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The Influence of Instrumental Line Shape Degradation on the Partial Columns of O_{3}, CO, CH_{4} and N_{2}O Derived from High-Resolution FTIR Spectrometry

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## Abstract

**:**

_{3}, CO, CH

_{4}, and N

_{2}O can be separated into multiple partial columns using the optimal estimation method (OEM). The retrieval of trace gas profiles is sensitive to the instrument line shape (ILS) of the FTIR spectrometer. In this paper, we present an investigation of the influence of ILS degradation on the partial column retrieval of O

_{3}, CO, CH

_{4}, and N

_{2}O. Sensitivities of the partial column, error, and degrees of freedom (DOFs) of each layer to different levels of ILS degradation for O

_{3}, CO, CH

_{4}, and N

_{2}O are estimated. We then evaluate the impact of ILS degradation on the long-term measurements. In addition, we derive the range of ILS degradation corresponding to the acceptable uncertainties of O

_{3}, CO, CH

_{4}, and N

_{2}O results. The results show that the uncertainties induced by the ILS degradation on the absolute value, error, and the DOFs of the partial column are altitude and gas species dependent. The uncertainties of the partial columns of O

_{3}and CO are larger than those on CH

_{4}and N

_{2}O. The stratospheric partial columns are more sensitive to the ILS degradation compared to the tropospheric part. Our result improves the understanding of the ILS degradation on the FTIR measurements, which is important for the quantification of the measurement uncertainties and minimizes the bias of the inter-comparison between different measurement platforms. This is especially useful for the validation of satellite observations, the data assimilation of chemical model simulations, and the quantification of the source/sink/trend from the FTIR measurements.

## 1. Introduction

_{3}, HCl, HF, ClONO

_{2}, HNO

_{3}, N

_{2}O, CH

_{4}, CO, C

_{2}H

_{6}, and HCN, which have been observed globally for more than 20 years and are extensively used in atmospheric physics and chemistry [1,2,3,4,5,6,7,8,9,10]. Within the NDACC network, the solar spectra are acquired with similar instruments and are then processed with dedicated algorithms [11], which ensures consistent results between different FTIR (Fourier transform infrared) sites. However, the instrumental line shape (ILS) of the FTIR spectrometers may drift slowly due to mechanical degradation over time or may change abruptly because of operator intervention [12]. Moreover, Sun et al. (2017) found that the ILS status is dependent on the optical settings, because the mechanical errors between different field stops may be inconsistent [13]. The routine observation may change the entrance field stop size if incident radiation changes, which may introduce inconsistency into ILS. All of these misalignments will result in biases if not properly characterized.

_{3}, CH

_{4}, CO, and N

_{2}O, can be divided into multiple independent partial columns [8,9,14]. In certain circumstances, such as validation of chemical model simulations or satellite observations or source/sink/trend estimations, the partial column is more useful than the total column because it is only integrated over the most relevant layers and eliminates the influence from the layers below and/or above [8]. Vigouroux et al. (2008, 2015) divided the O

_{3}measurements at eight NDACC FTIR stations, namely, Ny-Ålesund (79°N), Thule (77°N), Kiruna (68°N), Harestua (60°N), Jungfraujoch (47°N), Izaña (28°N), Wollongong (34°S), and Lauder (45°S), into four independent partial columns, one in the troposphere and three in the stratosphere up to about 45 km. These O

_{3}partial columns were validated with the coincident Sondes or LIDAR data and were then used for the investigation of O

_{3}trends and variabilities [8,15]. Zhou et al. (2018) studied the atmospheric CO and CH

_{4}variability with the partial column times series measured with FTIR spectrometers at two sites (St Denis and Maïdo) on Reunion Island (21°S) in the Indian Ocean. Meanwhile, these partial column times series were compared to the in situ measurements, the GEOS-Chem model simulations, and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite measurements [16]. García et al., (2018) used the NDACC FTIR measurements made between 2007 and 2017 in subtropical Izaña (28°N), the mid-latitude station of Karlsruhe (49°N) and the Kiruna polar site (68°N) for the validation of the IASI (Infrared Atmospheric Sounding Interferometer) CH

_{4}and N

_{2}O partial column products [17].

