Multi-Frequency Differential Absorption LIDAR (DIAL) System for Aerosol and Cloud Retrievals of CO2/H2O and CH4/H2O

1. Introduction

Active remote sensing using differential absorption LIDAR (DIAL) [1,2,3,4] and integrated path differential absorption LIDAR (IP-DIAL) [5,6,7,8,9] provides the necessary measurement precision and accuracy to study greenhouse gas (GHG) concentrations in the atmosphere. Of particular importance, the dry-air mixing ratios of the GHGs, CO₂ and CH_4, can be monitored on a global scale using satellites for climate studies [10,11,12,13] and with ground-based systems for local and large city emission studies [14]. Airborne systems have been used to develop spaceborne platforms and to later calibrate ground-based and spaceborne sensors [7,15,16,17]. Largely overcoming the inherent limitations of passive systems [18,19], DIAL and IP-DIAL systems can provide the measurement data to study and assess the current state and dynamics of Earth’s ecosystem under GHG fluctuations.

Current DIAL and IP-DIAL system development for remote sensing of CO₂ and CH₄ focuses on the NIR (near infrared) spectral regions (1572 nm (CO₂), 1602 nm (CO₂), 1645 nm (CH₄), 2050 nm (CO₂)). The main technological challenges are the selection of a suitable laser source and detector, both of which have been the subject of numerous studies for current research systems over the past decade (see the overview given in [9]). The latest developments for DIAL [4] and IP-DIAL [7,9] systems make use of multiple frequencies to sample the absorption line profile and use subsequent spectral line shape fitting to obtain the GHG mixing ratios. This method is found here to be particularly suitable for complicated line shapes consisting of multiple underlying transitions (e.g., CH₄) and/or multi-species determinations (e.g., CH₄ or CO₂ and H₂O). Additional advantages have been realized in studies to and through clouds to enhance sensitivity. For example, previous IP-DIAL studies from aircraft measured 30 points across the CO₂ line shape to perform studies to cloud tops and through clouds to the surface [7]. Cloud targets have also found importance in the specification of the overall spectral purity of the DIAL frequency converter from comparisons of the online and offline frequency returns [20].

The work presented here demonstrates the application of a 10-frequency DIAL system to study GHG mixing ratios of CO₂ and H₂O (denoted CO₂/H₂O) and CH₄/H₂O from aerosol and cloud aerosol returns using an optical parametric oscillator (OPO) [21]. We present the data processing and mixing ratios obtained from IP-DIAL retrievals to clouds and make comparisons to the column averaged DIAL mixing ratios to validate the methods and potentially identify disagreements with currently available line shape models.

This paper is structured as follows: Section 2.1 presents the experimental 10-frequency OPO transmitter and movable transceiver platform used to perform DIAL and IP-DIAL studies of CO₂/H₂O and CH₄/H₂O. Section 2.2 discusses the DIAL and IP-DIAL data processing including the line shape fitting procedure to obtain GHG concentrations and mixing ratios. Section 2.3 reviews the latest spectroscopic improvements to the CH₄ line shape near 1645 nm. Section 3 presents the data processing for IP-DIAL retrievals from diffuse clouds (Section 3.1), the IP-DIAL results for CO₂/H₂O and CH₄/H₂O (Section 3.2), the DIAL results for CO₂/H₂O and CH₄/H₂O (Section 3.2), and an overall comparison of the mixing ratios obtained from IP-DIAL, DIAL and a (cavity ringdown) point sensor. A discussion of the uncertainties relative to satellite calibration requirements is given in Section 4. The paper closes with conclusions and outlook in Section 5.

2. Experiment

2.1. Combined DIAL and IP-DIAL system

A schematic of the overall system setup at NIST is shown in Figure 1 and is similar to the system described in [2]. The system specifications are given in Table 1 [22]. Briefly, the seed source of the frequency converter is based on the fixed frequency output of an external-cavity diode laser (ECDL). A portion (10%) of the fiber-coupled ECDL output is first frequency shifted in an acousto-optic modulator (AOM), modulated in an electro-optic phase modulator (EOM₁) and then free-space coupled and mode-matched into a 50 cm Invar confocal filter cavity that is used to frequency lock the laser to the cavity in reflection (Pound-Drever-Hall method [23]). The remaining 90% is fiber coupled to a second broadband EOM₂ that is driven by a fast-switching microwave synthesizer. The microwave (MW) frequencies are defined such that only one of the second-order harmonics from the EOM (i.e., shifted from the carrier by 2×MW frequency) is transmitted through the filter cavity [9] at any given time with high spectral purity (> 30 dB).

