The use of weather data in data science is hugely varied, and there are applications in all applied verticals. However, at the heart of all data science projects are accurate, trustworthy weather datasets.
In this article, we’ll explain the types of weather data available for data science, how to get them, how to make sure they are trustworthy, and finally, how to incorporate the data into your project so that you can gain the maximum benefit and insight.
Many data science projects involve sourcing raw data to complete the project. Whether the task is data visualization, data analysis, machine learning, or other data science activity, all projects start with at least one set of data. If your task involves analyzing data or visualizing the weather itself, just a source of weather data may be enough.
If your project is to try to analyze how another set of data is influenced or correlates with weather and climate patterns, then you’ll need a dataset that supports accurately joining the data together. If you also want to make predictions, then you’ll need a source of weather forecast data.
Types of Weather Data – Weather Forecast & Historical Weather
Before we dive into the details of how to obtain and use weather data, it’s worth reviewing the types of weather data you will find and how they are typically used.
Historical weather data
Around the world, tens of thousands of weather stations are continuously monitoring the weather. These individual weather stations provide weather history observations that can be used as the input to a weather forecast or as a record of the weather for us to analyze in our own work.
The weather stations report at regular intervals and include weather elements such as temperature, precipitation (rain, snow, etc), wind, pressure, and many more weather variables.
Historical weather summaries and climate data
Historical and climate summaries are simply the aggregation of the raw historical weather observation data to provide a picture of what the typical weather for a location has been.
For example, we can take many years of historical weather data to calculate that the average temperature in Paris, France, for January is 8C/46F. On any given January day, the temperature may well be much colder or warmer than 8C but the long term climate average is 8C.
Such historical weather summaries can be calculated across years, months, weeks, and even days to help create a ‘typical’ weather picture for a location. They can also be used to give a picture of the possible weather extremes by identifying the highest and lowest values of any particular weather metric.
For the case of Paris, France, we can see that the highest temperature is 16C/61F and the lowest maximum temperature recorded is a chilly -6C/21F.
Weather forecast data
The last type of weather data available is the weather forecast. This provides a detailed prediction of how the weather will behave over the coming days. Weather forecasts typically range from three to 15 days out, with the first seven days being the most accurate.
Multiple organizations create weather forecasts, which all have their own strengths and weaknesses. The most accurate weather forecast data will combine the output from multiple forecast models to provide the best estimated forecast.
Ensuring the Weather Data is suitable for Data Science
Weather data for data science needs to achieve a number of goals. Firstly, the data must be accurate and complete, of course. In addition to being trustworthy, the data must be in a format that can be used in our chosen Data Science tool, be it R, Excel, a database, or a custom Python script.
Let’s first look at making sure the weather data is complete and accurate. For weather data, “completeness” is required geographically (there is data nearby to the locations I’m investigating) and temporally (there is accurate data for the date and time that I’m interested in).
Spatial and temporal resolution
One of the most important features of the weather data is that it includes enough spatial and temporal resolution. What does this mean?
For historical data, it means that it’s necessary to find a source of weather data that is close enough to the point of interest to be considered accurate. If the weather station is too far away from the target location, it is likely that the weather data will not be correct for the location we are analyzing.
Unfortunately, there are a limited number of weather stations, so it’s necessary to be careful if you load weather history records by simply using the ‘closest’ weather station. It’s better to look at all close-by weather stations and combine the results of the weather stations into the best estimate for the location of interest.
For example, if I am interested in the weather data for Fairfax, Virginia, there are multiple weather stations nearby – Washington Dulles Airport, Washington National Airport, and multiple other stations. They are all close, so which should I trust? The answer is to trust all of the stations!
By doing this, we validate that we are using the most representative weather values for the data. This technique helps eliminate localized effects, such as Washington National Airport tending to report warmer temperatures because of its proximity to the warm Potomac River.
We must also check that the data includes enough day and time accuracy to allow it to be analyzed effectively.
Weather data for data science that will be compared to another data set, such as business performance data, typically requires that there is at least hourly weather data available so that any time-of-day analysis can be performed accurately.
It is not possible to analyze hourly business data if the weather data is only reported as a daily summary – rain at night often has no bearing on activities during the daytime.
Error checking and observation completeness
Another problem with historical data is that weather stations don’t report the weather occasionally due to equipment failure, planned outages, or other causes. Using multiple stations helps mitigate this problem by being able to fall back on alternative weather stations if other stations do not report certain hourly records.
The final part of the historical data validation is to identify errors in the weather station observations. Unfortunately, some weather observations include errors in addition to omissions.
When using weather observations within data science applications, it’s important to have an understanding of whether the data has been analyzed and cleansed. If so, what procedures have been followed?
