Development of a laser heterodyne spectroradiometer for high-resolution measurements of CO2, CH4, H2O and O2 in the atmospheric column

Hao Deng; Chenguang Yang; Zhenyu Xu; Mingxing Li; An Huang; Lu Yao; Mai Hu; Bing Chen; Yabai He; Ruifeng Kan; Ruifeng Kan; Jianguo Liu; Jianguo Liu

doi:10.1364/OE.413035

Development of a laser heterodyne spectroradiometer for high-resolution measurements of CO₂, CH₄, H₂O and O₂ in the atmospheric column

Hao Deng, Chenguang Yang, Zhenyu Xu, Mingxing Li, An Huang, Lu Yao, Mai Hu, Bing Chen, Yabai He, Ruifeng Kan, and Jianguo Liu

Optics Express
Vol. 29,
Issue 2,
pp. 2003-2013
(2021)
•https://doi.org/10.1364/OE.413035

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Hao Deng, Chenguang Yang, Zhenyu Xu, Mingxing Li, An Huang, Lu Yao, Mai Hu, Bing Chen, Yabai He, Ruifeng Kan, and Jianguo Liu, "Development of a laser heterodyne spectroradiometer for high-resolution measurements of CO₂, CH₄, H₂O and O₂ in the atmospheric column," Opt. Express 29, 2003-2013 (2021)

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Abstract

We have developed a portable near-infrared laser heterodyne radiometer (LHR) for quasi-simultaneous measurements of atmospheric carbon dioxide (CO₂), methane (CH₄), water vapor (H₂O) and oxygen (O₂) column absorption by using three distributed-feedback diode lasers as the local oscillators of the heterodyne detection. The developed system shows good performance in terms of its high spectral resolution of 0.066 cm⁻¹ and a low solar power detection noise which was about 2 times the theoretical quantum limit. Its measurement precision of the column-averaged mole fraction for CO₂ and CH₄ is within 1.1%, based on the standard deviation from the mean value of the retrieved results for a clean sky. The column abundance information of the O₂ is used to correct for the variations and uncertainties of atmosphere pressure, the solar altitude angle, and the prior profiles of pressure and temperature. Comparison measurements of daily column-averaged atmospheric mole fractions of CO₂, CH₄ and H₂O, between our developed LHR and a greenhouse gas observing satellite, show a good agreement, which proves the reliability of our developed system.

1. Introduction

Water vapor (H₂O), carbon dioxide (CO₂) and methane (CH₄) are the primary greenhouse gases playing a role in climate change. H₂O is the most common greenhouse gas and an important component of the atmosphere. It regulates the temperature of the earth through absorbing and emitting radiation. CO₂ is the second most common greenhouse gas and a major contributor to global warming. It has a stable chemical properties and a long lifetime of several decades. Its concentration has increased from 178 parts per million (ppm) to 430 ppm since the first industrial revolution. CH₄ is the third most common greenhouse gas. It has a short lifetime of approximate 12 years, but its warming potential is about 25 times greater than that of CO₂. Therefore, there is a global demand for accurate and precise long-term measurements of these greenhouse gases to study their impacts on climate change. Currently, the most widely used tool for measuring H₂O, CO₂ and CH₄ columns and vertical profiles in the atmosphere, is a ground based Fourier-transform spectrometer (FTS) [1–4]. This is due to its high spectral resolution, high measurement precision and wide spectral region. However, the high instrument costs and huge size of the FTS instrument limits its applications.

Over the years, laser heterodyne radiometer (LHR) has received increasing attention because of its advantages, including high spectral resolution, high sensitivity and small size. It has been used for atmosphere detection [5–15] and planet observation [16–20]. Recently, with the availability of narrow-linewidth distributed feedback (DFB) quantum cascade laser (QCL) [5], mid-infrared LHR was developed to measure the vertical profiles of atmospheric CO₂, H₂O [11,12] and ozone (O₃) [14] by Weidmann and others. In order to extend the spectral detection range of LHR, an external cavity quantum cascade laser (EC-QCL) with a wide frequency tunability of 100 cm⁻¹ was employed as the local oscillator for atmospheric H₂O, CH₄, nitrous oxide (N₂O), O₃ and dichlorodifluoromethane (CCl₂F₂) detection [6,7]. Moreover, Weidmann et al. also reported a miniaturized quantum cascade laser heterodyne spectroradiometry, based on the hollow waveguide [8], which has been applied for spaceborne observation of atmospheric CH₄ isotopologues [13]. The QCL-based LHR mainly focuses on the atmospheric window of 8-12 µm. The other atmospheric window of 3-5 µm is also an attractive mid-infrared spectral region for atmospheric sensing, with commercially available interband cascade lasers (ICLs) providing access to this spectral region. More recently, Wang et al. [15] demonstrated an ICL-based LHR operating near 3.5 µm for measuring the vertical profiles of atmospheric CH₄ and H₂O. But both ICLs and QCLs still suffer from some drawbacks like extreme costs.

In contrast, DFB diode lasers operating in the near-infrared region – such as those used for the fiber-optical telecommunications – possess the merit of low price, robustness and small size. The absorption bands of H₂O, CO₂ and CH₄ in the 1.2–1.7 µm wavelength regions are suitable to be used for their atmospheric sensing. In recent years, Wilson et al. [21] performed pioneering works in developing an all-fiber near-infrared LHR for atmospheric CO₂ and CH₄ column measurements [22,23], which greatly enables an implementation of its miniaturization and lower costs. At present, it has formed an observation network of estimation of global carbon flux [24]. This LHR was applied for satellite observation of the variations of CO₂, CH₄ and H₂O column-averaged mole fraction in the stratosphere [25]. Recently, Bomse et al. adopted measurement of O₂ column abundance to improve the retrieval accuracy of H₂O through compensating for numerous systematic errors induced by erroneous surface pressure or utilized prior profiles [26], which is extensively used in ground-based FTS and satellite observations of greenhouse gas for dry air corrections of the other gases. Another recent work reported development of a laser heterodyne radiometer for measuring atmospheric CO₂ and CH₄ [27]. The earlier work of our own research on heterodyne radiometer for CO₂ measurement was described in [28].

The work we are reporting now is a significant extension of our previous work on near infrared heterodyne spectroradiometer. The new system is capable of making quasi-simultaneous atmospheric O₂, CO₂, CH₄ and H₂O column measurements in the solar occultation mode by using three DFB diode lasers operating near 1.277 µm, 1.571 µm and 1.654 µm as the local oscillators for heterodyne detection. The measurement of O₂ column abundance helps to minimize the errors induced by the variations of atmosphere pressure, the solar altitude angle, and the uncertainty of the prior profiles of pressure and temperature.

