电离层电子密度的高精度层析反演对提升卫星导航精度、优化短波通信稳定性至关重要，但传统网格离散化方法具有精度和分辨率受限、计算量大等问题，极端电离层扰动场景下误差更显著。本文提出基于深度算子网络（Deep Operator Network, DeepONet）的电子密度层析预测模型 ——DeepONet-TOMO，通过非线性算子映射的端到端学习，突破传统网格离散化局限，实现全球高分辨率电离层电子密度的精准反演。该模型创新性地融合多源异构数据构建输入，包括 NeQuick2 模型模拟数据、基于非差非组合精密单点定位（Undifferenced and Uncombined Precise Point Positioning, UC-PPP）技术提取的实测斜向总电子含量（Slant Total Electron Content, STEC）数据，以及 COSMIC-II 和 “云遥” 卫星掩星观测的电子密度剖面数据，借助多源信息互补提升模型鲁棒性。本文围绕2024年5月太阳极端活跃期间设计三种数据融合方案（基础数据集、实际观测层析数据集、多源融合数据集），系统验证模型在不同数据条件下的适应性。 结果表明融入掩星观测数据，基于算子的同化模型观测稀疏区域的电子密度重构误差较未加入掩星数据降低约 25%~ 42%。同时，该模型无需网格离散实现任意构型的电子密度重构，提升电子密度层析的计算速度。这项研究为电离层层析提供了全新高效的技术路径，直接提升卫星导航、短波通信等应用的精度与可靠性，更对推动空间物理观测与地球空间信息科学的交叉融合具有深远意义。

The accurate and stable radiometric calibration is a fundamental for quantitative remote sensing. This article addresses the evaluation and correction of radiometric calibration biases in the middle infrared channel (channel 6 centered at 3.8 μ m) and the thermal infrared channels (channels 7 and 8 centered at 10.8 and 12.0 μ m, respectively) of the MEdium Resolution Spectral Imager for the Rainfall Mission (MERSI-RM) on FengYun-3G (FY-3G) satellite against the channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua satellite using the double-difference (DD) method. First, an infrared radiative transfer model is constructed to simulate the observations in both middle infrared and thermal infrared channels, in which surface reflected solar irradiances are fully taken into account. Then, the matching samples between the MERSI-RM and MODIS observations in January, April, July, and October of 2024 are collected in terms of the matching criteria. Next, the radiances at top of atmosphere (TOA) are simulated using the infrared radiative transfer model. Finally, the radiometric calibration biases in the MERSI-RM channels 6–8 are evaluated and corrected. The results show that the impact of the simulation errors, the spectral response differences, and geolocation errors on the final intercalibration results can be ignored. The radiometric calibration of the MERSI-RM channel 6 is quite consistent with that of the MODIS channel 20, while the radiometric calibrations in the MERSI-RM channels 7 and 8 are about 0.5-K underestimated. Although more or less calibration biases exist in the MERSI-RM channels, their on-orbit calibrations are generally stable in the four months of 2024.

This article presents the evaluation and correction of the on-orbit calibration biases of the Microwave Humidity Sounder 2 (MWHS-2) aboard Fengyun 3D (FY-3D) against the advanced technology microwave sounder (ATMS) on Suomi National Polar-orbiting Partnership satellite using the double difference (DD) method. First, a microwave radiative transfer model (RTM) and a calibration bias correction method are developed. Then, the matching observations in the year of 2022 between the MWHS-2 and the ATMS channels are collected over both sea surfaces and land surfaces. Next, the brightness temperatures at top of atmosphere in the MWHS-2 and ATMS channels are simulated using the RTM constructed in this work. After that, the DDs and the theoretical observations are computed. Finally, the calibration biases in the MWHS-2 channels are corrected. The results show that the RTM in this work is valid and accurate. The simulations of the RTM in this work basically agree with that obtained by the latest version of radiative transfer for TOVS. The impact of the simulation differences between the two RTMs and the combined influence of the simulation errors and spectral response differences on the final intercalibration results are quantitatively analyzed. In June of 2022, the calibration biases in FY-3D MWHS-2 channels 1, 11–15 are −1.89 ± 5.13 K, −2.78 ± 1.02 K, −1.21 ± 1.05 K, −0.54 ± 1.14 K, 1.35 ± 0.88 K, and −1.19 ± 1.47 K, respectively. In the whole year of 2022, the calibration biases in the MWHS-2 channels are generally stable: in the MWHS-2 channels 1, 11∼15, the maximum variations in two consecutive months are 1.42, 0.31, 0.36, 0.43, 0.37, and 0.39 K, respectively, while the maximum variations in 2022 are 2.03, 0.37, 0.38, 0.53, 0.60, and 0.63 K, respectively. The on-orbit calibration biases in the MWHS-2 channels are corrected against the ATMS channels.

The Total Precipitable Water (TPW) is a key variable of atmospheres, and its spatiotemporal distribution is of great importance in global climate change. This paper addresses the TPW retrieval over both sea and land surfaces from the data acquired by the Microwave Humidity Sounder II (MWHS-II) on Fengyun 3D (FY-3D) satellite. First, the Back Propagation Neural Network (BPNN) algorithms are developed with the spatiotemporal matching samples of the MWHS-II data with the fifth-generation European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. Then, the TPWs at spatial resolutions of 0.25° in longitude and latitude between 65°S and 65°N over both sea and land surfaces are retrieved from the pixel-aggregated FY-3D MWHS-II data in 2022. Finally, the TPWs retrieved in this work are validated with the radiosonde TPWs over both sea and land surfaces, and they are also compared to the F18 Special Sensor Microwave Imager Sounder (SSMIS) TPWs over sea surfaces. The results indicate that the BPNN algorithms developed in this work are valid and superior to the D-matrix method, the Ridge method, the Lasso method, the physical method, the random forest (RF) method, the support vector machine (SVM) method, and the eXtreme Gradient Boosting (XGBoost) method. Against the radiosonde TPWs, the mean error (ME), the root mean square error (RMSE), and mean absolute error (MAE) of the TPWs retrieved in this work are −1.17 mm, 3.46 mm, and 2.63 mm over sea surfaces, respectively, and they are −0.80 mm, 4.04 mm, and 3.13 mm over land surfaces, respectively. The TPWs retrieved in this work are much more accurate than the F18 SSMIS TPWs.

Accurately capturing nonlinear, multiscale plasma dynamics remains a central challenge in plasma physics, typically addressed by incorporating appropriate closures into fluid models. While such models retain certain kinetic fidelity and avoid the high computational cost of full phase-space simulations, designing closures often requires sophisticated, system-specific analysis. Recent advances in machine learning have motivated the use of data-driven approaches to learn fluid closures directly from kinetic simulations. In this study, we introduce a physics-informed Fourier neural operator (PIFNO) that incorporates an implicit fluid closure within the neural network to predict the dynamical evolution of two-dimensional electrostatic plasmas from initial conditions. By incorporating fluid moment equations into its loss function, PIFNO exhibits strong spatial extrapolation capabilities beyond the coverage of the training data. Without prescribing a closure form, the model achieves high accuracy in predicting all physical quantities from fully kinetic simulations of electrostatic turbulence. These results position PIFNO as a promising framework for enabling fast and efficient fluid modeling of complex plasma systems.

This article addresses the directional land surface emissivity (LSE) retrieval from the data acquired by the MEdium Resolution Spectral Imager (MERSI) on Fengyun 3D and 3E (FY-3D/E) satellites and the Moderate-resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua satellites. First, a method to retrieve directional LSEs from multisatellite data is developed based on the radiative transfer model. Then, the directional LSEs are retrieved from the combined MERSI/FY-3D/E, MODIS/Aqua, and MODIS/Terra data in January 1–16 and July 1–16 of 2022 over a study area with longitude from 100 °E to 130 °E and latitude from 20 °N to 50 °N. Finally, the retrieved LSEs are, respectively, cross-validated with the MODIS/Terra land surface temperature (LST) and emissivity 8-day level 3 global 0.05° V61 (MOD11C2) product and the MODIS/Terra LST/3-band emissivity 8-day level 3 global 0.05° V61 (MOD21C2) product over the entire study area, and validated against the in situ data at three true desert and semi-arid sites. The results show that the multisatellite data provide more information of view angles and solar angles, which makes the determination of bidirectional reflectance distribution function (BRDF) model and the LSE retrieval more robust. The LSEs retrieved in this work have strong dependence on time and land cover types. Over the vegetated areas, the LSEs retrieved in this work basically agree with the MOD11C2 and MOD21C2 products, while over the true desert and semi-arid areas, the LSEs in the MOD11C2 and MOD21C2 products are obviously overestimated, especially the MOD11C2 product, but the LSEs in this work are consistent with the in situ data. In general, the method developed in this work is valid and the retrieved LSEs are accurate.

The ionosphere is a vitally dynamic charged particle region in the Earth’s upper atmosphere, playing a crucial role in applications such as radio communication and satellite navigation. The slant total electron contents (STECs) are an important parameter for characterizing wave propagation, representing the integrated electron density along the ray of radio signals passing through the ionosphere. The accurate prediction of STEC is essential for mitigating the ionospheric impact particularly on Global Navigation Satellite Systems (GNSS). In this work, we propose a high-precision STEC prediction model named deep neural operator network (DeepONet)-STEC, which learns nonlinear operators to predict the 4-D temporal-spatial integrated parameter for the specified satellite-ground station ray path globally. As a demonstration, we validate the performance of the model based on GNSS observation data for global and US Continuously Operating Reference Stations (CORS) regimes under ionospheric quiet and storm conditions. The DeepONet-STEC model results show that the three-day 72 h prediction in quiet periods could achieve high accuracy using observation data by the precise point positioning (PPP) with temporal resolution 30 s . Under active solar magnetic storm periods, the DeepONet-STEC also demonstrated its robustness and superiority than traditional deep learning methods. This work presents a neural operator regression architecture for predicting the 4-D spatiotemporal ionospheric state for satellite navigation system performance, which may be further extended for various space applications and beyond.

