Journal
REMOTE SENSING
Volume 10, Issue 12, Pages -Publisher
MDPI
DOI: 10.3390/rs10122041
Keywords
instrument line shape; FTIR; partial column; NDACC
Categories
Funding
- National High Technology Research and Development Program of China [2016YFC0200800, 2018YFC0213104, 2017YFC0210002, 2016YFC0203302, 2018YFC0213201]
- National Science Foundation of China [41605018, 41877309, 41503105, 41775025, 41575021, 51778596, 91544212, 41722501]
- Anhui Province Natural Science Foundation of China [1608085MD79]
- Outstanding Youth Science Foundation [41722501]
- National High-Resolution Earth Observation Project of China [05-Y20A16-9001-15/17-2]
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High resolution Fourier transform infrared (FTIR) measurement of direct sunlight does not only provide information of trace gas total columns, but also vertical distribution. Measured O-3, CO, CH4, and N2O 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, CH4, and N2O. Sensitivities of the partial column, error, and degrees of freedom (DOFs) of each layer to different levels of ILS degradation for O-3, CO, CH4, and N2O 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, CH4, and N2O 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 CH4 and N2O. 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.
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