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The High-Spectral Infrared Atmospheric Sounder (HIRAS) is a Fourier transform spectrometer onboard the fourth polar-orbiting FengYun 3D satellite (FY-3D). The FY-3D HIRAS provides interferogram measurements of Earth view radiance spectra in three infrared spectral bands at 29 cross-track positions, each with a $2 \times 2$ array of field of views (FOVs). The HIRAS level 1 radiance data cover the spectral bands from 650 to 1135 cm−1 [long-wave (LW) band], 1210 to 1750 cm−1 [mid-wave (MW) band], and 2155 to 2550 cm−1 [short-wave (SW) band] with a spectral resolution of 0.625 cm−1. The radiometric calibration algorithm and the methods of refining the nonlinearity (NL) and the polarization correction coefficients on orbit are summarized in this article. The NL correction coefficients are derived by minimizing the spread of the responsivity functions derived from the measurements of the internal calibration target with varying temperatures. The polarization correction coefficients are derived from the cold space observations and the routine Earth scene measurements. The radiometric accuracy is assessed by comparing the HIRAS measurements to the collocated Cross-track Infrared Sounder (CrIS) observations and radiance simulations. The results show that, compared to CrIS, the radiometric differences are about 0.3 and 0.7 K for the LW and MW bands, respectively, and 0.5 K for the CO absorption and window regions in the SW band. The consistency of the radiometric calibration among the four FOVs is estimated to be within 0.2 K for most of the spectral domain. Some remaining issues for the FY-3D HIRAS are also discussed.

The High Spectral Infrared Atmospheric Sounder (HIRAS) is the first Chinese Fourier Transform Michelson interferometer onboard the FengYun 3D (FY-3D) polar-orbiting meteorological satellite launched on November 15, 2017. The FY-3D HIRAS provides infrared (IR) radiance spectra measurements in three spectral bands: the long-wave IR (LWIR) band from 650 to 1135 cm−1, middle-wave IR (MWIR) band from 1210 to 1750 cm−1, and short-wave IR (SWIR) band from 2155 to 2550 cm−1. The ground system processes the interferogram measurements into calibrated radiance spectra. In each cross-track scan, there are 29 observations, each with a field-of-regard (FOR) comprising an array of $2\times 2$ field of views. In a six-month intensive campaign period, the HIRAS system was tuned, characterized, and validated. For the operational Level 1 product, the radiance noise levels meet the specifications. The spectral frequency accuracy was improved by maximizing the spectral correlation between the measured and simulated spectra by tuning the instrument-line-shape parameters. The absolute spectral frequency biases are less than 3 part per million (ppm) for all the three bands, and spectral bias standard deviations are less than 3 ppm in the LWIR and MWIR bands, and are about 3–5 ppm in the SWIR band. The radiometric calibration uncertainties were assessed by the comparisons of the radiance spectra between HIRAS and other IR hyperspectral sensors on different satellites. The radiance differences of the cross-sensor comparisons are in general less than 0.3, 0.7, and 1.0 K in the LWIR, MWIR, and SWIR bands, respectively. The HIRAS spectra were also compared with the spectra simulated with a fast radiative transfer model. Some remaining issues for the FY-3D HIRAS are also discussed.

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.

Fengyun-4A (FY-4A), the first of the Chinese next-generation geostationary meteorological satellites, launched in 2016, offers several advances over the FY-2: more spectral bands, faster imaging, and infrared hyperspectral measurements. To support the major objective of developing the prototypes of FY-4 science algorithms, two science product algorithm testbeds for imagers and sounders have been developed by the scientists in the FY-4 Algorithm Working Group (AWG). Both testbeds, written in FORTRAN and C programming languages for Linux or UNIX systems, have been tested successfully by using Intel/g compilers. Some important FY-4 science products, including cloud mask, cloud properties, and temperature profiles, have been retrieved successfully through using a proxy imager, Himawari-8/Advanced Himawari Imager (AHI), and sounder data, obtained from the Atmospheric InfraRed Sounder, thus demonstrating their robustness. In addition, in early 2016, the FY-4 AWG was developed based on the imager testbed—a near real-time processing system for Himawari-8/AHI data for use by Chinese weather forecasters. Consequently, robust and flexible science product algorithm testbeds have provided essential and productive tools for popularizing FY-4 data and developing substantial improvements in FY-4 products.

