q: Can I fetch hourly data instead of daily?
A: Yes. Replace the daily parameter with hourly and specify hourly metrics such as precipitation or temperature_2m.

Q: Is Open-Meteo data reliable for the United Kingdom?
A: Yes. The archive draws from ERA5 reanalysis data and aligns closely with UK Met Office historical records.

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