Considering scattering in radiative transfer calculations often leads to extensively long computation times that can be prohibitive, especially in the operational context of satellite missions. Over time, numerous methods were developed to accelerate scattering calculations. In this paper, based and further built upon the work of O'Dell [1], we describe the implementation of the Low-Stream Interpolation acceleration technique in the 4A/OP radiative transfer model. The method's acceleration relies on the execution of computationally expensive calculations only for representative points, which represent the regions in the gas absorption optical depth space called "bins". For all other points the calculation is performed at a low accuracy and by subsequent interpolation. We have considered a number of method's aspects, and introduced various modifications in order to optimise its accuracy and computation time. This includes: a) an extension of the method to Jacobians, b) modifications of bin and sub-bin divisions, c) implementation of automatic binning and its comparison to fixed bins, d) improvement of the computation of representative points, e) improvement of the definition of "significant scattering" used by the method, f) avoiding the redundancy of high-accuracy computation at large gas absorption optical depths, g) optimisations of the method computation domain size and h) evaluations of various possible accelerations of low-accuracy calculations. We applied the method on the MicroCarb O2 and CO2 bands in nearinfrared and shortwave-infrared over a large variety of geophysical cases and discussed the impacts of the modifications and the overall performance. We confirmed that the Low-Stream Interpolation is a powerful technique to accelerate the scattering calculations and, in our model, it provided relative accuracies on polarised and unpolarised radiances and Jacobians lower than 0.05 % with acceleration of 10-50 times.

The European Earth Observation programme Copernicus is implementing the next-generation system for atmospheric composition monitoring: after the success of the Sentinel-5 Precursor TROPOMI, a constellation of Sentinel-4 geostationary and Sentinel-5 Low-Earth orbiting missions will be launched in 2025 and beyond for air quality, ozone and climate variables monitoring, while the CO2M missions will observe greenhouse gases emissions and related proxies. Post-launch validation of the data products is essential to determine their quality and enable users to judge their fitness-for-purpose. Therefore, in 2021-2022 the European Union funded the H2020 Copernicus Cal/Val Solution (CCVS) project with the aim to review the status of existing validation infrastructures and methods for all Sentinel missions and to define a holistic solution to overcome limitations (https://ccvs.eu). In this contribution we report on the maturity assessment and gap analysis performed in this project. This assessment synthesizes lessons learned from earlier work in FP7 and H2020 projects, and from the operational/routine validation services run in the ESA/Copernicus Atmosphere Mission Performance Cluster (ATM-MPC), the EUMETSAT Atmospheric Composition Satellite Application Facility (AC SAF), the Copernicus Atmosphere Monitoring Service (CAMS) and the Copernicus Climate Change Service (C3S). The CCVS assessment includes feedback from space agencies, Copernicus stakeholders and the CEOS Working Group on Calibration and Validation (WGCV). The validation means, such as the precursor data sets and comparison methods, have evolved significantly in the past decade: (1) New ground-based instruments have been developed and networks have expanded in geographical coverage and in capabilities, (2) traceability to metrological standards and uncertainty characterization of the (Fiducial) Reference Measurements (FRM) has improved considerably, (3) rapid provision of FRM through data distribution services is becoming commonplace, (4) the advantages of advanced comparison methods have been demonstrated, and (5) all of this has facilitated the development of operational, near-real-time validation systems such as the Validation Data Analysis Facility (VDAF-AVS) of the ATM-MPC for the Sentinel-5P mission. On the other hand, a list of remaining challenges still restrain the scope and quality of the validation of several atmospheric data products: (1) Station-to-station differences in ground-based validation results suggest (poorly understood) intra-network and inter-network inhomogeneity, (2) the coverage offered by ground-based networks (of the full range of the measurand values and of the influence quantities affecting the retrieval) can have important gaps, (3) timeliness of ground-based data provision remains poor for several products, (4) comparability (representativeness) between ground-based and satellite measurements requires further methodological advances and supporting measurement campaigns, (5) the accuracy and breadth of scope of the latest generation of satellite sounders puts correspondingly tight and difficult-to-meet requirements on the FRM data quality, (6) cross-validation of the different satellites requires a coordinated approach, and (7) some networks and activities experience increased/recurrent funding difficulties. We conclude this overview of the CCVS gap analysis for atmospheric composition data with illustrations of concrete actions undertaken recently to address some of the validation challenges highlighted by the project.The CCVS project has received funding from the European Union’s Horizon 2020 programme under grant agreement No 101004242 (Project title: “Copernicus Cal/Val Solution).

