Distribution list. Ref NOV-7493-NT-5856 Issue 1 Date 16/12/15 Rev 0 Date 16/12/15 Page 3/61. Improved QUALITY INDICATORS FOR MTG-IRS LEVEL 2 PRODUCTS

Transcription

1

2

3 Improved QUALITY INDICATORS FOR MTG-IRS LEVEL 2 PRODUCTS Page 3/61 Distribution list INTERNAL EXTERNAL Name Name Company / Organisation Documentation NOVELTIS Paolo PRATI EUMETSAT Richard BRU Stephen TJEMKES EUMETSAT Pascale RABÉ Rolf STUHLMANN EUMETSAT Olivier LEZEAUX Emmanuel DUFOUR Cindy HORTALA Jean-François VINUESA

4 Page 4/61 Document status Improved Quality Indicators for MTG-IRS Level 2 products Technical Memorandum n 1 Issue Revision Date Reason for the revision /11/15 Draft version /12/15 Final version Modification status Issue Rev Status * Modified Reason for the modification 1 0 I 2.1 Discussion on the analysis of radiosonde data 1 0 I 2.2 Figure 2 Figure 3 Figure 4 exemplifying the research and selection of temperature inversion events. 1 0 M 4.1 Figure 10: MTG-IRS noise figure replaced (ambiguous line plot in the original version) 1 0 M Bottom panel of Figure 16 replaced due to wrong hour. (Brightness temperature scaled by the background of line determined in the spectral interval. IASI simulation, Spain 00h UTC case) 1 0 I Figure 18 and Figure 19 : illustration of the diurnal cycle of the normalized brightness temperature. 1 0 I Figure 27 : corresponding illustration of the diurnal cycle of the emission signal 1 0 I 5.3 Updated recommendations * I = Inserted D = deleted M = Modified

5 Page 5/61 Acronyms 4A/OP ACE AMSU AOD ATBD CfO CGS CNES ECMWF EPS EUMETSAT FFT FTS GEISA IASI IRS LATMOS LMD MACC MetOp MIST MODIS MOPD MSG MTG MWIR NG NWP RD RT RTM SOW ULB Automatized Atmospheric Absorption Atlas OPerational version Atmospheric Chemistry Experiment Advance Microwave Sounding Unit Aerosol Optical Depth Algorithm Theoretical Basis Document Call for Offers Core Ground Segment Centre National d Etudes Spatiales (French Space National Centre) European Centre for Medium-range Weather Forecast European Polar System EUropean organisation for the exploitation of METeorological SATellites Fast Fourier Transform Fourier Transform Spectrometer Gestion et Etude des Informations Spectroscopiques Atmosphériques Infrared atmospheric Sounding Interferometer InfraRed Sounder Laboratoire Atmosphères, Milieux, Observations Spatiales Laboratoire de Météorologie Dynamique Monitoring Atmospheric Composition and Climate Meteorological Operational satellite MTG-IRS Science Team MODerate resolution Imaging Spectroradiometer Maximum Optical Path Difference Meteosat Second Generation Meteosat Third Generation Mid Wave InfraRed New Generation Numerical Weather Prediction Referent Document Radiative Transfer Radiative Transfer Model Statement Of Work Université libre de Bruxelles

6 Page 6/61 Applicable and reference Documents N Reference Title [DA1] EUM/COS/LET/15/ Call for Offers (Cf0) 15/ Improved quality indicators for MTG IRS L2 products [DA2] EUM/MTG/SOW/15/ Statement of Work Improved quality Indicators for MTG-IRS L2 product [DA3] EUM/COS/DOC/09/1449 General Conditions of Tender [DA4] EUM/MTG/DOC/11/0188 v3. 26 February 2014 Algorithm Theoretical Basis Document for Level 2 Processing of the MTG Infra-Red Sounder Data N Reference Title [DR1] LAT-7493-PR-5383_tech_magnt-v1.0 (principal authors: C. Clerbaux, E. Dufour, L.Clarisse). LAT-7493-PR-5384_admin_contrac-v1.0 24/07/2015 [DR2] NOV-7032-NT v1.0, 31/08/2012 A. Klonecki, Lezeaux O., Tournier B. [DR3] NOV-7316-NT-4711-v1.0; 27/11/2014 E. Dufour, Klonecki A., Tournier B., Standfuss C. [DR4] Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., & Huang, H. L. (2007), Journal of Applied Meteorology and Climatology, 47, , doi: /2007jamc Improved quality Indicators for MTG-IRS L2 product Technical and Management Proposal Financial and Contract Proposal Test data for verification of the operational implementation of the MTG-IRS Atmospheric State Product Extraction Chain, Generation of Final Test Data (Phase 4) Validation of MTG-IRS Level 1B data using Earth scenes. Final Report Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multispectral Satellite Radiance Measurements [DR5] W. C. Snyder, Z. Wan, Y. Zhang, Y.-Z. Feng, Int. J. of Remote Sensing, 19, [DR6] A. Klonecki, P. Prunet, J. Donnadille, E. Dufour, C. Camy-Peyret, Henk Eskes, T. Phulpin, C. Clerbaux, 2nd IASI International Conference, Annecy, [DR7] [DR8] Marco Matricardi, RTIASI-4: a new version of the ECMWF fast radiative transfer model for the infrared atmospheric sounding interferometer, 2003 Justin M. Sieglaff, Timothy J. Schmit, W. Paul Menzel, and Steven A. Ackerman, 2009: Inferring Convective Weather Characteristics with Geostationary High Spectral Resolution IR Window Measurements: A Look into the Future. J. Atmos. Oceanic Technol., 26, doi: Classification-based emissivity for land surface temperature measurements from space Simulation of the MTG/IRS spectra and analysis of the potential of the sounder to characterise atmospheric pollution RTIASI-4: a new version of the ECMWF fast radiative transfer model for the infrared atmospheric sounding interferometer Inferring Convective Weather Characteristics with Geostationary High Spectral Resolution IR Window Measurements

7 Page 7/61 Table of contents 1. INTRODUCTION PRESENTATION OF THE DOCUMENT CONTEXT The MTG-IRS instrument and Level 2 operational processing Quality Indicators within the MTG-IRS Operational Level processor OBJECTIVES OF PHASE GENERAL APPROACH DOCUMENT STRUCTURE SELECTION AND GENERATION OF THE REFERENCE GEOPHYSICAL SCENARIOS INPUT DATA SOURCE DATA SELECTION GEOPHYSICAL DATASET CONSTRUCTION GENERATION OF THE PERTURBED GEOPHYSICAL SCENARIOS APPROACH Temperature gradient with no surface thermal contrast Surface temperature Temperature gradient with surface thermal contrast Level of atmospheric temperature inversion point H 2O content Satellite zenithal angle VERTICAL T AND H 2O PROFILES SCENE CLASSIFICATION SIMULATION OF THE SPECTRAL RADIANCE GENERAL APPROACH A/OP CALCULATION Main features of the 4A/OP model Generation of the 4A/OP atlases on 90 levels FULL BAND SPECTRAL RADIANCE QUALITY INDICATORS ANALYSIS AND RECOMMENDATIONS DESIGN OF THE QUALITY INDICATOR Principle Selection of absorption/emission lines Quality indicator design Optimisation of the indicator with respect to radiometric noise SENSITIVITY AND PERFORMANCE ANALYSIS Qualitative analysis to the perturbation parameter Global performance analysis RECOMMENDATIONS... 61

8 Improved QUALITY INDICATORS FOR MTG-IRS LEVEL 2 PRODUCTS Page 8/61 1. Introduction 1.1. Presentation of the document This document is the Technical Memorandum 1 (TM1) prepared in the frame of the phase 1 activity of the Improved Quality indicators for MTG-IRS Level 2 products study financed by EUMETSAT based on the technical / management, administrative and contractual proposal [DR1]. It reports the activity performed by NOVELTIS, sub-contractor of LATMOS, which was in charge of the phase 1 activity Context The MTG-IRS instrument and Level 2 operational processing MTG-IRS will observe upwelling radiance at the top of the atmosphere at a moderate spectral resolution of cm -1 in two spectral bands, namely the LWIR (Long Wave Infrared) band extending from cm -1 and the MWIR (Mid Wave Infrared) band extending from cm -1. MTG-IRS is based on a Fourier transform spectrometer. The instrument can cover the Earth disc once every 60 minutes, with routine operations allowing the sounding of most EU ( LAC4 area) every 30 minutes, with a spatial sampling of 4 km at sub-satellite point. The primary objective of the MTG-IRS mission is to provide frequent observations of temperature and water vapour, for operational now-casting. A second objective is to exploit the relatively high spectral resolution and high horizontal and temporal samplings to derive atmospheric concentrations for important trace gases absorbing in this spectral range, such as ozone (O3), carbon monoxide (CO) and ammonia (NH3). The derivation of the atmospheric state information from the MTG-IRS observations is envisaged to be performed using an operational Level 2 processor [[DA4]], at Day-1 for temperature and humidity profiles, and at Day-2 for trace gases Quality Indicators within the MTG-IRS Operational Level processor The functionalities of the operational Level 2 processor are described in the so called Algorithm Theoretical Basis Documents [DA4] which aims at providing all the necessary steps to generate the atmospheric state product from the cloud-free MTG-IRS observations. The quality of the L2 retrieved product depends on the presence of clouds and aerosol, which affects the L1 data and hence the retrieved products. Temperature inversion near the surface is another factor that may impact the retrieved products. Among other functionalities, the MTG-IRS Level 2 processor notably includes a Quality Assurance module dedicated to the provision of quality information to the Level 2 products users. This information is provided as Quality Indicators which are envisaged to be appended to the retrieved state product, for each individual retrieval. In the frame of the consolidation of the MTG-IRS Level 2 processing at EUMETSAT, the current study considers the specification of two new quality indicators for the MTG-IRS Level 2 product to be disseminated to the users: 1/ an indicator to determine the presence of an inversion layer in the temperature profile at low altitude levels; 2/ an indicator of the presence of aerosol within the instrument field of view. The phase 1 activity reported here considers the indicator for the temperature inversion. The aerosol indicator will be considered in phase Objectives of Phase 1 The purpose of phase 1 is to perform preliminary work to derive a quality indicator to determine the presence of an inversion in the low altitude levels of the vertical temperature profile. This indicator will be integrated as part of the level-2 products disseminated to the users. When MTG-IRS data is available, these indicators will be evaluated against real information and could be improved/optimized.

