display of msg satellite data, processing and application joseph kagenyi kenya meteorological...
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Display of MSG satellite Data, Processing and Application
Joseph KagenyiKenya Meteorological
Department
objective To obtain skills in display of Spectral
data To gain skills in Features identification To gain skills in identifying what
spectral channels best identifies your features
To gain skills in RGB Interpretation and applications
Basics of displaying MSG/SEVIRI images
Four processing and rendering methods:1. Images of individual channels, using a simple
grey wedge or LUTs for pseudo colours (typical for MFG channels);
2. Differences/ratios of 2 channels, using a simple grey wedge or LUTs for pseudo colours (e.g. fog, ice/snow or vegetation);
3. Quantitative image products using multi-spectral algorithms (e.g. SAFNWC/MSG software package) and discrete LUTs;
4. RGB composites by attributing 2 to 3 channels or channel combinations to individual colour (RGB) beams classification by addition ofRGB colour intensities
Simple display of individual SEVIRI channels4 solar (on black), two WV channels + 6 IR (on whitish)
Adequate for viewing information of 3 MFG channels;
Not very practical for 12 MSG/SEVIRI channels.
Rendering of individual SEVIRI channelsProper choice of grey wedge
Solar channels rendered similar to black & white photography (channel 03 with particular response from ice/snow) physical rendering using lighter shades for higher reflectivity and darker shades for lower reflectivity.
Rendering of individual SEVIRI channelsProper choice of grey wedge
solar: reflectivity (P mode only)
high
low
clouds
land / sea
Rendering of individual SEVIRI channelsProper choice of grey wedgeIR channels rendered either in P or S
mode: P mode: grey shades follow intensity of
IR emission: physical rendering with lighter shades for stronger IR emission and darker shades for weaker IR emission;
S mode: P mode inverted: traditional “solar-like” rendering, allowing for easy comparison to images from solar channels.
Rendering of individual SEVIRI channelsProper choice of grey wedge
IR: emission / brightness temperatureP mode
strong / warm
weak / cold
clouds / more absorption
land / sea / less absorption
Rendering of individual SEVIRI channelsProper choice of grey wedge
IR: emission / brightness temperatureS mode
strong / warm
weak / cold
clouds / more absorption
land / sea / less absorption
Differences/ratios of 2 channels
Simply displaying a larger set of single channels for comparison is neither efficient in mining useful information nor particularly focussed on phenomena of interest;
Displaying specific channel differences or ratios, a simple operation though, improves the situation awareness by enhancing particular phenomenon of interest (e.g. fog or ice clouds) in a particular situation;
Grey-scale rendering (small values in dark or light shades – large values in light or dark shades) is not standardised; mode may be inherited from similar products based on data of other imagers (e.g. AVHRR or MODIS).
Differences of 2 channels – examples
night - dark day - bright
04 – 09fog
03 – 01ice clouds
day (only)- dark
Some recommended differences
Clouds 03-01 04-09 05-06 05-09 06-09
Thin cirrus 07-09 04-09 10-09
Fog04-0907-09Snow03-01Volcanic ash (SO2)06-11
Dust04-0907-0910-09Vegetation02-01Fire04-09Smoke03-01
Quantitative image products using multi-spectral algorithms Quantitative algorithms (thresholding or pattern
recognition techniques) extract specific features from multi-spectral images and code them into a single-channel image quantitative image products;
Using discrete LUTs quantitative images are easy to read due to relation between identified features and colour values, but may have some drawbacks:
Feature boundaries appear very artificial (e.g. checker board due to use of ancillary data of different spatial scale);
Extracted features show unclassified or misclassified fringes;
Natural texture of features is lost (“flat” appearance); Depending on robustness of feature extraction, time
evolution of images is not necessarily very stable animated sequences somewhat confusing (e.g. erratically jumping classification boundaries).
