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Weather Data: Best Weather Datasets & Databases

What is Weather Data?

Weather data is information about atmospheric and meteorological conditions. Historical weather data is used for climate analytics. Real-time weather data and forecasts help farmers and logistics companies predict weather conditions for crop and transportation route planning. Learn more

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meteoblue weather API - Highest precision weather forecast and history data

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The Ultimate Guide to Weather Data 2023

Learn about weather data analytics, sources, and collection.

What is Weather Data?

Weather refers to the meteorological state of the atmosphere in a particular location (city, region, or country) over a specific period of time (usually one day to a whole week). Data which tracks and predicts patterns and trends on different weather conditions is what we consider ‘weather data’. In general, weather data tells the user about the state of the atmosphere based on a whole range of parameters, including temperature, air quality, wind speed, and precipitation level. Organizations from various industries are utilizing analytics-grade weather data for a surprising number of real-world business applications. More and more companies are investing in weather APIs to get instant and accurate insights into the current weather conditions and to get enhanced weather forecasts for the future. Commercial weather APIs are more powerful than the standard weather forecasts we can access for free online or watch on the news. Weather data providers offer live weather data APIs with the potential to forecast weather and climate events for the next hundred years, for virtually any location on earth.

Who is using Weather Data in 2021?

Businesses of all sizes belonging to diverse industries are turning to weather data analytics to drive their strategies and planning: product manufacturers, advertising agencies, events companies, transport and logistics groups, and even healthcare companies. All kinds of social and technological institutions are now merging external, analytics-grade weather data with their organization’s proprietary internal data. Various different business teams - from sales, marketing and legal operations - can make use of weather APIs to arrive at data-driven decisions. Here are some more detailed examples of exactly who is using weather data in 2021 - the type of organization, and the person within the organization’s role:

Weather Data for the Marketing and Advertising Industry

  • Marketing and advertising: both digital and OOH marketers are finding increasingly innovative ways to market their products and services in the most effective way. And these marketing campaigns are only effective if they’re tailored to the situation of the audience. In other words, marketing campaigns only secure the best conversion rates if they’re relevant to the target audience. In a real-life context, live weather data can help here. Weather-triggered advertising is a key example. With weather-triggered advertising, the ads which are displayed across web pages, apps, and digital billboards are served according to the current weather conditions in a given location. For example, if you’re an ice cream manufacturer, your products are more likely to sell when the weather is warm and sunny. Running a seasonal marketing campaign is one way of doing this, where you tailor your campaign to the time of year. But seasonal marketing isn’t always enough: although your campaign may be adjusted to suit the time of year, adverse weather conditions can mean that ads for specific products fall on deaf ears. To stick with the ice cream example: if the weather is unusual for the time of year, and you’re advertising your ice cream in a particularly cold and rainy July, your ROI for this particular campaign isn’t going to be good. To increase ROI and drive conversions, real-time weather data via a weather API allows you to create a weather-triggered advertising campaign which works ‘on the fly’, giving you the capability to respond to real-time weather irregularities. With weather-triggered ads, you only serve ads and pay for ad space when the current weather conditions fulfil the requirements you’ve set. So if you’re selling ice creams, you’d only pay for ad space when it’s a clear blue sky, warm days, no rain. And if you’re an outdoor clothes retailer, you’d only roll out ads for the latest raincoat in times where the weather is cold and wet. Here, weather data works hand-in-hand with location data for smarter digital advertising. The kind of ads that a cell phone user is served when they visit an app or website correlates to and depends on their location in the physical world - and the weather conditions in that location. As a sub-sector of programmatic advertising, weather-triggered advertising means that the ad reel varies according to the outdoor weather conditions. Temperature, cloud coverage, rainfall, and daylight hours all have a bearing on the algorithm which indicates which product or service the phone user is eventually presented with.

Weather Data for Events Management

  • Events management: inclement weather can mean important events, both indoor and outdoor, are postponed or even cancelled. This has an impact on any event: from small-scale family skiing vacations, to huge events such as the Olympics or the Superbowl. Health and safety regulations dictate that events like sports matches, concerts, festivals and group holidays can only take place as long as the weather doesn’t present a public risk. For example, soccer matches can only go ahead on the provision that the pitch isn’t too wet or slippery. Likewise, thousands of festival goers can be left disappointed when these huge events are ‘rained off’. This comes at a huge expense for the organisers when the cost of promotional campaigns, venue and entertainment hire, and transport and logistics arrangements aren’t covered. For this reason, climate and weather are huge factors in any event manager’s risk assessment reports and insurance policy procedures. In fact, carrying out thorough risk management and safety verification specifically in relation to the weather is a key part of most major events’ insurance policies. The only way to make the most accurate weather predictions and forecasts is with a commercial weather dataset or API. Weather data providers gather data points from the most advanced satellite, drone, and radar technology. By combining meteorological expertise with big data science, weather data vendors can provide weather predictions models which identify potential risks to a higher degree of accuracy than open source weather forecast services. This means event planners and organisers can select dates for their events which aren’t at risk of being cancelled due to bad weather, and that they can get the best deal when it comes to insurance policies.

Weather Data for Travel and Vacation Business

  • Travel and vacation businesses - companies across the travel and tourism industry can benefit from a weather data API. Accurate weather data allows them to predict consumer demand so that they can allocate resources more effectively. For example, the amount of people choosing to book ski holidays is heavily dependent on the weather conditions at the resort. People typically only choose to vacation in ski resorts when the snow coverage is suitable for skiing. With a weather API, resort management, travel companies, and airline carriers can gauge when there’s going to be peaks and slumps in demand amongst travellers to ski resorts. This allows them to implement smarter marketing, operational, and revenue forecast strategies. Also, like with event management, a weather API allows travel companies to identify risks to travel years in advance, so that they can avoid scheduling flights and other journeys for periods when the weather presents a risk to travel.

Weather Data for Logistic Companies

  • Logistics - 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. In a similar way to commercial travel and transport companies, logistics companies ensure that their freight and haulage delivery plans are weather-secure by using accurate forecasting from a weather API. Being able to plan their routes years in advance according to these ultra-accurate weather prediction tools means that logistics companies avoid costly on-the-fly adjustments to planned routes, or weather-related cancellations and delays. This way, they’re able to make deliveries as they’re scheduled and maintain good levels of customer satisfaction.

Elsewhere, weather data has been applied to some even more surprising use cases in fields such as medicine and justice. Forensic meteorology uses intelligence on weather conditions in legal proceedings on property disputes, accidents and theft. In cases of asthmatic patients, weather data attributes such as humidity, temperature and presence of dust particles have been instrumental in predicting when triggers could occur and refining medicinal tools to combat adverse reactions.

What’s the difference between Weather Data and Climate Data?

Weather and climate are both concerned with atmospheric conditions, and so the data attributes of weather data and climate data often overlap. The difference is that whilst weather data is concerned with atmospheric conditions as they change over the course of days, hours, and even minutes, climate data is concerned with long-term atmospheric phenomena. Similarly, weather data operates on a more granular geographical level than climate data, as well as temporal. What this means is that, whereas climate datasets will contain information about entire countries, continents, or the whole earth, weather datasets are usually focused on smaller, more local, geographical regions, such as a ZIP code or city. Weather data companies can provide weather insights with up to 90m of granularity, giving users information for their specific location of interest.

Whereas the ‘weather’ can change on a day-to-day basis, the ‘climate’ in a given location is typically much more stable. However, global climate change is causing more and more irregularities to occur in climate patterns which were once fixed. As both the weather and the climate become increasingly volatile, weather and climate data science is being used to keep track of these changes and track climate change across the world.

What are the different types of Weather Data?

Weather data is a relatively broad data category because it can be split according to temporal and geographical coverage. Whereas some users turn to weather data providers for historical weather data, other users want live weather data. Likewise, some weather data products offer global coverage, whereas others can be customised to focus only on a specific location. Weather data sub-categories include:

Real Time Weather Data

  • Real-time weather data - normally when you buy real-time weather data, it’ll be delivered via a real-time weather data API. This ensures that you get weather updates as soon as they occur, giving you the most current insights into the weather conditions you’re interested in.

Historical Weather Data

  • Historical weather data - if you’re wondering where to find historical weather data, take a look at Datarade’s selection of historical weather databases and historical weather APIs. This gives you access to databases of weather conditions spanning back several decades.

Local Weather Data

  • Local weather data - weather data can be categorised by location. Local weather data can tell you about the conditions in a specific county or region, or on an even smaller scale, such as ZIP code or field-level weather data.

Global Weather Data

  • Global weather data - a global weather database provides intelligence on weather and climate trends from across the world. A powerful global weather data feed provides information about weather and climate patterns across the whole planet, such as atmospheric global weather data and wind patterns between continents.

What are some typical Weather Data attributes?

Weather data analytics takes various data attributes and parameters into consideration. These weather data attributes and units of measurement available vary between data providers. However, in general, you can expect to find the following data points as part of a commercial weather dataset or API:

  • Wind speed - typically expressed in ms, mph or kmph.

  • Wind direction - typically expressed in ms, mph or kmph.

  • Precipitation - including rain, snow, hail, and sleet.

  • Humidity - expressed in hPa.

  • Cloud coverage - expressed in octas.

  • Daylight hours, sunrise, sunset - expressed as SQL date, datenum, or unix time stamp.

  • Pollen concentration - grain concentration per cubic meter.

  • Solar radiation - typically measured in watt per square meter (w/m2).

  • Weather peril risks and events - including freak weather phenomena and natural disasters e.g. hurricanes, tornadoes, storms, floods, wildfires.

What is Feels Like temperature in Weather Data?

Feels Like, or Real Feel, is used to express how the human body perceives the external temperature. In the context of weather data, Feels Like temperature is an important attribute as it provides additional context to the raw temperature value. For example, although the actual temperature may be relatively warm, it may seem as though it’s colder due to other weather factors such as high wind speed and low humidity. Feels Like and Real Feel are important parameters for weather-triggered strategies and use cases because they take into account how real people respond to outside conditions and how this affects their behavior.

