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
Weathering steel exhibits excellent corrosion resistance and is widely used in bridges,towers,railways,highways,and other engineering projects that are exposed to the atmosphere for long periods of time.However,before...Weathering steel exhibits excellent corrosion resistance and is widely used in bridges,towers,railways,highways,and other engineering projects that are exposed to the atmosphere for long periods of time.However,before the formation of stable rust layers,weathering steel is prone to liquid rust sagging and spattering,leading to environmental pollution and city appearance concerns.These factors limit the application and development of weathering steel.In this study,a rapid and environmentally friendly method was de-veloped by introducing alloying elements,specifically investigating the role of Sn in the rapid stabilization of rust layers in marine atmo-spheric environments.The rust layer formed on weathering low-alloy steel exposed to prolonged outdoor conditions and laboratory im-mersion experiments was explored using electron probe micro-analyzer(EPMA),micro-Raman,X-ray photoelectron spectroscopy(XPS),and electrochemical measurements.Results showed an optimal synergistic effect between Sn and Cr,which facilitated the accelerated densification of the rust layer.This beneficial effect enhanced the capability of the rust layer to resist Cl^(-)erosion and improved the protec-tion performance of the rust layer.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
This past year, 2024, is on track to be the warmest year, joining 2023 as the two hottest years on record. With the exceptional heat, weather and climate extremes were common across the world. In particular, 2024 has ...This past year, 2024, is on track to be the warmest year, joining 2023 as the two hottest years on record. With the exceptional heat, weather and climate extremes were common across the world. In particular, 2024 has seen a remarkable run of extreme precipitation events and resulting impacts. Here, we provide an overview of the most notable extreme events of the year, including extreme precipitation and floods, tropical cyclones, and droughts. The characteristics and impacts of these extreme events are summarized, followed by discussion on the physical drivers and the role of global warming.Finally, we also discuss the future prospects in extreme event studies, including impact-based perspectives, challenges in attribution of precipitation extremes, and the existing gap to minimize impacts from climate extremes.展开更多
Black soil is essential for maintaining regional food security and promoting global agricultural production.Understanding the weathering process of parent material and the accumulation of organic carbon is crucial to ...Black soil is essential for maintaining regional food security and promoting global agricultural production.Understanding the weathering process of parent material and the accumulation of organic carbon is crucial to comprehending the developmental history and future trends of black soil,especially against the background of large-scale global cultivation and climate change.Although the importance of black soil formation and evolution cannot be ignored,the relevant research is still very scarce.In this study,a typical eight-meter-deep soil core was collected from the Keshan area of the Songnen Plain,Northeast China,where surface black soil developed on paleo-sediments.Using^(14)C dating,the formation age of the black soil was determined.Based on the characteristics of the geochemical composition,grain size and the magnetic susceptibility of the sediments,it was demonstrated that the black soil and its parent material originated from reworked loess.Furthermore,the mass transfer coefficient(τ)of some elements was determined,in order to explore the soil weathering process.By calculating the transported amount of alkaline and alkaline-earth elements,the weathering rate of parent material to black soil was found to be weak,at 0.16 kEq·ha^(-1)·year^(-1).Combining the results of dating and carbon density in the different layers of black soil,the accumulation rate of organic carbon was determined as follows:rapidly increasing in the initial period of 13.2-2.2 ka,reaching its maximum average value of 34.0 g·cm^(-2)·a^(-1)at 2.2-0.8 ka,then showing a decreasing trend with an average value of-77.5 g·cm^(-2)·a^(-1).Compared with regional climate change,Keshan black soil has developed under a colder and wetter climate during the Holocene.Predictably,ongoing global warming may lead to the degradation of black soils in the Songnen Plain,as well as in other regions.Our results will enrich geological knowledge of black soil formation and future evolutionary trends.展开更多
The undrained mechanical behavior of unsaturated completely weathered granite(CWG)is highly susceptible to alterations in the hydraulic environment,particularly under uniaxial loading conditions,due to the unique natu...The undrained mechanical behavior of unsaturated completely weathered granite(CWG)is highly susceptible to alterations in the hydraulic environment,particularly under uniaxial loading conditions,due to the unique nature of this soil type.In this study,a series of unconfined compression tests were carried out on unsaturated CWG soil in an underground engineering site,and the effects of varying the environmental variables on the main undrained mechanical properties were analyzed.Based on the experimental results,a novel constitutive model was then established using the damage mechanics theory and the undetermined coefficient method.The results demonstrate that the curves of remolded CWG specimens with different moisture contents and dry densities exhibited diverse characteristics,including brittleness,significant softening,and ductility.As a typical indicator,the unconfined compression strength of soil specimens initially increased with an increase in moisture content and then decreased.Meanwhile,an optimal moisture content of approximately 10.5%could be observed,while a critical moisture content value of 13.0%was identified,beyond which the strength of the specimen decreases sharply.Moreover,the deformation and fracture of CWG specimens were predominantly caused by shear failure,and the ultimate failure modes were primarily influenced by moisture content rather than dry density.Furthermore,by comparing several similar models and the experimental data,the proposed model could accurately replicate the undrained mechanical characteristics of unsaturated CWG soil,and quantitatively describe the key mechanical indexes.These findings offer a valuable reference point for understanding the underlying mechanisms,anticipating potential risks,and implementing effective control measures in similar underground engineering projects.展开更多
A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and...A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.展开更多
The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