Purely data-driven ionospheric modeling fails to adequately obey fundamental physical laws. To overcome this shortcoming, we propose a novel ionospheric inversion model, Physics-Informed Neural Network based on fully physical models SAMI3 (PINN-SAMI3). The model incorporates the governing equations of the ionospheric physical model SAMI3 into the neural network to reconstruct the temporal-spatial distribution of ionospheric plasma parameters. The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. The PINN-SAMI3 framework enforces physical laws through the multiple ion species of continuity, momentum, temperature equations in the magnetic dipole coordinate system. The simulation results show that if sparse ion densities are used as training data, it is possible to retrieve ionospheric electron densities, ion velocities and ion temperatures, respectively. The optimal physical constraints have been also investigated for different inversion quantities. Furthermore, the impact of incorporating E × B velocity terms on inversion results during the periods of ionospheric calm and geomagnetic storm is analyzed. The PINN-SAMI3 achieves good inversion results even using sparse data in comparison to the traditional artificial neural networks (ANN). The framework will contribute to advance the future space weather prediction capability with artificial intelligence (AI).

Electromagnetic (EM) wave propagation and inversion in complex time-varying medium is a challenging problem, particularly for plasma applications. We extend the EM wave–plasma coupling physics computation mapping to the recurrent neural network (RNN). The system can be trained to learn inhomogeneous time-varying magnetized plasma parameters from temporal scattered field. As a proof-of-concept demonstration, a physics-encoded RNN has been verified, which encodes Maxwell's vector wave equation describing the multiphysics coupling system into the standard RNN architecture. The results demonstrate that time-varying plasma parameter inversion can be accomplished using only a few sets of transmitted electric fields. This model is interpretable and computationally efficient, benefiting from optimization strategies provided by deep learning, which may be extended for various EM–plasma interaction applications and beyond.

Actual spectral response function (SRF) of the quadruple-sideband channel of the microwave sounder is axisymmetrical, and its distortion degree affects the difference in observations between the actual microwave sounder and the ideal design situation. Effects of the actual quadruple-sideband channel’s SRF on observations has been evaluated by applying the actual SRF of the quadruple-sideband channel of FengYun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) and rapid radiative transfer model (RTM) of China Meteorological Administration-Global Forecast System (CMA-GFS). Compared with ideal SRF, actual SRF could improve microwave-radiance simulation, and the observation-minus-background could be decreased by about 0.7 K. The improvements in the mid-high latitudes of the south hemisphere are more evident than in other latitudes. Effects of distortion types of actual SRF on observations have been analyzed by comparing simulated radiance differences between reference SRF and originally proposed SRFs with eight typical distortion types. Inner sideband drifting type and outer sideband drifting type have the greatest influence of about 1 K on microwave radiance. Inner–outer sideband symmetrical difference type has the least impact of approximately 0.02 K. Effects of the axisymmetry of SRF on observations have also been evaluated. Compared microwave radiance simulation by reference SRF with axisymmetrically and nonaxisymmetrically distorted SRFs, respectively, simulation errors induced by axisymmetrically distorted SRFs are less than that by nonaxisymmetrically distorted SRFs of about 0.1–0.4 K.

Deriving governing equations of complex physical systems based on first principles can be quite challenging when there are certain unknown terms and hidden physical mechanisms in the systems. In this work, we apply a deep learning architecture to learn fluid partial differential equations (PDEs) of a plasma system based on the data acquired from a fully kinetic model. The learned multi-moment fluid PDEs are demonstrated to incorporate kinetic effect such as Landau damping. Based on the learned fluid closure, the data-driven, multi-moment fluid modeling can well reproduce all the physical quantities derived from the fully kinetic model. The calculated damping rate of Landau damping is consistent with both the fully kinetic simulation and the linear theory. The data-driven fluid modeling of PDEs for complex physical systems may be applied to improve the fluid closure and reduce the computational cost of multi-scale modeling of global systems.

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale or multiscale systems. One of the long-standing problems in plasma physics is the integration of kinetic physics into fluid models, which is often achieved through sophisticated analytical closure terms. In this paper, we successfully construct a multimoment fluid model with an implicit fluid closure included in the neural network using machine learning. The multimoment fluid model is trained with a small fraction of sparsely sampled data from kinetic simulations of Landau damping, using the physics-informed neural network (PINN) and the gradient-enhanced physics-informed neural network (gPINN). The multimoment fluid model constructed using either PINN or gPINN reproduces the time evolution of the electric field energy, including its damping rate, and the plasma dynamics from the kinetic simulations. In addition, we introduce a variant of the gPINN architecture, namely, gPINN⁢𝑝, to capture the Landau damping process. Instead of including the gradients of all the equation residuals, gPINN⁢𝑝 only adds the gradient of the pressure equation residual as one additional constraint. Among the three approaches, the gPINN⁢𝑝-constructed multimoment fluid model offers the most accurate results. This work sheds light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.

Ionospheric structure is important for estimating ionospheric delay for user stations in the global navigation satellite system (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture the ionospheric random structure and estimate the ionospheric parameter using data of disperse receivers to improve the accuracy. This article proposes a unifying method named ionosphere kernel estimation algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semiparametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted first to include physical correlations. In addition, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible global position system (GPS) and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential slant total electron content (dSTEC) in the root-mean-square error with respect to inverse distance weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make a robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Sea surface temperature (SST) is a key climate variable, which affects the behavior of the Earth’s atmosphere. In this article, a method coupled with microwave sea surface emissivities (SSEs) is developed to retrieve SST from the intercalibrated measurements acquired by the microwave radiation imager (MWRI) on Fengyun 3D (FY-3D) satellite. First, the spatiotemporal matching samples over sea surfaces in 0.25 ∘×0.25∘ between FY-3D MWRI measurements and the fifth generation of European center for medium-range weather forecast (ECMWF) atmospheric reanalysis (ERA5) data in January, April, July, and October 2020 are collected and used to determine the unknown coefficients of the SST retrieval algorithm. To improve the accuracy, besides grouping the samples by sea surface wind speed and SST, pseudo-SSEs are introduced into the SST retrieval algorithm. The root-mean-square errors (RMSEs) of the SST retrieval algorithm in the three steps are 1.18, 0.73, and 0.68 K, respectively. Then, the SSTs in 2020 between 60°S and 60°N are retrieved from the FY-3D MWRI measurements without precipitation and heavy clouds. Finally, the SSTs retrieved in this work are validated with the GMI SST and the iQuam in situ data. The errors of the retrieved SSTs in the three steps are 0.19 ± 1.23, 0.17 ± 1.15, and 0.01 ± 1.15 K against the GMI SST, respectively, while they are 0.48 ± 1.24, 0.37 ± 1.13, and 0.12 ± 1.10 K against the iQuam in situ data, respectively. The errors of both the retrieval algorithm and the derived SSTs in this work are obviously reduced after introducing the pseudo-SSEs into the algorithm, which proves that the SST retrieval algorithm developed in this work is valid and accurate.

Plasma parameter inversion is important for space plasma physics and applications, particularly for inhomogeneous magnetized plasmas. A physics-informed deep neural network for Maxwell’s plasma coupling system is proposed in this letter. The network architecture consists of inhomogeneous plasma parameter inversion and electromagnetic field reconstruction. We verified our physics-informed neural network method for one-dimensional (1-D) Maxwell’s plasma coupling system with inhomogeneous magnetized plasma parameters. The simulation results show that this meshless method can effectively achieve simultaneous inversion of inhomogeneous plasma parameter and global field based on sparse sampling. The physics-informed deep neural network for Maxwell’s plasma coupling system has a certain generalization ability, which may be applied for more complex plasma applications.

We report observations of stimulated electromagnetic emission (SEE) induced by high power high frequency (HF) radio waves near the third electron gyroharmonic (3mathematical equation) at European Incoherent Scatter Radar (EISCAT). It is discovered that stimulated Brillouin scattering (SBS) spectrum behaves similarly as spectral ion lines of the incoherent scatter radar (ISR) for HF pumping frequency above 3mathematical equation. The SBS spectral width shows correlation with electron to ion temperature ratio Te/Ti. A new inversion method is proposed by incorporating the SBS spectral width within an artificial neural network approach to achieve electron temperature inversion for ionospheric turbulent plasmas. This work provides a potential new technique to diagnose parameters in the modified ionosphere when the ISR is not available.

This letter addresses the intercalibration of the thermal infrared (TIR) channels 11 (8.0–9.0 μm ), 12 (10.3–11.3 μm ), 13 (11.5– 12.5 μm ), and 14 (13.2–13.8 μm ) of the Advanced Geostationary Radiation Imager (AGRI) on Chinese Fengyun 4A (FY-4A) satellite against the Advanced Himawari Imager (AHI) on the Himawari 8 using the double difference (DD) method with the data in January and July of 2020. To transfer the radiometric calibration from AHI to FY-4A AGRI, an accurate TIR radiative transfer model and intercalibration equations are constructed. The results show that AGRI channel 12 is well calibrated and keeps stable in the two months. However, in other AGRI TIR channels, radiometric calibration biases are obviously observed, especially AGRI channel 11, in which the calibration biases vary by hemisphere and month. The observations in AGRI channels 13 and 14 are about 0.83 and 0.50 K underestimated, respectively. In AGRI channel 11, the observations are averagely 2.36 and 4.08 K overestimated in Northern Hemisphere and Southern Hemisphere, respectively. Moreover, in Southern Hemisphere, the observations in AGRI channel 11 in July are averagely 0.55 K warmer than those in January. The intercalibration coefficients were obtained by linear regression, and finally, the radiometric calibration biases in the AGRI TIR channels were successfully removed.

