TiO2 pigments are typically coated with inert layers to suppress the photocatalytic activity and improve the weatherability. However, the traditional inert layers have a lower refractive index compared to TiO2, and th...TiO2 pigments are typically coated with inert layers to suppress the photocatalytic activity and improve the weatherability. However, the traditional inert layers have a lower refractive index compared to TiO2, and therefore reduce the lightening power of TiO2. In the present work, a uniform, amorphous, 2.9-nm-thick TiO2 protective layer was deposited onto the surface of anatase TiO2 pigments according to pulsed chemical vapor deposition at room temperature, with Ti Cl4 as titanium precursor. Amorphous TiO2 coating layers exhibited poor photocatalytic activity, leading to a boosted weatherability. Similarly, this coating method is also effective for TiO2 coating with amorphous SiO2 and SnO2 layers. However, the lightening power of amorphous TiO2 layer is higher than those of amorphous SiO2 and SnO2 layers. According to the measurements of photoluminescence lifetime, surface photocurrent density, charge-transfer resistance, and electron spin resonance spectroscopy, it is revealed that the amorphous layer can prevent the migration of photogenerated electrons and holes onto the surface, decreasing the densities of surface electron and hole, and thereby suppress the photocatalytic activity.展开更多
Although inorganic pigments in common spectral tuning materials show good weatherability and heat resistance,the limited color choices,weak coloring power,poor dispersibility,and a possibility of toxicity limit their ...Although inorganic pigments in common spectral tuning materials show good weatherability and heat resistance,the limited color choices,weak coloring power,poor dispersibility,and a possibility of toxicity limit their development.On the basis of organic pigments which possess a wide range of colors,high coloring power,good transparency,and high safety,herein,the modified pigment and biomimetic coating with improved weatherability,especially ultraviolet(UV)resistance(from 2 to 6 days),was achieved by intercalating acid green 25(AG25)pigment into Mg/Al-layered double hydroxides(Mg/Al-LDH).Furthermore,the heat resistance of AG25 was also significantly increased.Moreover,the spectral stability of pigments after heat treatment is superior with almost unchanged spectral profile and green reflection peak.The formation of strong N-H bonds and the S-M(Mg,Al)bonds between Mg/Al-LDH laminates and AG25 molecules contributes to the improvement.This work shows potential for biomimetic leaf materials in respect of reflective spectra stability.展开更多
Small things can show a person's true character Find lt What was the weather like when the girl came to the castle door?nce upon a time,there was a prince.He wanted to marry a real princess.He traveled all around ...Small things can show a person's true character Find lt What was the weather like when the girl came to the castle door?nce upon a time,there was a prince.He wanted to marry a real princess.He traveled all around the world looking for one.展开更多
China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the qua...China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the quantitative measurement ability of the PMR is critical.China operates more than 250 weather radars over the mainland.Consistency of the spaceborne radar with ground-based radars will enhance precipitation measurement ability,especially over oceans and mountains where observations are sparse.Additionally,the spaceborne radar can be used to evaluate the spatial and temporal homogeneity of the ground-based radar network.This paper focuses on comparing the PMR onboard the FY-3G satellite with S-band China New Generation Weather Radars(CINRADs).A comparison algorithm between the PMR and CINRADs has been developed,incorporating detailed quality control,attenuation correction,data optimization,spatiotemporal matching,non-uniform beam filling constraint,uniformity constraint,and frequency correction.The matched data in typical months of four seasons were selected to carry out the comparison.The data consistency between the PMR and CINRADs was analyzed.The correlation coefficient is 0.87,the deviation is 0.89 dB,and the standard deviation is 2.50 dB,based on 98226 matching samples.The results show the radar reflectivity of the PMR is quite comparable to that of the CINRADs,demonstrating that the PMR data quality is satisfactory and can be used to verify and correct data consistency among multiple ground-based radars.This work also paves the way for data fusion and joint application of satellite and ground radars in the future.展开更多
Understanding the hydrochemical dynamics of alpine arid basins is essential for ensuring sustainable water resources on the Tibetan Plateau.The Nianchu River,a typical alpine and arid basin on the Tibetan Plateau,is t...Understanding the hydrochemical dynamics of alpine arid basins is essential for ensuring sustainable water resources on the Tibetan Plateau.The Nianchu River,a typical alpine and arid basin on the Tibetan Plateau,is the focal area of the“One River,Two Tributaries”Comprehensive Development Project in Xizang Autonomous Region of China.However,no systematic studies on hydrochemistry in this basin have been conducted.This study investigated the spatio-temporal variations and controlling mechanisms of water chemistry in the Nianchu River basin with a focus on the understudied influence of widely distributed geothermal springs.During the dry and wet seasons,44 river water and 13 geothermal water samples were collected and analyzed for major ions.Results showed that the river water was weakly alkaline,with total dissolved solids and major ion concentrations(e.g.,Ca^(2+),HCO₃^(-),SO_(4)^(2-))significantly higher in the dry season(mean EC:372μS/cm)than in the wet season(mean EC:236μS/cm),and peaking in the midstream tributary of the Longma River.In contrast,the geothermal springs were moderately acidic and exhibited markedly higher mineralization(mean EC:1,936μS/cm),with Na^(+),K^(+),and Cl^(-)concentrations being 20.8,22.5,and 44.8 times those in river water,respectively.The direct discharge of geothermal water was identified as a key driver that significantly elevated Na^(+),K^(+),and Cl^(-)levels and altered the hydrochemical facies of the river in affected reaches.The main conclusions are that:(1)the hydrochemical evolution of the Nianchu River is predominantly governed by carbonate and silicate weathering,while geothermal water chemistry is controlled by evaporation-crystallization and deep water-rock interactions;and(2)sulfuric acid participates in carbonate weathering alongside carbonic acid,particularly in the mid-lower reaches,enhances the release of Ca^(2+),Mg^(2+),and SO_(4)^(2-).This study provides a holistic understanding of hydrogeochemical controls in a typical alpine basin and underscores the critical role of geothermal fluids,offering a scientific basis for protecting water resources under climate and anthropogenic pressures.展开更多
Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and dr...Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.展开更多
Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep...Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.展开更多
In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC rec...Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.展开更多
Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostation...Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.展开更多
Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately...Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures.In this study,numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia.Using the Weather Research and Forecasting Model coupled with Chemistry(WRF-Chem)and European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)data,the most severe SDS events in the spring of 2023 in East Asia were numerically simulated.The simulated results were compared and validated using meteorological observations and multisource remote sensing data.The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events.The primary dust sources in spring 2023 were identified as the western desert of Mongolia,the Gobi Desert,and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China.Peak dust load and maximum wind speed occurred almost simultaneously,indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events.Increased surface vegetation covers partially mitigated wind-driven dust emissions.In April,strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways.Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China.In contrast,their impact on the northwestern regions of China remains relatively limited.These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.展开更多
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.展开更多
Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the...Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.展开更多
Nanophase iron particles(np-Fe^(0))have multiple formation mechanisms in lunar soil,which are mostly related to meteorite and micro meteorite impacts.Thermal modification of the impact is critical.Metal oxides have un...Nanophase iron particles(np-Fe^(0))have multiple formation mechanisms in lunar soil,which are mostly related to meteorite and micro meteorite impacts.Thermal modification of the impact is critical.Metal oxides have unique chemical and physical properties that allow np-Fe^(0) to form at a lower initial reaction temperature.Through the insitu heating experiment of ilmenite in the Chang'e-5 sample,it was found that ilmenite can form np-Fe^(0) at 400℃under high vacuum(10-6 Pa).This fills in the missing information on the lowest measured temperature at which ilmenite forms np-Fe^(0).At 400-800℃,only np-Fe^(0) and vesicles were formed without new Ti-rich minerals.At the same time,thermodynamic calculations showed that decomposition of ilmenite occurs in two stages.The experiments correspond to the initial stage of ilmenite thermal decomposition under high vacuum.The study explains the thermal decomposition reaction of ilmenite in a vacuum environment,provides a reference for the minimum measured temperature required for the formation of np-Fe^(0),and further improves the formation mechanism of np-Fe^(0).展开更多
Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,...Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,we analyzed the effects of forest cover change on local temperature using the Weather Research and Forecasting(WRF)model from 2010 to 2020 in the Greater and Lesser Khingan Mountains(GLKM),Northeastern China,and explored the related driving factors.The conversions between forest and open lands(i.e.,cropland and grassland)were predominant.During the growing season,the conversion of cropland and grassland to forest resulted in warming(0.38±0.10 and 0.41±0.09℃,respectively)in air temperature(Ta),while the reverse conversion caused cooling(-0.31 peratur±0.08 and e-0.24±0.07℃,respectively),which was less than the changes observed in land surface tem(LST).Conversion of forest to impervious land caused warming(1.16 the±0.11℃),and opposite conversion resulted in cooling(can-0.88 t±0.17℃).These results indicate that radiative effects like albedo and net radiation drive the signifi net warming effect from afforestation on open lands within the temperate forest ecoregion.Conversely,conversion to impervious land produced the most substantial net warming impacts,driven by non-radiative effects like sensible heat,latent heat,and ground heat flux(GH).In these conversions,temperature can indirectly influence precipitation(Pre)through vapor pressure deficit(VPD),and Pre can also indirectly affect temperature via evapotranspiration(ET).This study highlights the need to thoroughly understand the impacts of afforestation in temperate forests while avoiding deforestation to regulate the climate effectively.展开更多
This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and bou...This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.展开更多
To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the ...To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.展开更多
Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examine...Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.展开更多
Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and...Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.展开更多
基金Supported by the National Key R&D Program of China(2018YFB0605700).
