In this work, an efficient thermal-aware placement optimization framework is developed by combining a fast thermal simulation method with intelligent optimization algorithm. The fast thermal simulation method for 2.5-D advanced packaging is proposed by improving global stiffness matrix and load vector reconstruction process of traditional finite element method. Its performance is verified by comparing it with the traditional finite element method, the fast steady-state solver of the open-source HotSpot, and commercial software in terms of computational accuracy and efficiency. Then, an improved genetic algorithm, the elite immigrant primal-dual genetic algorithm is developed for effective placement optimization. By combining the fast thermal simulation method with the elite immigrant primal-dual genetic algorithm, the efficient thermal-aware placement framework for 2.5-D advanced packaging is established. The performance of the proposed optimization framework is then evaluated through several cases studies, including comparisons with optimization frameworks with other intelligent algorithms in terms of effective layout area, maximum temperature, temperature uniformity, and total wirelength. Additionally, its time efficiency is compared with frameworks based on other thermal simulation methods.

As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing time than traditional BO and genetic algorithm, implying its practical application potential for dealing with high-dimensional multi-constraint problems.

Solar Fraunhofer lines used for on-orbit wavelength calibration of spaceborne hyperspectral instruments are not universally applicable across all payloads. Existing calibration methods often overlook asymmetric shifts introduced by spectral broadening and lack a unified criterion for standard line selection, which significantly limits cross-comparison of spectral data among different instruments. In this study, we developed a theoretical model of asymmetric broadening and proposed a symmetry-based metric derived from energy integration. Using Kurucz and TSIS-1 HSRS spectra, we verified a strong correlation between asymmetric wavelength shifts and line symmetry. These shifts are jointly influenced by the asymmetric distribution of nearby spectral features within the convolution bandwidth and the instrument’s spectral resolution. After evaluating 22 Fraunhofer lines, we established a line selection guideline with potential applicability to other payloads and applied it to the spectral calibration of FY-3E/SSIM. This work provides key insights into on-orbit wavelength calibration for spaceborne hyperspectral instruments.

The three-dimensional (3D) atmospheric temperature structure in typhoon regions plays a critical role in typhoon physics research and numerical prediction, yet direct observational methods have long been lacking. Microwave remote sensing, with its unique cloud-penetrating capability has become a key approach for obtaining thermal structures within typhoons. However, the insufficient temporal resolution of traditional polar-orbiting satellite observations severely limits the application timeliness of microwave data in monitoring rapidly evolving weather systems like typhoons. With the operational deployment of China’s new-generation dawn-dusk orbit satellites, a tri-orbit collaborative observation system comprising FY-3E (dawn), MetOp-C (morning), and NOAA-20 (afternoon) satellites achieves six daily high-frequency observations of typhoons. This study integrates microwave temperature sounder (MWTS-III/AMSU-A/ATMS) data from three orbital platforms and establishes a real-time 3D temperature field reconstruction system with six daily outputs, based on our previously developed typhoon-specific retrieval algorithm. Analyses demonstrate that the retrieved temperatures effectively capture 3D structural features and spatiotemporal variations of typhoon temperature fields. Validation against best-track data reveals a strong correlation (>0.9) between retrieved warm-core intensity and typhoon central pressure, showing 6-9 hours earlier detection capability of intensity changes compared to GDAS-FNL and ERA5 reanalysis datasets. Case studies from the 2023 Northwest Pacific typhoon season confirm that our product maintains more stable temporal correlations with typhoon intensity (>0.9) than GDAS-FNL (0.85) and ERA5 (0.72). These results prove the reliability of the tri-orbit satellite-based temperature retrievals for analyzing typhoon thermal evolution.

Microwave observations have become the primary means of acquiring internal information on typhoons because microwave radiation can penetrate clouds and precipitation. By exploiting the strong relationship between the observed microwave brightness and atmospheric temperature, it is possible to derive a 3-D temperature structure within typhoons. However, these retrievals are limited by the consistent overestimation of warm cores and excessive intensity of cold centers at lower levels. Through the optimization of clear-sky and cloudy data classification and the incorporation of a latitude zone regression algorithm, this study successfully established an improved algorithm for the retrieval of typhoon inner atmospheric temperature using Microwave Temperature Sounder-III (MWTS-III) on China’s FY-3E, the first civil dawn-dusk polar orbit satellite. The analysis of the actual retrieved results for typhoons Bolaven and Doksuri at different stages demonstrates that the new algorithm effectively reduces errors in the low cold center and provides a more reasonable position for the warm core. Further analysis indicated that the accurate separation of quasi-clear-sky and cloud data is crucial for adjusting the warm-core position and mitigating errors in the low-layer temperature bias. In addition, fitting within latitudinal bands helps minimize the overall warm-core bias resulting from latitudinal temperature gradients, allowing for better characterization of real-time changes in typhoon intensity through temperature retrieval. The optimized 3-D air temperature retrieval product for typhoon regions holds great promise for advancing research on typhoon mechanisms and forecasting.

The measured CO2mixing ratio is significantly influenced by atmospheric water vapor due to its rapid spatial-temporal variability. In greenhouse gas remote sensing, the dry-air mixing ratio is commonly used to represent CO2concentration, which reduces errors arising from water vapor fluctuations. Early satellite spectrometers were often equipped with the O2-A band to retrieve atmospheric O2concentration. Leveraging the stable ratio between O2and dry air, CO2can be normalized to derive its dry-air mixing ratio. This approach not only reduces systematic errors but also plays a crucial role in minimizing instrumental biases, thereby enhancing measurement accuracy. However, advancements in satellite instrument calibration have diminished the necessity for O2-A band normalization. Therefore, this study proposes an alternative normalization method based on meteorological data to alleviate hardware requirements and costs. CO2retrieval results from O2-A band normalization and meteorological data normalization were compared using observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite, and these results were validated against data from the Total Carbon Column Observing Network (TCCON).The findings indicate that, in comparison to TCCON, the mean biases of the O2-A band scheme are 0.17 ppm, 0.25 ppm, and - 0.005 ppm in glint, nadir, and target modes, whereas the biases for the meteorological normalization method are 0.35 ppm, 0.45 ppm, and 0.19 ppm, respectively. Overall, the systematic bias of the meteorological normalization method is approximately 0.2 ppm greater than that of the O2-A band scheme, potentially attributable to the spatial-temporal resolution limitations of the Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2) dataset. Nonetheless, this method remains a viable alternative to O2-A band normalization for CO2retrieval.

Sea surface wind speed (SSWS) is an important oceanic dynamical parameter, extensively utilized in numerical simulations of climate change and weather forecasts, as well as in storm intensity assessment. Microwave radiometers onboard sun-synchronous satellites can provide a large amount of SSWS data globally. Atmospheric attenuation caused by large raindrops and rainwater contamination can both lead to estimation errors, especially for the sensors without L-band and C-band channels such as the Microwave Radiation Imager (MWRI) sensor onboard the Fengyun-3D (FY3D) satellite. To investigate the potential of MWRI in SSWS detection under bad weather conditions, a lightweight deep neural network (LWDNN) is used based on the Global Change Observation Mission First-Water (GCOM-W1) Advanced Microwave Scanning Radiometer 2 (AMSR2) all-weather SSWS product in 2021. The SSWS product from AMSR2, soil moisture active passive (SMAP), buoys, and the ERA5 reanalysis data have been utilized to validate the LWDNN under all weather conditions. The overall root mean square error (RMSE) of MWRI SSWS is less than 2.0 m/s under all weather conditions and less than 1.5 m/s in the clear-sky region. In order to test the retrieval effectiveness of LWDNN in cyclone regions, the scenes of cyclones are collected. Results show that the RMSEs of the MWRI maximum wind speed (VMAX) product relative to the AMSR2 and SMAP data are 6.31 and 6.66 m/s, respectively, in the wind speed range of 20–70 m/s, and there are no systematic biases. The RMSEs of the MWRI SSWS relative to the Stepped-Frequency Microwave Radiometer (SFMR) is 5.21 m/s in the wind speed range of 6–56 m/s, and the SSWS of MWRI and SFMR is generally consistent.

The Chinese early-morning-orbiting Fengyun-3E (FY-3E) satellite fills the 6-h initial observation window for data assimilation in numerical weather prediction (NWP). The low-light band (LLB) on the medium-resolution spectral imager low light (MERSI-LL) of FY-3E can detect extremely low radiances at night, significantly enhancing nighttime observation capabilities as well as elevating data assimilation quality by improving the nighttime cloud mask algorithm. However, severe and nonlinear stray light contamination affects most nocturnal FY-3E/MERSI-LL LLB images, particularly those from the Southern Hemisphere, hindering further visualization applications. The analysis concluded that the stray light is closely associated with the refraction and reflection of sunlight entering the MERSI-LL, solar zenith angle (SZA), and detector number. To obtain clear and enhanced images, this study designed a fully automated and adaptive stray light correction and enhancement algorithm for the nocturnal low-light images of FY-3E/MERSI-LL. Three typical stray-light-contaminated scenarios were categorized for all nighttime images. The restored results showed that after processing, the “fog” stray light and stripes were essentially removed, and the details became richer and more prominent, significantly improving the visual effect and usability of the images. This algorithm is simple, efficient, and highly applicable, and will be integrated into the processing system of the FY-3E satellite to support near real-time applications of LLB images. However, some strong or unusual stray light still affects the local continuity of the images. Future low-light imagers of FY-3 satellites will feature more sophisticated instruments to reduce incident stray light in their optical system.

The first C- and Ku-band dual-frequency scatterometer (WindRAD) onboard the Chinese FengYun-3E (FY-3E) satellite was successfully launched in July 2021. The WindRAD scatterometer uses an advanced fan-beam conical scanning mechanism to acquire wind vector data of global ocean surfaces and other geophysical parameters through backscattering measurements of the Earth. This article provides an introduction to the WindRAD instrument, an overview of the data preprocessing, and assessment of WindRAD measurements. The numerical weather prediction-based ocean calibration (NOC) approach, natural targets, and cross-calibration against the Ku-band scatterometer onboard the HY-2B satellite based on collocated backscatter measurements, were used to validate WindRAD backscatter results. The evaluation results revealed that the performance of the WindRAD data is generally in good agreement with other scatterometer data currently in use, while WindRAD backscatter data may contain nonlinear calibration issues that require further investigation. WindRAD has the ability to provide high-quality global backscattering measurements, which can be used for the inversion of various geophysical parameters and assimilation applications.