_{3}, CO, CH

_{4}, and N

_{2}O derived from high-resolution FTIR spectrometry and deduces the range of the ILS degradation corresponding to the acceptable uncertainties of O

_{3}, CO, CH

_{4}, and N

_{2}O results.

## 2. Theoretical Analysis

#### 2.1. The Influence of ILS Degradation

_{s}, and the total error T

_{err}with the iterative Gauss−Newton scheme can be expressed as

_{i}) is the forward model calculation; S

_{a}is the a priori covariance matrix; x

_{a}is the a priori state vector; S

_{ε}is the measurement noise error covariance matrix; K

_{i}is the Jacobian matrix which links the measurement vector y to the state vector x

_{i}: $\Delta \mathrm{y}={\mathrm{K}}_{\mathrm{i}}\Delta {\mathrm{x}}_{\mathrm{i}}$; E

_{s}is the smoothing error calculated via Equation (7); E

_{m}is the measurement error calculated via Equation (8); and E

_{b}is the forward model parameter error calculated via Equation (9) ([21], Chapter 3).

_{a}and S

_{a}are independent of the ILS because they are a priori parameters. The S

_{ε}and S

_{b}depend on the ILS, since the ILS is part of the forward model calculation and can introduce correlations among the different spectral points, causing non-diagonal matrices of S

_{ε}and S

_{b}. The Jacobians also depend on the ILS because they represent the sensitivity of the spectra to the atmospheric status, and the analyzed spectra depend on the ILS.

_{ε}, S

_{b}, and K

_{b}turn into ${\mathrm{S}}_{\mathsf{\epsilon}}^{\prime}$, ${\mathrm{S}}_{\mathrm{b}}^{\prime}$, and ${\mathrm{K}}_{\mathrm{b}}^{\prime}$, respectively, when using an error ILS, the resulting biases for x, the DOFs, and the total error can be expressed as

_{1}and z

_{2}is calculated by integrating profile x by Equation (14), where A

_{m}is the air-mass profile derived from the FTIR retrievals:

#### 2.2. The Influence of Reference Selection

_{ref}is the same as X but is deduced with a nominal ideal ILS. We used the results with an ideal ILS as the reference. If the ILS of the actual measurements deviates from ideal conditions, it will cause an offset to both X and X

_{ref}. Assuming the offset is Δ, then Equation (15) turns into Equation (16). The NDACC network regularly performs cell measurements to diagnose misalignment of the spectrometer and to realign the instrument when indicated [11]. For a perfectly aligned spectrometer, (X

_{ref}– Δ) is close to X

_{ref}, and thus, the influence of ideal condition selection as the reference is of secondary significance (%D ≈ ${\%\mathrm{D}}^{\prime}$).

## 3. Experiment Description

#### 3.1. Experimental Scheme

_{3}, CO, CH

_{4}, and N

_{2}O.

#### 3.2. Retrieval Strategy

_{3}, CO, CH

_{4}, and N

_{2}O followed the NDACC standard conventions [25]. All spectroscopic line parameters were adopted from HITRAN 2008 [1]. A priori profiles of all gases except H

_{2}O were from a dedicated WACCM (Whole Atmosphere Community Climate Model) run. A priori profiles of pressure, temperature and H

_{2}O were interpolated from the National Centers for Environmental Protection and National Center for Atmospheric Research (NCEP/NCAR) reanalysis [25]. We assumed S

_{ε}to be diagonal, and set its diagonal elements to the inverse square of the signal-to-noise ratio (SNR) of the fitted spectra and its non-diagonal elements to zero. For all gases, the diagonal elements of S

_{a}were set to the standard deviation of a dedicated WACCM run from 1980 to 2020, and its non-diagonal elements were set to zero.

_{3}, CO, CH

_{4}, and N

_{2}O profiles in Hefei, China. The partial DOFs and altitude ranges obtained at each partial layer are also added in Table 2. The retrieved profiles of O

_{3}, CO, CH

_{4}, and N

_{2}O were separated into four, three, three, and four partial layers, respectively. The thus defined layers were independent on the basis of the resolution of the averaging kernels, as can be seen in Figure 1, where the partial column averaging kernels (PAVKs) of the four gases are given. Their PAVKs were resolved at their FWHM (Full Width at Half Maximum), where the averaging kernels peaked at the right altitude; i.e., at the middle of the chosen layer ranges.