2. Experimental details

A diagram of the near-infrared LHR we developed is shown in Fig. 1. A sun tracker (EKO, STR-32G) was used to track the sun with high tracking accuracy (< 0.01°). A reflective collimator (Thorlabs, RC08FC-P01, with a numerical aperture of 0.167) was mounted on the sun tracker for the collection of the sunlight into a single-mode optical fiber. The collected sunlight was chopped at 831 Hz by a mechanical chopper (Thorlabs, MC2000B-EC) after it formed a free-space collimated beam by a pair of collimators (Thorlabs, RC04FC-P01). The chopper frequency was chosen to avoid beat interference with the 50 Hz mains frequency, and could be set at other values. Three near-infrared diode lasers operating near 1.277 µm (NEL, NLK1B5EAAA), 1.571 µm (NEL, NLK1L5GAAA) and 1.654 µm (NEL, NLK1U5FAAA) were served as the local oscillators for the heterodyne detection. The wavelengths of the lasers could be tuned to the interested absorption range of the target species by properly adjusting their laser temperatures and injection currents with house-made laser temperature and current controllers. Three time-division multiplexing ramp signals were produced by a 16-bit data acquisition card (NI, USB-6363, 2 MHz bandwidth) to control the laser’s injection currents, respectively. A single-mode fiber combiner was used to couple the three laser radiations into one fiber. Afterwards, the combined beam was divided into two beams by a fiber splitter: one as the local oscillator beam for LHR and the second as a reference beam for laser frequency scan measurement via an etalon. The local oscillator beam was mixed with the chopper-modulated sunlight by another single-mode fiber coupler (Newport, F-CPL-F12131) and superimposed on a fast photodetector (Thorlabs, DET08CFC/M) with a bandwidth of 5 GHz. This fast photodetector generates radio frequency heterodyne beat signal for the selected sunlight components around the local laser oscillator wavelength. A bias-tee was used as an ac-coupler to block out the direct current term contained in the photodetector output. The ac-coupled signal passed through a four-stage RF amplifier. The power level of the amplified RF heterodyne beat signal was measured by a circuit based on a Schottky diode. The final RF power of the heterodyne signal was demodulated against the mechanic sunlight chopper by a digital lock-in amplifier (SRS, SR865A). The obtained heterodyne spectra were acquired via the data acquisition card and recorded by a laptop computer. At the same time, the reference beam passed through a quartz Fabry-Pérot etalon. The etalon transmission interference signal was detected by an InGaAs photodetector of 2 MHz bandwidth and acquired with the same data acquisition card.

Fig. 1. Layout of a laser heterodyne spectroradiometer for high-resolution column absorption measurements of multiple atmospheric species (CO₂, CH₄, H₂O and O₂).

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3. Data retrieval details and results analysis

3.1 Retrieval of column-averaged mole fraction of a target species

The most common approach to retrieve the column-averaged mole fraction of a target species [1–4] is via the ratio of the column abundance of the target species to that of O₂. This approach can minimize the error induced by the variations of atmosphere pressure, the solar altitude angle, and the uncertainty of the prior profiles of pressure and temperature. It can be simply written as:

(1)$${X_{gas}} = \frac{{colum{n_{\textrm{gas}}}}}{{colum{n_{{O_2}}}}} \times 0.2095$$

where refers to the column-averaged mole fraction of the target species, refers to the column abundance of the target species, and refers to the column abundance of O₂. The constant 0.2095 reflects that the atmosphere contains nominal 20.95% O₂ by volume. The column abundance of the target species can be expressed as:

(2)$$colum{n_{gas}} = \sum\limits_{j = 1}^\infty {{c_j}\cdot {k_j}} \cdot {n_j}\cdot {l_j}$$

where c_j, k_j, n_j, and l_j are the prior concentration, the proportionality coefficient, the total molecular number density, and the depth of the j-th layer of the whole atmosphere, respectively.

A flow diagram of the data processing is illustrated in Fig. 2. In order to improve the retrieval accuracy, the details of the sunlight transmission process must be considered. In this work, the whole atmosphere was divided into 70 layers. The vertical profiles of temperature, pressure and the prior concentration of the target species at different altitudes were provided by the National Centers for Environment Prediction (NCEP) of USA. The simulation for the spectral absorption was calculated based on HITRAN database [29], the line-by-line radiative transfer model (LBLRTM) [30,31], and our instrument’s line shape function. A nonlinear least-square method was used to obtain best-fit proportionality coefficients by adjusting and matching the simulated spectrum to the measured spectrum. It is worth noting that the initial proportionality coefficients in different divided layers are uniform. The column abundance and the column-averaged dry air mole fraction of the target species can be determined from the Eq. (1) and Eq. (2), respectively.

Fig. 2. Flowchart of the numerical analysis of measurements to retrieve the column abundance of the target species. LBLRTM: line-by-line radiative transfer model.

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3.2 Instrument performance

For laser heterodyne spectroscopy, the measured heterodyne spectrum is the convolution of the instrument line shape function and the atmospheric transmittance. The spectral resolution of the laser heterodyne system depends primarily on the bandwidth of the used radio frequency (RF) circuit. However, the influence of the lowpass filter of a lock-in amplifier cannot be neglected and also contributes to the final spectral resolution or instrument line shape function. Thus, the accurate measurement of instrument line shape function is of high importance for accurate atmospheric column retrievals of the target species.

Firstly, we checked the noise level of system (without solar radiation) as a function of time to determine an optimal averaging time for signal processing. For example, Fig. 3 shows the measurements with the 1.571 µm laser. The noise of the heterodyne-detected RF power detector in the absence of solar radiation is displayed in Fig. 3(a). An Allan variance technique was used to analyze the noise, as displayed in Fig. 3(b). It can be observed that the noise of the system reaches a minimum level at an averaging time of approximate 1300 seconds.

Fig. 3. Measurements and Allan deviation plot showing the noise level of the heterodyne-detected RF power detector in the absence of solar radiation. Estimations of 95% confidence ranges are indicated.