This study presents a novel approach to the radiometric inter-calibration between two sensors onboard the same satellite based on pseudo-invariant pixels (PIPs) using iteratively re-weighted multivariate alteration detection (IR-MAD) method. The IR-MAD algorithm can statistically select pseudo-invariant pixels from the multispectral image pair to assess the radiometric differences between them. Analysis of multiple image pairs from different acquisition times can provide long-term inter-calibration results of the two sensors. The procedure is applied to Fengyun(FY)-3A&3B Visible Infrared Radiometer (VIRR), with the Medium Resolution Spectral Imager (MERSI) onboard the same platform as the reference. Consistency of the spatial distribution of the PIPs selected by IR-MAD with pseudo-invariant calibration sites (PICS) given by other scientists demonstrates the effectiveness of our method. The long-term time series trending of top-of-atmosphere VIRR reflectance over LIBYA1 and LIBYA4 after inter-calibration correction shows that the inter-calibrated VIRR has good agreement with MERSI, with a mean bias of less than 1% and an uncertainty of less than 2% for most channels. The approach requires no prior knowledge of the inter-calibration targets and extends PICS to the pixel-level targets, which results in more diverse samples, broader dynamic ranges and lower uncertainty, yielding consistent and reliable long-term inter-calibration results.

The short-wave infrared Polarization and Multi-Angle Imager (PMAI) onboard Fengyun-3 precipitation satellite is a new spaceborne imaging polarimeter for clouds and aerosols, with polarization channels of 1030, 1370, and 1640 nm. This study presents a detailed description and assessment of the calibration model of PMAI. For radiometric intensity calibration, multiple parameters in the radiometric model are fitted into a single coefficient to simplify calibration. Results show that the radiometric calibration uncertainty of the full image plane is better than 0.02, and the calibration coefficient increases as field of view increases. The maximal unsaturated incident radiance of all channels is equivalent to 100% albedo, and signal-to-noise ratio at the referenced radiance is greater than 115 and 182 for the polarized and unpolarized channels, respectively. The response of all channels shows high linearity and good uniformity of the full image plane. Based on results of intensity calibration, a polarization calibration model using a fully linear polarized light source is introduced with a polarization measurement matrix established by a simplified method and a calculation method. Assessment of polarization measurement indicates that the uncertainties of the obtained degree of linear polarization (DoLP) and angle of linear polarization (AoLP) based on the two methods are highly consistent. When fully linearly polarized light is incident, the measurement error of DoLP using the simplified polarization measurement matrix is within 0.02 and that of AoLP is less than 1°. Therefore, the simplified radiometric intensity and polarization calibration model meets the measurement accuracy requirements and improves the calibration efficiency.

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 onboard calibration equipment and inconsistent in-orbit calibration methods, the MRESI historical data have 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 first 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 reevaluation of the in-orbit radiometric calibration parameters based on intercalibration 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) are reprocessed with the same calibration method. The reprocessed datasets show remarkable improvements in 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 intersensor consistency can provide valuable insights into global climate monitoring and model assessment.

Fine spectrum refines conventional spectral sounding channels by increasing the spectral resolution to improve further the vertical resolution for accurate detection of atmospheric parameters. The 50- to 60-GHz absorption band is mainly used to sound the vertical distribution of atmospheric temperature in the troposphere and stratosphere, and 53 GHz is located in the weak oxygen absorption line. FengYun-3E (FY-3E) Microwave Temperature Sounder-III (MWTS-III) has two sounding channels added to this absorption band, and initially realized the refinement of the sampling channels. In this article, by establishing the assimilation operator for the radiance data of MWTS-III in China Meteorological Administration Global Assimilation Forecasting System (CMA-GFS) 4D-Var, and using the in-orbit observations from FY-3E MWTS-III, we investigate the impact of refining sounding channels at 53 GHz on numerical weather prediction (NWP). Results show that the temperature increment has the largest value and the widest influencing area near the peak weighting functions of channels, and the intensity and range of the temperature increment also change as the number of assimilated channels increases. An increment index can help quantify the impact of channel refinement on the forecast field. After single-cycle assimilation, the root mean square error (RMSE) of atmospheric parameters in the analysis field shows a decreasing trend. After continuous assimilation, the RMSE of geopotential height has a positive effect on short-term forecasts as the number of assimilated channels increases. It can be seen that channel refinement at 53 GHz positively impacts the performance of NWP.

Precise spatial distribution of latent heat released during precipitation formation is crucial to enhance weather forecasting and climate prediction accuracy. This study introduces an innovative convolutional neural network heating (CNNH) algorithm. The algorithm incorporates the vertical gradient of precipitation rate and air temperature at different altitudes as key inputs. It combines additional information from adjacent vertical layers and neighboring horizontal areas. To mitigate possible misjudgments of negative heating, a punishing mechanism was introduced with a latent heating (LH)-structure loss function within the optimization framework. By employing the adaptive differential evolution (ADE) algorithm, the optimal configuration of the network structure optimizes accurate LH retrieval. To evaluate the CNNH algorithm’s efficacy, a self-consistency check using weather research and forecasting (WRF) model simulation data was conducted. Furthermore, an inter-comparison study with four other algorithms using real satellite observations was undertaken. Evaluations found that the CNNH algorithm could precisely retrieve the primary characteristics of the horizontal and vertical structure, along with the temporal evolution process and statistical information of WRF simulated LH, in eastern China in August 2017. It effectively addressed the issues of overestimation of near-surface cooling and mixing layer heating, thereby outperforming selected artificial intelligence (AI) and physics-based LH algorithms. The intercomparison study between LHCNNH with four other published LH products based on the same GPM observations reveals that CNNH got comparable performance among them. However, specific differences among these algorithms highlight considerable uncertainties in multiple LH satellite remote sensing products and underscore the necessity for further improvements in satellite LH algorithms.