A retrieval algorithm of integrated water vapor content above cloud, using shortwave infrared observations, is developed and evaluated through idealized and realistic atmospheric profiles, with its application currently limited to oceanic regions and latitudes within ±60°. Water vapor plays a crucial role in cloud formation and development, particularly in the formation of clouds resulting from convective processes. The resulting convective cloud locally influences the spatio-temporal variability of atmospheric water vapor content, through exchanges between cloud and its immediate environment. Therefore, a better understanding of the water vapor content above and around clouds is necessary to improve our comprehension of interactions between water vapor and cloud to better constrain Large-Eddy Simulations and numerical weather forecasting models. The algorithm was developed for the Cluster for Cloud evolution, ClImatE and Lightning space mission project. This mission, scheduled for 2028, aims to enhance our knowledge of the 3D convective cloud development velocities, the electrical activity associated with convective systems, and the water vapor content above and around the cloud. The retrieval algorithm presented in this study uses a Bayesian probabilistic approach, the optimal estimation method. The atmosphere is assumed to be composed of homogeneous plane-parallel layers, and synthetic radiance datasets were generated to test the developed retrieval algorithm. The feasibility of retrieving the integrated water vapor content above the cloud over the ocean from shortwave infrared radiances is shown to have, under idealized vertically homogeneous cloud profiles, absolute errors less than 2 kg m−2 for optically thick clouds or when the integrated water vapor content is below 20 kg m−2 and less than 1 kg m−2 for very thick clouds with an optical thickness exceeding 150. Tests using realistic water vapor and cloud extinction profiles that present non-homogeneous vertical distributions show that integrated water vapor content above water type clouds could be retrieved with a Root-Mean-Square Error related to cloud vertical penetration of approximately less than 1 kg m−2 except for optically thin and low-level clouds (cloud optical thickness less than 50 and cloud top height less than 2 km). For very low water vapor content encountered in the presence of high deep convective clouds, the retrieval algorithm tends to systematically overestimate the retrieved water vapor content due to an overestimation of the cloud extinction profile in the upper part of the cloud in the inversion model.

Observation from space, being global and quasi-continuous, is a first importance tool for aerosol studies. Remote sensing in the visible domain has been widely used to obtain better characterization of these particles and their effect on solar radiation. On the opposite, remote sensing of aerosols in the thermal infrared domain still remains marginal. However, knowledge of the effect of aerosols on terrestrial radiation is needed for the evaluation of their total radiative forcing. Infrared remote sensing provides a way to retrieve other aerosol characteristics, including their mean altitude. Moreover, observations are possible at night and day, over ocean and over land. In this context, six years (2003-2008) of the 2nd generation vertical sounder AIRS observations have been processed over the tropical belt (30°N-30°S).

Observation from space, being global and quasi-continuous, is a first importance tool for aerosol studies. Remote sensing in the visible domain has been widely used to obtain better characterization of these particles and their effect on solar radiation. On the opposite, remote sensing of aerosols in the thermal infrared domain still remains marginal. However, knowledge of the effect of aerosols on terrestrial radiation is needed for the evaluation of their total radiative forcing. Infrared remote sensing provides a way to retrieve other aerosol characteristics, including their mean altitude. Moreover, observations are possible at night and day, over ocean and over land.