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For the measurement of the instrument line shape function, we temporally replaced the incoming sunlight with a 2 MHz narrow-linewidth DFB diode laser at a fixed wavelength around 1.572 µm. Then, the local oscillator was scanned across this target wavelength. The heterodyne signal between the two lasers was photomixed at the photodetector, and further amplified by the four-stage RF amplifier. The output of the RF power detector was demodulated by the lock-in amplifier in combination with the optical chopper. Figure 4 shows the demodulated heterodyne-detected signal obtained with an averaging time of 1385 seconds, by averaging a total of 2770 individual scans at a 0.5 Hz rate. The raw measurement data points were displayed in black dots, which can be fitted very well with a Gaussian profile (in red line in Fig. 4) using a nonlinear least-squares method. The half width at half maximum (HWHM) of the Gaussian profile was 0.033 cm⁻¹, corresponding to the spectral resolution of the system, 0.066 cm⁻¹, according to full width at half maximum criteria. Moreover, the optical power of the narrow-linewidth DFB diode laser replacing the sunlight in this measurement was determined to be 1.2 nW by an optical power meter. When the local oscillator wavelength is at that of this DFB diode laser, the corresponding lock-in output peak value was 5.54 V, whereas the standard deviation (SD) of the off-resonance baseline section (highlighted green in Fig. 4) is 1.82×10⁻⁴ V. This resulted in a signal-to-noise ratio (SNR, at 3x SD) of 10146. Therefore, the noise equivalent power density of the system for SNR = 1 was evaluated to be 1.22×10⁻¹⁴ W/Hz^0.5, which is 2 times the theoretical quantum limit [32].

Fig. 4. Instrument line shape of the heterodyne spectroradiometer. The measurement signal was obtained by mixing a fixed-wavelength narrow-linewidth laser radiation with a wavelength-scanned local oscillator. A Gaussian line shape fitting (red line) models the measurements (black dots) very well.

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3.3 Measurements of column-averaged mole fractions of the target gas species

The measured raw experimental heterodyne-detected spectra are shown in Fig. 5(a). They were measured in Hefei, China, located at 31.9°N, 117.2°E. Three time-division multiplexing ramp signals with a slow scan frequency of 0.5 Hz were generated via the data acquisition card to drive frequency scanning of the lasers. The measured path-integrated absorption spectra of CO₂, O₂, and H₂O and CH₄ in the whole atmosphere have been displayed in Fig. 5(a) in black, red and blue colors, respectively. The corresponding etalon transmission interference signals, as shown in Fig. 5(b), were simultaneously measured for determining the scanning of the laser frequencies. The absolute frequencies of the target molecular absorption lines were taken from the HITRAN spectral database [29]. In order to compromise spectral SNR and the time resolution of retrieval, the data in Fig. 5(a) was obtained by averaging 50 scans within a total time of 5 minutes. It should be noted that the injected laser current was below its operation threshold at the beginning of each scan sweep. Therefore, the heterodyne-detected signal at the beginning of each scan sweep served as a zero baseline reference to remove the effects of detector offset and the variation of the solar radiation.

Fig. 5. (a) Time-multiplexed measurements of heterodyne-detected atmospheric transmission spectra of CO₂, O₂, and CH₄ and H₂O; (b) the corresponding etalon interference signals of the laser wavelength scans.

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Three examples of CO₂, O₂, and CH₄ and H₂O heterodyne-detected spectra have been analyzed and displayed (in black) in Fig. 6(a), Fig. 6(b) and Fig. 6(c), respectively, where the corresponding best-fit model curves are displayed in red. The absorption-free baselines were based on 3rd-order polynomials. The simulated transmission spectra of the target species show that the fitting curves have a close consistency with the raw heterodyne-detected measurement spectra. The corresponding residuals between the measurement spectra and the best-fit model simulations are less than 0.8%, as displayed in blue lines in Fig. 6. Such atmospheric transmission spectra and their modeling result in column abundances of these species. Repeated measurements reveal their variation as a function of time.

Fig. 6. Examples of the experimental heterodyne-detected atmospheric transmission spectra (black dots) and the model fitting results (red lines) for: (a) CO₂, (b) O₂, (c) CH₄ and H₂O. Their differences were plotted after a 20x expansion to show details. The absorption-free baselines were based on 3rd-order polynomials.

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Figure 7 presents the retrieved column abundances of continuous measurements of CO₂, O₂, CH₄ and H₂O represented by dots in different colors, in which Figs. 7(a)–7(d) refer to the retrievals from the measurements acquired on October 1, 2019, while Figs. 7(e)–7(h) represent the retrievals from the measurements on October 3, 2019. Although CO₂ and CH₄ concentrations near ground vary greatly, their column abundances during the day remain relatively stable, so do the O₂ column abundances, as shown in this figure. We can see that the column abundances of CO₂, O₂ and CH₄ have the same variation with a slow downward trend, while the column abundance of H₂O shows a different variation tendency. The measurement fluctuations for the column abundances of CO₂, O₂, CH₄ and H₂O can be estimated through analyzing their standard deviations from smooth trends of their mean column abundances. The corresponding measurement uncertainties have been indicated in each of the plots in Fig. 7. As can be seen, the retrieved results on October 3 are superior to that on October 1 in terms of the measurement fluctuations because of the collected solar radiation on October 3 was greater than that on October 1. The lowest measurement fluctuation of less than 1% for CO₂ column abundance is mainly due to the factor that the photodetector has higher gain at the CO₂ 1.571 µm wavelength region than at the two other 1.277 µm and 1.654 µm wavelength regions, leading to a better SNR for the recorded CO₂ spectra.

Fig. 7. Retrieved column abundances of continuous measurements of the four target species represent by dots in different colors, respectively; (a-d) results on October 1, 2019, and (e-h) results on October 3, 2019. The measurement fluctuations based on the standard deviations from smooth trend lines are indicated in each of the plots.

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Then, the column-averaged dry air mole fraction of the target species can be obtained by using Eq. (1). The final calculated mole fractions ${X_{\textrm{C}{\textrm{O}_2}}}$, ${X_{\textrm{C}{\textrm{H}_\textrm{4}}}}$ and ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ for the two measurement days are shown in Fig. 8, respectively. As can be observed, in general, the variations of ${X_{\textrm{C}{\textrm{O}_2}}}$ and ${X_{\textrm{C}{\textrm{H}_\textrm{4}}}}$ in daylight remain stable, which is consistent with the results reported in the Refs. [2,3]. Because H₂O distribution in the atmosphere varies with time and space, the variation of ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ is bigger. Moreover, the measurement errors for ${X_{\textrm{C}{\textrm{O}_2}}}$, ${X_{\textrm{C}{\textrm{H}_\textrm{4}}}}$ and ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ are important parameters for evaluating the performance of our developed LHR, and can be estimated by analyzing their standard deviations and daily mean values. Given October 3 2019 was a particularly clear day, the results from this day were utilized to calculate the measurement errors that correspond to 1%, 1.3%, and 5.1%, respectively. In comparison with the reported LHR, our developed system performs well in terms of the measurement error for a 5-minute acquisition time and has a better temporal resolution. In order to further prove the reliability of our developed LHR, the daily averaged ${X_{\textrm{C}{\textrm{O}_2}}}$, ${X_{\textrm{C}{\textrm{H}_\textrm{4}}}}$ and ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ measurements were compared with the greenhouse gases observing satellite (GOSAT, Japan) observations in October, 2019, as presented in Figs. 9(a)–9(c), respectively. The comparisons show that the daily averaged dry air column-averaged mole fraction obtained from the GOSAT and our system, have the same variation trend. The mean relative errors of 0.09% (${X_{\textrm{C}{\textrm{O}_2}}}$) and 0.6% (${X_{\textrm{C}{\textrm{H}_\textrm{4}}}}$) can be calculated, respectively, while all the relative errors for ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ exceed 7%. Furthermore, we found that the relative change rate of ${X_{{\textrm{H}_\textrm{2}}\textrm{O}}}$ within 2 minutes obtained from GOSAT is more than 40%. It also indicates that the spatial-temporal distribution of H₂O is not uniform.