In order to cover the warm end of Earth-scene brightness temperature (TB) range of passive microwave radiometers, intercalibration over warm scenes is necessary. This article presents a methodology to intercalibrate the microwave radiation imager (MWRI) on the Chinese second-generation meteorological satellite Fengyun 3C (FY-3C) with the Global Precipitation Measurement (GPM) Microwave Imager (GMI) over the warm scenes of dense forests using the double-difference (DD) method. Based on the microwave radiative transfer model (RTM), an intercalibration method is developed, in which a modified land surface emissivity (LSE) model for dense forests is proposed. The forests with optically thick canopy are identified in terms of polarization TB differences and normalized difference vegetation index (NDVI) extracted from the latest vegetation product of Moderate-Resolution Imaging Spectroradiometer (MODIS). The matching TBs between FY-3C MWRI and GMI over dense forest warm scenes are collected and analyzed together with the TBs over ocean surfaces obtained by Zeng and Jiang (2020). The results show that: 1) FY-3C MWRI’s observations are generally underestimated, and the intercalibration biases are polynomial functions of observations; 2) the intercalibration biases at the warm end are relatively smaller than those at the cold end; and 3) the calibration in the ascending orbits (MWRIA) is relatively better than that in the descending orbits (MWRID). At the tropical rain forest scene TBs defined in this work, the intercalibration biases (mean ± standard deviation at the mean) in the FY-3C MWRI channels of 10 V, 10 H, 18 V, 18 H, 23 V, 36 V, 36 H, 89 V, and 89 H are, respectively, −1.3 ± 0.7, −1.9 ± 1.1, 1.6 ± 0.6, 2.5 ± 0.8, −0.2 ± 0.5, −2.0 ± 0.6, −2.4 ± 0.7, −0.2 ± 0.6, and −0.1 ± 0.6 K for the ascending orbits, while they are, respectively, −4.0 ± 0.8, −5.4 ± 1.2, −1.4 ± 0.7, −1.2 ± 0.8, −2.9 ± 0.5, −4.9 ± 0.7, −5.5 ± 0.7, −2.7 ± 0.8, and −2.3 ± 0.7 K for the descending orbits. The in-orbit calibration coefficients of GMI are successfully transferred to FY-3C MWRI.

3-D computerized ionospheric tomography (CIT) is an ill-posed problem due to the insufficient amount of observations, it remains challenging for practical applications. In this article, we proposed an ionospheric tomography method that combined data-driven methods with compressed sensing (CS) to deal with the ill-posed problem. First, slant total electron content (STEC) data were extracted by undifferenced and uncombined precise point positioning (UCPPP) with known fixed station coordinates. Second, data-driven methods were adopted to construct the projection matrix from the ionospheric model. Third, compressed sensing was used to derive the sparse solution based on L1 norm. The ionospheric tomography can be achieved well by using observations during the shorter time interval and in a sparse receiver distribution based on the property of compressed sensing. Results of experiment based on real Global Positioning System (GPS) observation data verified the effectiveness of the proposed methods. By comparing with the colocated ionosonde, it is found that the CS methods are more consistent with the actual ionospheric fluctuation than the modified constrained algebraic reconstruction technique (CART). In terms of the differential STEC (dSTEC) analysis, the error of the tomography model by Compressed Sensing-Principal Component Analysis (CS-PCA) is less than 0.2 TEC unit (TECU), and the time resolution is 5 min. The UCPPP with constraint by CS-PCA shows the best performance of 12.2%, 40.9% and 0.31% improvement in positioning accuracy, convergence time, and fixed rate over the UCPPP with constraint by modified CART. The proposed data-driven methods may be important for high-resolution 4-D ionospheric tomography in the future.

Accurate radiometric calibration is fundamental to quantitative remote sensing. In this article, a thermal infrared radiative transfer model is first established, and then, the on-orbit radiometric calibration of the thermal infrared channels 23 (8.55 μm ), 24 (10.8 μm ), and 25 (12.0 μm ) of the advanced MEdium Resolution Spectral Imager (MERSI-2) on the Chinese Fengyun 3D (FY-3D) satellite is evaluated and corrected against the channels of the Advanced Himawari Imager (AHI) on Himawari 8 satellite using the double difference (DD) method. The results indicate that the on-orbit radiometric calibrations in FY-3D MERSI-2 channels 23, 24, and 25 are slightly biased and certain variations exist between January and July 2020. In January 2020, the on-orbit calibration of MERSI-2 channel 23 is statistically consistent with that of AHI channel 11, and the calibration bias (mean ±standard deviation at the mean) is 0.04 ±0.27 K, whereas they are 0.11 ± 0.49 K and −0.30±0.50 K in MERSI-2 channels 24 and 25, respectively. In July 2020, the on-orbit calibration of MERSI-2 channel 25 is very consistent with that of the AHI channels with a calibration bias of 0.06 ±0.31 K, while they are −0.18±0.23 K and −0.27±0.30 K in MERSI-2 channels 23 and 24, respectively. The radiometric calibration biases are finally corrected by the linear fits on the matching observations.