Quantitative image products using multi-spectral algorithms – an example
SAFNWC/MSG PGE03Cloud Top Temperature/Height (CTTH)
checkerboardboundary
green fringe around blue
feature
RGB image composites – additive colour schemeAttribution of images of 2 or 3 channels (or
channel differences/ratios) to the individual colour (RGB) beams of the display device;
RGB display devices produce colours by adding the intensities of their colour beams optical feature extraction through result of colour addition.
FAST BUT QUITE EFFICIENT ALternative FOR QUANTITATIVE FEATURE EXTRACTION
RGB image composites – additive colour scheme
R red beam
B bl
ue b
eam
G green beam
Click Color Selector.exe
• Tool reveals individual colour intensities adding to the colours shown in the circle;
• Close tool after use (also when calling it later on again).
RGB image composites – some RGB colours/values
Examples of colours (names) and 8-bit (octal and decimal) values loaded to the RGB beams:
Red 255,0,0 Fuchsia 255,0,255 Skyblue 153,206,235
RGB image composites – pros and cons
Drawback: Much more subtle colour scheme compared
to discrete LUTs used for quantitative image products interpretation more difficult;
Advantages: Processes “on the fly”; Preserves “natural look” of images by
retaining original textures (in particular for clouds);
Preserves spatial and temporal continuity allowing for smooth animation RGB image sequences.
RGB image composites – inside
+
+
Channel 03
Channel 02
Channel 01
Color Selector.exe
RGB image composites – inside
Optimum (and stable) colouring of RGB image composites depends on some manipulations:
Proper enhancement of individual colour channels requires:
Some stretching of the intensity ranges; Selection of either P or S mode for IR channels;
Attribution of images to individual colour beams depends on:
Reproduction of RGB schemes inherited from other imagers;
Permutation among colour beams and individual images more or less pleasant / high-contrast appearance of RGB image composite.
RGB 321 natural composite
Reveals fog and deep clds, water clds Cirrus /snow/Vegetation/bare ground
Channel attribution RGB321
CColor Selector.exe
RGB(6-5)(4-9)(3-1) Deep convection
Reveals atmospheric and surface features
Channel attributionR 06-05 G 04-09 B 03-01
RGB149 (Day Microphysics)
Reveals some cloud properties
Channel attribution:RGB 149
For 04 and 09 beams P (inverted) mode is used!
Daytime convection product
Also used RGB139
RGB image composites – using HRV (channel 12)
In order to preserve high resolution of HRV channel assign it to 2 colour beams (using only one colour beam blurs the image too much);
Attributing it to beams R and G is preferred rendering close to natural colours for surface features;
Beam B is then free for any other SEVIRI channel properly downscaled (factor of 3) to HRV.
Assigning an IR window channel in P mode to beam B (as a temperature profile surrogate) adds height information to a detailed cloud view eg RGB12,12,9 or RGB229
RGB image composites – using HRV (channel 12)
Reveals fine details of snow cover, fog patches and higher clouds
R 12 G 12 B 09 (09 in P mode (inverted)!)
Recommended schemes for RGB image composites
Convection 01,03,09
01,03,10 01,04,09
01,04,10 03,04,09
03,04,10
HRV (channel) 12,12,04 12,12,09
Dust 01,03,04 03,02,01
Vegetation 03,02,01
Fire/Smoke 03,02,01 04,02,01
Channel differences 06-05,04-09,03-
01
Summary of RGB image composites
Fast technique for feature enhancement exploiting additive colour scheme of RGB displays;
May require simple manipulation to obtain optimum colouring (choice of P or S mode for IR channels!);
More complex RGB schemes may require some time to get acquainted with;
Some RGB schemes may be inherited from other imagers (e.g. AVHRR or MODIS);
Combination of an IR channel with HRV feasible and much informative;
RGB image composites retain natural texture of single channel images;
RGB image composites remain coherent in time and space, i.e. ideal for animation of image sequences.