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Net primary productivity (NPP) is a key indicator of the health of forest ecosystems that offers important information about the net carbon sequestration capacity of these systems. Precise assessment of NPP is crucial for measuring carbon fixation and assessing the general well-being of forest ecosystems. Due to the distinct ecological characteristics of various forest types, accurately understanding and delineating the distribution of these types is crucial for studying NPP. Therefore, an accurate forest-type classification is necessary prior to NPP calculation to ensure the accuracy and reliability of the research findings. This study introduced deep learning technology and constructed an HRNet-CASA framework that integrates the HRNet deep learning model and the CASA model to achieve accurate estimation of forest NPP in Huangshan District, Huangshan City, Anhui Province. Firstly, based on VHR remote sensing images, we utilized the HRNet to classify the study area into six forest types and obtained the forest type distribution map of the study area. Then, combined with climate data and forest type distribution data, the CASA model was used to estimate the NPP of forest types in the study area, and the comparison with the field data proved that the HRNet-CASA framework simulated the NPP of the study area well. The experimental findings show that the HRNet-CASA framework offers a novel approach to precise forest NPP estimation. Introducing deep learning technology not only enables precise classification of forest types but also allows for accurate estimation of NPP for different types of forests. This provides a more effective tool for forest ecological research and environmental protection. [ABSTRACT FROM AUTHOR]

The operational stability of a power transformer exerts an extremely important impact on the power symmetry, balance, and security of power systems. When the grid load fluctuates greatly, if the load factor of the transformer cannot be maintained within a reasonable range, it leads to increased instability in grid operation. Adjusting the transformer capacity based on load changes is of great significance. The existing control methods for on-load capacity-regulating (OLCR) transformers have low timeliness, and the daily switching frequency of the capacity-regulating switch is not controlled. To ensure the safe and stable operation of transformers, this paper proposes a control method for OLCR transformers based on load prediction and fuzzy control. Firstly, the operating principle of OLCR transformers is analyzed, and a multi-strategy enhanced dung beetle optimizer (MSDBO) combined with a CNN−LSTM model is proposed for load forecasting. On this basis, the daily switching frequency of the capacity-regulating transformer is introduced, and hesitant fuzzy control is used to select the optimal capacity-regulating strategy relying on three factors: loss, economy, and switching frequency. Finally, simulation models are constructed using the MATLAB/SIMULINK platform and simulation analysis is conducted to verify the effectiveness and superiority of the proposed control method. For the three scenarios in this paper, the method reduces daily power loss by 28.5% to 56.3% and daily operating costs by 25.4% to 50.8%. The method used in this paper can sacrifice 3.5% to 9.2% of the loss reduction capability in exchange for reducing the number of switch operations by 28.6% to 57.1%, significantly extending the lifespan of the switches and thereby increasing the operational lifespan of the transformer. [ABSTRACT FROM AUTHOR]

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]

Blue honeysuckle (Lonicera caerulea L.), which belongs to the Caprifoliaceae family, is an emerging fruit crop worldwide. For the development of a transgenic system and multipurpose tissue culture, this study for the first time established an in vitro regeneration system via somatic embryogenesis, as well as improving the previously established indirect organogenesis-based regeneration system. For embryogenesis, Murashige and Skoog (MS) medium supplemented with 1.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D) showed the highest induction rate of the embryogenic callus (97.6%), and MS supplemented with 0.1 mg/L 6-benzyladenine (6-BA), 0.1 mg/L α-naphthaleneacetic acid (NAA), and 0.5 g/L activated carbon (AC) achieved the highest somatic embryo rate (28.3%). For indirect organogenesis, MS medium supplemented with 1.0 mg/L 6-BA and 0.1 mg/L NAA resulted in the highest non-embryogenic callus induction rate (98.9%) and adventitious shoot induction rate (51.6%). For adventitious root induction, MS supplemented with 1.0 mg/L indole-3-butyric acid (IBA) achieved the highest root induction rate (96.0%) and average root length (4.6 cm), whereas MS supplemented with 0.5 mg/L indole-3-acetic acid (IAA) resulted in the highest average regenerated root number (8.8). The total time for the regeneration from explants to soil-planted seedlings (10 euphylla) was 105 and 150 days with an efficiency of 44.1% and 23.9% through organogenesis and somatic embryogenesis, respectively. This study provides a powerful tool for rapid propagation, proliferation, and transformation, as well as laying a technological foundation for gene function research and genetic improvement of blue honeysuckle. [ABSTRACT FROM AUTHOR]

