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.

During transport by advection, atmospheric nonspherical particles, such as volcanic ash, desert dust or sea-salt particles experience several chemical and physical processes, leading to a complex vertical atmospheric layering at remote sites where intrusion episodes occur. In this paper, a new methodology is proposed to analyse this complex vertical layering in the case of a two/three-component particle external mixtures. This methodology relies on an analysis of the spectral and polarization properties of the light backscattered by atmospheric particles. It is based on combining a sensitive and accurate UV-VIS polarization lidar experiment with T-matrix numerical simulations and air mass back trajectories. The Lyon UV-VIS polarization lidar is used to efficiently partition the particle mixture into its nonspherical components, while the T-matrix method is used for simulating the backscattering and depolarization properties of nonspherical volcanic ash, desert dust and sea-salt particles. It is shown that the particle mixtures' depolarization ratio δ _p differs from the nonspherical particles' depolarization ratio δ_ns due to the presence of spherical particles in the mixture. Hence, after identifying a tracer for nonspherical particles, particle backscattering coefficients specific to each nonspherical component can be retrieved in a two-component external mixture. For three-component mixtures, the spectral properties of light must in addition be exploited by using a dual-wavelength polarization lidar. Hence, for the first time, in a three-component external mixture, the nonsphericity of each particle is taken into account in a so-called 2β + 2δ formalism. Applications of this new methodology are then demonstrated in two case studies carried out in Lyon, France, related to the mixing of Eyjafjallajökull volcanic ash with sulfate particles (case of a two-component mixture) and to the mixing of dust with sea-salt and water-soluble particles (case of a three-component mixture). This new methodology, which is able to provide separate vertical profiles of backscattering coefficient for mixed atmospheric dust, sea-salt and water-soluble particles, may be useful for accurate radiative forcing assessments.

The development of increasingly sensitive and robust instruments and new methodologies are essential to improve our understanding of the Earth’s climate and air pollution. In this context, Dual-Comb spectroscopy (DCS) has been successfully demonstrated as a remote laser-based instrument to probe infrared absorbing species such as greenhouse gases. We present here a study of the sensitivity of Dual-Comb spectroscopy to remotely monitor atmospheric gases focusing on molecules that absorb in the ultraviolet domain, where the most reactive molecules of the atmosphere (OH, HONO, BrO...) have their highest absorption cross-sections. We assess the achievable signal-to-noise ratio (SNR) and the corresponding minimum absorption sensitivity of DCS in the ultraviolet range. We propose a potential light source for remote sensing UV-DCS and discuss the degree of immunity of UV-DCS to atmospheric turbulences. We show that the characteristics of the currently available UV sources are compatible with the unambiguous identification of UV absorbing gases by UV-DCS.

We propose a new methodology to measure gas concentration by light-absorption spectroscopy when the light source spectrum is larger than the spectral width of one or several molecular gas absorption lines. We named it optical similitude absorption spectroscopy (OSAS), as the gas concentration is derived from a similitude between the light source and the target gas spectra. The main OSAS-novelty lies in the development of a robust inversion methodology, based on the Newton-Raphson algorithm, which allows retrieving the target gas concentration from spectrally-integrated differential light-absorption measurements. As a proof, OSAS is applied in laboratory to the 2ν₃ methane absorption band at 1.66 µm with uncertainties revealed by the Allan variance. OSAS has also been applied to non-dispersive infra-red and the optical correlation spectroscopy arrangements. This all-optics gas concentration retrieval does not require the use of a gas calibration cell and opens new tracks to atmospheric gas pollution and greenhouse gases sources monitoring.

While pollen is expected to impact public human health and the Earth’s climate more and more in the coming decades, lidar remote sensing of pollen has become an important developing research field. To differentiate among the pollen taxa, a polarization lidar is an interesting tool since pollen exhibit non-spherical complex shapes. A key attribute is thus the lidar particle depolarization ratio (PDR) of pollen, which is however difficult to quantify as pollen are large and complex-shaped particles, far beyond the reach of light scattering numerical simulations. In this paper, a laboratory π-polarimeter is used to accurately evaluate the PDR of pure pollen, for the first time at the lidar exact backscattering angle of 180.0°. We hence reveal the lidar PDR of pure ragweed, ash, birch, pine, cypress and spruce pollens at 355 and 532 nm lidar wavelengths, as presented at the ELC 2021 conference. A striking result is the spectral dependence of the lidar PDR, highlighting the importance of dual-wavelength (or more) polarization lidars to identify pollen taxa. These spectral and polarimetric fingerprints of pure pollen, as they are accurate, can be used by the lidar community to invert multi-wavelength lidar polarization measurements involving pollen.

Pollen is nowadays recognized as one of the main atmospheric particles affecting public human health as well as the Earth's climate. In this context, an important issue concerns our ability to detect and differentiate among the existing pollen taxa. In this paper, the potential differences that may exist in light scattering by four of the most common pollen taxa, namely ragweed, birch, pine and ash, are analysed in the framework of the scattering matrix formalism at two wavelengths simultaneously (532 and 1064 nm). Interestingly, our laboratory experimental error bars are precise enough to show that these four pollen taxa, when embedded in ambient air, exhibit different spectral and polarimetric light-scattering characteristics, in the form of 10 scattering matrix elements (5 per wavelength), which allow each to be identified separately. To end with, a simpler light-scattering criterion is proposed for classification among the four considered pollen taxa by performing a principal component (PC) analysis, which still accounts for more than 99 % of the observed variance. We thus believe this work may open new insights for future atmospheric pollen detection.