A re-calibration algorithm of MWRI (Microwave Radiation Imager) onboard FengYun series satellites was developed based on the error budget analysis of MWRI and the main calibration parameters simulation and iteration. SNO (Simultaneous Nadir Overpass) between MWRI and GMI were used to get the brightness temperature of stable radiation target including sea surface and rain forest. Based on those brightness temperature datasets of stable target, DD (double difference) were used with RTTOV simulation result to cancel the effect of different sensor parameters between MWRI and GMI. Finally, emissivity of hot reflector, back lobe of hot reflector, efficiency of hot load, non-linearity parameter of MWRI were simulated and corrected. Using the corrected parameters, re-calibration of MWRI was done and we get the 10 years’ time series dataset. Validation result shows that the dataset has very high accuracy and stability compared with operational dataset.

Reflected global navigation satellite system (GNSS) signals from Earth surface can be received by receivers at low Earth orbit for the remote sensing of geophysical parameters. While the technique has been studied for around 30 years, most early spaceborne GNSS reflectometry missions only adapted to receive GPS signals and the studies of reflected Galileo (GAL) signals in space are limited. The Navigation Satellite System Occultation Sounder II (GNOS-II) payload onboard the FY-3E satellite is the first mission that can operationally receive reflected GPS, BeiDou (BDS), and GAL signals at the same time. This letter presents the GAL reflectometry measurements from GNOS-II together with their calibration and wind speed (WS) retrieval methods. Results show that while GAL has a different signal modulation, the observables can be used to retrieve WSs using the same geophysical model functions (GMFs) of GPS after a dedicated calibration. The retrieved WSs from GAL also have a comparable accuracy as those from GPS and BDS.

Accurate cloud detection via satellites is important for cloud radiative forcing estimation and disaster weather monitoring. Current polar-orbiting satellite cloud observation are limited during early morning orbit and contain notable uncertainty due to dimness measurements in visible bands. FY-3E\MERSI-LL is the first early morning orbit satellite worldwide and can realize global cloud observation under early morning scenarios. In this study, a dynamic threshold cloud detection algorithm is proposed based on the FY-3E\MERSI-LL infrared channel, combined with auxiliary data such as sea surface temperature, land surface temperature, snow cover mask and terrain elevation. The algorithm can detect clouds against complex land surface background, but faces classification difficulties over some plateau, high-latitude and snow surface regions, especially during early morning observation periods. Compared to coincident Himawari-8 and GOES-16 cloud measurements in the Eastern and Western Hemispheres, respectively, our algorithm recognizes reasonable cloud distributions. Furthermore, Himawari-8 and GOES-16 cloud products are used for quantitative cloud algorithm evaluation. The results show that at low-middle latitudes (60°N-60°S), the average cloud and clear hit rates during the various seasons are 73.24% and 76.46%, respectively, the cloud leakage and false alarm rates are 14.46% and 8.15%, respectively, and the total accuracy (cloud and clear) is 77.33%. The algorithm performance is better over the ocean than over land. Ground site MPLCMASK products are also used to verify the FY-3E cloud results in middle- and high-latitude areas. This algorithm provides a cloud detection reference during early morning orbit based on infrared channels.

A fast piecewise-defined neural network (PDNN) method is presented to produce accurate atmospheric temperature and humidity profiles from satellite hyperspectral infrared (IR) and microwave (MW) observations in all-sky conditions. The PDNN method relies on a novel classification approach and a principal component-based neural network (NN) function to better capture the nonlinear relationship between the spectral radiances and atmospheric state vectors. The algorithm was designed to only rely on satellite measurements. Large datasets were used for network training to make the retrieval robust to random errors in the reference data. For each retrieved profile, an effective quality indicator (QI) was obtained by training against the absolute value of the retrieval error. In addition, a reliable rain cloud flag was generated based on the scattering difference of cloud particles between the 50 and 118 GHz channels. Besides the independent reanalysis of field data, the algorithm’s performance was also evaluated using radiosonde measurements. Preliminary validation shows that the best temperature retrieval occurred in the mid-troposphere (around 1.0 K). Depending on the reference data, errors in the boundary layer typically ranged from 1.8 to 2.5 K. For water vapor, the retrieval error was less than 22% in the low-troposphere and less than 35% in the mid- and upper-troposphere when validated against the reanalysis field. The humidity error was approximately 10% lower when compared to radiosondes. The PDNN is used operationally to produce atmospheric temperature and moisture soundings from the Vertical Atmospheric Sounding System (VASS) of the FengYun-3E (FY-3E) satellite, an early-morning-orbit meteorological satellite launched in 2021.

Radiative calibration of satellite spectral radiometers is essential for their downstream applications. The Medium Resolution Spectral Imager (MERSI-II) is a key instrument of the Chinese polar orbit Fengyun-3D (FY-3D) satellite. However, its calibration performance has not been sufficiently studied, which limits its broad application. This study revealed the feasibility of a cloud-target method for assessing the MERSI-II calibration performance in solar bands. The top-of-atmosphere (TOA) reflectances for six MERSI-II reflective solar bands (RSBs) were numerically simulated using a rigorous forward radiative transfer method and cloud properties from well-collocated and well-calibrated Moderate Resolution Imaging Spectroradiometer (MODIS) operational cloud products with strict constraints. Only ice cloud targets were examined in the collocation due to their better homogeneity. The excellent agreement between our simulated reflectance and the MODIS reflectance (relative differences (RDs) of over 90% are within a 5% uncertainty range in six bands) validates our models. The simulated results in MERSI-II bands 1–4 showed reasonable agreements with the MERSI-II operational reflectance, i.e., mean RDs$\sim $15% and$\sim $12% (in the three years), respectively. More importantly, we removed these seasonal and degradation biases to improve the current calibration accuracy to a stable value within 3%. Due to its robust performance, our cloud-target-based calibration method can be applied to future MERSI-II sensors to monitor solar band stability.

