We report new methods for retrieving atmospheric constituents from symmetrically-measured lidar-sounding absorption spectra. The forward model accounts for laser line-center frequency noise and broadened line-shape, and is essentially linearized by linking estimated optical-depths to the mixing ratios. Errors from the spectral distortion and laser frequency drift are substantially reduced by averaging optical-depths at each pair of symmetric wavelength channels. Retrieval errors from measurement noise and model bias are analyzed parametrically and numerically for multiple atmospheric layers, to provide deeper insight. Errors from surface height and reflectance variations are reduced to tolerable levels by “averaging before log” with pulse-by-pulse ranging knowledge incorporated.
© 2014 Optical Society of America
Future airborne and space missions call for unprecedented high precision for global measurement of atmospheric constituents and parameters [1, 2]. For example, the Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) mission  has been planned by NASA to measure the global distribution of carbon dioxide (CO2) mixing ratios (~400 ppm in average) to ~1 ppm precision. To meet such stringent requirements, nadir-viewing, direct-detection, and pulsed integrated-path differential-absorption (IPDA) lidar techniques are being developed to measure the two-way optical absorption of the target species from the spacecraft to the surface and back at multiple wavelength channels [3, 4]. From the absorption and altimetry measurements and other ancillary data of the atmosphere, the dry mixing ratios of the target species can be retrieved [5–7]. The errors of the retrieved mixing ratios essentially arise from the random measurement noise, the forward model bias, and errors in the forward model parameters.
Throughout this paper, a candidate ASCENDS lidar approach  being developed at NASA Goddard will be used as a concrete example. This IPDA lidar approach allows simultaneous measurement of CO2 and surface height in the same path [4, 6]. As shown in Fig. 1, a pulsed laser is wavelength-stepped across a single CO2 line at 1572.335 nm to measure the optical depths (ODs) at 4 pairs of symmetric laser frequency channels . The laser frequency fluctuation causes a variation in measured CO2 transmittance, resulting in an uncertainty in the target mixing ratio retrieval. To reduce this uncertainty, laser pulses at each fixed wavelength are carved from a frequency stabilized continuous-wave (CW) laser [8, 9]. The ~1-μs pulses need to be at least ~100 μs apart to eliminate crosstalk from cloud scattering. This approach has also been adopted to scan an O2 absorption line doublet for an atmospheric pressure measurement , and to measure atmospheric methane concentrations .
Comprehensive analyses of IPDA measurement errors and errors of column-averaged retrieval from differential absorption optical depth (DAOD) measured at two (on/offline) wavelength channels have been published [3–7, 12–15]. Nevertheless, the unprecedented precision targets call for more vigorous modeling and error reduction methods, and closer scrutiny of previously neglected error sources. We have recently reported new methods to quantify and reduce errors for DAOD measurement arising from the laser frequency noise, broadened laser line-shape, statistical bias in “log after averaging”, surface height and reflectance variations . The present paper generalizes the formulation from our previous publication  and uses established inversion methods  to address multiple-layer retrievals from the absorption spectra measured at multiple pairs of symmetric wavelength channels. Our forward model is essentially linearized by linking the estimated ODs rather than the transmittance values to the mixing ratios. This allows us to link both the relative random error (RRE) and the relative systematic error (RSE) of the retrieved mixing ratios, arising from the measurement noise and model bias, respectively, to characteristic parameters to provide deeper insight into system optimization and limitation. We show that errors from the laser frequency drift and spectral distortion (due to, e.g., etalon fringes and surface reflectance variations) can be substantially reduced by placing the laser frequency channels symmetrically about the center of the target absorption line profile and averaging the two ODs measured at each pair of symmetric channels to cancel out errors. Our model includes laser line-shape factor and thus remains accurate even when the laser line-shape is broadened for the suppression of the stimulated Brillouin scattering (SBS) in the laser amplifiers. Retrieval errors from imperfect forward model parameters (including spectroscopic parameters, surface pressure, atmospheric temperature profile, and the water vapor profile) have been thoroughly studied [5, 7, 12, 13, 18–21] and is outside the scope of this paper. Owing to the continuing advances in the absorption line-shape modeling studies [20, 21], the retrieval errors from the inaccuracy of the absorption line-shape modeling are diminishing.
Our forward model is presented in section 2. The retrieval and error analysis methods are derived in section 3. A numerical example of the parametric error analysis is presented in section 4. More considerations for the observing systems are discussed in section 5. Some supporting details are provided in the Appendices. Throughout this paper, matrices are denoted by bold face upper case, e.g., K, column vectors by bold face lower case, e.g. b, the transpose by superscript T, e.g., KT. represents an estimate of and the determinant of . represents the covariance of and . (or ) represents the variance, the standard deviation, the ensemble average of .
2. Forward model
2.1 Dependence of optical depth on mixing ratios
Using ancillary data of atmospheric temperature profile, water vapor mixing ratio and surface pressure, the atmospheric pressure at range r from the spacecraft can be determined for each vertical column of the atmosphere. p decreases monotonically with height and is conveniently used as the vertical coordinate in this paper. The laser frequency noise contributes to measurement errors through two complimentary factors, the energy spectral density (ESD) of each single laser pulse (as a function of the Fourier frequency ), and the fluctuation of the line-center frequency of The present model uses the effective two-way OD of the target species  to include the line-shape factor, and thus remains accurate even when the laser line-shape is broadened. can be related to the dry mixing ratio and the two-way effective weighting function of the target species as (see Appendix A for details). This relationship becomes linear and much simpler when is much narrower than the spectral width of the target absorption line. In such narrow line-width case, becomes twice the one-way monochromatic OD and twice the one-way monochromatic weighting function . and are given by
We now divide the atmospheric column into nq layers and approximate by a state vector. is a channel independent mixing ratio used to approximate the layer-averaged mixing ratio for layer j (j = 1, 2, …, nq). Here layer 1 is at the bottom of atmosphere, pj is the pressure at the top boundary of layer j, and . This approximation is valid when or (for all channels) are sufficiently uniform within layer j. can then be simplified to
2.2 Forward model for uncombined wavelength channels
Throughout this paper, we assume m symmetric pairs of frequency channels and use superscript (or subscript) i to index such channels at mean laser line-center frequencies (i = 1, 2, …, 2m). is the frequency offset about the absorption line center . As illustrated in Fig. 1, each pair is placed symmetrically about (i.e., ). We will establish the forward model for these uncombined channels in this subsection, and then combine each pair of channels into a symmetrically-combined channel i in the next subsection. The forward model for symmetrically-combined channels can be easily derived from the fundamental forward model for uncombined channels. The forward model for uncombined channels is adapted from our previous DAOD modeling work , and thus will be discussed only briefly herein. As to be seen in the next section, the retrieval requires knowledge of both the variance and cross-channel covariance of the OD measurements. The former has been quantified in  but the latter has not been well addressed previously. In this subsection, we further quantify the cross-channel covariance of the OD measurements.
Including other attenuation factors , the detected laser pulse energy backscattered from the surface is given by23, 24]
Let denote a certain mean of averaged across multiple laser pulses (k = 1, 2, …, np) in channel i. can be estimated from the following sum of normalized photon counts (SNK) accumulated across the np pulses
Our forward model is derived from Eq. (7) as follows. is estimated by where a bias correction factor (given by Eq. (30) in Appendix B) is added to improve the estimation accuracy. becomes negligible when the integrated photon count is sufficiently high. is related to the mixing ratios through Eq. (2). The offset can be modeled by a polynomial , where and so on, are unknown constants. This offset has little dependence on the frequency channels and thus can be nearly approximated by The odd terms (, and so on) are canceled out by averaging each pair of symmetric channels, and thus excluded from our model. For ease of analysis, we only keep the quadratic term to model any uncorrected spectral response of the lidar (due to, e.g., etalon fringes). We then arrive at the following forward model for channel i
We now turn to derive the covariance and variance of as needed for the retrieval. The covariance is nonzero due to a cross-channel correlation of the effective laser line-center frequency noise defined by8, 9], of the slave laser can be approximately divided into two components . The fast frequency noise component can be treated as uncorrelated among different pulses and uncorrelated to the slow frequency noise component arises solely from the master laser frequency drift, and thus is the same for all channels. It has been demonstrated that the slow drift of the frequency difference between the slave and master lasers can be eliminated [8, 9] and thus is neglected here. The correlation of arises solely from that essentially remains unchanged within each wavelength sweep, but varies slowly over longer time scales. From Eq. (9), can be split into , where and arise from and , respectively. Using  and , is found to beEq. (10) is numerically verified in Appendix C. The cross-channel covariance of arises solely from .
