Multiphysics simulation, which models the interactions between multiple physical processes, and multi-component simulation of complex structures are critical in fields like nuclear and aerospace engineering. Previous studies use numerical solvers or ML-based surrogate models for these simulations. However, multiphysics simulations typically require integrating multiple specialized solvers-each for a specific physical process-into a coupled program, which introduces significant development challenges. Furthermore, existing numerical algorithms struggle with highly complex large-scale structures in multi-component simulations. Here we propose compositional Multiphysics and Multi-component PDE Simulation with Diffusion models (M2PDE) to overcome these challenges. During diffusion-based training, M2PDE learns energy functions modeling the conditional probability of one physical process/component conditioned on other processes/components. In inference, M2PDE generates coupled multiphysics and multi-component solutions by sampling from the joint probability distribution. We evaluate M2PDE on two multiphysics tasks-reaction-diffusion and nuclear thermal coupling--where it achieves more accurate predictions than surrogate models in challenging scenarios. We then apply it to a multi-component prismatic fuel element problem, demonstrating that M2PDE scales from single-component training to a 64-component structure and outperforms existing domain-decomposition and graph-based approaches. The code is available at github.com/AI4Science-WestlakeU/M2PDE.

Monitoring aerosols over the oceans is critical for understanding Earth’s climate and air quality. Although polarization can substantially reduce uncertainty in aerosol retrievals, current algorithms rely mainly on multi-view polarimeters, and no dedicated algorithm is available for single-view polarimeters over the ocean. Here, we present the first ocean algorithm for a spaceborne single-view polarimeter, demonstrated with the Particulate Observing Scanning Polarimeter (POSP) onboard the GF-5(02) satellite. Our algorithm combines multi-spectral polarization with machine-learning-accelerated radiative transfer calculation and seasonally clustered global aerosol models. Validation with AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) data demonstrates high accuracy, with RMSEs of 0.061, 0.479, and 0.037 for AOD550, AE670-870, and SSA550using AERONET, and 0.030 and 0.259 for AOD550and AE670-870using MAN, respectively. Comparison with retrievals from the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm confirms that our algorithm performs comparably to GRASP products. These results underscore the necessity and feasibility of developing specialized aerosol retrieval algorithms for single-view polarimeters, and pave the way for global aerosol over the ocean monitoring.

The Qinghai-Tibet Plateau (QTP) stores a significant amount of organic carbon in permafrost regions, and the temporal dynamic changes under permafrost degradation remain uncertain. In this study, integrating on-site and multi-source remote sensing data, we proposed a dual-input small-sample deep learning framework for estimating the soil organic carbon (SOC) density and storage at the depth of 0-3m in permafrost regions based on attention mechanisms and deep learning (DL) methods. Our model achieved an improvement of 10.6% and 22.9% in the accuracy of SOC estimation compared to previous studies in the shallow (0-30 cm) and deep (0-100 cm) layers of permafrost regions, respectively. The SOC storage over permafrost regions to the depth of 3m were 14.27 ± 4.38 Pg and 12.26 ± 2.02 Pg in 2005 and 2020 respectively, suggesting a release of 2.01 ± 0.48 Pg of SOC over the past 15 years. The carbon release intensities per unit area in thermokarst lakes and retrogressive thaw slumps are approximately 24.6 times and 21.0 times higher than the mean value of whole permafrost regions, respectively. The factor analysis revealed that precipitation, NDVI (Normalized Difference Vegetation Index), and MAGT (Mean Annual Ground Temperature) are the primary controlling factors when estimating the shallow layers (0-30 cm). In contrast, at deeper layers, the spatial distribution of shallower SOC, soil water content, and DEM possess greater weight. This finding is crucial for modeling SOC storage and its dynamics in the permafrost regions on the QTP.

Polarized Scanning Atmospheric Corrector (PSAC) is an optical sensor onboard HuanjingJianzai-2 (HJ-2) A/B satellites. One of its missions is to monitor precipitable water vapor (PWV) by using its near-infrared (NIR) channels. Since the accuracy of the commonly used NIR PWV retrieval algorithm developed based on radiative transfer model (RTM) would be significantly affected by radiometric decay of sensors, and the recalibration of decayed sensors is a complex process, it is interesting and necessary to find a robust PWV retrieval algorithm that is not affected by sensor decay. At present, a semi-empirical algorithm constructed based on the matching results between ground-based PWV data and the actual PSAC observations has been used for the PWV retrieval of PSAC. Since the systematic calibration error of PSAC is considered in constructing the algorithm, it should be able to remove the negative effects of sensor decay on PWV retrieval results. Because the above inference has not been confirmed quantitatively, it is necessary to evaluate the accuracy of the algorithm in the presence of sensor decay. The evaluation results based on simulated data show that the accuracy of the semi-empirical algorithm does not change regardless of the presence or absence of radiometric decay in PSAC. Moreover, the algorithm is used for PWV retrieval of MODIS to test its effectiveness. Compared with the official PWV data developed based on RTM, the MODIS PWV data developed by using the semi-empirical algorithm are reduced by more than 50% in both absolute and relative errors.

