Accurate and in-situ monitoring of frost growth on plant leaves is crucial for disaster prevention in smart agriculture. To address the limitations of traditional methods in quantification and continuity, this study proposes a novel monitoring paradigm integrating dynamic dielectric spectrum analysis with hybrid intelligent algorithms. A mesh-electrode-based capacitive sensor was designed to capture in-situ and continuous dielectric spectrum changes on leaf surfaces. Subsequently, a hybrid SWT-SSA-LSTM model was constructed for high-fidelity denoising and prediction of the original signals. Field experiments demonstrated that this system could quantify frost layer mass and thickness with high precision. The established nonlinear regression models achieved coefficients of determination of 0.924 and 0.975, respectively. The prediction model exhibited outstanding performance, with a root mean square error as low as 1.475. This study establishes a complete technical closed-loop from physical perception to intelligent prediction, providing an innovative solution for precise frost monitoring in agriculture.

Accurately forecasting the Pan-Arctic sea-ice extent (SIE) in September is crucial for understanding climate change impacts and ensuring safe Arctic navigation. This study introduces the SWR DY-method, a novel prediction approach that combines the stepwise regression (SWR) and interannual increment methods and applies it to predictive performance. The model identifies grid points significantly correlated to September SIE using the temporal correlation coefficients between various monthly climate variables and the Pan-Arctic SIE in September. A database of potential predictors was established, from which SWR and long short-term memory (LSTM) neural networks were used to develop single-predictor models with monthly initializations from January to September. These single-predictor models were then ensemble-averaged to create multi-predictor ensemble prediction models. Evaluation of the models from 2014 to 2022 was carried out, comparing the performances of the SWR DY-method and LSTM DY-method. Results indicated that the SWR DY-method had higher single-predictor prediction skill than the LSTM DY-method. Multi-predictor models using the SWR DY-method demonstrated lower mean absolute errors and higher predictive skill for January to September initializations, outperforming the LSTM and the SIO (Sea Ice Outlook) median predictions. This study presents a new and effective strategy for improving seasonal predictions of Pan-Arctic SIE.摘要精准预测9月北极海冰范围对理解气候变化的影响与保障北极航道安全至关重要. 基于逐步回归和年际增量预测方法(SWR-DY), 本文研制了一种9月北极海冰范围预测新模型.该模型基于各月气候变量与9月北极海冰范围的时间相关系数, 筛选具有统计显著性的预测因子, 建立潜在预测因子库. 在此基础上, 分别采用SWR和长短期记忆神经网络(LSTM)构建1 − 9月逐月起报的单因子预测模型, 并通过集合平均得到多因子集合预测模型. 本文通过对比了不同模型对2014−2022年9月北极海冰范围的预测效能. 结果表明:SWR DY方法的单因子预测能力普遍优于LSTM DY方法; 其多因子模型在1 − 9月起报时均表现出更低的平均绝对误差和更高的预测能力. 新方法对2014−2022年9月北极海冰范围的预测效能也高于国际海冰预测网络活动多模型预测结果中位数的预测效能. 本研究为9月北极海冰范围预测提供了新方法.

Wave glider with webbed wings (WGWW) is an innovative unmanned surface vehicle, powered by wave energy and solar energy. Its energy supply conundrum has been thoroughly resolved by “exploiting local resources” and can achieve long-term and large-scale ocean data acquisition for its outstanding endurance. Flexible webbed wings (FWWs) are crucial constituents of WGWW, transforming wave energy into driving force through fluid–structure interaction (FSI) under their hyper-elastic deformation. It is of great significance to analyze the influence of FWW material parameters on their driving force to improve the wave energy conversion efficiency of WGWW. In this paper, the numerical simulation model of the fluid–structure interaction of FWWs is first established, based on the bidirectional FSI theory. Moreover, the overset method and dynamic mesh technology are introduced to solve the problems of large deformation and easy divergence in the thin plate structure of FWWs. The optimization analysis of the FSI dynamic performance of FWWs under different material parameters are accomplished, including the different materials, surface hardness, and shape size. Finally, a principle prototype of WGWW equipped with inertial navigation sensor is built and a wave tank experiment is carried out. The elastic deformation and driving force generation of FWWs are analyzed, and the feasibility of WGWW driven by FWW is verified. The relevant results provide scientific basis and reference for the material selection and optimal design of FWWs and have important significance for improving the dynamic performance of WGWW.