Assuming that the transmitted laser pulses in channel i have nearly the same pulse energy, the variance of the measurement is found to beEq. (29) in Appendix B for details). The first term in Eq. (11) arises from the signal shot noise, the second term from background solar radiation, receiver circuitry noise and the detector dark count, the third term from the laser line-center frequency noise, and the last term from the random altimetry error. is the measurement standard deviation for and is assumed to be the same for each of the np pulses. The effective absorption cross-section of the target species, defined by Eq. (26) in Appendix A, assumes its monochromatic value for the narrow line-width case. is the averaged height of a window function defined by Eq. (31) in Appendix C. is only slightly higher than 1/np and is taken to be 1/np for our numerical evaluations. Error contributions from , and these other noise sources will be further discussed in the next subsection.
(evaluated for deterministic) can be regarded as the model bias, and is found to be
2.3 Forward model for symmetrically combined channels
Since the pressure-shift of the line-center of varies with altitude, the target atmospheric absorption line profile becomes slightly asymmetric. In our numerical examples, is simply placed at the maximum absorption point. At each pair of symmetric channels about , the ODs are roughly the same, but the OD slopes are nearly opposite. This allows us to drastically reduce errors due to the laser frequency drift. Figure 2 illustrates this feature (left) and the weighting functions at the 4 pairs of channels (right) for the atmospheric CO2 line at 1572.335 nm. The CO2 absorption spectrum and weighting functions are computed for US standard atmospheric conditions with a constant dry CO2 mixing ratio of 400 ppm. The averaged OD is treated as the OD at the symmetrically-combined channel i. For the narrow line-width case, becomes . Due to the cancellation of the two nearly opposite OD slopes for each symmetric channel pair, the slope of the averaged OD shown in Fig. 2 is reduced from that of the original OD by a factor of several tens for any practical online channels. The four channels are placed at and Since the two weighting functions for each pair of channels are roughly the same, such channel combining essentially does not reduce the vertical resolution of the retrieval.
The new measurement vector is defined as , and the extended state vector is that combines and In practice, the photon counts for each of the 2m channels are measured separately before combining the pairs. From Eq. (8), we arrive at the following forward model
The covariance matrix of is found to be essentially diagonalized due to the cancellation of the two nearly opposite OD slopes for each symmetric channel pair16], and thus can be reduced to a negligible level by pulse averaging. However, does not decrease from this averaging and thus needs to be suppressed by laser frequency stabilization. By combining symmetric channels, the requirement for the laser frequency stabilization is substantially relaxed. To bound (the partial RRE of DAOD) to 0.03% for CO2 measurement illustrated in Fig. 2, the upper bound for can be relaxed from 0.23 MHz  to 6 MHz, which becomes much easier to satisfy [8, 9].
The error reduction can be simply explained as follows. Within each wavelength sweep, of each of the 2m pulses is shifted from its mean by essentially the same amount (and further shifted by an uncorrelated amount ). The common shift causes nearly opposite OD changes and in the two symmetric channels. By the pair combining, the two opposite OD changes nearly cancel out, resulting in a much reduced OD error from for the symmetrically combined channel. Consequently, it can be easily shown that contributes to the covariance and variance of as expressed by Eqs. (14)-(15). Similarly, a common laser frequency bias (including the Doppler shift arising from the high-speed cross-wind and the radial component of the spacecraft velocity ) will result in an OD bias ~for each uncombined channel i. By the pair combining, the bias towards is reduced substantially to . Compared to the single-channel measurement of with pulses, the pair combining essentially retains the same noise contributions to from sources other than (such as the signal shot noise and ) when each of the two symmetric channels is measured with pulses. These other noise contributions to are essentially inversely proportional to and thus can be reduced by pulse averaging.
