Satellite Imagery

Satellite data is available from the geostationary satellites GOES-16/17, Meteosat-8/11 and Himawari-8. The spatial resolution varies between 1 and 3 kilometers at the equator below the satellite. Images are available every 10 respectively 15 minutes depending on the source.

Please note that only certain parameters such as cloud_type(_transparent):idx or specific channels can be useful if queried as non-image data. The other parameters are intended for visualization purposes and are only meaningful if combined with an appropriate colormap (using png_default does that for you), as the values correspond to a specific color.


A global false color composite that uses data from visible and near-infrared wavelengths and is consistent across all the satellites can be queried with the parameter


As Meteosat-11 provides a high resolution scan for limited areas, we provide another composite where we integrate it to provide increased fidelity over Europe. It can be queried with the parameter



Infrared Channels

Infrared composites can be queried with the parameters

For the following wavelengths the brightness temperature is provided directly. Brightness temperatures can also be queried in °C by replacing K with C.

The number indicates the wavelength in tenths of micrometers, e.g. sat_ir_039:idx corresponds to the channel with a central wavelength of 3.9 μm.


Normalised Difference Vegetation Index

The Normalised Difference Vegetation Index (NDVI) is a satellite product which indicates vegetation greenness. It is calculated as a normalised ratio between the red and near infrared satellite channels. The NDVI varies between 0 and 1, with a value close to 1 indicating high vegetation greenness and hence high vegetation density and health.



Meteosat data for Europe

Over Europe, there are additional parameters available that provide more information on clouds as seen from space.

Cloud Types

By combining data from sensors at various wavelengths, it is possible to distinguish cloud types due to their differing temperature and constituents. Such an image can be queried with the two parameters

  • sat_cloud_type:idx: if cloud-free, the index differentiates between cloud-free land (0) and sea (1) as well as snow covered land (2) and snow/ice covered sea (3). The different cloud types are explained in the table below.
  • sat_cloud_type_transparent:idx: cloud-free areas are set to -666 instead of 0, 1, 2 and 3. Also unclassified clouds (14) are set to -666.
IndexCloud Type
0Cloudfree land
1Cloudfree sea
2Snow covered land
3Snow/ice covered sea
4Very low stratus
5Low clouds
6Mid level clouds
7High opaque clouds
8Very high opaque clouds
9Very thin cirrus
10Thin cirrus
11Thick cirrus
12Thin cirrus above mid/low clouds
13Fractional/broken low clouds

For visualization purposes, these indices are mapped onto colors according to the following legend:

Sat cloud type legend

If you wish to query an image that is transparent wherever there are no clouds and emplaces the HRV image data wherever there are clouds, you can do so by means of the parameter


Please note that since the image data stems from HRV, the clouds will be black wherever there is no sunlight, e.g. during the night.


Fog Images

A clever combination of satellite images at certain wavelengths into an RGB can highlight low clouds and fog as well as distinguish them from one another. Two such RGB composite images queried by the following parameters:


For the interpretation of sat_day_fog:idx images please refer to the PDF SEVIRI Dust RGB Quick Guide. As indicated by the title of the PDF, this is also helpful with tracking dust clouds.

A brief guide to reading the sat_night_fog:idx images can be found in the PDF Night Microphysics RGB Quick Guide, with a more detailed presentation containing a lot of examples located here. Please note that the composite is tuned for night time and the presence of sunlight has a significant impact on both the contrast and interpretation of the colors.


Visible Images

A high resolution visible (hrv) wavelength composite over Europe can be queried with