In the above article[1], a difference in the definitions of our simulated reflectance (with respect to instantaneous TOA radiance) and the operational MERSI-II L1 reflectance (with respect to solar constant) causes errors in their direct comparisons.

As the first hyperspectral infrared sounder onboard a geostationary satellite, FengYun 4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) plays an important role in high-impact weather forecasting with a high spatial and temporal resolution. Because hyperspectral infrared data are highly sensitive to clouds and these cloud-affected data are currently challenging to assimilate, cloud detection is a necessary step to assimilate hyperspectral infrared data better. FY-4A GIIRS has been operationally assimilated in the Chinese operational numerical weather prediction system CMA-GFS (China Meteorological Administration Global Forecast System), which uses an imager-assisted cloud detection method to detect the clear-sky field of view. This paper develops a new cloud detection method to detect clear channels for FY-4A GIIRS in CMA-GFS, extending the original method from two-dimensional to three-dimensional (3D) space. Compared to the original method, the 3D cloud detection method increases the number of observations to two to three times for the middle- and lower-tropospheric channels and three to four times for the upper-tropospheric channels. The additional observations are of the same quality as those retained by the original cloud detection method, and the averaged Observation minus Background (OMB) biases after bias correction for upper-tropospheric channels decreased 54% compared to the original. Experiments results indicate that this method successfully achieves the cloud detection in 3D for GIIRS data and shows the potential in improving data utilization. Also, the results show that 3D cloud detection has a slightly positive impact on temperature and wind forecasts.

To cope with global climate change and monitor global CO2concentration distribution, the first Chinese carbon dioxide satellite (TanSat) has been successfully launched in December 2016. In this study, we implemented a CO2retrieval scheme by calibrating the TanSat sun-glint (GL) mode spectra and adapting the Iterative Maximum$A$PosterioriDifferential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm for CO2spectral retrieval. The global terrestrial CO2total vertical column density (VCD) and column-averaged dry-air mole fractions of CO2($\text{X}_{\text {CO2}}$) were simultaneously retrieved from TanSat GL spectral observations. Then, a comprehensive verification was performed between TanSat CO2retrieval and other measurements including Total Carbon Column Observing Network (TCCON), the Japanese Greenhouse gases Observing SATellite (GOSAT), and the US Orbiting Carbon Observatory-2 (OCO-2). Further comparisons between our TanSat CO2retrieval and ground-based FTIR measurements from TCCON indicated a good correlation with the mean bias of −0.78 ppm, the standard deviation at 1.75 ppm, and the Pearson correlation coefficient of 0.81. In addition, cross-satellite CO2validations of TanSat with GOSAT and OCO-2 showed consistently spatiotemporal trends for both CO2VCD and$\text{X}_{\text {CO2}}$. In summary, we can conclude that the presented CO2retrieval scheme has achieved global CO2retrieval from TanSat GL mode spectra with high precision and accuracy, as suggested by the results of independent ground-based and satellite validations.

Under the assumption that clouds are homogeneous and single-layered (SL), most current operational cloud top height (CTH) products derived from passive radiometers may largely underestimate the CTH of overlapping clouds. This article proposes a statistics-based extrapolation algorithm for retrieving the CTHs of overlapping clouds using only existing cloud property products available for most operational radiometers, and the method is successfully employed for the advanced himawari imager (AHI) observations. Because regional clouds within the same “system” have relatively continuous geometric properties, especially CTH, due to similar atmospheric conditions, upper-layer ice cloud CTHs (ITHs) and lower-layer water cloud CTHs (WTHs) are inferred using the CTH retrievals of well-chosen neighboring SL ice and water clouds, respectively. The proposed algorithm uses the latest machine-learning-based model to reasonably distinguish overlapping clouds from SL clouds, and optimizes the extrapolation by considering three physical constraints on neighboring, cloud phase, and cloud optical thickness (COT). Validated using active observations from CloudSat and cloud-aerosol Lidar and infrared pathfinder satellite observation (CALIPSO), our algorithm improves the AHI CTH mean bias for overlapping clouds from −5.1 to −2.6 km. More importantly, the algorithm provides CTH information of underlying water clouds that are unavailable from existing radiometer-based products. With the simultaneous retrieval of ITH and WTH, this algorithm increases our capability to detect the vertical structures of overlapping clouds and better evaluate the cloud radiative effects (CREs).

Inter-calibration is a general method to harmonize the in-orbit spectral radiance of the target instrument with the reference instrument in the visible and infrared spectra. This study proposes and applies an inter-calibration method to the space-based ultraviolet and visible (UVIS) grating radiometers. Based on pixel pairs derived from simultaneous nadir overpasses (SNO), the in-orbit spectral radiances from the Environmental trace gas Monitoring Instrument onboard the Chinese high-resolution remote sensing satellite GaoFen-5 (EMI/GF-5) and the TROPOspheric Monitoring Instrument onboard the Sentinel-5 Precursor satellite (TROPOMI/S5p) are compared through the double difference method, where the individual window channel method is also adopted for comparison. Uncertainties from SNO thresholds, instrument spectral specification and radiative transfer simulation are estimated. The SNO thresholds, including time, distance, viewing angle and scene uniformity of 300 s, 3 km, 0.01 and 0.01, are determined to get collocations for UVIS instruments. Several factors, including instrument line shape (ILS), instrument spectral accuracy, reference solar spectrum, surface albedo and atmospheric polarization, can influence the radiometric difference obtained by the double difference method. Accurate SAO2010 solar spectrum and wavelength accuracy can substantially reduce the dependence of radiometric difference on wavelength. Errors introduced by scalar approximation and parameterized ILS can be neglected. The surface albedo greatly affects the simulated radiance at wavelengths longward of 330 nm. The results show that radiometric differences obtained by using the double difference method are consistent with those derived from individual window channel method, with the discrepancy within 0.4%. An ocean-land calibration difference of 1.5% is found between EMI and TROPOMI at the UVIS band. Inter-calibration using the double difference method can minimize the spectral variations in full spectral radiance inter-comparison for grating hyperspectral instruments, and the radiometric difference can be estimated comprehensively. The wavelength dependence can also be easily derived from the results of double difference method. The double difference method is recommended for radiometric comparison for UVIS hyperspectral spectrometers.