文摘TiO2 pigments are typically coated with inert layers to suppress the photocatalytic activity and improve the weatherability. However, the traditional inert layers have a lower refractive index compared to TiO2, and therefore reduce the lightening power of TiO2. In the present work, a uniform, amorphous, 2.9-nm-thick TiO2 protective layer was deposited onto the surface of anatase TiO2 pigments according to pulsed chemical vapor deposition at room temperature, with Ti Cl4 as titanium precursor. Amorphous TiO2 coating layers exhibited poor photocatalytic activity, leading to a boosted weatherability. Similarly, this coating method is also effective for TiO2 coating with amorphous SiO2 and SnO2 layers. However, the lightening power of amorphous TiO2 layer is higher than those of amorphous SiO2 and SnO2 layers. According to the measurements of photoluminescence lifetime, surface photocurrent density, charge-transfer resistance, and electron spin resonance spectroscopy, it is revealed that the amorphous layer can prevent the migration of photogenerated electrons and holes onto the surface, decreasing the densities of surface electron and hole, and thereby suppress the photocatalytic activity.
基金financially supported by the External Collaboration Fund(No.XM2022FH5079)。
文摘Although inorganic pigments in common spectral tuning materials show good weatherability and heat resistance,the limited color choices,weak coloring power,poor dispersibility,and a possibility of toxicity limit their development.On the basis of organic pigments which possess a wide range of colors,high coloring power,good transparency,and high safety,herein,the modified pigment and biomimetic coating with improved weatherability,especially ultraviolet(UV)resistance(from 2 to 6 days),was achieved by intercalating acid green 25(AG25)pigment into Mg/Al-layered double hydroxides(Mg/Al-LDH).Furthermore,the heat resistance of AG25 was also significantly increased.Moreover,the spectral stability of pigments after heat treatment is superior with almost unchanged spectral profile and green reflection peak.The formation of strong N-H bonds and the S-M(Mg,Al)bonds between Mg/Al-LDH laminates and AG25 molecules contributes to the improvement.This work shows potential for biomimetic leaf materials in respect of reflective spectra stability.
文摘Small things can show a person's true character Find lt What was the weather like when the girl came to the castle door?nce upon a time,there was a prince.He wanted to marry a real princess.He traveled all around the world looking for one.
基金jointly supported by the National Natural Science Foundation of China(Grant U2442214)the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN10)+1 种基金the National Defense Science and Technology Bureau’s 14th Five-Year Civil Aerospace Preresearch Project(Grant Nos.D030303 and D040204)the International Space Water Cycle Observation Constellation Program(Grant No.183311KYSB20200015).
