1. Inter-calibration of Meteosat’s heritage channels GSICS meeting, March 2013, Williamsburg, USA
- Change title to more general one.
- Focus can stay on the HIRS work, however, given the character of the workshop an introduction on the VIS/Near-infrared work is also needed.
Rob Roebeling, Tim Hewison, Sebastien Wagner, Ilse Decoster, Alessio Lattanzio and Jörg Schulz

2. Outline Motivation Inter-calibration IR and WV channels
Inter-calibration VIS (and NIR) channels
Reprocessing approach
Further work and Discussion

3. Inter-calibration IR and WV channels

4. Study Set-up Objective: Prerequisites: Method
To recalibrate time-series of Meteosat First Generation and Meteosat Second Generation IR radiances from 1982 till date using a superior instrument as reference.
Prerequisites:
Inter-calibration back to 1982
Target accuracy over the time-series better than 1 K
Inter-calibration with uncertainty estimate
Method
Select reference instrument
Assess the uncertainties through systematic review of spectral conversion functions
Define the inter-calibration approach
Reprocess, verify and validate the re-calibrated data record
Instrument drift is SRF shifts & sensor degradation

5. - Review Spectral Conversion Functions -
Preparatory studies
- Review Spectral Conversion Functions -

6. Spectral Conversion Functions - Review
Objective
To develop Spectral Conversion Functions that minimize the differences between reference and monitored instruments and optimise sampling and fitting methods.
Method
Evaluate for a sounding (~6μm) and a window (~10μm) channel.
Calculate MFG, MSG and HIRS-like IR radiances for a selection of ECMWF profiles (from Chevallier 2001) using RTTOV.
Assess uncertainties for different conditions and fitting methods.

7. Spectral Conversion Functions - Uncertainties different WV instruments
Monitored
Reference 
HIRS/2
NOAA6-14
HIRS/3
NOAA15-17
HIRS/4
NOAA18-MetopB
MVIRI
Meteosat 1-3
Meteosat 4-7
SEVIRI
Meteosat 8-11
0.04
1.03
1.07
0.07
0.16
0.41
0.78
0.05
0.06 X
0.67
0.51
0.84
0.03
0.74
0.57
Mean RMSD Tb [K] of Spectral Conversion Functions for each class of instrument: WV
What are the target accuracies and precisions that we aim at

8. Preparatory studies
- L1b Datasets-

9. HIRS L1b differences - 3 ground stations
 BT up to 0.2 K at channel 8
 BT up to 0.5 K at channel 12
Fig: Comparison of HIRS Level 1b Brightness Temperature for datasets retrieved from NOAA-GS (NOAA), EUMETSAT-GS (PPF) and via the EARS reprocessing service. Results are presented for Channel 8 and 12 for a subset of received data.
Courtesy of Joerg Ackermann, EUMETSAT

10. HIRS L1b differences – EUMETSAT vs. NOAA (Lei Shi)
  BT < 0.05 K for all channels
 NOAA files: BT = if BT < 170 or BT > 350
 NOAA files: duplicated scan lines round poles
I've performed the first step of the analysis you requested for. The results are attached for the three days separately (DAY091.txt, DAY092.txt, DAY093.txt). The three columns in each file contain the following information: channel number, mean difference EUMETSAT-NOAA in K, number of data.
This is a first step only and I think it would be useful to have a more comprehensive analysis by e.g. calculating the mean differences for individual temperature intervals and/or plotting the differences as a time series.
1.) The software does not use the little endian vs. big endian option. Therefore, I had to play around with the data until I found the following: two different byte swaps were necessary, before the data look reasonable on an AIX machine. Ask NOAA for either providing ASCII data or a software that is more elaborated than the one they have provided this time.
2.) For a very few number of cases (channels 17, 18, and 19 only), the NOAA files contain the number Apparently, the brightness temperatures calculated by Lei Shi exceed a distinct temperature range (about 170 K to 350 K) that she prescribes. Inform NOAA that this is very critical as it can bias the statistics.
3.) In the NOAA data duplicated scan lines, which occur over Arctic and Antarctic, are not removed. For these overlapping areas, the data sets contain different values for the brightness temperatures for coincident scan lines. Apart of the fact that those time inconsistencies make comparisons with time consistent data sets difficult (pre-processing steps are necessary to tailor the data), it will also bias a climate data record derived from those data. Ask NOAA if it is possible to correct for these duplicated scan lines
I will be away for the next two weeks and we can discuss the results and possible further steps of evaluation after my return on 6. August.
Cheers,
Jörg
Fig: Comparison of Eumetsat and NOAA (Lei Shi data) mean Brightness Temperatures for 3 examples days and 19 HIRS channels
Courtesy of Joerg Ackermann, EUMETSAT

11. - Assessment of HIRS stability -
Preparatory studies
- Assessment of HIRS stability -

12. Temporal Variogram Analysis (Tim Hewison)
Objective: to quantify the instrument calibration drift w.r.t. IASI;
Inter-compare 4 years of MetopA/IASI measurements against Meteosat7/MVIRI and MetopA/HIRS;
Weight the regression of collocated radiance;
Evaluate standard radiance bias as brightness temperature bias, ΔTb;
Calculate variograms from ΔTb time series:
Fig: Variograms Metop-A/HIRS against Metop-A/IASI.
Fig: Variograms Meteosat-7 against Metop-A/IASI.

13. Comparison HIRS against IASI reference
CH 12
Benefits of HIRS as reference:
 HIRS is stable
 Established instrument (operated since 70s)
 On-board calibration
 HIRS/4 on same platform as IASI
CH 11
Radiance Difference in BRT [K]
CH 8
Don’t know where to put this slide
Courtesy of Dorothee Coppens, EUMETSAT

14. Inter-calibration VIS and NIR channels

15. Visible and near-infrared recalibration (S. Wagner)
06 December 2018
Visible and near-infrared recalibration (S. Wagner)
Table : Summary of visible and near-infrared recalibration methods through intercalibration
Method
Channels
(μm)
Refl. Range (%)
Availability
Update Freq.
Errors (1σ)
Sun-glint
0.4–3.9
0-30
Tropics
Daily
~4% inter-band
DCC
0.4–1.1
80-100
Monthly 2%
LWC
30-50
Yes 4%
Deserts
25-45
No (sel. areas)
2-3% rel.
Moon
0.4–2.2 15
Needs scheduling
2-3% rel. 5% abs
Ray-matching
0-100
Global
3% now
2% with SRF
Rayleigh
0.4–0.85
5-10
Global (sel. oceans)
>2%
Stars (abs)
0-1?
Needs navigation
Yearly ??
SCOPE-CM requires a combined method that:
covers the full reflectance range (0-100)
covers at least the spectral range mm (full spectral coverage is desirable)
has a precision better than 3%
can be applied to all GEOs (and LEOs)

16. Preparatory Study - Spectral Aging Models (I. Decoster)
Objective:
To develop a model that describes the spectral aging of the visible channels onboard Meteosat First Generation.
Method:
Challenges
Fitting the model requires sufficiently long time series of observations
Gray degradation Spectral degradation
Instrument drift is SRF shifts & sensor degradation
Fig. Example of spectral aging MVIRI visible channel

17. Preparatory Study - Spectral Aging Models (I. Decoster)
Refl Ratio = ratio between observed broad reflectance and modeled broadband reflectance. Ideally RR should be 1 and show no temporal trend.
Fig. Time series of reflectance ratio before correction
Fig. Time series of reflectance ratio with SSCC model
Fig. Time series of reflectance ratio with Spect. Aging Mod

18. Propagating calibrations back in time

19. Inter-Calibration – Objectives and Method
To define and evaluate methods for generating a Fundamental Climate Data Record (FCDR) of MVIRI/SEVIRI infrared radiances.
Method
Employ Double-differences (DD) to differentiate “cross-platform bias” from “noise”
Use one monitored and two reference instruments in the DD equation:
where
Om: observed monitored data
Or1 and Or2: observed reference data from instrument 1 and 2.
Collect SNO’s of Om, Or1 and Or2;
Note 1: Monitored instrument calibration errors is cancelled out by DD
Note 2: Monitored instrument serves as transfer instrument
Monitored
Reference 2
Reference 1 DD
Fundamental Climate Data Record (FCDR) = long-term data record of calibrated and quality-controlled sensor data designed to allow the generation of homogeneous products that are accurate and stable enough for climate monitoring.

20. Transferring reference: Double Differencing
More explanation needed
Delta 20

21. Further work

22. Further work
To resolve differences between the NOAA and EUMETSAT HIRS level1b data;
To develop and test the Double Differencing approach to inter-calibrate HIRS and Meteosat using Meteosat as transfer instrument;
To prepare a dataset of Meteosat and HIRS Simulations Nadir Overpasses;
To produce the inter-calibrated Meteosat WV & IR FCDR by 2014;
To investigate potential of using aging models for the VIS/NIR channels;
To define a combined approach for the VIS/NIR channels;
To produce the inter-calibrated Meteosat VIS & NIR FCDR by 2016;
To link the IR/WV and VIS/NIR inter-calibration activities with SCOPE-CM projects.

23. Discussion WV and IR re-calibration VIS and NIR re-calibration
How can we best propagating back in time?
Was HIRS on previous missions as stable as it is on METOP?
Should we tie HIRS on Metop to IASI, and use it as reference?
VIS and NIR re-calibration
Should we apply spectral aging models?
What combination of VIS and NIR calibration methods is expected to be precise and applicable to all Geostationary satellites?
Should we focus only on VIS heritage channels, or also on NIR channels?

24. Thank You
Any Questions?