Top 10 Weather Databases for Weather Forecasting in 2023
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
Recommended Weather Data Products
CustomWeather - Historical Hourly and Daily Weather Observations - 100 Years
CustomWeather - High-Resolution Weather Forecasts and Historical Weather Forecasts
Weather Source: OnPoint Weather Historical - Back to 2000
Worldwide Daily Historical Weather Data | Climate Data | Human Checked Weather Data starting in the mid 1900s
Storm Glass Global Weather API - Marine & Terrestrial Weather - High Resolution (10 Day Forecasts & Historical)
Accurate Weather Forecast API - Meteosource
Customised Weather Data Validation
CustomWeather API: Real-Time Weather Data - Current Conditions for 4,300 global locations
Global Weather Mapping - Marcus Weather Mapping (MWM)
OikoLab Weather API - Global Weather Data, Delivered Hourly
More Weather Data Products
The Ultimate Guide to Weather Data 2023
Weather Data and its importance
Weather data is a collection of information about atmospheric conditions, including temperature, humidity, precipitation, wind speed and direction, atmospheric pressure, and more. The importance of weather data lies in its ability to provide valuable insights into the present and future weather patterns, allowing for better planning and decision-making in various industries and applications, such as agriculture, aviation, transportation, construction, and emergency management.
Accurate weather data also plays a crucial role in protecting lives and property by enabling early warning systems and emergency preparedness measures in the face of severe weather events such as hurricanes, tornadoes, and floods. Without reliable weather data, it would be impossible to understand and prepare for the unpredictable forces of nature.
Types of Weather Data
Basic Weather Data
Basic weather data refers to the most commonly measured variables in weather observations:
Temperature
Temperature is a measure of the degree of hotness or coldness of the air. It is usually measured in degrees Celsius (°C) or Fahrenheit (°F). Temperature data is collected using thermometers, which can be placed on the ground, in the air, or in water.
Precipitation
Precipitation refers to any form of liquid or solid water that falls from the atmosphere and reaches the ground. This includes rain, snow, sleet, and hail. Precipitation data is typically collected using rain gauges, which measure the amount of liquid precipitation, or snow gauges, which measure the amount of snow.
Wind Speed and Direction
Wind speed refers to the speed at which air is moving horizontally past a given point, while wind direction refers to the direction from which the wind is blowing. Wind data is usually collected using anemometers, which measure wind speed, and wind vanes, which indicate wind direction.
Advanced Weather Data
Advanced weather data includes more specialized measurements and observations that are not as commonly collected:
Humidity
Humidity is a measure of the amount of water vapor in the air. It can be expressed in several ways, including relative humidity, absolute humidity, and specific humidity. Humidity data is typically collected using a hygrometer.
Atmospheric Pressure
Atmospheric pressure is the pressure exerted by the weight of the atmosphere on the Earth’s surface. It is typically measured in millibars or inches of mercury. Atmospheric pressure data is collected using barometers.
Solar Radiation
Solar radiation refers to the energy from the sun that reaches the Earth’s surface. It is typically measured in watts per square meter. Solar radiation data is collected using pyranometers or other similar instruments.
Remote Sensing Data
Remote sensing data refers to data collected from instruments that are not in direct contact with the Earth’s surface:
Satellite Data
Satellite data is collected by sensors on satellites orbiting the Earth. This data includes images of the Earth’s surface, as well as data on temperature, precipitation, and other weather variables.
Radar Data
Radar data is collected by sending out pulses of electromagnetic radiation and measuring the time it takes for the signal to bounce back. This data is used to measure precipitation, wind speed, and other weather variables.
Sources of Weather Data
Weather data is gathered from daily observations using a variety of sources, including satellites, weather stations, radars, and more. Understanding these sources can help us better comprehend the weather and how it impacts us wherever we are in the world.