## 4. Sensitivity Study

_{3}, CO, CH

_{4}, and N

_{2}O with respect to different levels of ILS degradation, respectively. The partial column, partial error, and partial DOFs were calculated via Equations (5), (6), and (14), respectively, by setting the elements of the corresponding matrices to zero for the altitudes outside of the concerned partial column boundaries. The fractional differences in the partial column, partial error, and partial DOFs were calculated via Equation (15).

#### 4.1. O_{3}

#### 4.2. CO

#### 4.3. CH_{4}

#### 4.4. N_{2}O

## 5. Consistency Evaluation

_{3}, CO, CH

_{4}, and N

_{2}O as a function of SZA, respectively. Figures S10–S13 are the same as Figures S6–S9 but for negative ME degradation. The resulting statistics are summarized in Table 3. Generally, for both positive and negative ME degradation, the variability of the partial column, partial error, and partial DOFs at each partial layer for O

_{3}and CO is larger than that for CH

_{4}and N

_{2}O.

_{3}, the variability of the partial column, partial error, and partial DOFs at each partial layer are comparable. The variability of these quantities at PC1 and PC2 is larger than that at PC3 and PC4. With 4% of ILS degradation, the variability of fractional difference in the partial column at PC1, PC2, PC3, and PC4 was shown to be 3.3 ± 13.2%, 5.2 ± 21.1%, 0.7 ± 9.7%, and −3.2 ± 8.5%, respectively. With −4% of ILS degradation, the variability at PC1, PC2, PC3, and PC4 was shown to be 0.92 ± 13.0%, −3.3 ± 23.4%, −1.7 ± 9.4%, and 2.6 ± 9.1%, respectively.

_{4}, the partial column, partial error, and partial DOFs at each partial layer show good consistency over SZA. With 4% of ILS degradation, the variability of fractional difference in the partial column at PC1, PC2, and PC3 was shown to be 0.5 ± 0.1%, −0.6 ± 0.1%, and 3.8 ± 0.3%, respectively. With −4% of ILS degradation, the variability at PC1, PC2, and PC3 was shown to be 0.1 ± 0.04%, −0.4 ± 0.1%, and 1.4 ± 0.2%, respectively.

_{2}O, the partial column at each partial layer shows good consistency over SZA. The partial error and partial DOFs show lager variability at each partial layer than the partial column. With 4% of ILS degradation, the variability of the fractional difference in the partial column at PC1, PC2, PC3, and PC4 was shown to be 0.6 ± 0.8%, −0.4 ± 1.8%, −1.9 ± 3.1%, and −5.2 ± 4.1%, respectively. With −4% of ILS degradation, the variability at PC1, PC2, PC3, and PC4 was shown to be 0.03 ± 0.2%, 0.1 ± 1.1%, −0.5 ± 1.4%, and 3.4 ± 2.0%, respectively.

## 6. Discussion and Recommendations

_{3}, CO, CH

_{4}, and N

_{2}O is generally larger than that on the total quantities presented in Sun et al. (2018) [18]. The influence of ILS degradation on the partial quantities of all gases is altitude and gas species dependent, and the level of the influence varies across ILS degradation levels. Positive and negative ILS degradation generally have opposite influences on partial quantities. The influence of ILS degradation on partial quantities of O

_{3}and CO is larger than that on CH

_{4}and N

_{2}O. For all gases, the partial columns in the troposphere are more sensitive to positive ILS degradation than negative ILS degradation. Generally, the partial columns in the stratosphere are more sensitive to ILS degradation than those in the troposphere.

_{3}, CO, and CH

_{4}are smoother than that of N

_{2}O, because ILS degradation alters the S

_{ε}, S

_{b}, and Jacobian matrices of these gases in different ways. For O

_{3}, CO, and CH

_{4}, ILS degradation does not alter the number of iterations, and the sensitivity varies monotonously across ILS degradation levels, while for N

_{2}O, the number of iterations is occasionally altered, and the sensitivity is also altered.

