Meteorological Applications最新文献

{"title":"Identification and Forecasting of Typical Wind Shear and Shear Line Based on Two-Dimensional Wind Field","authors":"Anning Chen,&nbsp;Jinlong Yuan,&nbsp;Shuo Zhang,&nbsp;Yue Liu,&nbsp;Jiadong Hu,&nbsp;Jiawei Qiu,&nbsp;Haiyun Xia","doi":"10.1002/met.70229","DOIUrl":"https://doi.org/10.1002/met.70229","url":null,"abstract":"<p>The forecasting of clear-air wind shear presents a significant challenge in aviation meteorology. To improve the accuracy of wind shear identification and forecasting using coherent Doppler wind lidar, this study proposes an advanced method for identifying and forecasting wind shear and shear line. The method first performs point-by-point sliding traversal of potential wind shear along the radial and tangential direction based on retrieved two-dimensional wind vector. Subsequently, shear points are identified and extracted by the wind vector, and spatial clustering analysis is applied to reconstruct low-level wind shear line. Finally, the movement of wind shear line is forecasted with the average wind vector in the guidance region of shear line segments. This study simulated two simplified wind shear scenarios that retain key wind field features: gust front passage and divergent airflow. It compared the identification and forecasting performance of wind shear and shear line based on radial speed and two-dimensional wind vector. Results demonstrated that under controlled kinematic conditions, the two-dimensional wind vector method provides more accurate identification and improved forecast precision. The proposed method effectively identified and forecasted shear lines induced by gust front and severe convection in the airport field experiments. This study provides technical support for airport low-level wind shear early warning and flight safety assurance.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148647767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of Severe Turbulence Over China During 2018–2025 From In Situ EDR Data","authors":"Cai Xuewei,&nbsp;Liu Xinhua,&nbsp;Guo Jianping,&nbsp;Mao Xu,&nbsp;Wu Wenhui,&nbsp;Yi Zhihong","doi":"10.1002/met.70224","DOIUrl":"https://doi.org/10.1002/met.70224","url":null,"abstract":"<p>The nation-scale airborne turbulence features remain unclear in China. We investigated the characteristics of turbulence for the period 2018–2025 over China based on in situ eddy dissipation rate (EDR) observations of turbulence from Xiamen Airlines, in combination with pilot reports (PIREPs). Using different EDR thresholds, we quantitatively analyzed the seasonal variation characteristics of severe turbulence and its spatial distribution across different flight phases and examined the characteristics of various potentially hazardous persistent severe turbulence event. The National Centers for Environmental Prediction reanalysis data, along with precipitation observations from automatic weather stations, were used to summarize the weather factors that trigger turbulence through synoptic analysis. Results showed that the frequency of turbulence occurrence decreases with increasing altitude, and turbulence intensity in the upper troposphere is systematically weaker than PIREPs. The seasonal variation in the proportion of turbulence occurrence at different altitudes becomes more pronounced for stronger turbulence. Severe turbulence is rare below 3 km, but peaks at 3–8 km in summer (60%–70%) and above 8 km in winter. Diurnal variation in turbulence is confined mainly to low altitudes, where the frequency and intensity of turbulence increase simultaneously only in the afternoon. Those flight routes prone to more severe or greater turbulence (EDR ≥ 0.45 m^2/3 s⁻¹) are mainly the north–south routes over eastern China. In the vicinity of airports, severe turbulence is frequently observed at altitudes of &lt; 400 m and 2–3 km. Persistent severe turbulence events occur mostly in winter and spring, although turbulence intensity peaks in autumn and winter. They are characterized by either brief high-frequency bursts or prolonged persistence and are triggered primarily by upper-level jets, typhoons, trough/shear-line passages, and cold air outbreaks. Our findings advance the understanding of in situ EDR and offer a practical reference for its quantitative application in turbulence monitoring over China.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70224","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on an Intelligent Identification Method for the Operational Status of Ground-Based Automatic Weather Station Based on Multi-Source Information","authors":"Yun Wu,&nbsp;Ruo Zhou,&nbsp;Zhonghua Li,&nbsp;Weidong Hua,&nbsp;Fan Feng","doi":"10.1002/met.70213","DOIUrl":"https://doi.org/10.1002/met.70213","url":null,"abstract":"<p>To address the increasing operational pressure caused by the rapid expansion of ground-based automatic weather station (AWS) networks and the limitations of traditional reactive maintenance, this paper proposes an intelligent method for identifying AWS operational status based on multi-source information. By integrating heterogeneous data—including meteorological