The 10 transmitted sideband frequencies that define a scan are sequentially generated at a 100 Hz pulse repetition frequency (PRF). The frequency sequence is continuously repeated every 100 ms to give a 10 Hz overall scan rate. In second order, the spectral bandwidth coverage over the (±) sidebands is ≈ 50 GHz and centered near the ECDL frequency. The fiber-coupled output of the filter cavity is passed through EOM₃ driven at 150 MHz and amplified to ≈ 10 mW to enable the PDH lock of the optical parametric oscillator (OPO) to each injection seeded frequency.

The OPO contains two KTA crystals (type I phase matching) in a twisted ring resonator configuration for image rotation [24,25,26] and is pumped at 100 Hz PRF with 170 mJ/pulse at 1064 nm using an injection-seeded Nd:YAG laser. The signal wave output pulse energy used is ≈ 5 mJ and has a measured near-transform-limited 190 MHz line width [4]. The KTA crystals are cut so that the signal wave can be angle tuned over the spectral region from 1600 nm to 1650 nm to access the strongest lines in the 30013-00001 band of CO₂ (the R(22e) line at 1602.2 nm) and in the 2 ν₃ band of CH₄ (the R(6) manifold at 1645.2 nm). In both cases, nearby water lines are also sampled to determine dry-air mixing ratios. The signal wave of the OPO output is separated from the remaining pump and OPO frequencies using a home-built prism beam separator containing four SF₆ 60° prisms in a folded wavelength-compensating-path configuration and then propagated along a coudé path to a movable platform (91 cm x 152 cm). Two galvanometers in the path are used for steering the beam to an expanding telescope for Class I level transmission using both feed-forward and feed-back signals derived from the position of the pneumatic cylinder and a quadrant photodetector, respectively.

Two receiver telescopes are also mounted on the movable platform and consist of a near-field (NF) and far-field (FF) telescopes (data from the FF system was primarily used in this study). Both are equipped with altitude/azimuth mount for alignment with the fixed transmitted beam. The collected light is coupled into 1 mm multi-mode fibers and then free-space coupled to near-IR photomultiplier tubes (PMT). Narrow bandpass filters are used for CO₂ at 1602.2 nm and CH₄ at 1645.5 nm. To aid in reducing background light during daytime viewing, filter wheels are available with optical densities from 0 to 3. The movable platform weighing more than 300 kg can be scanned laterally by ± 30° using a stepper motor control stage and in altitude from 0° to 30° using a pneumatic control cylinder.

Two spectral regions targeted for these studies are illustrated in Figure 2a,b. For the CO₂/H₂O region shown in Figure 2a, five frequencies are selected on the R(22e) CO₂ line of the 30013-0001 band at 1602.205 nm (6241.40 cm^-1) and two frequencies on the H₂O line pair at 1602.133 nm and 1602.102 nm (6241.68 cm^-1 and 6241.81 cm^-1), and the remaining 3 frequencies sample the baseline regions at both ends of the spectrum. For CH₄/H₂O region shown in Figure 2b, six frequencies sample across the doublet structure of the R(6) manifold of the 2ν₃ band centered at 1645.534 nm (6077.05 cm^-1), one frequency on the H₂O line at 1645.471 (6077.28 cm^-1) and the remaining three frequencies sample the baseline regions on both ends. Over a range of 250 m (500 m path length), the optical densities of CO₂ and CH₄ are near 4% for ambient levels of CO₂ (≈ 420 ppm) and CH₄ (≈ 2 ppm).

2.2. Raw data processing and concentration fitting procedure

The pre-processing of the raw data follows the procedure discussed previously [1]. Briefly, a dual channel 8-bit digitizer samples the output voltages of the PMTs at a rate of 2 GS/sec. This is fast enough to resolve the amplified photon current signal from the PMT with 7 samples across the full width of each count. Double counting is prevented by skipping 6 samples following threshold detection. In software, the accumulated counts and current for both the NF and FF receivers are saved separately on 10 s intervals for each frequency in NetCDF format.

The processing begins with the accumulation of the photon counts and current over 10 consecutive points which reduces the range resolution to ≈ 1.5 m. For all ten frequencies, a non-linear least square fit (NLLSF) is used to determine the saturation parameter to correct for pulse pileup error. The individual parameters for each frequency and interval are typically fit over a region where the count rates range between N = (1-5) x10⁶ counts/s. The frequency averaged saturation parameter over all intervals is then fixed and NLLSF repeated to fit the current to the photon count signal using a scalar and offset parameter on each interval [27]. This hybrid detection scheme extends the linearity of the photon counting system to several billion counts/s. These same parameters are used for splices over the enhanced backscatter regions from cloud returns when the saturated count threshold levels are exceeded.

