Weather data API serving forecasts, reanalyses and NOAA GOES satellite observations as point or grid time series for applications and ML pipelines.

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GribStream is a weather data API that turns large public forecast and observation archives into queryable time series. Working directly with raw GRIB2 files from NOAA or ECMWF means owning ingest, indexing, decoding and storage; GribStream instead lets an application request only the variables, coordinates and time ranges it needs, returned as CSV, JSON, NDJSON or Parquet.
The catalogue lists 99 datasets: global and regional numerical forecasts and ensembles (including GFS, HRRR, NBM, ECMWF IFS, ICON and AROME), AI forecasts such as ECMWF AIFS and NOAA AIGFS, ERA5 reanalysis actuals, CAMS atmospheric composition, National Water Model hydrology, air-quality and aviation turbulence guidance. Its satellite layer serves NOAA GOES-18 and GOES-19 Advanced Baseline Imager Level 2 full-disk products (16-band cloud and moisture imagery, fire detection, aerosol optical depth, rainfall rate, land- and sea-surface temperature) and NOAA's hourly Global Mosaic of Geostationary Satellite Imagery (GMGSI).
Queries run through REST endpoints (/timeseries and /runs) with server-side expressions, condition filters, ensemble members and an asOf model-run cutoff for reproducible forecast backtesting. Access is also offered through a Python client on GitHub, a MySQL-compatible connection (public beta) and a hosted MCP connector for AI tools.
A free plan is available; Pro plans start at $9.90 per month with daily credit limits, and custom plans are available on request. Customers quoted on the site include the Chicago White Sox and the aviation weather app SkewTLogPro.
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Processing
Pre-processing & Enhancement
Cleans, calibrates, and standardises data for quality.
Delivered as
Data Distribution
Geographic Scope

GribStream
GribStream provides a weather data API for live and historical forecasts, ensembles, satellite and radar data, land-surface hydrology, atmospheric composition and reanalysis actuals. Users query by coordinates and time range, with calculated fields, condition filters, as-of model-run cutoffs for backtesting, and CSV, JSON or NDJSON output.
2024
Founded
1-10
Team size
Uruguay
Country
Provides processed data via standardised channels (APIs, feeds).