The Basis of RGB 20081117 · 2008-11-21 · 1 The Basis of RGB image composites Analysis Division...

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The Basis ofThe Basis ofRGB image compositesRGB image composites

Analysis DivisionAnalysis Division((Akihiro SHIMIZUAkihiro SHIMIZU))

Meteorological Satellite CenterMeteorological Satellite Center

Japan Meteorological AgencyJapan Meteorological Agency

First, this presentation will show the basic, general knowledge of satelliteimagery of each channels.

Thereafter, this presentation will show RGB image composites.

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ObjectivesObjectives

Learn the basic, general knowledge ofLearn the basic, general knowledge ofsatellite imagery of each channels.satellite imagery of each channels.

Especially, we learn the characteristics of theEspecially, we learn the characteristics of thesensors onboard MTSATsensors onboard MTSAT--1R.1R.

Learn how to use (interpret) the differences.Learn how to use (interpret) the differences.

Learn RGB image composites.Learn RGB image composites.

This material shows you the RGB compositeThis material shows you the RGB compositeexamples applied the technique of RGB toexamples applied the technique of RGB toMTSAT images.MTSAT images.

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MTSATMTSAT--1R1R ChannelsChannels

We will mention the feature of individual JAMI channels, before reference of RGBcomposite image.

Resolution

The characteristics of a sensor on board the MTSAT-1R are shown in Table. Thehorizontal spatial resolution of the MTSAT-1R is 1 km in the VIS image and 4 km in theIR images at the sub-satellite point (SSP). The more distant from SSP, the more theearth’s surface is viewed obliquely and the resolution deteriorates. In the vicinity ofJapan, the resolution is 1.4 km in the VIS image and 5 km in the IR images.

The gray scale of MTSAT-1R images is 10 bits (1024 levels) in the VIS and IR images.

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Half diskFull diskIRVISIRVIS

6 bits

10 bits

Quantization

8 bits

10 bits

5 km

4 km

# of obs. par dayNadia spatial res.

32241 kmMTSAT

(NA)281.25 kmGMS5

IR4(SWIR)

IR1 and IR2 Separated

Less transparent IR3(WV) Tb simulated by LBLRTM (AGR)

SRFs of infrared channels

MTSATMTSAT--1R/JAMI1R/JAMI

MTSAT-1R’s platform is as follows.

Nadia spatial resolution and quntization is modified compare to GMS-5.

Half disk observation started to derive AMVs.

This is SRFs (spectral response functions) of infrared channel.

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MSG ChannelsMSG Channels

Spectral channel characteristics of SEVIRI in terms of central, minimum and maximum wavelength ofthe channels and the main application areas of each channel.

ChannnelNo.

Spectral Band(μm)

Characteristics ofSpectral Band (μm)

Main Observational Application

Centre Min Max1 VIS 0.6 0.635 0.56 0.71 Surface, clouds, wind fields2 VIS 0.8 0.81 0.74 0.88 Surface, clouds, wind fields3 NIR 1.6 1.64 1.50 1.78 Surface, cloud phase4 IR 3.9 3.90 3.48 4.36 Surface, clouds, wind fields

5 WV 6.2 6.25 5.35 7.15Water vapor, high level clouds,

atmospheric instability6 WV 7.3 7.35 6.85 7.85 Water vapor, atmospheric instability

7 IR 8.7 8.70 8.30 9.10Surface, clouds, atmospheric

instability8 IR 9.7 9.66 9.38 9.94 Ozone

9 IR 10.8 10.80 9.80 11.80Surface, clouds, wind fields,

atmospheric instability

10 IR 12.0 12.00 11.00 13.00Surface, clouds, atmospheric

instability

11 IR 13.4 13.40 12.40 14.40Cirrus cloud height, atmospheric

instability12 HRV Surface, cloudsBroadband (about 0.4 - 1.1μm)

This table shows MSG (METEOSAT Second Generation) channels.

