Thanks to the recent advances in SIS (Space Information Science) and increasingly mature technologies, network simulations have attracted much attention, which play vital roles in research on validation of the correctness of SIN's (Space Information Network) solutions. In this paper, we constitute an architecture of a hardware-in-the-loop simulation system, where we use a server to act as a real satellite of SINs. Moreover, we model the channel of the satellite network relying on real cables and servers by referring to the Corazza model. Furthermore, based on the data of real weather events, we propose a coordinate mode for reducing LEO's (Low Earth Orbit) communication delay and validate the efficiency of our proposed method on simulation platform by simplifying the problem to a shortest path problem.

FY-4A GIIRS temperature profile products have provided unprecedented information for studying the atmospheric characteristics of thermal structures since 2020. The main objective of this paper is to evaluate GIIRS temperature profile products by using radiosonde observations and then apply them to the diagnosis of winter precipitation types in southern China. GIIRS temperature profile products for four types (clear sky perfect quality, cloudy sky perfect quality, cloudy sky good quality and cloudy sky bad quality) show different performances. Relatively, the cloud can affect the quality and quantity of GIIRS products. At different pressure levels, the perfect flagged data under the clear or cloudy sky show the best agreement with radiosonde observations, yielding the highest Pearson correlation coefficient and lowest mean bias as well as root mean square error. The good flagged data have a slight deviation from the perfect data. The impact on the quantity of the GIIRS temperature data is greater than that on the quality with an increase in cloud top height. A case investigation was carried out to analyze the performance of GIIRS temperature profiles for the diagnosis of precipitation types in a winter storm of 2022. The GIIRS temperature profiles represent the reasonable atmospheric thermal structures in the rain and snow in Hubei and Hunan provinces. The GIIRS temperature below 700 hPa is an important indictor to precipitation type diagnosis. Furthermore, two critical thresholds for GIIRS temperatures, which are below −2 °C at 850 hPa and below 0 °C at 925 hPa, respectively, are proposed for the occurrence of snowfall in this winter storm. In addition, the distribution of GIIRS temperature at different pressure levels is consistent with radiosonde observations in a freezing rain event in Guiyang, all of which show the warm rain mechanism by combining the cloud top information.

Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level (i.e., better than 0.5 K). Unfortunately, such an NL correction is still not used in spectrum calibration from the Geostationary Interferometric InfraRed Sounder (GIIRS) onboard the Fengyun-4A (FY-4A) satellite. Different from the classical NL correction method where the NL coefficient is estimated from out-band spectral artifacts in an empirical low-frequency region, originally with prelaunch results and updated under in-orbit conditions, a new NL correction method for a spaceborne Fourier transform spectrometer (including GIIRS) is proposed. In particular, the NL parameter μ, independent of different working conditions (namely the thermal fields from environmental components), can be determined from laboratory results before launch and directly utilized during in-orbit calibration. Moreover, to overcome the inaccurate linear coefficient from the two-point calibration that influences the NL correction, an iteration algorithm is established to make both the linear and the NL coefficients converge to their stable values, with relative errors less than 0.5 % and 1 %, respectively, which is universally suitable for NL correction of both infrared and microwave sensors. Using the onboard internal blackbody (BB), which is identical to the in-orbit calibration, the final calibration accuracy for all the detectors and all the channels with the proposed NL correction method is validated to be around 0.2–0.3 K at an ordinary reference temperature of 305 K. Significantly, the relative error in the classical method NL parameter immediately transmitting to that of the linear one in theory, which inevitably introduces some additional errors around 0.1–0.2 K for the interfering radiance no longer exists. Moreover, the adopted internal BB with higher emissivity produces better NL correction performance in practice. The proposed NL correction method is scheduled for GIIRS implementation on board the FY-4A satellite and its successor after modifying their possible spectral response function variations.

Non-linearity (NL) correction is a critical procedure to guarantee the calibration accuracy of a spaceborne sensor to approach a good level (i.e. better than 0.5 K). Unfortunately, such a NL correction is still unemployed in spectrum calibration of Geostationary Interferometric InfraRed Sounder (GIIRS) onboard Fengyun-4A (FY-4A) satellite. Different from the classical NL correction method where the NL coefficient is estimated from out-band spectral artifacts in an empirical low-frequency region originally with prelaunch results and updated under in-orbit condition, a new NL correction method for a spaceborne Fourier transform spectrometer (including GIIRS) is proposed. Particularly, the NL parameter μ independent of different working conditions (namely the thermal fields from environmental components) can be achieved from laboratory results before launch and directly utilized for in-orbit calibration. Moreover, to overcome the inaccurate linear coefficient from two-point calibration influencing the NL correction, an iteration algorithm is established to make both the linear and the NL coefficients to be converged to their stable values with the relative errors less than 0.5 % and 1 % respectively, which is universally suitable for NL correction of both infrared and microwave sensors. By using the onboard internal blackbody (BB) which is identical with the in-orbit calibration, the final calibration accuracy for both all the detectors and all the channels with the proposed NL correction method is validated to be around 0.2–0.3 K at an ordinary reference temperature of 305 K. Significantly, in the classical method, the relative error of NL parameter immediately transmitting to that of linear one in theory which will introduce some additional errors around 0.1–0.2 K for the interfered radiance inevitably, no longer exists. Moreover, the adopted internal BB with the higher emissivity will produce the better NL correction performance in practice. The proposed NL correction method is scheduled for implementation to GIIRS onboard FY-4A satellite and its successor after modifying their possible spectral response function variations.

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency.