1. Variogram Stability Analysis
Tim Hewison, Rob Roebeling EUMETSAT

2. HIRS Data – Objectives Prerequisites and Benefits
To recalibrate time-series Meteosat First Generation and Meteosat Second Generation infrared 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 and validate the Data processing and verification
Instrument drift is SRF shifts & sensor degradation

3. METEOSAT 1984-2005 Archive evaluation using radiosondes
Upgrade of calibration technique
(van de Berg, et al., 95)
Upgrade of calibration technique
(Schmetz, 1989)
ISCCP DX Normalized Instead of nominal
Comparisons between the METEOSAT BTs and the simulated BTs from radoisoundings: (+) represent the raw data, (◊) represent the homogeneised data. The histogram shows the nb of soundings used for comparison.
Can we do better than that and extend to SEVIRI?
Courtesy of Helene Brogniez and Rémy Roca, LMD

4. Traditional inter-calibration approach
Slide: 4

5. Proposed GSICS inter-calibration approach
Delta Correction to transfer from one reference to another
Defined as differences between inter-calibration functions
Defined in channel-space of monitored instrument
No need for direct comparisons of references
More explanation needed
Delta
Slide: 5 5

6. Proposed GSICS inter-calibration approach
Delta time steps inserted for illustration only
In practice, deltas defined from simultaneous double-differences
Make lines transparent
Delta
Slide: 6 6

7. Uncertainties of Meteosat-HIRS inter-calibration
Uncertainties introduced due to:
SBA due to SRF differences
Systematic and Random:
Spatial
Temporal
Radiometric Noise
Aliasing diurnal cycle variation
Due to orbital drift
Radiometric calibration drift
- Quantified with SBAF }
}- GEO-IASI Unc Anal
Negligible?
Quantified by Tett et al.
Quantified here!

8. Method Can quantify instrument calibration drift wrt IASI
Using 4-year time series of inter-comparisons
Meteosat7/MVIRI-MetopA/IASI
MetopA/HIRS-MetopA/IASI
(MetopA has been in stabilised 21:30 orbit )
Weighted Regression of collocated radiance
Evaluation of standard radiance bias (for 1976 US Std Atm)
As brightness temperature bias, ΔTb
Calculate temporal variogram from ΔTb time series:

9. Time Series of MetopA/HIRS Bias wrt MetopA/IASI
MetopA/HIRS Ch12 & Ch8
~ Meteosat/MVIRI “WV” and “IR”
Processed using GSICS GEO-IASI ATBD
GSICS RA Correction coeff
Evaluated Bias for Standard Scene radiance
Expressed as Tb
Ch12: small, irregular var
Ch8: small annual cycle
Time Series of Standard Biases for Ch12 and Ch8 of Metop-A/HIRS calculated with reference to Metop-A/IASI.

10. Variograms for MetopA/HIRS Ch12 & Ch8
“WV” and “IR” channels of Meteosat/MVIRI
Variograms increase with time interval 1-29d
Averaging period used in GSICS Correction
“WV” Maximum at Δt=1yr:
(2γt)1/2=33.8±2.2 mK
“IR” Minimum at Δt=1yr:
(2γt)1/2=9.0±0.6 mK
Error budget contributions
Negligible wrt SBAF
Temporal variograms [(2γt)1/2] calculated as RMS differences in Standard Biases for Ch12 and Ch8 of Metop-A/HIRS calculated with reference to Metop-A/IASI. Vertical lines show periods of 29d and 1yr.

11. Time Series of Meteosat7/MVIRI Bias wrt IASI
Meteosat7/MVIRI “WV” and “IR”
Processed using GSICS GEO-IASI ATBD
GSICS RA Correction coeff
Evaluated Bias for Standard Scene radiance
Expressed as Tb
WV: strong yearly cycle
IR: twice-yearly cycle
+ long-term drift
(ice contamination?)
Time Series of Standard Biases for “WV” and “IR” channels of Meteosat7/MVIRI calculated with reference to Metop-A/IASI.

12. Variograms for Meteosat-7/MVIRI “WV” and “IR”
“WV” and “IR” channels of Meteosat/MVIRI
Variograms increase with time interval 1-29d
Averaging period used in GSICS Correction
“WV” Minimum at Δt=1yr:
(2γt)1/2=65±4 mK
“IR” Minimum at Δt=1yr:
(2γt)1/2=187±12 mK
Higher “nugget values”
More collocation noise
Higher “sill values”
Faster calibration drift
Temporal variograms [(2γt)1/2] calculated as RMS differences in Standard Biases for “WV” and “IR” channels of Meteosat-7/MVIRI calculated with reference to Metop-A/IASI. Vertical lines show periods of 29d and 1yr.

13. Conclusions Calibration drift of MetopA/HIRS channels:
Ch12 (“WV”): 34mK over 1yr << SBAF uncertainty (~0.74K)
Ch8 (“IR”): 9mK over 1yr << SBAF uncertainty (~34mK)
HIRS may serve as stable inter-calibration reference for WV+IR
But HIRS on other platforms suffer from orbital drift
Changing instrument environment (temperature, stray light)
Aliasing diurnal cycle
Calibration drift of Meteosat7/MVIRI channels:
“WV”: 65mK over 1yr << SBAF uncertainty (~0.74K)
“IR”: 187mK over 1yr > SBAF uncertainty (~34mK)
Better to use HIRS to bridge time periods between Meteosats?
Or apply bridge at same time of year to minimise variance?