MTG Imaging Service

Meteorological applications will greatly benefit from the upcoming Meteosat Third Generation Imaging Service.

MTG-I LI instrument

Nowcasting applications are in the focus of the imaging services, be it through the visual identification and characterisation of the weather systems, assessment via more advance quantitative products or a short-term prediction using probabilistic products that rely on data from MTG imagers.

 

Spectral imaging

Applications benefiting from the Spectral imagery mission, achieved through the Flexible Combined Imager (FCI) instrument:

  • Improved meteorological information about the rapid processes of the atmospheric water cycle, resulting in improved severe weather forecasts & early warnings.
  • Daytime total column precipitable water retrieval enhanced with support of 0.91 µm channel, especially over land.
  • Improved detection of very thin cirrus clouds assisted with 1.375 µm channel.
  • Improved retrieval of cloud microphysics with additional 2.26 µm 'microphysical’ channel.
  • True colour imagery available for first time, supported by new 0.44 µm & 0.51 µm channels.
  • Improved aerosol retrievals, especially over land — important for volcanic ash & air quality monitoring.
  • Improved fire detection via extended dynamical range of the 3.8 µm channel.
  • Increase in the quality of climate relevant products, through higher spatial resolution.

Figures 2–10 are comparisons between current imagery from SEVIRI on MSG, the MODIS instruments on board NASA's Terra and Aqua satellites and the VIIRS instrument on Suomi-NPP. Imagery from MTG will have comparable resolution to that from the MODIS instruments.

Figure 2

Figure 3

Figure 2: Example of ash detection, SEVIRI Natural Colour RGB, 12:15 UTC, 26 November 2006 (left), MODIS True Colour RGB, 12:20 UTC, 26 November 2006
Figure 3: Example of dust monitoring, SEVIRI Dust RGB, 18:00 UTC, 29 October 2010 (left), MODIS Dust RGB, 18:15 UTC, 29 October 2010
 

Figure 4

Figure 5

Figure 4: Example of thin cloud detection, SEVIRI Dust RGB, 11:00 UTC, 23 May 2005 (left), MODIS NIR1.3, 11:00 UTC, 23 May 2005
Figure 5: Example of severe convection detection/cloud microphysics, SEVIRI RGB Composite (VIS0.6, NIR1.6, IR10.8), 11:45 UTC, 27 November 2009 (left), MODIS RGB Composite (VIS0.6, NIR2.2, IR11.0), 11:47 UTC, 27 November 2009
 

Figure 7

Figure 6

Figure 6: Example of convective cloud detection, 11 June 2018, 11:37 UTC; SEVIRI data (left), and FCI imagery (right) simulated over Central Europe based on data from the VIIRS instrument on the NOAA Suomi-NPP satellite; combining 0.865 µm imagery (background) and 11.45 µm (convective storms) to a ‘sandwich’ product.
Figure 7: Example of fog detection, 16 Nov 2018, 01.37 UTC; simulated FCI imagery at 2 km spatial resolution based on data from the VIIRS instrument on the NOAA Suomi-NPP satellite (right panel), and SEVIRI imagery at approximately 5 km spatial resolution over Czech Republic (3 km spatial resolution at sub-satellite point, left panel; Brightness Temperature differences (VIIRS I4 (3.7 µm)–I5 (10.8 µm); SEVIRI 3.9 µm–10.8 µm)
 

Figure 9

Figure 8

Figure 8: Example of fire detection: MSG SEVIRI 3.9 µm channel at 3 km sampling distance, with brightness temperature saturating at 344 K (red pixels) (left), MODIS 3.9 µm channel at 1 km sampling distance (right) simulating FCI imagery, showing more detailed fire locations and a range of fire brightness temperatures (yellow-orange-red colours); Botswana, 29 August 2008.
Figure 9: Example of fire detection: MSG SEVIRI RGB 3 km spatial sampling distance (left), MODIS RGB imagery as proxy for FCI at 1 km spatial sampling distance (right). Portugal, 5 August 2018.
 

Figure 10: Comparison of full-disc and rapid scanning between current SEVIRI (left) and future FCI (right)

Lightning imaging

Applications benefiting from the Lightning imagery mission, achieved through the Lightning Imager (LI) instrument:

  • Improvements for nowcasting and very short range forecasting of severe weather through information on total lightning (Intra Cloud (IC) lightning and Cloud to Ground (CG) lightning). Note: with optical pulse detection, it is not possible to distinguish between cloud-to-ground and cloud-to-cloud lightning.
  • Continuous and simultaneous observations of total lightning in geostationary field of view, with an extremely high timeliness (goal of reaching 30 seconds)
  • Detection, monitoring, tracking and extrapolate of the development of active convective areas and storm life cycles.
  • By providing data on the total lightning, the LI instrument will help improve the quality of information essential for air traffic routing and safety.
Figure 2
 
Figure 11: MTG Lightning Imager (LI): US Proxy Data, 4 May 2017, 13:07 UTC; 1-minute GLM lightning group density; ABI 11.2 µm imagery (preliminary, non-operational data). Source: G. Stano, NASA SPoRT.

Figure 12: Simulated lightning event for 24 hours on 16 June 2013
 

Service details

Figure 13
 
Figure 13: GOES-16 pre-operational data. Credit: NOAA/NASA

The information delivered to the users will be the time, position and intensity of detected optical pulses converted into geophysical flashes (point data).

Additionally, users will be provided with accumulated flashes over a period of 30 seconds (gridded data on the same grid as FCI instrument data, derived from accumulated point data), which can be further stacked for longer integration periods depending on the users’ application.

Continuous and simultaneous observations of total lightning with almost full-disc coverage, delivered to the users with very high timeliness provides (together with the associated FCI data) a valuable tool for assessment and nowcasting of severe weather from pure satellite data, over almost the full satellite field of view. An example of such an integrated view is provided from the GOES-16 pre-operational data (Figure 7).

LI and climate

For climate purposes the LI will:

Figure 14
 
Figure 14: Climate dataset from LIS and OTD instruments. Credit: NOAA/NASA
  • Help in assessing the impact of climate change on thunderstorm activity.
  • Aid the study and monitoring of the physical and chemical processes in the atmosphere regarding nitrogen oxide (NOx), which plays a key role in the ozone conversion process and acid rain generation.
  • Provide information on a global scale with known error-characteristics. This information is needed in regional models to improve very short-range forecasts of convective events and verification/validation of algorithms to nowcast convective initiation (time and location).

An example of statistics derived from similar climate dataset from LIS and OTD instruments is shown in Figure 14.

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