**SatWeave** is a global, analysis-ready dataset of multi-spectral geostationary satellite observations developed by [Google DeepMind](https://deepmind.google/) and [Google Research](https://research.google/). SatWeave harmonizes imagery from five operational geostationary satellite positions (**GOES-East** , **GOES-West** , **Meteosat Prime (0°)** , **Meteosat Indian Ocean (IODC)** , and **Himawari** ) into a single global mosaic across **17 spectral bands** : 7 visible, near-infrared, and shortwave-infrared reflectance bands (`0 to 100 %`) and 10 shortwave, water vapor, and thermal infrared brightness temperature bands (`K`). Updated **every hour** at **0.1° (\~10 km) spatial resolution** with **around 1 hour latency** alongside a 30-minute historical archive (`2016-2025`), SatWeave provides the live satellite observations used to initialize [WeatherNext 3](https://developers.google.com/weathernext/guides/models.md.txt) every hour and to generate near-real-time precipitation estimates in [PARDIG-NRT](https://developers.google.com/weathernext/guides/pardig.md.txt). [Access on Cloud Storage](https://developers.google.com/weathernext/guides/satweave.md.txt#access-on-google-cloud-storage) [Explore Variables](https://developers.google.com/weathernext/guides/satweave.md.txt#supported-variables) ## At a glance | Attribute | Details | | Temporal Coverage | **2016 to present** (`2016-01-01T00:00:00` to `2025-12-31T23:30:00` UTC historical archive + `2026-01-01T00:00:00` UTC to present operational archive and live stream) | | Spatial Coverage | Global (90°N to 90°S \[`90.0°` to `-90.0°`\], 0° to 360°E; `EPSG:4326` equirectangular grid; geostationary sensor coverage within approximately ±60°-65° latitude) | | Spatial Resolution | 0.1° (\~10 km) (1801 × 3600 grid) | | Temporal Resolution | 1 hour (`2026_to_present/` backfill and near-real-time stream, 24 timesteps/day) · 30 minutes (`2016_to_2025/` historical archive, 48 timesteps/day) | | Real-Time Latency | **\~1 hour** | | Variables \& Spectral Bands | **17 multi-spectral bands:** 7 Visible, Near-IR, and SWIR reflectance (0--100 %) · 10 Shortwave, Water Vapor \& Thermal IR brightness temperature (K) | | Satellite Constellation | GOES-East, GOES-West (NOAA) · Meteosat Prime 0°, Meteosat IODC (EUMETSAT) · Himawari (JMA) | | Dimensions \& Format | `(time, latitude, longitude)` · [Zarr v3](https://zarr.dev/) on Google Cloud Storage | | Available Surfaces | **[Google Cloud Storage (Zarr)](https://developers.google.com/weathernext/guides/satweave.md.txt#access-on-google-cloud-storage)** (`gs://satweave_1/`) | | License \& Access | [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) · Publicly accessible with any Google Cloud account (no access request form required) | |---|---| ## Key features - **Multi-satellite harmonization:** International satellite agencies publish raw imagery in distinct map projections, calibration tables, file formats, and scan intervals (10 to 30 minutes). SatWeave reprojects, cross-calibrates, and blends overlapping views from five geostationary positions onto a uniform `0.1°` global grid. - **Multi-spectral channels:** Provides 17 spectral channels spanning solar reflectance (`0 to 100 %`) for daytime cloud structure, aerosols, snow and ice phase, and particle size alongside water vapor and thermal infrared brightness temperature (`K`) for continuous day-and-night tracking of tropospheric moisture, cloud-top temperature, and deep convection. - **Hourly updates and historical archive:** Combines a multi-year historical record (`2016-2025` at 30-minute timesteps) with a continuous hourly archive and near-real-time feed (`2026 to present` at 