Improving Nowcasting of Intense Convective Precipitation by Incorporating Dual-Polarization Radar Variables into Generative Adversarial Networks.

The nowcasting of strong convective precipitation is highly demanded and presents significant challenges, as it offers meteorological services to diverse socio-economic sectors to prevent catastrophic weather events accompanied by strong convective precipitation from causing substantial economic losses and human casualties. With the accumulation of dual-polarization radar data, deep learning models based on data have been widely applied in the nowcasting of precipitation. Deep learning models exhibit certain limitations in the nowcasting approach: The evolutionary method is prone to accumulate errors throughout the iterative process (where multiple autoregressive models generate future motion fields and intensity residuals and then implicitly iterate to yield predictions), and the "regression to average" issue of autoregressive model leads to the "blurring" phenomenon. The evolution method's generator is a two-stage model: In the initial stage, the generator employs the evolution method to generate the provisional forecasted data; in the subsequent stage, the generator reprocesses the provisional forecasted data. Although the evolution method's generator is a generative adversarial network, the adversarial strategy adopted by this model ignores the significance of temporary prediction data. Therefore, this study proposes an Adversarial Autoregressive Network (AANet): Firstly, the forecasted data are generated via the two-stage generators (where FURENet directly produces the provisional forecasted data, and the Semantic Synthesis Model reprocesses the provisional forecasted data); Subsequently, structural similarity loss (SSIM loss) is utilized to mitigate the influence of the "regression to average" issue; Finally, the two-stage adversarial (Tadv) strategy is adopted to assist the two-stage generators to generate more realistic and highly similar generated data. It has been experimentally verified that AANet outperforms NowcastNet in the nowcasting of the next 1 h, with a reduction of 0.0763 in normalized error (NE), 0.377 in root mean square error (RMSE), and 4.2% in false alarm rate (FAR), as well as an enhancement of 1.45 in peak signal-to-noise ratio (PSNR), 0.0208 in SSIM, 5.78% in critical success index (CSI), 6.25% in probability of detection (POD), and 5.7% in F1.

Risk assessment of land subsidence based on GIS in the Yongqiao area, Suzhou City, China.

This study focuses on the Yongqiao District in Suzhou City, Anhui Province, China, aiming to analyze the current situation of ground settlement and its influencing factors in the area. The selected risk indices include settlement rate, cumulative settlement amount, groundwater level drop funnel, thickness of loose sediment layer, thickness of soft soil layer, and the number of groundwater extraction layers. Additionally, vulnerability indices such as population density, building density, road traffic, and functional zoning are considered. An evaluation index system for assessing land Subsidence risk was established. The risk evaluation of land Subsidence was conducted using the Hierarchical analysis-composite index method and ArcGIS spatial analysis, The evaluation results show that the area of higher risk area is about 2.82 km2, accounting for 0.96% of the total area, mainly distributed in the area of Jiuli village, Sanba Street. The middle risk area is distributed around the higher area, with an area of about 9.18 km2, accounting for 3.13% of the total area. The lower risk areas were distributed in most of the study area, covering an area of 222.24 km2, accounting for 75.82% of the total area. The low risk assessment area is mainly distributed in Bianhe Street and part of Zhuxianzhuang Town, with an area of about 58.88 km2, accounting for 20.09% of the total area. The findings of this study are not only crucial for informing local policies and practices related to land use planning, infrastructure development, and emergency response but also enhance our understanding of the complexities of land Subsidence processes and their interactions with human activities, informing future research and practice in environmental risk assessment and management.

Distribution, risk assessment, and quantitative source apportionment of heavy metals in surface sediments from the shelf of the northern South China Sea.

The concentration of heavy metals (Cu, Pb, Zn, Cr, Co, and Ni) from 421 sediment samples from the shelf of the northern margin of the South China Sea (SNSCS) was analyzed. The heavy metal content and calculated potential ecological indicators (Eri < 40 and RI < 160) indicate that there is low potential ecological risk of heavy metal pollution in the SNSCS. The mean effects range-median quotient (M-ERM-Q) and hazard quotient (HQ) values of sediment toxicological characteristics indicate that heavy metals are a potential toxicological risk. The high-risk area is mainly distributed in the southwest of the nearshore SNSCS. The positive matrix factorization (PMF) analysis results showed that major contributors to heavy metal pollution were natural sources and anthropogenic activities in the SNSCS. The government should pay particular attention to the monitoring of heavy metals in the nearshore southwest of the SNSCS.

Distribution and source determination of rare earth elements in sediment collected from the continental shelf off Hainan Island, China.

Contents of rare earth elements (REEs), major elements, and the total organic carbon (TOC) were determined for 152 surface sediment samples collected from the continental shelf off Hainan Island (CSHI). From high to low, the average contents of REEs were as follows: Ce > La > Nd > Pr > Sm > Gd > Dy > Er > Yb > Eu > Ho > Tb > Tm > Tm. The LREEs in the south are more abundant than in the north, which is shown by the higher LREE/HREE values in south than in the north. This resulted higher values for the LREE/HREE ratio in the south than in the north. The mean enrichment factor (EF) could be arranged from highest to lowest as follows: Tm > Sm > Pr > Er > La > Lu > Ce > Tb > Eu > Nd > Yb > Gd > Ho > Dy. The EF indicates that pollution as a result of human activity was more serious in the southeast of the study area than in the north. The factors affecting the REE concentrations in this area include naturally occurring minerals and industrial pollution. Based on the spatial variation of upper continental crust (UCC)-normalized REE concentrations, the CSHI was classified into three geochemical provinces. The sediment of province I was controlled by the Red and Pearl rivers. The composition of the province II is mainly controlled by the Red River and the Pearl River, although some sediments have originated from the South China Sea Island. Province III sediments mainly originated from sources on Hainan Island.

The concentration distribution and biohazard assessment of heavy metal elements in surface sediments from the continental shelf of Hainan Island.

The level of heavy metals (Cr, Co, Ni, Cu, Zn, and Pb) and Al2O3 were quantified in 140 surface sediment samples from the continental shelf of Hainan Island (CSHI). The mean heavy metal concentration in the decreasing order was: Cr > Zn > Pb > Ni > Cu > Co. Although heavy metals were locally enriched in the CSHI, the overall pollution level was relatively low. The biohazard assessment results of the mean effects range-median quotient (M-ERM-Q) and hazard quotient (HQ) for a single metal element (Cu, Pb, Zn) in the surface sediments showed that the exposure to individual heavy metals posed a low health risk. However, the biohazard assessment of multiple metals was higher than the single metals. Particular attention should be paid to the potential biological hazard from Cr and Ni in the CSHI.

Two new karlotoxins found in Karlodinium veneficum (strain GM2) from the East China Sea.

The dinoflagellate Karlodinium veneficum is a harmful algal bloom species with a worldwide distribution. This small athecate dinoflagellate makes a family of polyketide toxins that are hemolytic, cytotoxic and ichthyotoxic. The first chemical structure for karlotoxins from East China Sea (ECS) is reported here. The two new karlotoxins, namely 4,5-dihydro-KmTx 2 (compound 1) and 4,5-dihydro-dechloro-KmTx 2 (compound 2), were isolated and purified from monoalgal cultures of K. veneficum strain GM2. Their structures were determined by spectroscopic analysis, including tandem mass spectrometry as well as 1D and 2D NMR experiments. These new karlotoxin congeners feature a saturated polyol arm different from previously reported for KmTx 2 that appears to increase hemolytic activity.