An early warning approach for the rapid identification of extreme weather disasters based on phased array dual polarization radar cooperative network data.

In recent years, X-band phase array dual polarization weather radar technology has matured. The cooperative networking data from X-band phase array dual polarization weather radar have many advantages compared with traditional methods, namely, high spatial and temporal resolution (approximately 70 seconds in one scan, 30 m in radial distance resolution), wide coverages that can compensate for the observation blind spots, and data fusion technology that is used in the observation overlap area to ensure that the observed precipitation data have spatial continuity. Based on the above radar systems, this study proposes an improved hail and lightning weather disaster rapid identification and early warning algorithm. The improved thunderstorm identification, tracking, analysis, and nowcasting (TITAN) algorithm is used to quickly identify three-dimensional strong convective storm cells. Large sample observation experiment data are used to invert the localized hail index (HDR) to identify the hail position. The fuzzy logic method is used to comprehensively determine the probability of lightning occurrence. The comparative analysis experiment shows that, compared with the live observation data from the ground-based automatic station, the hail and lightning disaster weather warning algorithm developed by this study can increase warning times by approximately 7 minutes over the traditional algorithm, and its critical success index (CSI), false alarm ratio (FAR) and omission alarm ratio (OAR) scores are better than those of the traditional method. The average root mean square error (ARMSE) for identifying hail and lightning locations by this improved method is also significantly better than that of traditional methods. We show that our method can provide probabilistic predictions that improve hail and lightning identification, improve the precision of early warning and support operational utility at higher resolutions and with greater lead times that traditional methods struggle to achieve.

Automatic registration of urban high-resolution remote sensing images based on characteristic spatial objects.

Automatic registration of high-resolution remote sensing images (HRRSIs) has always been a severe challenge due to the local deformation caused by different shooting angles and illumination conditions. A new method of characteristic spatial objects (CSOs) extraction and matching is proposed to deal with this difficulty. Firstly, the Mask R-CNN model is utilized to extract the CSOs and their positioning points on the images automatically. Then, an encoding method is provided to encode each object with its nearest adjacent 28 objects according to the object category, relative distance, and relative direction. Furthermore, a code matching algorithm is applied to search the most similar object pairs. Finally, the object pairs need to be filtered by position matching to construct the final control points for automatic image registration. The experimental results demonstrate that the registration success rate of the proposed method reaches 88.6% within a maximum average error of 15 pixels, which is 28.6% higher than that of conventional optimization method based on local feature points. It is reasonable to believe that it has made a beneficial contribution to the automatic registration of HRRSIs more accurately and efficiently.

Janus sand filter with excellent demulsification ability in separation of surfactant-stabilized oil/water emulsions: An experimental and molecular dynamics simulation study.

Developing efficient separation materials for surfactant-stabilized oil/water emulsions is of great importance while significantly challenging. In this work, a sand filter with Janus channels was prepared by simply mixing superhydrophilic and superhydrophobic quartz sand in a mass ratio of 1:1. Due to the imbalanced force of droplets in those Janus channels, better separation performance under gravity was achieved for both surfactant-stabilized oil-in-water and water-in-oil emulsions than the superhydrophilic or superhydrophobic sand filter alone. It also received high flux (1080.13 L m-2 h-1 for dichloroethane-in-water emulsion and 1378.07 L m-2 h-1 for water-in-dichloroethane emulsion) and high separation efficiency (99.80% for dichloroethane-in-water emulsion and 99.98% for water-in-dichloroethane emulsion). Molecular dynamics based computational work and experimental studies revealed that the Janus channels of mixed sand layer exhibited greater interaction energy with emulsion droplets for more efficient adsorption, resulting in better demulsification capability and separation performance. The as-prepared Janus sand filters retained excellent separation performance after 50 cycles of the stability test. Together with the needs on only cheap and easily accessible raw materials and its environmentally friendly preparation method, this Janus sand filtration process exhibits its great potential for the separation of surfactant-stabilized oil/water emulsions.