Satellite infrared hyperspectral instruments can obtain a wealth of atmospheric spectrum information. In order to obtain high-precision atmospheric temperature and humidity profiles, we used the traditional One-Dimensional Variational (1D-Var) retrieval algorithm, combined with the information capacity-weight function coverage method to select the spectrum channel. In addition, an Artificial Neural Network (ANN) algorithm was introduced to correct the satellite observation data error and compare it with the conventional error correction method. Finally, to perform the temperature and humidity profile retrieval calculation, we used the FY-3D satellite HIRAS (Hyperspectral Infrared Atmospheric Sounder) infrared hyperspectral data and combined the RTTOV (Radiative Transfer for TOVS) radiative transfer model to build an atmospheric temperature and humidity profile retrieval system. We used data on the European region from July to August 2020 to carry out the training and testing of the retrieval system, respectively, and used the balloon-retrieved sounding data of temperature and humidity published by the University of Wyoming as standard truth values to evaluate the retrieval accuracy. Our preliminary research results show that, compared with the retrieval results of conventional deviation correction, the introduction of ANN algorithm error correction can improve the retrieval accuracy of the retrieval system effectively and the RMSE (Root-Mean-Square Error) of the temperature and humidity has a maximum accuracy of improvement of about 0.5 K (The K represents the thermodynamic temperature unit) and 5%, respectively. The temperature and humidity results obtained by the retrieval system were compared with Global Forecast System (GFS) forecast data. The retrieved temperature RMSE was less than 1.5 K on average, which was better than that for the GFS; the humidity RMSE was less than 15% as a whole, and better than the forecast profile between 100 hpa (1 hpa is 100 pa, the pa represents the air pressure unit) and 600 hpa. Compared with AIRS (Atmospheric Infrared Sounder) products, the result of the retrieval system also had a higher accuracy. The main improvement of the temperature was at 200 hpa and 800 hpa, with maximum accuracy improvements of 2 K and 1.5 K, respectively. The RMSE of the humidity retrieved by the system was also better than the AIRS humidity products at most pressure levels, and the error of maximum difference could reach 15%. After combining the two algorithms, the FY-3D/HIRAS infrared hyperspectral retrieval system could obtain higher-precision temperature and humidity profiles, and relevant results could provide a reference for improving the accuracy of business products.

The deep-space (DS) view spectra are used as a cold reference to calibrate the Hyperspectral Infrared Atmospheric Sounder (HIRAS) Earth scene (ES) observations. The DS spectra stability in the moving average window is crucial to the calibration accuracy of ES radiances. While in the winter and spring seasons, the HIRAS detector-3 DS view is susceptible to solar stray light intrusion when the satellite flies towards the tail of every descending orbit, and as a result, the measured DS spectra are contaminated by the stray light pseudo spectra, especially in the short-wave infrared (SWIR) band. The solar light intrusion issue was addressed on 13 December 2019 when the DS view angle of the scene selection mirror (SSM) was adjusted from −77.4° to −87°. As for the historic contaminated data, a correction method is applied to detect the anomalous data by checking the continuity of the DS spectra and then replace them with the proximate normal ones. The historic ES observations are recalibrated after the contaminated DS spectra correction. The effect of the correction is assessed by comparing the recalibrated HIRAS radiances with those measured by the Cross-track Infrared Sounder onboard the Suomi National Polar-orbiting Partnership Satellite (SNPP/CrIS) via the extended simultaneous nadir overpasses (SNOx) technique and by checking the consistency among the radiance data from different HIRAS detectors. The results show that the large biases of the radiance brightness temperature (BT) caused by the contamination are ameliorated greatly to the levels observed in the normal conditions.

The Hyperspectral Infrared Atmospheric Sounder II (HIRAS-II) onboard the Fengyun-3 satellites is a Fourier-transform infrared spectrometer that requires high radiometric calibration accuracy, making the characterization and correction of polarization effects essential. Although the gold-coated scan mirror introduces only weak polarization, its rotation changes the polarization orientation relative to the fixed polarization-sensitive axis of the downstream optics, producing scan-angle-dependent radiometric errors. To characterize and correct this effect, we designed and constructed a dedicated polarization test apparatus and used it to conduct a prelaunch thermal-vacuum (TVAC) polarization test. During the test, HIRAS-II observed the same stable 290 K area-source blackbody over a densely sampled scan-angle range, with the internal calibration target and cold shield serving as the warm and cold references, respectively. Guided by a polarization-induced radiometric error model formulated within the two-point calibration framework, we developed a decoupled two-step least-squares method to retrieve the polarization parameters from the resulting measurements. The method first estimates the equivalent polarization-axis angle of the downstream optical system from the phase of the band-averaged angular modulation and then retrieves the effective combined polarization parameter separately for each field of view (FOV) and spectral channel. The retrieved parameters were subsequently used to calculate the scan-angle-dependent polarization correction term and correct the calibrated spectra. After correction, the FOV-averaged standard deviation over the scan angle decreased from 0.023 to 0.007 K, from 0.024 to 0.007 K, and from 0.045 to 0.014 K in the long-wave (LW), mid-wave 1 (MW1), and mid-wave 2 (MW2) bands, respectively. The corresponding maximum reductions in brightness temperature deviation were 0.093, 0.064, and 0.174 K. The model, experimental approach, and retrieved prelaunch parameters establish a basis for the on-orbit evaluation and correction of scan-angle-dependent polarization-induced radiometric errors. Reducing these errors improves the radiometric calibration accuracy of HIRAS-II and helps provide more reliable Level-1 radiance data for atmospheric profile retrievals and data assimilation in global numerical weather prediction (NWP) systems.

Infrared Fourier transform spectrometers using interferometric spectroscopy are widely used in space remote sensing owing to their high spectral resolution and sensitivity. We investigated the distorted spectral characteristics introduced by nonlinear errors of different orders through simulation for infrared detectors with strong nonlinear effects. A high-order nonlinear correction scheme was proposed based on two iterative correction methods for in-band and out-of-band spectra. Further, the effects of second-order, third-order, in-band, and out-of-band correction methods were compared using prelaunch radiometric calibration experimental data from the DQ-2 satellite infrared hyperspectral atmospheric composition sounder. The results showed that the third-order in-band correction scheme performed the best, while various other correction schemes also effectively reduced nonlinear errors. The maximum average deviation was 0.18–0.25 K for the long-wave band and 0.11–0.19 K for the mid-wave band in the temperature range of 230–300 K. According to the correction evaluation and methods comparison, the proposed method is appropriate for nonlinearity detectors to improve radiometric calibration accuracy.