Fig. 8. Calculated dry air mole fractions of CO₂, CH₄ and H₂O on October 1, 2019 (left) and October 3, 2019 (right).

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Fig. 9. Comparison of our observations of daily column-averaged mole fractions of CO₂, CH₄, and H₂O vapor, against the retrieved data obtained from GOSAT.

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4. Conclusions

A multiple-species LHR in the solar occultation mode has been developed by using three DFB diode lasers operating near 1.277 µm (O₂ absorptions detection), 1.571 µm (CO₂ absorptions detection) and 1.654 µm (CH₄ and H₂O absorptions detection) as the local oscillators. The spectra resolutions of the developed LHR have been estimated to be 0.066 cm⁻¹. The noise equivalent power of the system has been evaluated to be 2 times the theoretical quantum limit. It has been applied for measurements of atmospheric greenhouse gases columns in Hefei of China. A measurement precision of about 1% can be achieved by the current LHR. The comparisons of daily averaged dry air mole fractions of CO₂, CH₄ and H₂O between our developed LHR and the GOSAT satellite observation show a good agreement. In general, all above results prove the reliability and performance of the developed portable multiple-species LHR system for atmospheric remote sensing. However, the performance of our instrument is worse than the ∼0.1% uncertainty reported by using commercial Fourier transform spectrometers [2,3]. Thus, in the future, we aim to further improve the performance of our developed LHR. Improvements would include improving the RF signal processing electronics, and improving the SNR of heterodyne signal by optimizing the integration time and increasing the size of the sunlight collimator.

Funding

National Key Research and Development Program of China (2019YFB2006003, 2018YFC0213103); National Natural Science Foundation of China (41901081).

Disclosures

The authors declare no conflicts of interest.

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23. E. L. Wilson, A. J. DiGregorio, G. Villanueva, C. E. Grunberg, Z. Souders, K. M. Miletti, A. Menendez, M. H. Grunberg, M. A. M. Floyd, J. E. Bleacher, E. S. Euskirchen, C. Edgar, B. J. Caldwell, B. Shiro, and K. Binsted, “A portable miniaturized laser heterodyne radiometer (mini-LHR) for remote measurements of column CH₄ and CO₂,” Appl. Phys. B 125(11), 211 (2019). [CrossRef] .

24. E. L. Wilson, A. J. DiGregorio, V. J. Riot, M. S. Ammons, W. W. Bruner, D. Carter, J. P. Mao, A. Ramanathan, S. E. Strahan, L. D. Oman, C. Hoffman, and R. M. Garner, “A 4 U laser heterodyne radiometer for methane (CH₄) and carbon dioxide (CO₂) measurements from an occultation-viewing CubeSat,” Meas. Sci. Technol. 28(3), 035902 (2017). [CrossRef] .

25. P. I. Palmer, E. L. Wilson, G. L. Villanueva, G. Liuzzi, L. Feng, A. J. DiGregorio, J. Mao, L. Ott, and B. Duncan, “Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide,” Atmos. Meas. Tech. 12(4), 2579–2594 (2019). [CrossRef] .

26. D. S. Bomse, J. E. Tso, M. M. Flores, and J. H. Miller, “Precision heterodyne oxygen-corrected spectrometry: vertical profiling of water and carbon dioxide in the troposphere and lower stratosphere,” Appl. Opt. 59(7), B10–B17 (2020). [CrossRef] .

27. J. J. Wang, C. Y. Sun, G. S. Wang, M. M. Zou, T. Tan, K. Liu, W. D. Chen, and X. M. Gao, “A fibered near-infrared laser heterodyne radiometer for simultaneous remote sensing of atmospheric CO₂ and CH₄,” Opt. Laser Eng. 129, 106083 (2020). [CrossRef] .

28. H. Deng, C. Yang, W. Wang, C. Shan, Z. Y. Xu, B. Chen, L. Yao, M. Hu, R. F. Kan, and Y. B. He, “Near infrared heterodyne radiometer for continuous measurements of atmospheric CO₂ column concentration,” Infrared Phys. Technol. 101, 39–44 (2019). [CrossRef] .

29. L. S. Rothman, I. E. Gordon, Y. Babikov, A. Barbe, D. C. Benner, P. F. Bernath, M. Birk, L. Bizzocchi, V. Boudon, L. R. Brown, A. Campargue, K. Chance, L. H. Coudert, V. M. Devi, B. J. Drouin, A. Fayt, J. M. Flaud, R. R. Gamache, J. Harrison, J. M. Hartmann, C. Hill, J. T. Hodges, D. Jacquemart, A. Jolly, J. Lamouroux, R. J. LeRoy, G. Li, D. Long, C. J. Mackie, S. T. Massie, S. Mikhailenko, H. S. P. Müller, O. V. Naumenko, A. V. Nikitin, J. Orphal, V. I. Perevalov, A. Perrin, E. R. Polovtseva, C. Richard, M. A. H. Smith, E. Starikova, K. Sung, S. A. Tashkun, J. Tennyson, G. C. Toon, V. G. Tyuterev, and G. Wagner, “The HITRAN2012 molecular spectroscopic database,” J. Quant. Spectrosc. Radiat. Transfer 130, 4–50 (2013). [CrossRef] .

30. S. A. Clough, M. J. Iacono, and J. L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor,” J. Geophys. Res. 97(D14), 15761–15785 (1992). [CrossRef] .

31. S. A. Clough and M. J. Iacono, “Line-by-line calculation of atmospheric fluxes and cooling rates: 2. Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons,” J. Geophys. Res. 100(D8), 16519–16535 (1995). [CrossRef] .