文摘China launched its first spaceborne Precipitation Measurement Radar(PMR)on the FY-3G satellite in April 2023.To achieve the scientific goal of measuring the three-dimensional precipitation structure,evaluating the quantitative measurement ability of the PMR is critical.China operates more than 250 weather radars over the mainland.Consistency of the spaceborne radar with ground-based radars will enhance precipitation measurement ability,especially over oceans and mountains where observations are sparse.Additionally,the spaceborne radar can be used to evaluate the spatial and temporal homogeneity of the ground-based radar network.This paper focuses on comparing the PMR onboard the FY-3G satellite with S-band China New Generation Weather Radars(CINRADs).A comparison algorithm between the PMR and CINRADs has been developed,incorporating detailed quality control,attenuation correction,data optimization,spatiotemporal matching,non-uniform beam filling constraint,uniformity constraint,and frequency correction.The matched data in typical months of four seasons were selected to carry out the comparison.The data consistency between the PMR and CINRADs was analyzed.The correlation coefficient is 0.87,the deviation is 0.89 dB,and the standard deviation is 2.50 dB,based on 98226 matching samples.The results show the radar reflectivity of the PMR is quite comparable to that of the CINRADs,demonstrating that the PMR data quality is satisfactory and can be used to verify and correct data consistency among multiple ground-based radars.This work also paves the way for data fusion and joint application of satellite and ground radars in the future.
基金jointly funded by the School Scientific Research Development Fund project(2022LFR091)。
文摘Understanding the hydrochemical dynamics of alpine arid basins is essential for ensuring sustainable water resources on the Tibetan Plateau.The Nianchu River,a typical alpine and arid basin on the Tibetan Plateau,is the focal area of the“One River,Two Tributaries”Comprehensive Development Project in Xizang Autonomous Region of China.However,no systematic studies on hydrochemistry in this basin have been conducted.This study investigated the spatio-temporal variations and controlling mechanisms of water chemistry in the Nianchu River basin with a focus on the understudied influence of widely distributed geothermal springs.During the dry and wet seasons,44 river water and 13 geothermal water samples were collected and analyzed for major ions.Results showed that the river water was weakly alkaline,with total dissolved solids and major ion concentrations(e.g.,Ca^(2+),HCO₃^(-),SO_(4)^(2-))significantly higher in the dry season(mean EC:372μS/cm)than in the wet season(mean EC:236μS/cm),and peaking in the midstream tributary of the Longma River.In contrast,the geothermal springs were moderately acidic and exhibited markedly higher mineralization(mean EC:1,936μS/cm),with Na^(+),K^(+),and Cl^(-)concentrations being 20.8,22.5,and 44.8 times those in river water,respectively.The direct discharge of geothermal water was identified as a key driver that significantly elevated Na^(+),K^(+),and Cl^(-)levels and altered the hydrochemical facies of the river in affected reaches.The main conclusions are that:(1)the hydrochemical evolution of the Nianchu River is predominantly governed by carbonate and silicate weathering,while geothermal water chemistry is controlled by evaporation-crystallization and deep water-rock interactions;and(2)sulfuric acid participates in carbonate weathering alongside carbonic acid,particularly in the mid-lower reaches,enhances the release of Ca^(2+),Mg^(2+),and SO_(4)^(2-).This study provides a holistic understanding of hydrogeochemical controls in a typical alpine basin and underscores the critical role of geothermal fluids,offering a scientific basis for protecting water resources under climate and anthropogenic pressures.
基金funded by the National Natural Science Foundation of China(42571311).
文摘Central Asia is characterized by an arid climate and widespread desert distribution,with its sustainable development severely constrained by dust events.An objective understanding of the spatiotemporal patterns and driving forces of dust weather is highly important in this area.Based on the meteorological observations from 2000 to 2020,we examined the spatiotemporal characteristics of dust weather in the five Central Asian countries(Kazakhstan,Uzbekistan,Kyrgyzstan,Turkmenistan,and Tajikistan)via Theil-Sen trend analysis and Geodetector modeling method,quantitatively revealing the influence of environmental factors,such as temperature,precipitation,and vegetation,on the frequency of dust weather.The results showed that:(1)dust weather in Central Asia was mainly distributed in a large''dust belt''extending from west to east from northern part of the Caspian lowland desert,and concentrated in basins,plains,and other low-altitude areas.Strong dust weather mainly occurred in northern areas of the Aral Sea and southern edge of Central Asia,with a maximum annual frequency of 21.9%;(2)strong dust weather in Central Asia has fluctuated and slightly decreased since 2001.The highest frequency(1.1%)occurred in spring(from March to June);(3)from 2000 to 2020,changes such as spot shifting and shrinking occurred in the four main source areas(north of the Aral Sea,Kyzylkum Desert,Karakum Desert,and Garabogazköl Bay region),where sandstorms occurred in Central Asia,and northern Caspian lowland desert became the most important low-emission dust source in Central Asia;and(4)the combined effect of soil moisture and air temperature has the most significant influence on dust weather in Central Asia.This study provides a theoretical basis for sand prevention and sand control in Central Asia.In the future,Central Asia should focus on the rational utilization of land and water resources,and implement human interventions such as vegetation restoration and optimization of irrigation methods to curb further desertification in this area.