To fit the concentrations of the DIAL and IP-DIAL profiles at each altitude (i.e., at each temperature and pressure), we first generate line shape templates for CO₂ and CH₄ at 400 ppm and 2 ppm, respectively. Given that the CO₂ line is a single transition and that we use only 5 online frequencies near the peak of the absorption to measure the line, a higher accuracy line shape function that includes line mixing and speed dependent effects was not necessary. Therefore, the template line shape function used was a Voigt profile based on the line broadening and shift parameters reported in the HITRAN 2020 database [28]. However, for CH₄, the most accurate line shape function was required and discussed separately below. Voigt functions for the nearby water vapor lines were added to the overall profile at the 1 % (10,000 ppm) level.

For the range resolved DIAL concentrations, the raw data are processed at the 10 frequencies using a numerical procedure described in [4]. The differential absorption optical depth, DAOD(R,ν_i), at each of the 10 frequencies, ν_i, is first determined from the integrated photon count data, N_i, at range, R=(R₁ + R₂)/2 with range resolution, ΔR=(R₂-R₁), from

$$DAOD\left(R,{\nu }_i\right)=ln\left(\frac{N_i\left(R_1\right)N_b\left(R_2\right)}{N_b\left(R_1\right)N_i\left(R_2\right)}\right)$$

(1)

where N_b is the one of the background frequencies conveniently chosen as the first point (i.e., $DAOD\left(R,{\nu }_1\right)$ = 0) and R=0 is where the pulse leaves the transmitter telescope. From our prior DIAL study of CO₂ [4], the OPO signal-wave frequency is known to be shifted by ≈ 120 MHz relative to the input seed frequency. To account for this shift (as well as other frequency drift factors), the initial center frequencies were determined from the fitted profiles obtained from averaging over of the first 6 range bins. Model-based adjustments to the center frequencies and line shapes were then made for changes in altitude. The final fits of optical density at the 10 frequencies were performed following iterative updates of the scaled templates. All components of a given species were scaled together to give concentrations in μmole/mole (ppm) for CO₂ and CH₄ and in percent mol/mol (PCM) for water vapor. We also note that the line width of the laser (190 MHz) was added in quadrature to the Gaussian component of the profile and the temperatures and pressures used at each altitude were derived from the U.S. Standard Atmosphere model [29] after correction for the weather station data at the base altitude of the transmitter in Boulder (1655 m). For the IP-DIAL measurements over regions with increasing altitude, the path length is first divided into equal sections with temperature and pressure corrected for altitude and the line shapes over each section are averaged prior to performing fits. Further details of the IP-DIAL processing procedures are given below.

2.3. Methane line shape profile

The line shape function of the R(6) manifold of the 2ν₃ band of CH₄ is complicated given that it is an unresolved profile under atmospheric conditions consisting of 6 major lines. This complexity has likely contributed to the significant changes in the cross sections and Voigt line shape parameters reported in the HITRAN databases over the past 20 years. Moreover, the line shape is further complicated by line mixing and speed-dependent effects which cannot be modeled by a simple Voigt profile used in HITRAN. Given the importance of the R(6) line for the upcoming active satellite IPDA measurements from MERLIN [10,11], several cavity ringdown studies have been performed to (i) measure the precise line centers of the 6 lines using saturation spectroscopy [30,31] and (ii) fit the temperature and pressure dependence of these transitions [32,33] to the Hartmann-Tran profile (HTP) [34]. As a result of the high sensitivity of the IP-DIAL returns, we found it necessary to make use of this model to minimize the residuals. It is further noted that the overall integrated intensity of the HTP R(6) profile is scaled to that of HITRAN 2020 [33] and that additional studies are underway at NIST to link the intensity scale to the International System of Units (SI).

3. Results

3.1. IP-DIAL studies of the LIDAR returns from diffuse clouds

The measurements of the CO₂/H₂O region were performed over the 7 h nighttime period on Nov. 24, 2021 beginning at 8:08 PM local time. The telescope was at an inclination of 2.5° off the horizon and pointed ≈ -10° off North across the city of Boulder. The measurements of the CH₄/H₂O region were performed over the 5 h nighttime period on Apr. 28, 2022 beginning at 10:05 PM local time. The telescope was at an inclination of 10.5° and pointed 2.4° off North across the city of Boulder.

