Clouds play an important role in the Earth’s climate system; however, various observational methods describe clouds differently, leading to cloud products being described with different characteristics, and affecting our understanding of cloud effects. To address this problem, this study integrates different cloud products into the transfer-learning procedure of a deep learning model and determined the Cloud Effective Radius (CER), Cloud Optical Thickness (COT), and Cloud Top Height (CTH) from Himawari-8 thermal infrared measurements. The retrieval results were independently evaluated against the Moderate-resolution Imaging Spectroradiometer cloud products and further compared with Himawari-8 cloud products during the day. The Root Mean Squared Errors (RMSE) of the model for the CER, COT, and CTH were 4.490 μm, 11.198, and 1.904 km, respectively, which are lower than those of Himawari-8 cloud products (RmSe:11.172 μm, 14.755, and 2.860 km). Moreover, validation results against active sensors show that the model performs slightly better during the day than at night, and both are generally better than the Himawari-8 cloud product. Overall, the model maintains stable performance during both day and night, and its accuracy is higher than that of Himawari-8 cloud products.

Atmospheric pollution is a critical issue in public health systems. The simulation of atmospheric pollution dispersion in urban blocks, using CFD, faces several challenges, including the complexity and inefficiency of existing CFD software, time-consuming construction of CFD urban block geometry, and limited visualization and analysis capabilities of simulation outputs. To address these challenges, we have developed a prototype system that couples 3DGIS and CFD for simulating, visualizing, and analyzing atmospheric pollution dispersion. Specifically, a parallel algorithm for coordinate transformation was designed, and the relevant commands were encapsulated to automate the construction of geometry and meshing required for CFD simulations of urban blocks. Additionally, the Fluent-based command flow was parameterized and encapsulated, enabling the automatic generation of model calculation command flow files to simulate atmospheric pollution dispersion. Moreover, multi-angle spatial partitioning and spatiotemporal multidimensional visualization analysis were introduced to achieve an intuitive expression and analysis of CFD simulation results. The result shows that the constructed geometry is correct, and the mesh quality meets requirements with all values above 0.45. CPU and GPU parallel algorithms are 13.3× and 25× faster than serial. Furthermore, our case study demonstrates the developed system’s effectiveness in simulating, visualizing, and analyzing atmospheric pollution dispersion in urban blocks.

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

Radiative transfer models are widely applied in climate models to simulate vertical temperature perturbations caused by external radiative forcings. A large part of radiative transfer models is the infrared gaseous spectral transmittance scheme, which quantifies the longwave atmospheric absorption. A rapid infrared gaseous spectral transmittance scheme, called the Optimized alternate Mapping Correlated K‐Distribution model (OMCKD), is introduced in this paper. To improve the accuracy of our scheme without increasing pseudo‐monochromatic calculations, we introduce the optimal iteration method to automatically tune the equivalent absorption coefficients in the cumulative probability subspace. In addition, a new expression weighted by black‐body radiation is introduced to calculate the equivalent absorption coefficient. The OMCKD simulates heating rate and radiation flux with errors of less than 0.12 K d⁻¹ and 0.35 W m⁻², respectively, below stratopause for standard atmospheric profiles. The OMCKD is also evaluated and compared with the rapid radiative transfer model for general circulation models (RRTMG) in realistic atmospheric profiles. We found that OMCKD can accurately produce heating rates and generally captures radiative forcings associated with large perturbations to the concentrations of main greenhouse gases. Furthermore, the number of pseudo‐monochromatic calculations in OMCKD is 11.4% less than that in RRTMG, which indicates less computational cost.

Five δ-two-stream and δ-four-stream schemes are compared in solar spectra using the RapidvRadiative Transfer Model for General Circulation Models Applications (RRTMG). By calculating the flux and heating rate in various atmosphere, it is found that, in accuracy, the δ-four-stream schemes overwhelmingly outperform the δ-two-stream schemes. The precision of adding algorithm of the δ-four-stream spherical harmonic expansion (δ-4SDA) is comparable to that of adding algorithm of the δ-four-stream discrete ordinates method (δ-4DDA). Further more, the accuracy of the adding algorithm of δ-Eddington approximation (δ-2SDA) is close to that of δ-two-stream approximation with Practical Improved Flux Method (δ-PIFM), while adding algorithm of δ-two-stream discrete ordinates method (δ-2DDA) produces the poorest results among the five approximate schemes. For the RRTMG model with radiative transfer calculation, the computational time of δ-4SDA is about 1.5 times that of δ-two-stream schemes, and the computational time of δ-4SDA is about 88% that of δ-4DDA.