How is Weather Data collected?

Weather data providers collect data taking geography, topography and elevation into consideration. This means that the weather data they provide is more accurate than standard weather reports available for free online. Weather data APIs are updated constantly based on signal collected via satellite and airport observation stations, as well as proprietary data collection tools such as drone technology and mapping devices.

Collecting daily weather data points can help understand long-term atmospheric patterns, and the findings are usually displayed in a tabular weather data collection chart or geographically, so using a map. The use of high-tech equipment for weather data science means collecting weather data can now be done to an amazing level of accuracy and at an ultra-granular degree. Weather data collection equipment and sources of weather data can include:

  • 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 and tidal patterns.

How to assess the quality of Weather Data?

The quality of the data varies depending on the weather data provider’s sources. Different weather data sources supply different kinds of information, and some are more reliable than others. For example, airport observation stations don’t always supply accurate weather insights because they can be located hundreds of miles away from the location the user is interested in. It’s estimated that airport observation stations generate inaccurate weather reports up to 25% of the time. So always check that your weather data provider doesn’t rely solely on airport observation stations for collecting their weather intelligence.

When assessing the quality of weather data, it’s important to understand the context in which the weather dataset is to be used. A key part of data quality assessment (DQA) is recognising that data quality is meaningful only when it relates to its intended use case. It’s always a good idea to sample weather data before you buy it, for example using a weather API demo or trial. This helps you establish whether a weather data company can provide the right types of parameters, quality, and quantity of weather data to support your specific use case. 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 using a weather data sample to validate the data, calculate basic statistics or generate appropriate graphs.
  3. Select the appropriate statistical method for summarizing and analyzing the data. See how the data performs when you subject it to weather data analysis
  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. This will help you separate high resolution weather data from weaker datasets.

What are some Weather Data websites?

We now understand how weather data is collected and how to assess the data quality. But how can you actually get weather data? Who provides weather data?

There are a number of weather data sources and weather data companies who now offer their weather datasets to buy. From forecast services like Dark Sky to analytics companies like Yahoo, weather data providers supply users with analytics-grade weather data for business use. If you’re wondering how to find weather data online and at scale, browse Datarade’s weather data providers to compare weather APIs, datasets and feeds from a range of weather data services.

How is Weather Data typically priced?

Weather data providers offer a wide variety of pricing models to 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 usually categorized according to the level of data access and volume they require, so for example as ‘basic’, ‘premium’ or ‘premium gold’ depending on how much they pay per month or annually. Subscriptions to weather APIs ensure that users can access the most up-to-date weather intelligence and that their data covers their entire desired time window.
  • Pay per view access to content. The user pays for a single or one-time access to the data.Sometimes called on-demand pricing, this model allows users to access the weather data they require only as and when they need to, so is a more flexible model for users with tighter budgets.

What are some common challenges when buying Weather Data?

A common issue which occurs when you’re looking to buy weather data is comparing weather data providers. Lots of companies sell weather data, but it may not be the kind of weather data which suits your use case. For example, a real-time weather data broker will provide live weather updates, whereas another provider will offer exclusively historical weather datasets. This means it’s vital to get a weather data sample before you buy data or subscribe to a provider. This way, you can make sure that you’re getting exactly the right weather intelligence for your needs.

Questions for Weather Data providers

To get the best weather data, ask your provider the following:

  • How far back does their historical weather data stretch?
  • How far into the future do their weather forecasts go?
  • How fast can they update their dataset to match the volatility of the weather?
  • Can the provider’s dataset be integrated with your current business technologies and what does this integration process look like?
  • What quality assurance methodologies does the weather data provider use to remove errors?
  • If applicable, who carries out external data quality assessment for the weather data provider?
  • Does their intelligence come from the best weather data sources? Do they collect their data from multiple sources, and if so, what are these sources?
  • Can you get a weather data sample or demo their weather API with a free trial?
  • What format is their weather data delivered in? CSV? JSON? REST API?

Where can I buy Weather Data?

Data providers and vendors listed on Datarade sell Weather Data products and samples. Popular Weather Data products and datasets available on our platform are CustomWeather - Historical Hourly and Daily Weather Observations - 100 Years by CustomWeather, CustomWeather - High-Resolution Weather Forecasts and Historical Weather Forecasts by CustomWeather, and Worldwide Daily Historical Weather Data | Climate Data | Human Checked Weather Data starting in the mid 1900s by AWIS Weather Services.

How can I get Weather Data?

You can get Weather Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Weather Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Weather Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Weather Data?

Weather Data is similar to Pollen Data, Climate Data, Marine Data, Land Use Data, and Water Data. These data categories are commonly used for Weather Forecasting and Weather Observation.

What are the most common use cases for Weather Data?

The top use cases for Weather Data are Weather Forecasting, Weather Observation, and Climate Change Analytics.

Translations for this page

Datos del clima (ES)