Launched in 2013, and 2017 respectively, Chinese Fengyun-3C and 3D meteorological satellites are equipped with two microwave sounders, Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder -2 (MWHS-2), whose observations play an important role in numerical weather prediction by data assimilation. Data quality control should be carried out before assimilation to filter out bad data, such as cloud- or rain-polluted data and questionable data. This work can’t be accomplished purely depending on MWTS or MWHS-2 themselves. MWHS-2 is taken as an example to do quality control in the paper, and the method is suitable for MWTS too. Multi-source information from other instruments onboard FY-3 is extracted to assist in the work. Cloud mask product from VIRR (Visible and InfraRed Radiometer), oceanic cloud liquid water content product from MWRI (Microwave Radiation Imager), and global rain rate product from MWRI are mapped to MWHS-2 for quality control in combination with oceanic rain detection product from MWHS-2 itself. 6 kinds of cloud and rain detection schemes are then designed to get the best choice by analyzing the characteristics of background departure. RTTOV v10 is adopted to simulate brightness temperature of MWHS-2 at all channels. The results suggested that scheme RI RC (MWRI cloud and rain information ingested) and scheme RC (all information ingested) are the two best choices for numerical assimilation application, and scheme RI RC can retain more samples. Questionable data can also be found in the way to help monitor the operational status of instruments.

Considering the characteristics of nonlinear problems, a new method based on the L-curve method and including the concept of entropy was designed to select the regularization parameter in the one-dimensional variational analysis-based sounding retrieval method. In the first iteration, this method uses an empirical regularization parameter derived by minimizing the entropy of variables. During subsequent iterations, it uses the L-curve method to select the regularization parameter in the vicinity of the regularization parameter selected in the last iteration. The new method was employed to select the regularization parameter in retrieving atmospheric temperature and moisture profiles from Atmospheric Infrared Sounder radiance measurements selected from the first day of each month in 2008. The results show that compared with the original L-curve method, the new method yields 5.5% and 2.5% improvements on temperature and relative humidity profiles, respectively. Compared with the discrepancy principle method, the improvements on temperature and relative humidity profiles are 1.6% and 2.0%, respectively.

Fengyun (FY) satellite program is becoming the important component of global earth observing system. Its instrumental performance, i.e., data quality and stability, play a critical role to support the quantitative applications, such as atmospheric and surface parameter retrievals, numerical weather prediction (NWP) and the production of Climate Data Records (CDRs). For monitoring the instrumental performance, the radiative transfer models (RTMs) together with NWP fields are used to simulate the reference radiance in FY satellite instrument monitoring schemes (FY-SIMS). Through monitoring the difference of observed and simulated brightness temperature of satellite instruments (OMB) against instrument parameters, the quality and stability can be evaluated from different viewpoint of geographic location, viewing geometry, time and spectral ranges. Two components of FY-SIMS, FY satellite data status monitoring system (FY-SDSMon) and satellite instrument characterization (FY-SIChar), are introduced. FY-SDSMon can provide near-real-time and long-time monitoring using fast RTMs and the China Meteorological Administration operational NWP fields, giving a routine and general knowledge of the instrument performance and its potential risk warning. FY-SIChar helps on further characterization of instruments by using more kinds of RTMs and comparison with counterpart instruments. Several examples show that FY-SIMS gives the insight to understand the instrument performance and characterize the potential bias. With the FY-SIMS, the performance of IR and MW band is widely monitored, and recently the VIS-NIR band is also in test to be monitored. Due to the improved quality and stability, the FY data is well evaluated and assimilated by worldwide NWP community to improve the forecast skill.