32. T. G. Blaney, “Signal-to-noise ratio and other characteristics of heterodyne radiation receivers,” Space Sci. Rev. 17(5), 691–702 (1975). [CrossRef] .

References

View by:
Article Order
|
Year
|
Author
|
Publication

M. Zhou, B. Langerock, M. K. Sha, N. Kumps, C. Hermans, C. Petri, T. Warneke, H. Chen, J. M. Metzger, R. Kivi, P. Heikkinen, M. Ramonet, and M. De Mazière, “Retrieval of atmospheric CH4 vertical information from ground-based FTS near-infrared spectra,” Atmos. Meas. Tech. 12(11), 6125–6141 (2019)..
[Crossref]
W. Wang, Y. Tian, C. Liu, Y. W. Sun, W. Q. Liu, P. H. Xie, J. G. Liu, J. Xu, I. Morino, V. A. Velazco, D. W. T. Griffith, J. Notholt, and T. Warneke, “Investigating the performance of a greenhouse gas observatory in Hefei, China,” Atmos. Meas. Tech. 10(7), 2627–2643 (2017)..
[Crossref]
Y. Tian, Y. Sun, C. Liu, W. Wang, C. Shan, X. Xu, and Q. Hu, “Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China,” Atmos. Environ. 173, 198–209 (2018)..
[Crossref]
C. P. Rinsland, N. B. Jones, B. J. Connor, J. A. Logan, N. S. Pougatchev, A. Goldman, F. Murcray, T. M. Stephen, A. S. Pine, R. Zander, E. Mahieu, and P. Demoulin, “Northern and Southern Hemisphere ground-based infrared spectroscopic measurements of tropospheric carbon monoxide and ethane,” J. Geophys. Res. 103(D21), 28197–28217 (1998)..
[Crossref]
D. Weidmann, W. J. Reburn, and K. M. Smith, “Ground-based prototype quantum cascade laser heterodyne radiometer for atmospheric studies,” Rev. Sci. Instrum. 78(7), 073107 (2007)..
[Crossref]
D. Weidmann and G. Wysocki, “High-resolution broadband (>100 cm-1) infrared heterodyne spectro-radiometry using an external cavity quantum cascade laser,” Opt. Express 17(1), 248–259 (2009)..
[Crossref]
D. Weidmann, T. Tsai, N. A. Macleod, and G. Wysocki, “Atmospheric observations of multiple molecular species using ultra-high-resolution external cavity quantum cascade laser heterodyne radiometry,” Opt. Lett. 36(11), 1951–1953 (2011)..
[Crossref]
D. Weidmann, B. J. Perrett, N. A. Macleod, and R. M. Jenkins, “Hollow waveguide photomixing for quantum cascade laser heterodyne spectro-radiometry,” Opt. Express 19(10), 9074–9085 (2011)..
[Crossref]
S. R. King, D. T. Hodges, T. S. Hartwick, and D. H. Barker, “High-resolution atmospheric-transmission measurement using a laser heterodyne radiometer,” Appl. Opt. 12(6), 1106–1107 (1973)..
[Crossref]
R. T. Ku and D. L. Spears, “High-sensitivity infrared heterodyne radiometer using a tunable-diode-laser local oscillator,” Opt. Lett. 1(3), 84–86 (1977).
[Crossref]
A. Hoffmann, N. A. Macleod, M. Huebner, and D. Weidmann, “Thermal infrared laser heterodyne spectroradiometry for solar occultation atmospheric CO2 measurements,” Atmos. Meas. Tech. 9(12), 5975–5996 (2016)..
[Crossref]
A. Hoffmann, M. Huebner, N. Macleod, and D. Weidmann, “Spectrally resolved thermal emission of atmospheric gases measured by laser heterodyne spectrometry,” Opt. Lett. 43(16), 3810–3813 (2018)..
[Crossref]
D. Weidmann, A. Hoffmann, N. Macleod, K. Middleton, J. Kurtz, S. Barraclough, and D. Griffin, “The methane isotopologues by solar occultation (miso) nanosatellite mission: spectral channel optimization and early performance analysis,” Remote Sens. 9(10), 1073 (2017)..
[Crossref]
D. Weidmann, W. J. Reburn, and K. M. Smith, “Retrieval of atmospheric ozone profiles from an infrared quantum cascade laser heterodyne radiometer: results and analysis,” Appl. Opt. 46(29), 7162–7171 (2007)..
[Crossref]
J. Wang, G. Wang, T. Tan, G. Zhu, C. Sun, Z. Cao, W. Chen, and X. Gao, “Mid-infrared laser heterodyne radiometer (LHR) based on a 3.53 µm room-temperature interband cascade laser,” Opt. Express 27(7), 9610–9619 (2019)..
[Crossref]
G. Sonnabend, D. Wirtz, V. Vetterle, and R. Schieder, “High-resolution observations of Martian non-thermal CO2 emission near 10 um with a new tunable heterodyne receiver,” Astron. Astrophys. 435(3), 1181–1184 (2005)..
[Crossref]
H. Nakagawa, S. Aoki, H. Sagawa, Y. Kasaba, I. Murata, G. Sonnabend, M. Sornig, S. Okano, J. R. Kuhn, J. M. Ritter, M. Kagitani, T. Sakanoi, M. Taguchi, and M. Kagitani, “IR heterodyne spectrometer MILAHI for continuous monitoring observatory of Martian and Venusian atmospheres at Mt. Haleakalā, Hawaii,” Planet. Space Sci. 126, 34–48 (2016)..
[Crossref]
H. Rothermel, H. U. Käufl, and Y. Yu, “A heterodyne spectrometer for astronomical measurements at 10 micrometers,” Astron. Astrophys.. 126, 387–392 (1983).
G. Sonnabend, M. Sornig, P. Krötz, D. Stupar, and R. Schieder, “Ultra high spectral resolution observations of planetary atmospheres using the Cologne tuneable heterodyne infrared spectrometer,” J. Quant. Spectrosc. Radiat. Transfer 109(6), 1016–1029 (2008).
[Crossref]
K. Fast, T. Kostiuk, F. Espenak, J. Annen, D. Buhl, T. Hewagama, M. F. A’Hearn, D. Zipoy, T. A. Livengood, G. Sonnabend, and F. Schmülling, “Ozone abundance on Mars from infrared heterodyne spectra: I. Acquisition, retrieval, and anticorrelation with water vapor,” Icarus 181(2), 419–431 (2006)..
[Crossref]
E. L. Wilson, M. L. McLinden, J. H. Miller, G. R. Allan, L. E. Ott, H. R. Melroy, and G. B. Clarke, “Miniaturized laser heterodyne radiometer for measurements of CO2 in the atmospheric column,” Appl. Phys. B 114(3), 385–393 (2014)..
[Crossref]
H. R. Melroy, E. L. Wilson, G. B. Clarke, L. E. Ott, J. Mao, A. K. Ramanathan, and M. L. McLinden, “Autonomous field measurements of CO2 in the atmospheric column with the miniaturized laser heterodyne radiometer (Mini-LHR),” Appl. Phys. B 120(4), 609–615 (2015)..
[Crossref]
E. L. Wilson, A. J. DiGregorio, G. Villanueva, C. E. Grunberg, Z. Souders, K. M. Miletti, A. Menendez, M. H. Grunberg, M. A. M. Floyd, J. E. Bleacher, E. S. Euskirchen, C. Edgar, B. J. Caldwell, B. Shiro, and K. Binsted, “A portable miniaturized laser heterodyne radiometer (mini-LHR) for remote measurements of column CH4 and CO2,” Appl. Phys. B 125(11), 211 (2019)..
[Crossref]
E. L. Wilson, A. J. DiGregorio, V. J. Riot, M. S. Ammons, W. W. Bruner, D. Carter, J. P. Mao, A. Ramanathan, S. E. Strahan, L. D. Oman, C. Hoffman, and R. M. Garner, “A 4 U laser heterodyne radiometer for methane (CH4) and carbon dioxide (CO2) measurements from an occultation-viewing CubeSat,” Meas. Sci. Technol. 28(3), 035902 (2017)..
[Crossref]
P. I. Palmer, E. L. Wilson, G. L. Villanueva, G. Liuzzi, L. Feng, A. J. DiGregorio, J. Mao, L. Ott, and B. Duncan, “Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide,” Atmos. Meas. Tech. 12(4), 2579–2594 (2019)..
[Crossref]
D. S. Bomse, J. E. Tso, M. M. Flores, and J. H. Miller, “Precision heterodyne oxygen-corrected spectrometry: vertical profiling of water and carbon dioxide in the troposphere and lower stratosphere,” Appl. Opt. 59(7), B10–B17 (2020)..
[Crossref]
J. J. Wang, C. Y. Sun, G. S. Wang, M. M. Zou, T. Tan, K. Liu, W. D. Chen, and X. M. Gao, “A fibered near-infrared laser heterodyne radiometer for simultaneous remote sensing of atmospheric CO2 and CH4,” Opt. Laser Eng. 129, 106083 (2020)..
[Crossref]
H. Deng, C. Yang, W. Wang, C. Shan, Z. Y. Xu, B. Chen, L. Yao, M. Hu, R. F. Kan, and Y. B. He, “Near infrared heterodyne radiometer for continuous measurements of atmospheric CO2 column concentration,” Infrared Phys. Technol. 101, 39–44 (2019)..
[Crossref]
L. S. Rothman, I. E. Gordon, Y. Babikov, A. Barbe, D. C. Benner, P. F. Bernath, M. Birk, L. Bizzocchi, V. Boudon, L. R. Brown, A. Campargue, K. Chance, L. H. Coudert, V. M. Devi, B. J. Drouin, A. Fayt, J. M. Flaud, R. R. Gamache, J. Harrison, J. M. Hartmann, C. Hill, J. T. Hodges, D. Jacquemart, A. Jolly, J. Lamouroux, R. J. LeRoy, G. Li, D. Long, C. J. Mackie, S. T. Massie, S. Mikhailenko, H. S. P. Müller, O. V. Naumenko, A. V. Nikitin, J. Orphal, V. I. Perevalov, A. Perrin, E. R. Polovtseva, C. Richard, M. A. H. Smith, E. Starikova, K. Sung, S. A. Tashkun, J. Tennyson, G. C. Toon, V. G. Tyuterev, and G. Wagner, “The HITRAN2012 molecular spectroscopic database,” J. Quant. Spectrosc. Radiat. Transfer 130, 4–50 (2013)..
[Crossref]
S. A. Clough, M. J. Iacono, and J. L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor,” J. Geophys. Res. 97(D14), 15761–15785 (1992)..
[Crossref]
S. A. Clough and M. J. Iacono, “Line-by-line calculation of atmospheric fluxes and cooling rates: 2. Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons,” J. Geophys. Res. 100(D8), 16519–16535 (1995)..
[Crossref]
T. G. Blaney, “Signal-to-noise ratio and other characteristics of heterodyne radiation receivers,” Space Sci. Rev. 17(5), 691–702 (1975)..
[Crossref]