Satellites
Weather satellites are essential tools for meteorologists and climatologists. They orbit the earth and provide data on various weather conditions, including cloud cover, atmospheric temperature, and precipitation. They capture images of the Earth’s surface that can be used to map weather patterns and identify severe weather events.
Weather Stations
A weather station is a ground-based facilities that collect meteorological data from the local area. They measure a range of weather conditions, including temperature, humidity, wind speed and direction, and barometric pressure. Weather stations can be fully automated or operated by humans, and data is usually transmitted to a central location for processing.
Radars
Weather radars use radio waves to detect the movement and location of precipitation. This information is used to map weather patterns, as well as to report on the intensity and direction of precipitation, as well as the presence of hail and tornadoes. Radars are an essential tool for predicting severe weather events and providing early warnings to the public.
Weather Balloons
Weather balloons are used to gather information about the atmosphere, such as temperature, pressure, and humidity. They are typically launched twice a day at weather stations around the world and can rise up to 30 kilometers into the atmosphere. The data collected by weather balloons helps to improve weather forecasting models and understand long-term climate patterns.
Aircraft
Aircraft are equipped with sensors that can measure a range of meteorological data, including temperature, pressure, and wind speed. This data is transmitted to weather agencies and can be used to improve weather forecasting models, especially in areas where ground-based observations are limited.
Citizen Science
Citizen science projects allow members of the public to contribute to weather data collection efforts. For example, individuals can install personal weather stations in their homes or participate in cloud observation projects. Citizen science is a valuable tool for expanding the reach of weather data collection and improving the accuracy of weather models. It builds networks of people with an interest in climate services. The networks link people across a city or county, forming a community approach to weather services.
Weather data is a valuable resource that can help individuals, organizations, and governments make informed decisions. Weather data is collected and analyzed at different levels, including regional, national, and continental levels.
Weather data on a region is collected and analyzed for specific areas, such as states, provinces, or cities. This data is critical for local decision-making within a region, such as planning for emergency response, agriculture in a certain county, transportation between regions, and tourism. Regional weather data is collected through a network of ground-based weather stations in a county that measure various weather parameters, including temperature, precipitation, flood risk, wind speed, and humidity.
At the national level, weather data is collected and analyzed by government agencies responsible for weather forecasting and climate monitoring across regions. In the United States, the National Oceanic and Atmospheric Administration (NOAA) is responsible for collecting and analyzing national weather data. National weather data is used for a variety of purposes, including aviation safety, environmental monitoring, disaster response, and national security.
At the continental level, weather data is collected per continent and analyzed by international organizations, such as the World Meteorological Organization (WMO), which is responsible for coordinating global weather data collection and analysis. Continental weather data is used for climate research, weather forecasting, and disaster management.
Applications of Weather Data
Weather data has dozens of applications in various industries. One of the most important applications of weather data is in weather forecasting. Meteorologists use weather data to produce forecasts that help people plan their daily activities, as well as provide early warnings for severe weather events such as hurricanes, tornadoes, and thunderstorms. Weather forecasting is crucial for industries such as agriculture, transportation, and aviation, as it helps them plan their operations and mitigate risks associated with adverse weather conditions like a flood or heavy snow.
Another critical application of weather data is in agriculture. Farmers rely on weather data to make decisions about planting, harvesting, and irrigation. They use weather data to determine the right time to plant their crops, as well as when to irrigate them. Weather data also helps farmers predict and prepare for weather-related disasters, such as floods, heavy snowfall or droughts, which can have a significant impact on crop yields and profitability.
The aviation industry also heavily relies on weather data to ensure safe and efficient flights. Pilots and air traffic controllers use weather data to plan flight routes and avoid severe weather conditions, such as thunderstorms and turbulence. Weather data also helps airlines optimize fuel consumption and reduce flight delays, ultimately leading to cost savings and improved customer satisfaction.