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MSG andMSG and MTSATMTSAT--1R1R

3.48~4.363.48~4.36IR3.9IR3.93.5~4.03.5~4.0IR4(3.8IR4(3.8µµm)m)

5.35~7.155.35~7.15WV6.2WV6.26.5~7.06.5~7.0IR3(WV)IR3(WV)

11.00~13.0011.00~13.00IR12.0IR12.011.5~12.511.5~12.5IR2IR2

9.80~11.809.80~11.80IR10.8IR10.810.3~11.310.3~11.3IR1IR1

0.56~0.710.56~0.71VIS0.6VIS0.60.55~0.900.55~0.90VISVIS

Wave length (Wave length (µµm)m)MSG ChannelsMSG ChannelsWave length (Wave length (µµm)m)MTSAT ChannelsMTSAT Channels

This table shows the correspondence of each channel of MSG and MTSAT-1R.

There are many common things on the image interpretation in the correspondingchannels.

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Properties of each channelProperties of each channel

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Visible (VIS) imageVisible (VIS) image

•• Reflection intensitiesReflection intensitiesdiffer depending upondiffer depending uponcloud thickness.cloud thickness.

•• Lower clouds can be seenLower clouds can be seenwell.well.

•• Intensities varyIntensities varydepending upon locationsdepending upon locationsamong the earth, the sunamong the earth, the sunand the satellite.and the satellite.

•• Available only duringAvailable only duringdaytimedaytime

03UTC 21 July 2006 MTSAT-1R image

Visible (VIS) image

(1) Features of VIS image

VIS image represents the intensity of sunlight reflected from clouds and/or the earth’s surface andmakes it possible to monitor the conditions of the ocean, land and clouds. Portions of highreflectance are visualized bright and low reflectance dark. In general, the snow surface andclouds look bright because they have high reflectance, the land surface is darker than the clouds,and the sea surface looks the darkest because of its low reflectance. Note, however, that theappearance differs depending on the solar elevation at the observed point. In the morning andevening and in high–latitude districts, the image looks darker because there is little incident lightdue to the oblique sunlight and the small amount of reflected rays.

(2) Use of VIS image

A. Distinction between thick and thin clouds

The reflectance of a cloud depends on the amount and density of the cloud droplets and raindropscontained in the cloud. In general, low-level clouds contain a larger amount of cloud droplets andraindrops and therefore they appear brighter than high clouds. Cumulonimbus and other thickclouds that have developed vertically contain a lot of cloud droplets and raindrops and theyappear bright in a VIS image. Through some thin high-level clouds, the underlying low-levelclouds and land or sea surface can be seen.

B. Distinction between convective and stratiform types

Cloud types can be identified from the texture of the cloud top surface. The top surface of astratiform cloud is smooth and uniform while the top surface of a convective cloud is rugged anduneven. The texture of a cloud top surface is easily observed when sunlight is hitting the cloudtop obliquely.

C. Comparison of cloud top height

If clouds of different heights coexist when sunlight is hitting them obliquely, it may happen thatthe cloud of the higher top height casts a shadow onto the cloud surface of the lower top height.Comparison of cloud height is possible by this shadow.

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IR imageIR image

Watching overWatching overmeteorologicalmeteorologicalphenomenaphenomena

Observation ofObservation ofcloud top heightcloud top height

03UTC 21 July 2006MTSAT-1R image

150.2 K 329.7 K

Infrared (IR) image

(1) Features of IR image

The IR image represents a temperature distribution and can be observed without adifference between day and night. Therefore, it is useful for watching cloudsand/or the earth’s surface temperature. In the IR image, portions of lowtemperature are visualized bright and portions of high temperature dark.

(2) Use of IR image

A. Watching over meteorological phenomena

Unlike the VIS image, observation is possible with the IR image under the sameconditions against day or night. This is the most advantageous point in watchingmeteorological disturbances.

B. Observation of cloud top height

It is possible to know the cloud top temperature with the IR image. If thetemperature profile of atmosphere is known, the cloud top temperature can beconverted into cloud top height. For the estimation of the temperature profile,values by objective analysis or Numerical Weather

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Use of WV imageUse of WV image

Grasp of airflow inGrasp of airflow inthe upper andthe upper andmiddle airmiddle air


174.2 K 299.8 K

Use of WV image

A. Grasp of airflow in the upper and middle air

The WV image is possible to represent the radiation from the water vapor contentin the upper and middle layer. That is, the airflow in the upper and middle layercan be visualized using water vapor as a tracer even if no cloud is present. Theposition of troughs, vortices and jet streams in the upper and middle layer can beestimated from the distribution of bright and dark areas on the WV image.

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Use of 3.8Use of 3.8m imagem image

Main application:Main application:identification of loweridentification of lowerclouds in the nightclouds in the night

Identification of upperIdentification of uppercloudsclouds

Detection of firesDetection of fires

Microphysical cloudMicrophysical cloudproperties (particleproperties (particlesize)size)


222.7 K 319.9 KSee Annex for further details.

Use of 3.8-m image

A. Identification of lower clouds in the night

Lower clouds have a small temperature difference with the surrounding cloudfree areas and it is difficult to detect them at night in the IR image alone. Thecloud top temperature is calculated lower at 3.8-m than at IR1 and thedifference of calculated temperature is 2 to 10 degrees negative. The 3.8-mdifferential image is used for the lower cloud identification in the night becausethe differences between cloud free area and lower cloud area are more intensifiedin the 3.8-m differential image than in the 3.8-m image.

B. Identification of upper clouds

The 3.8-m rays have properties that are close to visible light and they easilypenetrate the higher cloud with ice crystals. At night, the radiations from theearth’s surface at high temperature penetrate thin higher clouds and are added tothe radiations from the cloud top, so the cloud top temperature calculated at 3.8-m is higher than actual. Because the transmission effect is larger at 3.8-m thanat IR1, the cloud top temperature is higher than the infrared temperature and thetemperature difference has a positive value. In this regard, the area of thin higherclouds can be identified with the 3.8-m differential image. For example, it ispossible to distinguish between cumulonimbus, which brings about rainfall, andanvil cirrus, which does not.

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Differences/ratios of 2 channelsDifferences/ratios of 2 channels(Extraction from the materials of MSG, EUMETSAT)(Extraction from the materials of MSG, EUMETSAT)

Simply displaying a larger set of single channels forSimply displaying a larger set of single channels forcomparison is neither efficient in meaning usefulcomparison is neither efficient in meaning usefulinformation nor particularly focussed on phenomenainformation nor particularly focussed on phenomenaof interest;of interest;

Displaying specific channel differences or ratios, aDisplaying specific channel differences or ratios, asimple operation though, improves the situationsimple operation though, improves the situationawareness by enhancing particular phenomenon ofawareness by enhancing particular phenomenon ofinterest (e.g. fog or ice clouds) in a particularinterest (e.g. fog or ice clouds) in a particularsituation;situation;

GreyGrey--scale rendering (small values in dark or lightscale rendering (small values in dark or lightshadesshades –– large values in light or dark shades) is notlarge values in light or dark shades) is notstandardised; mode may be inherited from similarstandardised; mode may be inherited from similarproducts based on data of other imagers (e.g.products based on data of other imagers (e.g.AVHRR or MODIS).AVHRR or MODIS).

The text on this slide is practically self-explaining.

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Some recommended differencesSome recommended differences

CloudsClouds

IR10.8IR10.8--IR12.0IR12.0

IR10.8IR10.8--IR3.9IR3.9

Thin cirrusThin cirrus

IR10.8IR10.8--IR12.0IR12.0

FogFog

IR10.8IR10.8--IR3.9IR3.9

Volcanic AshVolcanic Ash

IR10.8IR10.8--IR12.0IR12.0

DustDust

IR10.8IR10.8--IR12.0IR12.0

FireFire

IR3.9IR3.9--IR10.8IR10.8

Note difference of day and night on channel4.

