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Climate model evaluation work has made progress both in theory and practice, providing strong support for better understanding and predicting climate change. However, at the weather scale, there is relatively little assessment of climate models in terms of daily-scale climate phenomena, such as storm frequency and intensity. These weather-scale variables are of significant importance for our understanding of the impacts of climate change. In order to assess the capability of climate models to simulate weather-scale climate patterns, this study employs Self-Organizing Maps (SOMs) for weather pattern classification. By combining different evaluation metrics, varying the number of SOM types, changing the size of the study area, and altering the reference datasets, the climate models are evaluated to ensure the robustness of the assessment results. The results demonstrate that the size of the study area is positively correlated with observed differences, and there are correlations among different evaluation metrics. The highest correlation is observed between evaluation metrics in large-scale and small-scale spatial domains, while the correlation with SOM size is relatively low. This suggests that the choice of evaluation metrics has a minor impact on model ranking. Furthermore, when comparing the correlation coefficients calculated using the same evaluation metrics for different-sized regions, a significant positive correlation is observed. This indicates that variations in the size of the study area do not significantly affect model ranking. Further investigation of the relationship between model performance and different SOM sizes shows a significant positive correlation. The impact of dataset selection on model ranking is also compared, revealing high consistency. This enhances the reliability of model ranking. Taking into account the influence of evaluation metric selection, SOM size, and reanalysis data selection on model performance assessment, significant variations in model ranking are observed. Based on cumulative ranking, the top five models identified are ACCESS1-0, GISS-E2-R, GFDL-CM3, MIROC4h, and GFDL-ESM2M. In conclusion, factors such as evaluation metric selection, study area size, and SOM size should be considered when assessing model ranking. Weather pattern classification plays a crucial role in climate model evaluation, as it helps us better understand model performance in different weather systems, assess their ability to simulate extreme weather events, and improve the design and evaluation methods of model ensemble predictions. These findings are of great significance for optimizing and strengthening climate model evaluation methods and provide valuable insights for future research. [ABSTRACT FROM AUTHOR]

Autumn is the transitional season when the atmospheric circulation pattern changes from summer to winter. The temperature and precipitation in Southeastern China in autumn are significantly influenced by the change in circulation patterns, and both show significant uniqueness. The clustering method can be used to observe the changes of circulation patterns in detail and to observe and analyze the transition from warm to cold seasons from a detailed view of the daily circulation pattern perspective. This method may have important research implications on how to study the generation and dissipation of extreme weather events. The Self-Organizing Maps (SOM) method is used to a 500 hPa geopotential height and 850 hPa wind and sea level pressure for 1981–2020 to identify the characteristic weather patterns (WTs) in autumn (September–November) over Southeastern China. Characteristics of the captured WTs are also analyzed in terms of the distribution characteristics of weather patterns, occurrence frequency, typical progression, precipitation and extreme precipitation (EP), temperature and extreme high temperature (EHT), and the relationship with atmospheric teleconnection. Nine WTs were identified in autumn, which represents a series of weather situations consisting of troughs and ridges. On this basis, these WTs were used to carry out the differentiation of seasonal differences between early and late autumn. The maximum mean and extreme precipitation occur in several early season patterns (WT1, WT2, WT4, and WT7). It is highly likely that extremely high temperatures occur in the WT1 and WT2 patterns. The most common progression between WTs is WT7−WT1−WT2−WT4 in the early season. This seasonality allows us to distinguish between early and late seasons based on daily weather types. A preliminary trend analysis suggests that patterns in the early season occur more frequently and last longer in the early season, and patterns in the late season occur less frequently and later. That is, the longer cool season pattern is shifting to the shorter warm season pattern. In addition, the persistence of both cool and warm patterns increased during 2001–2020 relative to 1981–2000, and the risk of both flooding and drought occurrence is on the rise. [ABSTRACT FROM AUTHOR]

