1. Radiometric and spectral inter-comparison of IASI
performed at CNES
Presented by Denis Jouglet (CNES)
Contributions from:
- CNES: C. Maraldi, E. Jacquette, T. Phulpin
- LMD: N. Scott, L. Crépeau, R. Armante
- C-S: C. Quang, X. Lénot
- JC Calvel, C. Baqué
GSICS Annual Meeting, March 2015, New Delhi, India

2. Introduction Objectives of the inter-comparison
For the IASI TEC: External monitoring of the IASI calibration
Participation to the IASI-B in-flight commissioning and routine
Ensure the continuity of IASI-A, IASI-B and IASI-C
For the users (Climatology, Meteorology, GSICS):
To ensure the consistency of the IASI calibration with the TIR sensors community
Checks the long term data quality
3 independent methodologies / tools
Statistics on common geophysical scenes (SNOs and quasi-SNOs) observed by two sensors, to assess the calibration difference only
Quasi-SNOs for IASI-A / IASI-B, SNOs for IASI-A(B) / CRIS, IASI-A(B) / AIRS
Massive means of IASI-A and IASI-B
Comparison of measurements with simulations with NWP + TR (4AOP) for IASI
Progresses
Methodologies 2 & 3 close to be operational
Update with more than 2 years of data now

3. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

4. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

5. Methodology for direct IASI-A / IASI-B
“Similar” scenes:
IASI-A and B are on the same orbit with a 180° shift
 Numerous common observations (CO) between 2 consecutive tracks, but:
never simultaneous: ~50min temporal shift
off-nadir: from 0° to 39°, opposite angles
Selection on the most relevant scenes
Use of geoloc., geom., IIS, AVHRR, ECMWF data
Focus on stable and homogeneous scenes
= Night, mostly oceans, 0% or 100% clouds
Balance “A before B” and “A after B”
For each common observation
Focus on the central area (same atmospheric thickness)
Regional averaging of the soundings (300*300km)
ΔT calculated at elementary channel level
Mean and stdev computed over the dataset
IASI-A Track 2
IASI-B Track 1
IASI-A Track 1

6. Direct IASI-A / IASI-B inter-comparison: results
Biases and standard deviation over the selected dataset
(homogeneous and stable scenes, night, as many “A before B” as “A after B”)
2013
With 1 year of data:
Biases < ~0.1K
 Very good cross calibration
Compliant with the radiometric absolute specification of 0.5K
Highest bias in B1
 Shape still under investigation
2014

7. Temporal evolution
The inter-comparison IASI-B / IASI-A is very stable with time B1 B3 B2

8. Trend with other parameters
● Is the effect in B1 due to non-linearity?
Plot of NEDT vs BT (spectrally integrated in B1, B2 and B3) + Sliding means
Small increase in B1, amplitude -0.1K (cold scenes) to 0 (warm scenes)
Slight non-linearity residuals between IASI-A and B B1 B2 B3

9. Direct IASI-A / IASI-B spectral inter-comparison
Specification for each IASI: Δν/ν < 2 ppm
Methodology:
Definition of 30 spectral windows
For each window, cross-correlation of the IASI-A and -B spectra for different spectral shifts
The maximum of correlation gives the actual spectral shift
Based on the same dataset as the radiometric calibration
Results:
 Performances largely compliant with the requirement
 Very stable with time (after cal/val)

10. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

11. Reminder of AIRS, IASI, CRIS characteristics
Typical IASI, AIRS and CRIS spectra

12. Methodology for IASI / AIRS, IASI / CRIS
Similar scenes: SNOs (Simultaneous Nadir Overpasses)
Tolerance in simultaneity : 20 min
~30 scenes every 3 days for IASI / AIRS (12000 in 5 years)
Always at high latitudes
Spatial match:
 Regional averaging of the soundings pixels over a 300km*300km area around the orbit crossing point
Spectral match:
Construction of 33 broad pseudo-bands
Each PB = intelligent averaging of ~100 elementary channels to get the similarity of the PB spectral functions
The AIRS missing channels and varying spectral resolution are considered when calculating the IASI coefficients
NB: the convolution of IASI by the CRIS or AIRS SRFs has been performed but is still under exploitation
For each pseudo-band,

13. IASI-A / CRIS & IASI-B / CRIS inter-comparison 2014
Biases and standard deviations (no filtering)
Biases < ~0.2K  Very well cross calibrated
Same shape, highest bias in B1, stronger for IASI-B. Spectral slope?
Similar datasets of IASI and CRIS:

14. IASI-A / CRIS & IASI-B / CRIS inter-comparison 2013
Biases and standard deviations (no filtering)
Similar to 2014

15. IASI-A / AIRS & IASI-B / AIRS inter-comparison 2014
Biases and standard deviations (no filtering)
Biases < ~0.2K  Very well cross calibrated
Same shape, highest bias in B1, stronger for IASI-B. Atmospheric shape?
Similar datasets between IASI and AIRS:

16. IASI-A / AIRS & IASI-B / AIRS inter-comparison 2013
Biases and standard deviations (no filtering)
Similar to 2014

17. Temporal Evolution of IASI/AIRS and IASI/CRISB1 B2 B3
IASI-A / CRIS
IASI-B / CRIS
IASI-A / AIRS
IASI-B / AIRS
 All couples are very stable with time (a bit lower in B1)

18. Non-linearity effects of IASI/AIRS and IASI/CRIS ?
Trend of B1 NeDT wrt B1 BT
 No obvious trend?

19. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

20. Indirect IASI-A / IASI-B through AIRS and CRIS
Combination of IASI / AIRS and IASI / CRIS for IASI-B / IASI-A
IASI-B / CRIS – IASI-A / CRIS
2014 (1 year)
IASI-B / AIRS – IASI-A / AIRS
2014 (1 year)
IASI-B / CRIS – IASI-A / CRIS
2013 (1 year)
IASI-B / AIRS – IASI-A / AIRS
2013 (1 year)
 Very similar from year to year

21. Indirect IASI-A / IASI-B through AIRS and CRIS
Combination of IASI / AIRS and IASI / CRIS for IASI-B / IASI-A
IASI-B / CRIS – IASI-A / CRIS
2014 (1 year)
IASI-B / AIRS – IASI-A / AIRS
2014 (1 year)
All biases agree: ~0.1K
 Confirms the very good cross calibration
Always an effect on B1, IASI origin?
Small differences in B1: dataset selection, e.g. colder?
Direct IASI-B / IASI-A in PB

22. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

23. Difference of massive means IASI-B - IASI-A (from T. Phulpin)
Independent method: averaging of all L1C radiance spectra for IASI-B and IASI-A
Hyp: in 1 year, both sounders globally sees the same scenes
Data from Feb 2014 to Jan 2015
~65% cloudy; surf. temp. distrib. (1σ): 250K-290K
Difference IASI-B – IASI-A (NEDT) from these mean radiances:
Same amplitude (bias ~0.1K in B1) and same shape as IASI-B / IASI-A from SIC
Influence of the dataset: here also the input dataset may be a bit different between A and B (Ext. Cal., different AVHRR cloud flags, etc.)
Massive means on all scenes

24. Difference of massive means IASI-B - IASI-A (from T. Phulpin)
Independent method: averaging of all L1C radiance spectra for IASI-B and IASI-A
Hyp: in 1 year, both sounders globally sees the same scenes
Data from Feb 2014 to Jan 2015
~65% cloudy; surf. temp. distrib. (1σ): 250K-290K
Difference IASI-B – IASI-A (NEDT) from these mean radiances:
The AVHRR cloud mask differs from MetOp-A to MetOp-B, causing larges differences in massive means (calibration of channel 5?)
Massive means on all scenes
Massive means on clear scenes
Massive means on cloudy scenes

25. Introduction
IASI-A / IASI-B direct inter-comparison by quasi-SNOs
IASI / CRIS, IASI / AIRS direct inter-comparison by SNOs
IASI-A / IASI-B inter-comparison via CRIS & AIRS
IASI-A / IASI-B inter-comparison by massive means
IASI-A / IASI-B inter-comparison via NWP+4AOP
Conclusions

26. LMD Protocol (*) for the Quality assessment of satellite L1 and L2 data: Stand Alone Approach
(*) Since mid 1990s and the NOAA/NASA TOVS Pathfinder Programme
Scene classification: Airmass?
Clear? Cloud? Aerosols?
Obs’d Satellite Data (BTs)
ECMWF Analysis +
Simulated Satellite Data (BTs)
Statistics
Residuals
« simulated_observed » BTs
Land/Sea/Day/Night/Secant, Airmass…

27. Work dedicated to IASI
The tool IASI stand-alone is being operationalized to be operated by CNES
1 year has been processed (Dec 2013 – Nov 2014)
Space (2.5km) and time coloocated with ECMWF analysis (0h, 6h, 12h, 18h; 137 vertica levels) but future with reanalysis or ARSA
Entire globe -180°:+180° / -90°:90°
Here only see at night, but future land/sea and day/night
Only clear scenes (clouds determined by the LMD Cloud/Aerosol Detection Alg. Based on MetOp IASI/AMSU/MHS/HIRS data
Done for IASI-A and IASI-B  Double differences

