Soil moisture (SM) is an important index of soil drought, and it directly controls the energy balance and water cycle of the land surface. As an indicator and amplifier of global warming, the Tibetan Plateau (TP) is becoming warmer and wetter. Because of its particular geographical environment, large‐scale measurements of SM on the TP can only be achieved by satellite remote sensing. The resolution of current SM product of the Soil Moisture Active Passive (SMAP) satellite is 36 km, which is insufficient for many practical applications. In this study, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model (DEM) are applied to increase the resolution of SM down to 1 km using the Random Forest (RF) algorithm. The preliminary results of the proposed algorithm are evaluated by station observations and other reanalysis products. The downscaled results are more consistent with the in situ observations, the Land Data Assimilation System (CLDAS) from China Meteorological Administration (CMA), and the Global Land Data Assimilation System (GLDAS) from National Aeronautics and Space Administration (NASA) than the original SMAP product. The downscaling algorithm is most effective for grasslands. It is demonstrated that high‐resolution SM products can be generated by fusing various features using machine‐learning algorithms.

Weather radars are commonly used to track the development of convective storms due to their high resolution and accuracy. However, the coverage of existing weather radar is very limited, especially in mountainous and ocean areas. Geostationary meteorological satellites can provide near global coverage and near real-time observations, which can compensate for the lack of radar observations. In this paper, a deep learning method was used to estimate the radar composite reflectivity from observations of China’s new-generation geostationary meteorological satellite FY-4A and topographic data. The derived radar reflectivity products from satellite observations can be used over regions without radar coverage. In general, the deep learning model can reproduce the overall position, shape, and intensity of the radar echoes. In addition, evaluation of the reconstruction radar observations indicates that a modified model based on the attention mechanism (Attention U-Net model) has better performance than the traditional U-Net model in terms of all statistics such as the probability of detection (POD), critical success index (CSI), and root-mean-square error (RMSE), and the modified model has stronger capability on reconstructing details and strong echoes.

In the atmosphere, cloud particles have different shapes. The study of cloud particle shapes plays an important role in understanding cloud precipitation processes, radiative transfer, and weather modification. The image resolution and data quality of cloud probes affect the accuracy of the classification of particle shapes. To solve the occlusion of the photosensitive edge of the particle image and achieve automatic, high-precision ice-crystal classification of airborne Cloud Imaging Probe (CIP) ice-crystal images, this study uses a traditional image processing algorithm for data quality control and applies artificial intelligence algorithms to classify ice-crystal images. At present, there are mainly two types of ice-crystal classification methods, one classifies the shape of ice crystals using a pattern parameterization scheme, and the other uses an artificial intelligence network model to classify the shape. Combined with data quality control, the dataset was tested on eight models, and the TL-EfficientNet-b6 model was found to be the most accurate. Therefore, the TL-EfficientNet-b6 classifier model was used in this study, which is a newly developed convolutional neural network (CNN) based on a transfer learning method. Experimental results show that the TL-EfficientNet-b6 model can reach 100% in the single-class precision of tiny and hexagonal ice crystals, and the average precision can reach 98%. These results are more accurate than those using traditional classification methods. This method could be valuable in cloud microphysics research and weather modification.

Doppler spectra measured by vertically pointing radars are inherently linked to raindrop size distributions (DSDs). But, accurate estimation of DSDs remains challenging because raindrop spectra are broadened by atmospheric turbulence and shifted by vertical air motions. This paper presents a novel method to estimate vertical air motions that there is no need to assume a model for DSD at each range gate. The theory of the new method is that the spectral difference between the adjacent range gates is contributed by vertical air motions and the variability of DSDs. The contribution of the change of DSDs is estimated by looking up the prepared tables (LUTs) of raindrop velocity difference and shape function difference. Then, the vertical air motions can be estimated by minimizing the cost function of the two spectra between the adjacent range gates. The retrieval algorithm is applied to three cases including a stratiform and two convective observed by a C band vertically pointing radar in Longmen, Guangdong province of China in June 2016. Before that, the spectrum broadening effect is removed by the traditional deconvolution method with a wind profiler. The vertical profiles of precipitation parameters are also retrieved to investigate the microphysical process. The precipitation parameters retrieved near the surface are compared with the ground data collected by a two-dimensional video disdrometer(2DVD) and the results show good agreements.

With the wide application of advanced communication and information technology, false data injection attack (FDIA) has become one of the significant potential threats to the security of smart grid. Malicious attack detection is the primary task of defense. Therefore, this paper proposes a method of FDIA detection based on vector auto-regression (VAR), aiming to improve safe operation and reliable power supply in smart grid applications. The proposed method is characterized by incorporating with VAR model and measurement residual analysis based on infinite norm and 2-norm to achieve the FDIA detection under the edge computing architecture, where the VAR model is used to make a short-term prediction of FDIA, and the infinite norm and 2-norm are utilized to generate the classification detector. To assess the performance of the proposed method, we conducted experiments by the IEEE 14-bus system power grid model. The experimental results demonstrate that the method based on VAR model has a better detection of FDIA compared to the method based on auto-regressive (AR) model.

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments.