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Abstract In this work, we proposed a method of extracting feature parameters for deep neural network prediction based on the vectorgraph storage format, which can be applied to the design of electromagnetic metamaterials with sandwich structures. Compared to current methods of manually extracting feature parameters, this method can automatically and precisely extract the feature parameters of arbitrary two-dimensional surface patterns of the sandwich structure. The position and size of surface patterns can be freely defined, and the surface patterns can be easily scaled, rotated, translated, or transformed in other ways. Compared to the pixel graph feature extraction method, this method can adapt to very complex surface pattern design in a more efficient way. And the response band can be easily shifted by scaling the designed surface pattern. To illustrate and verify the method, a 7-layer deep neural network was built to design a metamaterial broadband polarization converter. Prototype samples were fabricated and tested to verify the accuracy of the prediction results. In general, the method is potentially applicable to the design of different kinds of sandwich-structure metamaterials, with different functions and in different frequency bands.

With the development of the mobile communication technology and the acceleration of 5G commercial network deployment, energy consumption of 5G, which will continue to raise the operating expense significantly.How to maximize the energy efficiency while ensuring service experience and equipment safety has always been one of the research focus in the industry.With the challenges including complexity of network architecture and variety of base station types, an AI-based energy-saving technology including policy generation and closed-loop security assurance of “perception, prediction, analysis, and decision” was introduced.After calibration and validation based on the offline dataset, the false-switch-off rate is less than 2%, and the recall rate is not fewer than 84%.Further study shows that the technology has greater potential on energy-saving.

Abstract Surface downward longwave radiation (SDLR) is a major component of the energy budget. Although studies have reported the spatiotemporal variations of SDLR in China, the spatiotemporal coverage of the situ measurements used is always limited. In this study, the gradient boosting regression tree (GBRT) was developed to reconstruct SDLR based on air temperature (Ta), relative humidity (RH), and downward shortwave radiation (DSR). Ground measurements collected at the Baseline Surface Radiation Network (BSRN) and the Arid and Semi‐arid Region Collaborative Observation Project (ASRCOP) were used to build and evaluate the GBRT model. The evaluation results showed that the daily SDLR estimates are correlated well with the SDLR in situ, with an overall root mean square errors (RMSE) of 16.5 Wm−2 and a correlation coefficient (R) value of 0.91 for the validation data set. Comparison with existing SDLR products showed that accuracy and trends of the SDLR estimates based on the GBRT method are reasonable. To obtain long‐term SDLR data for spatiotemporal analysis over China, densely distributed reconstructed DSR and ground measured Ta and RH collected at 756 Chinese Meteorological Administration (CMA) stations were used as input to estimate the SDLR based on the GBRT method over China during 1958–2015. The long‐term estimated SDLRs at the selected 563 stations showed that SDLR increased at an average rate of 1.3 Wm−2 per decade over China from 1958 to 2015. The trend of SDLR is positively correlated with the trend in Ta and water vapor pressure, whereas negatively correlated with the trend in DSR.

Metropolis’ contribution (anthropogenic aerosols) to solar dimming and brightening remains a hot topic of special concern over the past several decades. However, urbanization effects on surface incident solar radiation ( R _s ) have not been comprehensively investigated. In this study, the urbanization effects on solar dimming and brightening were addressed using the densely distributed reconstructed R _s data at 375 stations and 92 urban–rural station pairs over the time period of 1960–2019 in China. The results indicate that the impacts of urbanization on the monthly mean R _s is 0.86 ± 7.99 W m ^−2 during the study period, while the impact is 0.90 ± 8.30 W m ^−2 and 0.82 ± 8.26 W m ^−2 for the solar dimming (1960–1992) and brightening (1992–2019) phase, respectively. The urbanization effects on the trend of R _s is −0.39 and 0.16 W m ^−2 per decade during dimming and brightening, respectively. It also found that urbanization effects on R _s trend differs strikingly in magnitudes for specific regions in China. Generally, urbanization speeds up China’s dimming in the dimming phase and slows down China’s brightening in the brightening phase.