A deep, large-scale warmth occurred in the Arctic from January to April 2016, but the roles of various physical processes in this period have not been quantified. Here, we utilize an updated version of the coupled atmosphere‒surface climate feedback response analysis method to quantitatively attribute the extreme warmth. Our results show distinct characteristics associated with the warm anomaly in January‒February and March‒April. This extreme Arctic warmth is largely explained by the positive contributions of atmospheric dynamics, which are dominated by horizontal advection in January‒February and by adiabatic heating and vertical terms in March‒April. Compared with January‒February, an increase in solar radiation leads to an enhanced positive contribution from surface albedo processes in March‒April. Water vapor processes provide considerable positive contribution during both periods. In contrast, surface dynamic processes provide positive contribution in January‒February but negative contribution in March‒April, while cloud processes provide nearly negative contribution during both periods, primarily through their longwave effects.

A wave glider with webbed wings (WGWWs) is a new type of unmanned surface robot that combines wave energy and solar energy as its energy supply, driven by flexible webbed wings (FWWs). Wave gliders with webbed wings are already playing an important role in marine science research. Flexible webbed wings are significant components of wave gliders with webbed wings that achieve the absorption and conversion of wave energy through bidirectional fluid-structure coupling with water flow. To address the issues of large deformations and nonconvergence under the strong coupling action of flexible webbed wings, a dynamic model of flexible webbed wings is first established on the basis of an analysis of the motion principle of a wave glider with webbed wings. The Mooney-Rivlin model was subsequently applied to describe the stress-strain relationship of a rubber hyperelastic material for flexible webbed wings. By adopting the overset method and dynamic mesh technology, employing the system coupling interface based on the data interaction platform of the ANSYS Workbench, as well as mechanical and fluid solvers, the transient dynamic characteristics of fluid-structure coupling of flexible webbed wings under different working conditions are obtained. Finally, by setting different sea conditions, the influences of wave height and period on the dynamic characteristics of flexible webbed wings are analyzed. The results indicate that the greater the wave height and the smaller the wave period are, the greater the power output of the flexible webbed wings is. Additionally, the influence of the wave height ratio period on the dynamic characteristics of flexible webbed wings is more pronounced.

Global climate change has led to more frequent and intense dry and wet extremes, causing considerable socio-economic losses, but whether these extremes in distant regions are linked and what mechanisms are driving their changes remain unclear. Based on the standardized precipitation-evapotranspiration index and ERA5 reanalysis data, this study reveals a dry-wet teleconnection between southwestern China (SWC) and northeastern China (NEC) from January to April: when SWC was extremely dry, NEC tended to be anomalously wet, and vice versa. Although the seesawing teleconnection is most significant on interannual time scales, it also experienced interdecadal changes, with wet SWC and dry NEC in 1979–1998 and 2019-present and dry SWC and wet NEC in 1999–2018. Further investigations suggest that the pattern of dry SWC and wet NEC is related to anomalous anticyclones (cyclones) over SWC (NEC), which lead to significant changes in surface temperature and total precipitation in the respective regions. The dryness in western (eastern) SWC is mainly influenced by the changes in temperature (precipitation), while the NEC wetness is affected mainly by the changes in temperature. Observational and modeling studies further suggest that the pressure anomalies over SWC and NEC are caused by zonally and meridionally propagating Rossby wave trains, triggered by the North Atlantic Oscillation and the enhanced Indo-Pacific convection, respectively. These wave trains further lead to hydroclimatic extremes in North America, southern Europe, and the Middle East by regulating the atmospheric circulation anomalies over these regions.

Based on the impact of large-scale circulation anomalies on sea -ice melting, this paper develops a statistical forecasting model for the seasonal sea -ice early melt onset (EMO) in the Bering Sea using the interannual increment prediction method. The prediction model considers three physically meaningful predictors: the January Beaufort High (P1 -H500), the November sea -level pressure (P2-SLP) over eastern Siberia, and the November snow cover over the eastern European Plain (P3-Snowc). P1 -H500 can influence the sea surface temperature (SST) anomaly in the Bering Sea through ocean-atmosphere interactions, and this SST anomaly can persist from January to March. Subsequently, it affects the EMO in the Bering Sea. P2-SLP exhibits a close association with the east part of the midlatitude North Pacific SST in November. The colder midlatitude North Pacific SST anomalies, which persist from November until January and February of the following year, will be accompanied by warmer SST anomalies in the Bering Sea, which result in a decreased sea -ice extent and a later -than -usual EMO. The Arctic dipole anomaly in January is one of the ways in which P3-Snowc affects the EMO in the following year. The predicted EMO shows good agreement with the observed EMO in the cross -validation test for 1981-2022, with a temporal correlation coefficient of 0.45, exceeding the 99% confidence level. The prediction accuracy of the prediction model for positive and negative abnormal years of EMO is 60% and 41%, respectively.

In the boreal summer and autumn of 2023, the globe experienced an extremely hot period across both oceans and continents. The consecutive record-breaking mean surface temperature has caused many to speculate upon how the global temperature will evolve in the coming 2023/24 boreal winter. In this report, as shown in the multi-model ensemble mean (MME) prediction released by the Institute of Atmospheric Physics at the Chinese Academy of Sciences, a medium-to-strong eastern Pacific El Niño event will reach its mature phase in the following 2–3 months, which tends to excite an anomalous anticyclone over the western North Pacific and the Pacific-North American teleconnection, thus serving to modulate the winter climate in East Asia and North America. Despite some uncertainty due to unpredictable internal atmospheric variability, the global mean surface temperature (GMST) in the 2023/24 winter will likely be the warmest in recorded history as a consequence of both the El Niño event and the long-term global warming trend. Specifically, the middle and low latitudes of Eurasia are expected to experience an anomalously warm winter, and the surface air temperature anomaly in China will likely exceed 2.4 standard deviations above climatology and subsequently be recorded as the warmest winter since 1991. Moreover, the necessary early warnings are still reliable in the timely updated medium-term numerical weather forecasts and sub-seasonal-to-seasonal prediction.

中国是自然灾害频发的国家,气象灾害造成的损失占自然灾害造成损失的70％.2020年夏季出现超长梅雨期,长江和淮河发生洪水;2021年夏季,华北雨季开始早,结束晚,期间发生了"21·7"河南地区特大暴雨事件.这些气象灾害都对人民生命财产造成严重损失.因此,有必要提前对气候异常进行预测,以提高国家的防灾减灾能力.2022年3月,中国科学院大气物理研究所开展汛期(6～8月)的全国汛期气候趋势预测会商会.通过综合大气所各个数值模式和统计模型的结果,在未来4～6个月全球短期气候仍处在La Ni?a事件恢复到ENSO正常状态的背景下,预计2022年汛期(6～8月),东北东部和中部、华北大部分地区、黄河中下游、东南沿海、西北地区中部、西藏大部分地区、西南地区东部和云南大部分地区降水正常略偏多,其中环渤海湾地区降水偏多2～5成,可能发生局地洪涝灾害.全国其他大部分地区降水正常略偏少,其中长江下游地区和新疆北部降水偏少2～5成.预计今年登陆台风数正常略偏多.由于未来ENSO的趋势演变具有一定的不确定性以及夏季降水受到中高纬大气环流季节内变化的影响,因此,此次汛期预测结果具有一定的不确定性.我们将根据2022年春末、夏初大气环流和海洋等因子的实际演变趋势,做进一步补充订正预测.

Ocean observation is the prerequisite for the human to cognize and develop the ocean. Most autonomous ocean-observation platforms (AOOPs), for their limited endurance, cannot cope with the further application in large range and long-term marine operations. Gliding robots have become one category of the most powerful platforms in ocean observation for their super endurance, with marine renewable energy acquisition or new driving modes. This paper starts with the comparation of the performance characteristics of several typical AOOPs, and introduces the definition and classification of the gliding robots according to their certain features. The research progresses of each gliding robot to date are discussed, including underwater glider, wave glider and multifunction hybrid glider. The prospects for the future development of related technologies in gliding robots are also represented in this paper, which will provide a reference for novel AOOPs’ construction and application selection in ocean observation, based on carrying different sensors.

As a promising semiconductor photocatalyst, BiOCl has been widely used in the field of environmental protection. However, due to its weak ability to absorb visible light, the application of BiOCl in other important photocatalytic fields has been significantly limited, such as organic syn-thesis. In this work, a facile method was used to prepare a highly efficient heterogeneous nano-photocatalyst BiOCl/cellulose nanocrystal (CNC). Subsequently, the BiOCl/CNC was verified by XPS, ESR, BET and other characterization methods. The results show that not only the strong interaction between BiOCl and CNC increases the visible light absorption intensity of the compos-ite, but also the combination of BiOCl and CNC makes the specific area of the catalyst more than twofold. In addition, a large number of hydroxyl groups contained in CNC can be combined with the BAO bond in BiOCl through hydrogen bonds, forming abundant oxygen vacancies on BiOCl/ CNC. Excitedly, these changes enable BiOCl/CNC to exhibit excellent photocatalytic performance and regeneration performance in the sulfonation reaction of arylacetylene acid and sodium arylsulfinate, with a yield of up to 96%. This work represents a step towards a low-cost, environmentally friendly composite of cellulose and BiOCl, which will provide useful enlightenment for future exploration in related fields. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of King Saud University.

In this paper, we propose a hybrid forecasting model to improve the forecasting accuracy for depth-averaged current velocities (DACVs) of underwater gliders. The hybrid model is based on a discrete wavelet transform (DWT), a deep belief network (DBN), and a least squares support vector machine (LSSVM). The original DACV series are first decomposed into several high- and one low-frequency subseries by DWT. Then, DBN is used for high-frequency component forecasting, and the LSSVM model is adopted for low-frequency subseries. The effectiveness of the proposed model is verified by two groups of DACV data from sea trials in the South China Sea. Based on four general error criteria, the forecast performance of the proposed model is demonstrated. The comparison models include some well-recognized single models and some related hybrid models. The performance of the proposed model outperformed those of the other methods indicated above.

Accurate prediction of spring drought in China is helpful toward reducing associated agricultural losses. In this study, spring drought is defined as the three‐month Standardized Precipitation Evapotranspiration Index (SPEI) ending in May. Based on the year‐to‐year increment and downscaling method, three single‐predictor prediction models (P1 model, P2 model, and P3 model) and two multi‐predictor models for spring drought at 677 stations in China are developed for the period 1983–2020. As physical processes affecting spring drought in the China region, the tropical Pacific–Indian Ocean sea surface temperature (SST) in winter, the Davis Strait–Barents sea‐ice concentration (SIC) in winter, and the 500‐hPa spring vertical velocity, predicted by the Climate Forecast System, version 2 (CFSv2), are considered in the prediction models. The prediction skill of the downscaling models for the spring SPEI is measured by cross‐validation for the period 1983–2020. The CFSv2 model only shows convincing prediction skill for the spring SPEI at 35 of 677 stations. However, among the 677 stations, the temporal correlation coefficient between the observed and predicted spring SPEI at 621 stations for P1 model, 598 stations for P2 model, 545 stations for P3 model, 674 stations for the statistical downscaling model (SD model), and 675 stations for the hybrid downscaling dynamical–statistical prediction model (HD model) exceeds the 95% confidence level. Therefore, compared to the CFSv2 model, the prediction skill for spring drought is improved by the single‐predictor and multi‐predictor models. The prediction skill of HD model for spring drought, which combines preceding observational predictors and the simultaneous predictor of the CFSv2 model, is higher than that of SD model. In addition, the severe drought that occurred in Northeast and North China in spring 2017 can be successfully predicted by HD model.