The range corrected LIDAR returns from the first offline frequency in the CO₂ region are shown in Figure 3b for one of the initial 10 min intervals. A cross section cut of the LIDAR returns is shown in Figure 3a at each of the 10 different frequencies. In the first 2 h period, normal aerosol returns extend to about 10 km, at which point the scattering is significantly enhanced upon passing through a ≈ 2 km cloud segment centered at ranges from 11 km to 15 km. The offline cloud signal returns (shown in blue) exceed 200×10⁶ count/s and are of the same magnitude as seen near full overlap at 0.5 km. From a comparison of the online (red) and offline returns in the cloud region, the CO₂ absorption signal over this 20+ km pathlength represents > 70% fractional absorption.

The range corrected LIDAR returns for the CH₄ region are shown in Figure 3d and a cross section cut of the raw returns at all 10 frequencies is shown in Figure 3c for ranges up to 22 km. The enhanced backscatter from the more diffuse clouds extends over a 6 km range beginning at ≈ 14 km and is 50 times less than the CO₂ region shown in Figure 3a. With the pathlength being nearly double that of the CO₂ region, online fractional absorption of the CH₄ region is > 90 % of the offline returns. Furthermore, the larger returns for the CO₂ region are above the saturated count threshold and require the use of the PMT’s current channel, while the returns in the CH₄ region remained linear in photon counts. Therefore, these two systems represent near limiting cases for backscatter studies from clouds.

The range dependence of the optical depth gives a better representation of the linearity and signal-to-noise ratio of the returns. The natural logarithm of the ratios for each frequency, k, relative to the first offline color gives the optical depth, OD(R), as a function of range, R, from,

$${OD}_k\left(R\right)=\mathrm{l}\mathrm{n}\left(\frac{N_k\left(R\right)}{N_1\left(R\right)}\right)$$

(2)

where $N_k\left(R\right)$ are the accumulated counts in a 10 min interval. Since the online optical depths are proportional to the GHG concentrations at the different frequencies, they are expected to increase approximately linearly with range, as is evident in Figure 4a,b, respectively. The SNR decreases with range since the accumulated counts decrease by the inverse square of the range (i.e., SNR(R) ≈ $\sqrt{N_k/R^2}$ in a uniform aerosol field). However, beyond 10 km in Figure 4a, the SNR increase significantly from the enhanced backscatter from the cloud aerosol returns. Furthermore, the near linear increase in ${OD}_k\left(R\right)$ across the cloud region indicates sufficient transparency to perform integrated path measurements. A similar trend is observed in Figure 4b for CH₄/H₂O, although the enhancement in SNR from cloud aerosols is not as pronounced because the 2-fold increase in range results in a significant decrease in the total backscatter.

In contrast to DIAL processing that is laser energy independent (see Eqs. 1 and 2), the IP-DIAL processing of the integrated cloud returns requires knowledge of the relative energy in each pulse. The integrated path optical depth, ${IPOD}_k\left(R\right)$, of point, k, can be expressed as,

$${IPOD}_k\left(R\right)=\mathrm{l}\mathrm{n}\left(\frac{N_1\left(0\right)}{N_k\left(0\right)}\right)+\mathrm{l}\mathrm{n}\left(\frac{N_k\left(R\right)}{N_1\left(R\right)}\right)$$

(3)

where the first term on the right represents the log ratio of the transmitted laser pulse energies (LPE). Typically, for IP-DIAL studies, the pulse energies of the reference channel are measured independently by sampling the output beam before expansion and transmission [9,35]. However, unlike the output from single-mode fiber-based sources [9], the shape of our free-space OPO beam depends slightly on frequency. Consequently, the energy ratios of the signal and reference channels at the different frequencies were found to vary slowly with time. Other factors such as the unmatched filter bandwidths and differing detector quantum efficiencies also contributed to the observed variations in these ratios. As previously discussed [35], careful designs and detailed calibrations are required to proportionally match the relative energies in the signal and reference channels.

The simultaneous measurement of DIAL and IP-DIAL returns enables an alternative method to determine the LPEs. The method takes advantage of the linear range dependence of the optical depths shown in Figure 4a,b. From Equation (3), the LPEs can be obtained from the linear fits of this data following small corrections for changes in concentration and altitude. By extrapolating the curves back to when the laser (OPO) pulse leaves the transmitter (R=0), the LPE terms in Equation (3) are obtained directly. The curve fits are performed over the region from 0.625 km to 5 km (see vertical dashed lines in Figure 4a,b) where the intercepts at R=0 represent the LPEs for each 10 min average. The uncertainties of the intercepts are typically < 0.005 in optical density and therefore have a minor impact on the fitted concentrations.

