Meteorologisk Institutt

Overview

Meteorologisk Institutt (MET Norway) provides public APIs that grant access to comprehensive weather data, serving a broad range of applications from personal projects to academic research and small-scale commercial ventures. Established in 1866, MET Norway operates as the national meteorological institute of Norway, contributing to public safety and infrastructure planning through its data services. The institute's developer platform, developer.yr.no, hosts documentation and access points for its API offerings, which are primarily designed to deliver real-time observations and future forecasts.

The core of MET Norway's API services is the Locationforecast API. This API enables developers to retrieve detailed weather forecasts for specific geographical coordinates. It provides parameters such as temperature, precipitation, wind speed and direction, atmospheric pressure, and cloud cover at various time intervals. This granular level of data makes it suitable for applications requiring precise local weather information, such as outdoor activity planners, agricultural tools, and smart home systems that adjust based on weather conditions. The data is often updated frequently, supporting near real-time applications.

Beyond the Locationforecast API, MET Norway offers specialized APIs like the Nowcast API, which focuses on very short-term forecasts (up to 90 minutes) with high temporal resolution, ideal for immediate weather-sensitive decisions. The Textforecast API provides human-readable weather descriptions for larger regions, useful for content generation or voice-based assistants. For specific environmental concerns, the Frost API delivers data relevant to frost conditions, which can be critical for sectors like agriculture, construction, and transportation. The extensive free tier for non-commercial and limited commercial use, coupled with detailed data policy and usage guidelines, positions MET Norway's APIs as an accessible option for developers seeking reliable meteorological data without significant initial investment.

Developers utilizing these APIs must adhere to the specified attribution requirements, typically by displaying a statement such as "Weather data from MET Norway" or "yr.no" alongside the presented data. This ensures proper credit for the publicly funded data. While the APIs are free for most non-commercial and limited commercial applications, larger commercial deployments or those exceeding usage thresholds may require direct contact with MET Norway for specific licensing agreements. This approach provides flexibility for a wide user base while ensuring sustainable data provision. The comprehensive developer portal offers guides and examples to facilitate integration, making it relatively straightforward to incorporate weather data into new or existing applications.

Key features

• Location-specific forecasts: Access detailed weather predictions for any geographical point using latitude and longitude via the Locationforecast API.
• Short-term forecasts (Nowcast): Obtain highly granular, short-duration weather predictions (up to 90 minutes) for immediate decision-making.
• Text-based forecasts: Retrieve human-readable weather descriptions for broader regions, suitable for content integration and accessibility features.
• Frost data: Access specialized data related to frost conditions, beneficial for agriculture, infrastructure, and other frost-sensitive industries through the Frost API.
• Historical data access: While primarily focused on current and forecast data, some historical observations are available, supporting trend analysis and research.
• High data resolution: Data provided includes temperature, precipitation, wind speed, wind direction, pressure, and cloudiness at fine temporal and spatial resolutions.
• Free for non-commercial use: Offers a generous free tier for personal projects, academic research, and limited commercial applications, making it highly accessible.
• Attribution requirement: Clear guidelines for attributing data to MET Norway (yr.no) when used in applications.
• Regular updates: Weather data is frequently updated to ensure accuracy and relevance for dynamic applications.

Pricing

Meteorologisk Institutt's API services are primarily available free of charge for non-commercial use and for commercial applications meeting specific limited criteria. This model supports broad accessibility for developers, researchers, and hobbyists. Commercial use beyond these defined limits, or use cases requiring higher request volumes or specialized data access, necessitate direct contact with MET Norway for a licensing agreement. All usage, regardless of commercial status, requires proper attribution as specified in their data policy.

Pricing summary as of 2026-05-28. For the most current details, refer to the official MET Norway developer portal.