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Applications of IR1Applications of IR1 –– IR2IR2

Identification of thinIdentification of thin(high) clouds(high) clouds

Identification ofIdentification ofvolcanic ash and dustvolcanic ash and dust

LowLow--level humiditylevel humidity

Sea surfaceSea surfacetemperaturetemperature

......

MTSAT-1R, 21 July 2006 03UTC

Infrared differential image

Features of infrared differential image

The infrared differential image is a visualization of the temperature at IR1 fromwhich the temperature at IR2 is subtracted. These infrared bands are called theatmospheric windows where there is little absorption by water vapor and theatmosphere. However, the absorption by water vapor cannot be always neglected.Absorbency is larger in the IR1 band than in the IR2 band though the differenceis slight. The difference in absorbency between IR1 and IR2 depends on thewater vapor content of the atmosphere, and the infrared differential image isvisualized darker for larger values of this difference.

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MTSAT Example: Frontal systemMTSAT Example: Frontal system

IR1-IR2 (11μm - 12μm) IR1-IR4 (11μm - 3.8μm)

Thick ormulti-layer

cloud

MTSAT-1R, 25 July 2006, 03 UTC

In certain case, it is often difficult to analyze the imagery without animation.

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Summary of MTSATSummary of MTSATRepresentationRepresentation

VIS high albedo lowIR (IR1,IR2) low temp. highWV (IR3) wet humidity dry

3.8μm (IR4, day) low albedo high3.8μm (IR4, night) low temp. high

IR1-IR2 dust,ash thin CiIR4-IR1 (day) large ice small iceIR4-IR1 (night) fog thin Ci

Comparison of images

In the VIS image, the gray scale is represented white for high reflectance andblack for low reflectance. In the IR image, it is represented black for hightemperature and white for low temperature as same as the gray scales ordinaryused. In the infrared differential image, it is represented black for large (positive)differences and white for small (negative) differences. In the 3.8-m image, thegray scale in the day is represented black for high reflectance and white for lowreflectance. Note that this is opposite to the VIS image. At night, the same grayscale as the IR image is used. The 3.8-m differential image is represented blackfor positive differences and white for negative differences (Table).

Table. Appearance in images

Image types Appearance in images

White Light gray Gray Dark gray Black

Visible image Reflection large Reflection small

Infrared image Temperature low Temperature high

Water vapor image Wet Dry

3.8-m image (daytime) Reflection small Reflection large

3.8-m image (nighttime) Temperature low Temperature high

Infrared differential image Negative Positive

3.8-m differential image Negative Positive

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(Cloud) Physical Properties represented(Cloud) Physical Properties representedby the MTSAT Channelsby the MTSAT Channels

VIS(0.55-0.90): optical thickness and amount of cloud water and iceIR4 (IR3.8): particle size and phaseWV (IR3): mid- and upper level moistureIR1 (IR11.0), IR2 (IR12.0): top temperature

IR2 - IR1: optical thicknessIR4 - IR1: optical thickness, phase, particle sizeWV - IR1: top height, overshooting tops

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RGB image compositesRGB image composites

We often analyze images by using each individual channel alone, and it isimportant to determine the kind of the cloud by animation. However,considerable skill is necessary for that.

RGB image composites can be determine easily by appearance, it is veryeffective for the neph-analysis.

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（Wikipedia http://ja.wikipedia.org/wiki/）

RGB image compositesRGB image compositesThe three primary colorsThe three primary colors of theof the lightlight

RGB: red, green, and blue

RGB means the three primary colors of the light, and it is composed of the color spacerepresenting an additive color system.

The scheme on this slide shows the system;

Primary colors: red, green, and blue

Secondary colors: yellow = red + green

cyan = green + blue

magenta = red + blue

All colors: white = red + green + blue

black = no light

RGB image composite is a technique to display the color imagery by using this propertyof the three primary colors of the light.

