FCI A Spot

High latitude simulator for MTG/FCI instrument


FCI A Spot
FCI A Spot

Guidance for using simulated data for Meteosat Third Generation FCI data.

Last Updated

26 January 2023

Published on

26 January 2023

Simulation methodology

MTG FCI flow simulation
Figure 1: A schematic representation of the processing steps in the image simulation from polar satellite instrument data (left image) to geostationary satellite grid (middle) and, finally, to the target area imagery (right).

To simulate the high resolution data of MTG/FCI polar satellite data are used. A schematic representation of the processing steps is shown in Figure 1. Depending on the case, VIIRS, MODIS and/or AVHRR data are used. GOES/ABI data are used to demonstrate how data comparable to FCI performs polar satellite data. Parallax shift is not simulated, but the difference in viewing geometry is shown with comparison images from SEVIRI or GOES/ABI. In addition to MTG/FCI, MSG/SEVIRI instrument data are also simulated so that the methodology can be compared against actual SEVIRI data.

Transforming polar data to geostationary SEVIRI data

For simulating MSG/SEVIRI, the polar satellite data are collected to the SEVIRI 3km sub-satellite point grid using bucket resampling (middle image in Figure 1). In this method all polar satellite pixels that are within one SEVIRI pixel are averaged. This method leaves some holes (missing data) where no polar data are coinciding with the SEVIRI grid.

Transforming polar data to geostationary FCI data

As a first step, the polar satellite data are resampled to a grid that would correspond to the FCI instrument at 250m sub-satellite resolution using nearest neighbour resampling. This data are then aggregated to the actual resolution of the FCI channel in question, by taking an average suitable grouping of the pixels. Both nominal and high resolution modes can be used for the channels that have both distribution modes.

The bucket resampling approach isn't used due to the high resolution of the FCI instrument. The selected approach will provide a good estimate without leaving any missing data in the images.

Target area imagery

The target area images are resampled from the intermediate data in geostationary projection using bilinear interpolation with gradient search algorithm for fast resampling. The target image resolutions are selected so they would best demonstrate the instrument resolution.

In addition to simulated SEVIRI and FCI images, the original polar data are resampled directly to the target area to further give references.

Limitations of the method

Simulating future satellite instrument imagery is very complex if all influencing factors are considered. In practice, there are two ways to approach this issue (e.g. Schmit et al 2005). One way is to utilise radiative transfer model calculations, the other is to use existing satellite data. This simulation uses existing satellite data. The methodology used in the simulator only takes into account the change in the resolution of the images. Therefore, the simulator has limitations that should be kept in mind when viewing images. The issues related to the properties of the instruments, including measurement angle and the transfer of radiation from surface or clouds to instrument, affects to final 'real' image. In this simulation these factors are not applied to the simulations due to its complexity. Below is a brief introduction on these matters and references for further reading.

Instrument properties

Each satellite instrument measures the radiation differently. For different applications, meteorological satellite instruments measures atmospheric and surface properties through different channels, carefully selected parts of the visible and infrared electromagnetic spectrum. The spectral response function (SRF) of an instrument describes its relative sensitivity to energy of different wavelengths. When an instrument has its own spectral response functions, there may be differences in the colours of RGB images, when comparing the simulated image (Figure 2b) with the observed one (Figure 2a). Below is an example from the case study Dense fog over southern Finland.

SEVIRI Natural Colour RGB
Figure 2a: Observed SEVIRI Natural Colour RGB, 11 September 2021 12:00 UTC.
Simulated SEVIRI Natural Colour RGB
Figure 2b: SEVIRI simulated Natural Colour RGB from VIIRS, 11 September 2021 11:56 UTC.
VIIRS Natural Color RGB
Figure 2c: Observed VIIRS Natural Color RGB, 11 September 2021 11:56 UTC.

There are also additional factors that make visible differences in simulated images. Zhang et al (2006) has described the factors in instrument that has blurring influence on image quality in following way:

For an ideal instrument, the detector output is proportional to the pixel radiance within footprint i.e., the geometrical field of view (GFOV). However, real instruments do not exactly reproduce the spatial radiance field of the pixel; a nontrivial portion of the energy comes from the surrounding area. This energy from outside the GFOV introduces the blurring influence, which degrades the image quality.

Diffraction is one of the unavoidable factors that generate a blurring influence. Diffraction in a satellite sensor occurs because the diameter of the optical system has a limited size. It reduces the detector response from the radiances within the GFOV and increases its response from the radiances outside the GFOV. Other factors, such as the time response of the detector, electronics (analog-to-digital conversion), scan pattern,atmospheric effects, as well as image resampling can cause additional blurring influence

Because of the high orbital altitude, an instrument on a geostationary satellite (GEO) has a larger blurring.

Further information

Schmit et al (2005): Introducing the next-generation Advanced Baseline Imager on GOES-R, https://doi.org/10.1175/BAMS-86-8-1079

Zhang et al (2006): Impact of Point Spread Function on Infrared Radiances From Geostationary Satellites, https://doi.org/10.1109/TGRS.2006.872096

Instrument pixel geometries

A schematic representation of behaviour of pixels towards the edge of the swath for selected polar orbit satellites
Figure 3: A schematic representation of behaviour of pixels towards the edge of the swath for selected polar orbit satellites.

