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.

Two near‐real‐time and one post‐processing products from the Integrated Multi‐satellite Retrievals for Global Precipitation Measurement Mission (GPM IMERG) were evaluated during the period from 2016 to 2018 in the wet seasons (June–November) over the Sichuan Basin, China. Results indicated the following: (1) The three products could generally replicate strong precipitation well; however, significantly large biases were observed when detecting weak precipitation. (2) All three products replicated summer precipitation more accurately than autumn precipitation. The IMERG “early run” product (IMERG‐E) largely underestimated precipitation in the wet season, while the “latter run” (IMERG‐L) and “final run” products (IMERG‐F) could counteract this to a certain degree. (3) IMERG‐F captured weak, moderate, and strong precipitation well during the wet season, and IMERG‐E showed excellent potential in detecting different precipitation intensities. All the three products could replicate the diurnal cycle of the wet season precipitation. The findings of this study can facilitate the application of IMERG products in regions with complex topography and have also highlighted the potential of IMERG‐E for rapid early warning and forecast systems.

Convolutional neural networks (CNNs) have been utilized extensively to improve the resolution of weather radar. Most existing CNN-based super-resolution algorithms using PPI (Plan position indicator, which provides a maplike presentation in polar coordinates of range and angle) images plotted by radar data lead to the loss of some valid information by using image processing methods for super-resolution reconstruction. To solve this problem, a weather radar that echoes the super-resolution reconstruction algorithm—based on residual attention back-projection network (RABPN)—is proposed to improve the the radar base data resolution. RABPN consists of multiple Residual Attention Groups (RAGs) connected with long skip connections to form a deep network; each RAG is composed of some residual attention blocks (RABs) connected with short skip connections. The residual attention block mined the mutual relationship between low-resolution radar echoes and high-resolution radar echoes by adding a channel attention mechanism to the deep back-projection network (DBPN). Experimental results demonstrate that RABPN outperforms the algorithms compared in this paper in visual evaluation aspects and quantitative analysis, allowing a more refined radar echo structure, especially in terms of echo details and edge structure features.

Large-area soil moisture (SM) data with high resolution and precision are the foundation for the research and application of hydrological and meteorological models, water resource evaluation, agricultural management, and warning of geological disasters. It is still challenging to downscale SM products in complex terrains that require fine spatial details. In this study, SM data from the Soil Moisture Active and Passive (SMAP) satellite were downscaled from 36 to 1 km in the summer and autumn of 2017 in Sichuan Province, China. Genetic-algorithm-optimized backpropagation (GABP) neural network, random forest, and convolutional neural network were applied. A fusion model between SM and longitude, latitude, elevation, slope, aspect, land-cover type, land surface temperature, normalized difference vegetation index, enhanced vegetation index, evapotranspiration, day sequence, and AM/PM was established. After downscaling, the in situ information was fused through a geographical analysis combined with a spatial interpolation to improve the quality of the downscaled SM. The comparative results show that in complex terrains, the GABP neural network better captures the soil moisture variations in both time and space domains. The GDA_Kriging method is able to merge in situ information in the downscaled SM while simultaneously maintaining the dynamic range and spatial details.

The vertical structure of radar echo is crucial for understanding complex microphysical processes of clouds and precipitation, and for providing essential data support for the study of low-level wind shear and turbulence formation, evolution, and dissipation. Therefore, finding methods to improve the vertical data resolution of the existing radar network is crucial. Existing algorithms for improving image resolution usually focus on increasing the width and height of images. However, improving the vertical data resolution of weather radar requires a focus on improving the elevation angle resolution while maintaining distance resolution. To address this challenge, we propose a network for super-resolution reconstruction of weather radar echo vertical structures. The network is based on a multi-scale residual feedback network (MR-FBN) and uses new multi-scale feature residual blocks (MSRB) to effectively extract and utilize data features at different scales. The feedback network gradually generates the final high-resolution vertical structure data. In addition, we propose an elevation upsampling layer (EUL) specifically for this task, replacing the traditional image subpixel convolution layer. Experimental results show that the proposed method can effectively improve the elevation angle resolution of weather radar echo vertical structure data, providing valuable help for atmospheric detection.