Common integrations

• Web Applications: Embedding real-time and forecast weather widgets on websites for travel, news, or local services.
• Mobile Applications: Providing location-based weather updates for Android and iOS apps, such as outdoor activity trackers or daily planners.
• Smart Home Systems: Integrating weather data to automate home functions like irrigation systems or thermostat adjustments.
• Agricultural Tools: Using frost data and precipitation forecasts to inform planting, harvesting, and irrigation schedules.
• Logistics and Transportation: Incorporating weather conditions into route planning or delivery schedule optimization.
• Data Visualization Platforms: Connecting weather data streams to dashboards for analysis and visual presentation.
• IoT Devices: Feeding weather sensor data into Internet of Things devices for environmental monitoring or automated responses.

Alternatives

• OpenWeatherMap: Offers a wide range of weather APIs, including current, forecast, and historical data, with a freemium model and extensive global coverage.
• AccuWeather: Provides commercial-grade weather data and forecasting services, often used by media and enterprise clients, with a focus on accuracy and global reach.
• Tomorrow.io: Specializes in hyper-local, minute-by-minute weather forecasting using proprietary data sources, suitable for businesses needing precise, real-time weather intelligence.

Getting started

To begin using the Meteorologisk Institutt's Locationforecast API, you typically make HTTP GET requests to the API endpoint. The primary data format is XML or JSON. Below is a simple Python example demonstrating how to fetch a weather forecast for a specific location (Oslo, Norway) using the Locationforecast 2.0 API.

First, ensure you have the requests library installed:

pip install requests

Then, you can use the following Python code to retrieve the forecast:

import requests
import json

# Define the location (Oslo, Norway) - Latitude and Longitude
latitude = 59.9139
longitude = 10.7522

# Construct the API endpoint URL for Locationforecast 2.0
# You can specify format=json for JSON output, otherwise it defaults to XML.
api_url = f"https://api.met.no/weatherapi/locationforecast/2.0/compact?lat={latitude}&lon={longitude}"

# Set a User-Agent header as required by MET Norway's API policy
headers = {
    "User-Agent": "apispine-wiki/1.0 (https://apispine.com)"
}

try:
    # Make the GET request to the API
    response = requests.get(api_url, headers=headers)
    response.raise_for_status()  # Raise an HTTPError for bad responses (4xx or 5xx)

    # Parse the JSON response
    weather_data = response.json()

    # Print some relevant information from the forecast
    print(f"Weather forecast for Latitude: {latitude}, Longitude: {longitude}")
    
    # The 'properties' key contains the forecast data
    if 'properties' in weather_data and 'timeseries' in weather_data['properties']:
        # Get the forecast for the next hour (first entry in timeseries)
        next_hour_forecast = weather_data['properties']['timeseries'][0]
        
        time = next_hour_forecast['time']
        temperature = next_hour_forecast['data']['instant']['details']['air_temperature']
        wind_speed = next_hour_forecast['data']['instant']['details']['wind_speed']
        
        print(f"Time: {time}")
        print(f"Temperature: {temperature}°C")
        print(f"Wind Speed: {wind_speed} m/s")
        
        # Check for precipitation forecast if available
        if 'next_1_hours' in next_hour_forecast['data']:
            precip_amount = next_hour_forecast['data']['next_1_hours']['details']['precipitation_amount']
            print(f"Precipitation (next 1 hr): {precip_amount} mm")
    else:
        print("No timeseries data found in the response.")

except requests.exceptions.HTTPError as err_h:
    print(f"HTTP Error: {err_h}")
except requests.exceptions.ConnectionError as err_c:
    print(f"Error Connecting: {err_c}")
except requests.exceptions.Timeout as err_t:
    print(f"Timeout Error: {err_t}")
except requests.exceptions.RequestException as err:
    print(f"An error occurred: {err}")
except json.JSONDecodeError:
    print("Error decoding JSON response.")

This script demonstrates how to make a basic API call, handle the response, and extract key weather parameters. Remember to replace the User-Agent string with an identifier specific to your application, as this is a requirement for using the MET Norway APIs to help them understand traffic patterns and assist with debugging. For more detailed usage and available parameters, consult the Meteorologisk Institutt Locationforecast 2.0 API documentation.