3.2. IP-DIAL studies of CO₂/H₂O and CH₄/H₂O

Unlike hard target backscatter where the integrations are effectively performed over the width of the laser pulse, the diffuse nature of cloud aerosols leads to an extended source of the enhanced backscatter that ranges from 2 km for the CO₂/H₂O region to > 6 km ranges for the CH₄/H₂O region. The inclination angle for the CO₂/H₂O study was 2.5° which corresponds to clouds at an altitude of ≈ 0.44 km at a range of 10 km from the transmitter. The inclination angle for the CH₄/H₂O study was 10.5°, which corresponds to clouds at an altitude of ≈ 3.5 km at distance of 19 km from the transmitter. Since the model line shape function depends on temperature and pressure, it will change with altitude over the extended region of the cloud. As a result, separate calculations are required over multiple and contiguous range bin slices to properly model the integrated path concentrations.

The path integrated concentrations to cloud targets were determined on 10 min intervals, and fits were performed over consecutive 250 m slices where for each slice, the beam path was divided into typically 5 or more sections depending on the inclination of the transceiver system. As described above, the line shape for each section was calculated at the temperature and pressure predicted using the U.S. Standard Atmosphere model [29] and the concentrations for each slice were determined from fits to the averaged line shape over these sections. The parameters varied in the NLLSF included a baseline offset, slope and the concentrations of CO₂ or CH₄ and H₂O where all lines of the given species were scaled together as a group.

The series of best-fit line shapes to the absorption data over the CO₂/H₂O and CH₄/H₂O regions are shown on 250 m intervals in Figure 5a,b, respectively. The absorption data were obtained for a single 10 min interval near the beginning of both measurement periods (see Figure 4a,b) after processing using Equation (3). The fit residuals shown in the sub-panels increase with range as expected for ≈ 1/R decrease in SNR. However, in the cloud aerosol region of CO₂/H₂O between 12 km and 13.5 km, the SNR improves significantly, leading to an obvious reduction in the scatter of the residuals. The improvement in the corresponding cloud aerosol region of CH₄/H₂O between 18 km and 22 km is not as apparent because of the much weaker signal returns.

More detailed views of the fits and residuals are illustrated in Figure 6a–d at both short range from normal aerosol returns (1.5 km to 1.75 km) and at long range from cloud aerosol returns (near 12.5 km for CO₂ and 18.75 km for CH₄). In both short and long range, the relative magnitude of the residual scatter for CO₂/H₂O is on the order of 1 % relative to the peak online optical density. For the CH₄/H₂O region, residuals are again near 1 % at short ranges but increase to ≈ 3 % at long ranges. This increase may arise from the very low online count rates at 19 km (see Figure 3c). However, some systematic structure appears to remain in the residuals which may suggest an error in the calculated temperature and pressure (T&P) profile. The additional uncertainties (biases) of the T&P profile and those associated with the spectroscopic parameters are discussed in Section 4. We note that relative to CO₂, the CH₄ cloud region was measured at more than eight times the altitude (0.43 km vs 3.5 km), resulting in significant gradients in the temperature and pressure over the range. The model line shape narrows at this altitude and contributions to the average line shape lead to a more well-resolved doublet profile as the data in Figure 4d reveal.

The enhanced backscatter from the extended cloud aerosol regions affords an opportunity to reduce the measurement uncertainties of the column-averaged mixing ratios. The column integrated concentrations may be obtained from the mean over all slices, ${\overline{x}}_{wtd}$, each weighted by the integrated counts for that slice relative to the total returns (i.e., total weight). The uncertainties, $s_{wtd}$, can then be evaluated from the weighted standard deviation of the mean of the slices as given by

$${\overline{x}}_{wtd}=\frac{{\sum }_{i=1}^n{w}_i{x}_i}{{\sum }_{i=1}^n{w}_i}$$

(4)

$${var\left(x\right)}_{wtd}=\left[\frac{{\sum }_{i=1}^n{w}_i{x}_i^2}{{\sum }_{i=1}^n{w}_i}-{\left({\overline{x}}_{wtd}\right)}^2\right]\frac{n}{n-1}$$

(5)

$$s_{wtd}=\sqrt{\frac{{var\left(x\right)}_{wtd}}{n}}$$

(6)

where $w_i$ are the weights of the individual measurements, $x_i$, n is the number of slices and ${var\left(x\right)}_{wtd}$ is the weighted variance.

