Wiki Categories

Model Evaluation

Weather Data

What is weather data?

Weather data provides information about the weather and climate of a region. It tracks patterns and predicts trends.

Weather refers to short-term atmospheric conditions of a region and can include indicators such as minimum /maximum temperature, humidity, or wind speed. Climate is the weather of a region averaged over a long period of time. Climate data covers details such as seasonal average temperatures or decade-long patterns of rains and contributes to climate prediction.

Weather data is tremendously important to agriculture and infrastructure planning. Various industries use weather data for real-world business cases, such as travel planning, demand forecast, and supply chain management. The easy availability of weather data for practically any region makes it possible to incorporate it in diverse analytical cases.

Where does the data come from?

Several governmental, semi-governmental, and private organizations (such as weather stations, the NOAA - national oceanic and atmospheric administration, the NWS - national weather service, and the world meteorological organization) collect weather data and make weather observations from various locations using different methods. They consider geography, topography, and elevation of the region to make the data more accurate. Some of the methods used for gathering weather data are:

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

The data gets updated constantly based on the information collected through satellites, airport observation stations, drone sensors, mapping devices, and other sources. The use of sophisticated technology and weather models can deliver more accurate and highly granular details.

Past weather data is usually available to download in bulk. For real-time data on current conditions and weather forecasts,  weather forecast APIs, feeds, and streams are used.

What types of attributes should I expect?

Common attributes of this type of data include daily weather summary and the following data points:

  • Average temperature, median temperature, minimum/maximum temperature, expressed in degree Celsius or Fahrenheit
  • Wind speed in mph or kmph, wind direction
  • Humidity in hPa, relative humidity in percentage
  • Precipitation including the precipitation types of rain, snow, hail, and sleet, precipitation amount, precipitation probability
  • Dew point expressed in degree Celsius or Fahrenheit
  • Cloud coverage expressed in oktas
  • Air pressure in millibars or mm/inches of mercury, air current
  • Ozone information
  • UV index
  • Pollen concentration per cubic meter
  • Daylight hours, Sunrise and sunset time, moon phase
  • Solar radiation - typically measured in watts per square meter (w/m2)
  • Severe weather risks and events including freak weather phenomena and natural disasters such as hurricanes, tornadoes, storms, floods, or wildfires

Additional attributes are delivered around specific requirements and upcoming events. For example, farmers need temperature and humidity information, while marine vessels need to track ocean currents. In another example, for any expected hurricanes, weather data provides regular updates on their location, movement, direction, and speed.

What are the different types of weather data?

Weather data is a broad data category. It can be divided into subcategories based on temporal and geographical coverage. Some customers need historical data, while some need real-time data. Some may focus on a smaller region or a city, while others may want the data for a state or across the globe. For climate change tracking, accurate analysis needs to take into account state, country, region, and global data.

  • Real-time data: It is typically delivered through real-time weather APIs. This approach makes data available as soon it is updated and provides real-time insights.
  • Historical weather data: It is available as a downloadable database and also through historical weather APIs. This approach enables access to weather conditions spanning several decades, helping identify patterns and anomalies.
  • Local data: This data is specific to a small region, a state, or a country. Local weather data in real time is valuable for forecast models which can help the local community for planning their work and making provisions for any natural disasters. Historical local weather data helps track patterns and make long-term development policies. Modern technology makes local data available for a farm, too, to support agricultural activities.
  • Global weather data: This data provides information on weather and climate trends from across the world. It helps detect climate patterns, track climate change, and power divers analytical cases.

How should I test the quality of the data?

The easiest way to test the quality of the data is by checking historical data to see if conclusions match predictions. On a more granular level, data coming from sensors need to be tested and thoroughly vetted.

The key factors for sensor data are type, location, and the number of sensors. Some types of sensors are more reliable and produce more accurate data. For example, images captured from planes are not as detailed as images captured by satellites, particularly near an urban area or a body of water. More sensors will deliver more accurate data by aggregating measurements from all of them. More sensors also provide an opportunity to compare results and identify faulty sensors.

The accuracy of sensor data depends on how the sensors are maintained and how often they get recalibrated.

To test the quality of the data:

  • Ensure the reliability of sensors and the frequency of their testing, maintenance, and recalibration.
  • Test the data for accuracy, consistency, and completeness.
  • Validate that the vendor provides the data attributes matching your requirements and quality criteria.

Who uses the data?

Government and semi-governmental agencies use this type of data for strategic infrastructure planning.

The next biggest industries that use this data extensively are agriculture and food processing.  The energy sector and land management services also use it to track plant and animal populations. Traffic management is a key use of weather data to alert travelers and manage diversion. Emergency services use it to assess the nature of potential natural disasters and plan their response. It can also help disease specialists to forecast, track, and plan to eliminate disease outbreaks in a region.