As a critical system of the East Asian winter monsoon, the Siberian high has an important impact on the winter weather and climate anomalies in Eurasia. Using the National Center for Environment Prediction-Climate Forecast System version 2 (NCEP-CFSv2), this study comprehensively evaluates the seasonal and monthly prediction of the Siberian high intensity during the winter time (November to February). Results show that the NCEP-CFSv2 model can skillfully predict the Siberian high intensity only in November, the reasons for which are that the local thermal process, dynamic process, and Siberian snow cover extent mainly affect the Siberian high intensity in November. In terms of the thermal process, the NCEP-CFSv2 can better reproduce the Siberian high intensity in November and its related surface soil temperature, upward long-wave radiation, and other thermal factors in Siberia. In terms of the dynamic process, the NCEP-CFSv2 can better reproduce the Siberian high strength in November, which is associated with the low-level tropospheric divergent circulation and the sinking movement of the upper and middle layers in the Siberian area. The model also reproduces the relationship between the snow cover extent over Siberia and the Siberian high intensity in November. The thermodynamic process of the Siberian high and snow cover extent in the area are predictability sources of the Siberian High intensity in November, and the NCEP-CFSv2 can reasonably reproduce these predictability sources in November.

In this paper, the data feature of depth-averaged current velocities (DACVs) derived from underwater gliders is analyzed for the first time. Two features of DACVs have been proposed: one is the complex ingredients and small samples, and the other is the stationarity that occurs as the length of a DACV sequence increases. With these features in mind, a set of methods combining statistical analysis and machine learning are proposed to realize the prediction of DACVs. Four groups of DACV data of different gliders from sea trials in the South China Sea are used to verify the prediction method. Based on three general error criteria, the prediction performance of the proposed model is demonstrated. The persistence method is used as a comparison model. The results show that the prediction methods proposed in this paper are effective.

There were no TCs generated in July 2020 over the western North Pacific (WNP), which was the first time this had happened during since 1980. This study attempts to understand the cause of there having been no TCs generated in July 2020, and evaluates the prediction skill for the large-scale environmental conditions associated with the TC genesis number (TCGN). Results show that the main causes were the abnormal warming of sea surface temperature (SST) in the North Indian Ocean (NIO) and North Atlantic in July and the abnormal decrease in SST from April and May in the Niño4 region. The NIO SST can affect the large-scale environmental conditions via the SST–precipitation–wind feedback mechanism. Through the interaction between the tropical North Atlantic and the NIO, the abnormally warm North Atlantic SST further strengthened the impact of the NIO SST on the environmental conditions. The monthly difference (MD) of the Niño4 index from April to May is significantly correlated with the TCGN in July. Not only can the Niño4 MD in May affect the environmental conditions by affecting the WNP anticyclone, but it can also affect the NIO SST and precipitation anomalies through a shift in the position of the Walker circulation. Besides, the activity of the MJO also had a certain impact on the absence of TC genesis in July 2020. Although CFSv2 can successfully predict the local feedback affecting the July TCGN, it fails to forecast the large-scale environmental conditions associated with the absence of TC genesis over the WNP in July 2020.