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RGB image compositesRGB image composites –– additive colour schemeadditive colour scheme(Extraction from the materials of MSG , EUMETSAT)(Extraction from the materials of MSG , EUMETSAT)

Attribution of images of 2 or 3 channels (or channelAttribution of images of 2 or 3 channels (or channeldifferences/ratios) to the individual colour (RGB) beamsdifferences/ratios) to the individual colour (RGB) beamsof the display device;of the display device;

RGB display devices produce colours by adding theRGB display devices produce colours by adding theintensities of their colour beamsintensities of their colour beams optical featureoptical featureextraction through result of colour addition.extraction through result of colour addition.

FAST BUT QUITE EFFICIENT SURROGATE FORFAST BUT QUITE EFFICIENT SURROGATE FORQUANTITATIVE FEATURE EXTRACTIONQUANTITATIVE FEATURE EXTRACTION


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RGB image compositesRGB image composites–– how to dohow to do

Optimum (and stable) colouring of RGB imageOptimum (and stable) colouring of RGB imagecomposites depends on some manipulations:composites depends on some manipulations:

Proper enhancement of individual colour channelsProper enhancement of individual colour channelsrequires:requires: Some stretching of the intensity ranges;Some stretching of the intensity ranges;

Reflectivity enhancement at lower solar angles applying e.g.Reflectivity enhancement at lower solar angles applying e.g.sun angle compensation orsun angle compensation or histogrammehistogramme equalisation;equalisation;

Selection of either inverting or not IR channels.Selection of either inverting or not IR channels.

Attribution of images to individual colour beamsAttribution of images to individual colour beamsdepends on:depends on: Reproduction of RGB schemes inherited from other imagers;Reproduction of RGB schemes inherited from other imagers;

Permutation among colour beams of individual imagesPermutation among colour beams of individual images more or less pleasant / highmore or less pleasant / high--contrast appearance of RGBcontrast appearance of RGBimage composite.image composite.

Extraction from the materials of MSG,EUMETSAT

(Extraction from the materials of MSG)

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RGB image compositesRGB image composites–– pros and conspros and cons

DrawbacksDrawbacks:: Much more subtle colour scheme compared toMuch more subtle colour scheme compared to

discrete LUT used in quantitative image productsdiscrete LUT used in quantitative image products interpretation more difficult;interpretation more difficult;

RGBsRGBs using solar channels loose colour nearusing solar channels loose colour neardawn/dusk (even with reflectivity enhancement).dawn/dusk (even with reflectivity enhancement).

AdvantagesAdvantages:: ProcessesProcesses ““on the flyon the fly””;; PreservesPreserves ““natural looknatural look”” of images by retainingof images by retaining

original textures (in particular for clouds);original textures (in particular for clouds); Preserves spatial and temporal continuity allowingPreserves spatial and temporal continuity allowing

for smooth animation of RGB image sequences.for smooth animation of RGB image sequences.

Extraction from the materials of MSG,EUMETSAT


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RGB in MTSAT ImageRGB in MTSAT ImageWhen we use the channels of MTSAT, we can use theWhen we use the channels of MTSAT, we can use thecombinations of EUMETSAT, such as "Day Microphysicalcombinations of EUMETSAT, such as "Day Microphysical““,,"Night Microphysical" and"Night Microphysical" and ““Severe ConvectionSevere Convection”” asascombinations of RGB.combinations of RGB.

~G~General cloud analysis, cloud microphysicseneral cloud analysis, cloud microphysics~~““ Day MicrophysicalDay Microphysical““

VIS(0.55VIS(0.55--0.90), IR4(3.8), IR1(11.0)0.90), IR4(3.8), IR1(11.0)

““ Night MicrophysicalNight Microphysical““IR2(12.0)IR2(12.0)--IR1(11.0), IR1(11.0)IR1(11.0), IR1(11.0)--IR4(3.8), IR1(11.0)IR4(3.8), IR1(11.0)

~ S~ Severe convective storms, typhoonsevere convective storms, typhoons~~““ Severe ConvectionSevere Convection““

VISVIS(0.55(0.55--0.90), IR4(3.8)0.90), IR4(3.8)--IR1(11.0), IR1(11.0)IR1(11.0), IR1(11.0)

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Lower clouds

Upper clouds

Mixture imageof VIS ,IR1and

IR4

VIS: RedIR4: GreenIR1: Blue

Sc or Fog

VIS

IR1

IR4

Coloring in SATAID ImageColoring in SATAID ImageDay MicrophysicalDay Microphysical

~The first step, the function of SATAID~~The first step, the function of SATAID~

(MTSAT-1R, 26 July 2007, 02UTC)

MTSAT-1R have only 5 channels, so we can't have various combination of RGBcomposite image,

but we will enumerate some examples that seem to be effective to watch imagery.