observations, equipment status, maintenance records, and monitoring images—a health evaluation system with 35 indicators across four dimensions (data quality, equipment status, maintenance support, and site environment) is constructed. To overcome the limitations of the Analytic Hierarchy Process (AHP) in high-dimensional weighting, Maximum Difference Scaling (MaxDiff) is introduced to improve efficiency and reduce subjective bias. In addition, deep learning models (YOLOv5s-SE and ResNet) are employed to quantify environmental factors from monitoring images, enabling the detection of interference such as occlusion and foreign objects. Experimental results show that the proposed method can effectively identify sub-healthy states of AWS and significantly improve the detection of environmental and latent faults, providing support for the transition from reactive to predictive maintenance.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-Surface Air Temperature Measurements: Contribution to the Overall Uncertainty due to Wind Speed and Relative Humidity","authors":"Natali Giselle Aranda,&nbsp;Alberto Bottacin,&nbsp;Graziano Coppa,&nbsp;Chiara Musacchio,&nbsp;Andrea Merlone","doi":"10.1002/met.70218","DOIUrl":"https://doi.org/10.1002/met.70218","url":null,"abstract":"<p>In the context of global warming, ensuring the comparability and traceability of air temperature measurements is essential for accurately characterising climate change. However, quantifying the environmental effects under real measurement conditions is still a key challenge for the meteorological community. This study addresses this issue by improving the understanding of air temperature observation uncertainties from ground-based stations. Data from six identical thermometers at 1.60 m and 2.10 m, protected with the same ventilated solar shield and connected to the same datalogger, were analysed during summer and winter. Air temperature difference (Δ<i>T</i>) was calculated as the difference between the readings of each thermometer and the mean, while wind speed and relative humidity were considered quantities of influence. The analysis shows that Δ<i>T</i> depends on season, sensor height, wind speed and relative humidity. During winter nights, Δ<i>T</i> reflects the influence of fog and condensation, intensified by low wind speed. Δ<i>T</i> also exhibits a clear diurnal cycle, indicating the presence of a solar heating effect. The uncertainty of Δ<i>T</i> was then calculated across different ranges of air temperature, wind speed, and relative humidity, obtaining values between 0.03°C and 0.12°C for both thermometer heights. For instance, during a summer daytime case, the air temperature at 1.60 m reached 32.25°C with an uncertainty due to wind speed and relative humidity equal to 0.06°C. Overall, the results show that the uncertainty decreases as wind speed increases, independently of seasonal conditions and thermometer height. This work provides uncertainty values derived from real environmental conditions that can be incorporated in the environmental effects component of the overall measurement uncertainty, in line with the World Meteorological Organisation requirements. These results also support the evaluation of uncertainty budgets for climate reference stations, providing uncertainty values applicable to other stations with similar measurement setups.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70218","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Generalisation of the Signal-to-Noise Ratio Using Proper Scoring Rules","authors":"Jochen Bröcker,&nbsp;Eviatar Bach","doi":"10.1002/met.70223","DOIUrl":"https://doi.org/10.1002/met.70223","url":null,"abstract":"<p>A generalised concept of the signal-to-noise ratio (SNR) (or equivalently the ratio of predictable components, or RPC) is provided, based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows one to define and analyse the signal-to-noise properties of any type of forecast that is amenable to scoring, thus drastically widening the applicability of these concepts. The methodology is illustrated through numerical examples of ensemble forecasts, scored using the continuous ranked probability score (CRPS), and of probability forecasts of a binary event, scored using the logarithmic score. Numerical examples are carried out using both synthetic data with prescribed SNRs as well as seasonal ensemble hindcasts of the North Atlantic Oscillation (NAO) index. The latter have previously been interpreted as having a signal-to-noise ‘paradox’, or anomalous SNR, using the RPC statistic. For the synthetic data, the RPC statistic as well as the scoring rule–based ones agree regarding which data sets exhibit anomalous SNRs, but exhibit different variance, indicating different statistical properties. For the NAO data, on the other hand, the different statistics are more equivocal on whether the SNR is anomalous.