Compress · Weather & climate
Forecasts, radar, observations and climate archives.
Weather is a data business: models produce terabytes every run, radars scan every few minutes and climate archives are kept for decades. Compress shrinks all of it with error bounds set per variable, so forecasts reach users sooner and archives cost less to keep.
What it compresses.
The data weather & climate teams bring to Compress most.
- NWP output (GRIB2, NetCDF)
- Raw radar volumes
- Station, buoy and radiosonde observations
- Reanalysis and climate projections
Use cases
Compress in weather & climate.
Climate & weather archives
Petabytes of model output in a fraction of the space.
See howForecast distribution
Get each model run to users before the next one lands.
See howWeather radar
Every sweep from every radar, kept.
See howWeather stations & observations
More readings from remote stations, for less airtime.
See howFeatures used
The parts of Compress that do the work.
Neural codecs
Codecs trained on your kind of data.
Learn moreError bounds
Set the error per variable; we guarantee it.
Learn moreScientific formats
NetCDF, GRIB, Zarr, GeoTIFF and more.
Learn moreEdge & streaming
Compress at the sensor, before the link.
Learn moreSDK & CLI
One line in Python, a command in the shell.
Learn moreCloud, on-premises or air-gapped
Runs where your data already lives.
Learn moreIndustries
Other industries.
Related articles
- Scientific data
What an error bound actually guarantees
· 6 min read
- Codecs
Learned codecs against zstd, SZ and ZFP
· 9 min read
- News
GRIB2 and Zarr v3 support in the Compress SDK
· 2 min read
- Storage & cost
The real cost of keeping a petabyte
· 5 min read
- News
Research partnership on compressing climate model output
· 2 min read
We’ll cover
- Compress run on a sample of your data
- Size, speed and error compared with what you use now
- Fitting it into your pipeline
- Deployment: cloud, on-premises or edge