The averaging procedure has been applied over a large enough range to capture all of the cloud features in both regions. Signals below a certain threshold in the slices were excluded in the weighted average. The weighted dry-air mixing ratios are shown for 10 min intervals in Figure 7a,b for the CO₂/H₂O and CH₄/H₂O regions, respectively. For comparison, the measured dry-air mixing ratios from a cavity ringdown point sensor are included. The point sensor samples the outside air within 2 m of the transceivers. As found in our previous studies [4,9], the point sensor measurements overestimate the mixing ratios from addition of local sources of CO₂ and CH₄ that include a utility plant located on the NIST Boulder campus. For dry-air CO₂, the uncertainties range from about 0.3 % to 1 % over the initial 3+ h measurement period. Interestingly, while the first 120 min showed enhanced backscatter from the clouds and therefore improved residuals, the weighted averages show little in the way of improvement relative to the cloudless regions after the first 120 min. We attribute the lack of improvement to difficulties in splicing the current to the counts for the signal returns that are near the upper dynamic range limit of the PMT. In contrast, the weighted mixing ratios for CH₄ in Figure 7b show substantial improvement in the initial 2.5 h when clouds were present relative to the final 2.5 h when they were absent or too distant. We find the relative uncertainties over the cloud period range from 0.1 % to 0.4 % for CH₄ and increase to 2 % to 3 % in the cloudless period.

3.3. DIAL Studies of CO₂/H₂O and CH₄/H₂O and comparison with the IP-DIAL results

The range-resolved dry-air concentration maps for CO₂ and H₂O obtained from the DIAL fits using Equation (1) are shown in Figure 8a,b, respectively and those for CH₄ and H₂O are shown in Figure 8d,e, respectively. Because of technical issues, the results are from the far-field receiver only where full overlap begins near 0.625 km. In both cases, the range resolution is 250 m beginning at 0.75 km and is linearly increased to about 1.5 km at a range of 10 km to help offset the ≈ 1/R drop in SNR. The corresponding range-corrected LIDAR maps are shown in the lower panels on an expanded color bar scale which enhances variations in the aerosol backscatter over the first 10 km. Consequently, the cloud aerosol backscatter is off scale as indicated by the absence of color.

For the CO₂/H₂O region, the inclination angle is only 2.5° and, at 1.25 km, the beam path is only ≈ 50 m above two emission stacks from a natural gas burning power plant on the University of Colorado campus that was running at full capacity this time of year. As evident in Figure 8) near this range, particularly in the second half of the measurement period, additional contributions from the plant’s plume emission result in a (20-30) ppm increase above the background levels of ≈ 425 ppm. Also apparent in Figure 8c at this range is a sharp and distinct increase (spike) in the LIDAR signals from the plume condensate. A recently reported DIAL study of ours using a fiber amplifier system made use of the emission spike to measure the emission flux from this power plant [36]. We also note in Figure 8c, the abrupt decrease in the LIDAR signal intensity after the first 4 h which reduces the SNR at long range for the latter part of the night. This led to the missing data in regions beyond 7 km as indicated by the absence of color in Figure 8a,b.

A cross check of the DIAL concentration maps can be obtained through comparisons with the IP-DIAL results reported in Section 3.2. Given that all data were acquired with the same transceiver system, filter and detector, and that the LPEs are not needed in the DIAL retrievals, differences in the column average mixing ratios from these two independent techniques can provide insight into any systematic errors associated in the data processing and/or assumptions in the models to retrieve concentration. For this, the mixing ratios of the path averaged DIAL (PA-DIAL) data displayed in Figure 8a,b are needed. In contrast to the weighted mean method performed over multiple slices in the cloud region, the range resolved mixing ratios need to be weighted equally and the overall uncertainties must be added in quadrature. For a fixed range bin size of 250 m, the uncertainties would be dominated by the largest uncertainties associated with the weakest returns at long range. As discussed above, the range bin size was linearly increased to largely “equalize” these uncertainties over range.

It is further noted that this comparison is not complete in the sense that the DIAL mixing ratios are missing in the first 0.75 km and for the CH₄/H₂O region, the “gap” between the DIAL data and the IP-DIAL data is too large. Nevertheless, barring any unknown sources and sinks in these regions, the path-averaged values should largely agree with one another.

The comparison of the dry-air mixing ratios of the path averaged DIAL (PA-DIAL) and the IP-DIAL measurements are shown in Figure 9a,b for CO₂ and CH₄, respectively. At most of the 10 min intervals, the PA-DIAL uncertainties for CO₂ and CH₄ range from 1 % to 2 % which are 5- to 10-fold larger than those from the IP-DIAL data. More importantly, IP-DIAL data are well-centered on the PA-DIAL mixing ratios for most of the measurement periods with perhaps a slight bias for lower IP-DIAL values at a few points. Nevertheless, for nearly every interval, the corresponding uncertainties well within those of the PA-DIAL values.