1-hour timesteps, `~1 hour` latency), removing the need to build custom multi-agency satellite ingestion pipelines. - **Direct input for AI weather models:** Conventional numerical weather prediction analysis cycles update every 6 hours. Direct ingestion of SatWeave allows [WeatherNext 3](https://developers.google.com/weathernext/guides/models.md.txt) to initialize new global forecasts every hour. *** ** * ** *** ## Supported spectral bands and grid schema SatWeave provides 17 multi-spectral bands on a uniform global `0.1°` (\~10 km) grid indexed across `(time, latitude, longitude)`: 7 visible, near-infrared, and shortwave-infrared top-of-atmosphere reflectance bands (`0 to 100 %`) and 10 shortwave, water vapor, and thermal infrared top-of-atmosphere brightness temperature bands (`K`). | Variable Name | Central Wavelength | Band \& Physical Application | Units / Range | Constellation Coverage | |---|---|---|---|---| | Visible, Near-Infrared \& Shortwave-Infrared Reflectance (7 Bands) ||||| | `mosaic_470_nm` | 0.47 μm (470 nm) | Blue visible (aerosols, dust, smoke, daytime clouds) | 0--100 % | GOES-East, GOES-West, Himawari (3 satellites) | | `mosaic_509_nm` | 0.51 μm (509 nm) | Green visible (true-color composites, vegetation, aerosols) | 0--100 % | Himawari only (1 satellite) | | `mosaic_640_nm` | 0.64 μm (640 nm) | Red visible (daytime clouds, fog, solar insolation, snow and ice) | 0--100 % | All 5 satellites | | `mosaic_863_nm` | 0.86 μm (863 nm) | Near-IR vegetation band (vegetation, land and water contrast, daytime clouds) | 0--100 % | All 5 satellites | | `mosaic_1370_nm` | 1.37 μm (1370 nm) | Near-IR cirrus band (daytime thin high-altitude cirrus clouds) | 0--100 % | GOES-East, GOES-West only (2 satellites) | | `mosaic_1610_nm` | 1.61 μm (1610 nm) | Snow and ice SWIR (water versus ice cloud phase, snow versus cloud discrimination) | 0--100 % | All 5 satellites | | `mosaic_2250_nm` | 2.25 μm (2250 nm) | Cloud particle size SWIR (droplet effective radius, aerosol size, hot spots) | 0--100 % | GOES-East, GOES-West, Himawari (3 satellites) | | Shortwave, Water Vapor \& Thermal Infrared Brightness Temperature (10 Bands) ||||| | `mosaic_3830_nm` | 3.83 μm (3830 nm) | Shortwave IR window (nighttime low clouds and fog, fire and hot-spot detection) | K | All 5 satellites | | `mosaic_6200_nm` | 6.20 μm (6200 nm) | Upper-level water vapor IR (upper-tropospheric moisture, jet streams) | K | All 5 satellites | | `mosaic_6900_nm` | 6.90 μm (6900 nm) | Mid-level water vapor IR (mid-tropospheric moisture, atmospheric winds) | K | GOES-East, GOES-West, Himawari (3 satellites) | | `mosaic_7330_nm` | 7.33 μm (7330 nm) | Lower-level water vapor IR (lower and mid-level moisture, convection initiation) | K | All 5 satellites | | `mosaic_8590_nm` | 8.59 μm (8590 nm) | Cloud-top phase IR (ice versus water cloud phase, dust, volcanic SO~2~) | K | All 5 satellites | | `mosaic_9620_nm` | 9.62 μm (9620 nm) | Ozone absorption IR (total column ozone, stratospheric-tropospheric dynamics) | K | All 5 satellites | | `mosaic_10400_nm` | 10.40 μm (10400 nm) | Clean longwave IR window (cloud-top temperature, surface temperature) | K | GOES-East, GOES-West, Himawari (3 satellites) | | `mosaic_11200_nm` | 11.20 μm (11200 nm) | Standard longwave IR window (deep convection, cloud-top \& sea surface temperature) | K | All 5 satellites | | `mosaic_12400_nm` | 12.40 μm (12400 nm) | Dirty longwave IR split-window (low-level moisture, volcanic ash, sea surface temperature) | K | All 5 satellites | | `mosaic_13300_nm` | 13.30 μm (13300 nm) | CO~2~ longwave IR absorption (cloud-top height assignment, air temperature) | K | All 5 satellites | ### Coordinate