3. Retrieval and error analysis methods
3.1 Retrieval method
In general, the forward model is nonlinear with respect to x. Although is evaluated from and measured at x, an approximate value xi of x is needed to evaluate the factors in . The evaluated , denoted as , is a nonlinear function of xi. Nevertheless, the forward model and can be linearized about a priori state within the natural variability of x about17] where the measurement error and the error of are assumed to be Gaussian with known error covariance matrixes Sy and Sa, respectively. Using the Newton-Gauss method, the following iteration is expected to converge to 17]
For the narrow line-width case, becomes independent of x and the remaining dependence of on q1 can be minimized by choosing so that . is then simplified to17]
can be approximated by a constant if the surface is sufficiently flat within the averaging time , or if the laser beam and the receiver are pointed to a fixed surface spot during the pulses to be averaged. When there is no a priori information available, . By setting , the above MAP solutions in Eqs. (17), (18), and (20) become the same as the corresponding maximum likelihood (ML) solutions. The linear ML solution in Eq. (20) with is equivalent to the weighted linear least-square solution with weighting covariance matrix . This linear ML solution can serve as the start point for the iterations of Eq. (17).
3.2 retrieval error analysis
Discretizing into the state vector is based on the approximation . When and are not uniform within layer j, the bias for estimating with may become significant. Neglecting , the retrieval error in the extended state vector can be expressed by 
It is adequate to represent with a column-averaged retrieval (nq = 1) when the mixing ratio of the target species is sufficiently uniform in the column. When this is not the case, it is desirable to retrieve in two or even more layers. For example, when in the planetary boundary layer (PBL) is significantly different from that in the column above the PBL (see, for example ,), double-layer retrievals are desirable.
We now turn to link the retrieval RRE and RSE, arising from the measurement noise and model bias, respectively, to characteristic parameters to gain deeper insight into system optimization and limitation. For ease of analysis, the linear ML retrieval solution is used without including the quadratic correction term and the small covariance elements are neglected. In the following derivations, represents an average of elements (i = 1, 2, …, m) across m channels, weighted by . represents a variance of , and a covariance between elements and across m channels. is the estimated two-way OD of the target species within layer j at channel i.
From Eq. (18), the RRE of arising from the experimental variance is found to beEquation (22) indicates that the RRE of is equal to the RRE of . Since for single layer retrieval, can be regarded as an effective measurement variance of the effective two-way DAOD for the column. When retrieving from a combined online and a combined offline channel that have the same , the single-layer becomes the conventional DAOD , and becomes the variance of the DAOD . Without changing of the existing channels, adding more channels in the retrieval (and the computation of ) reduces . Compared with the single-layer retrieval, the RRE of each retrieved for multiple layers is increased by two factors: a factor, and a smaller than the single layer . For double-layer retrievals, for example, .
Following the argument leading to the Cramer's rule, the linear retrieval can be found from Eq. (20) asEq. (23) with or , respectively. The RSE of from is found to be
To reduce both the RRE and RSE, it is desirable to have a large . The online and offline channels are equivalent in their contributions to and . can be increased by shifting online channels towards the absorption peak while keeping the offline channels in the adjacent low-absorption window regions. However, this also increases . For the simple case of column-averaged retrieval from two channels, the RRE is typically minimized when the online OD (and hence ) is not maximized. The online points often need to be placed on the sides of the absorption line to uniformly sense concentrations in the lower troposphere , resulting in an increased RRE. Further error reduction considerations will be discussed along with the numerical example in the next section.
When the laser signal shot noise term in Eq. (15) is well above other noise terms (as is the case for the numerical example in section 4), so that . In other words, the shot-noise limited retrieval RRE is inversely proportional to the square root of the accumulated photon count of the pulses from all m combined channels. In general, the retrieval RRE and RSE are reduced by using information collectively from all measurement channels. Averaging multiple retrievals will further reduce the retrieval RRE arising from the measurement noise by the square root of the number of retrievals being averaged. However, this averaging may not effectively reduce the retrieval RSE arising from some persistent bias .