_{k}, L

_{k}, and M

_{k}refer to the absorption intensity, the ideal line shape, and the molecular mass for the k-th absorber, respectively. Typically, L

_{k}can be expressed by a Voigt function as a consequence of translational effects and collisional effects. $\mathrm{P}\left({\mathrm{z}}^{\prime}\right)$ and $\mathrm{T}\left({\mathrm{z}}^{\prime}\right)$ refer to the pressure and temperature at altitude ${\mathrm{z}}^{\prime}$, respectively. The absorption intensity, molecular mass, and fitting frequency are gas dependent, and the line shape, pressure, and temperature are altitude dependent. The influence of ILS degradation on partial quantities is thus altitude and gas species dependent. Specifically, the absorption line shape in the stratosphere is narrower than that in the troposphere, and stratospheric quantities are more sensitive to ILS degradation.

- For O
_{3}and CO, a precise ILS should be used because the variability in ILS degradation influence at each partial layer is very large. We recommend the incorporation of the measured ILS in partial column retrieval. One option is to use the output of routine cell measurements with LINEFIT code [11]. - For CH
_{4}, the influence of positive ILS degradation at PC2 (7–16 km) and negative ILS degradation at PC1 (0–7 km) can be regarded as negligible. The maximum positive ILS degradations at PC1 (0–7 km) and PC3 (16–37 km) should be less than 13% and 1.5%, respectively. The maximum negative ILS degradation at PC2 (7–16 km) and PC3 (16–37 km) should be less than 12% and 4.5%, respectively. - For N
_{2}O, the maximum positive ILS degradation at PC1 (0–5 km), PC2 (5–11.5 km), PC3 (11.5–20 km) and PC4 (20–35 km) should be less than 6%, 4%, 1.5%, and 1.5%, respectively. The influence of negative ILS degradation at PC1 (0–5km) and PC2 (5–11.5 km) can be regarded as negligible. The maximum negative ILS degradation at PC3 (11.5–20 km) and PC4 (20–35 km) should be less than 2% and 1.5%, respectively.

## 7. Conclusions

_{3}, CO, CH

_{4}, and N

_{2}O derived from high-resolution FTIR spectrometry. The sensitivities of the partial column, partial error, and partial DOFs (degrees of freedom) with respect to different levels of ILS degradation for O

_{3}, CO, CH

_{4}, and N

_{2}O were first investigated, and then the consistency of the resulting deductions was evaluated with one year of measurements. Finally, the maximum ILS deviations allowable for limiting the influence within 2% were deduced.

_{3}and CO is larger than that for CH

_{4}and N

_{2}O. For all gases, the partial columns in the stratosphere are more sensitive to ILS degradation than those in the troposphere. In order to limit the fractional difference in the partial column within 2%, it is recommended that a precise ILS deduced from cell measurements is used for O

_{3}and CO; for CH

_{4}, the influence of positive ME (modulation efficiency) degradation at PC2 (7–16 km) and negative ME degradation at PC1 (0–7 km) can be regarded as negligible. The maximum positive ME degradation at PC1 (0–7 km) and PC3 (16–37 km) should be less than 13% and 1.5%, respectively. The maximum negative ME degradation at PC2 (7–16 km) and PC3 (16–37 km) should be less than 12% and 4.5%, respectively; for N

_{2}O, the maximum positive ME degradation at PC1 (0–5 km), PC2 (5–11.5 km), PC3 (11.5–20 km) and PC4 (20–35 km) should be less than 6%, 4%, 1.5%, and 1.5%, respectively. The influence of negative ME degradation at PC1 (0–5 km) and PC2 (5–11.5 km) can be regarded as negligible. The maximum negative ME degradation at PC3 (11.5–20 km) and PC4 (20–35 km) should be less than 2% and 1.5%, respectively.

## Supplementary Materials

_{3}at each partial layer as a function of SZA from August 2015 to August 2016 where ILS j with a maximum ME deviation of 4% is used; Figure S7: The same as Figure S6 but for CO; Figure S8: The same as Figure S6 but for CH

_{4}; Figure S9: The same as Figure S6 but for N

_{2}O; Figure S10: The same as S6 but for a maximum ME deviation of −4%; Figure S11: The same as S7 but for a maximum ME deviation of −4%; Figure S12: The same as S8 but for a maximum ME deviation of −4%; Figure S13: The same as S9 but for a maximum ME deviation of−4%.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Partial column averaging kernels (PAVK) (ppmv/ppmv) for O

_{3}, CO, CH

_{4}, and N

_{2}O retrieval.