Technologies available to import the weather data
Once we have identified a reliable source of weather data, we can now prepare to obtain the data so we can integrate it within our data science application. There are a number of typical ways to retrieve weather data.
Downloadable data files
Some data providers will provide you with weather data in a flat file format. These formats may be in a standard format, such as Comma Separated Values (CSV) format. In this case, the data can often be used or imported directly into a Data Science project.
Other data providers will provide raw historical data in a format that is not so easily read into normal data science tools. This is typical for raw data from government meteorological departments because weather observation data can be very large.
Earth Science has developed data formats such as GRIB and NetCDF to allow such large data volumes to be processed.
Raw historical weather data sets such as this will generally require pre-processing to be able to use them in a data science project. In addition, most will not include full error checking or the ability to interpolate the observations from multiple nearby weather stations.
Commercial weather data providers (some of which include a free trial) will often perform additional data processing and formatting that makes the data more easily consumed into a data science project. Data formats can include CSV, JSON, or other plain text file formats.
Weather API for automated data retrieval and loading
Downloading weather data and then importing it into the database or other application is generally suitable for a small number of data loads.
For example, importing a fixed amount of historical weather data for analysis with a fixed set of dates. Unfortunately, it is often too restrictive to deal only with a fixed set of weather data. Weather forecasts, for example, generally need to be refreshed at least daily.
If the application uses recent weather history, that too can need a regular refresh. The latter can often require an update at least every hour so that the dashboard, visualization, or other output is able to display the very latest information.
To achieve such a frequent data refresh requires an automated procedure to retrieve the weather data and then load it into the application.
One of the best ways to do this is via a web service API. Web services use the same technologies as the general World Wide Web (HTTP and HTTPS network protocols) to transfer the weather data from the provider to the client.
Web services will sometimes use a custom scripting client such as Python, PHP, JavaScript, or Java to retrieve the data in JSON or CSV format. After retrieving the data, the script may process the data and then save the data or upload the data into another application for analysis.
Many applications that are used for Data Science can import data directly from such web services.
For example, Microsoft Excel, Power BI, and many business intelligence applications, such as MicroStrategy and Tableau, can read information from a web service directly.
In this case, it’s often helpful to have a weather data provider that can supply the weather data in a standard data format, such as a CSV, so that the application can easily import the data.
ODATA
ODATA (short for Open Data Protocol) is a standard form of RESTful web service that some applications, such as Excel and SAP Analytics Cloud, support.
ODATA acts just like a web service, except that the exchange format is very formalized so that data science clients know how to consume the data without any modifications. If your application and data provider both support ODATA then this will provide an easy path to importing the weather data into your application.
Accurately analyzing weather data
We have now found and imported our weather data into the application so we can perform our data science. Now let’s look at the typical data we can expect within a weather data report.
The typical weather data dataset will include multiple columns such as temperature, precipitation, and wind speed, etc. If the dataset is using a short time period for each item of data, such as an hourly weather forecast or hourly historical observations, then not a lot of post-processing will occur on the dataset that is imported.
However, if the data is aggregated to summarize a day, month, or year, then multiple weather data observations are aggregated into a single report. There are different ways for it to happen, and it’s important to understand how a particular aggregated weather variable value is obtained.
Temperature is typically aggregated in three ways: the maximum temperature, the minimum temperature, and the arithmetic mean of the temperature.
The mean temperature can be the mean of all hourly values or the average of the maximum and minimum values. In a typical day, the maximum temperature often occurs in the afternoon, and therefore, simply reporting the maximum temperature is a good substitute for the overall temperature of the day.
In some circumstances, maximum temperature does not occur in the afternoon, such as when colder or warmer air masses are moving through a location.
In these cases, using the maximum daily temperature to compare against business performance may not produce accurate analysis and results. It is generally better to analyze temperature at the hourly level.
For some applications, it is necessary to understand more about the maximum and minimum temperatures when investigating the typical weather for a time and location.
For example, consider the normal temperature for a location in January. We would like to understand the normal maximum temperature (mean maximum temperature) plus the possible variability.
What temperature range do 80% of the days fall between? What is the maximum maximum or minimum maximum temperature possible at this location?
The maximum temperature is a good guide to the typical weather at a location, particularly when additional statistical values are considered, so a full understanding of the typical temperature and its variability is understood.
Rainfall is typically summed over the aggregation period. The precipitation coverage, the amount of time the rain fell for, is often as important a driver for business metrics as the amount of rain that falls.
A short, sharp but heavy thunderstorm at the end of the day in Miami, Florida, may well have less impact on tourist activities than a longer but lighter all-day light rain. However, the former may well produce significantly more rainfall and therefore look worse in the daily weather observation data.