This case is tentative (trial) RGB image composite.

This composite corresponds to “Day Microphysical” (RGB 02,04r,09).

Notice that the image of IR4 (3.8μm) contains total (solar, thermal)contributions.

The true composites “Day Microphysical” contain only solar contribution for3.9μm channel.

Moreover, while gamma value of IR4 is 1 in this case, the value for “DayMicrophysical” is 2.5.

However, we can derive useful information from this image.

In addition to lower cloud and upper cloud, we can distinguish fog (or Sc)!

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Mixture imageVIS: Red

IR4: GreenIR1: Blue

Day Microphysical in SATAID ImageDay Microphysical in SATAID Imagewith brightness/contrast adjustmentswith brightness/contrast adjustments

Typhoon No.13 Sinlaku

(MTSAT-1R, 17 September 2008, 0230UTC)

Thick Ice Cloud(large ice)

Low-level Cloud(water)

Thick Ice Cloud(small ice)

This case is also tentative (trial) RGB image composite.

This composite pattern correspond to Day Microphysical (RGB 02,04r,09) inMSG.

Notice that the image of IR4 (3.8μm) contains total (solar, thermal)contributions.

However, we can get the image similar to MSG image.

This image is obtained by adjusting brightness and the contrast of the images ofeach channel.

In this image, thick clouds (large ice) of typhoon appear in reddish color, thickice clouds (small ice) appear in orangish color, low-level water clouds appear inwhite-greenish color.

Thus we can analyze the structure of typhoon easily.

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IR11.0－IR12.0differential image

IR3.8－IR11.0differential image

IR11.0（Inversion）image

RGB image compositesRGB image compositesforfor NightNight MicroMicropphysicalhysical

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RGB image compositesRGB image composites ｂｙｂｙ MTSATMTSAT--1R1R imageimagess( Night( Night MicroMicropphysical )hysical )

R：IR 1１.0– IR12.0

(MTSAT-1R, 25June 2007, 12UTC)

convective clouds/ thick clouds

low-levelclouds / fog

high-levelclouds

mid-levelclouds

low-level thick clouds

G：IR 3.8 – IR 11.0

B：IR 11.0（ Inversion ）

・low-levelclouds・mid-levelclouds・thick high-level clouds

・low-levelclouds・mid-levelclouds

・low-levelclouds・mid-levelclouds・thin high-level clouds

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Mixture imageIR2-IR1: Red

IR1-IR4: GreenIR1: Blue

Night MicrophysicalNight Microphysicalin SATAID Imagein SATAID Image

Typhoon No.6 PRAPIROON(MTSAT-1R, 1 August 2006, 18UTC)

Very Cold,Very Cold,Thick, HighThick, HighLevel CloudLevel Cloud

This composite pattern correspond to Night Microphysical (RGB 10-09,09-04,09)in MSG.

Notice that the image of IR4 (3.8µm) is not CO2-corrected brightnesstemperature.

However, we can get the image similar to MSG image.

This image is obtained by adjusting brightness and the contrast of the images of

each channel.

In this image, thick Cb of typhoon at night appear in sprenkled orange-red color.

The sourse of noise are IR4 (3.8µm).

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Typhoon No.12 IOKE

CirrusVery Cold,

Thick, HighLevel Cloud

(MTSAT-1R, 24 August 2006, 12UTC)

This composite pattern correspond to Night Microphysical (RGB 10-09,09-04,09) in MSG.

Notice that the image of IR4 (3.8μm) is not CO2-corrected brightnesstemperature.