dimensions | Coordinate | Shape \& Type | Grid Convention | |---|---|---| | `time` | `datetime64[ns]` | Observation valid timestamp in UTC (30-minute intervals in `2016_to_2025/mosaic.zarr`; 1-hour intervals in `2026_to_present//mosaic.zarr`). | | `latitude` | `1801` (`float64`) | Global 0.1° grid ordered north-to-south from `90.0°` to `-90.0°` (`degrees_north`). | | `longitude` | `3600` (`float64`) | Global 0.1° grid in **0° to 360°** convention (`0.0°` to `359.9°`, `degrees_east`). Convert standard `-180°..180°` longitudes using `lon_360 = lon % 360`. | ### Sensor coverage and historical constellation evolution Geostationary satellites orbit above the equator and observe up to approximately `±60°-65°` latitude. High-latitude polar grid cells beyond the geostationary viewing limb, channels not carried on a given satellite sensor, and nighttime solar reflectance values are encoded as `NaN`: - **Per-band satellite availability (modern constellation, November 2018 onward):** - **All 5 satellites (\~85-87% global valid pixels; 11 bands):** `mosaic_640_nm`, `mosaic_863_nm`, `mosaic_1610_nm`, `mosaic_3830_nm`, `mosaic_6200_nm`, `mosaic_7330_nm`, `mosaic_8590_nm`, `mosaic_9620_nm`, `mosaic_11200_nm`, `mosaic_12400_nm`, and `mosaic_13300_nm` cover GOES-East, GOES-West, Meteosat Prime (`0°`), Meteosat IODC, and Himawari. - **3 satellites (\~69% global valid pixels; 4 bands):** `mosaic_470_nm`, `mosaic_2250_nm`, `mosaic_6900_nm`, and `mosaic_10400_nm` are carried on GOES-East ABI, GOES-West ABI, and Himawari AHI (`NaN` over the Meteosat Prime and IODC sectors). - **2 satellites (\~50% global valid pixels; 1 band):** `mosaic_1370_nm` (1.37 μm cirrus band) is carried on GOES-East ABI and GOES-West ABI only (`NaN` outside the Americas and the eastern and central Pacific). - **1 satellite (\~34% global valid pixels; 1 band):** `mosaic_509_nm` (0.51 μm green visible band) is carried on Himawari AHI only (`NaN` outside the Asia-Pacific sector). - **Historical Western Hemisphere transitions (`2016-2018` in `2016_to_2025/mosaic.zarr`):** The `2016_to_2025` archive spans three distinct satellite sensor regimes in the Western Hemisphere as NOAA transitioned from legacy GOES Imagers to the GOES-R Series Advanced Baseline Imager (ABI): - **January 1, 2016 -- July 10, 2017 (`21:30 UTC`) --- Pre-GOES-16 era:** Before GOES-16 became operational, the Western Hemisphere was observed by legacy **GOES-13 (East)** and **GOES-15 (West)** Imagers, which carried only **5 spectral bands** (`640`, `3830`, `6200`, `11200`, and `13300 nm`) and sometimes scanned at **3-hourly** intervals rather than every 30 minutes. During this period, `mosaic_1370_nm` is `100% NaN` globally (`0%` valid), `470`, `509`, `2250`, `6900`, and `10400 nm` are Himawari-only (`~34%` valid), `863`, `1610`, `7330`, `8590`, `9620`, and `12400 nm` cover only the Meteosat and Himawari sectors (`~65%` valid, `NaN` over the Americas), and intermediate 30-minute timestamps between 3-hourly GOES scans contain `NaN` values over the Americas. - **July 10, 2017 (`22:00 UTC`) -- November 13, 2018 (`23:30 UTC`) --- GOES-16 (East) transition:** **GOES-16 ABI** begins covering the GOES-East sector across all 16 ABI channels at 30-minute intervals (`mosaic_1370_nm` rises to `~35%` valid globally and the four 3-satellite ABI+AHI bands rise to `~68%` valid), while the GOES-West sector remains on legacy 5-band GOES-15. - **November 14, 2018 (`00:00 UTC`) onward --- Full 5-satellite constellation:** **GOES-17 ABI** (later succeeded by **GOES-18 ABI**) joins in the GOES-West sector, completing full multi-spectral coverage across the Western Hemisphere. ### Data quality and satellite artifacts