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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]

The economic externality of transportation infrastructure is related to the local policy environment. China's high-speed rail (HSR) is developing simultaneous with urban agglomeration (UA). This study examines the synergy of HSR operation and UA policy implementation on the urban economy. With the data on 285 prefecture-level cities of China joining the HSR network and UAs from 2003 to 2018, we use staggered difference-in-differences (DID) design to test the individual and joint impacts of HSR and UA policy on urban economy respectively. Our result shows that the joint impact of the two interventions is significantly stronger than the individual impact, which means the existence of the synergy effect. Exploration of the synergy mechanism identifies three channels through which the HSR effect is strengthened by UA policy: (a) Institutional integration effect: UA policy has eliminated the administrative barriers in HSR operation. (b) Spillover effect: UA policy has diffused the benefits of HSR, and (c) Efficiency effect: UA policy planning has strengthened the drive of HSR to urban efficiency. In addition, we find the specific sequence of intervention, the inauguration of HSR after the implementation of UA policy, can better promote the economy. Finally, a heterogeneity analysis is performed to study the impacts on center cities and non-center cities. We emphasize the importance of the cooperation of transportation infrastructure and regional policies, and provide a theoretical basis for the development of HSR industry and the implementation of UA strategy in China. • HSR has a positive effect on urban economy. • The effect of HSR synergizes with UA policy. • There are institutional integration, policy network spillover, and efficiency improvement in synergy mechanisms. • The inauguration of HSR after the introduction of UA policy is more beneficial to the urban economy. • The joint effect of HSR and UA policy increases the economic gap between center cities and non-center cities. [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]

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]

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]

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]

• The paper proposed the heterogeneous stochastic frontier model with undesirable outputs to measure the environmental efficiency. • The average urban environmental efficiency increased by 48.7% in China from 2005 to 2017; however, differences between cities continued to expand. • The instrumental variable strategy with minimum spanning tree was inserted into DID model. • The implementation of HSR significantly improved the environmental efficiency of the investigated cities. • The effects of HSR tended to weaken in cities with an existing ordinary-speed rail (OSR) system. • The treatment effect of HSR on environmental efficiency was found to vary by region, economic development, and population size. High-speed rail (HSR) is among the most efficient and environmentally friendly transportation methods available today. Following a phase of rapid HSR development, China has built 35,000 km of operational rail network—the largest in the world. It is important to evaluate the role HSR has played in promoting the sustainable development of Chinese cities. In this study, a heterogeneous stochastic frontier model with undesirable outputs was implemented to measure the environmental efficiency of 273 cities in China from 2005 to 2017. Results showed that the average environmental efficiency increased by 48.7% during this period although there was differentiation between cities. Next, we introduced a difference-in-differences model employing an instrumental variable strategy for further analysis. Results revealed that the implementation of HSR significantly improved the environmental efficiency of the investigated cities. Considering that ordinary-speed rail (OSR) accounts for 74.8% of the total railway network area in China, this study also examined the interactive effect of HSR and OSR. The findings suggested that the positive environmental effects of HSR were lower in cities with an existing OSR system. Therefore, we suggest that the Chinese government consider the presence of existing railway networks when planning new HSR routes to maximize the efficiency of both railway systems. Moreover, we found the treatment effect of HSR on environmental efficiency to vary according to region, economic development, and population size. Specifically, we determined that cities in western China, with lower economic development or smaller population sizes, benefit more from HSR. These findings may suggest that policymakers could utilize HSR as a tool to promote balanced economic development. [ABSTRACT FROM AUTHOR]