In polar satellite instrument scanning geometry also affects on the spatial coverage and shape of the pixel. Difference between instruments is most visible at the edge of the swath as can been seen from the Figure 3.

Further information

Visible Infrared Imaging Radiometer Suite (VIIRS) Imagery Environmental Data Record (EDR) user's guide: https://doi.org/10.7289/V5/TR-NESDIS-150, page 13, Figure 7

Miller, Sh., K. Grant and J. J. Puschell (2013a): VIIRS Improvements Over MODIS. 2013 EUMETSAT Meteorological Satellite Conference and 19 th AMS Satellite Meteorology, Oceanography and Climatology Conference. Vienna, Austria, 16-20 September 2013.
COMET Module Introduction to VIIRS Imaging and Applications

Geometrical effects

Figure 4a
Figure 4a: Illustration of the sun's variable elevation angle relative to nadir view. Source: U.S. Geological Survey Landsat users handbook, figures 5-8, page 97.
Figure 4b
Figure 4b: Effects of seasonal changes on solar elevation angle for particular latitude. Source: U.S. Geological Survey

Geostationary satellite viewing angle does not change with time, because satellite is situated in a fixed location. In the case of polar satellites, viewing angles changes as a satellite moves along its orbital path. The solar and satellite viewing directions significantly influence satellite measurement when sun elevation is low (Figure 4b). This affects the calculated RGB composite, so the same composite could look very different for LEO compared to GEO. From late autumn to early spring, when the solar elevation angle is very low at high latitudes, GEO satellite measurements are more often saturated than LEO satellites due to unfavorable sun-satellite geometry.

Examples in Figure 5a and b, from the case study Freezing precipitation at Helsinki-Vantaa airport

SEVIRI Day Microphysics RGB
Figure 5a: Observed SEVIRI Day Microphysics RGB, 11 December 2021 10:00 UTC
AVHRR Day Microphysics RGB
Figure 5b: Observed AVHRR Day Microphysics RGB, 11 December 2021 08:16 UTC

Parallax effects

When the satellite viewing angle is away from the sub-satellite point (nadir), an object’s position is shifted (Figure 6a & b). Displacement grows more quickly with increasing distance from the nadir. At large viewing angles, high clouds move further than low clouds and the distortion of the shape of the original feature is also greater. At very slated view, the satellite actually measures the side of clouds rather than the top.

GEO parallax shift
Figure 6a: Parallax shift of an elevated object, as observed from geostationary orbit. Credit: Martin Setvak.
LEO parallax shift
Figure 6b: Parallax shift for a satellite at polar orbit. Credit: Martin Setvak

On Figure 6a the small red circle at top of the imaginary 'overgrown' storm represents, for example, an overshooting top. The two red dotted lines indicate the apparent location of this feature, as observed from two different places in space — one representing a nadir view (e.g. from a satellite in polar orbit, with the storm at its swath centre), and the other a view from a geostationary satellite. While the nadir view places the feature properly, at its real location, the slant view from the geostationary position shifts the apparent location of the feature significantly northward (up in this drawing) with respect to the real position of the feature. This difference in apparent location of the feature (indicated by the thick red double arrow) is called the 'parallax shift'.

On Figure 6b the two red dotted lines indicate the edges of the image swath, the longer dotted blue line its centre (nadir view, centre of the satellite swath). The thick red double arrow indicates the parallax shift for a storm further away from the swath centre.

Parallax shift and shape degradation affects both GEO and LEO satellites. As polar satellites orbit is at a much lower altitude than geostationary satellites, resolution is much better at high latitudes and also cloud displacement is negligible near the nadir. Despite this, it is worth keeping in mind that for lower orbits the viewing angle and parallax displacement grows more quickly with increasing distance from nadir (Figure 7). At the edge of the swath, especially high Cb clouds are displaced relative to the surface but also a semi-transparent cloud appears thicker because the measurement reaching the satellite took a longer path through it.

Displacement error of the cloud due to parallax
Figure 7: The look angle to the cloud feature for the lower satellite A is greater than the angle for satellite B, so the displacement error of the cloud due to parallax is greater for satellite A.

Further information

Parallax shift effect

The Problem With Parallax: Part 1-3

Limb cooling

The limb-cooling is a result of an increase in measurement (optical) path length of the absorbing atmosphere, as viewing zenith angle increases (see this NASA technical report). Major absorbing variables are water vapour, ozone and carbon dioxide. Satellite instrument IR measurements are colder at locations that are far from sub-satellite point (nadir). This affects RGB composite interpretation by causing anomalous reductions in brightness temperature, thus especially changing the colour interpretation of the water vapour RGB composites. Example from the case study Development of storm Valtteri (Figure 8).

300 hPa level isotachs (light blue) and geopotential (cyan) from ECMWF model +12h forecast valid time at 12 UTC on 29.1.2022. Background Airmass RGB imagery (Meteosat-11, SEVIRI) 29.1.2022 12:00 UTC overlayed with polar satellite Airmass RGB (Aqua, MODIS) 29.1.2022 12:10 UTC.
Figure 8:  Meteosat-11Airmass RGB overlaid with Aqua MODIS, 29 January 2022 12:10 UTC

Further information

Instructions for using the simulator

Limb cooling effect (EUMeTrain)

Elmer et al. (2016): Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites