We are pleased to announce that a new dataset has been published today, 18 March 2025, in the Climate Data Store (CDS):
ERA5 hourly time-series data on single levels from 1940 to present
This dataset is a subset of some parameters of the full CDS ERA5 single levels hourly dataset on 0.25 degrees resolution and is stored in Analysis Ready Cloud Optimised (ARCO) format, which has been implemented for retrieving long time-series for a single point in a efficient way.
Please be aware that this is an experimental catalogue entry which uses an alternative source for the ERA5 data designed for fast access long-timeseries for a single point, and it is not recommended for use operational systems .
It is also the source of ERA5 data that is used by the new ERA-Explorer also launched today! The ERA-Explorer is an interactive web-application which lets you calculate daily, monthly and annual statistics in seconds clicking on a location on the map.
For any enquiries regarding this dataset or the application, please contact us .
ECMWF Support
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Hi Michela,
The date format seems to have changed, and it is causing me some problems.
In February 2025 I have downloaded the ERA5 monthly time-series on single levels from 1990 to 2024, and the data format is: X631152000, X633830400, etc . I downloaded the same data in October 2024 but only for years 2011-2024, and the data format was then: (ymd) X20110101, X20110201, etc.
Could anybody help me with the new time format with ERA5, and convert it to standard calendar (ymd)?
I use R to download the data and my analysis. Could you please help me? Thank you
Gabriela
This is fantastic!
I work in renewable energy, and constantly want to access long-term wind speed and irradiance data for sites. Previously I’ve had to store and then reformat the data myself, due to the long download times, but this is much more efficient 
Could you add irradiance (Surface solar radiation downwards - SSRD) too please , so that I can use this data for solar sites as well?
Thank you!
Charlie
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Hello Charlie, for this parameter see this alternative archive https://earthdatahub.destine.eu/collections/era5/datasets/reanalysis-era5-single-levels, it seems it’s there. All the best. Rémi.
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This is no short of a game changer! Coming from the renewable energy community, I can’t sufficient emphasize the impact that this will have. It is very important that this does not stay an experimental feature (or worse gets removed), but that it becomes part of the operation services!
Edit: Realizing that solar fluxes aren’t included this significantly reduces the utility for renewable energy. Sincerely hope this gets added ASAP!
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I agree. It would also be useful to have surface solar radiation downwards, and surface thermal radiation downwards, as well snow cover and snow depth.
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Dear ECMWF Copernicus
At Open-Meteo, I am tackling similar challenges with a larger range of ERA5 variables and additional datasets, including ERA5-Land, CERRA, ECMWF IFS HRES, and various national weather service datasets.
To maximize efficiency, I opted against traditional formats like NetCDF, HDF5, or Zarr, instead developing a novel chunked, compressed format optimized for time-series compression. Benefits include significantly smaller data sizes and faster compression/decompression speeds. The downside is that it’s not yet a widely adopted format, but we are actively developing Python, Rust, Swift, and TypeScript WASM libraries. Open-Meteo is fully open-data on AWS and open-source.
Your engineers are likely encountering similar challenges with handling massive datasets, IO bottlenecks, storage constraints, and memory limitations—issues that will only intensify with ERA5-Land and the upcoming ERA6. If you’re interested in collaborating on converting the entire Copernicus database into a chunked time-series architecture, feel free to reach out!
Interesting. Does this new method have a name and/or a description?
We just refer to them as “om-files”. You can find the underlaying C implementation and format specification here with Python binds here. Visualisation using WASM here. We hope to finish the Python bindings in the next weeks and improve integrations with xarray and other libraries.
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Thanks RĂ©mi. Yep, that dataset works well for me 
For most of my daily tasks I’ve switched to this zarr archive. Upon request they add new parameters. See you around 