With photovoltaic (PV) penetration increasing, PV-output prediction has become a research hotspot. Due to the close correlation between PV-output fluctuation and weather conditions, PV-output prediction models often vary different weather types, while the historical/forecast weather types for modeling are mostly obtained from weather-service providers. However, weather-service providers generally have deficiencies in forecast accuracy, spatio-temporal resolution, and investment/operating costs. Based on the above, this paper changes the current acquisition way of the weather types, and proposes a framework of reversely determining weather types from historical PV-output data. First, the symbol-sequence histograms (SSH) are used to describe the PV-output volatility in a coarse-grained manner. Then, the SSHs are partitionally clustered and a classification rule for weather-types is proposed to label the historical weather types. Next, considering the chaotic characteristics of PV output, a prediction method combining phase-space reconstruction with an extremely learning machine based single-layer forward net is developed to predict the SSH. Finally, the day-ahead weather type is forecasted. Simulations were implemented on the weather-type classification and forecasting using a campus PV-system in East China. The PV-output prediction results show that, compared with weather information from a weather-service supplier, 75-day mean errors are significantly reduced by 15.55% (MAPE) and 12.69% (rRMSE), respectively. • A framework to reversely label weather type according to historical PV-output data. • The volatility of PV-output is quantified based on a coarse-grained manner. • A weather-type classification rule is proposed based on symbol histogram clustering. • Symbol-sequence histogram is forecasted using a phase-space reconstruction-ELM model. • The effect of the method is evaluated through the accuracy of PV-output prediction. [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 order to ascertain the contamination status of Salmonella from carcass, equipment, environment and the intestinal contents in a pig slaughterhouse in Huaian, China, we investigated the prevalence and characteristics of Salmonella isolates in one slaughterhouse. A total of 636 samples were collected; Salmonella was laboratory-confirmed from 154/441 (34.9%) carcass swab-samples, 13/57 (22.8%) pieces of equipment, 30/86 (34.9%) environmental samples and the intestinal contents of 19/52 (36.5%) animals. Salmonella was found to be at its highest and lowest frequency during splitting (55.9%) and flaming (17.2%) processes, respectively. All Salmonella isolates were characterized by serotyping, multilocus sequence typing (MLST) and 69 S . Derby isolates were analyzed using pulsed-field gel electrophoresis (PFGE). Four serovars ( S . Derby, S . Rissen, S . Typhimurium, and S . London) were shared from the four sources and predominant serovars were S . Derby and S . Rissen. MLST results indicated that ten different STs were distinguished, of which ST40 was identified as the most prominent ST. Moreover, ST40, ST469, ST34 and ST155 comprised Salmonella isolates obtained from carcass, equipment, the slaughterhouse environment and intestinal contents. PFGE results showed that 33 pulsotypes (PF1-PF33) were identified. PF21, PF25 and PF29 suggested that many isolates from carcass-swab samples at different slaughterhouse process points were of the same pulsotype which also partly comprised isolates from equipment, the environment and intestinal contents. The present study provides an insight into Salmonella distribution in a pig slaughterhouse and revealed that cross-contamination occurs frequently at different sites, which provides data support for proposing some interventions to control Salmonella . [ABSTRACT FROM AUTHOR]

Background: HTLV-1 and HTLV-2 are retroviruses linked etiologically to various human diseases, and both of them can be transmitted by vertical route, sexual intercourse, blood transfusion and intravenous drug use. Recently, some HTLV-infected cases have been reported and this virus is mainly present in the Southeast coastal areas in China, but has not been studied for the people in Central China. Objectives: To know the epidemiologic patterns among different population samples in Central China and further identify risk factor for HTLV-1 and HTLV-2 infection. Methods: From January 2008 to December 2011, 5480 blood samples were screened for HTLV-1/2 antibodies by using enzyme immunoassay, followed by Western Blot. Results: The prevalence of HTLV-1 and HTLV-2 was found with infection rates 0.13% and 0.05% among all population samples for HTLV-1 and HTLV-2, respectively. The highest percentages of infection, 0.39% and 0.20%, were found in the high risk group, while only 0.06% and 0.03% in the blood donor group. There was only one case of HTLV-1 infection (0.11%) among patients with malignant hematological diseases. Of seven HTLV-1 positive cases, six were co-infected with HBV, two with HCV and one with HIV. Among three HTLV-2 positive individuals all were co-infected with HBV, one with HCV. Conclusions: HTLV-1 and HTLV-2 have been detected in the Central China at low prevalence, with the higher infection rate among high risk group. It was also found that co-infection of HTLV-1/2 with HIV and HBV occurred, presumably due to their similar transmission routes. HTLV-1/2 antibody screen among certain population would be important to prevent the spread of the viruses. [ABSTRACT FROM AUTHOR]