Tomorrow.io’s Statistical Weather Data API: Go Beyond the Forecast

Analyze and interpret weather events with Tomorrow.io’s best-in-class Statistical Weather Data API.

Tomorrow.io's Statistical Weather Data API allows you to analyze and interpret past, current, and future weather events.

The Value of a Statistical Weather Data API

Statistical weather data allows you to collect indicators that could be later used to predict weather events. You need more than just basic weather data to make these predictions, which is why aggregated data from the Tomorrow.io Weather API is so useful. With it, developers can create analytics tools for business intelligence, which they can then leverage to optimize operations, reduce loss, and boost revenue.

Access a Robust Library of Parameters for Comprehensive Statistical Weather Data

The Tomorrow.io Weather API collects all data from multiple sources and organizes it into layers and insight categories. Insight categories are a collection of events that can be used as convenient methods to identify upcoming weather situations. Data layers from 80 different Weather API fields can be used to build custom weather analytics for your specific industry. Analytics from a Statistical Weather Data API allow you to analyze weather trends so you can optimize operations before events happen.

When you think of weather, you might think of basic information such as temperature, rain, snow, or winds, but the Tomorrow.io Statistical Weather Data API offers much more than that. With historical weather data up to seven years in the past, you can predict future events and conditions, including solar information for radiation levels, pollen and mold spores information for consumer health (e.g., allergies), maritime information to determine wind and swells that could affect international trade, air quality data that could affect consumer health, and much more.

Tomorrow.io's weather API offers over 80 data layers including weather parameters like snow, ice, and rain.

Take a Statistical Approach to Weather Data

With a variety of API data points, businesses can build their own statistical-based analytic tools. Some Tomorrow.io customers work with the API to build data models and algorithms that help make predictions. Historical and analytical data gives businesses a competitive advantage as they identify customer behavior patterns and changes based on weather.

Using weather data, organizations already save millions on future events. Take the insurance industry as an example. Weather events damage homes, threaten lives, and damage other assets like cars. Not only do insurance companies care about weather, but their customers do too. Insurance companies currently use the Tomorrow.io API to gather data and build statistical tools to then alert their customers of impending weather events. A survey showed that 90% of their customers appreciated the notifications and found value in messages warning of future weather events. Notifications warn users of floods, hurricanes, fires, high winds, hail and other events that could damage property. With these notifications, insurance customers could prepare ahead of time so that there is less damage to their property. The statistical information helps customers avoid damage but also saves the insurance company millions in claims.

Another example of the power of statistical data is the way Uber uses Tomorrow.io to optimize their estimated time of arrival (ETA) shown to riders and better prepare for an increase in ride requests. Not only does Uber optimize operations, but they use Tomorrow.io’s API to drive revenue. Using statistical data from our API, Uber’s algorithms more accurately provides riders with ETA information and makes decisions in real-time. The API contains numerous data points for minute-by-minute granular weather patterns so that any changes are already accounted for in the app. Uber also works with the Tomorrow.io API historical data to create artificial intelligence-driven insights so that they can make changes to operations quickly and provide safe transportation for their drivers and passengers across the globe.

Using Statistical Parameters

Developers can build hyper-accurate applications using the Tomorrow.io API due to the numerous data points available across locations and even get historical data to identify patterns and trends that would not be available with basic API data. Several industry leaders already use the Tomorrow.io API for statistical analysis of weather events that drive consumer patterns, buying habits, preparation, and travel and transportation habits.

Because the Tomorrow.io has so many data layers, it’s completely flexible to your own use case and industry needs. The API returns JSON data sets, allowing developers to integrate it into any platform, language, application or environment. You can integrate today, tomorrow, or yesterday’s events into algorithms and applications. Choose from the many parameters available to build your own analytics applications.

Leverage our Statistical Weather Data API for a Variety of Real-World Use Cases

Cities often need to warn residents and governments about impending weather conditions in order to ensure their safety. Residents seek shelter during thunderstorms with lightning, and car owners must garage their vehicles during hail to prevent damage. These cities and municipalities use the Tomorrow.io API to predict when these conditions will affect local areas so that they can prepare and warn residents. You can approach this problem in several ways. A full list of data fields are available in the Tomorrow.io API documentation. Let’s look at a simple Python request for visibility at a specific location, because poor visibility would increase traffic accidents.
import requests

url = "https://api.tomorrow.io/v4/timelines?location=YOUR_LOCATION&fields=visibility&units=metric×teps=1h&apikey=YOUR_KEY"

headers = {"Accept": "application/json"}
response = requests.request("GET", url, headers=headers)
print(response.text)
In the request, we use the metric system, but imperial is also an option. Timesteps for this example are set to “1h,” but developers can set any timestep as little as minute-by-minute to get more real-time data. The JSON result:
{
  "data": {
    "timelines": [
      {
        "timestep": "1h",
        "endTime": "2022-03-08T10:00:00Z",
        "startTime": "2022-03-03T22:00:00Z",
        "intervals": [
          {
            "startTime": "2022-03-03T22:00:00Z",
            "values": {
              "visibility": 16
            }
          },
          {
            "startTime": "2022-03-03T23:00:00Z",
            "values": {
              "visibility": 16
            }
          },
          {
            "startTime": "2022-03-04T00:00:00Z",
            "values": {
              "visibility": 16
            }
          },
          {
            "startTime": "2022-03-04T01:00:00Z",
            "values": {
              "visibility": 16
            }
          },
	snipped for brevity....
      }
    ]
  }
}

Each interval in the JSON data set contains values for the parameters we requested, which was visibility. This example returns only one field, but you can choose from 60 fields listed in the core documentation.