However, we can get the imagery similar to MSG’s composite imagery.


In this image, thick Cb of typhoon at night appear in sprenkled orange-red color,and thin cirrus appear in dark bluish color.

The source of noise are IR4 (3.8µm).

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Convective Clouds and Cirrus: Pacific Ocean

Very Cold,Convective Clouds

Cirrus


This composite pattern correspond to Night Microphysical (RGB 10-09,09-04,09)in MSG, too.

Notice that the image of IR4 (3.8μm) is not CO2-corrected brightnesstemperature.

However, we can get the imagery similar to MSG imagery.


In this image, the convective clouds appear in sprenkled orange-red color, thincirrus appears in dark bluish color.

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Low cloud over the Bering Sea

UpperCloud

Fog or lowlevel cloud


This composite pattern correspond to Night Microphysical (RGB 10-09,09-04,09)in MSG, too.


In this image, night-time fog ( or low cloud ) appears in pinkish white.

We have to adjust this more, but we could make a clear distinction.

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Night MicrophysicalNight MicrophysicalGammaGamma CorrectionCorrection

Low cloud over the Bering Sea

Γ=1.0 Γ=1.5

Γ=2.0

Example:Gamma correction with IR1-IR4


This composite pattern correspond to Night Microphysical (RGB 10-09,09-04,09) in MSG, too.

These images are the same as the image of the previous slide and are obtained byadjusting brightness and the contrast of the images of each channel.

In these images, night-time fog ( or low cloud ) appears in pinkish white.

Notice that SATAID does not have the function for gamma correction, thereforewe have to use the retouch software in common use.

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Mixture imageVIS: Red


SevereSevere ConvectionConvection in SATAID Imagein SATAID Image

Convective Cloud :Pacific Ocean

Very cold,Convective

Clouds (MTSAT-1R, 24 August 2006,23UTC)

This case is also tentative (trial) RGB image composite.

This composite pattern correspond to Severe Convection (RGB 02,04-09,09) inMSG.


In this image, convective cloud appears in yellowish - orangish color.

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Mixture imageIR4 (Rev): RedIR1-IR4: Green

IR1: Blue

Fire detectionFire detectionin SATAID Imagein SATAID Image

Fire (yellow pixels)

Forest Fire : North-westernChina and Siberia

Fire (yellow pixels)

(MTSAT-1R, 30 May 2006, 12UTC)

IR4 RGB

This example is RGB composite imagery of fire detection in north western China and Siberia.

The forest fires are highlighted by 3.8μm（IR4）and its brightness temperature difference (IR1-IR4)imagery.

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Summary of RGB image compositesSummary of RGB image composites

Fast technique for feature enhancement exploitingFast technique for feature enhancement exploitingadditive colour scheme of RGB displays;additive colour scheme of RGB displays;

May require simple manipulation to obtain optimumMay require simple manipulation to obtain optimumcolouring (choice of inverting for IR channels!);colouring (choice of inverting for IR channels!);

More complex RGB schemes may require some timeMore complex RGB schemes may require some timeto get acquainted with;to get acquainted with;

Some RGB schemes may be inherited from otherSome RGB schemes may be inherited from otherimagers (e.g. AVHRR or MODIS);imagers (e.g. AVHRR or MODIS);

Combination of an IR channel with HRV feasible andCombination of an IR channel with HRV feasible andmuch informative;much informative;

RGB image composites retain natural texture ofRGB image composites retain natural texture ofsingle channel images;single channel images;

RGB image composites remain coherent in time andRGB image composites remain coherent in time andspace, i.e. ideal for animation of image sequences.space, i.e. ideal for animation of image sequences.

Extraction from the materials of MSG , EUMETSAT)Extraction from the materials of MSG , EUMETSAT)



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Summary of RGB imageSummary of RGB imagecomposites for MTSATcomposites for MTSAT--1R1R

MTSATMTSAT--1R has only 5 channels, so we can't have1R has only 5 channels, so we can't havevarious combination of RGB composite image.various combination of RGB composite image.