The construction industry also benefits from weather data, as weather conditions can significantly impact construction projects. Extreme weather events such as hurricanes, floods, and blizzards can cause delays and disruptions in construction projects, resulting in increased costs and lost productivity. By using weather data as a source of additional information, construction companies can plan their projects better, avoid adverse weather conditions, and reduce the risks associated with extreme weather events.
Finally, weather data is also essential for disaster management and emergency response. Local authorities use weather data to predict and prepare for natural disasters, such as hurricanes, floods, and wildfires. They use this information when searching and evacuating people from affected areas, to provide emergency supplies, and to allocate resources to respond to the disaster. Weather data also helps emergency responders to coordinate their search and rescue efforts and respond quickly and efficiently to weather-related emergencies.
Collecting, processing, and analyzing Weather Data
Weather data is collected at a weather station through a variety of methods such as ground-based instruments, weather balloons, radar, stations, and satellites. Ground-based instruments, such as thermometers, anemometers, and rain gauges, are the most common way to collect weather data. These instruments are installed at various locations and are used to measure temperature, wind speed, precipitation, and other atmospheric conditions. Weather balloons are used to collect data on temperature, humidity, pressure, and wind at different altitudes. Radar and satellites are used to collect data on precipitation, cloud cover, and temperature from above the earth’s surface.
Once weather data is collected at weather stations, it must be processed to make it useful for analysis. This science involves converting raw data into a format that can be easily analyzed. For example, temperature readings may be converted from Celsius to Fahrenheit or pressure readings may be adjusted for altitude. Data may also need to be cleaned to remove errors or anomalies. This process involves checking for inconsistencies or outliers in the data and removing them or replacing them with more accurate values.
Analyzing weather data involves using statistical methods to research and identify patterns, trends, and relationships in the data. This analysis can be used to predict future weather patterns, such as the likelihood of a hurricane forming, or to understand how weather patterns are changing over time. This type of analysis is often used by meteorologists to produce weather forecasts and to monitor and record the impacts of climate change on the earth’s climate. Additionally, weather data analysis can also help inform decision-making in industries such as agriculture, transportation, and construction, which are heavily influenced by weather patterns.
Assessing the reliability and accuracy of Weather Data
Weather data accuracy and reliability are critical for making important decisions related to public safety, agriculture, transportation, and other industries. However, weather forecasting is not an exact science, and there are several factors that can affect the accuracy and reliability of the data accessed.
One of the most significant factors affecting the accuracy of weather data is the quality of the data itself. Data quality can be impacted by several factors such as equipment failure, data transmission errors, or human error. For example, a faulty sensor or a poorly calibrated instrument can record incorrect readings, which can significantly affect the accuracy of the forecast. Additionally, weather data can be disrupted by natural events such as lightning strikes or high winds that can damage equipment, further reducing the accuracy of the data. Likewise, the availability of technology varies from on continent to another. Whereas most European countries have a sophisticated network of weather stations, governments in countries across the continent of Africa lacks automated stations which reduces the frequency of weather reports.
Another important factor that affects the accuracy and reliability of weather data is the complexity of the Earth’s atmosphere. The atmosphere is incredibly complex, and it is challenging to make accurate predictions of weather patterns. Small variations in temperature, pressure, and humidity can lead to significant changes in weather conditions, making it challenging to predict weather patterns accurately. Additionally, natural phenomena such as hurricanes, tornadoes, and thunderstorms can develop quickly, and it is challenging to predict their behavior accurately.
Despite these challenges, weather forecasting has come a long way in recent years, thanks to advances in technology and improved forecasting models. Weather data is collected from a vast array of sources, including satellites, weather stations, and radar, which can help to improve the accuracy and reliability of the data. Additionally, machine learning and artificial intelligence algorithms are being developed to analyze and interpret weather data, allowing forecasters to make more accurate predictions on an automated basis.
However, there is still much work to be done to improve the accuracy and reliability of weather data, and ongoing research and development are crucial to achieving this goal.