Accurate and stable in-orbit radiometric calibration of a satellite instrument is fundamental to Earth geophysical parameter estimation. This article addresses the intercalibration of the microwave temperature sounder (MWTS) on the Chinese second-generation polar-orbiting meteorological satellite, Fengyun 3D (FY-3D), against the advanced technology microwave sounder (ATMS) aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. First, ocean and land microwave radiative transfer models (RTM) are constructed by combining the sea and land surface emissivity models and atmospheric absorption model, as well as the intercalibration equations. Then, the MWTS and ATMS observations are resampled into a 1° × 1° regular grid space, and the matching brightness temperatures (TBs) under clear-sky/near clear-sky conditions are collected. Next, the TBs at top-of-atmosphere are simulated using the RTM and the fifth generation of European Centre for Medium-Range Weather Forecast atmospheric reanalysis (ERA5) data. After that, the double differences between FY-3D MWTS and S-NPP ATMS and the theoretical observations in FY-3D MWTS channels are calculated. Finally, the radiometric calibration coefficients of FY-3D MWTS are successfully derived from the observations of S-NPP ATMS by linear fits on the matching TBs. In contrast to the ATMS measurements, FY-3D MWTS observations are generally overestimated, and the in-orbit radiometric calibration errors (mean ± standard deviation at the mean) are 1.83 ± 1.45, 0.45 ± 0.94, 1.87 ± 0.60, −0.20 ± 0.36, −0.02 ± 0.37, 0.19 ± 0.24, 1.69 ± 0.28, 2.25 ± 0.29, 1.97 ± 0.33, 1.74 ± 0.42, 2.84 ± 0.42, 0.07 ± 0.65, and 0.32 ± 1.18 K in FY-3D MWTS channels 1–13, respectively. The results with Hewison's semi-empirical land surface emissivity (LSE) model and the results with LSEs derived from the coincident ATMS observations at 50.3 GHz are consistent. Moreover, the intercalibration results obtained by the RTM in this work also agree well wi...

This work reports a holistic experimental investigation of stimulated Brillouin scattering (SBS) features and electron temperature inversion near the third electron gyroharmonic 3fce using the European Incoherent Scatter (EISCAT) heating facility. The evolution of SBS features including spectral offset, width, and power varies asymmetrically near 3fce. The asymmetries among SBS, electron temperature, and high frequency-enhanced ion lines are clearly exhibited for pumping above f0 ≥ 3fce. Electron temperature Te at the resonance regime has been retrieved from the measured SBS spectra based on the wave matching theory. The inversion results by SBS are consistent with measurement by the EISCAT UHF incoherent scatter radar (ISR) at the resonance altitude. The comparison of electron temperature Te and ion temperature Ti between SBS and ISR enlightens great potentials for developing realistic ionospheric diagnostic technique.

The first report is made of a holistic investigation of stimulated electromagnetic emission (SEE) during ionospheric electron gyroharmonic heating using a diagnostic approach at the High Frequency Active Auroral Research Program facility. The evolution of SEE polarization and plasma irregularity development near the third electron gyroharmonic 3fce is investigated, which provides new insights into SEE generation mechanisms by the associated parametric decay instabilities. New more complex SEE spectral line behavior is clearly observed for varying transmitter beam angles. New SEE spectral emissions are observed at ~75 kHz when pumping below 3fce. The high-frequency radar echoes and SEE polarimetry appear asymmetric for pumping above 3fce, which implies that the broad upshifted maximum spectral line formation involves plasma irregularities scattered by high-frequency radar echoes. This has important implications for understanding artificial ionization layer generation during ionospheric modification.

Semantic segmentation of LiDAR point clouds enables fine-grained understanding of large-scale 3D scenes, yet remains challenging due to the complexity and non-uniformity of real-world data. This paper presents Point AFNO-Transformer, a novel architecture that integrates adaptive frequency-domain attention into a point-based transformer framework. By incorporating a 3D adaptive Fourier neural operator, the network captures contextual dependencies in the frequency domain, complementing the spatial attention mechanism. Experiments on the Toronto-3D benchmark demonstrate that the proposed method achieves superior mean Intersection-over-Union and overall accuracy compared to spatial-domain baselines across most semantic categories. These results suggest that frequencydomain attention offers an effective and efficient alternative to purely spatial attention for large-scale point cloud segmentation, and can foster further advances in 3D scene understanding.

This paper presents the intercalibration of the solar reflective channels of the advanced MEdium Resolution Spectral Imager (MERSI-II) on Fengyun 3D (FY-3D) satellite against the channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua satellite and of the Visible infrared Imaging Radiometer (VIIRS) on the Suomi National Polar-Orbiting Partnership (S-NPP) spacecraft using the double difference (DD) method. First, an intercalibration method is constructed in terms of the radiative transfer model. Then, the reflectance at top of atmosphere (TOA) is simulated using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) code for the matching samples. Next, the DDs in the MERSI-II solar reflective channels are calculated. Finally, the intercalibration coefficients are determined using a quadratic regression. The results indicate that the intercalibration method constructed in this work is valid and accurate. Against the MODIS and VIIRS channels, the radiometric calibration accuracy of the MERSI-II solar reflective channels is within 3%. Finally, the calibration biases in the MERSI-II solar reflective channels are successfully removed.