28. Calc – Obs (IASI-A, IASI-B) and double differences
The inter-comparison IASI-B – IASI-A by double differences is almost< 0.1K
Exceptions where the TR is not well modeled
No larger bias in B1, but the scene distribution is different from quasi-SNO method

29. Residuals for B1 wrt Brightness Temperature
For Channel cm-1
Left : IASI-A, Right : IASI-B
In cyan : mean +-stdv of residuals within 10 BTs classes [210:310:10] (in K)
The trend wrt brightness temperature seen by quasi-SNO method is not clear here

30. CONCLUSIONS ● Progresses:
3 operational tools, independent methodologies
Trends confirmed with >2 years of data mission
Major result: very accurate cross-calibration!
IASI-B very close to IASI-A (bias < ~0.1K)  continuity of the IASI mission
IASI / AIRS / CRIS: Bias between 0K and 0.2K, < radiometric absolute specification of 0.5K
All are very stable with time
3 methodologies agree: very confident
Larger bias in IASI B1, stronger in IASI-B: non-linearity in IASI?
The observed bias is still high for climatic time series
On-going work:
Go further in the interpretation of the shape of the bias curves:
Impact of the linearity tables and maybe update in IASI board processing
Get the bias curves for each IASI pixel
Fine tuning of the selection rules to reduce the IASI-B/IASI-A quasi-SNOs dataset
IASI complete uncertainty budget (GRWG_14.13)

31. Thank you for your attention

32. Backup slides

33. Methodology for direct IASI-A / IASI-B
Similar scenes:
IASI-A and B are on the same orbit with a 180° shift
 Numerous common observations between 2 consecutive tracks, but:
never simultaneous: ~50min temporal shift
off-nadir: from 0° to 39°, opposite angles
The numerous common observations makes a pre-filtering compulsory
 On the most relevant scenes
IASI-A Track 2
IASI-B Track 1
IASI-A Track 1
Spatial match:
Regional averaging of the soundings
(area 300km * 300km or less)
Spectral match:
ΔT calculated at elementary channel level
 Mean and stdev computed over the dataset

34. IASI-A / IASI-B : selection of scenes
Selection aims at reducing the effects of :
50 min time delay
Need for stable scenes
Need for homogeneous atmospheres (same atm. even if moving)
Off-nadir geometry = The lines of sight A & B are always different
Focus on the central area  same atmospheric thickness
Need for homogeneous atmospheres (typical size ~16km)
Selection by a relevance index
Assessment of several parameters, each parameter is given a mark, then a global mark is computed with weights
For homogeneous scenes:
Low inter-pixel and intra-pixel variance of the IIS imager for A & B
Clouds & snow: none or full in A & B
For stable scenes:
Focus on oceans at night
Low differences in IIS imager A & B temperatures
Clouds & snow: same amount between A & B
Low variations in ECMWF profiles (“Geophysical NeDT”)
“Geophysical” daily NeDT
0.74K
19.92K

35. IASI-B BB and CS1 measurements
The radiometric calibration uses two reference points (internal Cold Space and warm black body)
CS is colder for IASI longest wavelengths
 The non-linearity is likely to be higher in IASI longest wavelengths

36. IASI / AIRS spectral matching
Work with IASI L1C, AIRS L1B
Method: 33 broad pseudo-bands (PBs)
1 PB = summation of ~100s of elementary channels (most widths between 23 and 63 cm-1)
Reduces noise and spectral resolution differences
AIRS spurious channels: taken into account through a weighted summation of the IASI channels (weighs are computed to make the resulting PB response functions similar in IASI and AIRS)
Comparison of ΔT = TIASI - TAIRS in each PB
Other methods under progress
similar channels (statistical similar behavior)
convolved channels
Instrumental functions of one PB for AIRS (including spurious channels), for IASI without weighting in the channels summation and for IASI with weighting

37. Necessity of tuning the dataset selection
Nominal case (night, oceans, as many “A before B” as “A after B”):
Only “A before B”:
Stringent quality index:
 Standard deviation mainly due to geophysics
Only diurnal data:
 Major impact of the input dataset, all parameters must be balanced

38. Features of the selected dataset
Goal for these parameters:
diversity
Latitudes
Longitudes
IASI 870cm-1
Sat Zen Angle
Goal for these parameters:
minimization
Intra-pixel IIS variance
Inter-pixel IIS variance
Difference of mean IIS
Difference of cloud fraction
Cloud fraction
Continental fraction