The objective of the H2020 project “Copernicus Cal/Val Solution (CCVS)” is to define a holistic Cal/Val strategy for all ongoing and upcoming Copernicus Sentinel missions. This includes an improved calibration of currently operational or planned Copernicus Sentinel sensors and the validation of Copernicus core products generated by the payload ground segments. CCVS will identify gaps and propose long-term solutions to address currently existing constraints in the Cal/Val domain and exploit existing synergies between the missions. An overview of existing calibration and validation sources and means is needed before starting the gap analysis. In this context, this survey is concerned with measurement capabilities for aerial campaigns. Since decades airborne observations are an essential contribution to support Earth-System model development and space-based observing programs, both in the domains of Earth Observation (radar and optical) as well as for atmospheric research. The collection of airborne reference data can be directly related to satellite observations, since they are collected in ideal validation conditions using well calibrated reference sensors. Many of these sensors are also used to validate and characterize postlaunch instrument performance. The variety of available aircraft equipped with different instrumentations ranges from motorized gliders to jets acquiring data from different heights to the upper troposphere. In addition, balloons are also used as platforms, either small weather balloons with light payload (around 3 kg), or open stratospheric balloons with big payload (more than a ton). For some time now, UAVs/drones are also used in order to acquire data for Cal/Val purposes. They offer a higher flexibility compared to airplanes, plus covering a bigger area compared to in-situ measurements on ground. On the other hand, they also have limitations when it comes to the weight of instrumentation and maximum altitude level above ground. This reflects the wide range of possible aerial measurements supporting the Cal/Val activities. The survey will identify the different airborne campaigns. The report will include the description of campaigns, their spatial distribution and extent, ownership and funding, data policy and availability and measurement frequency. Also, a list of common instrumentation, metrological traceability, availability of uncertainty evaluation and quality management will be discussed. The report additionally deals with future possibilities e.g., planned developments and emerging technologies in instrumentation for airborne and balloon based campaigns. This presentation gives an overview of the preliminary survey results and puts them in context with the Cal/Val requirements of the different Copernicus Sentinel missions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 101004242.

In the context of MERLIN (MEthane Remote LIdar missioN), a French–German spatial lidar mission dedicated to monitoring the atmospheric methane content, two software programs have been developed: LIDSIM (LIDar SIMulator) and PROLID (PROcessor LIDar). The objectives are to assess whether the instrument design meets the performance requirements and to study the sensitivity of this performance to geophysical parameters. LIDSIM is an end-to-end mission simulator and PROLID is a retrieval processor that provides mole fractions of methane in dry air, averaged over an atmospheric column. These two tools are described in this paper. Results of the validation tests and the first full orbit simulations are reported. Merlin target performance does not seem to be reachable but breakthrough performance is reached.

MEthane Remote LIdar missioN (MERLIN) is a German‐French space mission, scheduled for launch in 2024 and built around an innovative light detecting and ranging instrument that will retrieve methane atmospheric weighted columns. MERLIN products will be assimilated into chemistry transport models to infer methane emissions and sinks. Here the expected performance of MERLIN to reduce uncertainties on methane emissions is estimated. A first complete error budget of the mission is proposed based on an analysis of the plausible causes of random and systematic errors. Systematic errors are spatially and temporally distributed on geophysical variables and then aggregated into an ensemble of 32 scenarios. Observing System Simulation Experiments are conducted, originally carrying both random and systematic errors. Although relatively small (±2.9 ppb), systematic errors are found to have a larger influence on MERLIN performances than random errors. The expected global mean uncertainty reduction on methane emissions compared to the prior knowledge is found to be 32%, limited by the impact of systematic errors. The uncertainty reduction over land reaches 60% when the largest desert regions are removed. At the latitudinal scale, the largest uncertainty reductions are achieved for temperate regions (84%) and then tropics (56%) and high latitudes (53%). Similar Observing System Simulation Experiments based on error scenarios for Greenhouse Gases Observing SATellite reveal that MERLIN should perform better than Greenhouse Gases Observing SATellite for most continental regions. The integration of error scenarios for MERLIN in another inversion system suggests similar results, albeit more optimistic in terms of uncertainty reduction.

The CNES (French Space Agency) and DLR (German Space Agency) project MERLIN is a future integrated path differential absorption (IPDA) lidar satellite mission that aims at measuring methane dry-air mixing ratio columns (XCH4) in order to improve surface flux estimates of this key greenhouse gas. To reach a 1 % relative random error on XCH4 measurements, MERLIN signal processing performs an averaging of data over 50 km along the satellite trajectory. This article discusses how to process this horizontal averaging in order to avoid the bias caused by the non-linearity of the measurement equation and measurements affected by random noise and horizontal geophysical variability. Three averaging schemes are presented: averaging of columns of XCH4, averaging of columns of differential absorption optical depth (DAOD) and averaging of signals. The three schemes are affected both by statistical and geophysical biases that are discussed and compared, and correction algorithms are developed for the three schemes. These algorithms are tested and their biases are compared on modelled scenes from real satellite data. To achieve the accuracy requirements that are limited to 0.2 % relative systematic error (for a reference value of 1780 ppb), we recommend performing the averaging of signals corrected from the statistical bias due to the measurement noise and from the geophysical bias mainly due to variations of methane optical depth and surface reflectivity along the averaging track. The proposed method is compliant with the mission relative systematic error requirements dedicated to averaging algorithms of 0.06 % (±1 ppb for XCH4 = 1780 ppb) for all tested scenes and all tested ground reflectivity values.