How to predict and study persistent extreme precipitation events (PEPEs) with a prediction period of 1–2 weeks is an important scientific problem faced by the meteorological circles at home and abroad. Based on the accurate description of the flood range caused by 14-day PEPEs, the comprehensive analysis method was used to obtain the weather characteristics related to 14-day PEPEs (including abnormal trough/ridge, westerly jet, atmospheric river (AR) activity, teleconnections, etc.). First, we selected three regions in China, North China (NC), the Yangtze River valley (YRV), and South China (SC), analyzed their 14-day PEPEs in summer (June to August), and composited them into an average circulation (500 hPa geopotential height field) to compare the weather patterns related to PEPEs in these regions. Then, several variables are composited to understand the evolution of the atmospheric state before and during the occurrence of PEPEs. Finally, potential applications of several teleconnection types and composites in advance prediction are studied. The main findings include: the common weather signals during the occurrence of PEPEs are characterized by obvious and continuous a high-low-high saddle field circulation configuration (conducive to the formation of frequent heavy rainfall), active westerly jet (westerly jet is the controlling factor of precipitation), and enhanced water vapor transport (significantly increased atmospheric river activity). In this study, some key characteristics and development of PEPEs were identified, the formation mechanism of China's 14-day PEPEs was revealed, the role of ARs in PEPEs was recognized, and the PEPEs precursor signal was extracted. Furthermore, PEPEs in different regions were also compared, which played an important role in understanding and predicting similar events. [ABSTRACT FROM AUTHOR]

Multi-pass conventional spinning is the preferable forming technology for the forming of thin-walled conical part with curved surface (TCPCS) in aerospace field. In multi-pass conventional spinning, the design of roller path is a critical problem due to its sensitive effect on the deformation mode and forming defect during spinning process. However, at present, the roller path is still mainly designed based on experience and trial and error, which seriously restricts the high-performance spinning of TCPCS. In this work, a new quantitative method based on circumferential strain distribution was developed for the roller path design in multi-pass conventional spinning of TCPCS. In this method, the total required circumferential strain for the forming of final TCPCS by conventional spinning was firstly determined. Then, the spinning passes number was obtained through dividing the total required circumferential strain by the ultimate circumferential strain producing the spinning instability. As for the roller path profile in each pass, it is divided into two sections and determined, respectively, i.e., the attaching mandrel section and the preforming section. The attaching mandrel section presents the same profile of mandrel. The profile of preforming section is determined point by point by distributing the rest of circumferential strain to produce the final TCPCS. The point-by-point distributed circumferential strain is half of the the total required circumferential strain in the initial stage until it reaches the half of the ultimate circumferential strain, and then, it will keep the half of the ultimate circumferential strain to the end. The proposed new method of roller path design was validated by finite element simulation, where well spinning stability, wall thickness distribution and roundness were obtained. This method provides a quantitative, high-efficient and universal way for the roller path design in conventional spinning of TCPCS. [ABSTRACT FROM AUTHOR]

Using the self-organizing maps (SOM) method, we ranked and compared the simulation results of atmospheric circulation and precipitation for 32 global climate models (GCMs) in the Coupled Model Intercomparison Project Phase 5 (CMIP5) over China, and found that the ranking of the GCM's ability to simulate the frequency of sea level pressure (SLP) weather patterns (WPs) was not correlated with the ranking of its ability to simulate annual precipitation WPs. Then, we attributed the precipitation simulation differences and identified three main components for the differences in the multi-model simulation results: internal variability, frequency differences, and the combined term of the two, with internal variability being the largest of the three components. These three deviations depend ultimately on two factors: the ability to simulate the frequency of WPs and the ability to simulate the corresponding average daily precipitation generated by these WPs, with the second factor playing a decisive role. Then, to address the drawback that the model ensemble results cannot be effectively improved when each single model that makes up the ensemble model is dry or wet, a solution was proposed to correct for the simulation differences: the nodal precipitation differences of each WP were corrected. After the correction of the simulation differences, the simulation capability of all the individual models was greatly improved, which increases our confidence in using the CMIP5 models for future weather patterns and precipitation simulation and forecasting. [ABSTRACT FROM AUTHOR]

Background: As a result of the continuous rise in the incidence of type 2 diabetes mellitus (T2DM), related cardiovascular diseases (CVDs) have been a main healthy burden worldwide. This study aimed to investigate the potential role of FGD5-AS1 as a biomarker for the diagnosis of T2DM and predicting cardiovascular complications in T2DM. Methods: Three hundred subjects were recruited in this study, including 100 T2DM patients without CVDs, 100 T2DM patients with CVDs as well as 100 healthy subjects. Plasma FGD5-AS1 level was quantified using RT-qPCR assay. The correlation of FGD5-AS1 level with other key variables was assessed using Pearson correlation analysis. ROC curve analysis was performed to evaluate the diagnostic value of FGD5-AS1 for T2DM and related CVDs. The effect of FGD5-AS1 on AC16 and HA-VSMCs was determined. Results: FGD5-AS1 level showed a stepwise decrease in individuals with T2DM and CVDs compared to healthy persons. FGD5-AS1 was associated with BMI, systolic blood pressure, diastolic blood pressure, fasting glucose, 2-h postprandial blood glucose, HbA1c, triglycerides, usCRP, and HDL-cholesterol. The ROC analysis indicated FGD5-AS1 had a significant overall predictive ability to diagnose T2DM, T2DM with CVDs, and the combination of both. FGD5-AS1 increases the growth but alleviates apoptosis and fibrosis of high glucose-induced AC16 cells. FGD5-AS1 attenuate the growth and calcification but induced apoptosis of high glucose-treated HA-VSMC cells. Conclusions: These results suggest that FGD5-AS1 are associated with T2DM and measuring FGD5-AS1 could potentially contribute to T2DM screening and prediction for risk of cardiovascular complication. [ABSTRACT FROM AUTHOR]

