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

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

Inter-calibration IR and WV channels

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

- Review Spectral Conversion Functions - Preparatory studies - Review Spectral Conversion Functions -

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.

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

Preparatory studies - L1b Datasets-

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

HIRS L1b differences – EUMETSAT vs. NOAA (Lei Shi)   BT < 0.05 K for all channels  NOAA files: BT = 9999.99 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 9999.99. 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

- Assessment of HIRS stability - Preparatory studies - Assessment of HIRS stability -

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.

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

Inter-calibration VIS and NIR channels

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 0.4-1.1 mm (full spectral coverage is desirable) has a precision better than 3% can be applied to all GEOs (and LEOs)

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

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

Propagating calibrations back in time

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.

Transferring reference: Double Differencing More explanation needed Delta 20

Further work

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.

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?

Thank You Any Questions?