How weather and climate data is being used for research and for policy-making
Weather and climate data play an essential role in both research and policy-making. Scientists and researchers utilize this data to better understand the patterns and dynamics of weather and climate change, and to report the trends and impacts of climate change on the environment, human health, and the economy. Policy-makers use weather and climate data to develop and implement strategies to mitigate the effects of climate change and to adapt to the status quo. They also use this data to assess the effectiveness of policies and measures that have been implemented to address climate change. In short, weather and climate data are crucial tools for scientists, researchers, and policy-makers to understand and address the challenges of climate change.
Future trends and innovations in weather data collection and analysis
In the coming years, the field of weather data collection and analysis is expected to witness significant advancements in technology and innovation. One of the most notable trends is the increasing use of artificial intelligence (AI) and machine learning (ML) in weather data analysis. These technologies can analyze large amounts of data quickly and accurately, allowing meteorologists to produce more accurate and reliable weather forecasts. Furthermore, AI and ML can also help in detecting weather patterns that may not be visible to the human eye, leading to better predictions and a deeper understanding of the earth’s climate.
Another future trend in weather data collection and analysis is the use of unmanned aerial vehicles (UAVs), also known as drones. These devices can be used to collect weather data in areas that are difficult or dangerous to access, such as mountain ranges or oceans. Drones equipped with weather sensors can collect data in real-time, providing more accurate and up-to-date information about weather conditions. Additionally, drones can also be used to monitor and predict natural disasters such as hurricanes, floods, and wildfires, improving emergency response and disaster management.
Finally, the use of big data analytics and cloud computing is another trend in weather data collection and analysis. With the increasing amount of weather data being collected, it is becoming more difficult to store, process, and analyze this data using traditional methods. Big data analytics and cloud computing can help meteorologists store and analyze large amounts of data quickly and efficiently, leading to more accurate weather forecasts and a better understanding of the earth’s climate. Cloud computing also enables data sharing and collaboration among researchers and meteorologists around the world, leading to more comprehensive and global insights into weather patterns and climate change.
Dataset breakdown:
10 Best Weather APIs and Datasets
| # Rank | Provider Name | Dataset Name | Review |
|---|---|---|---|
| 1 | CustomWeather | Historical Hourly and Daily Weather Observations - 100 Years | CustomWeather provides an extensive historical weather dataset with hourly and daily weather observations for over 8,400 global locations. The dataset dates back to 1930 and includes temperature, precipitation, and other variables. |
| 2 | Weather Source | Weather Source: OnPoint Weather Historical - Back to 2000 | OnPoint Weather Historical data provides hourly and daily weather values from the year 2000 to present. This database is a stable source of historical information from 2007 for North American and back to 2000 for all other international locations because once the data is archived no further changes or edits are made. |
| 3 | CustomWeather | High-Resolution Weather Forecasts and Historical Weather Forecasts | CustomWeather offers high-resolution weather forecasts for over 85,000 global locations, available in any of 95 languages. The dataset also includes extended long-range forecasts and historical weather forecasts. |
| 4 | AWIS Weather Services | Worldwide Daily Historical Weather Data - Climate Data - Human Checked Weather Data starting in the mid 1900s | AWIS Weather Services provides a comprehensive historical weather dataset that is constantly updated with daily Max Temperature, Min Temperature, Precipitation, and other variables. The dataset dates back to the early 1900s and covers locations worldwide. |
| 5 | Meteosource Weather API | Accurate Weather Forecast API - Meteosource | Meteosource offers a comprehensive hyperlocal weather dataset for any location or GPS coordinate, updated in real-time. The dataset includes minute-by-minute, hour-by-hour, and daily weather forecasts. |