基金supported by the National Natural Science Foundation of China[grant number 62376217]the Young Elite Scientists Sponsorship Program by CAST[grant number 2023QNRC001]the Joint Research Project for Meteorological Capacity Improvement[grant number 24NLTSZ003]。
文摘Deep learning-based methods have become alternatives to traditional numerical weather prediction systems,offering faster computation and the ability to utilize large historical datasets.However,the application of deep learning to medium-range regional weather forecasting with limited data remains a significant challenge.In this work,three key solutions are proposed:(1)motivated by the need to improve model performance in data-scarce regional forecasting scenarios,the authors innovatively apply semantic segmentation models,to better capture spatiotemporal features and improve prediction accuracy;(2)recognizing the challenge of overfitting and the inability of traditional noise-based data augmentation methods to effectively enhance model robustness,a novel learnable Gaussian noise mechanism is introduced that allows the model to adaptively optimize perturbations for different locations,ensuring more effective learning;and(3)to address the issue of error accumulation in autoregressive prediction,as well as the challenge of learning difficulty and the lack of intermediate data utilization in one-shot prediction,the authors propose a cascade prediction approach that effectively resolves these problems while significantly improving model forecasting performance.The method achieves a competitive result in The East China Regional AI Medium Range Weather Forecasting Competition.Ablation experiments further validate the effectiveness of each component,highlighting their contributions to enhancing prediction performance.
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.42077242 and 42171407)the Graduate Innovation Fund of Jilin University.
文摘Accurate and rapid recognition of weathering degree(WD)and groundwater condition(GC)is essential for evaluating rock mass quality and conducting stability analyses in underground engineering.Conventional WD and GC recognition methods often rely on subjective evaluation by field experts,supplemented by field sampling and laboratory testing.These methods are frequently complex and timeconsuming,making it challenging to meet the rapidly evolving demands of underground engineering.Therefore,this study proposes a rock non-geometric parameter classification network(RNPC-net)to rapidly achieve the recognition and mapping ofWD and GC of tunnel faces.The hybrid feature extraction module(HFEM)in RNPC-net can fully extract,fuse,and utilize multi-scale features of images,enhancing the network's classification performance.Moreover,the designed adaptive weighting auxiliary classifier(AC)helps the network learn features more efficiently.Experimental results show that RNPC-net achieved classification accuracies of 0.8756 and 0.8710 for WD and GC,respectively,representing an improvement of approximately 2%e10%compared to other methods.Both quantitative and qualitative experiments confirm the effectiveness and superiority of RNPC-net.Furthermore,for WD and GC mapping,RNPC-net outperformed other methods by achieving the highest mean intersection over union(mIOU)across most tunnel faces.The mapping results closely align with measurements provided by field experts.The application of WD and GC mapping results to the rock mass rating(RMR)system achieved a transition from conventional qualitative to quantitative evaluation.This advancement enables more accurate and reliable rock mass quality evaluations,particularly under critical conditions of RMR.