It is very important to identify the possible mechanism of precipitation generation in southeastern China. To determine the weather type (WT) that is likely to generate precipitation, the Jenkinson–Collison classification method was applied to wintertime daily mean sea level pressure data across southeastern China. We found that all WTs with easterly components are prone to generate precipitation. These WTs were merged into one type named the ensemble of easterly wind components (the EE type). The persistence and transformation rules of the EE type are studied. Anticyclone type is the easiest one to convert to EE type. The persistence and transformation rules of the EE type provide favourable conditions for the formation of precipitation. Furthermore, we examined the influence of two common teleconnections (Western Pacific [WP] and Eurasian [EU]) in East Asia in winter on the WTs. When WP is in the positive phase and EU is in the negative phase, EE type weather patterns will appear frequently in southeastern China. At this time, it is most conducive to the formation of precipitation. On the one hand, warm and cold air converge in this area. On the other hand, the prevailing east wind can provide sufficient water vapour. The study implies that whether a WT is easy to produce precipitation can be judged by wind direction. The results will help us better understand the physical mechanism of precipitation generation in this area. [ABSTRACT FROM AUTHOR]

It is very important to identify the possible mechanism of precipitation generation in southeastern China. To determine the weather type (WT) that is likely to generate precipitation, the Jenkinson–Collison classification method was applied to wintertime daily mean sea level pressure data across southeastern China. We found that all WTs with easterly components are prone to generate precipitation. These WTs were merged into one type named the ensemble of easterly wind components (the EE type). The persistence and transformation rules of the EE type are studied. Anticyclone type is the easiest one to convert to EE type. The persistence and transformation rules of the EE type provide favourable conditions for the formation of precipitation. Furthermore, we examined the influence of two common teleconnections (Western Pacific [WP] and Eurasian [EU]) in East Asia in winter on the WTs. When WP is in the positive phase and EU is in the negative phase, EE type weather patterns will appear frequently in southeastern China. At this time, it is most conducive to the formation of precipitation. On the one hand, warm and cold air converge in this area. On the other hand, the prevailing east wind can provide sufficient water vapour. The study implies that whether a WT is easy to produce precipitation can be judged by wind direction. The results will help us better understand the physical mechanism of precipitation generation in this area. [ABSTRACT FROM AUTHOR]

Archaeological evidence demonstrates that China was one of many early centers of pig domestication. Yet, it remains uncertain whether there was a single center or multiple centers within China. A recent ancient DNA study of archaeological pig remains hypothesized that in addition to the North China Plain, Northeast China may have also been a pig domestication center. In this study, we conducted ancient DNA analysis on 35 pig remains from Wanfabozi (6–2.2 kBP) and Dashanqian (4–2.2 kBP) in order to better understand the regional history of ancient pigs in Northeast China. Analysis of a 279 bp fragment of the mitochondrial D-loop indicates that pigs at these sites, particularly Wanfabozi, exhibit high haplotype diversity relative to sites in the North China Plain. Our analysis also revealed multiple haplotypes at these sites that are unique to Northeast China as well as some haplotypes shared with pigs in the North China Plain. The high genetic diversity and unique haplotypes at these sites likely reflect the use of wild boars, which are still abundant in the region today. Moreover, the presence of haplotypes shared with the North China Plain suggests domestic pigs were not likely independently domesticated in Northeast China but imported from the North China Plain. The results of our study also highlight the difficulties surrounding the identification of wild versus domestic swine using mtDNA alone and addresses the need to incorporate other lines of evidence when assessing the domestications status of archaeological pigs. • Pigs from Northeast China and earlier pigs from North China share mtDNA haplotypes. • Data suggest Northeast China was not an independent center of pig domestication. • Extensive haplotype sharing between Chinese domestic pigs and wild boars. • Results demonstrate that domestication was a continuous process. • To understand domestication, need to integrate DNA and archaeological data. [ABSTRACT FROM AUTHOR]