2020 (2)

D. S. Bomse, J. E. Tso, M. M. Flores, and J. H. Miller, “Precision heterodyne oxygen-corrected spectrometry: vertical profiling of water and carbon dioxide in the troposphere and lower stratosphere,” Appl. Opt. 59(7), B10–B17 (2020)..
[Crossref]

J. J. Wang, C. Y. Sun, G. S. Wang, M. M. Zou, T. Tan, K. Liu, W. D. Chen, and X. M. Gao, “A fibered near-infrared laser heterodyne radiometer for simultaneous remote sensing of atmospheric CO2 and CH4,” Opt. Laser Eng. 129, 106083 (2020)..
[Crossref]

2019 (5)

H. Deng, C. Yang, W. Wang, C. Shan, Z. Y. Xu, B. Chen, L. Yao, M. Hu, R. F. Kan, and Y. B. He, “Near infrared heterodyne radiometer for continuous measurements of atmospheric CO2 column concentration,” Infrared Phys. Technol. 101, 39–44 (2019)..
[Crossref]

E. L. Wilson, A. J. DiGregorio, G. Villanueva, C. E. Grunberg, Z. Souders, K. M. Miletti, A. Menendez, M. H. Grunberg, M. A. M. Floyd, J. E. Bleacher, E. S. Euskirchen, C. Edgar, B. J. Caldwell, B. Shiro, and K. Binsted, “A portable miniaturized laser heterodyne radiometer (mini-LHR) for remote measurements of column CH4 and CO2,” Appl. Phys. B 125(11), 211 (2019)..
[Crossref]

P. I. Palmer, E. L. Wilson, G. L. Villanueva, G. Liuzzi, L. Feng, A. J. DiGregorio, J. Mao, L. Ott, and B. Duncan, “Potential improvements in global carbon flux estimates from a network of laser heterodyne radiometer measurements of column carbon dioxide,” Atmos. Meas. Tech. 12(4), 2579–2594 (2019)..
[Crossref]

M. Zhou, B. Langerock, M. K. Sha, N. Kumps, C. Hermans, C. Petri, T. Warneke, H. Chen, J. M. Metzger, R. Kivi, P. Heikkinen, M. Ramonet, and M. De Mazière, “Retrieval of atmospheric CH4 vertical information from ground-based FTS near-infrared spectra,” Atmos. Meas. Tech. 12(11), 6125–6141 (2019)..
[Crossref]

J. Wang, G. Wang, T. Tan, G. Zhu, C. Sun, Z. Cao, W. Chen, and X. Gao, “Mid-infrared laser heterodyne radiometer (LHR) based on a 3.53 µm room-temperature interband cascade laser,” Opt. Express 27(7), 9610–9619 (2019)..
[Crossref]

2018 (2)

A. Hoffmann, M. Huebner, N. Macleod, and D. Weidmann, “Spectrally resolved thermal emission of atmospheric gases measured by laser heterodyne spectrometry,” Opt. Lett. 43(16), 3810–3813 (2018)..
[Crossref]

Y. Tian, Y. Sun, C. Liu, W. Wang, C. Shan, X. Xu, and Q. Hu, “Characterisation of methane variability and trends from near-infrared solar spectra over Hefei, China,” Atmos. Environ. 173, 198–209 (2018)..
[Crossref]

2017 (3)

W. Wang, Y. Tian, C. Liu, Y. W. Sun, W. Q. Liu, P. H. Xie, J. G. Liu, J. Xu, I. Morino, V. A. Velazco, D. W. T. Griffith, J. Notholt, and T. Warneke, “Investigating the performance of a greenhouse gas observatory in Hefei, China,” Atmos. Meas. Tech. 10(7), 2627–2643 (2017)..
[Crossref]

D. Weidmann, A. Hoffmann, N. Macleod, K. Middleton, J. Kurtz, S. Barraclough, and D. Griffin, “The methane isotopologues by solar occultation (miso) nanosatellite mission: spectral channel optimization and early performance analysis,” Remote Sens. 9(10), 1073 (2017)..
[Crossref]

E. L. Wilson, A. J. DiGregorio, V. J. Riot, M. S. Ammons, W. W. Bruner, D. Carter, J. P. Mao, A. Ramanathan, S. E. Strahan, L. D. Oman, C. Hoffman, and R. M. Garner, “A 4 U laser heterodyne radiometer for methane (CH4) and carbon dioxide (CO2) measurements from an occultation-viewing CubeSat,” Meas. Sci. Technol. 28(3), 035902 (2017)..
[Crossref]

2016 (2)

A. Hoffmann, N. A. Macleod, M. Huebner, and D. Weidmann, “Thermal infrared laser heterodyne spectroradiometry for solar occultation atmospheric CO2 measurements,” Atmos. Meas. Tech. 9(12), 5975–5996 (2016)..
[Crossref]

H. Nakagawa, S. Aoki, H. Sagawa, Y. Kasaba, I. Murata, G. Sonnabend, M. Sornig, S. Okano, J. R. Kuhn, J. M. Ritter, M. Kagitani, T. Sakanoi, M. Taguchi, and M. Kagitani, “IR heterodyne spectrometer MILAHI for continuous monitoring observatory of Martian and Venusian atmospheres at Mt. Haleakalā, Hawaii,” Planet. Space Sci. 126, 34–48 (2016)..
[Crossref]

2015 (1)

H. R. Melroy, E. L. Wilson, G. B. Clarke, L. E. Ott, J. Mao, A. K. Ramanathan, and M. L. McLinden, “Autonomous field measurements of CO2 in the atmospheric column with the miniaturized laser heterodyne radiometer (Mini-LHR),” Appl. Phys. B 120(4), 609–615 (2015)..
[Crossref]

2014 (1)

E. L. Wilson, M. L. McLinden, J. H. Miller, G. R. Allan, L. E. Ott, H. R. Melroy, and G. B. Clarke, “Miniaturized laser heterodyne radiometer for measurements of CO2 in the atmospheric column,” Appl. Phys. B 114(3), 385–393 (2014)..
[Crossref]

2013 (1)

L. S. Rothman, I. E. Gordon, Y. Babikov, A. Barbe, D. C. Benner, P. F. Bernath, M. Birk, L. Bizzocchi, V. Boudon, L. R. Brown, A. Campargue, K. Chance, L. H. Coudert, V. M. Devi, B. J. Drouin, A. Fayt, J. M. Flaud, R. R. Gamache, J. Harrison, J. M. Hartmann, C. Hill, J. T. Hodges, D. Jacquemart, A. Jolly, J. Lamouroux, R. J. LeRoy, G. Li, D. Long, C. J. Mackie, S. T. Massie, S. Mikhailenko, H. S. P. Müller, O. V. Naumenko, A. V. Nikitin, J. Orphal, V. I. Perevalov, A. Perrin, E. R. Polovtseva, C. Richard, M. A. H. Smith, E. Starikova, K. Sung, S. A. Tashkun, J. Tennyson, G. C. Toon, V. G. Tyuterev, and G. Wagner, “The HITRAN2012 molecular spectroscopic database,” J. Quant. Spectrosc. Radiat. Transfer 130, 4–50 (2013)..
[Crossref]

2011 (2)

D. Weidmann, T. Tsai, N. A. Macleod, and G. Wysocki, “Atmospheric observations of multiple molecular species using ultra-high-resolution external cavity quantum cascade laser heterodyne radiometry,” Opt. Lett. 36(11), 1951–1953 (2011)..
[Crossref]

D. Weidmann, B. J. Perrett, N. A. Macleod, and R. M. Jenkins, “Hollow waveguide photomixing for quantum cascade laser heterodyne spectro-radiometry,” Opt. Express 19(10), 9074–9085 (2011)..
[Crossref]

2009 (1)

D. Weidmann and G. Wysocki, “High-resolution broadband (>100 cm-1) infrared heterodyne spectro-radiometry using an external cavity quantum cascade laser,” Opt. Express 17(1), 248–259 (2009)..
[Crossref]

2008 (1)

G. Sonnabend, M. Sornig, P. Krötz, D. Stupar, and R. Schieder, “Ultra high spectral resolution observations of planetary atmospheres using the Cologne tuneable heterodyne infrared spectrometer,” J. Quant. Spectrosc. Radiat. Transfer 109(6), 1016–1029 (2008).
[Crossref]

2007 (2)

D. Weidmann, W. J. Reburn, and K. M. Smith, “Ground-based prototype quantum cascade laser heterodyne radiometer for atmospheric studies,” Rev. Sci. Instrum. 78(7), 073107 (2007)..
[Crossref]

D. Weidmann, W. J. Reburn, and K. M. Smith, “Retrieval of atmospheric ozone profiles from an infrared quantum cascade laser heterodyne radiometer: results and analysis,” Appl. Opt. 46(29), 7162–7171 (2007)..
[Crossref]