In recent years, the growth rate of China's real industry has slowed down while the financial industry has entered a phase of rapid development. Driven by the profit-seeking motive of capital, real enterprises tend to carry out financial investments, and the degree of corporate financialization has been rising. This paper selects A-share listed enterprises in Shanghai and Shenzhen from 2009 to 2020 as research samples to study the impact of corporate financialization on technological innovation and the mediating effect of financing constraints from the perspective of financial asset holding. The study found that the financialization of enterprises' crowding out effect on technological innovation has led to the phenomenon of "turning from real to virtual". We also found that the crowding-out effect had experienced lag. This conclusion still held when we controlled for endogeneity. The heterogeneity analysis showed that the financialization of non-state-owned enterprises had an excessive inhibitory effect on technological innovation, and the financialization of enterprises in eastern China has had a remarkable inhibitory effect on technological innovation. The influence mechanism analysis showed how financing constraints played a crucial mediating role in corporate financialization inhibiting technological innovation, and corporate financialization has inhibited technological innovation by exacerbating financing constraints. Based on this research, we propose targeted suggestions to prevent the excessive financialization of enterprises on both government and enterprise levels. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) object detection based on point cloud data plays a critical role in the perception system of autonomous driving. However, this task presents a significant challenge in terms of its practical implementation due to the absence of point cloud data from automotive-grade hybrid solid-state LiDAR, as well as the limitations regarding the generalization ability of data-driven deep learning methods. In this paper, we introduce SimoSet, the first vehicle view 3D object detection dataset composed of automotive-grade hybrid solid-state LiDAR data. The dataset was collected from a university campus, contains 52 scenes, each of which are 8 s long, and provides three types of labels for typical traffic participants. We analyze the impact of the installation height and angle of the LiDAR on scanning effect and provide a reference process for the collection, annotation, and format conversion of LiDAR data. Finally, we provide baselines for LiDAR-only 3D object detection. [ABSTRACT FROM AUTHOR]

Pedestrian tracking is an important aspect of autonomous vehicles environment perception in a vehicle running environment. The performance of the existing pedestrian tracking algorithms is limited by the complex traffic environment, the changeable appearance characteristics of pedestrians and the frequent occlusion interaction, which leads to the insufficient accuracy and stability of tracking. Therefore, this paper proposes a detector–tracker integration framework for autonomous vehicle pedestrian tracking. Firstly, a pedestrian objects detector based on the improved YOLOv7 network was established. Space-to-Depth convolution layer was adopted to improve the backbone network of YOLOv7. Then, a novel appearance feature extraction network is proposed, which integrates the convolutional structural re-parameterization idea to construct a full-scale feature extraction block, which is the optimized DeepSORT tracker. Finally, experiments were carried out on MOT17 and MOT20 public datasets and driving video sequences, and the tracking performance of the proposed framework was evaluated by comparing it with the most advanced multi-object tracking algorithms. Quantitative analysis results show that the framework has high tracking accuracy. Compared with DeepSORT, MOTA improves by 2.3% in the MOT17 dataset and MOTA improves by 4.2% in the MOT20 dataset. Through qualitative evaluation on real driving video sequences, the framework proposed in this paper is robust in a variety of climate environments, and can be effectively applied to the pedestrian tracking of autonomous vehicles. [ABSTRACT FROM AUTHOR]

Age-related macular degeneration (AMD) has been implicated as a risk factor for severe consequences from COVID-19. We evaluated the genetic architecture shared between AMD and COVID-19 (critical illness, hospitalization, and infections) using analyses of genetic correlations and pleiotropy (i.e., cross-phenotype meta-analysis) of AMD (n = 33,976) and COVID-19 (n ≥ 1,388,342) and subsequent analyses including expression quantitative trait locus (eQTL), differential gene expression, and Mendelian randomization (MR). We observed a significant genetic correlation between AMD and COVID-19 infection (rG = 0.10, p = 0.02) and identified novel genome-wide significant associations near PDGFB (best SNP: rs130651; p = 2.4 × 10−8) in the pleiotropy analysis of the two diseases. The disease-risk allele of rs130651 was significantly associated with increased gene expression levels of PDGFB in multiple tissues (best eQTL p = 1.8 × 10−11 in whole blood) and immune cells (best eQTL p = 7.1 × 10−20 in T-cells). PDGFB expression was observed to be higher in AMD cases than AMD controls {fold change (FC) = 1.02; p = 0.067}, as well as in the peak COVID-19 symptom stage (11–20 days after the symptom onset) compared to early/progressive stage (0–10 days) among COVID-19 patients over age 40 (FC = 2.17; p = 0.03) and age 50 (FC = 2.15; p = 0.04). Our MR analysis found that the liability of AMD risk derived from complement system dysfunction {OR (95% CI); hospitalization = 1.02 (1.01–1.03), infection = 1.02 (1.01–1.03) and increased levels of serum cytokine PDGF-BB {β (95% CI); critical illness = 0.07 (0.02–0.11)} are significantly associated with COVID-19 outcomes. Our study demonstrated that the liability of AMD is associated with an increased risk of COVID-19, and PDGFB may be responsible for the severe COVID-19 outcomes among AMD patients. [ABSTRACT FROM AUTHOR]

