Double differencing of IASI-A/B against Meteosat/SEVIRI IR Channels Tim Hewison (EUMETSAT) Introduction: EUMETSAT Calibration Team + GSICS IR Sub-Group + co-authors Aims: To introduce GSICS and our activity to generate the IR Reference Sensor Traceability & Uncertainty Report Also, consideration for the handling of processing changes to the reference sensors (such as IASI).

Overview Double-Difference MetopB/IASI - MetopA/IASI IR Reference Sensor Traceability & Uncertainty Report Possible strategy for migrating GSICS products from IASI-A to IASI-B

GSICS GEO-LEO IR Double Differences Time series of Bias in Meteosat-10/ SEVIRI IR13.4 wrt IASI-A wrt IASI-B For standard scene radiance (267K) Over 1st 4 yr overlap Biases vary Ice contamination Range -0.4 to -2.7K Differences <0.1K The SEVIRI imagers on MSG generally have much smaller biases, except the 13.4µm channel shown here In fact, we can use the GSICS Corrections to estimate the bias wrt two reference instruments – IASI A & B - These vary of their first 3 years overlap period With small, systematic differences

GSICS GEO-LEO IR Double Differences Time series of Bias in Meteosat-10/ SEVIRI IR13.4 wrt IASI-A wrt IASI-B For standard scene radiance (267K) Last 4 yr overlap Biases vary Ice contamination Range -0.5 to -2.0K Differences <0.1K The SEVIRI imagers on MSG generally have much smaller biases, except the 13.4µm channel shown here In fact, we can use the GSICS Corrections to estimate the bias wrt two reference instruments – IASI A & B - These vary of their first 3 years overlap period With small, systematic differences Operational GSICS Products – Zoom on period Jan 2013 - Nov 2017 - IASI-B step change -0.1K in August 2017

Time series of Double Differences (SEVIRI-IASIA)-(SEVIRI-IASIB) No Obvious Trend in Any Channel!  We can extend the analysis to calculate the double difference between IASI-B-A in each of SEVIRI’s 8 IR channels No trend in any channel over this 4 year period Good news – implies their calibration is stable – so we can combine whole period However, there are small differences in the long-wave channels Small differences in long-wave channels 

Statistics of Double Difference Time Series (MSG3-IASIA)-(MSG3-IASIB) OPE RAC Std Bias over 2013-03-18/2017-08-02: Channel Double Difference Trend [K/yr] Mean Double Difference [K] IR3.9  -0.003 +   0.004 -0.010 0.005 IR6.3  -0.007 -0.002 0.004 IR7.4   0.003 0.001 0.003 IR8.7  -0.005   0.002 -0.013 IR9.7  0.003   0.008 -0.048 0.007 IR10.8  -0.008 -0.024 IR12.0 -0.029 IR13.4  -0.006 -0.040 No statistically significant trend in any channel Within standard uncertainty of ~3mK/yr Can combine entire period Consistent results from other Meteosats But larger uncertainties No statistically significant difference between IASI-A and -B in Short- and Mid-bands in any channel Small, but significant difference in long-wave band Larger for colder scenes Table just says the same thing in numbers Highlighting the relative stability, which can be measured to ~3mK/yr And the small size of the relative bias ~40mK

2013-03/2017-03 (SEVIRI-IASIA)-(SEVIRI-IASIB) - Tb Mean Difference dTb [K] Channel 50% [cm-1] 200 210 220 230 240 250 260 270 280 290 300 K IR13.4 714 782 -0.30 -0.24 -0.19 -0.15 -0.12 -0.09 -0.06 -0.04 -0.02 0.00 0.02 IR12.0 800 870 -0.16 -0.13 -0.11 -0.07 -0.05 -0.03 IR10.8 885 971 -0.27 -0.21 -0.10 -0.08 IR9.7 1018 1047 -0.14 -0.01 IR8.7 1124 1177 IR7.3 1316 1409 0.01 IR6.2 1493 1724 0.03 IR3.9 2385 2751 Meteosat-10/SEVIRI IR But the magnitude of these double differences strongly varies with scene radiance - It is much larger for colder scenes

