Tjemkes.pdf
Towards a consolidated
MTG-IRS L2 processor
Stephen Tjemkes, Xavier Calbet, Alessio Lattanzio,
Rolf Stuhlmann

Slide: 1

Meteosat Third Generation Infrared Sounder (MTG-IRS)
• Hyperspectral sounder in geostationary orbit
• 2 spectral bands (700 – 1210 cm-1; 1600 – 2175 cm-1)
• Moderate spectral resolution (0.625 cm-1)
• Spatial Sampling: 4 km at Sub Satellite Point
• System definition in Phase B.
• System development and on-ground testing (Phases
C/D), commissioning and routine operations (Phase E)
subject to EUMETSAT Special Council decision, 15 march
2010 – Opening for voting on MTG programme proposal.

Slide: 2

Meteosat Third Generation Infrared Sounder (MTG-IRS)
Mission Objective
Monitor of atmospheric state, in particular moisture
Support to emerging air chemistry and air quality
applications
Instrument Characteristics:
large detector array
integration time of 10 sec
large data volume
IRS approx. 2500 spectra / sec
(cf. IASI: 15 spectra / sec)

Slide: 3

Processing flow chart

OFFLine

ACC

SCE

QI
PHYS

PRE

OFFLine
Slide: 4

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 5

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 6

Apodisation

Base the L2 processor on apodised or unapodised data?
Issues are: increased spectral resolution, retrieval
stability, sensitivity to errors in spectroscopy, or
spectral calibration, capabilities of FRTM.

Slide: 7

Radiance at toa

Ri: Radiance at TOA in specific channel

Ri   S   o R d
S: Convolution function
Sinc in case of unapodised data (FFT of box car)
FFT of apodisation function in case of apodised data
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Convolution functions

Slide: 9

Radiance difference at toa for 2 experiments

Calculated radiance at toa with LBLRTM
Experiment 1: Spectral calibration error of 5 ppmv
Experiment 2: Enhanced CO2
Compared to Baseline

Slide: 10

Radiance difference Unapodised

Slide: 11

Radiance difference after Norton-Beer Strong
Apodisation

Slide: 12

Moisture Jacobians unapodised

Slide: 14

Moisture Jacobians after Norton-Beer Strong
apodised

Slide: 15

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 16

Data acceptance:

Dwell 45
Zone 4

Full Disc divided in 4 zones

Zone 3
Zone 2
FDC

Zone 1
Dwell 1

Slide: 17

Scanning Pattern
Zone 4 observed every 30 min in 15 min.

Operational practice for IRS scanning pattern

4.5

0

-4.5

Elapsed tim e (repeated every 6 hours)

6:0
0

5:0
0

4:0
0

3:0
0

2:0
0

1:0
0

-9

0:0
0

IRS elevation viewing angle (degrees)

9

Forw ard
Retrace

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Data acceptance

Dwell 45
Zone 4

Process only Zone 4 data
(at day0)

Zone 3
Zone 2
FDC

Zone 1

Data reduction: 1/2

Dwell 1

Slide: 19

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 20

Scene Analysis

Consider only cloud free scenes
We might consider only one out of a cluster of 3x3
cloud free scenes

Additional Data reduction: 1/4 – 1/40

Slide: 21

First comparison between 2 different methods
AVHRR

EUMETSAT

DIFA

G. Grieco, C. Serio

Slide: 22

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 23

Preprocessing: First guess

Current implementation: Linear statistical regression
based upon EOF.
Currently studying:
Linear: Functional Sliced Inversion
Non-Linear: Artificial Neural Networks, Support Vector
Machines
Objective:
Improved quality
Relax the need for iterative retrieval

Slide: 24

Example preliminary result

G. Camps-Valls et al., Univ. Of Valencia
Slide: 25

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 26

How to handle trace gas information

Mission objective is to support AQ, although IRS is not a
AQ mission. Need to provide information on CO and O3
from IRS observations. Requires active retrieval.
Not clear how to perform this active retrieval to ensure
that the trace gas radiation signature does not
contaminates the T and H2O retrievals.

Slide: 27

Issues being considered
General Processing Issues:
Apodisation
Use of compact representation of radiances (PCA)
Channel Selection
Data Acceptance
LAC 4
Specific Scene Analysis
Pre-Processing
Surface Properties retrieval
Specific Statistical retrieval method to generate First Guess
Iterative Retrieval
Background state and error covariance for iterative retrieval
How to handle CO and O3
Specific Quality Indicators
Offline Processing

Slide: 28

Offline

Radiance monitoring based on collocated upper air and
nwp analysis with detailed LBL calculations.
Product monitoring comparison between L2 products and
collocated independent upper air observations

Slide: 29

IASI NWP based Radiance Monitoring: 24h average
Brightness Temperature at 280K

CO2

Surf

2nd IASI Conference January 2010

O3

CH4

H2O

CO CO2
Surf
Slide: 30
Adopted
from: L. Fiedler

Summary

Briefly reported on the development of a consolidated IRS
L2 concept processor. This development is supported by
large group of scientists working together in the MTGIRS Science Team.

Slide: 31