SatWeave composites observations directly from operational weather satellites, so individual frames may contain upstream sensor and mosaicking artifacts such as missing scan lines or sectors, calibration differences along overlap boundaries, solar glint, or outlier pixels. These artifacts come from various satellites, including limb artifacts in the IR bands on the Meteosat Second Generation satellites and nighttime glare in the visible bands over their coverage regions. Consumers can inspect the data for missing (`NaN`) or anomalous values and filter or post-process as needed for their downstream workflow. *** ** * ** *** ## Accessing SatWeave data > [!NOTE] > **Open access:** SatWeave is publicly available on Google Cloud Storage under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Anyone with a Google Cloud account can query the Zarr stores immediately without submitting an access request form. ### Directory structure SatWeave is published as [Zarr v3](https://zarr.dev/) stores in the `gs://satweave_1/` bucket: gs://satweave_1/ └── zarr/ ├── 2016_to_2025/ │ └── mosaic.zarr # Historical archive (2016-01-01T00:00 to 2025-12-31T23:30 UTC · 30-min timesteps) └── 2026_to_present/ # 2026 backfill + live hourly mosaics (~1 hour latency) ├── / │ └── mosaic.zarr # Single hourly mosaic · 1h timesteps · (1, 1801, 3600) └── ... Both `2016_to_2025/mosaic.zarr` and the hourly stores in `2026_to_present//mosaic.zarr` share identical Zarr v3 variable attributes and `float64` coordinate grids (`latitude`: `90.0` to `-90.0`, `longitude`: `0.0` to `359.9`), so slices from the two archives can be concatenated directly along the `time` dimension in Xarray. ### Common query recipes Install the required Python libraries: ```bash pip install xarray zarr obstore ``` #### 1. Open an hourly mosaic and inspect the schema Open an hourly SatWeave mosaic directly from Google Cloud Storage (or open the multi-year `zarr/2016_to_2025/mosaic.zarr` historical archive) using `obstore` and `xarray`:
**Python**
```python import obstore import xarray as xr import zarr # Open an hourly SatWeave Zarr store on Google Cloud Storage # (For the 2016-2025 30-minute archive, use prefix="zarr/2016_to_2025/mosaic.zarr") gcs_store = obstore.store.GCSStore( bucket="satweave_1", prefix="zarr/2026_to_present/20260901T120000/mosaic.zarr", ) ds = xr.open_zarr(zarr.storage.ObjectStore(gcs_store), chunks={}) print(ds) ``` Expected dataset structure: Dimensions: (time: 1, latitude: 1801, longitude: 3600) Coordinates: * time (time) datetime64[ns] 2026-09-01T12:00:00 * latitude (latitude) float64 90.0 89.9 89.8 ... -89.8 -89.9 -90.0 * longitude (longitude) float64 0.0 0.1 0.2 ... 359.7 359.8 359.9 Data variables: mosaic_470_nm (time, latitude, longitude) float32 dask.array mosaic_509_nm (time, latitude, longitude) float32 dask.array mosaic_640_nm (time, latitude, longitude) float32 dask.array mosaic_863_nm (time, latitude, longitude) float32 dask.array mosaic_1370_nm (time, latitude, longitude) float32 dask.array mosaic_1610_nm (time, latitude, longitude) float32 dask.array mosaic_2250_nm (time, latitude, longitude) float32 dask.array mosaic_3830_nm (time, latitude, longitude) float32 dask.array mosaic_6200_nm (time, latitude, longitude) float32 dask.array mosaic_6900_nm (time, latitude, longitude) float32 dask.array mosaic_7330_nm (time, latitude, longitude) float32 dask.array mosaic_8590_nm (time, latitude, longitude) float32 dask.array mosaic_9620_nm (time, latitude, longitude) float32 dask.array mosaic_10400_nm (time, latitude, longitude) float32 dask.array mosaic_11200_nm (time, latitude, longitude) float32 dask.array mosaic_12400_nm (time, latitude, longitude) float32 dask.array mosaic_13300_nm (time, latitude, longitude) float32 dask.array