4. Discussion

The measurement uncertainties of the IP-DIAL mixing ratios from cloud aerosol retrievals shown in Figure 7 approach the stringent requirements specified for global satellite measurements to accurately quantify the global changes in GHG mixing ratios and to adequately characterize sources and sinks. However, a few sources of systematic uncertainty (biases) remain in our ground-based spectroscopic studies that prevents us from assigning them as absolute mixing ratio uncertainties.

As mentioned in Section 2.3, systematic errors in the form of repeating patterns in the residuals from fits of the CH₄ data to the HITRAN database required us to implement the more refined Hartmann-Tran profile (HTP) [33]. From comparisons of the HTP CH₄ line shape with the HITRAN 2008, 2012, 2016 and 2020, the overall best shape relative to HTP was found for HITRAN 2008 (after scaling the overall peak intensity by 0.98). It is clear from the fit residuals to HITRAN 2008 shown in Figure 10a that the systematic errors are even more pronounced relative to those for HTP in Figure 10b. The HTP integrated line intensity in Ref. [33] was adjusted to that of HITRAN 2020 database which corresponds to a peak intensity reduction of ≈ 0.91 of the HITRAN 2020 profile because of the reduced line broadening in HTP. Since our fits are to peak intensity, this led to > 8 % increase in the best fit mixing ratios of CH₄ relative to HTP. From these comparisons, there is a clear need for an SI standard that is traceable to the World Meteorological Organization (WMO) scale for the absolute intensities of the CH₄ lines. We again note that while the CO₂ profile is also impacted by line mixing and speed dependent effects, the effects on the profile are much smaller relative to CH₄ and are not revealed in the residuals in this study because of the smaller spectral coverage of the online CO₂ measurements.

Another unknown that impacts the absolute uncertainties, particularly for the IP-DIAL results of CH₄ at an altitude of ≈ 3.5 km, is in the calculated temperature and pressure (T&P) profile using the U.S. Standard Atmosphere model [29]. While the CO₂ study was performed over a nearly horizontal path where T&P changes along the column are expected to be minimal, some evidence for systematic residuals in the CH₄ line shape residuals remain in Figure 10b. To investigate the impact the T&P profile, we have refitted this data using two different inclination angles, θ, of 6.5° and 12.5°. The results are shown in Figure 10c,d, respectively. In contrast to the substantial increase in residuals for θ=12.5°, the reduction seen for θ=6.5° is significant. This angle difference of 4° corresponds to an altitude difference of ≈ 1.4 km at a range of 20 km.

Given the strong pressure dependence of the shape of the CH₄ doublet on the residuals, we have performed pressure fits over two series of IP-DIAL line shape profiles. The 10 min intervals chosen are shown in Figure 11c as vertical white lines superimposed on the LIDAR map. The best-fit path-averaged pressures and uncertainties (1σ or Type A, k=1) are shown in Figure 11a,b for the 2^nd and 7^th intervals, which represent high and low altitude cloud aerosol regions, respectively. The trends expected for the path-averaged T&P profile of the U.S. Standard Atmosphere are superimposed on the plots with yellow and red lines for inclination angles, θ=6.5° and 10.5°, respectively. As found above, the profile for θ=6.5° is in much better agreement with the observed pressure changes. Finally, we also perform pressure fits to profile differences that are separated by 6 km as illustrated by the yellow boxes in Figure 11c. This 6 km separation corresponds to the difference between the cloud ranges in the two columns. We note that as a result of the reduced uncertainties in the cloud regions at 19 km and 13 km in Figure 11a,b, respectively, the relative uncertainties of the pressure fits near the 16 km average range in Figure 11d are significantly reduced. The results shown in Figure 11d are again in much better agreement with the T&P profile at an inclination angle of θ=6.5°.

There may be several possible reasons why the predicted inclination angle from the pressure fits in Figure 11 is about 4° less than the actual inclination angle determined from the platform pitch sensor (which has a confirmed accuracy of ±0.5°). Any effect on the CH₄ line shape that tends to fill in the central dip region will result in fits to higher pressures that will correlate to a lower inclination angle (and lower altitude). One possible mechanism that will fill in the dip region is from additional line broadening by H₂O vapor. The pressure broadening in the HTP model used here is from dry air only. Previous studies [37] have found that CH₄ line widths are, on average, 34 % larger than for dry air under humid conditions (> 5 % relative humidity). However, the relative humidity in this study was relatively low (≈ 1 %) as seen in Figure 8e, and while some additional broadening by H₂O is expected, it is unlikely to account for the observed angle difference of ≈ 4°. A second possibility that will decrease the dip depth is from averaging over the Doppler line shifts associated with changes in wind shear over the integrated path. While a Doppler wind LIDAR system is planned for future measurement campaigns, the only atmospheric wind profile measurements available in this study are from two Denver International Airport (DIA) soundings performed several hours before and after the IP-DIAL study. The wind profile measurements, shown in Appendix B, Figure A1a,b, vary between 11 m/s and 18 m/s at altitudes > 4 km relative to near ground level speeds between 1 m/s and 5 m/s. Over this range, the predicted Doppler frequency shifts for winds parallel to the beam path (i.e., worst case scenario) would be in the range from ±3 MHz to ±10 MHz. Tests on the integrated HTP line shapes over the largest of these shifts indicates the impact on the CH₄ dip depth is small. We also note that the maximum laser frequency shift from the thermal drift the filter cavity over periods longer than these 10 min averaging times is expected to be of this magnitude or less [4]. A final possible reason is the U.S. Standard Atmosphere T&P profile is not a good approximation in this study. Some evidence for this is found from comparisons between the two DIA soundings shown in Appendix B, Figure A3a,b. We note especially the large differences in temperature profiles and an overall pressure shift of ≈ 15 hPa over this 12 h period. Significant differences are also seen relative to the T&P profile of the U.S Standard Atmosphere shown in Figure A3c. Given the impact of a such factors on the online/offline measurements planned for the MERLIN mission [10,11] where such effects will not be evident in the data, current efforts are underway to preform simultaneous 3D wind profile measurements using a Doppler wind LIDAR system and to measure the individual T&P profiles based on the intensity ratio and widths of two nearby water lines.

Finally, we note that while the position of the transceiver platform is controllable, active feedback to track the optimal location for the transmit beam through a cloud region has yet to be implemented. Consequently, the cloud backscatter returns in Figure 3b,d were limited in duration (i.e., 2 h for CO₂ and 2.5 h for CH₄). More importantly, the overall backscatter signal returns were far from optimal; in the CO₂/H₂O region, the offline returns were near the saturation limit of the PMT detection system, which is in sharp contrast to the online returns in the CH₄/H₂O region that were near the noise floor of the detection system. It would clearly be advantageous to work well between these two limiting cases. We expect the SNR ratios will improve significantly once an independent active tracking system under development is completed.

5. Conclusions and Outlook

In this work, we have demonstrated a combined DIAL/IP-DIAL system to measure range-resolved and integrated path mixing ratios of dry-air CO₂ and CH₄ above the city of Boulder, Colorado. The integrated path measurements are performed to cloud targets up to 20 km in range and 3.5 km in altitude. The technique uses 10 frequencies to sample across ≈ 1.5 cm^-1 spectral regions and to measure the absorption line shapes of CO₂, CH₄ and H₂O as a continuous function of range. A hybrid photon counting/current detection system enhanced the dynamic range to measure range-resolved backscatter signals from aerosols as well as from distant cloud targets.

The DIAL/IP-DIAL multi-frequency method overcomes two challenging tasks associated with standoff methods. For IP-DIAL, one task is the measurement of the pulse energies with high accuracy at each of the transmitted frequencies. This challenge is overcome through fitting the range resolved DIAL data to extrapolate pulse energies back to the time of emission thereby eliminating the difficulties associated with the uniform unbiased sampling of the transmitted pulses and with the calibration of multiple detectors’ responsivities [35]. A second challenge of two-point online/offline DIAL and IP-DIAL systems is with the stringent frequency stabilization of the frequency converter. Because of the multi-frequency sampling and line shape fits performed here, direct determinations of the center frequencies were obtained thereby eliminating systematic errors associated with the OPO seed-signal frequency shift and other thermal drift factors.

Two independent studies were performed in the CO₂/H₂O and CH₄/H₂O regions to simultaneously measure both DIAL and IP-DIAL mixing ratios. Over the cloud aerosol regions, the uncertainties in the IP-DIAL retrievals were found to vary between and 0.2 to 1.0 % for CO₂ and 0.1 % and 1 % for CH₄ while the uncertainties in the range-resolved DIAL measurements varied between 1 % and 2 % in both regions. From a comparison of both techniques, the mixing ratios for the IP-DIAL measurements were found to be well-centered and within the error bars of the DIAL data. Finally, we note that because of the current inability to adjust in real time the transmitter location, the overall signal returns were found to be far from optimal; the backscatter signal intensities were near the detector saturation level in the CO₂/H₂O region while the online returns in the CH₄/H₂O region approached the detector noise floor. Current efforts are directed towards the development of a higher pulse repetition rate LIDAR system operating at 1.6 μm to survey the cloud base and provide active feedback for better positioning of the DIAL/IP-DIAL transceiver system.