What is Weather Data & How to Use It

Learn everything about Weather. Understand data sources, popular use cases, and data quality.

What is Weather Data?

Weather in simple terms is the state of the atmosphere of a particular place over a specific period of time (usually one to a couple of days). Data which provides patterns and trends on weather conditions are considered weather data. Weather data comprises of any facts and figures about the state of the atmosphere. Social and technological institutions are now considering and merging weather data (as the main external data) with internal data such as sales and operational data to optimize risk management. Weather conditions do not only affect planning but also have operational impacts on businesses. The business value of weather data can only be measured on a grand scale as economists have used weather forecasts to predict supply and demand patterns, improve marketing, streamline operations and even create economic models. Due to this invaluable nature of weather data, scientists and geographers are working continuously to improve the art of accurate weather forecasting. They make use of methodologies which involve data mining, processing and analysis so as to produce empirical evidence with accuracy.

Who uses Weather Data and for what use cases?

With weather and climate being regarded as important data-driven activities, the use of weather data has become invaluable to almost every aspect of our daily lives. Not only does weather data determine what we could wear or eat, it has now become important to incorporate weather data in almost every business. Apart from trivial use cases in the fields of agriculture, aviation, construction and other open-field operations, common use cases in logistics, utility and fast food companies have shown how weather data has been incorporated into their risk management analysis to optimize safety and customer’s satisfaction. Logistics companies minimize risk by merging real-time weather data with Global Positioning System (GPS) data so as to determine suitable routes to traverse in times of unfavorable weather conditions. Just as utility companies merge real-time weather data with that of their assets across their service territory to determine which assets could be affected by unstable weather conditions, fast food companies also merge real-time data with location so as to determine demands on items such as soft drinks, ice cream and water (usually influenced by temperature. It is no news that Ice cream shops cash out more during the Summer.)

More sophisticated operations in fields such as medicine and justice have made significant use of weather data. Forensic meteorology has been effective in creating weather conditions relevant in legal proceedings on property disputes, accidents and theft. In cases of asthmatic patients, weather data on components such as humidity, temperature and presence of dust particles have been instrumental in predicting when triggers could occur.

Adding more contexts to the vast scope of Insurance policies, weather data and insurance policies are seemingly a great match. Not only can insurance companies use weather data to evaluate claims, it can also help increase customers engagement with weather alerts, thus managing risk of weather-stimulated accidents.

What are some typical Weather Data attributes?

The art of accurate weather forecasting is beyond simply gazing out the window to see if it is rainy or the type of clouds shading the sun rays from basking the Earth. This is due to a number of attributes (components) which make up the weather data of a specific place. Weather data is generated from a combined analysis of these measure components amongst others. These components include Wind, Precipitation, Temperature, Cloudiness, Humidity and atmospheric pressure. Other non-common factors which contribute to weather conditions are Mountains Oceans, Forest and other natural bodies.

How is weather data is collected?

Collecting daily weather data can help understand atmospheric patterns and with the use of high- tech equipment a vast array of weather data can be collected with amazing accuracy. How and what is collected will depend on the data type and source;

  • Thermometers are used to measure temperature.
  • Radar systems to measure the movement of rain clouds.
  • Barometers measure atmospheric pressure.
  • Wind vanes and anemometers gauge wind speed and direction.
  • Transmissometers measure atmospheric visibility.
  • Hygrometers measure humidity.
  • Weather Satellites locate clouds, wildfires, snow cover, and indicate ocean temperatures

How to assess the quality of weather data?

Data does not exist in a vacuum. Before assessing the quality of weather data, the reviewer must know the context the data set is to be used. Data quality assessment (DQA) revolves around the premise that data quality is meaningful only when it relates to its intended use. Thus, DQA refers the statistical and scientific evaluation of data to determine if they are of the right type, quality, and quantity to support their intended use. This involves some key simple steps that should be followed chronologically;

  1. Begin with a review of the sampling design and data collection method used to check for consistency with data collection objectives.
  2. Conduct a preliminary data review to validate data, calculate basic statistics or generate appropriate graphs.
  3. Select the appropriate statistical method for summarizing and analyzing the data. Identify the key underlying assumptions associated with the statistical test selected.
  4. Verify the underlying assumptions of the statistical method and evaluate whether they hold, or whether deviations are acceptable, given the actual data.
  5. Draw conclusions from the data and use appropriate labels to describe the quality for example as good, fair or poor.

How is Weather Data typically priced?

Weather data providers offer a wide variety of pricing models to end users. Some providers offer weather data APIs (Application Programming Interface) while others may offer a weather mapping platform. Whatever the case, different pricing models are offered depending on the data package and product chosen. Most Data as a Service (DaaS) pricing models in the market with regards to weather data are volume-based and include;

  • Subscription to access content. Typically, users pay a monthly or annual subscription fee to access weather data content. Subscribers are categorized as basic, premium or premium gold depending on how much they pay per month or annually. The higher the amount the more data content they can access.
  • Pay per view access to content. The user pays for a single or one-time access to the data.

Common challenges with Weather Data?

Customers of weather data are often faced with issues of trust. The absence of transparency and the opportunity to clearly compare two or more data sets from different providers make it hard on customers. For a customer to compare different data sets, they will have to pay for them first. This is not often the case, in other industries like hotel and restaurant where the prices of their products are easy to compare. Most weather data providers do not offer instant online assistance or agent on standby to answer customers’ queries and provide clarifications on data interpretations.

Questions for weather data providers

A regular data buyer would probably pose a weather data provider the following questions for clarification;

  • How fast can they update their data set to match the volatility of the weather?
  • Can the provider’s dataset be integrated with our current business technologies and how does the process look like?
  • Who carries out external data quality assessment for the data provider?
  • How can a large volume of data be interpreted and utilized in a short time?

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