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China. [ABSTRACT FROM AUTHOR]

Linking sinuosity, a fairly recently developed metric, with high temperature extremes (HTEs) can be both useful and insightful to better understand the physical mechanisms behind HTEs. However, it is not clear whether there exists a relationship between the sinuosity changes and HTE changes in present and future climate conditions over southeastern China. In this paper, the anomalous characteristics of the atmospheric circulation are quantified by sinuosity. Three sinuosity metrics are used in this study: individual sinuosity (SIN), aggregate sinuosity (ASIN), and comprehensive sinuosity (CSIN). Furthermore, we examine the relationship between sinuosity changes and HTE changes in present and future climate conditions. ASIN is strongly correlated with surface air temperature (SAT). We find that the influence of individual sinuosity (SIN) at different latitudes on the SAT of southeastern China is different. The SIN of low (middle) latitude isohypses has significant positive (negative) correlations with the SAT of southeastern China. The SIN of high-latitude isohypses is rather limited and can therefore be ignored. The projected relationship between the sinuosity changes and HTE changes in the late 21st century suggests similar results. The change in SAT is related to the changes in climate variables over southeastern China in the future, and these changes increase with the increase in Z500 or V850 and the decrease in U500. Moreover, the frequencies of large (small) comprehensive sinuosity (CSIN) values at low (mid) latitudes will increase. At the end of the 21st century, Z500 isohypses at different latitudes will have an obvious poleward shift. Our results indicate that measuring the aggregate waviness of the midtropospheric flow (via sinuosity) can provide insight regarding HTEs, and the climate model output can be used to examine the future likelihood of increased HTE. [ABSTRACT FROM AUTHOR]

Extreme precipitation has often occurred in Southeastern China, while the possible mechanism is not clear. In order to bridge the scale gap between large-scale circulation and extreme precipitation, in this paper, the k-means clustering technique—a common method of weather-type (WT) analysis—was applied to regional 850-hPa wind fields. The reasonable determination of k values can make the later WT analyses more reliable. Thus, the Davies–Bouldin (BD) criterion index is used in the clustering process, and the optimal value of the k was determined. Then, we obtain and analyze the frequency, persistence, and progression of WTs. The rule of transitions from one WT to another may help explain some of the physical processes in winter. We found a special evolutionary chain (WT3→WT1→WT2→WT5→WT3) that can be used to explain the cold wave weather process in winter. Different WTs in the evolutionary chain correspond well to different stages of the cold wave weather process (gestation (WT3), outbreak (WT1), eastward withdrawal (WT2), and extinction (WT5)). In addition, we found that there are obvious differences in precipitation between December and February. After reassembling five kinds of WTs, two modes are formed: dry WTs and wet WTs. Our research shows that the intraseasonal variation of precipitation can be attributed to the fluctuation between the wet and dry WTs, and the different phases of teleconnection can correspond well with it. For example, the relative frequencies of wet WTs are higher in February. These WTs correspond to the positive phase of the WP and ENSO, the negative phase of the EA and EU, and the strong MJO state of the second, third, and eighth phase. Our work has well established the relationship between synoptic scale and large-scale circulation, which provides a reference for climate model simulation and future climate prediction. [ABSTRACT FROM AUTHOR]

In the die-less spinning of the conical part, the forming state changes during the spinning process and depends on the forming parameters, which has a critical effect on the forming quality of the spun part. In this paper, a criterion of the forming states is established, and its dependence on the forming parameters is investigated. It is found that the formed wall thickness changes during die-less spinning. According to the formed wall thickness variation degree, three forming states are defined: shear spinning state ( t f = t 0 s i n α , where t 0 and t f are the initial and formed wall thicknesses, respectively, and α is half-cone angel), the conventional spinning state ( t f > 0.9 t 0 ), and transition state between the shear and conventional spinning states ( t 0 s i n α < t f ≤ 0.9 t 0 ). The distribution of stress and strain in three forming states is obviously different. The die-less spinning always proceeds in the sequence of shear spinning state, transition state, and conventional spinning state. The initial forming state may be any one of them, which is mainly determined by the diameters of the blank and general mandrel. Thus, the quantitative dependence of initial forming state on the diameters of blank and general mandrel is further analyzed, and a criterion for the initial forming state is correspondingly established. In addition, the influence of forming parameters, including the blank parameters, mold parameters, and process parameters, on the criterion for the initial forming state in die-less spinning is revealed. This paper could improve the understanding of the deformation mechanism in die-less spinning. [ABSTRACT FROM AUTHOR]