Polarized Scanning Atmospheric Corrector (PSAC) onboard the Huanjing Jianzai (HJ)-2 A/B satellites is a cross-track scanning polarimetric remote sensor that measures the intensity and direction of light reflected by the Earth and its atmosphere by 9 full polarized spectral bands from near-ultraviolet (near-UV) to shortwave infrared (SWIR). In particular, geolocation accuracy is an important factor for polarization observations. An automatic coastline inflection method (CIM) is implemented for PSAC geolocation error estimation. Over five months of globally middle or low latitude coastline area measurements are used to obtain statistical result. The results of the comparison with the Global Self-consistent, Hierarchical, High-resolution Geography Database (GSHHG) show PSAC geolocation error is smaller than 0.38 ground sample distance (GSD) or 3.25 km in 95% confidence level. In cross-track direction, the geolocation error estimation is affected by the instrument sampling characteristics like spatial response function (SRF). Thus, the correction method is proposed by establishing relationship between measurement radiance in CIM and offset proportion of PSAC GSD. The biases are obviously reduced after correction.

As the successors of the overdue HuanjingJianzai-1 (HJ-1) satellites and new members in Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianZai-2 series satellites (HJ-2 A/B) have been launched on September 27, 2020. Each satellite carries four sensors, including the Polarized Scanning Atmospheric Corrector (PSAC), the charge-coupled device (CCD) camera, the hyperspectral imager (HSI) and the infrared spectroradiometer (IRS). Among them, PSAC is mainly used for the monitoring of atmospheric parameters to provide data support for atmospheric environmental monitoring and atmospheric correction of data from other sensors. To test the in-orbit performance of PSAC, we develop the “day-1” aerosol and water vapor retrieval algorithms. The preliminary validation results based on ground-based observations show that the aerosol optical depth (AOD) and columnar water vapor (CWV) datasets developed based on PSAC data have high accuracy and can effectively characterize the temporal trends of AOD and CWV. The accuracy of PSAC AOD dataset is better than the expected error ±(0.05 + 0.2 * AODAERONET), and the accuracy of PSAC CWV dataset is better than the expected error ±(0.5 + 0.15 * CWVAERONET). To eliminate the negative impact of the atmosphere on CCD data and expand its application range, aerosol and water vapor data developed based on PSAC are used for atmospheric correction of CCD data. Compared with L1 CCD data, the texture details and clarity of CCD data after atmospheric correction have been significantly improved.

As the successors of the HuanjingJianzai-1 (HJ-1) series satellites in the Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HJ-2 A/B satellites have been successfully launched on the September 27 of 2020. The Polarized Scanning Atmospheric Corrector (PSAC) sensors, onboard the HJ-2 A/B satellites, are served as the synchronously atmospheric correction instrument requiring high speed and accurate aerosol optical depth (AOD) algorithm. For this purpose, we proposed a neural network based AOD retrieval model (named the AODNet), which takes full advantage of the multispectral measurements of PSAC for AOD retrieval with a high speed. The training of AODNet is conducted by the simulated observation data (currently applicable for the China region) from the forward calculation using the radiative transfer model. In this way, the land surface reflectance (LSR) is no need for our well trained model. It is expected to be one of the effective ways to solve the ill-pose problem in the decoupling of the atmosphere and surface information in AOD retrieval. Either of Sun-sky radiometer Observation NETwork (SONET) AOD or AErosol RObotic NETwork (AERONET) AOD was used to validate the AODNet AOD. The correlation coefficient is higher than 0.85 and more than 60% of the AODNet AOD can fall into the expected error envelope of ±(0.05+20%). The cross-comparison shows that the AODNet has better accuracy than MODIS Dark Target (DT) and Deep Blue (DB) algorithm. The air pollution episode is well characterized by the AODNet AOD using PSAC data.