From data to products
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From data to products

Meteosat-8 colour composite by Meteo-France
Colour composite from Meteosat-8
Courtesy of Meteo-France

Observational data acquired by satellite instruments are processed up to different levels before distribution to the users. The result of processing is a product.

Provided that the data are accurately calibrated and geo-located, quantitative derived products provide measurements of geophysical characteristics of the atmosphere or the globe's surface. Derived products are extensively used either in a qualitative way or in a quantitative way, in meteorological and environmental applications, namely for assimilation in Numerical Weather Prediction models and climate monitoring.

 

 

Data processing levels

Level

Generic description of data processing levels
(to be adapted to each instrument)
0 Instrument and auxiliary data reconstructed from satellite raw data after removing communication artefacts
1 Instrument data extracted at full original resolution, with geolocation and calibration information

Sub-levels named 1a, 1b, 1c for LEO data
and 1.0, 1.5 for GEO data

1a (1.0) - Instrument counts with geolocation and calibration information attached but not applied

1b (1.5) - Geolocation and calibration information applied to the instrument counts

1c - Instrument specific
For example, 1b data converted to Brightness temperature (IR) or Reflectance factor (VIS)

1d - Instrument specific
For example, same as level 1c with cloud flag ( for sounding data)

2 Geophysical quantity retrieved from single instrument data in original instrument projection
Note: For example, temperature, humidity, radiative flux
3

Geophysical quantity retrieved from single instrument data, mapped on uniform space and time grid
Note: Can be retrieved on a multi-orbital (LEO) or multi-temporal (GEO) basis.

4 Composite multi-sensor and/or multi-satellite product or result of model analysis of lower level data

 

Major categories of products

The CGMS "Directory of applications" describes the generation and use of a wide range of products that can be derived from satellite observations, including in particular the categories described below. Direct access to data described here is possible through the product portal , providing links to data providers.

Imagery in general

Imagery products result from calibration, geolocation, remapping and dynamics adjustment of level 1 data. Imagery and radiance products are a step for derivation of higher level products. Imagery products are used on a routine basis by direct interpretation either as single-channel images or after further processing such as multispectral compositing, temporal combination of animated sequences, or multisatellite mosaics.

Imagery products are also routinely used in a qualitative way in support of weather forecasting in particular for nowcasting, severe weather short-range forecasting and forecast verification

Cloud characteristics

An accurate cloud mask product is a prerequisite for deriving meaningful quantitative surface and sounding products. Cloud detection can be performed through comparison with infrared brightness temperature thresholds or visible reflectance thresholds, with adjustments depending on the underlying surface (sea or land), the latitude and the season.

Cloud characteristics products are an essential support from satellite imagery to nowcasting and regional short-term forecasting. Multispectral discrimination techniques applied to visible and infrared imagery allow identification of cloud types. Cloud top temperature or pressure level can be derived from infrared imagery.

Vertical temperature and humidity sounding

Vertical temperature and humidity soundings by polar satellites are mainly derived from IR sounder data, in clear-sky, and from microwave sounders in cloudy areas. Sounding data from NOAA/ATOVS instrument package are operationally available from NESDIS on the GTS in SATEM code at reduced resolution. ATOVS data have been shown to have a significant positive impact on NWP in both hemispheres. Advanced NWP centres are increasingly assimilating radiance data directly, rather than the temperature and humidity soundings retrieved from them. Information on the error characteristics of retrieved products or radiances, including biases, is essential for their successful assimilation. Regional sets of locally received ATOVS radiance data are available through RARS. Hyperspectral infrared sounders of the AIRS and IASI generation allow a considerable progress in accuracy and vertical resolution.

Additional observations of atmospheric Temperature and Humidity profile are also provided by measuring the occultation of GPS radio-signals.

Atmospheric Motion Vectors

Atmospheric Motion Vectors are generated automatically by applying a correlation algorithm to sequences of two or three images. They are extracted classically by tracking the movement of cloud fields from a geostationary satellite. Similar approach can be used in the absence of clouds, in particular with water vapour features detected in water vapour absorption channel images. Such geostationary winds are computed within 60° latitude and longitude of the sub-satellite point, at least four times daily at 0000, 0600, 1200 and 1800 UTC, and distributed in SATOB and/or BUFR code by geostationary satellite operators. The operators are generally moving towards the production of higher resolution products and the use of quality indicators to assist in their use. For polar regions, advantage can be taken of frequent overpasses by polar-orbiting satellites for the derivation of winds, as routinely demonstrated with water vapour radiances from the MODIS instruments aboard NASA‘s Aqua and Terra satellites.

Land and sea surface temperature

Sea Surface Temperature (SST) may be derived from IR images of both polar and geostationary meteorological satellites in cloud-free areas. Polar data provide global coverage with good spatial resolution. Geostationary satellites provide frequent data coverage, which gives a better chance to find cloud-free pixels and allows monitoring diurnal variations. Regional AVHRR based products are available at high resolution (typically 2 km) or global products at lower resolution (e.g. 10 km). With Metop it should be possible to derive operationally high resolution products on the global scale. High accuracy SST can be derived from AATSR (Envisat) and MODIS (Aqua and Terra). Microwave passive imagery support SST measurements of cloud-free or cloudy pixels, though with lower spatial resolution than IR.

Land Surface Temperature (LST) can be derived from IR imagery in combination with visible imagery to take into account the effect of vegetation.

Snow and ice

Snow and ice areas are identified by visible, infrared and microwave imagery (AVHRR, MODIS, SSM/I). Active microwave sensors such as scatterometer or SAR imagers are useful to characterize the superficial snow or ice layer.

Vegetation

Characterizing the land surface has direct applications for food security and environmental management. It is also important to determine lower boundary conditions for NWP and is a major component of climate monitoring from space.

Daylight AVHRR imagery provides an essential indicator of the overall vegetation status, the Normalised Difference Vegetation Index (NDVI) based on difference between reflectance of AVHRR channel 1 (0.6 µm) and channel 2 ( 0.9 µm). The index is used to assess fire risk, and estimating vegetation growth and crop yields. Improved vegetation products are generated from more recent imagers like MODIS. Dedicated products can be derived to monitor vegetation growth or status: Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), vegetation stress or drought.

Ocean surface

Active microwave sensors are essential to monitor the ocean surface: altimetry data provide sea level information for ocean current monitoring and oceanographic modelling, but also provide information on sea state (significant wave height and wind intensity).

Ocean surface wind is an essential input to NWP and tropical cyclone monitoring. Wind vectors (speed and direction) at the ocean surface can be derived from scatterometer data, e.g. from SeaWinds on QuikSCAT and ASCAT on Metop or from passive conical scanning microwave imagery with full polarization.

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