Measurement and Modeling of Air‐Broadened Methane Absorption in the MERLIN Spectral Region at Low Temperatures

Abstract

The ability to precisely model methane absorption in the R(6) manifold of the 2ν3 band at atmospheric pressure and temperature conditions is a key technical requirement of the German‐French, Methane Remote Sensing Lidar (MERLIN) space mission. To this end, 27 high‐resolution and high‐signal‐to‐noise‐ratio absorption spectra of air‐broadened 12CH4 were recorded using a variable‐temperature frequency‐stabilized cavity ring‐down spectroscopy apparatus. The measurement conditions corresponded to sample temperature, pressure, and methane molar fraction values spanning 220–290 K, 4–110 kPa, and 4–7 μmol/mol, respectively. The measured spectra were fit using the sum of isolated Hartmann‐Tran profiles with the addition of line mixing. For each line within the manifold, all spectroscopic parameters at room temperature were fixed to our previously obtained values (Delahaye et al., 2016, https://doi.org/10.1002/2015JD024524) and only the temperature dependences of the model parameters were adjusted. The results show that the fitted model agrees with the measured methane absorption to better than 0.3% for the entire R(6) manifold spectral region and to within 0.1% at the online position of the MERLIN for all considered pressure and temperature conditions. A first comparison with ground‐based atmospheric measurement was also made showing significant improvement with respect to existing spectroscopic modeling of methane absorption.

Toward measurements of the speed-dependence of line-mixing

“…This figure also shows that neglecting the SD of LM then leads to an underestimation of the doublet width, with errors on the absorption of typically 1% of the peak value that have a M shape. This could be expected (recalling that 12 w and 21 w have negative values) from the well known fact ( [1,2], and [17] for NH 3 ) that the speed dependence of the broadening results, for isolated lines, in a narrowing with changes of the absorption of similar magnitudes and a W shape (a M shape if transmissions are considered). Fig.…”

“…From the experimental point of view, the situation is the same: All studies in which LM parameters have been deduced from measured spectra have disregarded their speed dependence, even though the SD of the pressure-broadening and shifting coefficients were included in the fitting model (e.g. [2,[11][12][13][14] and those cited therein).…”

Twenty-Five-Fold Reduction in Measurement Uncertainty for a Molecular Line Intensity

“…For example, the high-resolution JAXA Greenhouse Gases Observing Satellite (GOSAT) [10] and NASA Orbiting Carbon Observatory (OCO) [11] passive remote sensing missions have, for more than a decade, relied upon accurate spectroscopic forward models of carbon dioxide (CO 2 ) and molecular oxygen (O 2 ) to retrieve CO 2 column densities: the measurement target being a precision of 1 μmol/mol, or approximately 0.3 % of the nominal mean column density [12–14]. Additionally, monitoring global trends in atmospheric methane (CH 4 ) [15–18] and performing point-source attribution [19] currently motivates laboratory research into accurate first-principles models in congested spectral regions [20, 21], and open-path dual-comb spectroscopy has achieved the following low relative instrumental variations for mole fractions at 30 s of integration: 0.14 % for CO 2 , 0.35 % for CH 4 , and 0.40 % for water (H 2 O) [22]. Consequently, reference values for the absorption cross-sections (derived from the oscillator strengths) of these and many more molecules (e.g., [23]) must be known with sufficiently low relative uncertainty (≤0.1 %) to ensure accurate retrievals from highly precise instruments.…”

“…At values of T and p routinely encountered within the Earth’s atmosphere, σ can comprise a relatively smooth function of frequency, v , in congested spectral regions (e.g., volatile organic compounds in air [24, 25]), or when the absorber lifetime is short (e.g., collision-induced absorption [8]). When clusters of overlapping lines are resolved (e.g., the overtone spectrum of methane [20, 21]), we can express σ as a summation over a physically justified choice of line shape profiles (including line mixing) scaled by the respective molecular line intensities ( S ), which can be calculated in terms of invariants of nature. Importantly, this approach yields first-principles models for σ .…”

Optical Energy Variability Induced by Speckle: The Cases of MERLIN and CHARM-F IPDA Lidar

“…where σCH4 is the absorption cross sections of a mole of methane depending on wavelength, pressure p and temperature T, g is the acceleration due to gravity, Mdry is the average molar mass of dry-air, MH2O is the molar mass of water vapor and XH2O is water vapor dry-air volume mixing ratio. WF(p), DAODH2O and DAODCO2 are computed from data provided by meteorological centers and spectroscopic data found in GEISA database [40] with specific improvements [41,42]. Slant DAOD is determined as the ratio of the back-scattered energy estimates for pulses ON and OFF (Pon and Poff) normalized by emitted energy estimates for each pulse (Eon and Eoff) to deal with the fluctuations of the energy delivered by the laser source [38][39].…”