基金supported by the National Natural Science Foundation of China (NSFC) (Grant No.42205044)Feng Yun Application Pioneering Project (FY-APP) Innovation Center for Feng Yun Meteorological Satellite (FYSIC) Special Project (FY-APP-XC-2023.04)the Wuxi University Research Start-up Fund for Recruited Talent。
文摘Ground-based radar is the primary means by which severe storms are monitored and tracked;however, due to limited coverage, important data is often missed over ocean and mountainous areas. On the other hand, geostationary(GEO)weather satellites provide continuous observations with seamless coverage with advanced imager, despite their limited capability to penetrate clouds. Combining satellite and ground-radar observations could exploit the advantages of both techniques, providing tracking capability close to that of ground radar while maintaining full spatial coverage. This study presents a novel method called Multi-dimensional satellite Observation information for Radar Estimation(MORE) to reconstruct radar composite reflectivity(CREF). Deep learning techniques are important components of MORE for estimating CREF from China's Fengyun-4B(FY-4B) GEO satellite observations. Two models are developed: an infraredonly(IR-Single) model available for all times, and a visible-infrared(VIS+IR) model for daytime applications. These models incorporate multi-dimensional satellite observation information, including temporal, spatial, spectral, and viewing angle information, to enhance the accuracy of radar echo reconstruction. Results demonstrate that the VIS+IR model outperforms the IR-Single model, and both models achieves a root-mean-square error(RMSE) of less than 6 dBZ and a coefficient of determination(R~2) of greater than 0.7. The models effectively reconstruct radar echoes, including strong echoes exceeding 50 dBZ, and show good agreement with precipitation data in radar-blind areas. This study offers a valuable solution for severe weather monitoring and tracking in regions lacking ground-based radar observations, and provides a potential tool for enhanced data assimilation in numerical weather prediction(NWP) models.
基金supported by the Science&Technology Fundamental Resources Investigation Program(2023FY100700)the Key Project of Innovation LREIS(KPI006)+1 种基金the Key R&D and Achievement Transformation Program of Inner Mongolia Autonomous Region(2023KJHZ0027)the Construction Project of China Knowledge Centre for Engineering Sciences and Technology(CKCEST-2023-1-5).
文摘Sand and dust storms(SDSs)are natural disasters that frequently occur during spring in arid and semi-arid areas,causing serious impacts on human health,air quality,transportation,and agricultural production.Accurately simulating the occurrence and evolution of SDSs is of great significance for identifying dust sources and formulating effective disaster prevention measures.In this study,numerical simulations were conducted to reveal the dynamic spatiotemporal evolution and transport of dust load across East Asia.Using the Weather Research and Forecasting Model coupled with Chemistry(WRF-Chem)and European Centre for Medium-Range Weather Forecasts Reanalysis v5(ERA5)data,the most severe SDS events in the spring of 2023 in East Asia were numerically simulated.The simulated results were compared and validated using meteorological observations and multisource remote sensing data.The results showed that the simulated dust load in the peak regions showed close agreement with ground-based observations during the events.The primary dust sources in spring 2023 were identified as the western desert of Mongolia,the Gobi Desert,and the Taklimakan Desert in Xinjiang Uygur Autonomous Region of China.Peak dust load and maximum wind speed occurred almost simultaneously,indicating that high wind speed was the primary driver of sand and dust mobilization during individual SDS events.Increased surface vegetation covers partially mitigated wind-driven dust emissions.In April,strong winds over the Gobi Desert on the Mongolian Plateau predominantly drove cross-border SDSs along northwestern and northward transport pathways.Dust originating from Mongolia exerts a substantial influence on particulate dust load in the central and eastern parts of Inner Mongolia Autonomous Region of China.In contrast,their impact on the northwestern regions of China remains relatively limited.These findings contribute to understanding the source areas of SDS events in East Asia by simulating the dynamic evolution of SDSs and elucidating the relationships between SDS events and local geographical and environmental factors.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
基金supported by the Science and Technology Project of Sichuan Electric Power Company“Power Supply Guarantee Strategy for Urban Distribution Networks Considering Coordination with Virtual Power Plant during Extreme Weather Event”(No.521920230003).
文摘Ensuring reliable power supply in urban distribution networks is a complex and critical task.To address the increased demand during extreme scenarios,this paper proposes an optimal dispatch strategy that considers the coordination with virtual power plants(VPPs).The proposed strategy improves systemflexibility and responsiveness by optimizing the power adjustment of flexible resources.In the proposed strategy,theGaussian Process Regression(GPR)is firstly employed to determine the adjustable range of aggregated power within the VPP,facilitating an assessment of its potential contribution to power supply support.Then,an optimal dispatch model based on a leader-follower game is developed to maximize the benefits of the VPP and flexible resources while guaranteeing the power balance at the same time.To solve the proposed optimal dispatch model efficiently,the constraints of the problem are reformulated and resolved using the Karush-Kuhn-Tucker(KKT)optimality conditions and linear programming duality theorem.The effectiveness of the strategy is illustrated through a detailed case study.