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postprocessing Suspiciously High Localised Precipitation in ERA5 for Improved Hydrological Simulations","authors":"Nikolaos Mastrantonas,&nbsp;Ervin Zsoter,&nbsp;Angela Iza Wong,&nbsp;Gonçalo Gomes,&nbsp;Peter Salamon,&nbsp;Christel Prudhomme","doi":"10.1002/met.70216","DOIUrl":"https://doi.org/10.1002/met.70216","url":null,"abstract":"<p>Hydrological simulations critically depend on the quality of precipitation inputs, particularly, for representing extreme events. The ERA5 reanalysis dataset is a widely used product and a key input to the Global Flood Awareness System (GloFAS) of the Copernicus Emergency Management Service (CEMS). Despite its general reliability, ERA5 exhibits a known issue: isolated grid cells with unrealistically high precipitation values, often termed ‘rainbombs’. These anomalies can inflate river discharge estimates in both hindcasts and forecasts, increasing the risk of false flood alerts. In this study, we present a simple yet effective method to reduce the magnitudes of such high-intensity localised events by leveraging data from neighbouring ERA5 grid points and statistical benchmarks from unaffected datasets. The postprocessing was applied to daily data spanning from 1979 until 2024. Over 7% of ERA5 grid points exhibited at least one rainbomb, with 722 locations, primarily in tropical regions and complex terrain such as the Andes, East African highlands and Papua Island, experiencing more than 100 events. The hydrological impact of these anomalies was evaluated for two basins with frequent occurrences: the Tana basin in Kenya and the Guayas basin in Ecuador. Results show that the postprocessed dataset not only reduces unrealistic discharge peaks from individual events but also significantly alters return period thresholds, yielding more realistic hydrological indicators. While the approach effectively targets spatial inconsistencies and it is flexible enough to be applied to different datasets, some uncertainty remains regarding the treatment of highly localised extreme precipitation events. This modified precipitation dataset will be integrated into the next major release of GloFAS (version 5), scheduled for operational deployment in 2026.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70216","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Drought Projections and Trend Analysis Through Redundancy-Controlled Multi-Model Climate Simulations","authors":"Muhammad Shakeel,&nbsp;Zulfiqar Ali,&nbsp;Hussnain Abbas,&nbsp;Gulzar Ahmad,&nbsp;Miklas Scholz,&nbsp;Nada Ahmed,&nbsp;Salah Knani,&nbsp;Rizwan Niaz","doi":"10.1002/met.70209","DOIUrl":"https://doi.org/10.1002/met.70209","url":null,"abstract":"<p>Among natural hazards, drought stands out for its complexity and severe impacts on ecosystems and societies. Global Climate Models (GCMs) play a crucial role in projecting future climate variability and associated extremes. However, the presence of biases and inter-model correlations within GCM outputs often compromises the precision of drought representation. This research proposes a new ensemble weighting method, termed Less Redundant Medially Weighted Aggregation (LRMWA). The procedure of LRMWA is based on the consideration of Mutual Information (MI) and model-to-model deviations in the simulated precipitation data. In the application, we used precipitation simulations from 18 global climate models participating in CMIP6, considering three projected future scenarios over the Tibetan Plateau. Results indicate that LRMWA consistently outperforms alternative approaches with lower average RMSE (1.59) and RAE (1.98), reflecting improved error reduction and spatial robustness. Furthermore, the proposed framework extends the drought standardization procedure through weighted probabilistic modeling across multiple time scales, enhancing drought characterization under changing climatic conditions. In addition, steady-state drought probability statistics demonstrate the prevailing of Normal drought overall. However, mean state probabilities also highlight the increase in drought risk from low to high scenario especially over the longer time scale. In summary, the study contributes to advancing robust weighting schemes for GCMs, improving drought assessment, and supporting informed decision-making for effective drought management.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70209","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148615357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Synchronous Changes of Surface and Near-Surface Air Temperature: Insights From a Field-Scale Experiment”","authors":"","doi":"10.1002/met.70222","DOIUrl":"https://doi.org/10.1002/met.70222","url":null,"abstract":"<p> <span>Ma, W.</span>, <span>Y. Yu</span>, <span>P. Mo</span>, <span>J. Li</span>, and <span>Q. Chen</span>. <span>2026</span>. “ <span>Synchronous Changes of Surface and Near-Surface Air Temperature: Insights From a Field-Scale Experiment</span>.” <i>Meteorological Applications</i>, <span>33</span>, no. <span>3</span>: e70204. https://doi.org/10.1002/met.70204.</p><p>In the originally published article, the first name of fourth author is incorrect. The first name of the fourth author has been corrected from “Jiqin Li” to “Jiqing Li”.</p><p>The online version of this article has been corrected accordingly.