4. Numerical estimation of retrieval errors
In this section, we numerically estimate the errors of atmospheric CO2 retrievals from the lidar-sounding spectra measured across the 1572.335 nm CO2 absorption line. A fast pulse rate (8 kHz) is chosen to lower the required pulse energy and reduce the spectral distortion arising from the surface reflectance variations. The calculations are based on parametric formulas derived in section 3 and realistic parameters listed in Table 1. The pulses in all channels are assumed to have the same transmitted pulse energy The overall optical efficiency of the lidar’s receiving path (including filters) is assumed to be 51%. The additional OD bias and variance due to imperfect ranging are negligible for a ranging bias ≤ 0.66 m and a ranging precision ≤ 20 m , and are neglected hereafter. The speckle noise can also be neglected due to the large telescope diameter of 1.5 m and large laser beam spot size of 50 m on the surface [3, 6]. The measurement noise is computed from Eq. (15) and plotted as a function of in Fig. 3 (left). Partial contributions to from the signal shot noise , frequency noise, solar background, receiver circuitry noise, and detector dark count are also shown in Fig. 3 (left).
Referring to Eq. (22), the effective single-layer DAOD is found to be 1.17, and its effective measurement error is found to be 0.00039 when there are 3200 detected photons (in average) for each offline pulse, resulting in a retrieval RRE of 0.034% from the measurement noise. This photon count can be achieved with for an average surface reflectance ρ = 0.17. The signal shot noise contribution is well above the noise contributions from the receiver circuitry and the solar background count, as desired. The detector specifications listed in Table 1 can be met with a state-of-the-art HgCdTe avalanche photodiode (APD) detector . The background solar photon count rate is estimated for a zenith angle of 75°. Its noise contribution is larger than that from the receiver transimpedance amplifier (TIA). The noise from the detector dark count is negligible. The frequency noise contribution arises essentially from the 3 MHz slow frequency drift , and the contribution from the 2 MHz fast frequency noise is averaged down to a negligible level within the 10-s averaging time. For the frequency noise contribution is less than that from the solar background radiation and it is safe to assume . The bias towards due to the laser line-center frequency noise is shown in Fig. 3 (right). The partial retrieval RSE due this bias is found to be as small as 10−5 and thus negligible.
Next, we estimate the single-layer retrieval RSE arising from the model bias . Referring to Eq. (12), is taken to be and other terms of are neglected. Since the retrieval bias from varies from one path to another, the corresponding RSE needs to be kept to a small fraction of the 0.1% RRE. For realistic error estimation, we use measured surface reflectance data in  to quantify the RSE.
As shown in Fig. 1, the laser pulses are assumed to repeatedly cycle through wavelength channels 1 to 8 and the laser beam spot at the surface is assumed to travel at the same speed as the spacecraft. The same set of surface reflectance data used in  for its strong variations is reused for this evaluation. The surface reflectance measurement was taken in southern Spain using ~10-m laser spot size and a step size of ~6 m . To convert this data to the reflectance for our beam size, a 1-D running average is taken within our beam size (50 m) and the averaged reflectance is used as in our calculation. The raw and averaged reflectance data are plotted in Fig. 4 (left). For an averaging time of 1 s, there are n = 1000 pulses for each wavelength and the path length is 7 km. In contrast, the separation between spots of adjacent channels is only 0.875 m. As a result, the spectral distortion due to variations of across the 8 channels is quite small. Referring to Eq. (24), (and hence the RSE) is further reduced by the factor that rejects variations of uncorrelated to . This point is confirmed by our calculation results shown in Fig. 4 (right). The RSE calculated from the surface reflectance data is for 1-s averaging starting at any position along the path. The RSE is increased when the averaging time (hence np) is reduced. Figure 4 (right) also shows an average of 10 values of RSE, each computed over 0.1 s averaging time consecutively along the path. This averaged RSE is essentially reduced to the same level of the 1-sec RSE. This is also the case for the following double-layer retrievals. The retrieval from the multiple symmetric channels rejects anti-symmetric variations in and appears to randomize the RSEs observed along consecutive sections of the path. This allows shorter time of averaging before log, without increasing the overall RSE averaged across the consecutive sections of the path. It is desirable to shorten the time of averaging before log, to minimize the impact of time-varying spectral distortion (due to, e.g., etalon fringes in the lidar’s spectral response).
