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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]

Green technology innovation is a crucial factor in the global quest for sustainability. In 2017, China approved the establishment of Pilot Zones for Green Financial Reform and Innovations to build and improve the green financial system. Using a difference-in-difference (DID) model, this paper examines the impact of this pilot policy on corporate green technology innovation and its transmission mechanism based on the data of green patents of Chinese A-share listed manufacturing companies between 2014 and 2019. It is found that Pilot Zones for Green Financial Reform and Innovations have contributed to the growth of green technology innovations. This conclusion is confirmed after a series of robustness tests. The pilot policy's promotion of green technology innovations is mainly reflected in the application of corporate green invention patents. However, it is more significant for non-heavy polluting enterprises, high-tech certified enterprises, and large enterprises. The paper also finds that the pilot policy promotes corporate green technology innovation by promoting social financing, talent support, and government financial support. [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]

Mental stress has been recognized as an essential risk factor for hypertension. Therefore, experts specializing in cardiology, psychiatry, and Traditional Chinese Medicine organized by the Psycho-cardiology Group, College of Cardiovascular Physicians of Chinese Medical Doctor Association, and Hypertension Group of the Chinese Society of Cardiology proposed the expert consensus on the diagnosis and treatment of adult mental stress-induced hypertension in March 2021, which includes the epidemiology, etiology, diagnosis, and treatment of the mental stress-induced hypertension. This consensus will hopefully facilitate the clinical practice of this disorder. In addition, the COVID-19 pandemic has become one of the primary global sources of psychosocial stressors since the beginning of 2020, and the revision of this expert consensus in 2022 has increased the relevant content. This consensus consists of two parts. The sections of Part A include (I) Background and epidemiological characteristics, (II) Pathogenesis, and (III) Diagnosis. The sections of Part B contain (IV) Treatment recommendations, and (V) Prospects. This article presents Part B of the consensus. [ABSTRACT FROM AUTHOR]

Crataegus bretschneideri C. K. Schneid. is one of the species cultivated in China. Due to its unclear taxonomic classification status, the conservation and utilization of this germplasm resource have been limited. In this study, we analyzed the chloroplast genomes and nuclear sequences to reveal the taxonomic relationships among C. bretschneideri and related species. We assembled the chloroplast genomes of C. bretschneider and related species and varieties, including C. maximowiczii C. K. Schneid., C. maximowiczii var. ninganensis S. Q. Nie & B. J. Jen., C. pinnatifida Bunge, and C. pinnatifida var. major N. E. Br. The lengths of the chloroplast genomes ranged from 159,644 bp (C. bretschneideri) to 159,947 bp (C. pinnatifida var. major). The five Crataegus chloroplast genomes had similar features and possessed 86 to 88 protein-coding genes, 37 tRNA genes, and eight rRNA genes which were arranged in the same order. Eight mutation hotspot regions, including matk, psaB, accD, petA, clpP, trnD-GUC, psbH-petB, and trnN-GUU-trnR-ACG could be used as potential molecular markers for further studies of Crataegus genetic diversity. Phylogenetic analyses based on 17 chloroplast genomes of Crataegus and Amelanchier indicated that C. bretschneideri was related to C. maximowiczii and C. maximowiczii var. ninganensis. However, the phylogenetic trees constructed by nuclear sequences of 36 Crataegus accessions reflected a closer relationship between C. bretschneideri and C. pinnatifida. Furthermore, divergence time estimation suggested that C. bretschneideri and C. maximowiczii diverged in the late Miocene and that speciation of C. pinnatifida occurred during the middle to late Miocene. These findings revealed that C. bretschneideri is an independent species and may be of hybrid origin. [ABSTRACT FROM AUTHOR]

This study empirically analyzes the impacts of manufacturing intelligentization on technological innovation using panel data from 29 manufacturing industries in China for the period 2012–2019. The research findings indicate that manufacturing intelligentization has significantly positive impacts on intra-industry technological innovation. Moreover, technological innovation exhibits path dependence, in which prior innovation accumulation is advantageous for current technological innovation. Furthermore, the spatial Durbin model indicates that intelligentization has both horizontal and vertical technology spillover effects on technological innovation. Specifically, the intelligentization of horizontal- and forward-linked industries has positive impacts on technological innovation in the focal industry. Additionally, technological innovation exhibits significant spatiotemporal dependence, as manifested by the negative spillover effects of prior technological achievements in other industries on current technological innovation in the focal industry. • Manufacturing intelligentization can significantly promote intra-industry technological innovation. • Technological innovation exhibits path dependence: prior innovation fosters current technological innovation exhibits. • Manufacturing intelligentization has horizontal and vertical technology spillover effects on technological innovation. • Technological innovation exhibits negative spatiotemporal dependence. [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]

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]

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]