Surface upward longwave radiation (SULR) is an indicator of thermal conditions over the Earth’s surface. In this study, we validated the simulated SULR from 51 Coupled Model Intercomparison Project (CMIP6) general circulation models (GCMs) through a comparison with ground measurements and satellite-retrieved SULR from the Clouds and the Earth’s Radiant Energy System, Energy Balanced and Filled (CERES EBAF). Moreover, we improved the SULR estimations by a fusion of multiple CMIP6 GCMs using multimodel ensemble (MME) methods. Large variations were found in the monthly mean SULR among the 51 CMIP6 GCMs; the bias and root mean squared error (RMSE) of the individual CMIP6 GCMs at 133 sites ranged from −3 to 24 W m−2 and 22 to 38 W m−2, respectively, which were higher than those found between the CERES EBAF and GCMs. The CMIP6 GCMs did not improve the overestimation of SULR compared to the CMIP5 GCMs. The Bayesian model averaging (BMA) method showed better performance in simulating SULR than the individual GCMs and simple model averaging (SMA) method, with a bias of 0 W m−2 and an RMSE of 19.29 W m−2 for the 133 sites. In terms of the global annual mean SULR, our best estimation for the CMIP6 GCMs using the BMA method was 392 W m−2 during 2000–2014. We found that the SULR varied between 386 and 393 W m−2 from 1850 to 2014, exhibiting an increasing tendency of 0.2 W m−2 per decade (p < 0.05).

It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (Ld, 4–100 μm) dataset. Although a number of global Ld datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily Ld dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (Sd), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated Ld dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily Ld dataset were 17.78 W m−2, 0.99 W m−2, and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated Ld dataset performed better than the clouds and earth’s radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated Ld dataset showed an increasing trend of 1.8 W m−2 per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated Ld dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

The downward longwave radiation (Ld, 4–100 μm) is a major component of research for the surface radiation energy budget and balance. In this study, we applied five machine learning methods, namely artificial neural network (ANN), support vector regression (SVR), gradient boosting regression tree (GBRT), random forest (RF), and multivariate adaptive regression spline (MARS), to estimate Ld using ground measurements collected from 27 Baseline Surface Radiation Network (BSRN) stations. Ld measurements in situ were used to validate the accuracy of Ld estimation models on daily and monthly time scales. A comparison of the results demonstrated that the estimates on the basis of the GBRT method had the highest accuracy, with an overall root-mean-square error (RMSE) of 17.50 W m−2 and an R value of 0.96 for the test dataset on a daily time scale. These values were 11.19 W m−2 and 0.98, respectively, on a monthly time scale. The effects of land cover and elevation were further studied to comprehensively evaluate the performance of each machine learning method. All machine learning methods achieved better results over the grass land cover type but relatively worse results over the tundra. GBRT, RF, and MARS methods were found to show good performance at both the high- and low-altitude sites.

Downward surface solar radiation (Rs) plays a dominant role in determining the climate and environment on the Earth. However, the densely distributed ground observations of Rs are usually insufficient to meet the increasing demand of the climate diagnosis and analysis well, so it is essential to build a long-term accurate Rs dataset. The extremely randomized trees (ERT) algorithm was used to generate Rs using routine meteorological observations (2000–2015) from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA). The estimated Rs values were validated against ground measurements at the national scale with an overall correlation coefficient value of 0.97, a mean bias of 0.04 Wm−2, a root-mean-square-error value of 23.12 Wm−2, and a mean relative error of 9.81%. It indicates that the estimated Rs from the ERT-based model is reasonably accurate. Moreover, the ERT-based model was used to generate a new daily Rs dataset at 756 CDC/CMA stations from 1958 to 2015. The long-term variation trends of Rs at 454 stations covering 46 consecutive years (1970–2015) were also analyzed. The Rs in China showed a significant decline trend (−1.1 Wm−2 per decade) during 1970–2015. A decreasing trend (−2.8 Wm−2 per decade) in Rs during 1970–1992 was observed, followed by a recovery trend (0.23 Wm−2 per decade) during 1992–2015. The recovery trends at individual stations were found at 233 out of 454 stations during 1970–2015, which were mainly located in southern and northern China. The new Rs dataset would substantially provide basic data for the related studies in agriculture, ecology, and meteorology.