9 Page 9/61 The study aims at establishing a recommendation for the use of selected radiance channels in order to establish a useful index. These recommendations will be demonstrated through radiative transfer calculations relying on temperature data, carefully chosen to represent geophysical situations where capping inversion near the surface is identified General approach The identification and analysis of the temperature inversion indicator have relied on the following steps: The specification and performance of the temperature inversion indicator have been analysed through RT simulations of radiance spectra in the thermal infrared domain for several identified and characterized low level temperature inversion situations. These RT calculations have been performed starting off using a baseline set of 14 low-temperature inversion cases and 2 nominal cases without a temperature inversion, identified among a set of ECMWF data. The RT calculations have then been extended to a larger dataset of perturbed profiles generated based on the reference profiles and aiming at testing the impact of different parameter of the temperature capping inversion. A total of 6 perturbation scenarios have been defined (atmospheric temperature gradient without a surface thermal contrast, atmospheric temperature gradient with a surface thermal contrast, surface temperature gradient without atmospheric gradient, altitude of the atmospheric inversion level, amount of water vapour, air-mass factor). A total of 5 perturbation degrees have been considered for each of these scenario. These results in an extended set of 16 * 6 * 5 = 480 simulated scenes, in which scenes with and without low-level temperature inversion are present. Both IASI and MTG-IRS (specifications) have been considered as a reference for the instrumental parameters (spectral band, spectral resolution and sampling), with and without radiometric noise added, resulting in 4 different instrumental scenarios. The generated radiance and Brightness temperature spectra have been analysed in the vicinity of several spectral regions of interest. This has resulted in a baseline specification of a quality indicator flag associated to given absorption lines with specific property. A theoretical reliability index of the proposed flag has also been proposed. In parallel a preliminary selection of 9 eligible absorption/emission lines have been identified in the observable spectral domain of IASI and IRS. The proposed quality indicator has then been calculated for the 9 candidate absorption/emission lines for the extended datasets of 480 atmospheric/surface scenario and the 4 instrumental configurations. The extended dataset profiles have been classified in terms of temperature inversion, thus providing a reference for benchmarking the results of the proposed quality indicator and provide preliminary elements for qualifying its performance Document structure Chapter 2 describes the selection and generation of the reference geophysical scenarios; Chapter 3 describes the generation of the perturbed geophysical scenarios; Chapter 4 depicts the generation of the infrared radiance spectra; Chapter 5 establishes a preliminary specification of the temperature inversion indicator, provides elements for the performance analysis and finally gives general recommendations on the indicator setting and the optimal position of lines to be used.

10 Page 10/61 2. Selection and generation of the reference geophysical scenarios 2.1. Input data source The geophysical parameters were fixed using datasets of different sources: High resolution (HRES) ECMWF deterministic forecast system provided on a global X resolution with 137 hybrid sigma coordinates, 4 times a day (00H, 06H, 12H, 18H UTC) o o Profiles (3D): temperature, humidity, Surface data (or 2D): Sea surface temperature (SSTK), Skin temperature (SKT), Surface pressure (SP), Land/sea mask (LSM), Orography (Z), Total cloud cover (TCC), Sea Ice Cover (CI); MACC operational forecasts (1.125x1.125 ): o Profiles (3D): O3, CO profiles Other trace gases fixed at a typical average vertical profile (GASCON database) o O2, N2, CH4, CO2, N2O, NH3, NO, NO2, SO2, HNO3, CFC11, CFC12, CCl4; Algorithm - and accompanying MODIS data by Borbas et al (2005) for the description of land surface spectral emissivity. The algorithm uses MODIS data from six IR atmospheric window channels together with a conceptual model developed from laboratory measurements of surface emissivity, to create a fitting method that allows calculating continuous, high-resolution surface emissivity spectra. MODIS data from year 2012 have been used. This emissivity spectra are calculated at 1 cm -1 interval. Snyder et al (1998) water emissivity spectrum over ocean (emissivity index 101 ) (not used in this work). The use of the high resolution ECMWF model for collecting the temperature and humidity offers several advantages: The 137 vertical level version of the current operational version of the ECMWF model has about 20 levels in the lowest 1 km, and can thus resolve the vertical structure of the inversion layer. The ECMWF model resolution (0.125 X ) is for most of the MTG-IRS samples observed in LAC-4, of the same order of magnitude as the MTG-IRS sampling (4km at sub satellite point). The meteorological variables provided by the model and needed for the radiative transfer are all present and consistent, including information on the cloud coverage and surface temperature. Also, the model profiles generally extend to pressure lower than 0.1 hpa and therefore directly cover the vertical range needed for the RT calculation of the TOA radiance spectra. These data are available on request through the ECMWF Operational Atmospheric Model Data Sets Archive. In the frame of previous study for EUMETSAT ([DR3]), NOVELTIS had previously assembled a database of high resolution profiles from this archive for selected days of 2013, and has achieved/validated the integration of these datasets into the RT calculation chain used for the simulation of IASI and MTG-IRS data. In addition, in order to reproduce realistic vertical patterns of the temperature profiles using model data, the selected ECMWF profiles have been chosen with different vertical profile structure (inversion and non-inversion cases, different gradients and levels of inversion, presence of local maxima etc.). The temperature profile selection and perturbation are described in the next section (2.2) and in chapter 2 respectively. One possible caveat of using model data for the objective of the current study is that the latter might not represent the fine vertical structure presumably present in the low Tropospheric temperature. To explore this question, a series of vertical temperature profiles observed overnight by rawinsonde soundings over the Topeka upper air Station (Kansas, United States) during winter, have been examined. An important proportion of low level temperature inversion situations can be observed in these conditions (Figure 1). The high discretization rate of these radiosonde data (6 second i.e. 30 m, given the balloon ascent rate) makes it possible to observe the fine vertical variability of the temperature profile and indeed reveals irregular patterns and several local