Similarly, the RREs and RSEs of the double-layer retrievals are computed and shown in Fig. 5 (left) and (right), respectively. The RREs are plotted as functions the boundary pressure p1 between the two layers. Also plotted in Fig. 5 (left) are , , and the correlation coefficient as functions of p1. As expected, the RREs and RSEs become larger than the single-layer values. When retrieving the PBL with a top boundary ~2 km above the surface (corresponding to p1 ~795 hPa), the RRE and RSE of are found to be as large as 0.52% and 0.011%, respectively, due to a small ~0.210 and a large correlation coefficient of 0.933. The RRE and RSE of either layer become smaller as the layer becomes thicker. To reduce (the degree of linear dependence between and of the two layers), it is desirable that each layer has significant overlap (i.e., ) with at least one online weighting function (while the offline absorption is minimized) and each online weighting function has much more overlap within one layer than other layers.
The triple-layer retrieval RREs are computed and plotted in Fig. 6 (left), and the factor and effective DAOD are plotted in Fig. 6 (right), as functions of the boundary pressure p2 between layers 2 and 3 while p1 is fixed at 795 hPa. The RREs are significantly higher than the corresponding double-layer RREs due to much increased .
Without averaging each pair of symmetric channels, the same results can still be achieved if the 2m by 2m covariance matrix of is used. However, this requires knowledge of the non-zero covariances among different channels. By averaging each pair of symmetric laser frequency channels, the covariance matrix of y is diagonalized and reduced to m elements. This further simplifies the parametric analysis and numerical computation. The formulation can be further extended to allow each pair of symmetric channels to transmit pulses simultaneously. This could double the SBS-limited laser peak power. There is no need to measure and separately, only the sum is needed. However, this requires accurate measurement of the pulse energy ratio (in addition to the pulse energy sum ), which is difficult to achieve when the simultaneous pulses come from the same laser.
A wavelength channel becomes redundant for the retrieval of if its weighting function can be approximated by a linear combination of weighting functions of other channels. Nevertheless, it can still provide an independent piece of information to allow for inclusion of a term in the model (such as ) to further correct spectral distortion. To retrieve , only the relative ratios of the transmitted pulse energies are needed. There is no need to measure the absolute values of because scaling with a common factor only shifts , not . The spectral distortion from the lidar’s receiving path can be substantially removed from the offset . One way to do this is to normalize by the spectral response measured for the receiving path. Another way is to pass a small fraction of the transmitted laser through the receiving path and measure from it at the end of the path .
It should be noted that the weighting function can be also defined as , or (as a function of the altitude z). Although different definitions lead to different weighting function curves, they produce the same integrals and thus are equivalent.
Despite the variations of from one wavelength sweep cycle to the next, the retrievals remain accurate as long as are the same for all m combined channels. Even when varies across the 2m uncombined channels, the retrieval RSEs arising from this spectral distortion are substantially reduced by the cancelation of the anti-symmetric spectral distortion and rejection of the symmetric spectral distortion component that is uncorrelated to . This applies to all contributing factors to , including the surface reflectance and the optical detector responsivity for the receiver and the transmitted laser pulse energy monitor. For example, the slow responsivity drift of either detector does not affect the retrievals as long as the detector responsivity remains constant during each wavelength sweep cycle (~1 ms).
The retrieval errors can be further reduced if a priori constraints for the gas mixing ratios are included. Fixed and Sa have been used for CO2 retrieval . When the measurements and retrievals are made at consecutive time steps, it would be more accurate if and Sa could be estimated from neighboring measurements taken before (and after) the current time step. Similarly, the range at a beam spot k can be estimated from multiple altimetry measurements at beam spot k and neighboring beam spots. Since there are 57 such neighboring beam spots within a beam spot size of 50 m for our CO2 sounder example, the estimation accuracy of can be significantly improved.
To simplify retrievals, narrow-line width lasers have been used to scan the target absorption lines. The narrow laser linewidth leads to SBS in the laser amplifiers that limits the laser peak power and laser pulse energy. It would be highly desirable if the IPDA measurements can be made with spectrally-broadened laser pulses, in order to suppress the SBS. The laser line-shape needs to be broadened deterministically so that the resulting effective OD is deterministic and can be accurately calculated. The present model allows us to examine the feasibility and limitation of retrievals from such measurements. Our research on this topic will be reported in future publications.
New modeling and error reduction methods are presented for retrieving atmospheric constituents from symmetrically measured lidar-sounding absorption spectra. The forward model accounts for laser line-center frequency noise and broadened line-shape, and is essentially linearized by linking estimated ODs to the mixing ratios of the target species. Errors from the spectral distortion and laser frequency drift are substantially reduced by averaging ODs at each pair of symmetric wavelength channels. This allows the tolerance for the laser frequency drift to be relaxed from 0.23 MHz to 6 MHz for the ASCENDS’ CO2 transmitter. Retrieval errors from measurement noise and model bias are analyzed parametrically and numerically for multiple atmospheric layers, to provide deeper insight. For each atmospheric layer, an effective DAOD and its effective measurement variance are introduced. The RRE of the mixing ratio is equal to the RRE of the effective DAOD. In general, the retrieval RRE and RSE are reduced by using information collectively from all measurement channels. When the signal shot noise is predominant, the retrieval RRE decreases approximately by the square root of accumulated photon count of participating pulses from all measurement channels. Errors from surface height and reflectance variations are reduced to tolerable levels by “averaging before log” with pulse-by-pulse ranging knowledge incorporated. Error contributions from other sources are also taken into account.
A. Effective optical depth and weighting function
The effective two-way OD  can be linked to the dry mixing ratio byEq. (1), , and are defined below. is the effective absorption cross-section of the target molecules defined asEq. (1). For broadened laser line-shape, the effective weighting function also depends on so that is no longer linear with respect to . Similar to Eq. (2), for an arbitrary vertical position can be simplified to
B. Additional details for forward model
We now summarize noise contributions from the background solar radiation, detector dark count, and receiver circuitry noise for the lidar receiver. is estimated from the total count within a pulse duration minus a derived background count arising from background solar radiation, detector dark count and receiver circuitry noise. To reduce the background variance, the background count is measured in a longer duration βΔt between the pulse measurements, and scaled to within Δt. Referencing Eq. (5), the variance of is found to be 
C. Numerical verification of
The covariance of is the Fourier transform of its PSD that in turn can be derived from the measured master laser frequency noise PSD  shown in Fig. 7 (left). is taken to be the slow (“flicker” noise) portion of , and it is truncated to zero for f > 20 Hz. is computed by integrating the product of and the following window function16]. As shown in Fig. 7 (right), are computed for np = 100 and 1000 using the relevant parameters listed in Table 1 and taking the measured surface reflectance as As(i) as described in section 4. , and are plotted for the path segment giving rise to the most relative change in between channels 1 and 7. (including ) among the 8 channels are found to be essentially identical within f ≤ 160 Hz. This is due to the small, and the fact that closely tracks when the separation between the corresponding beam spots of channel i and channel j is much smaller than the beam spot diameter and the path length of the np pulses. (including ) among the 8 channels are found to be the same within 0.001%, even when np is as small as 100. They are only slightly smaller than (smaller by < 0.3% for np = 100, and by < 2% for np = 1000). This verifies that from the slow frequency drift essentially does not decrease from the pulse averaging, and .
The authors gratefully acknowledge Dr. J. Mao and Dr. X. Sun of NASA Goddard for fruitful discussions. They are also indebted to Dr. A. Amediek of Deutsches Zentrum für Luft- und Raumfahrt (DLR) for sharing surface reflectance measurement data, Dr. J. Abshire and other members of the Goddard CO2 sounder team for their support. This work was supported by the NASA Goddard Internal Research and Development program and the NASA Earth Science Technology Office Instrument Incubator Program.
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