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A record-breaking precipitation event, with a maximum 24-h (1-h) precipitation of 624 mm (201.9 mm) observed at Zhengzhou Weather Station, occurred in Henan Province, China, in July 2021. However, all global operational forecast models failed to predict the intensity and location of maximum precipitation for this event. The unexpected heavy rainfall caused 398 deaths and 120.06 billion RMB of economic losses. The high-societal-impact of this event has drawn much attention from the research community. This article provides a research review of the event from the perspectives of observations, analysis, dynamics, predictability, and the connection with climate warming and urbanization. Global reanalysis data show that there was an anomalous large-scale circulation pattern that resulted in abundant moisture supply to the region of interest. Three mesoscale systems (a mesoscale low pressure system, a barrier jet, and downslope gravity current) were found by recent high-resolution model simulation and data assimilation studies to have contributed to the local intensification of the rainstorm. Furthermore, observational analysis has suggested that an abrupt increase in graupel through microphysical processes after the sequential merging of three convective cells contributed to the record-breaking precipitation. Although these findings have aided in our understanding of the extreme rainfall event, preliminary analysis indicated that the practical predictability of the extreme rainfall for this event was rather low. The contrary influences of climate warming and urbanization on precipitation extremes as revealed by two studies could add further challenges to the predictability. We conclude that data sharing and collaboration between meteorological and hydrological researchers will be crucial in future research on high-impact weather events. [ABSTRACT FROM AUTHOR]

Tea plants are evergreen broad-leaved perennial shrubs, and their spectral characteristics are very similar to those of other evergreen vegetation, making it difficult to distinguish them. Currently, the most commonly used method of tea plantation extraction is machine learning, which classifies tea plantation through various feature combinations and algorithms. The disadvantage of these methods is that they require a large number of local training samples, making it challenging to produce an accurate model applicable to a large region. Furthermore, complex feature combinations and indicators may result in over-fitting, reducing the accuracy of the results. Therefore, it is necessary to develop a new algorithm suitable for tea plantation extraction in extensive regions. This paper uses Shihe District, Henan Province, China as a case in 2019, combined with Landsat-7/8 and Sentinel-2A/B images, and develops a new phenological-based algorithm to extract the tea plantation area. Firstly, we generated an evergreen vegetation map. Secondly, based on high-quality time series curves, the tea plant growth period was divided into seven parts to extract phenological indicators for classification. Finally, the tea plantation in the study area was extracted on a per-pixel basis. The overall accuracy of the algorithm is 87.59%, and the Kappa coefficient is 0.80. This study demonstrates the potential of the phenology-algorithms in extracting tea plantation areas and provides an advanced scheme and scientific basis for extracting tea plantation in other years, and also offers a reference for identifying tea plantation in other regions. Additionally, this paper generated a map of classified phenological indicators to provide guidance for monitoring tea plants growth. [ABSTRACT FROM AUTHOR]

Snow depth is an important parameter to characterize the characteristics of snow cover, and it is also one of the most sensitive response factors to regional climate change. However, the extent of snow depth variability and its driving mechanisms are still unknown in China. Therefore, in this study, we used the regression analysis, root-mean-square error analysis, anomalous year analysis, and correlation analysis methods to explore the spatiotemporal variation characteristics of snow depth in China from 1979 to 2019 based on the reanalysis snow depth dataset. The results show that (1) the snow distribution in China is obviously spatially heterogeneous, and the southeastern, western, and southern regions of the Qinghai-Tibet Plateau, northern Xinjiang, and northeastern China have high values of snow depth; (2) the high-value regions are also the sensitive regions for anomalous variations in snow depth in China; (3) in the past 41 years, the interannual variability of snow depth in China has shown a significantly decreasing trend, and the linear tendency of snow depth is − 0.093 cm/10 a (p < 0.01) and the snow depth in four seasons showed a decreasing trend (p < 0.05); and (4) the driving factors of snow heterogeneity are dissimilar in different regions and seasons. In temperate zones, average air temperature is the main factor affecting snow depth in cold temperature, mid temperature, and warm temperature zones; the maximum air temperature is the main factor affecting snow depth in mid temperate and warm temperate zones. Both the minimum air temperature and the average land-surface temperature are important factors affecting the snow depth in the cold temperate, mid temperate and warm temperate zones, and all passed the significance test of 0.01. [ABSTRACT FROM AUTHOR]

Smartphones are increasingly being equipped with atmospheric measurement sensors providing huge auxiliary resources for global observations. Although China has the highest number of cell phone users, there is little research on whether these measurements provide useful information for atmospheric research. Here, for the first time, we present the global spatial and temporal variation in smartphone pressure measurements collected in 2016 from the Moji Weather app. The data have an irregular spatiotemporal distribution with a high density in urban areas, a maximum in summer and two daily peaks corresponding to rush hours. With the dense dataset, we have developed a new bias-correction method based on a machine-learning approach without requiring users' personal information, which is shown to reduce the bias of pressure observation substantially. The potential application of the high-density smartphone data in cities is illustrated by a case study of a hailstorm that occurred in Beijing in which high-resolution gridded pressure analysis is produced. It is shown that the dense smartphone pressure analysis during the storm can provide detailed information about fine-scale convective structure and decrease errors from an analysis based on surface meteorological-station measurements. This study demonstrates the potential value of smartphone data and suggests some future research needs for their use in atmospheric science. [ABSTRACT FROM AUTHOR]