基金funding support from the National Natural Science Foundation of China(Grant Nos.42441804,42403043,42273042,42303041,and U24A2008)Youth Innovation Promotion Association CAS awards+5 种基金"From 0 to 1"Original Exploration Cultivation Project,Institute of Geochemistry,Chinese Academy of Sciences(Grant No.DHSZZ2023.3)Bureau of Frontier Sciences and Basic Research,CAS,(Grant No.QYJ-2025-0103)Guizhou Provincial Foundation for Excellent Scholars Program(Grant No.GCC[2023]088)Provincial Key Research and Development(R&D)Plan Projects of Heilongjiang(Grant No.2024ZXDXB52)The Innovation and Development Fund of Science and Technology of Institute of Geochemistry,Chinese Academy of SciencesGuizhou Province Basic Research Program Project(QKHJC-ZK[2023]-General 473)。
文摘Nanophase iron particles(np-Fe^(0))have multiple formation mechanisms in lunar soil,which are mostly related to meteorite and micro meteorite impacts.Thermal modification of the impact is critical.Metal oxides have unique chemical and physical properties that allow np-Fe^(0) to form at a lower initial reaction temperature.Through the insitu heating experiment of ilmenite in the Chang'e-5 sample,it was found that ilmenite can form np-Fe^(0) at 400℃under high vacuum(10-6 Pa).This fills in the missing information on the lowest measured temperature at which ilmenite forms np-Fe^(0).At 400-800℃,only np-Fe^(0) and vesicles were formed without new Ti-rich minerals.At the same time,thermodynamic calculations showed that decomposition of ilmenite occurs in two stages.The experiments correspond to the initial stage of ilmenite thermal decomposition under high vacuum.The study explains the thermal decomposition reaction of ilmenite in a vacuum environment,provides a reference for the minimum measured temperature required for the formation of np-Fe^(0),and further improves the formation mechanism of np-Fe^(0).
基金funded or supported by the National Natural Science Foundation of China(Nos.32371878,32001251)the Natural Science Foundation of Jiangsu Province(No.BK20200781)+1 种基金the Youth Science and Technology Talent Lifting Project of Jiangsu Province(No.JSTJ-2024-324)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)。
文摘Temperate forests are vital for maintaining ecological security and regulating the global climate.Despite considerable controversy surrounding the biophysical impacts of temperate forests on mid-latitude temperatures,we analyzed the effects of forest cover change on local temperature using the Weather Research and Forecasting(WRF)model from 2010 to 2020 in the Greater and Lesser Khingan Mountains(GLKM),Northeastern China,and explored the related driving factors.The conversions between forest and open lands(i.e.,cropland and grassland)were predominant.During the growing season,the conversion of cropland and grassland to forest resulted in warming(0.38±0.10 and 0.41±0.09℃,respectively)in air temperature(Ta),while the reverse conversion caused cooling(-0.31 peratur±0.08 and e-0.24±0.07℃,respectively),which was less than the changes observed in land surface tem(LST).Conversion of forest to impervious land caused warming(1.16 the±0.11℃),and opposite conversion resulted in cooling(can-0.88 t±0.17℃).These results indicate that radiative effects like albedo and net radiation drive the signifi net warming effect from afforestation on open lands within the temperate forest ecoregion.Conversely,conversion to impervious land produced the most substantial net warming impacts,driven by non-radiative effects like sensible heat,latent heat,and ground heat flux(GH).In these conversions,temperature can indirectly influence precipitation(Pre)through vapor pressure deficit(VPD),and Pre can also indirectly affect temperature via evapotranspiration(ET).This study highlights the need to thoroughly understand the impacts of afforestation in temperate forests while avoiding deforestation to regulate the climate effectively.
基金supported by the Special Project-Original Exploration(Grant No.42450163)the National Youth Science Foundation of China Project(Grant No.4240050560)the Research and Development of Key Technologies for Artificial Intelligence Regional Typhoon Forecasting Model project.