2006 (1)

K. Fast, T. Kostiuk, F. Espenak, J. Annen, D. Buhl, T. Hewagama, M. F. A’Hearn, D. Zipoy, T. A. Livengood, G. Sonnabend, and F. Schmülling, “Ozone abundance on Mars from infrared heterodyne spectra: I. Acquisition, retrieval, and anticorrelation with water vapor,” Icarus 181(2), 419–431 (2006)..
[Crossref]

2005 (1)

G. Sonnabend, D. Wirtz, V. Vetterle, and R. Schieder, “High-resolution observations of Martian non-thermal CO2 emission near 10 um with a new tunable heterodyne receiver,” Astron. Astrophys. 435(3), 1181–1184 (2005)..
[Crossref]

1998 (1)

C. P. Rinsland, N. B. Jones, B. J. Connor, J. A. Logan, N. S. Pougatchev, A. Goldman, F. Murcray, T. M. Stephen, A. S. Pine, R. Zander, E. Mahieu, and P. Demoulin, “Northern and Southern Hemisphere ground-based infrared spectroscopic measurements of tropospheric carbon monoxide and ethane,” J. Geophys. Res. 103(D21), 28197–28217 (1998)..
[Crossref]

1995 (1)

S. A. Clough and M. J. Iacono, “Line-by-line calculation of atmospheric fluxes and cooling rates: 2. Application to carbon dioxide, ozone, methane, nitrous oxide and the halocarbons,” J. Geophys. Res. 100(D8), 16519–16535 (1995)..
[Crossref]

1992 (1)

S. A. Clough, M. J. Iacono, and J. L. Moncet, “Line-by-line calculations of atmospheric fluxes and cooling rates: Application to water vapor,” J. Geophys. Res. 97(D14), 15761–15785 (1992)..
[Crossref]

1983 (1)

H. Rothermel, H. U. Käufl, and Y. Yu, “A heterodyne spectrometer for astronomical measurements at 10 micrometers,” Astron. Astrophys.. 126, 387–392 (1983).

1977 (1)

R. T. Ku and D. L. Spears, “High-sensitivity infrared heterodyne radiometer using a tunable-diode-laser local oscillator,” Opt. Lett. 1(3), 84–86 (1977).
[Crossref]

1975 (1)

T. G. Blaney, “Signal-to-noise ratio and other characteristics of heterodyne radiation receivers,” Space Sci. Rev. 17(5), 691–702 (1975)..
[Crossref]

1973 (1)

S. R. King, D. T. Hodges, T. S. Hartwick, and D. H. Barker, “High-resolution atmospheric-transmission measurement using a laser heterodyne radiometer,” Appl. Opt. 12(6), 1106–1107 (1973)..
[Crossref]

A’Hearn, M. F.

Allan, G. R.

Ammons, M. S.

Annen, J.

Aoki, S.

Babikov, Y.

Barbe, A.

Barker, D. H.

Barraclough, S.

Benner, D. C.

Bernath, P. F.

Binsted, K.

Birk, M.

Bizzocchi, L.

Blaney, T. G.

T. G. Blaney, “Signal-to-noise ratio and other characteristics of heterodyne radiation receivers,” Space Sci. Rev. 17(5), 691–702 (1975)..
[Crossref]

Bleacher, J. E.

Bomse, D. S.

Boudon, V.

Brown, L. R.

Bruner, W. W.

Buhl, D.

Caldwell, B. J.

Campargue, A.

Cao, Z.

Carter, D.

Chance, K.

Chen, B.

Chen, H.

Chen, W.

Chen, W. D.

Clarke, G. B.

Clough, S. A.

Connor, B. J.

Coudert, L. H.

De Mazière, M.

Demoulin, P.

Deng, H.

Devi, V. M.

DiGregorio, A. J.

Drouin, B. J.

Duncan, B.

Edgar, C.

Espenak, F.

Euskirchen, E. S.

Fast, K.

Fayt, A.

Feng, L.

Flaud, J. M.

Flores, M. M.

Floyd, M. A. M.

Gamache, R. R.

Gao, X.

Gao, X. M.

Garner, R. M.

Goldman, A.

Gordon, I. E.

Griffin, D.

Griffith, D. W. T.

Grunberg, C. E.

Grunberg, M. H.

Harrison, J.

Hartmann, J. M.

Hartwick, T. S.

He, Y. B.

Heikkinen, P.

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Appl. Opt. (3)

Appl. Phys. B (3)

Astron. Astrophys. (1)

Astron. Astrophys.. (1)

H. Rothermel, H. U. Käufl, and Y. Yu, “A heterodyne spectrometer for astronomical measurements at 10 micrometers,” Astron. Astrophys.. 126, 387–392 (1983).

Atmos. Environ. (1)

Atmos. Meas. Tech. (4)

Icarus (1)

Infrared Phys. Technol. (1)

J. Geophys. Res. (3)

J. Quant. Spectrosc. Radiat. Transfer (2)

Meas. Sci. Technol. (1)

Opt. Express (3)

Opt. Laser Eng. (1)

Opt. Lett. (3)

R. T. Ku and D. L. Spears, “High-sensitivity infrared heterodyne radiometer using a tunable-diode-laser local oscillator,” Opt. Lett. 1(3), 84–86 (1977).
[Crossref]

Planet. Space Sci. (1)

Remote Sens. (1)

Rev. Sci. Instrum. (1)

D. Weidmann, W. J. Reburn, and K. M. Smith, “Ground-based prototype quantum cascade laser heterodyne radiometer for atmospheric studies,” Rev. Sci. Instrum. 78(7), 073107 (2007)..
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Space Sci. Rev. (1)

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Fig. 1. Layout of a laser heterodyne spectroradiometer for high-resolution column absorption measurements of multiple atmospheric species (CO₂, CH₄, H₂O and O₂).

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Fig. 2. Flowchart of the numerical analysis of measurements to retrieve the column abundance of the target species. LBLRTM: line-by-line radiative transfer model.

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Fig. 8. Calculated dry air mole fractions of CO₂, CH₄ and H₂O on October 1, 2019 (left) and October 3, 2019 (right).

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Fig. 9. Comparison of our observations of daily column-averaged mole fractions of CO₂, CH₄, and H₂O vapor, against the retrieved data obtained from GOSAT.

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Equations (2)

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X_{g a s} = \frac{c o l u m n_{gas}}{c o l u m n_{O_{2}}} \times 0.2095

c o l u m n_{g a s} = \sum_{j = 1}^{\infty} c_{j} \cdot k_{j} \cdot n_{j} \cdot l_{j}