Abstract. The CNES/DLR project MERLIN is a future IPDA lidar satellite mission that aims at measuring methane dry-air mixing ratio columns (XCH4) in order to improve surface flux estimates of this key greenhouse gas. To reach a 1 % relative random error on XCH4 measurements, MERLIN signal processing performs an averaging of data over 50 km along the satellite trajectory. This article discusses how to process this horizontal averaging in order to avoid the bias caused by the non-linearity of the measurement equation with measurements affected by random noise and horizontal geophysical variability. Three averaging schemes are presented: averaging of columns of XCH4, averaging of columns of Differential Absorption Optical Depth (DAOD) and averaging of signals. The three schemes are affected both by statistical and geophysical biases that are discussed and compared and correction algorithms are developed for the three schemes. These algorithms are tested and their biases are compared on modeled scenes from real satellite data. To achieve the accuracy requirements that are limited to 0.2 % relative systematic error (for a reference value of 1780 ppb), we recommend performing the averaging of signals corrected from the statistical bias due to the measurement noise and from the geophysical bias mainly due to variations of methane optical depth and surface reflectivity along the averaging track. The proposed method is compliant with the mission relative systematic error requirements dedicated to averaging algorithms of 0.07 % (± 1 ppb for XCH4 = 1780 ppb) for all tested scenes and all tested ground reflectivity values.

The CNES/DLRMERLINsatellite mission aims at measuring methane dry-air mixing ratio column (XCH4) and thus improving surface flux estimates. In order to get a 1% precision on XCH4measurements, MERLIN signal processing assumes an averaging of data over 50 km. The induced biases due to the non-linear IPDA lidar equation are not compliant with accuracy requirements. This paper analyzes averaging biases issues and suggests correction algorithms tested on realistic simulated scenes.

The SIFTI (Static Infrared Fourier Transform Interferometer) instrument aims at supporting an important part in a mission for atmospheric pollution sounding from space, by providing high spectral resolution and high Signal to Noise Ratio spectra of the atmosphere. They will allow to resolve tropospheric profiles of ozone (03) and carbon monoxide (C0), especially down to the planetary boundary layer (PBL), an altitude region of very high interest, though poorly monitored to date, for air quality and pollution monitoring. The retrieved profile of ozone, resp. C0, will contain 5 to 7, resp. 2.5 to 4, independent pieces of information. The French space agency CNES (Centre National d'Etudes Spatiales) has proposed and is studying an instrument concept for SIFTI based on a static interferometer, where the needed optical path are generated by a pair of crossed staircase fixed mirrors (replacing the moving reflector of dynamic Fourier transform interferometers like IASI or MIPAS). With the SIFTI design, a very high spectral resolution (~0.1 cm-1 apodised) is achieved in a very compact optical setup, allowing a large throughput, hence a high SNR. The measurements are performed in the 9.5 μm band for 03 and in the 4.6 μm band for C0. The science return of the sounder can be further increased if an "intelligent pointing" process is implemented. This consists in combining the TIR sounder with a companion TIR imager, providing information on the cloud coverage in the next observed scene. 0nboard, real-time analysis of the IR image is used to command the sounder staring mirror to cloud free areas, which will maximize the probability for probing down to the surface. After the first part of the phase A, the architecture of SIFTI was studied as a trade-off between performance and resource budget. We review the main architecture and functional choices, and their advantages. The preliminary instrument concept is then presented in its main aspects and in terms of main subsystem functions. The preliminary budgets of mass, volume, size and power are also evaluated. Eventually the science performances are estimated, at instrument level and at mission level, and are compared to the specifications. To finish, the ways forward are discussed.