11 Page 11/61 extrema in addition to the general low scale structure of the tropospheric profile. Nevertheless, the observed variability has a relatively moderate scale (typically 100 hpa) which - it is believed is taken into account correctly in the meteorological model. Also it is clear this vertical variability observed locally will be largely smoothed out using the space measurements due to the integration of the spectral radiance signal within the instrument (MTG-IRS or IASI) PSF. Figure 1: Tropospheric vertical temperature profiles from Station TOPEKA station Kansas (US). December h00 UTC. X-axis: temperature (Celcius degrees). Y-axis: Pressure (hpa). Source: high vertical resolution Radiosonde 6 second data, SPARC data initiative ( Data selection In order to select the baseline dataset of scenes, we have sampled the data using the following criteria as follows: We have restricted to the date of 15 December 2013 (winter situation in the northern hemisphere). A temperature inversion, defined as a negative gradient between Tmax (temperature at the inversion level) and T1 (temperature of in the first ECMWF layer) should be happening at least once in the considered day. 4 land pixels chosen spread over the MTG LAC-4 region (covering both Europe and North/central Africa). 4 consecutive hours (00h, 06h, 12h, 18H) (corresponding to the ECMWF model temporal sampling) are chosen for each considered pixel. Clear sky should be present over the whole day, using as a criterion the total cloud cover information contained in the HRES ECMWF deterministic forecast system. The selection of the ECMWF model grid points has been identified by manually screening 2D maps of low level temperature gradient for the considered date and times. This research has also been guided using 3 dedicated filtering: A mask aiming at isolating the spatial regions having significant and positive gradient amplitude (Tmax T1 > 0) at least once a day (Figure 2) A mask for eliminating the cloudy regions using as a criterion that the ECMWF total cloud cover information should not exceed 0.1 (Figure 2). The cloudy or partly cloudy regions are not of interest as they will not be treated by the MTG-IRS L2 processing. The level of the temperature inversion point was also considered - together with the temperature gradient amplitude - to avoid too low level temperature inversion (see example in Figure 3 and Figure 4). After this selection is processed, a total of 16 scenes (4 ECMWF grid points x 4 hours) have been identified. Among the 16 considered scenes, 14 scenes have a vertical profile of temperature where a low-level atmospheric inversion is present (i.e. Tmax > T1).

12 Page 12/61 The selected scenes time and geolocation are given in Table 1. For each of the selected grid point, the actual presence of a low-level atmospheric inversion point is also indicated. In addition, the table provides the prescribed observation geometry considered for the RT calculation: the satellite zenithal angle corresponds to the MTG-IRS viewing geometry the sun zenithal angle is set equal to 90 (no solar contribution considered in the RT calculation) Figure 2 : Research of temperature capping inversion situations over Europe. 15/12/ H00 UTC. Left panel: temperature gradient amplitude (negative values masked). Middle panel: ECMWF total cloud cover information. Right panel: same as left panel with cloud mask applied (cloud cover > 0.1) Figure 3 : Research of temperature capping inversion situations over Romania. 15/12/ H00 UTC. Temperature gradient amplitude at different inversion level.

13 Page 13/61 Figure 4 : Identified temperature inversion profile targeted in Figure 3. Romania case. 15/12/ H00 UTC. X-axis: Temperature [K]. Y-axis: model level.

14 Page 14/61 Table 1: Geolocation and time of the selected scenes Geographical region Longitude [ ] Latitude [ ] ECMWF Cloud cover Applied Satellite zenithal angle [ ] Applied Sun zenithal angle [ ] Time UTC 15/12/2012 Scene index Atmospheric inversion (yes/no) 00H 00 y Libya H 04 y 12H 08 n 18H 12 y 00H 01 y Tchad H 05 y 12H 09 n Spain (Lleida area) H 13 y 00H 02 y 06H 06 y 12H 10 y 18H 14 y 00H 03 y Romania H 07 y 12H 11 y 18H 15 y 2.3. Geophysical dataset construction Once the pixels latitude/longitude and date/time are identified, 3 major processing steps are followed for the generation of the geophysical profiles Coupling of the ECMWF and MACC database. This is done through horizontal interpolation of MACC on the ECMWF grid). The resulting ECMWF and MACC profiles are regrouped /saved by MTG stare, hour and month for the selected scenes. Profiles selection and extraction from the database according to lat/lon coordinates. Integration of the corresponding surface emissivity data: This is done for land pixels using the program that uses the U Wisconsin algorithm (Borbass). The spatial sampling of the land emissivity data and the considered ECMWF data are harmonized as follows: the land emissivity products originating from the Borbas et al. (2005) algorithm have their spatial resolution scaled on the MODIS products (0.05 x0.05 ), which is close to the IASI footprints. It was therefore chosen that the central MODIS pixel in each ECMWF gridbox is taken for the calculations of the land surface emissivity. Once the NetCDF files with emissivity are generated, they are merged with the profile files. This final file with all information is then used as an input of the RT code to run the spectral radiance simulation.