There is a possibility that this method can be appliedThere is a possibility that this method can be appliedto the imagery of MTSATto the imagery of MTSAT--1R, though there are some1R, though there are somelimitations.limitations.

It is necessary to use 3.8It is necessary to use 3.8µµm channel that we have tom channel that we have toconsider solar and thermal contributions.consider solar and thermal contributions.

Other channel selection, gamma correction,Other channel selection, gamma correction,stretching of intensity ranges, etcstretching of intensity ranges, etc……


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THE ENDTHE ENDTHE END

SATAID: mscweb.kishou.go.jp/VRL/

JMA: www.jma.go.jp/jma/indexe.html

The author is most appreciativeThe author is most appreciative

of the kind cooperation of Training Division (EUMETSAT).of the kind cooperation of Training Division (EUMETSAT).

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IR3.9: SOLAR AND THERMALCONTRIBUTION

(Extraction from the materials of MSG, EUMETSAT)(Extraction from the materials of MSG, EUMETSAT)

AnnexAnnex

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SEVIRISEVIRICHANNELS:CHANNELS:

IR3.9IR3.9 mm

Planck blackbody radiance curves

Signal in IR3.9 channel comes fromreflected solar and emitted thermalradiation !

Consequence for Planck relationbetween radiance and temperature:

during day-time, temperature is notrepresentative of any in situtemperature (see next slide) !

AnnexAnnex

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Schematic: BlackbodyRadiation for T=300K

(actual scene temperature)

Wavelength3.9

IR3.9 Radianceat 300K

IR3.9 RadianceMeasurement:

300K + reflectedsunlight

Schematic: BlackbodyRadiation for T=350K

(satellite measured scenetemperature)

RadianceIntensity

AnnexAnnex

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IR3.9: CLOUD PHASEAND PARTICLE SIZE(MAINLY DAY-TIME)

AnnexAnnex

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Reflection of Solar Radiation at IR3.9• Reflection at IR3.9 is sensitive to

cloud phase and very sensitive toparticle size

• Higher reflection from waterdroplets than from ice particles

• During daytime, clouds with smallwater droplets (St, Sc) are muchdarker than ice clouds

• Marine Sc (large water droplets) isbrighter than Sc over land

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MSG-1, 07 July 2003, 11:00 UTC, Channel 04

Due to the high reflectionfrom water droplets atIR3.9, low-level waterclouds are much darker

than high-level ice clouds(during day-time)

IR3.9: CloudPhase

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MSG-1, 20 May 2003, 13:30 UTC

IR3.9: Cloud Particle Size

Channel 04 (IR3.9) Channel 09 (IR10.8)

IR3.9 shows much more cloud top structures thanIR10.8 (very sensitive to particle size)

1 1

1= ice clouds with very small particles2= ice clouds with small particles3= ice clouds with large ice particles

31 1

1

3

3

1

1

1

3

3

3

2 2

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NOISE IN THE IR3.9 CHANNEL

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Noise in the IR3.9 ChannelRadiance

Wavelength (m)

• At IR10.8, equivalent brightness temperatures can be determined very accurately atboth warm and cold scene temperatues

• At IR3.9, the radiance increases rapidly with increasing temperature (see next slide)

• Since measurement accuracy is constant, the result is a much less accuratetemperature measurement at cold scene temperatures in the IR3.9 channel

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Noise in the IR3.9 Channel

At IR3.9, a smallchange in radiancecorrisponds to alarge change intemperature

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During the night, the IR3.9 channelcannot be used for cold cloud tops.

Below BTs of 220 K the IR3.9channel is very noisy (radiancesclose to zero).

RAW RAD TEMP[count] [mW/m2] [K]

54 0.01 21853 0.01 21352 0.00 20551 0.00 131

Noise in the IR3.9 Channel: Example

MSG-1, 14 July 2003, 02:00 UTC, IR3.9

Interpretation: IR3.9 imagery does afine job for warm scene temperatures,but at night it is not useful for coldscenes like thunderstorm tops.

IR3.9: noisy picture

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