| 6 | Ambee | Ambee: Water Vapour Data API - Global Coverage | Ambee provides the world’s first-ever water vapour API, allowing users to integrate global real-time water vapour information into their applications. The dataset offers accurate and actionable data insights. |
| 7 | OikoLab Weather | OikoLab Weather API - Global Weather Data, Delivered Hourly | OikoLab Weather provides a global weather dataset with hourly time-series weather parameters for any location dating back to 1940, as well as a 16-day ahead forecast. |
| 8 | WeatherLogistics | Customised Weather Data Validation | WeatherLogistics offers customised solutions for the validation, scoring, post-processing, calibration, and data cleansing of weather data products. The dataset includes tailored solutions to meet specific needs. |
| 9 | Marcus Weather | Global Weather Mapping - Marcus Weather Mapping (MWM) | Marcus Weather Mapping is a web-based weather/commodity data mapping application that allows users to create their own maps of interest with a variety of variables. The dataset aids in the decision-making process and mitigating weather-based risk. |
| 10 | Barchart | Barchart Weather Data on zip code | Regardless of your industry weather can have a dramatic impact on your business. Our clients in agriculture, energy, insurance and commodity trading trust Barchart for timely and accurate weather data. |
Pros and Cons of Different Weather APIs and Datasets
1. CustomWeather - Historical Hourly and Daily Weather Observations - 100 Years
CustomWeather provides historical hourly and daily weather observations for more than 8,400 global locations. The data is available from 1930 or earlier until today. The dataset covers various weather parameters such as temperature, humidity, pressure, precipitation, wind direction, and speed. The data is sourced from various government and commercial sources, and it is suitable for various industries such as agriculture, energy, and insurance.
| Pros | Cons |
|---|---|
| Covers a vast number of locations | No real-time data |
| Historical data available for more than 100 years | Limited data parameters |
| Suitable for various industries | Data sources not specified |
2. CustomWeather - High-Resolution Weather Forecasts and Historical Weather Forecasts
CustomWeather provides high-resolution weather forecasts and historical weather forecasts for over 85,000 global locations. The data is available in hourly and extended long-range forecasts and can be accessed in any of the 95 languages supported by the provider. The dataset covers various weather parameters such as temperature, humidity, pressure, precipitation, wind direction, and speed. The data is sourced from various government and commercial sources, and it is suitable for various industries such as agriculture, energy, and transportation.
| Pros | Cons |
|---|---|
| Covers a vast number of locations | No real-time data |
| High-resolution weather forecasts | Limited data parameters |
| Suitable for various industries | Data sources not specified |
3. Worldwide Daily Historical Weather Data - Climate Data - Human Checked Weather Data starting in the mid 1900s
AWIS Weather Services offers a comprehensive dataset containing worldwide daily historical weather data dating back to the mid-1900s. The data includes variables such as daily maximum and minimum temperature, precipitation, snowfall, wind speed and direction, and sunshine duration. The data is verified by human experts and updated regularly. The dataset is suitable for various industries such as agriculture, energy, and insurance.
| Pros | Cons |
|---|---|
| Historical data available for more than 70 years | Limited weather parameters |
| Verified and updated regularly by human experts | No real-time data |
| Suitable for various industries | Limited information on data sources |
4. Accurate Weather Forecast API - Meteosource
Meteosource Weather API provides comprehensive and accurate hyperlocal weather data for any location or GPS coordinate. The dataset includes minute-by-minute, hourly, and daily weather forecasts that are updated in real-time based on the latest measurements. The dataset covers various weather parameters such as temperature, humidity, pressure, precipitation, and wind direction and speed. The data is suitable for various industries such as transportation, energy, and tourism.
| Pros | Cons |
|---|---|
| Accurate and real-time data | Limited historical data |
| Covers various weather parameters | Limited coverage areas |
| Suitable for various industries | Paid API |
5. Ambee: Water Vapour Data API - Global Coverage
Ambee provides the world’s first-ever water vapor API that offers global real-time water vapor information. The dataset covers various weather parameters such as temperature, humidity, pressure, and precipitation. The data is suitable for various industries such as agriculture, energy, and transportation.