This paper presents the directional land surface emissivity (LSE) retrieval from the mid-infrared observations acquired by the advanced MEdium Resolution Spectral Imager (MERSI-II) on Fengyun 3D (FY-3D) satellite. First, the radiometric calibration biases in the MERSI-II channels 20 (~3.8 pm) and 21 (~4.05 pm) are corrected against the channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua satellite. Then, a method, which combines a cubic split-window method to estimate bi-directional reflectance and the RossThick-LiSparse-R model, is developed to retrieve the directional LSEs from the daytime MERSI-II mid-infrared observations. Next, the directional LSEs in the MERSI-II channels 20 and 21 in July of 2020 over the Eastern Asia (18°N~50°N, 95°E~130°E) are retrieved from the daytime MERSI-II observations. Finally, the results are cross-validated with the MOD11C2 product. The results show that the calibration biases in the MERSI-II channels 20 and 21 are 0.51±0.44 K and -0.33±0.41 K, respectively. The directional LSEs range between 0.75 and 1.0 depending on land cover types. Over the sparsely vegetated and bare areas, where the normalized difference vegetation index (NDVI) is less than or equal to 0.3 (NDVI≤0.3), the LSEs vary from 0.75 to 0.9, and the retrieval errors are about 0.06 against the MOD11C2 product. Over the densely vegetated areas (NDVI>0.3), the LSEs change from 0.9 to 1.0, and the retrieval errors are about 0.004 against the MOD11C2 product.

Purely data-driven ionospheric modeling inadequately adheres to fundamental physical laws. PINN-SAMI3 [1], as a novel data assimilation framework, integrates the control equations of the fully physical model SAMI3 with neural networks to construct a physics-informed ionospheric parameter inversion framework. This paper compares PINN-SAMI3 with traditional data assimilation methods EKF [2] and 4D-Var. The test results indicate that PINN-SAMI3 outperforms EKF but has lower accuracy than the 4D-Var method. This framework has important implications for the future construction of a hybrid model of space weather prediction that integrates physical models and neural networks.

Total Precipitable water (TPW) is an important parameter to characterize the water vapor content of the atmosphere, and its distribution characteristics are of great significance to the understanding and prediction of global climate change. Satellite passive microwave remote sensing, with its all-weather and all-day observation capability and high global coverage, is currently the main means of atmospheric detection in meteorological research. The Advanced Microwave Humidity Sounder (MWHS-II) on the Chinese second-generation polarorbiting operational meteorological satellite Fengyun 3D (FY-3D) has an excellent capability of detecting the vertical structure of atmospheric humidity and precipitation parameters. In this work, a Back Propagation neural network (BPNN) algorithm is firstly developed to retrieve high-precision global TPW over sea surfaces from MWHS-II/FY-3D data. Because of high accuracy, the fifth-generation of European Centre for Medium-Range Weather Forecast atmospheric reanalysis (ERA5) data serve as ground truths, and the spatiotemporal matching points between the MWHS-II/FY-3D observations and ERA5 data are collected and used as samples to train the BPNN algorithm. The training results show that the root mean square error (RMSE) and correlation coefficient are 2.04mm and 0.99 for the test set, respectively. Then, global TPWs over sea surfaces are retrieved from the MWHS-II/FY-3D data in 2022. Finally, the retrieved TPWs are validated with the radiosonde data, and a RMSE of 3.46mm and a coefficient of 0.97 are obtained. The RMSE and coefficient of F18 SSMIS TPW product in 2022 against the radiosonde data are 4.24mm and 0.96, respectively. The validation results indicate that the TPWs retrieved in this work are more accurate than the F18 SSMIS TPW product.

This paper addresses the retrieval of land surface temperature (LST) from the measurements acquired by the Medium Resolution Spectral Imager – Low Light (MERSI-LL) on Fengyun 3E (FY-3E) satellite. First, a generalized split-window algorithm is developed using radiative transfer modelling experiments. Then, LSTs are retrieved from the FY-3E MERSI-LL measurements in Sept. of 2022 over a study area with longitude from 105°E to 135°E and latitude from 25°N to 50°N. Finally, the LSTs retrieved in this work are validated against the MOD11C1 V61 product, in which the LST product of the Advanced Geostationary Radiation Imager on Fengyun 4A (FY-4A) satellite serves as a connecting bridge. The results show that the accuracy of the LSTs retrieved from the FY-3E MERSI-LL measurements in this work is 0.01±0.75 K against the MOD11C1 V61 product.

Sea surface temperature (SST) plays an important role in tropical cyclogenesis and formation of sea fog and sea breezes. Satellite remote sensing is the only way to obtain a global and continuous coverage of SST. In this paper, a novel method combining microwave sea surface emissivity (SSE) is proposed to retrieve SST from the measurements acquired by Microwave Radiation Imager (MWRI) on Fengyun 3D (FY-3D), which has ten channels at 10.65, 18.7, 23.8, 36.5 and 89.0 GHz with both vertical and horizontal polarizations. The algorithm is applied to FY-3D MWRI data in 2020, and then the derived SSTs are validated against the NOAA iQuam in situ SST data and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) SST product. Against the iQuam in situ data and the GMI SST, the errors of the retrieved SST in the final step are 0.04±1.13 K -0.07±1.18 K, respectively. The result indicates that the SST retrieval algorithm is valid and accurate in the SST retrieval.

