RGB Products: an easy and practical
way to display multispectral satellite
data (in combination with derivedproducts)

Dr. Jochen Kerkmann
Training Officer
EUMETSATMulti-channel GEO satellites today
Him-08 (140.7 E)

GOES-16 (89.5 W)

Met-10 (0)Met-08 (41.5 E)

20 March 2017, First Global Airmass RGB Composite
Credits: HP Roesli

How to display multispectral data?
Why do we need RGB products, when
we have derived (cloud) products?

Multispectral view of tomatoes & clouds

NorainRain

Credits: Prof. Danny Rosenfeld, Jerusalem (HUJ)

MODIS Airborne Simulator-L1B
02-954-06
26 Jul 2002
18:44 GMT
Caribbean Sea

Flight level 20 km
Resolution 50 m at NADIR
Swath ± 45°, With=37 km

Red:0.63 mm

Green: 2.06 mm
Blue: 2.11/2.21 mm

Credits: Prof. Danny Rosenfeld, Jerusalem (HUJ) 12/20/2017

Cloud Properties seen in Day Microphysics RGB

Cirrus with large
crystals

Nimbus

Dissipating Nimbus“warm” large ice
particles
Layer clouds oflarge water drops

Mixed phaselayer clouds

snow
surface

8 October 2003, 12:00 UTC
Source: D. Rosenfeld

Snow in the air downhill the stau
cloud after its supercooled clouddrops evaporated

Comparison 1: RGBs vs Cloud Type Product21Cloud Type Product

Day Microphysics RGB

Texture gets lost, no cloud phase info

Met-8, 21 March 2007, 12:00 UTC

Comparison 2: RGBs + Ash ProductBest use: RGBs + Derived ProductDerived product (ash mask) overlaid on Ash RGB

15 April 2010, 09:00-15:00 UTC

Source: M. Pavolonis

Main Improvements from multi-channel(GEO) Imagers

New Generation Improvements: Clouds at Night

India
Fog/Low
Stratus (night)

AHI IR10.4 Channel

AHI Night Micro RGB

New Generation Improvements: VegetationGrasslandRiceFields

MFG VIS Channel

MSG Natural Colours RGB

New Generation Improvements: Cloud / Snow DiscriminationFog

MFG VIS Channel

MSG RGB NIR1.6, HRV, HRVNew Generation Improvements: Haze, Dust, Ash, Smoke

DustCloudMFG IR Channel

MSG Dust RGB

New Generation Improvements: Cloud Particle Size

SmallIce
Particles

MFG IR Channel

MSG Convection RGB

New Generation Improvements: Thin Clouds

Thin Ci
Clouds

GOES-16 Nat. Colour RGB(non-operational data)

GOES-16 Dust RGB

New Generation Improvements: Cloud Classification

Thin HighClouds

Thick HighClouds
Low Clouds

GOES-16 Cloud Type RGB(non-operational data)

GOES-16 Dust RGB

New Generation Improvements: Moisture Boundaries

Diurnal development of thesea-breeze front in Yemen

New Generation Improvements: Wind Shear Observation
Himawari-8 product courtesy JMA/CIMSSAsh RGB Himawari-8 data (2km resolution)

MTSAT images courtesy JMA/BOM
11 micron IR MTSAT data (4km resolution)

Klyuchevskoy

3.7 micron NIR MTSAT data (4km resolution)

11-12 micron IR MTSAT data (4km resolution)

Raung Plume, Java (10th July 2015)

GUIDELINES FOR CREATING RGBs

Rules for Creating “Good” RGB Products
Select three channels or channel differences that
represent three different physical properties !!!
Example: MSG Window Channels
ChannelMain Cloud Physical Properties (clouds, NADIR viewing)

01 (VIS 0.6)

optical thickness, amount of cloud water and ice

02 (VIS 0.8)

optical thickness, amount of cloud water and ice

03 (NIR 1.6)

optical thickness, particle size & shape, phase

04 (MIR 3.9)07 (IR 8.7)

Day-time: top temperature, particle size & shape, phase
Night-time: top temperature (very noisy below -50°C)
top temperature

09 (IR 10.8)

top temperature10 (IR 12.0)

top temperature

Not Recommended RGB IR8.7, IR10.8, IR12.0
20/12/2017

MSG-1, 21 January 2005, 12:00 UTC21

Recommended RGB VIS0.8, IR3.9r, IR10.8
20/12/2017

MSG-1, 21 January 2005, 12:00 UTC22

RGB PRODUCTS FOROPERATIONAL FORECASTINGMost important RGBs (for Operational Forecasting)

24-h Microphysics (Dust) RGB

28 January 2013, 12:00 UTC

Airmass RGB

High

PV Anomaly

Deformation Zone

Jet (polar)

Jet (subtropical)

Example: Jet Streaks

Him-08, 17 October 2017, 12:00 UTC

Example: Jet Streaks + Moisture Rivers

Him-08, 17 October 2017, 12:00 UTC

Example: Deformation ZoneExample: Deformation Zone

XCOLX

COLNX

NXNXNNNExample: Deformation Zone
Ash cloud from Iceland as tracer

Source: HP. Roesli

Example: Two PV anomalies merge over Europe(Fujiwhara effect)Met-10, 5-6 April 2017