Data assimilation of satellite microwave sounders are very important for numerical weather prediction. Fengyun-3 (FY-3) polar satellites carry two such sounders: MicroWave Temperature Sounder (MWTS) and MicroWave Humidity Sounder (MWHS). The microwave observations should be quality-controlled before assimilation and rain check is one of the crucial steps. Microwave Radiation Imager (MWRI), on board FY-3 too, can provide useful rain information due to more sensitive to precipitation than sounders. A remapping methodology is described in this paper to make full use of data from sounders and imager simultaneously. Observations and products from MWRI can thus be remapped to field of view of sounders. Comprehensive information obtained in this way can be used to not only assimilation in numerical weather prediction models but also satellite observations quality monitoring.

Using the sample of companies listed on Chinese GEM between 2009 and 2015, we examines the impact of government subsidy on companies’ future stock price crash risk and explores how the earning information opacity moderates the relation between government subsidy and crash risk. We find that: 1) the government subsidies for the listed companies increase their crash risk; 2) the firms with higher information opacity are exposed to higher stock price crash risk; 3) considering the cross effect of opacity and government subsidy, the positive correlation between government subsidy and crash risk is weakened under the high information opacity environment. With further analysis, we find that that government subsidy dominates the earning management level of Jones model while measuring firm’s information opacity. This paper not only enriches the study of external influencing factors of crash risk, but also broadens the study of government subsidy efficiency and provides a new decision basis for the investors to recognize the firms’ earning information quality.

Terrain and cloud height heavily impact ozone information despite ozone being concentrated in the stratosphere. The ozone weighting function (OWF) provides important information towards understanding the capabilities and limitations of a given channel. The factors that impact the OWF can be analyzed using radiative transfer theory and modeling. At the 9.6-μm infrared spectral region, both the OWF values and peaks are related to the surface temperature, terrain altitude, and cloud height. Warmer surface temperatures, lower terrain altitude, or lower cloud levels will give larger weighting function values, and the peak of the weighting function slightly increases with the increase in surface temperature, terrain altitude, or cloud height. For longer UV bands such as 306 and 318 nm, OWF shows smaller values for higher terrains, while showing larger values when clouds are present. However, in the shorter UV bands such as 274 and 288 nm, OWF has almost no relationship with the surface and clouds. Therefore, with satellite-based infrared ozone remote sensing, high terrain and cloud presence will reduce ozone sensitivity and information content. In addition, for UV bands, the effect is spectrally dependent: lower terrain altitude and the presence of clouds will increase the zone information content in the longer UV band, but they have no effect in the short UV band. A simulation of an ozone retrieval in the infrared band shows that higher terrain results in lower precision for colder emitting surface temperatures and less ozone absorption signal.

High spectral infrared sounders possess distinct advantages that were exhibited in application of atmospheric temperature and humidity profiles retrieval, numerical weather prediction (NWP), climate change study and trace gas measurement due to high spectral resolution, low radiometric noise and high spectral and radiometric accuracy. A high spectral infrared atmospheric sounder (HIRAS) that was designed and manufactured completely by China will be carried onboard FY-3D and it is a sign of new age of infrared high spectral vertical sounding system in China. HIRAS is a Fourier interferometer and high radiometric precision is required for better application of data and also because radiometric noise and calibration errors are magnified in the inversion process to derive atmospheric parameters. Noise equivalent-temperature errors and calibration precision of the order of 0.1 °C and absolute errors of less than 0.3 °C are required to obtain temperature profile with rms errors of less than 1 °C. To develop calibration algorithm software and validate instrument data, complex calibration method is applied which using Cold Screen of 17 K and Internal Calibration Target of 313 K as calibration reference source and based on engineering model instrument vacuum calibration experiment data. Preliminary calibration results revealed that a strong absorbing peak at 800 cm−1 and need deep investigation. For FOV1, 2 and 4 the bias are less than 1 K for wide spectral range and are about 1–2 K just in location of less than 700 cm−1. FOV3 reflects bias of 2–4 K in whole LWIR band and need more data to verify it. Future work will be focused on non-linearity and spectral correction and the radiometric bias can be expected reach less than 0.7 K.