**Figure 2.**Sensitivity of the partial column, partial error, and partial degrees of freedom (DOFs) at each partial layer for O

_{3}with respect to the modulation efficiency (ME) deviation. The solid red line (P_PC), solid blue line (P_Perr), and solid black line (P_Pdofs) represent the sensitivity of the partial column, partial error, and partial DOFs with respect to positive ME deviation, respectively. The dashed red line (N_PC), dashed blue line (N_Perr), and dashed black line (N_Pdofs) represent the sensitivity of the partial column, partial error, and partial DOFs with respect to negative ME deviation, respectively. The ME deviations were modelled by ALIGN60. The results were deduced from the spectra recorded at Hefei on 16 February 2016.

**Figure 3.**The same as Figure 2 but for CO.

Gas | Date (d-m-y) | Tropopause Height (km) | Solar Zenith Angle (°) | Total Column (10 ^{18}) | Total Error (%) | Total DOFs (-) |
---|---|---|---|---|---|---|

O_{3} | 16-02-2016 | 16.4 | 57.6 | 8.62 | 5.26 | 5.2 |

CO | 16-02-2016 | 16.4 | 32.3 | 2.97 | 5.06 | 3.8 |

CH_{4} | 16-02-2016 | 16.4 | 33.9 | 40.13 | 5.21 | 3.5 |

N_{2}O | 16-02-2016 | 16.4 | 33.9 | 6.84 | 4.96 | 4.0 |

**Table 2.**Typical degrees of freedom (DOFs) for the signal and sensitivity ranges of the retrieved O

_{3}, CO, CH

_{4}, and N

_{2}O profiles in Hefei, China.

Gas | Total DOFs | Sensitive Range (km) | Partial Layers (km) | Partial DOFs |
---|---|---|---|---|

O_{3} | 4.8 | Ground–44 | PC1: Ground–9 | 1.3 |

PC2: 9–18 | 1.2 | |||

PC3: 18–27 | 1.1 | |||

PC4: 27–44.4 | 1.2 | |||

CO | 3.7 | Ground–27 | PC1: Ground–3.5 | 1.2 |

PC2: 3.5–12 | 1.3 | |||

PC3: 12–68 | 1.2 | |||

CH_{4} | 3.5 | Ground–31 | PC1: Ground–7 | 1.2 |

PC2: 7–16 | 1.2 | |||

PC3: 16–37 | 1.1 | |||

N_{2}O | 4.0 | Ground–31 | PC1: Ground–5 | 1.0 |

PC2: 5–11.5 | 1.0 | |||

PC3: 11.5–20 | 1.0 | |||

PC4: 20–35 | 1.0 |

**Table 3.**The variability (mean ± standard deviation) of fractional differences in the partial column, partial error, and partial DOFs for O

_{3}, CO, CH

_{4}, and N

_{2}O as a function of the solar zenith angle (SZA).

Gas | Partial Limits (km) | N | Partial Column | Partial Error | Partial DOFs | |||
---|---|---|---|---|---|---|---|---|

Positive ME | Negative ME | Positive ME | Negative ME | Positive ME | Negative ME | |||

O_{3} | PC1: Ground–9 | 119 | 3.3 ± 13.2 | 0.92 ± 13.0 | 1.3 ± 13.6 | 0.6 ± 13.6 | 1.0 ± 17.3 | 1.5 ± 17.8 |

PC2: 9–18 | 5.2 ± 21.1 | −3.3 ± 23.4 | −0.2 ± 14.9 | 2.3 ± 17.3 | 0.1 ± 16.0 | 0.8 ± 15.2 | ||

PC3: 18–27 | 0.7 ± 9.7 | −1.7 ± 9.4 | −0.8 ± 9.8 | 0.8 ± 10.1 | 0.0 ± 12.1 | −0.2 ± 12.0 | ||

PC4: 27–44.4 | −3.2 ± 8.5 | 2.6 ± 9.1 | 0.8 ± 14.3 | −1.4 ± 14.6 | 0.0 ± 12.1 | −0.6 ± 12.4 | ||

CO | PC1: Ground–3.5 | 102 | −3.9 ± 41.2 | −4.2 ± 38.7 | 12.9 ± 25.9 | 8.2 ± 25.1 | −0.4 ± 6.2 | −0.3 ± 6.4 |

PC2: 3.5–12 | 3.7 ± 23.5 | 3.3 ± 25.7 | −5.7 ± 43.9 | −4.6 ± 43.3 | 0.1 ± 5.7 | 0.1 ± 7.5 | ||

PC3: 12–68 | 3.4 ± 37.8 | 6.5 ± 36.5 | 14.6 ± 32.1 | 6.8 ± 31.9 | −1.2 ± 8.0 | −2.7 ± 10.0 | ||

CH_{4} | PC1: Ground–7 | 291 | 0.5 ± 0.1 | 0.1 ± 0.04 | −0.02 ± 0.1 | −0.2 ± 0.04 | 0.3 ± 0.1 | −0.1 ± 0.1 |

PC2: 7–16 | −0.6 ± 0.1 | −0.4 ± 0.1 | 0.9 ± 0.1 | 0.1 ± 0.04 | 0.7 ± 0.1 | −0.3 ± 0.1 | ||

PC3: 16–37 | −3.8 ± 0.3 | 1.4 ± 0.2 | 1.4 ± 0.2 | −1.0 ± 0.1 | 1.3 ± 0.2 | −0.7 ± 0.1 | ||

N_{2}O | PC1: Ground–5 | 371 | 0.6 ± 0.8 | 0.03 ± 0.2 | −0.3 ± 5.5 | −0.04 ± 2.9 | 0.4 ± 5.2 | −0.2 ± 2.4 |

PC2: 5–11.5 | −0.4 ± 1.8 | 0.1 ± 1.1 | –0.1 ± 17.8 | −0.02 ± 8.3 | −0.3 ± 13.1 | −0.1 ± 5.4 | ||

PC3: 11.5–20 | −1.9 ± 3.1 | −0.5 ± 1.4 | 3.4 ± 17.7 | 0.9 ± 9.0 | 2.2 ± 13.3 | −0.1 ± 7.0 | ||

PC4: 20–35 | −5.2 ± 4.1 | 3.4 ± 2.0 | 0.1 ± 22.5 | −0.5 ± 13.3 | 0.7 ± 2.9 | −0.6 ± 1.8 |

**Table 4.**Recommendations for limiting the fractional difference in the partial columns of all gases within 2%.

Gas | Partial Limits (km) | Positive ME | Negative ME |
---|---|---|---|

O_{3} | PC1: Ground–9 | measured | measured |

PC2: 9–18 | measured | measured | |

PC3: 18–27 | measured | measured | |

PC4: 27–44.4 | measured | measured | |

CO | PC1: Ground–3.5 | measured | measured |

PC2: 3.5–12 | measured | measured | |

PC3: 12–68 | measured | measured | |

CH_{4} | PC1: Ground–7 | <13% | * |

PC2: 7–16 | * | <12% | |

PC3: 16–37 | <1.5% | <4.5% | |

N_{2}O | PC1: Ground–5 | <6% | * |

PC2: 5–11.5 | <4% | * | |

PC3: 11.5–20 | <1.5% | <2% | |

PC4: 20–35 | <1.5% | <1.5% |

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sun, Y.; Liu, C.; Chan, K.; Wang, W.; Shan, C.; Hu, Q.; Liu, J. The Influence of Instrumental Line Shape Degradation on the Partial Columns of O_{3}, CO, CH_{4} and N_{2}O Derived from High-Resolution FTIR Spectrometry. *Remote Sens.* **2018**, *10*, 2041.
https://doi.org/10.3390/rs10122041

**AMA Style**

Sun Y, Liu C, Chan K, Wang W, Shan C, Hu Q, Liu J. The Influence of Instrumental Line Shape Degradation on the Partial Columns of O_{3}, CO, CH_{4} and N_{2}O Derived from High-Resolution FTIR Spectrometry. *Remote Sensing*. 2018; 10(12):2041.
https://doi.org/10.3390/rs10122041

**Chicago/Turabian Style**

Sun, Youwen, Cheng Liu, Kalok Chan, Wei Wang, Changong Shan, Qihou Hu, and Jianguo Liu. 2018. "The Influence of Instrumental Line Shape Degradation on the Partial Columns of O_{3}, CO, CH_{4} and N_{2}O Derived from High-Resolution FTIR Spectrometry" *Remote Sensing* 10, no. 12: 2041.
https://doi.org/10.3390/rs10122041