#### 2. Plot tropical cyclone infrared cloud-top brightness temperature
**Python**
```python import matplotlib.pyplot as plt import obstore import xarray as xr import zarr gcs_store = obstore.store.GCSStore( bucket="satweave_1", prefix="zarr/2026_to_present/20260901T120000/mosaic.zarr", ) ds = xr.open_zarr(zarr.storage.ObjectStore(gcs_store), chunks={}) # Select 11.2 µm longwave IR window band over the Western North Pacific # (lat: 40°N to 5°N, lon: 115°E to 165°E) and convert Kelvin to Celsius ir_temp_c = ( ds["mosaic_11200_nm"] .isel(time=0) .sel(latitude=slice(40.0, 5.0), longitude=slice(115.0, 165.0)) - 273.15 ) ir_temp_c.plot( cmap="Greys", cbar_kwargs={"label": "11.2 µm Brightness Temperature (°C)"}, ) plt.title("SatWeave 11.2 µm Longwave IR Window") plt.show() ```

*** ** * ** *** ## Starter guides For an interactive Colab notebook with end-to-end sample code and multi-spectral visualization workflows: - [SatWeave Starter Guide - Zarr on Google Cloud Storage](https://storage.googleapis.com/weathernext-public/colabs/SatWeave_Starter_Guide_Zarr_on_Google_Cloud_Storage.ipynb) *** ** * ** *** ## Terms of service and disclaimers SatWeave is an experimental satellite mosaic dataset under active research. Data and mosaic outputs are provided "as-is" for informational and research purposes and don't constitute official weather observations, forecasts, watches, or warnings. Users are responsible for their use of the data. For the protection of life and property, never rely on SatWeave as a sole source of information and always defer to official alerts and advisories from your national meteorological service and local emergency authorities. ### Data licensing and terms of use SatWeave is licensed under the [Creative Commons Attribution International License, Version 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). For complete terms, liability provisions, and acceptable use restrictions, see the [Terms of Service and Disclaimers](https://developers.google.com/weathernext/guides/disclaimers.md.txt) page. ### Acknowledgements and third-party attributions SatWeave incorporates geostationary satellite observations from the following third-party providers: - **Meteosat (1st, 2nd, and 3rd Generation):** Includes calibrated satellite observations supplied by EUMETSAT under the [Creative Commons Attribution 4.0 International (CC BY 4.0)](https://data.eumetsat.int/) license. Modification: Google incorporated the data into a global coverage geostationary satellite mosaic (SatWeave). - **GOES-R Series (16, 17, 18, and 19):** NOAA Geostationary Operational Environmental Satellites (GOES) data is accessed on an ongoing basis from [Google Cloud Public Datasets (NOAA GOES)](https://console.cloud.google.com/marketplace/product/noaa-public/goes). - **Himawari (8 and 9):** Source: Meteorological Satellite Center of Japan Meteorological Agency (JMA) via [NOAA NODD on Google Cloud](https://console.cloud.google.com/marketplace/product/bigquery-public-data/himawari). Modification: Google incorporated the data into a global coverage geostationary satellite mosaic (SatWeave). Use of the third-party materials referred to in the acknowledgements may be governed by separate terms and conditions or license provisions. Your use of third-party materials is subject to any such terms, and you should verify that you can comply with any applicable restrictions before use. *** ** * ** *** See the [Glossary](https://developers.google.com/weathernext/guides/glossary.md.txt) for definitions of meteorological terms, data formats, and Google Cloud platforms.