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What Is a Data Layer?

Data layers serve as structured collections of weather information that enable users to quickly obtain the precise weather insights they need.

Filip Dimkovski
By Filip Dimkovski
Joel Taylor editor profile picture
Edited by Joel Taylor

Updated May 15, 2024.

A blue sky with white clouds and holographic lines signifying layers of weather data

Over the last few years, weather forecasting technology has undergone a remarkable transformation in data analysis and digital infrastructure. One reason behind this evolution is the integration of data layers within weather APIs.

This shift toward detailed and organized weather data has revolutionized forecasting, offering tailored insights that meet diverse needs from agriculture and aviation to urban planning.



Exploring Weather Data Layers

Simply put, a weather data layer is a structured format within weather information systems that organizes and categorizes various types of meteorological data for easy access and analysis.

Data layers serve as structured collections of weather information, such as temperature, humidity, precipitation, and more, each categorized into distinct segments for precise analysis.

This organization into layers facilitates the efficient processing and interpretation of data, enabling users to quickly obtain the precise weather insights they need. Platforms like Tomorrow.io expand the utility of weather data layers by offering a comprehensive suite of options that go beyond conventional meteorological variables, such as:

  • Air quality: Quantifies the presence of pollutants in the atmosphere, offering important data for public health advisories, environmental impact assessments, and regulatory compliance monitoring.
  • Lightning: Captures data on lightning strikes, such as lightning flash rate density, which is the total number of lightning flashes per unit time per unit area.
  • Solar radiation: Measures the intensity of solar energy reaching the Earth's surface, pivotal for renewable energy management, agricultural planning, and understanding the effects of cloud cover and atmospheric conditions on solar exposure.

» Learn more about the full list of weather data layers from the Tomorrow.io documentation

Accessing Data Layers With the API

To access a specific field of the Tomorrow.io data layers, you need to use the Weather Timelines API. This allows you to query weather conditions by specifying the location, time steps, start time, and end time. So, let's take the solar data layer as an example and start by creating a JavaScript file, which we'll name "timelines.js".

Creating the Request

Once you've created the file and opened it in a code editor, you need to paste the following code snippet:

const options = {
  method: 'POST',
  headers: {
    accept: 'application/json',
    'Accept-Encoding': 'gzip',
    'content-type': 'application/json'
  },
  body: JSON.stringify({
    location: 'Copenhagen',
    fields: ['solarGHI', 'solarDNI', 'solarDHI'],
    units: 'metric',
    timesteps: ['1h'],
    startTime: 'now',
    endTime: 'nowPlus1h'
  })
};

fetch('https://api.tomorrow.io/v4/timelines?apikey=YOUR_API_KEY', options)
  .then(response => response.json())
  .then(response => console.log(JSON.stringify(response, null, 2)))
  .catch(err => console.error(err));

The code snippet we have makes a POST request to the Timelines API to retrieve solar radiation data for Copenhagen over the next hour. Specifically, it requests all three types of solar data in the data layer, including "solarGHI" (Global Horizontal Irradiance), "solarDNI" (Direct Normal Irradiance), and "solarDHI" (Diffuse Horizontal Irradiance).

Note: "YOUR_API_KEY" must be replaced with a valid API key from Tomorrow.io to successfully make the request. Here's how you can get your API key in JavaScript.

Breaking Down the Response

{
  "data": {
    "timelines": [
      {
        "timestep": "1h",
        "endTime": "2024-04-06T14:00:00Z",
        "startTime": "2024-04-06T13:00:00Z",
        "intervals": [
          {
            "startTime": "2024-04-06T13:00:00Z",
            "values": {
              "solarDHI": 141.56,
              "solarDNI": 628.56,
              "solarGHI": 511.57
            }
          },
          {
            "startTime": "2024-04-06T14:00:00Z",
            "values": {
              "solarDHI": 117.55,
              "solarDNI": 610.92,
              "solarGHI": 432.5
            }
          }
        ]
      }
    ]
  }
}

As we can see, the response provides solar radiation data for the location we requested (Copenhagen, Denmark), and it details three key measurements of solar radiation: solarDHI, solarDNI, and solarGHI. The time period starts at 13:00, and ends an hour later at 14:00, with a slight decrease in all key fields due to changes in atmospheric conditions.

Remember: You can fetch any field out of the data layers in the API, including soil, lighting, maritime, aviation, pollen, fire, and more.

Leverage Weather Data Layers With Tomorrow.io

The advanced capabilities of AI weather APIs with the inclusion of data layers have opened up new horizons in how we access, analyze, and leverage weather data. By structuring data fields into distinct layers, platforms like Tomorrow.io provide invaluable and detailed insights that cater to a wide array of needs. This not only enhances our understanding of atmospheric conditions but also empowers a diverse range of applications, driving informed decision-making and innovation across industries.