15 Page 15/61 3. Generation of the perturbed geophysical scenarios 3.1. Approach An extended dataset of profiles is generated for each of the 16 selected reference profiles. A total of 6 perturbation scenarios have been considered. For each scenario, 4 perturbed profiles have been generated in addition to the reference profile, addressing each time a specific parameter: temperature gradient with no surface thermal contrast; surface temperature; temperature gradient with surface thermal contrast; level of atmospheric temperature inversion point; H2O content; Satellite zenithal angle. The reference profile is the same in the first 5 (geophysical) scenarios. Also, in the first 4 scenarios, no perturbation is applied if no inversion of temperature is present in the reference scene (Tmax is considered equal to T1). The details of the perturbation applied in each scenario are itemized in the following sections Temperature gradient with no surface thermal contrast We consider here a perturbation of the low level atmospheric temperature gradient amplitude, defined as the difference between the maximum temperature Tmax and the temperature of the first ECMWF level T1, with the surface temperature Tsurf set to T1 (no surface thermal contrast). The generation of the perturbed profile is based on a vertically constant modulation of the Tmax T departure below the temperature inversion level down to the surface (factor-0.5, + 0.5, 2.0, 1.5). A total of 4 perturbations are applied in addition to the reference case leading to the following configurations: T1 < T1 ref < Tmax (Accentuated inversion) T1 = T1ref < Tmax (Reference inversion) T1 ref < T1 < Tmax (Attenuated inversion) T(z) = T1 = Tmax (Exact isothermal) Tmax < T1 (no inversion, positive lapse rate) Surface temperature We consider here the perturbation of the surf ace temperature Tsurf with a constant low level atmospheric temperature gradient (so Tmax and T1 are fixed). The values of the perturbed surface temperature is set equal to the different T1 values of the previous scenario. A total of 4 perturbations are applied in addition to the reference case allowing to span different Tsurf vs T1 (surface thermal contrast) configurations and different Tsurf vs Tmax configurations as follows: Tsurf < T1 < Tmax Tsurf = T1 < Tmax (reference inversion profile) T1 < Tsurf < Tmax T1 < Tsurf = Tmax T1 < Tmax < Tsurf

16 Page 16/ Temperature gradient with surface thermal contrast We consider now the perturbation of the low level atmospheric temperature gradient amplitude below the temperature inversion level (similar as in the first scenario described in 3.1.1) but with the surface temperature Tsurf set to T1 of the reference scenario (surface thermal contrast). A total of 4 perturbations are applied in addition to the reference case leading to the following configurations: T1 < Tsurf < Tmax Tsurf = T1 < Tmax (reference) Tsurf < T1 < Tmax Tsurf < T1 = Tmax T1 < Tmax < Tsurf Level of atmospheric temperature inversion point In this scenario, the temperature profile perturbation is done through a perturbation of the pressure grid localized near the inversion point. The perturbed vertical temperature profile is generated as follows: First, taking as a starting point the reference temperature profile defined as a function of pressure level (Pi, Ti) we determine the pressure level Pmax for which the temperature reaches a maximum (atmospheric inversion point). Then we distort the pressure grid in order to reach Pmax + delta Pmax at the inversion point (this results in a perturbed pressure grid Pi pert ), without changing the original temperature grid value Ti. Finally, we interpolate the resulting perturbed (Pi pert, Ti) profile onto the original pressure grid Pi, resulting in the perturbed temperature profile (Pi, Ti pert ). Note that vertical profile of H2O and other geophysical parameter profiles are unchanged. Both an elevation (2 scenes) and a descent (2 scenes) of the maximum temperature level are considered. A total of 4 perturbations of the inversion level (Pmax = P(Tmax)) are applied in addition to the reference case leading to 5 scenes: Pmax = Pmax ref hpa; Pmax = Pmax ref + 50 hpa; Pmax = Pmax ref ; Pmax = Pmax ref hpa; Pmax = Pmax ref hpa H 2O content The perturbation of the water vapour profile is a vertically constant scaling factor applied to the reference H2O profile from the surface up to the top of the atmosphere. Two diminution and two increases of/in the reference water vapour content are considered in addition to the reference profiles leading to the following scenes: XH2O = 0.5; XH2O = 0.75; XH2O = 1; XH2O = 1.50; XH2O = 2.0.

17 Page 17/ Satellite zenithal angle The perturbed satellite zenithal angle values v are chosen to sample regularly the airmass factor (1, 2, 3, 4, 5), all other thermodynamical and geophysical parameters being left fixed. This leads to the 5 following scenes: v = 0 ; v = 60 ; v = ; v = ; v =

18 Page 18/ Vertical T and H2O profiles The reference and perturbed T (and H2O) vertical profiles are shown in Figure 5 to Figure 8 for all the 16 considered scenes and all the perturbing scenarios.

19 Page 19/61 Figure 5: Low tropospheric temperature and H2O profiles of the extended dataset for the different perturbing scenarios, successively shown for the Spain, Libya, Tchad and Romania cases at 00H UTC time. From left to right, top to bottom: atmospheric temperature gradient, surface temperature, atmospheric temperature gradient with fixed surface temperature, altitude of the temperature inversion point, water vapour profile). The asterisks in the first 4 figures indicate the surface temperature.

20 Page 20/61

21 Page 21/61 Figure 6: same as Figure 1 at 06H00 UTC

22 Page 22/61

23 Page 23/61 Figure 7: same as Figure 1 at 12H00 UTC

24 Page 24/61

25 Page 25/61 Figure 8: same as Figure 1 at 18H00 UTC

26 Page 26/ Scene classification Each generated scene has been classified according to the two following criteria: T1 < Tmax; this condition is met in the presence of a low level temperature inversion in the atmosphere; Tsurf < Tmax; this condition is met in the presence of a low level temperature inversion between the atmosphere and the surface. The first criterion is meant to identify scenes targeted as the primary objective of the study. The second criterion defines a more general class of scenes having an atmospheric temperature inversion present but with respect to the surface temperature, rather than the lower atmospheric layer temperature, which might also be interesting to detect for the L2 MTG processing. The second class of scenes is a priori more directly distinguishable from the analysis of the IR spectral radiance measurement, as it takes into account the radiative effect of the surface. Note that, rigorously, the surface emissivity could also be taken into account in this criterion for example considering an effective surface temperature whose radiative effect would be modulated by the surface emissivity. This would allow a more precise representation of the radiative contribution of the surface in the radiative transfer. But this would then imply that the scene classification is dependent on the wavelength. The proposed double classification (using both criterions), was applied to the extended profile dataset and was used to generate reference tables that have been used for benchmarking the results of the quality indicator (see section 5.2).

