Identify cloud types and their characteristics
A conceptual model on DIBS: A special kind of Cirrus clouds formed by dust
The strongest influence of DIBS, and especially of dusty cirrus, on surface weather is mainly the reduced solar radiation reaching the ground as dust particles act as condensation nuclei and form clouds where otherwise no clouds would have been present. As current numerical weather prediction models do not accurately account for microphysical cloud formation processes caused by the additional dust at higher atmospheric levels, predicted maximum temperatures often have a considerable bias towards higher values.
Pilar Rípodas talks about the improvements that the MTG will bring in regards to NWC SAF products.
The Nowcasting SAF (NWC SAF) develops and distributes software packages to generate satellite derived products with application in nowcasting. Cloud products, precipitation products, stability product, wind product, convection products, products related to turbulence and extrapolation imagery are current products in the NWC SAF portfolio. A version of the NWC SAF software that supports the new EUMETSAT satellite MTG-I is been developed by the NWC SAF team. The first version (MTG day-1) is expected to be released early 2024. The improvements expected in the NWC SAF products in this version are presented. Some preliminary products with MTG-I data can be presented depending on the availability of data. A full exploitation of the new capabilities of MTG-I to improve the current NWC SAF products and to develop new ones will come in the following versions. The plans in this respect are presented.
Ivan Smiljanic shows how to detect low level moisture with the FCI.
This talk will provide insights into how FCI instrument can be used to detect moisture in the layers close to the surface. Up until the introduction of FCI instrument, the concept of low-level moisture estimation, using solely data from imagers on board GEO satellites was to high degree limited to so-called split window difference (e.g. SEVIRI BTD12.0-10.8). Perhaps the biggest down side of this approach is the fact that BT difference relies heavily on the vertical temperature profiles of the atmosphere (the temperature of moisture level). With introduction of water vapour absorption channel in the NIR spectral region this dependency is avoided. Hence the novel NIR0.91 FCI channels is seen as one of the crucial tools for nowcasting of severe storms, i.e. assessment of pre-conditions and moisture feeding dynamics of convective systems.
Mária Putsay talks about the new Cloud Phase RGB.
The presentation is about the Cloud Phase RGB, a new product for European users of GEO satellite data, which can be constructed using data from the Flexible Combined Imager (FCI) on the Meteosat Third Generation (MTG) satellite system. The aim of this RGB is to provide improved microphysical information about the cloud tops. This is achieved by using two near-infrared microphysical channels together. It is a daytime RGB and can be used in low-, mid- and high-latitude regions. The main application areas of the Cloud Phase RGB are in cloud analysis: convective clouds, fog and low clouds; aerosol-cloud interaction. The main characteristics of this RGB are demonstrated using proxy data from Japanese and American satellites: Himawari/AHI, GOES/ABI, NPP and NOAA-20/VIIRS.
Carl Jones talks about his experience with the Cloud Type RGB, that will be the RGB using the new 1.38 μm channel on the FCI.
The Day Cloud Type RGB (1.38, 0.64, 1.61) is a multispectral imagery product made with the original intent of more easily observing cirrus clouds. However, it has shown utility in monitoring convection, particularly through the use of the 1.38 μm channel. This presentation will explore potential convection applications offered by the Day Cloud Type RGB as seen by the Advanced Baseline Imager (ABI).
Johan Strandgren talks about the FCI True Colour Imagery.
The Flexible Combined Imager (FCI) on-board MTG-i1 introduces a unique capability: generating geostationary true colour imagery over Europe and Africa. This is typically achieved by combining data from three channels centred at red, green and blue wavelengths. However, FCI's green channel (0.51 microns) partially misses the spectral reflectance peak of chlorophyll around 0.55 microns, leading to inaccurate depiction of vegetation and barren surfaces. To address this limitation, a novel green band correction technique using the normalized difference vegetation index has been developed and utilized for the first release of true colour images from FCI. The new FCI true colour composite is also the corner stone for the ongoing development of the FCI GeoColor RGB composite. This composite incorporates the elements from the ABI GeoColor composite, by blending true colour imagery with night-time infrared imagery and city lights, as well as other relevant features such as wildfires and LI lightning events.
Lecture slides
Martin Setvak demonstrates comparison between new FCI data with MSG and VIIRS, focusing on convective storms.
The presentation will address preliminary comparison of the MTG-I1 FCI imagery (based on FCI commissioning data) with MSG SEVIRI and NPP/JPSS VIIRS data, with focus on convective storms.
Cloud Phase RGB is a new product for European users of GEO satellite data, which can be constructed using data from the Flexible Combined Imager (FCI) on the Meteosat Third Generation (MTG) satellite system. The aim of this RGB is to provide improved microphysical information on cloud tops, in particular discrimination between thick water clouds and thick ice clouds, and cloud top particle size.
This extended guide is about the Cloud Phase RGB, a new product for European users of GEO satellite data, which can be constructed using data from the Flexible Combined Imager (FCI) on the Meteosat Third Generation (MTG) satellite system. It uses one of the new FCI channels, not available with the SEVIRI instrument. This document is an extended guide discussing its characteristics in detail; a quick guide is also available on the EUMeTrain webpage. In this guide, the imagers of Japanese and American geostationary satellites (Himawari/AHI and GOES/ABI) and polar satellites (NPP and NOAA-20/VIIRS) are used to provide proxy data for the FCI.
Identify clouds made of water droplets, ice particles or a mixture and discriminate between clouds with small or large cloud particles.
In this part of the course you will not only learn more about the identification of clouds such as Stratus, Cumulus and Cirrus from satellite images, and you will also discover various methods to derive cloud height information. Microphysical properties of clouds like cloud phase and cloud particle size are also addressed.
To access the resource click here.
Note: all resources are provided as an external link which redirects you to https://eumetcal.eu where you will need to create a user account in order to gain access to the course
Deduce cloud top heights based on brightness temperatures, surface observations and sounding data.
In this part of the course you will not only learn more about the identification of clouds such as Stratus, Cumulus and Cirrus from satellite images, and you will also discover various methods to derive cloud height information. Microphysical properties of clouds like cloud phase and cloud particle size are also addressed.
To access the resource click here.
Note: all resources are provided as an external link which redirects you to https://eumetcal.eu where you will need to create a user account in order to gain access to the course
Identify contrails and ship trails.
In this part of the course you will not only learn more about the identification of clouds such as Stratus, Cumulus and Cirrus from satellite images, and you will also discover various methods to derive cloud height information. Microphysical properties of clouds like cloud phase and cloud particle size are also addressed.
To access the resource click here.
Note: all resources are provided as an external link which redirects you to https://eumetcal.eu where you will need to create a user account in order to gain access to the course
Identify fogs and discriminate between fog and low cloud.
In this part of the course you will not only learn more about the identification of clouds such as Stratus, Cumulus and Cirrus from satellite images, and you will also discover various methods to derive cloud height information. Microphysical properties of clouds like cloud phase and cloud particle size are also addressed.
To access the resource click here.
Note: all resources are provided as an external link which redirects you to https://eumetcal.eu where you will need to create a user account in order to gain access to the course