This study mainly focuses on the diurnal and long-term variation characteristics of the calibration performance of the geostationary (GEO) meteorological satellite Fengyun-4A/ Advanced Geostationary Radiation Imager (FY-4A/AGRI) infrared (IR) channels from June 1, 2017 to December 31, 2020. An improved algorithm is developed in this research based on the in situ observations from ocean drifters, which can capture the diurnal variations of the calibration performance of FY-4A/AGRI IR channels. The results suggest that there are significant and nonnegligible diurnal variations of the uncertainties and biases for the brightness temperature (TB) observed by the FY-4A/AGRI, especially the IR channels at 3.71, 8.61, 10.83, and $12.07 \mu \text{m}$ . Among them, the calibration performance from 16:00 to 18:00 UTC (around the time of local midnight at the subsatellite point of FY-4A) is worse at $3.71 \mu \text{m}$ , while TB biases from 04:00 to 16:00 UTC are relatively large at $12.07 \mu \text{m}$ . Moreover, after the operational calibration and update in June 2020, the long-term TB biases at channel 08 ( $3.71 \mu \text{m}$ ) obviously decrease from about 5–2.5 K, and the TB biases at channel 14 ( $13.54 \mu \text{m}$ ) increase from about 0.5–3 K, implying a possible positive or negative impact of the calibration update on the calibration performance of IR channels. Overall, this method based on the in situ drifter data can well monitor the on-orbit calibration performance of GEO satellite imaging sensor’s IR channels such as the FY-4A/AGRI, allowing an objective assessment on the effect of calibration update events.

Dust detection is essential for environmental protection, climate change assessment, and human health issues. Based on the Fengyun-4A (FY-4A)/Advance Geostationary Radiation Imager (AGRI) images, this paper aimed to examine the performances of two classic dust detection algorithms (i.e., the brightness temperature difference (BTD) and normalized difference dust index (NDDI) thresholding algorithms) as well as two dust products (i.e., the infrared differential dust index (IDDI) and Dust Score products (DST) developed by the China Meteorological Administration). Results show that a threshold below −0.4 for BTD (11–12 µm) is appropriate for dust identification over China and that there is no fixed threshold for NDDI due to its limitations in distinguishing dust from bare ground. The IDDI and DST products presented similar results, where they are capable of detecting dust over all study areas only for daytime. A validation of these four dust detection algorithms has also been conducted with ground-based particulate matter (PM10) concentration measurements for the spring (March to May) of 2021. Results show that the average probability of correct detection (POCD) for BTD, NDDI, IDDI, and DST were 56.15%, 39.39%, 48.22%, and 46.75%, respectively. Overall, BTD performed the best on dust detection over China with its relative higher accuracy followed by IDDI and DST in the spring of 2021. A single threshold for NDDI led to a lower accuracy than those for others. Additionally, we integrated the BTD and IDDI algorithms for verification. The POFD after integration was only 56.17%, and the fusion algorithm had certain advantages over the single algorithm verification.

The quality of thermal infrared (TIR) radiances from the FY-4A advanced geostationary radiation imager (AGRI) and their derived products is very important for weather predictions. The demand for more accurate weather forecasting models is increasing, so it is necessary to evaluate and improve the calibration accuracy of AGRI. This study aims to evaluate the AGRI TIR radiometric calibration accuracy by comparing two well-calibrated hyperspectral radiometers, the cross-track infrared sounder (CrIS), and the infrared atmospheric sounding interferometer (IASI). Most of the AGRI TIR channels are calibrated with an absolute brightness temperature (Tb) bias less than 0.5 K currently. The Tb bias of AGRI and CrIS\IASI depends on the target temperature, and it is smaller and more stable for higher Tb and increases for lower Tb. CH_14 shows the largest Tb bias (−0.53 K) and standard deviation (0.64 K), CH_10 and CH_11 record slightly positive Tb biases, and the other four channels exhibit negative Tb biases. The diurnal mean Tb bias is very consistent except for CH_11, which varies greatly among different time bins. The Tb bias of CH_09 is very consistent after the calibration update in February 2018. CH_10 shows a gradual bias decrease with bias from 1.0 to 0.6 K. CH_11 shows a gradual bias decrease with bias from 0.4 to 0.1 K with large fluctuations. CH_14 shows a gradual bias decrease with large fluctuation and like CH_11. CH_12 and CH_13 exhibit seasonal variations where the Tb bias reaches its maximum around June and minimum around February. In general, the Tb bias shows different spatial patterns in different channels and CH_11 shows obvious spatial distribution characteristics that is monthly dependent.

The current satellite management system mainly relies on manual work. If small faults cannot be found in time, it may cause systematic fault problems and then affect the accuracy of satellite data and the service quality of meteorological satellite. If the operation trend of satellite will be predicted, the fault can be avoided. However, the satellite system is complex, and the telemetry signal is unstable, nonlinear, and time-related. It is difficult to predict through a certain model. Based on these, this paper proposes a bidirectional long short-term memory (BiLSTM) deep leaning model to predict the operation trend of meteorological satellite. In the method, the layer number of the model is designed to be two, and the prediction results, which are forecasted by LSTM network as the future trend data and historical data, are both taken as the input of BiLSTM model. The dataset for the research is generated and transmitted from Advanced Geostationary Radiation Imager (AGRI), which is the load of FY4A meteorological satellite. In order to demonstrate the superiority of the BiLSTM prediction model, it is compared with LSTM based on the same dataset in the experiment. The result shows that the BiLSTM method reports a state-of-the-art performance on satellite telemetry data.

The medium resolution spectral imager (MERSI) onboard the Fengyun-3 (FY-3) series satellites can provide the long-term series data with favorable spectral and spatial resolution on the global scale since 2008. Such datasets are valuable for the studies of climate change. However, due to the lack of stable and reliable on-board calibration equipment and inconsistent in-orbit calibration methods, the MRESI historical data has poor long-term stability and unreliable accuracy, which affects the quantitative application of the data. This study reveals the overall status of the FY-3A/B/C MERSI-I historical data and proposes the recalibration methods for the reflective solar bands (RSBs) and thermal emission bands (TEBs). For the RSBs, by using FY-3A as the radiative transfer reference, an integrated transfer calibration method is developed for the calibration of FY-3B, which is then used to recalibrate FY-3C. The degradation tracking model of FY-3 MERSI-I is established firstly in the recalibration process by integrating multiple calibration methods. Then, based on the overlapping observations over the Libyan Desert, the linear consistency transfer model of the reference and target satellites is established, and the consistent correction coefficient between them is obtained. For the TEBs, a retrospective transfer recalibration scheme is proposed to achieve the re-evaluation of the in-orbit radiometric calibration parameters based on inter-calibration and to conduct the recalibration of historical data without permanent dependence on reference instruments. All the historical data of FY-A/B/C MERSI-I (from February 2008 to March 2017) is reprocessed with the same calibration method. The reprocessed datasets show remarkable improvements in the calibration accuracy and stability compared with the operational datasets. The overall radiometric biases are found to be small and highly stable during the entire mission cycle of the instrument. The calibration biases of reprocessed data are less than 3% and 0.5 K for the RSBs and TEBs, respectively, much better than those of the operational datasets. There are also substantial improvements in the seasonal fluctuations and deviation discontinuities. This reprocessed long-term MERSI data with high inter-sensor consistency can provide valuable insights into the global climate monitoring and model assessment.