</p><p>We apologize for this error.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Structural Properties and Reflectivity Patterns of Precipitation Systems Linked to Post-Fire Debris Flows in California","authors":"Silvana Castillo-Guerra,&nbsp;Charles Jones,&nbsp;Leila M. V. Carvalho","doi":"10.1002/met.70219","DOIUrl":"https://doi.org/10.1002/met.70219","url":null,"abstract":"<p>Post-fire debris flows are triggered by short-duration, high-intensity rainfall that mobilizes sediment on recently burned slopes. These events present a serious hazard in California, where intense rainfall often follows wildfire activity. This study uses radar reflectivity data to introduce a modified tracking algorithm that identifies and tracks coherent precipitation systems intersecting burned areas. The algorithm is applied to an inventory of post-fire debris flow events to characterize storm-scale structural properties, including area, propagation speed, direction, and eccentricity, as well as reflectivity patterns occurring within fire perimeters. The algorithm was applied successfully across long- and short-duration storms and revealed significant seasonal differences. Fall and winter storms generally featured larger, more elongated, and faster-moving reflectivity precipitation systems, while spring and summer storms were typically smaller, more circular, and slower. Within fire perimeters, warmer-month precipitation systems more frequently produced localized bursts of high reflectivity (&gt; 45 dBZ), whereas colder-month events showed broader zones of moderate reflectivity (25–45 dBZ). Temporal comparisons show that the timing of maximum radar reflectivity over burned areas and at reported debris flow locations has stronger alignment with peak 15-min rainfall intensities from nearby rain gauges than reflectivity-threshold–based metrics. Together, these findings demonstrate that storm-scale reflectivity tracking provides meaningful spatial and temporal context for identifying near-peak rainfall conditions over burned landscapes and offers a practical framework for characterizing precipitation system structure in support of fire-wide situational awareness of post-fire debris flow hazards.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70219","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148462819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating the Prediction of Wind Power Ramping Events in the Belgian Offshore Zone","authors":"Ruoke Meng,&nbsp;Geert Smet,&nbsp;Dieter Van den Bleeken,&nbsp;Aaron Van Poecke,&nbsp;Hossein Tabari,&nbsp;Peter Hellinckx,&nbsp;Piet Termonia,&nbsp;Joris Van den Bergh","doi":"10.1002/met.70203","DOIUrl":"https://doi.org/10.1002/met.70203","url":null,"abstract":"<p>This study provides a comprehensive evaluation for the prediction of wind power ramping events in the Belgian Offshore Zone. These rapid, large-scale power fluctuations pose significant challenges to grid reliability. The research uses operational Numerical Weather Prediction (NWP) models from the Royal Meteorological Institute of Belgium, as well as its version enhanced with Wind Farm Parameterization (WFP). Power predictions are generated with both typical power curves and machine learning approaches. Standard verification metrics, such as Mean Absolute Error (MAE), often fail to capture the operational significance of ramp events. To address this, we develop a flexible verification framework designed to assess ramp forecast performance. This framework incorporates adjustable time and power buffers, which tolerate minor, operationally acceptable discrepancies in the timing and magnitude of predicted events. Application of this framework to both intraday and day-ahead forecasts reveals that WFP-enhanced models consistently improve ramp predictions over the operational baseline. Further analysis reveals that while the WFP model with power curves effectively reduced false alarms, it comes at the cost of more misses. In contrast, ML-based approaches achieve slightly higher overall skill scores by striking a better balance between reducing these error types. Moreover, we introduce the Ramp Alignment Score (RAS), an event-based metric that quantifies the temporal alignment between predicted and observed ramps, to supplement the model evaluation by lead time. RAS analysis demonstrates that WFP models achieve better temporal alignment and reveals a distinct diurnal cycle in ramping prediction errors. Finally, we investigate the impact of a specific meteorological driver, finding an association between severe precipitation and large, highly predictable ramp events. Conversely, moderate and light precipitation are linked to a higher incidence of missed events and false alarms. This work provides both an operationally relevant evaluation methodology and insights into ramp predictions under specific meteorological conditions.</p>","PeriodicalId":49825,"journal":{"name":"Meteorological Applications","volume":"33 4","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/met.70203","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148381142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}