Sources of Weather Data
Visual Crossing Microsoft Data provides historical weather data and weather forecast data available for download, web service, and ODATA access.
Get started with our free trial and free data tier at Weather Data Services.
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FAQs About Using Weather Data in Data Science Projects
What types of weather data are available for data science projects?
Data scientists use several types of weather data that support their workflows: historical weather observations, climate summaries, and weather forecast data.
Visual Crossing provides access to all three, including raw historical weather data collected from global weather stations, long-term climate data for analyzing patterns and extremes, and accurate weather forecasts based on multiple global and regional models.
These datasets support everything from machine learning to data visualization and statistical analysis. For example, if you’re modeling how climate change affects crop yields or energy usage, access to both forecast and historical data in one API simplifies development and increases confidence in your results.
How can I find accurate historical weather data for analysis?
Accurate historical weather data should be both geographically relevant and free from gaps. Visual Crossing sources its data from thousands of high-quality stations, including those used by the US government, national centers, and other vetted networks.
To improve precision, Visual Crossing intelligently blends data from multiple nearby sources, reducing the risk of distortion caused by outlier readings or local effects like proximity to large bodies of water or elevation shifts.
This is critical when matching weather conditions to metrics like property damage, retail foot traffic, or economic data. Clean, aligned data means fewer assumptions and better model performance.
What is the difference between weather observations and climate summaries?
Weather observations record actual events—like temperature, precipitation, or wind speed—hourly or daily. These are perfect for short-term studies or event-driven analysis.
Climate summaries, however, show long-term averages and variations over months or years, providing insight into seasonal patterns, weather extremes, or trends associated with climate change.
Visual Crossing offers both. You can retrieve exact hourly data for a specific event (e.g., a storm on April 3rd) or get 30-year averages to understand how typical that day’s weather was. This dual access supports both forecasting and back-testing in your data science project.
How do I ensure wthat eather data is complete and reliable for my dataset?
To ensure your weather data is trustworthy, evaluate its spatial accuracy, temporal resolution, and cleansing process.
Visual Crossing solves spatial issues by considering multiple weather stations for each location, reducing reliance on any single data point that may be skewed due to geography. This blended approach provides the best estimate for real-world conditions.
For time-sensitive analysis, the platform offers hourly resolution, perfect for aligning with business, economic, or environmental datasets.
The system also includes automated gap-filling and error detection, making it an excellent fit for machine learning models where clean, complete inputs are essential for reliable predictions.
What formats are best for importing weather data into data science tools?
Formats like CSV and JSON are ideal for most data science tools, as they are easy to load into tools such as Python, R, Excel, Power BI, and even cloud databases.
Visual Crossing supports both formats and ensures consistency in structure, which means you spend less time cleaning and reshaping data and more time running your statistical analysis.
For larger or more complex datasets, especially those used in Earth science, Visual Crossing’s clean structure is a big advantage over raw government agency files like GRIB or NetCDF, which often require extra preprocessing and custom scripts to interpret.
Can I use APIs to automate weather data updates for my project?
Yes, using a weather API allows you to automate recurring data pulls and integrate weather information directly into your application or analysis pipeline.
Visual Crossing offers a reliable, scalable API with free access tiers and affordable paid options. It supports both historical and forecast data, and returns information in CSV or JSON, perfect for automated ingestion into dashboards, databases, or BI tools.
The API is easy to use across common programming languages and supports scheduled calls that keep your data set fresh for near real-time use cases. It’s particularly helpful in machine learning pipelines, where current or forecast weather can be a key feature.
What is ODATA, and how does it help with weather data integration?
ODATA is a standardized protocol used to integrate data sets directly into platforms like Excel, Power BI, and SAP Analytics Cloud.
Visual Crossing supports ODATA, making it easy for many data analysts to link weather updates into their reports without writing code. This is particularly useful for enterprise teams working with collaborative tools and automated dashboards.
ODATA compatibility ensures that weather records—such as temperature, wind direction, or atmospheric pressure—can be filtered, sorted, and analyzed just like database records. For teams that lack full-time developers, this lowers the barrier to including weather and climate data in everyday workflows.
Why is hourly weather data better for time-sensitive analysis?
When your goal is to align weather with economic activity, sales, or other time-specific events, hourly data gives you the clarity you need.
Visual Crossing delivers this granularity, so you know not just that it rained, but when it rained—and whether it coincided with a dip in store traffic or a spike in power use.
Daily summaries may be too coarse for this kind of analysis. If you’re building machine learning models, trying to visualize data trends, or comparing environmental variables hour-by-hour, Visual Crossing’s high-resolution datasets offer the precision you need to draw meaningful conclusions from your data.