The use of this type of data is widespread. Almost all small, medium, and large companies from diverse industries use weather analytics to drive their strategies and planning. These companies include manufacturers, marketers, advertising agencies, hospitality companies, entertainment businesses, transport and logistics groups, shipping companies, financial service providers, and healthcare companies.

Different business teams in sales, marketing, and manufacturing use the data in data-driven decision-making processes

What are the common challenges when buying the data?

The most significant challenge when buying weather data is ensuring data accuracy. Data from different sensors produce varying levels of accuracy, and assessing source credibility is essential. The data can be historical or real-time, some attributes get updated more often than others, and it is challenging in ascertaining their recency.

  • Source credibility: The data coming from sensors depend on the type, location, and the number of sensors. While some sensors deliver reliable, accurate, and recent data, some may not provide accurate or updated data. The vendor must ensure the credibility of sensor sources to supply data that matches your quality requirements.
  • Data accuracy: Data provided by sensors may not be sufficiently detailed or accurate, based on their maintenance frequency. Data from one source validated with other sources can be a good measure of data accuracy. Verifying the accuracy of data is a major challenge when buying weather data.
  • Data timeliness: Some attributes of the data need more frequent updates, such as temperature, wind speed, or rain.  On the other hand, sunrise and sunset times are predicted much in advance. The significance of data timeliness depends on the use cases, and you may need to ensure the availability of the most recent data.

What are similar data types?

Weather data is similar to air quality index, climate data, and marine data, commonly used for weather forecasting. Other environmental data categories such as energy data, natural disaster data, and sustainability data are often used in conjunction with weather data and climate data.

You can find a variety of examples of time-based data in the Explorium Data Gallery.

Sign up for Explorium’s 14-day free trial to access the data available on the platform.

Access Weather Data wit Explorium

What are the most common use cases of weather data?

The most common use cases are weather forecasting, precision farming, agriculture waste management, traffic management, and fleet management.

  • Weather Forecasting: Leveraging data and technology, weather forecasting predicts the atmospheric conditions for a specific location and time. It analyzes a variety of data attributes indicating the state of atmosphere, land, and ocean to create a model forecasting potential weather conditions.
  • Precision Farming: It indicates the management of farms leveraging analytics and data from various sources. With highly detailed topological and soil information, farmers can achieve year-round crops and a better crop yield. Using precision farming, farmers can efficiently manage fertilizer use, crop watering, and progress monitoring. Precision farming uses comprehensive weather data to plan proactively.
  • Agriculture Waste Management: The waste generated by crops or livestock can be used in multiple ways. Agriculture waste management helps prevent, reduce, or reuse this waste. Farmers often use such waste as animal feed or convert it into biofuel or fertilizer. However, a data-driven management approach targets prevention and reduction to minimize pollution and carbon footprint. Weather data helps farmers to plan their activities in a way that manages agriculture waste efficiently.
  • Traffic Management: It involves monitoring and managing the traffic flow in the most efficient way. Using data from radars, cameras, and other sensors, vehicular or air traffic management avoids congestion and accidents. Traffic management leverages weather data to alert travelers of potential hazards.
  • Fleet Management: Leveraging technology and IoT (Internet of Things), a fleet of cars, trucks, or ships can be managed more efficiently. Commercial shipping and transport companies use fleet management to improve their ROI, while military and police use it for improving their response time. Fleet management facilitates faster and more efficient transportation while improving safety and reducing maintenance. Weather data plays a critical role in data-driven decisions for effective fleet management.

Which industries commonly use this type of data?

Practically all governmental, semi-governmental, and private industries use this data. They include agriculture, food processing, energy, emergency services, healthcare, manufacturing, retail, CPG, travel, transport, logistics, hospitality, leisure, entertainment, financial institutes, insurance providers, banks, and hi-tech companies.

How can you judge the quality of your vendors?

The quality of vendors is critical in ensuring the success of your projects.  Customer reviews, case studies, and demos are a good measure of vendor quality. Personal interaction with the vendor rep can help you resolve queries about their datasets matching your requirements.

  • Customer reviews and case studies: Most vendor websites list customer ratings, reviews, or testimonials. If they also provide case studies, you can consider all the information together and shortlist the vendors.  Case studies discuss the scale and attributes of the datasets, along with the challenges customers face. This information is valuable in assessing the vendor suitability to your projects.
  • Demo: A demo showcases the range of datasets and how quickly you can start using them in your projects. Most vendors also provide sample datasets to check if the datasets can easily integrate with your systems.
  • Interacting with vendor reps:  Discussing your requirements directly with a vendor rep can help you resolve queries quickly. An effective way of evaluating the vendor capability, engagement, and commitment, interacting with vendor reps is also an opportunity to build a long-term relationship.

Additional Resources:

Explorium delivers the end-game of every data science process - from raw, disconnected data to game-changing insights, features, and predictive models. Better than any human can.
Request a demo
We're Hiring! Join our global family of passionate and talented professionals as we define the future of data science. Learn More