# Scheimpflug-like processing of differential absorption lidar data on water vapor

Tancrède Esnouf, Jean-Baptiste Dherbecourt, Myriam Raybaut, Jonas

Hamperl, Jean-Michel Melkonian, Cyrille Flamant

► To cite this version:

Tancrède Esnouf, Jean-Baptiste Dherbecourt, Myriam Raybaut, Jonas Hamperl, Jean-Michel Melkonian, et al.. Scheimpflug-like processing of differential absorption lidar data on water vapor. EPJ Web of Conferences, 2025, EOS Annual Meeting (EOSAM 2025) Delft, The Netherlands, August 24-28, 2025, 335, pp.03043. 10.1051/epjconf/202533503043. hal-05289186

HAL Id: hal-05289186

https://hal.science/hal-05289186v1

Submitted on 29 Sep 2025

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# Scheimpflug-like processing of differential absorption lidar data on water vapor.

Tancrède Esnouf<fnref n="1" />*, Jean-Baptiste Dherbecourt<fnref n="1" />, Myriam Raybaut<fnref n="1" />, Jonas Hamperl<fnref n="1" />, Jean-Michel Melkonian<fnref n="1" /> and Cyrille Flamant²

¹DPHY, ONERA, Université Paris-Saclay, 91123 Palaiseau, France

²Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), UMR 8190, CNRS-SU-UVSQ, 75005, Jussieu, France

**Abstract:** Differential Absorption Lidar (DiAL) method is widely used for a number of ground-based and airborne greenhouse gas measurement applications. However, for a direct detection in the 2µm range, other sources of random noise can be neglected in front of the detection noise which increases with ranging. We therefore propose to evaluate the effect of non-constant Scheimpflug-like averaging to reduce the signal-to-noise ratio along the line of sight, during a 5-hour measurement campaign in the Paris region.

The multi-species WaVIL lidar has already demonstrated the feasibility of measuring low abundant water vapor isotopes at 2 µm in the atmosphere [1]. This differential absorption lidar (DiAL) makes use of a specific parametric source emitting in the 1.98µm region, coupled to commercial extended InGaAs photodiodes. However, the lack of maturity of these IR sensors results in detection noise being the main source of random errors for this lidar. On February, 11th 2022, the WaVIL Lidar took part in a 5-hour long campaign to measure water vapor isotopic ratio. To improve the system's signal-to-noise ratio, we plan to enhance the telescope's field of view and increase the output energy. Here, we propose to focus on data measurement processing, using a new averaging method inspired by Scheimpflug imaging to degrade spatial resolution over the line of sight and reduce random noise [2].

## 1 Differential absorption lidar theory

$$DOD(r) = \frac{1}{2} \ln \left( \frac{P_{on} \left( r + \frac{\delta r}{2} \right) \cdot P_{off} \left( r - \frac{\delta r}{2} \right)}{P_{on} \left( r - \frac{\delta r}{2} \right) \cdot P_{off} \left( r + \frac{\delta r}{2} \right)} \right) \quad (1)$$

The DiAL method consists in sending alternately laser pulses into the atmosphere whose wavelength are tuned to a specific absorption line (on-line) and a non-absorption line (off-line) of the targeted gas. The gas concentration over a given range is then determined (with a few assumptions) by the variation of the backscattered light intensities [3]. Indeed, from

<footnote n="1">¹ Corresponding author: tancrede.esnouf@onera.fr</footnote>
the established lidar equation, the differential optical depth DOD at range $r$ can be determined as follows (1). $P_i$ is the received power signal at a certain wavelength, and $\delta r$ is the range sampling over which the differential optical depth is calculated. By repeating this procedure, the gas concentration is estimated for altitude slices as a linear function of the differential optical depth. The same system specifications were used for this campaign as ones presented in [1]. The intercomparaison is made with two near meteorological stations, located at 20 km (MétéoFrance's Radio Sounding) and 3 km (Sirta meteoairsol station) from the measurement site.

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Profile of water vapor measured with WaVIL Lidar during the 2022 campaign in Parisian suburb at 3pm. The profile processed with the Scheimpflug method is labelled ‘WaVIL Scheimpflug’. The boundary layer was around 1000 m AGL.</figcaption></figure>

## 2 Scheimpflug-like processing

Scheimpflug-like data processing involves averaging the signal over increasing range $\delta r$ depending on the square of the ranging $r$. In **Fig. 1**, we note that relative uncertainties are almost constant around 3% along the sight path. But the spatial resolution is downgraded from 75 meters up to 288 meters sampling. With this processing method, we expect to be able to improve future lidar measurements on less abundant water isotopes.

The authors would like to acknowledge SIRTA for providing the lidar data used in this study.

## References

1. J. Hamperl et al., Range-resolved detection of boundary layer stable water vapor isotopologues using a ground-based 1.98 µm differential absorption LIDAR, Opt. Express, **vol. 30**, no 26, p. 47199 (2022). https://insu.hal.science/insu-03908665v1

2. E. Malmqvist, M. Brydegaard, M. Aldén, et J. Bood, Scheimpflug Lidar for combustion diagnostics. Opt. Express. **vol. 26**, no 12, p. 14842 (2018). https://doi.org/10.1364/OE.26.014842

3. J. Bösenberg, Ground-based differential absorption lidar for water-vapor and temperature profiling: methodology. Appl. Opt., **vol. 37**, no 18, p. 3845 (1998). doi:10.1364/AO.37.003845.