系统评估了美国第二代气候预测系统(CFSv2)对1983～2019年北半球春季逐月南极涛动(AAO)的预测效能及可能成因.结果表明,CFSv2模式对3月、4月和5月AAO空间模态预测效能较好,但是耦合模式仅对3月的AAO年际变化具有较好的预测能力,对4月和5月AAO年际变化的预测能力较差.热带中东太平洋和澳大利亚以东太平洋海温异常有可能是3月AAO年际变化的可预测性来源.一方面,3月厄尔尼诺—南方涛动(ENSO)与AAO之间关系显著,而4、5月两者之间关系减弱.3月ENSO激发PSA(Pacific–South American)波列传播到南太平洋上,通过影响南太平洋海温异常以及低层气旋性环流异常影响3月AAO的年际变化.另一方面,3月澳大利亚以东太平洋海温异常在副热带急流核心区域激发活跃的Rossby波列,该波列由澳大利亚东部向东南频散到南太平洋中高纬地区,造成该地区位势高度异常,使得副热带地区30°S西风减弱,南半球高纬60°S西风加强,进而影响AAO的变化.CFSv2对3月AAO的预测效能高于4月和5月的主要原因是CFSv2模式能够很好再现3月AAO与ENSO、澳大利亚以东海温之间的关系及其影响机制.

As an unmanned surface vehicle, wave glider utilizes wave energy and solar power through its multi-body structure, including floating body, cable and underwater glide body. Its super endurance (up to years) is very suitable for large-scale and long-term ocean environment observation applications. Through the introduction of flexible webbed wings (FWWs), this paper proposes a new design method to overcome the deficiencies in former wave glider, such as complex structure, poor maneuverability, and low wave energy conversion efficiency, etc. Obviously, the FWWs are significant components of the wave glider to achieve the wave energy conversion and generate driving force. Firstly, the calculation model of the FWWs driving force is established based on the movement principle of the wave glider with webbed wings (WGWW). Then, the analysis on the driving force of FWWs under different bending deformation conditions is completed through the CFD method. Finally, the feasibility of the FWWs is verified through the WGWW prototype and its pool experiments.

天气预报是指一周内至两周时间尺度的气象预报,而月季及以上时间尺度的预报则属于气候预测范畴.中国的气候预测起步很早,无论在研究工作中还是在业务应用上都取得了显著成就.文中扼要回顾了这些研究和业务发展成就,重点包括:对于季风和梅雨、寒潮的早期认知和后期研究发现、早期气候预测业务发展概况、动力气候预测的早期探索、动力-统计气候预测方法的研制和应用、气候预测模式的发展以及初始化和多模式集合预测、东亚气候系统变异的全方位探索、气候预测范畴的不断拓展和气候预测研究的不断创新.也对未来气候预测研究和业务发展提出了几个重大挑战性课题,涉及不同时间尺度气候变异过程之间的相互作用、季节内至年代际气候预测、气候系统模式及初始化、动力-统计相结合的气候预测方法等方面.

The prediction skill of the Climate Forecast System, version 2 (CFSv2), for the North Atlantic Oscillation (NAO) is evaluated in three winter months (December, January, and February). The results show that the CFSv2 model can skillfully predict the December NAO one month in advance. There are two main contributors to NAO predictability in December. One is the predictability of the relationship between the North Atlantic sea surface temperature anomaly (SSTA) tripole and the NAO and the other is the second empirical orthogonal function (EOF) mode of the geopotential height at 50 hPa (Z50-EOF2). The relationship between the NAO and SSTA tripole index in December is the most significant in the three winter months. The significant monthly differences of surface heat fluxes in December over the whole North Atlantic are favorable for promoting the interaction between the NAO and North Atlantic SSTAs, in addition to improving the predictability of the December NAO. When the NAO is in a positive phase, easterly anomalies are located at the low and high latitudes and westerly anomalies prevail in the mid-latitudes of the troposphere. The correlation between the December Z50-EOF2 and zonal-mean zonal wind anomalies shows a similar spatial structure to that for the NAO. The possible reason why the CFSv2 model can predict the December NAO one month ahead is that it can reasonably reproduce the relationship between the December NAO and both the North Atlantic SST and stratospheric circulation.