Spectral calibration of Fengyun-3 satellite high-spectral resolution infrared sounder

Chengli, 漆成莉 Qi; Fang, 周 方 Zhou; Chunqiang, 吴春强 Wu; Xiuqing, 胡秀清 Hu; Ming-jian, 顾明剑 Gu

doi:10.3788/ope.20192704.0747

Cited by 9 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three small arrays simultaneously observe the same field of view of the target on the ground, and four different target areas are simultaneously observed by the small array detectors in each wave band. Each detector has an earth observation angle of 1.1 • , the instantaneous field of view of the corresponding substellar point is about 16 km, and the ground distance between pixels is 26.17 km [16,17]. Specific performance parameters are shown in Table 1.…”

Section: Fy-3d/hiras-imentioning

confidence: 99%

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Xie,

Gu,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Fengyun-3E (FY-3E)/Hyperspectral Infrared Atmospheric Sounder-II (HIRAS-II) is an extension Fengyun-3D (FY-3D)/HIRAS-I. It is crucial to fully explore and analyze the detection capabilities of these two instruments for atmospheric gas composition. Based on the observed spectral data from the infrared hyperspectral detection instruments FY-3D/HIRAS-I and FY-3E/HIRAS-II, simulated radiance data and Jacobian matrices are obtained using the Rapid Radiative Transfer Model RTTOV (Radiative Transfer for TOVS (TIROS Operational Vertical Sounder)). By perturbing temperature (T), surface temperature (Tsurf), water vapor (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrous oxide (N2O), the brightness temperature differences before and after the perturbations are calculated to analyze the sensitivity of temperature and various atmospheric gas components. The Improved Optimal Sensitivity Profile (OSP) algorithm is used to select the channels for atmospheric gas retrieval. The observation error covariance and background error covariance matrices are calculated, and then the information capacity is calculated, specifically the degrees of freedom for signal(DFS) and the entropy reduction (ER). Based on this, a comparative analysis is conducted on the information capacity of atmospheric water vapor and ozone components contained in the hyperspectral detection data from HIRAS-I and HIRAS-II instruments, respectively, to explore the retrieval capabilities of the two instruments for atmospheric gas components. We selected clear-sky data from the African oceanic region and the Chinese Yangtze River Delta terrestrial region for quantitative analysis of the information capacity of HIRAS-I and HIRAS-II. The results show that FY-3D/HIRAS-I and FY-3E/HIRAS-II exhibit different sensitivities to atmospheric gas components. In different experimental regions, temperature and water vapor show the most dramatic sensitivity changes, followed by ozone, methane, and nitrous oxide, while carbon monoxide and carbon dioxide exhibit the lowest variability. Regarding channel selection, HIRAS-II identifies more gas channels compared to HIRAS-I. The experiments concluded that HIRAS-II has a significantly higher information capacity than HIRAS-I, and the information capacity of atmospheric gas components varies across different experimental regions. Water vapor and ozone exhibit the highest information capacity, followed by nitrous oxide and methane, while carbon monoxide and carbon dioxide demonstrate the lowest capacity. The H2O ER (DFS) contained in FY-3E/HIRAS-II is 1.51 (0.35) higher than that in FY-3D/HIRAS-I, the O3 ER (DFS) in FY-3E/HIRAS-II is 1.51 (0.36) higher than that in FY-3D/HIRAS-I, while the N2O ER (DFS) in FY-3E/HIRAS-II is 0.17 (0.19) higher and the CH4 ER (DFS) is 0.07 (0.04) higher than that in FY-3D/HIRAS-I.

show abstract

Section: Fy-3d/hiras-imentioning

confidence: 99%

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Xie,

Gu,

Zhang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Suppose the points on the detector are represented by the polar coordinates (r, ϕ), in which r is the distance between the detector and the optical axis, and ϕ is defined as the opening angle of the arc segmented by the detector, and the half width and half height of the square detector are A and B, respectively. The coordinates of the four corner points can be expressed as follows: As shown in Figure 7b, the arc segmented by the rectangular detector occupies the proportion of the entire ring; that is, the normalized arc segment can be expressed as follows: (11) in which, and are opening angles of different arcs. In Figure 7b, the rectangular detector is divided into three regions by circles of different radii, and the expression of the ILS function is synthesized using these three arcs.…”