Example: Low Clouds

Comparison Met-8 vs Met-9

1 May 2007, 14:00 UTC

Source: HP. Roesli

Example: Dust crosses the Atlantic Ocean

Source: HP. Roesli

22-25 June 2014

Example: Volcanic Ash (Etna, Italy)

Source: HP. Roesli

12 August 2011, 09:00-13:00 UTCExample: Thin Ice Clouds (Lee Clouds)

MSG-1, 12 November 2006, 23:00 UTC

20/12/2017

Example: Cloud Phase

IceWatercloudcloudMet-8, 16 Aug 2006, 04:00 UTC36

OTHER RGB PRODUCTS

Day Microphysics RGB, 19 January 2017, Example of polluted airmasses

LH
Snow

L

Convection RGB, … dust changes cloud microphysics

small iceparticles(IR3.9r > 10%)24-h Dust

Day Microphysics

Convective Storms

Met-8, 22 February 2007, 12:00 UTC

Night Microphysics RGB, 19 January 2017, snow detection at night

Snow

Stratus
Snow
Mid-levelwater clouds
17 January 2006, 16:00 UTC

Convection RGB, small ice particles in severe thunderstorms

Multispectral View of Convective Storms

HRVRGB Convective Storms
Met-8, 29 June 2006

New Developments
Presented at 2017 RGB workshop

StandardTropicalTropical Airmass RGB (tuned for OTs)

Overshooting Tops in Typhoon Koppu (Pacific)
15 October 2015, HIM-08, AHI, Tropical Airmass RGB
2 km / 2.5 minutes

Tuning of AHI Dust RGB (for dust, cloud phase, moisture)
Red: IR12.4 – IR10.4, Green: IR11.2 – IR8.6, Blue: IR10.4(uses 4 (not 3) IR channels)A: thick dust
B: thin dust
C thin Cirrus
D: Thick Cirrus
E: Mid clouds
F: Low clouds
G: thin dust

ABGFC

DE

Credits: D. Gencic

Him-08 Tuned Dust RGB

Convective outflow boundaryIndia

Him-08 Ash RGB (source: CIRA)
Kamchatka

Him-08 Ash RGB (source: CIRA)

Animation of volcanic plume and contrailsHim-08 Tuned Dust RGB

moist

dry

Australia
ice
water
water
ice

thin ice

New
Him-08 ofRGB:
Phase
RGB
The
dilemma
4 IRCloudwindow
channels- Tuning of the Dust RGB to ABI / AHI –- J. Kerkmann, EUMETSATRed: IR12.4-IR10.4 (-6, +4 K)
Green´: IR11.2-IR8.6 (0, +10 K)
Blue: IR10.4 (273, 300 K)

AHI, Dust RGB (tuned)
Him-08, AHI, Cloud Phase RGB (05-06-02)

Animation Cloud Phase RGB, Australia

GOES-16 Cloud Phase RGB (non-operational data)

Cloud Phase RGB

Convection RGB

Supercell, Texas, 16 May 2017, 21:27 – 23:02 UTC (5 min intervals)

Fires in Australia, AHI, New Fire Temperature RGB
16-17 October 2017

TemperatureLevel
1 2 3Credits: CIRA

Him-08 True Colour RGB (source: CIRA)

A wall of pollution crosses the Sea of Japan
27-28 May 2017

Him-08 True Colour RGB (source: CIRA)
Spiegel Online: Smog alarm in India
7 November 2017, 03:20 UTC

Him-08 Geo Colour RGB (source: CIRA)

Smog in India

Him-08 True Colour RGB (source: CIRA)
Spiegel Online: Smog alarm in India
7 November 2017, 09:30 UTC

GOES-16 True Colour RGB (source: CIRA)(non-operational data)
Jose’

Mariadustsmoke

21 September 2017, 13:45 UTC

Him-08 Dust RGB (source: ePort, EUMeTrain)

High level smoke

Lake Baikal

23 June 2017, 12:00 UTC

MODIS image (NASA)Him-08 Dust RGB (source: ePort, EUMeTrain)

Russia (Siberia)
Very cold
land surfaces

27 January 20176, 06:00 UTC

Him-08, Convection RGB (source: JMA)

High-base convection

India

2 May 2016, 02:30-12:00 UTC

Him-08, Convection RGB (source: ePort, EUMeTrain)

Australia

High
SupercooledClouds

28 September 2017, 00:00 UTC

Impact of viewing angle on dust detection
Source: ePort Pro (EUMeTrain)

Thin
Dust

SenegalMet-08

Met-104 July 2017, 06:00 UTC

Dust (and smoke) often better detected at high viewing angles(NADIR view is not always the best one)

Impact of viewing angle on fog detection
Source: ePort Pro (EUMeTrain)

Qatar

Low viewing angle

High viewing angle

Met-08

Met-10Thin low clouds look thicker (better contrast) when viewed at high viewing angles !

Limb cooling in Airmass RGB

India

Met-08Him-085 September 2016, 12:00 UTC
Bluish limb area
In Met-08 on eastern side
In Him-08 on western side

Summary (Dust RGB Animation, source: CIRA)High winds produce massive dust storm along China-Mongolia border- 3 May 2017 -