Congested regions in videos put forward higher requirements for target detection algorithms, and the key detection of congested regions provides optimization directions for improving the accuracy of detection algorithms. In order to make the target detection algorithm pay more attention to the congested area, an automatic selection method of a traffic congestion area based on surveillance videos is proposed. Firstly, the image is segmented with superpixels, and a superpixel boundary map is extracted. Then, the mean filtering method is used to process the superpixel boundary map, and a fixed threshold is used to filter pixels with high texture complexity. Finally, a maximin method is used to extract the traffic congestion area. Monitoring data of night and rainy days were collected to expand the UA-DETRAC data set, and experiments were carried out on the extended data set. The results show that the proposed method can realize automatic setting of the congestion area under various weather conditions, such as full light, night and rainy days. [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]

This work investigates the structure, rheological properties, and film performance of wheat flour hydrocolloids and their comparison with that of a wheat starch (WS)–gluten blend system. The incorporation of gluten could decrease inter-chain hydrogen bonding of starch, thereby reducing the viscosity and solid-like behavior of the film-forming solution and improving the frequency-dependence, but reducing the surface smoothness, compactness, water vapor barrier performance, and mechanical properties of the films. However, good compatibility between starch and gluten could improve the density of self-similar structure, the processability of the film-forming solution, and film performance. The films based on wheat flours showed a denser film structure, better mechanical properties, and thermal stability that was no worse than that based on WS–gluten blends. The knowledge gained from this study could provide guidance to the development of other flour-based edible packaging materials, thereby promoting energy conservation and environmental protection. [ABSTRACT FROM AUTHOR]

Renewable energy sources (RES) generation has huge environmental and social benefits, as a clean energy source with great potential. However, the difference in the uncertainty characteristics of RES and electric–thermal loads poses a significant challenge to the optimal schedule of an integrated energy system (IES). Therefore, for the different characteristics of the multiple uncertainties of IES, this paper proposes a type-II fuzzy interval chance-constrained programming (T2FICCP)-based optimization model to solve the above problem. In this model, type-II fuzzy sets are used to describe the uncertainty of RES in an IES, and interval numbers are used to describe the load uncertainty, thus constructing a T2FICCP-based IES day-ahead economic scheduling model. The model was resolved with a hybrid algorithm based on interval linear programming and T2FICCP. The simulations are conducted for a total of 20 randomly selected days to obtain the advance operation plan of each unit and the operation cost of the system. The research results show that the T2FICCP optimization model has less dependence on RES output power and load forecasting error, so can effectively improve the economy of IES, while ensuring the safe and stable operation of the system. [ABSTRACT FROM AUTHOR]

The spatial resolution of remote sensing images directly affects the accuracy, efficiency, and computational cost of extracting the fractional vegetation cover (FVC). Taking the Liyang woodland region, Jiangsu Province, as the study area, FVCs with varying spatial resolutions were extracted separately from Sentinel-2, Landsat-8, MOD13Q1, and MOD13A1. The variations in FVCs extracted from remote sensing images with varying spatial resolutions were analyzed at one specific time and time series within a year. The results show that (i) the overall mean FVC values of the four spatial resolution images did not differ substantially; however, FVCs with varying spatial resolutions present with a regular pattern of overestimation or underestimation at different vegetation levels. (ii) Taking the 10 m spatial resolution FVC as the reference, the accuracy values of FVC extraction at 30 m, 250 m, and 500 m resolutions were 91.0%, 76.3%, and 76.7%, respectively. The differences in the spatial distribution of FVCs are the most obvious at water–land interfaces and at the edge of each woodland patch. (iii) The highest accuracy of time-series FVC extraction from lower-resolution images is in the range of 0.6~0.7 for FVC. The degree of variation in FVC of time series varying spatial resolutions depends on the season and vegetation cover conditions. In summary, there are considerable differences in the need to monitor high-resolution images depending on the FVC level of the land surface. This study provides a reference for selection and accuracy research of remote sensing images for FVC extraction. [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]

Traditional villages are historically, culturally, scientifically and aesthetically valuable, and a beautiful landscape is the primary embodiment of a traditional village environment. Urbanization and modernization have had a great impact on village landscapes. As an important aspect of traditional village landscapes, creating beautiful public spaces is an effective way to attract tourists and improve the well-being of residents. Landscape aesthetic activities are the result of the interaction between landscape objects and aesthetic subjects. Research on the relationship between the form of traditional village public spaces and subjective aesthetic preferences has long been neglected. This research examined 31 public spaces in traditional villages in the Dongshan and Xishan areas in Lake Taihu, Suzhou. An index system of public spatial forms in traditional villages was created, basic data of spatial forms were collected using a hand-held 3D laser scanner, and the value of the spatial forms index was calculated using R language. The scenic beauty estimation (SBE) method was improved, with the estimation of the beauty of the scenic environment based on VR panorama rather than traditional photo media. Parameter screening was performed using correlation analysis and full subset regression analysis, and four models were used to fit the SBE scores and grades. The results show that the majority of public spaces had lower than average SBE scores, and the four key indicators of average contour upper height, solid-space ratio, vegetation cover, and comprehensive closure predicted SBE. In addition, the linear model (R2 = 0.332, RMSE = 64.774) had the most accurate SBE level prediction and the stochastic forest model (R2 = 0.405, RMSE = 63.311) was better at predicting specific SBE scores. The model provides managers, designers, and researchers with a method for the quantitative evaluation of visual landscape preferences and quantitative landscape spatial forms and provides a reference for the protection and renewal of traditional village landscapes. [ABSTRACT FROM AUTHOR]

Although it is one of the promising candidates for pseudocapacitance materials, Ni(OH)2 is confronted with poor specific capacitance and inferior cycling stability. The design and construction of three-dimensional (3D) nanosphere structures turns out to be a valid strategy to combat these disadvantages and has attracted tremendous attention. In this paper, a 3D α-Ni(OH)2 nanosphere is prepared via a facile and template-free dynamic refluxing approach. Significantly, the α-Ni(OH)2 nanosphere possesses a high specific surface area (119.4 m2/g) and an abundant porous structure. In addition, the as-obtained α-Ni(OH)2 electrodes are investigated by electrochemical measurements, which exhibit a high specific capacitance of 1243 F/g at 1 A/g in 6 M KOH electrolyte and an acceptable capacitive retention of 40.0% after 1500 charge/discharge cycles at 10 A/g, which can be attributed to the sphere's unique nanostructure. Furthermore, the as-assembled Ni(OH)2-36//AC asymmetric supercapacitor (ASC) yields a remarkable energy density of 26.50 Wh/kg, with a power density of 0.82 kW/kg. Notably, two ASCs in series can light a 2.5 V red lamp sustainably for more than 60 min, as well as power an LED band with a rated power of 25 W. Hence, this 3D α-Ni(OH)2 nanosphere may raise great potential applications for next-generation energy storage devices. [ABSTRACT FROM AUTHOR]

Corynebacterium glutamicum is an important strain for the industrial production of amino acids, but the fermentation of L-methionine has not been realized. The purpose of this study is to clarify the effect of reducing power NADPH on L-methionine synthesis. Site-directed mutagenesis of zwf and gnd genes in pentose phosphate pathway relieved feedback inhibition, increased NADPH supply by 151.8%, and increased L-methionine production by 28.3%; Heterologous expression of gapC gene to introduce NADP+ dependent glyceraldehyde-3-phosphate dehydrogenase increased NADPH supply by 75.0% and L-methionine production by 48.7%; Heterologous expression of pntAB gene to introduce membrane-integral nicotinamide nucleotide transhydrogenase increased NADPH by 89.2% and L-methionine production by 35.9%. Finally, the engineering strain YM6 with a high NADPH supply was constructed, which increased the NADPH supply by 348.2% and the L-methionine production by 64.1%. The analysis of metabolic flux showed that YM6 significantly increased the glycolytic flux, including the metabolic flux of metabolites such as glycosyldehyde-3-phosphate, dihydroxyacetate phosphate, 3-phosphoglycate and pyruvate, and the significant increase of L-methionine flux also confirmed the increase of its synthesis. This study provides a research basis for the systematic metabolic engineering construction of L-methionine high-yield engineering strains. [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]

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]

Addressing society's most complex challenges, such as climate change, requires bringing together stakeholders from the business, government, and nonprofit sectors. At the municipal level, multi-stakeholder partnerships are often formed to implement community sustainability plans. However, these partnerships can create new challenges, as it is cumbersome to coordinate action among a group that is made up of such diverse stakeholders. Past research suggests that it is important for these partnerships to have the appropriate structures in place to mitigate some of the coordination challenges to which they are prone. Yet, very few studies have examined the influence that different structural features have on plan outcomes. This article seeks to address this important research gap by using quantitative methods to examine five different features that can compose partnership structures—oversight, monitoring and evaluation, partner engagement, communication, and community wide-actions and their impact on climate change mitigation outcomes. Based on data collected through a global survey and publicly available greenhouse gases emission data from 72 different partnerships that implement community sustainability plans (CSPs), this study finds that structural features related to oversight and community-wide actions are positively associated with climate change mitigation outcomes. These results indicate that certain features of partnership structures may be more important for achieving desirable climate change mitigation outcomes, and thus contribute to research on collaborative governance structures and climate action. [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]