27 Page 27/61 4. Simulation of the spectral radiance 4.1. General approach Forward calculations in the frame of the current study have been processed through the use of elements of a processing simulation chain historically dedicated to the synthesis of MTG L1B data and developed/used at NOVELTIS in the framework of previous scientific support studies funded by EUMETSAT ([DR2], [DR3]). The radiative transfer calculations have been made to generate the infrared radiance spectrum at the top of the atmosphere covering the spectral domain between 500 and 3000 cm -1 at high spectral resolution, for each of the 480 scenes (profiles, surface and associated viewing geometry) of the extended dataset described in chapter 3. The generated high resolution spectra have then been convolved by the instrument functions to generate two datasets of radiance spectra: IASI dataset, matching the IASI spectral domain, spectral sampling and spectral resolution; MTG-IRS dataset, matching an ideal FTS system for MTG-IRS with a MOPD of 0.8 cm and a spectral domain between cm -1 and cm -1. The calculations of the synthetic IASI and MTG-IRS radiance spectra with 4A/OP (forward simulation) have been carried out as follows. In a first step, noise-free interferograms have been generated from the RTM simulated infinite resolution spectra; In a second step, the interferograms have been multiplied by an apodisation function truncated according to the selected value of MOPD (2 cm for IASI and 0.8 cm for MTG-IRS); In a third step, the interferograms from the previous step have been converted back to the radiance space to give the apodised radiance spectra (L1C); In a final step a simulated radiometric noise signature, calculated with Gaussian random noise generator has been produced and added to the radiance spectra. The noise has been calculated using a random realisation of the error consistent with the following prescription: For IASI, the radiometric error performance measured for MetOp-A on the 09/11/2015 (Figure 10) and including a spectral correlation due to apodisation derived from the CNES covariance matrix for IASI L1C.. Figure 9: Radiative transfer calculation chain used for the generation of the synthetic IASI and MTG-IRS spectral radiance datasets For MTG-IRS, the specification of Level 1b error at 1-sigma (Figure 10), with no application of a spectral correlation. Passing by the intermediate step of generating interferograms has mainly two advantages: First, it allows for a fast (postprocessing) recalculation of the spectra with another apodisation function applied. Secondly, it makes it easier to apply the spectral response function in the interferogram domain (simple multiplication with the truncated apodisation function vs. convolution by the spectral response in the spectrum domain).

28 Page 28/61 Figure 10: Left: IASI noise level spectra expressed in NedT at 280 K measured for MetOp-A (09/11/2015); Right: MTG-IRS noise specification A/OP calculation The high resolution calculations of the radiance spectra dataset from the generated scenes has relied on the 4A (Automatized Atmospheric Absorption Atlas) radiative transfer model. These calculations have been performed using a specifically adapted vertical discretization of the pre-built absorption optical thicknesses database (atlas) on 90 levels, rather than the historically existing 43 levels. This improvement was made with the objective to optimize the representation of the fine structure of the vertical temperature and H2O profile in the low levels, below the atmospheric inversion point. After the main characteristics of this model are shortly recalled (next section), we briefly describe the generation of the atlas on 90 levels (section 4.2.2) Main features of the 4A/OP model 4A is a fast and accurate radiative transfer model particularly efficient in the infrared and the near infrared parts of the spectrum. The 4A line-by-line model is an advanced version of the nominal line-by-line STRANSAC model. 4A notably integrates the following features, relevant to the study: Fast computation of the transmittances and the radiances, thanks to the use of a comprehensive database (atlases) of monochromatic optical thicknesses for up to 43 atmospheric molecular species. Accurate computations: the atlases are created through the use of the line-by-line and layer-by-layer model, STRANSAC, with state-of-the-art physics. It uses spectroscopy from the latest GEISA spectral line catalogue. Other spectroscopy databanks can be used. Computation of the gaseous absorption for each atmospheric model layer in a spherical atmosphere, at a user defined observation level for zenith, nadir or limb observations (including refraction). Wide variety of surface and earth atmospheric conditions, including user-defined spectral emissivity functions and solar contribution. High spectral resolution (the nominal spectral resolution is cm -1 ). Convolution with various types of instrument functions Generation of the 4A/OP atlases on 90 levels The 4A calculation relies on a multi-dimensional interpolation using a pre-built absorption optical thicknesses database called «Atlases». This database of monochromatic optical thicknesses is based on the discretization of the atmosphere in pressure and temperature.

29 Page 29/61 The new discretization is defined as follows: 90 atmospheric levels between surface and top of the atmosphere ; 12 nominal atmospheres : 12 temperature profiles, 7K distant ; The spectral step is nominally defined at cm -1 ; Separation into 15 cm -1 blocks for each gas: several matrices compressed in wave numbers/layer/temperature. The 90 level atlases were created using the line-by-line and layer-by-layer model, STRANSAC, with state-of-the-art physics and up-to-date spectroscopy from the latest edition of the GEISA spectral line catalogue. The atmospheric levels were chosen identical to the 90 levels from the RTIASI-4 code between and 1050 hpa (see Matricardi, 2003, [DR7]). Figure 11: Atlas temperature discretization (black lines) and user temperature profile examples (coloured lines) Full band spectral radiance The generation of spectral radiance data is performed for the three spectral bands of IASI and the two spectral bands of MTG-IRS for all the reference and perturbed scenes. These radiance spectra are shown as an example in Figure 12 and Figure 13 for all the extended (reference + perturbed) database of profiles for the Spain grid point considered at 00H00. This scene will also be used as a reference case for illustrating the next results.

30 Page 30/61 Figure 12: Simulation of the IASI brightness temperature in SB1 (top), SB2 (middle)and SB3 (bottom) for the different considered perturbation scenarios (from left to right, top to bottom : atmospheric temperature gradient, surface temperature, atmospheric temperature gradient with fixed surface temperature, altitude of the temperature inversion point, water vapour profile, air mass factor). Noise free radiance. Baseline scene: Spain grid point 00H.

31 Page 31/61 Figure 13: Simulation of the MTG-IRS brightness temperature in SB1 (top), SB2 (bottom) for the same perturbation scenarios as Figure 12. Noise free radiance. Baseline scene: Spain grid point 00H.

32 Page 32/61 5. Quality indicators analysis and recommendations 5.1. Design of the quality indicator Principle In order to estimate a quality indicator for the detection of temperature inversion, we look at specific spectral regions where small intensity spectral lines of the water vapour are present. In the case of temperature inversion, these weak intensity spectral lines appear in an emission configuration (peak of the line pointing up) instead of appearing in absorption (peak of the line pointing down). This suggests that, once such absorption/emission lines are identified, we can produce a quality indicator based on the difference of two TOA radiance levels, the first measured in a spectral sample sensitive to the change of configuration of the absorption/emission line and the second in one or several presumably non sensitive spectral sample(s) away from the absorption/emission line, supposed to represent the background level Selection of absorption/emission lines. The selection of the spectral lines used to estimate the quality indicators was performed analysing the sensitivity of the radiance spectra to the presence of temperature profile inversion (and the different considered perturbing parameters) in the different spectral bands covered by IASI and MTG-IRS. As a starting point, different spectral regions / windows of interest have been particularly targeted: 780 cm cm -1. This spectral region includes a series of H2O lines and CO2 lines notably explored by Sieglaff et al. (2009) for short-term convective forecasts purposes. They were shown to provide useful information for monitoring the evolution in time of lower-tropospheric thermo-dynamical state, in rapidly changing atmosphere ([DR8]) 830 cm cm -1. Region of H2O absorption. 940 cm cm -1. Series of CO2 absorption lines, notably used by Claude Camy-Peyret for detection of measurements with high probability risk of bad XCO2 retrieval, in the frame of a IASI /GOSAT XCO2 retrieval exercise (see annex B in [DR1]) 1180 cm cm -1. This region, lying at the beginning of IASI band B2, also contains a set of weakly absorbing H2O lines that might be of interest. These spectral regions are exemplified in Figure 14 where the noise free simulations of spectral radiance for IASI are shown for the perturbation along the Spain reference scene considered at 00H.

33 Page 33/61 Figure 14: Zoom of the IASI radiance simulations over the different spectral regions of interest. Spain grid point, 00H UTC.

34 Page 34/61 The most relevant lines for detecting low-tropospheric temperature inversion events are the lines turning over from a pointing-down (absorption) configuration to a pointing-up (emission) configuration in the observed TOA spectra radiance, as follows: very weak H2O lines lying in the spectral intervals cm -1 (Figure 15); and another set of slightly more absorbing H2O lines present in the cm -1 (Figure 15).

35 Page 35/61

36 Page 36/61 Figure 15: Detail of the H2O absorption lines present in the interval cm -1, cm -1, cm -1, cm -1 and cm -1. IASI simulation. Spain grid point 00H0 UTC In these spectral intervals, a set of 9 water vapour absorption/emission lines of interest have been identified. These weak water vapour lines are very good candidates for observing lower tropospheric temperature profile inversion because most of the emission signal, which is also related to the vertical profile of humidity, comes from the lower troposphere where the vertical temperature profile has its maximum in case of temperature inversion. For each of these lines, one spectral sample sensitive to the change from pointing-down to pointing-up referred as ν ON, together with two spectral samples chosen in the line background, ideally present at each side of the line center and referred as ν OFF_1 and ν OFF_2 have been defined (see Table 2). Depending on the considered line shape and how it is modified as a function of the different thermodynamic profiles, the sensitive sample was either chosen in the vicinity of the line center (for V or line shape), or lying in a bit off the line center, in the line wings area for more complex line shape in M or W ).