The spectral response function (SRF) is a crucial parameter in multispectral radiometers, and it influences the radiometric calibration accuracy and quantitative application capabilities. The in-flight SRF often has errors due to prelaunch contamination or postlaunch degradation. This study proposes an innovative new method to retrieve SRFs of multispectral radiometers based on intercomparisons with hyperspectral sounders via the functional data analysis (FDA) technique. Under the FDA framework, all variables, including the hyperspectral radiance and SRF, are regarded as functions rather than discrete data by expanding in the Fourier functional basis. The forward convolution equation is processed directly into a functional integration model rather than a normally pointwise summation; this ensures that the unknown quantities are transformed from numerous SRF samples to several function parameters, thus avoiding the ill-posed problem. The proposed algorithm is verified with both simulated and real data from multiple thermal infrared bands of the FY-3 IRAS and FY-4 AGRI using collocations with METOP-B IASI. All these results demonstrate our algorithm’s qualitative and quantitative effectiveness for infrared SRF retrieval. Although the demonstrations are particularly relevant to infrared spectra, the algorithm is universal and also applicable to other spectral bands.

Due to the limitation of the satellite attitude measurement accuracy and the system servo control error of the payload scanning mechanism, an optimal use of Micro-Wave Radiation Imager (MWRI) observations requires high geolocation accuracy. In the operational system, the MWRI geolocation accuracy reaches 1 pixel, and there still exists room for improvement. In this article, we improve upon the coastline inflection point method (CIM) and propose to assign the accurate correspondence by employing a nonrigid point set registration method. First, the method identifies a set of latent variables to recognize outliers and then applies nonparametric geometric constraints to the correspondence asa prioridistribution. Second, the maximuma posteriori(MAP) estimation is applied by the expectation–maximization (EM) algorithm to obtain correct inliers. The comparison with other methods demonstrates that the proposed method can provide more accurate estimation of geolocation bias. In addition, the pixel error and changes in spacecraft attitude with the long-term geolocation data in FY-3C MWRI before and after correction were analyzed during the period from April 1 to August 30, 2018. The results have shown that the geolocation errors are reduced from [0.50, 0.60] pixels to [0.20, 0.33] pixels in the along- and cross-track directions after the attitude correction. In addition, the reduction of the standard deviation shows that the geolocation quality of MWRI is improved.

Known as input in the numerical weather prediction (NWP) models, microwave radiation imager (MWRI) data have been widely distributed to the user community. Nevertheless, the current operational geolocation accuracy is still on the pixel scale due to the presence of geolocation uncertainty. In this letter, we propose a new method to estimate the geolocation errors in MWRI data. Compared to the traditional coastline inflection method (CIM), the proposed method has two innovations. First, we establish a surface fitting interpolation model by involving more observations to detect the coastline. Second, we employ the iterative closest point (ICP) algorithm to determine the correspondences between the detected coastline and the actual coastline. Simulated experimental results demonstrate that the proposed method can provide a more accurate geolocation error estimation than the CIM. By applying our method, we have processed an MWRI data set from January 1 to February 28 in 2016. The experimental results have shown that the operational FY-3C MWRI geolocation errors are 0.4813 and 0.4909 pixels in the along-track and cross-track directions, respectively, which can be significantly reduced to 0.1299 and 0.1497 pixels after the attitude correction. It means that the geolocation accuracy has an average improvement up to 70%.

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 2×2 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 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 × 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.

Recently, deep learning-based saliency prediction methods have achieved significant accuracy improvements. However, they are hard to embed in practical multimedia applications due to large memory consumption and running time caused by complicated architectures. In addition, most methods are fine-tuned from pre-trained models for classification tasks, and networks cannot flexibly be transferred for a new task. In this paper, a condensed and randomly initialized student network is employed to achieve higher efficiency by transferring knowledge from complicated and well-trained teacher networks. This is the first use of knowledge distillation for efficient pixel-wise saliency prediction. Instead of directly minimizing Euclidean distance between feature maps, we propose two statistical representations of feature maps (i.e., first-order and second-order statistics) as knowledge. We conduct experiments on three kinds of teacher networks and four benchmark datasets to verify the effectiveness of the proposed method. Compared with the teacher networks, the student networks achieve an acceleration ratio of 4.56-4.73. Compared with state-of-the-art approaches, the proposed model achieves competitive accuracy with faster running speed (up to 4.38 times) and smaller model size (up to 93.27% reduction). We further embedded the proposed saliency prediction model into a video captioning application. The saliency-embedded approaches improve video captioning on all test metrics with a small complexity cost. The student-model embedded approach achieves 25% time saving with similar performance to the teacher embedded one.

The advanced MEdium Resolution Spectral Imager (MERSI II) is a major instrument onboard the Chinese FengYun 3D satellite, which was launched in November 2017. Extensive measurements were performed during MERSI II prelaunch testing to ensure effective characterization for on-orbit calibration. This paper gives a brief overview of the prelaunch performance testing conducted for MERSI II, as well as its improvements in terms of instrument design compared to MERSI I. The prelaunch calibration methodology and radiometric performance are detailed, including dynamic range, signal-to-noise ratio, noise equivalent differential temperature, linearity, and response uniformity. The assessment results indicate that most bands perform effectively with mirror specification noncompliances in a few reflective solar bands (RSBs). In addition, investigation of the stability and uniformity of the spherical integrating source indicates that they have a critical impact on the performance assessment and prelaunch calibration of RSBs. The temperature-dependence features of thermal emissive bands' performances are also discussed in terms of their sensitivity to the operating temperature of the focal plane assembly and instrument circumstance. This paper also shows that the self-stability of the calibration source and the representation of the assessment methods are important as they affect the results of instrument performance evaluation.

So far, the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) are the only two missions designed to measure the column-averaged CO2dry air mole fraction (XCO2). To improve our understanding of global carbon source and sink, these two XCO2products are compared in this study. The result reveals that the OCO-2 XCO2product show the wider spatial coverage than those of GOSAT from 30°S ∼ 90°N latitude. At the same time, GOSAT and OCO-2 XCO2products shows a good agreement with correlation coefficient (R2) of 0.69 and bias of −1.0 ppm. However, the discrepancy is still existed in some region. The discrepancy between these two products implies that it is necessary to make them complement each other to better improve our knowledge of global carbon cycle or even climate change.

Infrared Difference Dust Index (IDDI) is often used as a satellite dust product to detect the change of mineral dust aerosols in the atmosphere. And aerosol optical depth (AOD) is also a main measurement for mineral dust aerosol. To qualify dust loading on the regional or global scale, it is very necessary to understand the relation between IDDI and AOD. Therefore, this study investigates the impact of sensitivity factors including surface temperature and surface type to the IDDI by using MODTRAN (Moderate Resolution Transmittance code) model to better evaluate the relation between IDDI and AOD. The result shows that the simulated IDDI from MODTRAN are extremely sensitive to the surface temperature and surface type. The IDDI is growing with the increased surface temperature. And the sensitivity of farm and forest type to IDDI is similar and their difference is very small. But the sensitivity of desert type and ocean to IDDI is obviously different from other two types and the surface type is also a key parameter to IDDI. The result also implies that there is an exponential relationship between IDDI and AOD. These results will be very helpful to further establish the relation between IDDI and AOD.

The history, current status and future program of the Chinese Meteorological Satellite, i.e. Fengyun satellite (FY) is introduced in this presentation. Currently, there are 2 FY polar satellites and 3 FY geostationary satellites are in operational. The type of the instruments amounted on FY satellites includes the optical imager, the atmospheric sounder, the microwave imager, the atmospheric composition detector, and the radiation budget mapper. The ground segment and data application of FY satellites are introduced in general in this paper.

The Medium Resolution Spectral Imager (MERSI) was installed as a key payload on the FengYun-3C (FY-3C) polar-orbit meteorological satellite, which was successfully launched on September 23, 2013. We used 14 months of continuous FY-3C/MERSI Level-1B data (from November 2013 to December 2014) to evaluate the on-orbit image spatial quality. Based on a polar ice block image, a sharp target modulation transfer function (MTF) estimation method is used to quantitatively estimate the MTF value at the Nyquist frequency, which is an index for the spatial quality. The results show very good stability in the first year and a relatively lower spatial quality (MTF approximately 0.15) along the FY-3C/MERSI scan direction. This lower spatial quality is primarily attributed to the known and fixed 27% overlapped scan mode of MERSI, which can significantly reduce the image contrast. By using this fixed overlapped proportion (27%), we develop a fast and robust image restoration algorithm based on the Gaussian elimination (GE) method with lower and upper triangular matrix decomposition (LU). The speed-up ratio of this GE with LU decomposition method can attain a value of 626.30 compared with the traditional GE method when it solves linear equations with 2048 MERSI scan pixels. After the image restoration process, significant enhancement in the image spatial quality along the scan direction for every band of FY-3C/MERSI can be found with an increased MTF value of approximately 0.30. However, we evaluate the possible effect of this restoration algorithm on the original digital number (DN) and reflectance values. We find a slight decrease in the total averaged DN (0.5) and reflectance (<; 0.5%, relative bias) values. The variation in DN or reflectance after the image restoration process exhibits a positive correlation with homogeneity of the original target. Moreover, a sensitivity study on the reflectance reveals that it has a more significant impact on the inhomogeneous pixel with a low DN value.

Hyperspectral radiances from the Infrared Atmospheric Sounding Interferometer (IASI) and Atmospheric Infrared Sounder (AIRS) are used as a reference to improve the calibration accuracy for FengYun-2 (FY-2) infrared (IR) channel radiances. It is shown that the previous FY-2 operational calibration for IR bands produces significant bias in brightness temperatures that can exceed 1.1 K. In particular, the FY-2 IR3 band (6.7 μm) has the largest bias of 2.0 K. The daytime double-difference temperature (DDT) between AIRS and IASI using FY-2 imagers as a transfer medium showed an excellent consistency, is within 0.2 K at 290 K, and is stable over time for FY-2C/2D/2E. This only indicates the robust calibrations applied for both the AIRS and IASI measurements. During the nighttime of the Earth observation, stray light in space affects the long-term stability of the FY-2 DDT, particularly for the Earth scene at 220 K. FY-2E satellite which was launched in 2009 has an instrument design improvement. Intercalibrating FY-2 four times using AIRS and IASI data can reveal the diurnal features of the FY-2 instrument calibration. The temporal DDT appears very large during the spring and autumn eclipse times. Not only can the global-space-based-intercalibration-system intercalibration method provide an excellent operational calibration for the FY-2 imager, but it can also help improve the design of future instruments and onboard blackbody calibration.