The second generation of Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Chinese Fengyun 3E (FY-3E) has 3053 channels and its measurements are useful to retrieve atmospheric temperature and humidity profiles. In this paper, the FY-3E HIRAS-II channels for atmospheric temperature and humidity profile retrieval are selected using the principal component analysis and weighting functions. First, the channels strongly sensitive to the trace gases are excluded Then, the Jacobi matrices for temperature and humidity profiles are subjected to principal component analysis separately to calculate the cumulative influence coefficients of each channel on the principal components, and the channels are ranked according to the magnitude of the cumulative influence coefficients. Next, more channels are selected using the weighting functions. 190 and 154 FY-3E HIRAS-II channels are selected for the retrievals of atmospheric temperature and humidity profiles, respectively. Finally, the channel selections are evaluated using the retrieval performance. The results show that the channel selection is valid, and the accuracy of both atmospheric temperature and humidity profile retrieval is improved with the channels selected by the PCA method and the weighting function method, especially near the surfaces.

Kinetic approaches are generally accurate in dealing with microscale plasma physics problems but are computationally expensive for large-scale and multi-scale systems. One of the long-standing problems in plasma physics is the integration of kinetic effects into fluid models, which is often achieved through analytical closure terms. In this work, data-driven approaches are adopted to incorporate fluid closures in a multi-moment fluid model, and consequently, it can accurately capture the collisionless Landau damping. We investigate two different machine learning approaches 1) the mPDE-Net architecture with an explicitly formulated fluid closure and 2) the physics-informed neural network (PINN) with an implicit fluid closure. The learned multi-moment fluid models are constructed from and tested against fully kinetic Vlasov simulation data. The newly constructed fluid models can successfully capture the time evolution of the electric field energy, including its damping rate. This work sheds light on the accurate and efficient modeling of large-scale systems, which can be extended to complex multiscale laboratory, space, and astrophysical plasma physics problems.

This paper addresses typhoon identification and positioning by using the thermal infrared data acquired by the Advanced Geostationary Radiation Imager (AGRI) on the Chinese Fengyun 4A (FY-4A) satellite. First, a training dataset, a validation dataset, and a test dataset of typhoons in the West Pacific Ocean close to China are created from the FY-4A AGRI thermal infrared data. Then, the YOLOX neural network is configured and trained, in which an average precision (AP) of 33.2 is obtained for the test dataset. Finally, with the prior knowledge that the brightness temperature of the typhoon eye is higher than that of its surroundings, the typhoon eyes are located using the morphological image processing method. The results of typhoon eye positioning are generally consistent with the Optimal Path (OP) dataset of tropical cyclones created by the China Meteorology Administration (CMA), and the mean errors in latitude and longitude are 0.0391° and 0.0334°, respectively.

Accurate calibration of satellite instrument is the premise of Earth geophysical parameter estimation. This paper presents the intercalibration of the Microwave Temperature Sounder (MWTS) on Fengyun 3D (FY-3D) against the Advanced Technology Microwave Sounder (ATMS) on Suomi National Polar-orbiting Partnership (S-NPP) satellite using microwave radiative transfer model fed with the fifth generation of European Centre for Medium-Range Weather Forecast (ECMWF) atmospheric reanalysis (ERA5) data. The results show that the calibration biases (mean ± standard deviation) are 1.83±1.45, 0.45±0.94, 1.87±0.60, 0.60±0.27, -0.02±0.37, 0.19±0.24, 1.69±0.28, 2.25±0.29, 1.97±0.33, 1.74±0.42, 2.84±0.42, 0.07±0.65, and 0.32±1.18 K in the thirteen MWTS channels, respectively. In addition, the results with the semi-empirical Land Surface Emissivity (LSE) model and the results with LSEs derived from ATMS observations are consistent, and the use of LSEs derived from ATMS observations for the rest of channels is feasible.

This talk will report on new observations of the complex dynamics of wideband stimulated electromagnetic emission (SEE) generation with simultaneous measurement of field aligned irregularities (FAIs) near the third electron gyroharmonic frequency by high power high frequency HF radio waves from High Frequency Active Auroral Research Program (HAARP) facility. The time evolution of the wideband SEE spectral component is more clearly investigated with the benefit of tilted beam angle heating experiments. It is observed for the first time that HF radar echoes intensify for pump frequencies above electron gyroharmonic frequencies at HAARP, which indicates correlation between enhanced FAIs and the formation of the so-called Broad Upshifted Maximum BUM spectral line. The polarimetry of SEE spectral lines are also analyzed to reveal that the downshifted maximum DM spectral line has a higher degree of circular polarization for heating above gyroharmonic frequencies. To explain the asymmetric enhanced FAIs and correlated BUM emissions, a four-wave parametric process is investigated by using two-dimensional electrostatic particle-in-cell simulations for the first time. The variation of the four-wave decay instability is investigated for a range of plasma and pump wave frequency parameters. The numerical simulation results show the dependence of the maximum parametric decay process on the wave propagation angle with respect to the magnetic field, which agrees with theoretical analysis. The threshold of the BUM and 2BUM is also investigated for different electron oscillating velocity and pump wave frequency near electron gyroharmonics. The temporal-spatial characteristics for small scale plasma irregularities is also obtained. The comparison of the BUM observations with theoretical models provides a powerful tool for specifying nonlinear processes for plasma irregularity excitation.