The MEthane Remote sensing Lidar missioN (MERLIN) aims at demonstrating the spaceborne active measurement of atmospheric methane, a potent greenhouse gas, based on an Integrated Path Differential Absorption (IPDA) nadir-viewing LIght Detecting and Ranging (Lidar) instrument. MERLIN is a joint French and German space mission, with a launch currently scheduled for the timeframe 2021/22. The German Space Agency (DLR) is responsible for the payload, while the platform (MYRIADE Evolutions product line) is developed by the French Space Agency (CNES). The main scientific objective of MERLIN is the delivery of weighted atmospheric columns of methane dry-air mole fractions for all latitudes throughout the year with systematic errors small enough (<3.7 ppb) to significantly improve our knowledge of methane sources from global to regional scales, with emphasis on poorly accessible regions in the tropics and at high latitudes. This paper presents the MERLIN objectives, describes the methodology and the main characteristics of the payload and of the platform, and proposes a first assessment of the error budget and its translation into expected uncertainty reduction of methane surface emissions.

The column‐average dry air‐mole fraction of carbon dioxide in the atmosphere (XCO2) is measured by scattered satellite measurements like those from the Orbiting Carbon Observatory (OCO‐2). We show that global continuous maps of XCO2 (corresponding to level 3 of the satellite data) at daily or coarser temporal resolution can be inferred from these data with a Kalman filter built on a model of persistence. Our application of this approach on 2 years of OCO‐2 retrievals indicates that the filter provides better information than a climatology of XCO2 at both daily and monthly scales. Provided that the assigned observation uncertainty statistics are tuned in each grid cell of the XCO2 maps from an objective method (based on consistency diagnostics), the errors predicted by the filter at daily and monthly scales represent the true error statistics reasonably well, except for a bias in the high latitudes of the winter hemisphere and a lack of resolution (i.e., a too small discrimination skill) of the predicted error standard deviations. Due to the sparse satellite sampling, the broad‐scale patterns of XCO2 described by the filter seem to lag behind the real signals by a few weeks. Finally, the filter offers interesting insights into the quality of the retrievals, both in terms of random and systematic errors.

Measuring the concentration of greenhouse gases from space is a current challenge. This measurement is achieved via a precise analysis of the signature of chemical gaseous species (CO2, CH4, CO, etc.) in the spectrum of the reflected sunlight. First at all, two families of spectrometers have been studied for the MicroCarb mission. The first family is based on the phenomena of interference between two radiation waves (Michelson Interferometer). The second family is based on the use of dispersive optical components. The second family has been selected for the forthcoming studies in the MicroCarb project. These instruments must have high radiometric and spectral resolutions, in narrow spectral bands, in order to discriminate between absorption lines from various atmospheric chemical species, and to quantify their concentration. This is the case, for example, for the instrument onboard the OCO-2 satellite (NASA/JPL). Our analysis has led us to define a new instrumental concept, based on a dispersive grating spectrometer, with the aim of providing the same accuracy level as the OCO-2, but with a more compact design for accommodation on the Myriade Evolution microsatellite class. This compact design approach will allow us to offer a moderate-cost solution to fulfil mission objectives. Two other studies based on dispersive grating are in progress by CNES prime contractors (ASTRIUM and THALES ALENIA SPACE). A summary of the main specifications of this design will be described, in particular the approach with the so-called “merit function”. After a description of such a space instrument, which uses a specific grating component, a preliminary assessment of performances will be presented, including the theoretical calculations and formula. A breadboard implementation of this specific grating has allowed us to show the practicality of this concept and its capabilities. Some results of this breadboard will be described. In addition, an instrument simulator is being developed to validate the performances of this concept. A grating component prototype has been built, and the specifications, together with the expected performances, will be described, in particular the polarisation ratio. Some elements about detectors will be also given regarding their suitability for the mission. This preliminary design is encouraging and shows that such a spectrometer may be compatible with a microsatellite platform (low mass, low power and compact design). Some prospects of improvements will also be considered.

The concept of static Fourier transform interferometry at thermal infrared wavelengths is well suited in the case of narrow spectral bands that are looked at for targeted molecular species as CO and O3 for pollution and air quality monitoring, or H20 and CO2 for weather forecast, down to the troposphere. It permits a high spectral resolution and a very good radiometric performance, with the advantage of a static interferometer, including no moving part. Along with other molecules sounded in the UV-VIS domain, as for instance in the TRAQ mission, SIFTI will provide scientists with a complete set for pollution measurements and air quality survey. Our paper presents the principles of static Fourier transform spectrometry, the work led on the instrument performance model and our study of the SIFTI instrument. We describe the instrument, its main dimensions and characteristics, and its architecture and major subsystems. We eventually make a preliminary survey of the SIFTI performance budget items. As a conclusion, we introduce the future CNES phase A study of this instrument that is started in 2006