Focusing on Asian dust aerosols, the Community Radiative Transfer Model (CRTM) developed at JCSDA under NOAA/NESDIS is used to simulate the effects of dust on the observations from 10-12μm split-window channels of the Visible and InfraRed Radiometor(VIRR) on Chinese FengYun-3A (FY-3A) satellite. Based on the simulation, an infrared dust retrieving algorithm is developed with VIRR data, in which the effective radius of Asian dust is defined with the ground measurements from SKYNET. The optical thickness and height of the dust layer are retrieved simultaneously with this algorithm. The results show the optical thickness of dust layer is reliable comparing with the height. The errors in the calibration of sensors and surface temperature may have large effect on the retrieving.

The payload on board the FengYun 3 (FY-3) satellite series has been providing critical observations to the Earth, and the observation data have been processed and distributed to various users by the corresponding ground application system. In this paper, the FY-3 payload and its future development plan are presented, and its ground system is introduced. The major characteristics of the FY-3 instruments and the requirement on the instruments for major applications are discussed as well. There are nine new instruments, including the Microwave Temperature Sounder (MWTS), Microwave Humidity Sounder (MWHS), Infrared Atmospheric Sounder (IRAS), Medium-Resolution Spectral Imager (MERSI), Microwave Radiation Imager (MWRI), Solar Backscatter Ultraviolet Sounder, Total Ozone Unit, Solar Irradiation Monitor, and Earth Radiation Measurement. The data quality from the IRAS, MWTS, MWHS, and MWRI has proved to be good, and according to our evaluation at the European Centre for Medium-Range Weather Forecasts (ECMWF), the data have a very high availability. The MERSI has 20 visible (VIS) and infrared (IR) channels, of which three are VIS channels with a 250-m spatial resolution, one is a near-IR channel with a 250-m spatial resolution, and one is a long-wave IR channel with a 250-m spatial resolution. The global data at a 250-m spatial resolution are very useful in weather, disaster, and environmental monitoring. Compared with the ground observations, the ozone products derived from the FY-3 series are highly accurate. We have completed the calibration and validation of these sensors and most products.

On 27 May 2008, China has successfully launched the FY-3A meteorological satellite as a research and development (R&D) satellite, with 11 payloads mounted, and the three instruments among the 11 payloads are vertical atmospheric sounding instruments similar to ATOVS: MicroWave Temperature Sounder (MWTS), MicroWave Humidity Sounder and InfraRed Atmospheric Sounder, generally called Vertical Atmospheric Sounder System, i.e., VASS, which will help to improve NWP forecast skill. Chinese Meteorological Administration has developed a three dimension variational data assimilation system (Global/Regional Assimilation and Prediction System, shorten as Grapes 3Dvar), and with the ATOVS radiance data directly assimilated by RTTOV as observation operator. In this study some challenging research works related to assimilating the FY-3A VASS data into Chinese 3Dvar assimilation system Grapes 3Dvar are performed, such as generation of the RTTOV coefficients for FY-3A VASS three instruments, data quality control, channel selection and bias correction, etc. To investigate the effects of the FY-3A VASS data in NWP model, 2 sets of assimilation experiments conducted: one is single instrument data assimilation experiment and the other is combinatorial data assimilation experiments. The preliminary results indicate that: the forecast skills are improved greater after FY-3A VASS data assimilation than before, and the different instrument has different effects on NWP forecast; the microwave sounders have more contribution than infrared sounder; the combinatorial data assimilation experiments get more positive effect than single instrument data assimilation experiment; the positive impact on the South Hemisphere is more clear than on North Hemisphere.

The blurring from diffraction for the infrared (IR) radiances on a geostationary satellite (GEO) e.g., the next generation of Geostationary Operational Environmental Satellite (GOES-R) was simulated by using Moderate Resolution Imaging Spectroradiometer Airborne Simulator data and the point spread function (PSF) model for an unobscured telescope. The portion of the total radiance contributed from each nearby geometrical field of view (GFOV) was calculated. For 90% ensquared energy (EE) (equivalent to 10% of the energy coming from outside the footprint), the closest GFOVs contribute 7%; the contribution from the closest GFOVs increases to 22% for 70% EE. The increased portion from the nearby GFOVs causes larger blurring and degrades the pixel-based retrieval product accuracy. Radiance contamination from the nearby field for the GEO IR radiances with 90%, 80%, and 70% EE causes 0.2-, 0.3-, and 0.4-K blurring errors, respectively, in the 12-mum IR longwave window band in clear 300-K scenes. The blurring error is doubled in cloudy 230-K scenes. For the 13.8-mum absorption band, the blurring error will be smaller than that of the 12-mum band because the atmospheric layer where the temperature sensitivity peaks for the 13.8 mum is more uniform than the surface where the 12 mum is most sensitive. This indicates that the PSF has a greater impact on a heterogeneous surface. Similar blurring errors occur at both 4- and 10-km spatial resolution IR sensors. The blurring error is not random, and it varies spectrally. These conclusions are very relevant to the design of a cost-effective GEO IR sounder that meets the science requirements