| Pros | Cons |
|---|---|
| Unique water vapor data | Limited coverage areas |
| Covers various weather parameters | Paid API |
| Suitable for various industries | Limited historical data |
6. OikoLab Weather
OikoLab Weather provides an API for global weather data that is delivered hourly. This data includes hourly time-series weather parameters for any location from 1940 up to 16 days ahead. The data is available for both historical and future forecasts. The API can be easily integrated into different applications, such as weather forecasting, agriculture, or energy management.
| Pros | Cons |
|---|---|
| Provides global weather data that is updated hourly. | The data is limited to hourly time-series weather parameters. |
| Historical data is available dating back to 1940. | The data is only available up to 16 days ahead. |
| Data can be used for a wide variety of applications. | Â |
| The API is easy to integrate into different applications. | Â |
7. WeatherLogistics
WeatherLogistics offers customized solutions for validation, scoring, post-processing, calibration, and data cleansing of weather data products. This provider helps businesses in different industries to ensure the accuracy and reliability of their weather data by developing tailored solutions that fit their needs. WeatherLogistics works with businesses to validate weather data, develop forecasts, and provide climate risk assessments.
| Pros | Cons |
|---|---|
| Offers customized solutions to validate and improve data | WeatherLogistics doesn’t provide raw weather data |
| Helps businesses develop forecasts and risk assessments | The services provided are mainly geared towards businesses, rather than individual users |
| Can work with businesses to create tailored solutions | Â |
8. Marcus Weather
Marcus Weather provides a global web-based weather/commodity data mapping application that allows clients to create their own maps of interest with a variety of variables to aid in the decision-making process, in the mitigation of weather-based risk. The data includes information on temperature, wind speed, and direction, precipitation, and many other variables. Users can customize their maps based on their specific needs and interests.
| Pros | Cons |
|---|---|
| Provides a web-based mapping application. | The service is geared more towards businesses. |
| Offers a wide variety of weather variables. | The customization options may be overwhelming. |
| Can help businesses mitigate weather-based risk. | Â |
What is weather data, and why is it important?
Weather data refers to information related to atmospheric conditions, such as temperature, humidity, wind speed, precipitation, and air pressure. It is important because it helps us to predict and prepare for weather events, such as storms, hurricanes, and heatwaves, which can have significant impacts on human lives, agriculture, transportation, and other industries.
How is weather data collected and processed?
Weather data is collected using a variety of methods, including weather stations, radars, satellites, and buoys. The data is then processed using computer models and algorithms to generate forecasts and other weather-related products, such as maps and charts.
What are some common sources of weather data?
Common sources of weather data include the National Oceanic and Atmospheric Administration (NOAA), the European Centre for Medium-Range Weather Forecasts (ECMWF), private weather companies such as The Weather Channel and AccuWeather, and crowd-sourced weather data from citizen science projects.
How accurate and reliable is weather data?
The accuracy and reliability of weather data depends on the quality of the data sources, the data processing methods, and the specific weather phenomenon being forecasted. Overall, weather data has become more accurate in recent years due to advancements in technology and data analysis techniques.
How is weather data used in different industries and applications?
Weather data is used in a wide range of industries and applications, including aviation, agriculture, energy production, transportation, construction, and emergency management. It helps to inform decision-making processes, improve safety and efficiency, and minimize the impact of weather-related risks.
What are some future trends and innovations in weather data collection and analysis?
Future trends in weather data collection and analysis include the use of more advanced sensors and data platforms, the integration of artificial intelligence and machine learning techniques, and the development of new predictive models that can provide more accurate and timely forecasts. Additionally, there is a growing focus on climate change and its impact on weather patterns, which is expected to drive more research and innovation in this field.
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 Weather Source: OnPoint Weather Historical - Back to 2000 by Weather Source.
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.