Time series of Double Differences – IASIB-IASIA - Zoom 2016/7 2017-08-02 Changed IASI-B on-board processing Improved non-linear correction Will update for IASI-A in 2018 And zooming in on the last 2 years highlights a change in the IASI-A/B double differences Corresponding to the change in the onboard processing introduced on 2 Aug to improve the non-linear correction Causing the previous small negative bias in IASI-B to become a small positive one of similar magnitude This is expected to change again to ~zero when the counterpart change is rolled out to IASI-A in 2018 (which is smaller)

Overview Double-Difference MetopB/IASI - MetopA/IASI IR Reference Sensor Traceability & Uncertainty Report Possible strategy for migrating GSICS products from IASI-A to IASI-B

IR Reference Sensor Traceability & Uncertainty Report Aims To support choice of reference instruments for GSICS To provide traceability between reference instruments (IASI, AIRS, CrIS) To seek consensus on uncertainties in absolute calibration of reference sensors By consolidating pre-launch test results and various in-flight comparisons Limitations No new results, just expressing results of existing comparisons in a common way, reformatting where necessary, to allow easy comparisons 1. Error Budget & Traceability Focus on radiometric and spectral calibration – for AIRS, IASI, CrIS 2. Inter-comparisons Introduction: Pros and Cons of each method Direct Comparisons: Polar SNOs, Tandem SNOs (AIRS+CrIS), Quasi-SNOs, Double-Differencing: GEO-LEO, NWP+RTM, Aircraft campaigns Other Methods: Regional Averages (“Massive Means”), Reference Sites (Dome-C..) So within GSICS we have started to develop a report to …

Suomi-NPP CrIS Radiometric Uncertainty Estimates – D. Tobin Differential error analysis of the calibration equation, aimed at providing a useful estimate of the absolute accuracy of the mean of a large ensemble of observations. Input parameter uncertainties are based on the design of the sensor and engineering estimates of the calibration parameters; i.e. no external information via external “Cal/Val” used. Tobin et al. (2013), Suomi-NPP CrIS radiometric calibration uncertainty, J. Geophys. Res. Atmos. Example 3-sigma RU estimates for a typical warm, ~clear sky spectrum: Simplified On-Orbit Radiometric Calibration Equation: REarth = Re {(C’Earth – C’Space) /(C’ICT-C’Space)} RICT ‘ with: Nonlinearity Correction: C’ = C  (1 + 2 a2 VDC) ICT Predicted Radiance: RICT = eICT B(TICT) + (1-eICT) [ 0.5 B(TICT, Refl, Measured) + 0.5 B(TICT, Refl, Modeled)] Parameter Nominal Values 3-s Uncertainty TICT 280K 112.5 mK eICT 0.974-0.996 0.03 TICT, Refl, Measured 1.5 K TICT, Refl, Modeled 3 K a2 LW band 0.01 – 0.03 V-1 0.00403 V-1 a2 MW band 0.001 – 0.12 V-1 0.00128 – 0.00168 V-1 A nice example of the first is the differential error analysis of S-NPP/CrIS conducted by Dave Tobin: Where he takes the basic radiometer equation and estimates the uncertainty on each input term, and propagates these through the equation to estimate the Radiometric Uncertainty (RU) on the calibrated radiance Again, expressed here in BT – and as 3-sigma, which is generally <0.2K and ~0.1K for warmer scenes and the mid- and short-wave channels RU is generally 0.2K 3-sigma or less, and similar results for JPSS-1 CrIS Currently working to include (relatively small) polarization effects into the calibration algorithm and corresponding RU estimates

IR Reference Sensor Inter-Comparisons Mean Difference dTb [K] Pseudo Channel [cm-1] Min Freq [cm-1] Max Freq [cm-1] 200 220 240 260 280 300 Start date 2013-08-01 655 650 660 -0.11 -0.10 -0.08 NaN End date 2016-06-30 665 670 -0.09 Start time 21:53 675 680 -0.06 -0.07 End time 23:21 685 690 -0.05 Min Latitude [°] -74.31 695 700 -0.04 Max Latitude [°] 74.31 705 710 -0.03 Min Longitude [°] -179.87 715 720 Max Longitude [°] 180 725 730 -0.02 Min Scan Angle [°] 735 740 -0.01 Max Scan Angle [°] 3.5 745 750 -0.12 755 760 Collocation method: Big circle SNO 765 770 Collocation dist [km] 50 775 780 -0.50 Collocation time [s] 1200 785 790 -0.15 Collocation sec(theta) 795 800 Filtering applied see ref 805 810 -0.24 815 820 -0.17 Algorithm Ref JGR Tobin 2016 825 830 -0.18 Dataset Ref 835 840 Monitored Instrument IASI-A L1C 845 850 -0.16 Processing Version latest 855 860 CLASS 865 870 Reference Instrumemt CrIS NSR SDRs 875 880 -0.13 CSPP 885 890 895 900 Form consensus on relative calibration Re-binning results of existing comparisons Biases with respect to Metop-A/IASI With standard uncertainties (k=1) At full spectral resolution In 10cm-1 bins within AIRS bands Or average results over broad-band channels Converted into Brightness Temperatures For specific radiance scenes i.e. 200K, 210K, … 300K For all viewing angles and/or for specific ranges - e.g. nadir ±10° Over specific period Common 4-year period from IASI-B start Meteosat-10/SEVIRI IR 2013-03-01/2017-03-01 Himawari-8/AHI IR 2015-07-01/2017-06-30 For the second part we have set up a common spreadsheet to allow us to compare a variety of inter-comparisons between hyperspectral reference sensors On a common scale to form a consensus on their relative calibration … For example results are shown here for the double-differences for all the IR channels of Met-10 and Himawari-8 Again as a function of the scene TB Together with the uncertainties of the comparisons Both of which follow similar patterns

IR Reference Sensor Traceability & Uncertainty Report Conclusions IASI Changes IASI-A/B double difference was stable over >4yr Small differences in long-wave band – addressed by changes to non-linearity Gradual roll-out allows characterization of impact Cannot be completely corrected in re-processing IR Reference Sensor Traceability & Uncertainty Report supports choice of GSICS reference instruments provides traceability between reference instruments consolidates pre-launch test results and various in-flight comparisons forms consensus on uncertainties in absolute calibration of reference sensors So to conclude that: While GSICS now provides operational calibration corrections for the IR channels … … We are now establishing routine comparison of results to monitor relative calibration We will use these as part of a report on the Traceability and Uncertainty of our IR reference instruments, which … These comparisons can also highlight any changes in the references instruments’ calibration For example, IASI-A/B, where the double difference was stable over >4yr, With Small differences in long-wave band, which are being addressed by introducing changes to non-linearity Important that the gradual roll-out of these changes allows characterization of impact However, they cannot be completely corrected in re-processing and may require an empirical approximation to support the generation of FCDRs

Possible strategy for migrating GSICS products from IASI-A to IASI-B Tim Hewison (EUMETSAT) Introduction: EUMETSAT Calibration Team + GSICS IR Sub-Group + co-authors Aims: To introduce GSICS and our activity to generate the IR Reference Sensor Traceability & Uncertainty Report Also, consideration for the handling of processing changes to the reference sensors (such as IASI).

Options for NRTC Generation Just switch RefA->RefB Single reference w/jumps Switch RefA->RefB+∆ After period to test stability Single reference, no jumps Merge all Refn+∆n Multiple refs, no jumps Prime GSICS Corrections Just average all* Refs Multiple refs, with jumps Assign uncertainty to cover RefA ∆AB RefB ∆BC RefC Time

Pragmatic Approach for NRT GSICS Corrections IRRefUTable analysis of reference sensors’ performance: Ascertain which reference instruments consistent With consensus (of all references) Within given uncertainty, uLref(λ, Tb) Accept all qualified references Quote absolute uncertainty of GSICS Corrections As uLref(λ, Tb) Simple! But acceptable?

Options for FCDR Generation Define ∆ Corrections Based on stable overlaps Apply to all* references * at least all with stable references Merge together Applying delta corrections “Prime GSICS Correction” Version changes: Or Split time series When reprocessing impossible (e.g. onboard changes) Reprocess whole dataset Where possible Need to regenerate FCDR When any component changes Introduce review cycles RefAv1 RefAv2 ∆A1B1 ∆A2B2 RefBv1 RefBv2 Time