Meteostat is a JSON API which focuses on historical weather and climate data. With artificial intelligence and machine learning on the rise, meteorological time series data is becoming increasingly important. There are various use cases for weather and climate statistics. Think about predictive analytics or clarification of insurance claims due to storms. Meteostat’s goal is simple: serving as a record keeper for earth’s weather and climate.

The roots of Meteostat lie in the open source community and remain the project’s overall focus. You can find most of our code basis under the MIT license on GitHub. …

We are thrilled to announce that Meteostat has joined RapidAPI. The API marketplace makes it easy for developers to use thousands of APIs through a common platform. It also allows Meteostat to offer a much requested feature: a freemium API which provides developers the ability to scale.

Head over to RapidAPI and give it a try. The API playground is an amazing tool to explore the different endpoints and query parameters. Also, if you run into issues, please get in touch, so we can further improve the experience.

Effective now, we’re replacing Meteostat JSON API v2 with the new RapidAPI-based…

On May 1, 2021 Meteostat will shut down version 1 of its JSON API. We urge all developers building on top of this version to migrate to version 2 as soon as possible. The documentation is available here.

If you never want to miss an update from Meteostat Developers, follow us on Medium and bookmark the development feed.

We have just published the 1.1.0 release of the Meteostat Python library — and it comes with an exiting new feature! Thanks to the new Point data interface you are now able to obtain historical weather and climate data for any geographical location.

Point data provides more complete time series, as observations of multiple stations are joined together. The data output is being interpolated based on the geographical distance between the different weather stations and the reference point of the query. Additionally, Meteostat adjusts measurements based on difference in altitude.


All you need is the latest version of the Meteostat…

Today we are announcing the first stable release of the Meteostat Python library on PyPI. With version 1.0.0, the library becomes much more mature and performant. Furthermore, future minor releases will guarantee full compatibility from version 1.0 upwards.

With our Python library we wanted to build a scalable solution which allows users to analyze historical weather and climate data on a large scale — both time-wise and geographically. In contrast to the Meteostat JSON API, the library provides unlimited data access and doesn’t require users to sign up. This is made possible with the help of Cloudflare, a global CDN…

Update: We’ve published a first stable version of the Meteostat Python library. Please read this article for more information.

Just one week after the release of our open weather station directory, we are now launching an official Meteostat Python library. The library will co-exist with our JSON API and provides a more flexible interface which targets the data science community. It’s build on top of the Meteostat bulk data interface and utilizes Pandas for data analysis. We invite everyone to test the 0.1.0 version of the Meteostat Python library. …

Today, Meteostat takes the next step in the effort of making meteorological data open and accessible for everyone. We are launching a GitHub repository which serves the purpose of collecting information about public weather stations worldwide. From now on, everyone is able to download and contribute to the full list of weather stations available via Meteostat.


If you want to add a new weather station, update some information or correct an error, you can either correct/update the affected file(s) & create a pull request or fill an issue & describe your concern. Your pull requests will be reviewed by the…

Today, a new chapter begins for the Meteostat project. With the release of our updated product portfolio we are laying the foundation for the project’s next growth wave. The update includes version 2 of our API and an updated user interface for the website.

Furthermore, we are rolling out a new feature which allows both developers and users of our website to access historical weather and climate data for any geographic location. We call it point data.

Point data is an incredible opportunity for Meteostat, as it allows even more people to consume weather statistics in a more feasible way…

As the meteorological winter came to an end this month we want to take the opportunity to have a look on the statistics. If you are based in Central Europe it probably does not come to your surprise that this winter was one of the warmest on record in Europe.

Temperature anomaly for Europe in winter 2019/2020.
Temperature anomaly across Europe for winter 2019/2020. Source: Deutscher Wetterdienst.

In terms of precipitation Europe was mostly divided in half. While Italy and Spain experienced a dry season with little rain, we saw northern Europe reporting precipitation records well above average.

Climate and climate change have become ever-present topics in our news streams. As the earth is warming and human impact on climate change is frequently discussed on social media, the demand for open and reliable climate data sources is obvious. While governments and national weather agencies are beginning to adapt the open data trend, one problem remains: accessibility.

The National Oceanic and Atmospheric Administration (NOAA) is doing a great job collecting global climate data and making it available to science, education and other non-commercial activities. However, the data format is hard to decode for most people. …


A free online service which provides weather and climate…

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