InfraRed Atmospheric Sounder (IRAS) instruments were successfully launched onboard the FengYun-3A (FY3A) and FengYun-3B (FY3B) satellites on May 27, 2008, and November 5, 2010, respectively. They aim at providing multichannel radiances within the spectral range of visible to infrared (IR) wavelengths for many environmental applications, including data assimilation and retrievals of global atmospheric temperature and humidity profiles. However, the velocity of the filter wheel of the first IRAS onboard FY3A is unstable and, therefore, induced a discontinuity in the measurement. The IRAS onboard FY3B works well in normal and stable operational mode since its launch without any anomaly. A variety of postlaunch calibration/validation tasks are conducted using on-orbit data during a period of three months. This paper presents on-orbit verification of IRAS instrument performance, including long-term trends of the space and warm calibration counts and noise equivalent delta radiance. The Earth scenes observed simultaneously by IRAS and Meteorological Operational Satellite Programme (METOP)/Infrared Atmospheric Sounding Interferometer were obtained and compared to demonstrate a close similarity between the two measurements. Furthermore, the IR channel observations from FY3B/IRAS are compared with those from National Oceanic and Atmospheric Administration-19/High Resolution Infrared Radiation Sounder (HIRS) equivalent channels and simulations from a radiative transfer model. The results show that some of IRAS IR channels perform very well, particularly for channels 1-10, 15, 19, and 20, compared to those of HIRS. Several channels, such as 13, 16, and 18, however, display some large biases. The causes of these increased biases are still under investigation.

Characteristics of the total clear-sky greenhouse effect (GA) and cloud radiative forcings (CRFs), along with the radiative-related water vapor and cloud properties simulated by the Spectral Atmospheric Model developed by LASG/IAP (SAMIL) are evaluated. Impacts of the convection scheme on the simulation of CRFs are discussed by using two AMIP (Atmospheric Model Inter-comparison Project) type simulations employing different convection schemes: the new Zhang-McFarlane (NZH) and Tiedtke (TDK) convection schemes. It shows that both the climatological GA and its response to El Nino warming are simulated well, both in terms of spatial pattern and magnitude. The impact of the convection scheme on GA is not significant. The climatological longwave CRF (LWCRF) and its response to El Nino warming are simulated well, but with a prominently weaker magnitude. The simulation of the climatology (response) of LWCRF in the NZH (TDK) run is slightly more realistic than in the TDK (NZH) simulation, indicating significant impacts of the convection scheme. The shortwave CRF (SWCRF) shows large biases in both spatial pattern and magnitude, and the results from the TDK run are better than those from the NZH run. A spuriously excessive negative climatological SWCRF over the southeastern Pacific and an insufficient response of SWCRF to El Nino warming over the tropical Pacific are seen in the NZH run. These two biases are alleviated in the TDK run, since it produces vigorous convection, which is related to the low threshold for convection to take place. Also, impacts of the convection scheme on the cloud profile are discussed.

The Vertical Atmospheric Sounding Suits (VASS) onboard FY-3C satellite includes The Infrared Atmospheric Sounder (IRAS), Microwave Temperature Sounder (MWTS) and Microwave Temperature and moisture sounder (MWTHS-II). The IRAS is similar to that onboard FY-3A/B, while the MWTS-II/MWTHS-II is more sophisticated than their precursors. MWTS has 13 channels mainly at the window region and 57 O2 absorption band, and MWTHS has 15 channels mainly at the 118 O2 absorption band and the 183 H2O absorption band. A package has been developed to retrieve the atmospheric temperature profile, moisture profile, atmospheric total ozone, and other parameters in both clear and cloudy atmospheres from the VASS measurements, which is remap to IRAS Field of view. The algorithm that retrieves these parameters contains four steps: 1) cloud and precipitation detection, 2) bias adjustment for VASS measurements, 3) regression retrieval processes, and 4) a nonlinear iterative physical retrieval. The package does not process precipitation FOV, and for non-precipitation cloud FOV, the measurements from middle to low channels of IRAS are excluded. Till now all instruments are under orbit examination stage, and the primary results show that temperature soundings can be produced under partial cloud cover with RMS errors on the order of, or better than, 2.0 K in 1-km-thick layers from the surface to 700 mb, 1-km layers from 700–300 mb, 3-km layers from 300–30 mb, and 5-km layers from 30–1 mb; and moisture profiles can be obtained with an accuracy better than 20% absolute errors in 2-km layers from the surface to nearly 300 mb.