Section: Computation Of the Ilsmentioning

confidence: 99%

“…Rayleigh found that the interferogram generated from the Michelson interferometer has a mathematical relationship with the spectrum of the light source, and he obtained the spectrum using the Fourier transform [3]. Under this principle, a series of spectrometer based on the Michelson interferometer have been designed, such as the Infrared Atmospheric Sounding Interferometer (IASI) carried on the MetOp satellite [4][5][6], the Cross-Track Infrared Sounder (CrIS) carried aboard the Suomi National Polar-Orbiting Partnership (NPP) satellite [7][8][9], the High-Spectral Resolution Infrared Atmospheric Sounder (HIRAS) aboard on FY-3D satellite [10][11][12], and the Geostationary Interferometric Infrared Imager (GIIRS) aboard on the FY-4A satellite [13,14]. Interferometric spectroscopy technology, taking the advantages of high luminous Figure 1 depicts the schematic of the Michelson interferometer [28].…”

Section: Introductionmentioning

confidence: 99%

Intensity Simulation of a Fourier Transform Infrared Spectrometer

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper introduces an intensity simulation for the Fourier transform infrared spectrometer whose core element is the Michelson interferometer to provide support for the on-orbit monitoring of the instrument and to improve the data processing and application of the Fourier transform spectrometer. The Geostationary Interferometric Infrared Imager (GIIRS) aboard on Fengyun-4B (FY-4B) satellite, which will be launched in 2020, aims to provide hyperspectral infrared observations. An intensity simulation of the Michelson interferometer based on the GIIRS’s instrument parameters is systematically analyzed in this paper. Off-axis effects and non-linearity response are two important factors to be considered in this simulation. Off-axis effects mainly cause the wavenumber shift to induce a large brightness temperature error compared with the input spectrum, and the non-linearity response reduces the energy received by the detector. Then, off-axis effects and a non-linearity response are added to the input spectrum successively to obtain the final spectrum. Off-axis correction and non-linearity correction are also developed to give a full simulation process. Comparing the corrected spectrum with the input spectrum, we can see that the brightness temperature errors have a magnitude of 10−3 K, and this fully proves the reliability and rationality of the whole simulation process.

show abstract

Spectrum calibration of the first hyperspectral infrared measurements from a geostationary platform: Method and preliminary assessment

Guo

Yang

Wei

et al. 2021

Quart J Royal Meteoro Soc

View full text Add to dashboard Cite

The level-1 (L1) radiance spectra from the first Geostationary Interferometric InfraRed Sounder (GIIRS) have been publicly available since January 2019. On account of the inherent observation characteristics, operation modes, and capabilities, a complete spectrum calibration method of GIIRS/L1 products is originally proposed to form the latest version (V3) algorithm for implementation. Particularly, four targeted improvements in three aspects are independently established: subsample location alignment to yield integrated interferograms in both forward and backward directions with almost zero phase, rough spectral scale unification as well as accurate spectral scale correction to resolve spectral non-uniformity due to a seriously asymmetric configuration of focal plane array, and an additional double-reflected compensation to mitigate the influence of non-ideal onboard blackbody reference upon radiometric accuracy. Preliminary assessments from both domestic and international sources indicate that the spectral and radiometric accuracies of the measured spectra from the latest GIIRS/L1 V3 algorithm show a well-behaved performance in both longwave (LW) and midwave (MW) bands, that is, lower than 10 ppm of spectral scale errors, which is of sufficient accuracy for numerical weather prediction use, and around 1 K for most uncontaminated channels within the LW band. However, non-linearity correction of interferograms and spectral quality improvements, especially for the MW band, should be developed further. In general, a feasible solution of spectrum calibration for a hyperspectral sounder on geostationary platform is provided in detail for reference, which is expected to benefit users of GIIRS data as well as designers responsible for L1 data processing of other similar sensors.

show abstract

Spectral calibration of Fengyun-3 satellite high-spectral resolution infrared sounder

Cited by 9 publications

References 0 publications

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Comparative Study of the Atmospheric Gas Composition Detection Capabilities of FY-3D/HIRAS-I and FY-3E/HIRAS-II Based on Information Capacity

Intensity Simulation of a Fourier Transform Infrared Spectrometer

Spectrum calibration of the first hyperspectral infrared measurements from a geostationary platform: Method and preliminary assessment

Contact Info

Product

Resources

About