• Sentinel-1/-2 data complement each other in evergreen forest mapping. • The new phenology indicator was proposed to map evergreen forest. • First 10 m resolution map of evergreen forest in the Ta-pieh Mountain. • The new method has the potential to extend the other region and years. Evergreen forests are sensitive to climate change and their role in the exchange of carbon, water, and energy in terrestrial ecosystems is irreplaceable. The temporal and spatial variation of the evergreen forest is closely related to forest monitoring and management in China, which has a far-reaching impact on forest protection and sustainable development. However, the lack of annual fine resolution maps of evergreen forests limits our exploration of the evolution of the spatiotemporal patterns of evergreen forests. Therefore, it is necessary to timely and accurately maps evergreen forests with high spatial resolution. We used a new phenology index of NDVI max -NDVI winter_max to identify evergreen forests at the 10-m scale. First, we mapped annual 2019 land cover types to obtain the forest mask. Second, we calculated the classification phenology index by extracting the difference between evergreen forests and deciduous forests in phenological characteristics. Finally, we extracted the evergreen forest in 2019 from the annual forest map based on the constructed phenological indicators. The kappa coefficient of our annual forest map for 2019 was 0.92, and the overall, producer and user accuracies were 97.98%, 90.09%, and 97.46%, respectively. The kappa coefficient of the annual evergreen forest map in 2019 was 0.98, and the overall, producer and user accuracies were 99.73%, 97.84%, and 99.68%, respectively. Our study shows that the new phenological index can identify and map evergreen forests on complex landforms dominated by evergreen-deciduous mixed forests, which can be applied to other regions and years in China. The results of this study have reference value for evaluating the spatial distribution and resource management of evergreen forests. [ABSTRACT FROM AUTHOR]

As a Chinese traditional patent medicine, Tripterygium wilfordii glycosides (TWG) have been approved by the China State Food and Drug Administration (Z32021007) for autoimmune and inflammatory diseases. Application of TWG leads to significant decrease of the inflammatory cytokines, such as IL-6, IL-1β, and TNF-α. However, little is known whether TWG could regulate the anti-inflammatory cytokines and what the mechanism is. Here, we found that TWG could induce the upregulation of IL-37 which is a new anti-inflammatory cytokine. Furthermore, the inhibitors of ERK1/2 and/or p38 MAPK pathways suppressed IL-37 expression induced by TWG, indicating that the two pathways took part in this process. In conclusion, TWG could upregulate the anti-inflammatory cytokine IL-37 and ERK1/2 and p38 MAPK signal pathways were involved in the upregulation of IL-37 induced by TWG. The results showed that TWG had a potent activity on promoting the expression of IL-37, a new anti-inflammatory cytokine, which help further understanding the anti-inflammatory mechanism for the clinical application of TWG in therapy of diseases. [ABSTRACT FROM AUTHOR]

Double cropping is an important cropping system in China, with more than half of China's cropland adopting the practice. Under the background of global climate change, agricultural policies, and changing farming practices, double-cropping area has changed substantially. However, the spatial-temporal dynamics of double cropping is poorly understood. A better understanding of these dynamics is necessary for the northern limit of double cropping (NLDC) to ensure food security in China and the world and to achieve zero hunger, the second Sustainable Development Goal (SDG). Here, we developed a phenology-based algorithm to identify double-cropping fields by analyzing time-series Moderate Resolution Imaging Spectroradiometer (MODIS) images during the period 2000–2020 using the Google Earth Engine (GEE) platform. We then extracted the NLDC using the kernel density of pixels with double cropping and analyzed the spatial-temporal dynamics of NLDC using the Fishnet method. We found that our algorithm accurately extracted double-cropping fields, with overall, user, and producer accuracies and Kappa coefficients of 95.97%, 96.58%, 92.21%, and 0.91, respectively. Over the past 20 years, the NLDC generally trended southward (the largest movement was 66.60 km) and eastward (the largest movement was 109.52 km). Our findings provide the scientific basis for further development and planning of agricultural production in China. [ABSTRACT FROM AUTHOR]

The emergent highly pathogenic avian influenza A (H7N9) (HPAI) virus is a major public concern in China. Therefore, it is crucially important to develop an effective vaccine against this virus. In this study, we constructed a baculovirus vaccine expressing the hemagglutinin (HA) of H7N9 strain A/Chicken/Jiaxing/148/2014 (JX148). The recombinant baculovirus (rBac-JX148HA) generated in this study showed good growth in insect cells and good safety, and it stably expressed the HA protein. We compared the immunogenicity and efficacy of the inactivated whole-virus vaccine JX148 and rBac-JX148HA. One chicken in the JX148-treated group died on day 4 post-challenge, and three chickens had typical clinical symptoms (survival rate, 90%; morbidity, 40%). However, no chickens immunized with rBac-JX148HA showed clinical signs during the 14-day observation period. An analysis of viral shedding and viral replication demonstrated that rBac-JX148HA more efficiently inhibited viral shedding and viral replication than the inactivated whole-virus vaccine. Taken together, these results indicate that the inactivated recombinant baculovirus vaccine induces a high hemagglutination inhibition antibody titer, provides complete protection against challenge with the highly pathogenic H7N9 virus, and effectively inhibits viral shedding. Therefore, the candidate vaccine has potential utility in the prevention and control of H7N9 avian influenza and is also appropriate for veterinary vaccines using cell suspension culture technology.