文摘This study traces the development of the Shanghai Typhoon Model(SHTM)from a traditional physics-based regional model toward a data-driven,machine-learning typhoon forecasting system.After upgrading its initial and boundary conditions,SHTM now leverages large-scale constraints from machine-learning weather prediction(MLWP)models,resulting in an ML–physics hybrid framework.During Typhoon Danas(2025),the hybrid SHTM achieves substantially lower track errors than both the advanced ECMWF Integrated Forecasting System(IFS)and leading MLWP models such as PanGu and FuXi.Furthermore,the hybrid SHTM consistently maintains mean track errors below 200 km up to a forecast lead time of 108 hours,representing a significant advancement in forecast accuracy.In addition,this study highlights the technical roadmap for transitioning from a physics-based typhoon model to a fully data-driven ML typhoon forecast system.It also emphasizes that advances in the physical modeling framework provide a critical foundation for further improving the performance of future data-driven ML typhoon models.
基金National Key R&D Program of China of the 13th Five-Year Plan(No.2018YFD1100704)。
文摘To meet the challenge of mismatches between power supply and demand,modern buildings must schedule flexible energy loads in order to improve the efficiency of power grids.Furthermore,it is essential to understand the effectiveness of flexibility management strategies under different climate conditions and extreme weather events.Using both typical and extreme weather data from cities in five major climate zones of China,this study investigates the energy flexibility potential of an office building under three short-term HVAC management strategies in the context of different climates.The results show that the peak load flexibility and overall energy performance of the three short-term strategies were affected by the surrounding climate conditions.The peak load reduction rate of the pre-cooling and zone temperature reset strategies declined linearly as outdoor temperature increased.Under extreme climate conditions,the daily peak-load time was found to be over two hours earlier than under typical conditions,and the intensive solar radiation found in the extreme conditions can weaken the correlation between peak load reduction and outdoor temperature,risking the ability of a building’s HVAC system to maintain a comfortable indoor environment.
基金supported by the Guangdong Basic and Applied Basic Research project (No.2020B0301030004)the Key-Area Research and Development Program of Guangdong Province (No.2020B1111360003)+1 种基金the National Natural Science Foundation of China (No.42105103)the Guangdong Basic and Applied Basic Research Foundation (No.2022A1515011554).
文摘Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns(SWPs),however,the consistency of different classification methods is rarely examined.In this study,we apply two widely-used objective methods,the self-organizing map(SOM)and K-means clustering analysis,to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022.We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities.In the case of classifying six SWPs,the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods,and the difference in themean frequency of each SWP is less than 7%.The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature,lower cloud cover,relative humidity,and wind speed,and stronger subsidence compared to climatology mean.We find that during 2015-2022,the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 days/year,faster than the increases in the ozone exceedance days(3.0 days/year).The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6.In particular,the significant increase in ozone-favorable SWPs in 2022,especially the Subtropical High type which typically occurs in September,is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022.Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.
基金supported by the National Natural Science Foundation of China(Nos.U19A2044,42105132,42030609,and 41975037)the National Key Research and Development Programof China(No.2022YFC3700303).
文摘Extreme ozone pollution events(EOPEs)are associated with synoptic weather patterns(SWPs)and pose severe health and ecological risks.However,a systematic investigation of themeteorological causes,transport pathways,and source contributions to historical EOPEs is still lacking.In this paper,the K-means clustering method is applied to identify six dominant SWPs during the warm season in the Yangtze River Delta(YRD)region from 2016 to 2022.It provides an integrated analysis of the meteorological factors affecting ozone pollution in Hefei under different SWPs.Using the WRF-FLEXPART model,the transport pathways(TPPs)and geographical sources of the near-surface air masses in Hefei during EOPEs are investigated.The results reveal that Hefei experienced the highest ozone concentration(134.77±42.82μg/m^(3)),exceedance frequency(46 days(23.23%)),and proportion of EOPEs(21 instances,47.7%)under the control of peripheral subsidence of typhoon(Type 5).Regional southeast winds correlated with the ozone pollution in Hefei.During EOPEs,a high boundary layer height,solar radiation,and temperature;lowhumidity and cloud cover;and pronounced subsidence airflow occurred over Hefei and the broader YRD region.The East-South(E_S)patterns exhibited the highest frequency(28 instances,65.11%).Regarding the TPPs and geographical sources of the near-surface air masses during historical EOPEs.The YRD was the main source for land-originating air masses under E_S patterns(50.28%),with Hefei,southern Anhui,southern Jiangsu,and northern Zhejiang being key contributors.These findings can help improve ozone pollution early warning and control mechanisms at urban and regional scales.