37 Page 37/61 ν ON, ν OFF_1 ν OFF_ Table 2 ν ON, ν OFF_1, ν OFF_2 wavenumbers, expressed in cm -1 for the identified H2O absorption/emission lines of interest Quality indicator design A quality indicator for detecting a temperature inversion can be obtained from the analysis of the TOA radiance difference value between ON and OFF spectral samples. The emission signal S is given by the difference between the radiance level at line maximum R ON and the background radiance level R OFF : S = [R ON R OFF ] The general principle of the proposed detection algorithm relies on the sign (and amplitude) of this emission signal: in the common case (no temperature inversion), the emission signal is expected to be negative. A positive signal is an indication of the presence of a temperature inversion event. Also, the stronger the amplitude, the more confidence can be given for making the distinction. To take into account rigorously the spectral variation of the background radiance level (due to the Planck emission and surface emissivity variation), the background level R OFF is estimated at the spectral sample ν ON by linear interpolation in wavenumber from R(ν OFF_1 ) and R(ν OFF_2 ) i.e. the radiance observed in the background samples lying at both sides of the absorption/emission line. The emission signal can be also expressed in brightness temperature difference: where B 1 is for the Planck inverse function. S TB = Tb ON Tb OFF = B 1 {R ON } B 1 {R OFF } Taking off the background level radiance allows to distinguish more clearly the effect of the absorption/emission line turning over from the pointing up to pointing down configuration, independently of the background level (which is mainly driven by the surface temperature). This is illustrated for IASI and MTG-IRS in Figure 16 and Figure 17 respectively which highlight the presence of an emission signal in certain scenes, after conversion of the radiance in brightness temperature and normalization by the background level (Spain grid point at 00h00). The diurnal variation of the emission signal is also shown for the Spain case and the Tchad case in Figure 18 and Figure 19 respectively. In the Tchad case, a diurnal cycle of the emission signal can be clearly observed in the reference profiles (black) and associated perturbed profile. Notably, in the accentuated inversion profile (magenta), a switch in the sign of the emission signal is clearly occurring (from positive at 00 and 06 to negative at 12 and 18), translating the extinction of the temperature profile inversion situation probably due to the gradual warming of the surface by the sun.

38 Page 38/61 Figure 16: Effect of taking off the background contribution shown in the vicinity of the H2O absorption/emission line around cm -1. Top panel: Brightness temperature. Bottom: Brightness temperature scaled by the background of line determined in the spectral interval. IASI simulation, Spain 00h0 UTC case.

39 Page 39/61 Figure 17: same as Figure 16 for the MTG-IRS simulation

40 Page 40/61 Figure 18: Diurnal variation of the normalized brightness Temperature in the vicinity of the H2O absorption/emission line around cm -1. Perturbation scenario: Temperature gradient with no surface thermal contrast. IASI simulation without noise. Spain case at 00h, 06h, 12h and 18h. Figure 19: Diurnal variation of the normalized brightness Temperature in the vicinity of the H2O absorption/emission line around cm -1. Perturbation scenario: Temperature gradient with no surface thermal contrast. IASI simulation without noise. Tchad case at 00h, 06h, 12h and 18h Optimisation of the indicator with respect to radiometric noise To allow a comparison of the observed signal with the noise level using consistent unit, we express the signal in NedT (Noise equivalent differential Temperature):

41 Page 41/61 S NedT (ν) = S(ν) R(ν) T Tref Once we have a measure that is directly comparable to the noise level, we can define the ratio Q of the normalised emission level as Q = S NedT(ν) σ NedT (ν) where σ NedT is the 1-sigma level of the radiometric noise for the considered instrument and wavenumber. The quality indicator for detecting the temperature profile inversion can then be proposed as a binary quality flag Q FLAG simply calculated as: 0 if Q 1 Q FLAG = { 1 if Q > 1 where 0 stands for no detection of the temperature inversion and 1 for detection of the temperature inversion. Besides, the proposed flag can be associated to a theoretical reliability index Q LEVEL giving an information on the a priori estimated performance of the detection flag. Considering radiometric noise as the main source of error in the proposed detection process, this index should be directly linked to the signal to noise ratio Q and could be, for example discretized as follows: Q LEVEL = int(q) 1 = 1σ level = 69% confidence 2 = 2σ level = 95.4 % confidence 3 = 3σ level = 99.7% confidence 4 = 4σ level = 99.99% confidence In practice, the geophysical parameters (in particular vertical temperature and H2O profiles) together with the observing geometry, are expected to condition the robustness /performance of the indicator, in addition to radiometric noise, as will be demonstrated and discussed in section (performance analysis) Sensitivity and performance analysis Qualitative analysis to the perturbation parameter For each individual scene (reference or perturbed) the detection flag associated to the considered spectral line is triggered when the emission signal is larger than the 1-sigma noise level taken at the line central wavenumber. It is set to zero otherwise. The prediction of the indicator is compared to the actual presence of a temperature inversion event in the considered scene. The performance of the indicator is then classified in 4 categories: successful detection of temperature inversion, missed detection of temperature inversion, successful detection of nominal T profile, missed detection of a nominal T profile (i.e. false alarm). After the reference and perturbed profiles of the Spain scene at 00h00 are recalled in Figure 20, Figure 21 to Figure 33 show the corresponding emission signal in NedT and its sensitivity to the different thermodynamical, geophysical and geometrical parameters using the same colour convention. Both IASI and MTG-IRS are considered. In each of these figures, the detection performance of the indicator is also represented for all the reference /perturbed profiles and all the candidate absorption/emission lines, taking as a benchmark both the strict atmospheric inversion criterion and atmospheric vs surface inversion criterion.

42 Improved QUALITY INDICATORS FOR MTG-IRS LEVEL 2 PRODUCTS Page 42/61 Figure 20: Low-tropospheric temperature and H2O profiles of the extended dataset shown for the Spain 00H scene.

43 Improved QUALITY INDICATORS FOR MTG-IRS LEVEL 2 PRODUCTS Page 43/61 Figure 21: NedT on off difference for the temperature gradient scenario (without surface thermal contrast). Perturbation scenario: Temperature gradient with no surface thermal contrast. IASI with no noise. Top panel: performance of the atmospheric inversion detection Bottom panel: performance of the atmospheric vs surface inversion detection