I.Circle the correct options.1.Nancy is picking what to wear to school on Wednesday.She goes outside and feels the air.Nancy decides to wear a coat.What is the weather like?A.Cool and sunny.C.Cool and cloudy.B.Warm an...I.Circle the correct options.1.Nancy is picking what to wear to school on Wednesday.She goes outside and feels the air.Nancy decides to wear a coat.What is the weather like?A.Cool and sunny.C.Cool and cloudy.B.Warm and sunny.D.Warm and cloudy.2.It was cool and sunny in the morning when Nancy went to school.Now it is the afternoon.She doesn't need her coat anymore.How did the weather change?展开更多
Nature is amazing!Sometimes you don’t need a weather App to predict rain or shine-you just need nature!Feel the heat:listen to crickets Want to know the outside temperature?Listen to the crickets!Count how many times...Nature is amazing!Sometimes you don’t need a weather App to predict rain or shine-you just need nature!Feel the heat:listen to crickets Want to know the outside temperature?Listen to the crickets!Count how many times a cricket chirps in 15 seconds and add 40.That’s the temperature in Fahrenheit(华氏温度,1℉≈0.56℃).Do it several times and find the average.展开更多
Enhanced silicate weathering(ESW)is a geoengineering method aimed at accelerating carbon dioxide(CO_(2))removal(CDR)from atmosphere by increasing the weathering flux of silicate rocks and minerals.It has emerged as a ...Enhanced silicate weathering(ESW)is a geoengineering method aimed at accelerating carbon dioxide(CO_(2))removal(CDR)from atmosphere by increasing the weathering flux of silicate rocks and minerals.It has emerged as a promising strategy for CDR.Theoretical studies underscore ESW’s substantial potential for CDR and its diverse benefits for crops when applied to croplands.However,the well-known significant discrepancies in silicate weathering rates between laboratory and field conditions introduce uncertainty in CDR through ESW.By compiling data from recent literature,we calculated and compared CDR efficiency(t CO_(2)t^(-1)_(silicate)ha^(-1)y^(-1))observed in mesocosm experiments and field trials.The findings indicate that CDR efficiencies in field trials are comparable to or exceeding that observed in mesocosm experiments by 1-3 orders of magnitude,particularly evident with wollastonite application.The hierarchy of CDR efficiency among silicates suitable for ESW is ranked as follows:olivine≥wollastonite>basalt>albite≥anorthite.We suggest the potential role of biota,especially fungi,in contributing to higher CDR efficiencies observed in field trials compared to mesocosm experiments.We further emphasize introducing fungi known for their effectiveness in silicate weathering could potentially enhance CDR efficiency through ESW in croplands.But before implementing fungal-facilitated ESW,three key questions need addressing:(i)How does the community of introduced fungi evolve over time?(ii)What is the long-term trajectory of CDR efficiency following fungal introduction?and(iii)Could fungal introduction lead to organic matter oxidation,resulting in elevated CO_(2)emissions?These investigations are crucial for optimizing the efficiency and sustainability of fungal-facilitated ESW strategy.展开更多
Precise high-temperature weather forecasts are essential, as heatwaves are increasing in frequency under the ongoing climate change. Land-surface schemes have been demonstrated to be crucial to numerical weather predi...Precise high-temperature weather forecasts are essential, as heatwaves are increasing in frequency under the ongoing climate change. Land-surface schemes have been demonstrated to be crucial to numerical weather predictions.However, few studies have explored the impact of land surface schemes on short-range high-temperature weather forecasts via operational numerical weather prediction models. To evaluate the impact of the soil thermal process on high-temperature weather forecasts, we coupled the soil thermal process of the state-of-the-art Common Land Model(CoLM) with the South China operational numerical weather prediction model(CMA-TRAMS) and compared the coupled model with the original CMA-TRAMS, which incorporated the Simplified Model for land Surface(SMS). Contrast experiments based on two versions of CMA-TRAMS were conducted for the year 2022 when persistent extreme heatwaves were observed in Central-East China. The results are as follows:(1) Short-range high-temperature weather forecasts were sensitive to soil thermal process schemes. The original CMA-TRAMS clearly underestimated the summertime near-surface air temperature(T2m) over almost all areas of China, whereas the CoLM led to a reduction of the negative biases by approximately 0.5°C.(2) The more accurate initial soil temperatures and the deeper soil structure used in the CoLM test contributed to actual predictions of soil heat flux, soil temperature, and T2m. Nevertheless, the SMS test failed to capture upward heat transport from deeper to shallower soil layers at night due to the shallow soil structure and lower accuracy of the bottom and initial soil temperatures.(3) Higher soil temperatures resulted in increased near-surface moisture and cloud cover in the CoLM test, which led to the warmer soil and further mitigated the cold biases of T2m through reduced longwave and shortwave radiation losses at the land surface.展开更多
Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteo...Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteorological disruptions compromise the reliability of renewable energy generation forecasts,particularly in photovoltaic(PV)systems.However,current predictive methodologies exhibit notable deficiencies in extreme weather monitoring,systematic transient phenomena analysis,and preemptive operational strategies,especially for cold-wave weather.In order to address these limitations,we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks(TimeGAN)for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data quality verification.In order to better extract the spatiotemporal features of the model input simultaneously,we develop a hybrid neural architecture integrating Convolutional Neural Networks with Long Short-Term Memory modules(CNN-LSTM).To avoid the problem of hyperparameters getting trapped in local optimal solutions,we use the Whale Optimization Algorithm(WOA)algorithm to obtain the global optimal solution by simulating the hunting of humpback whales,further enhancing the generalization ability of the model.Experimental validation demonstrates performance improvements,with the proposed model achieving 30%higher prediction accuracy compared to Genetic Algorithm-Backpropagation Neural Network(GA-BPNN)and Radial Basis Function-Support Vector Regression(RBF-SVR)benchmarks,promoting the renewable energy prediction in data-constrained extreme weather scenarios for future power networks.展开更多
In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu...In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu-Weather,FengWu,and FuXi have emerged as promising alternatives for numerical weather prediction in weather forecasting.However,these models have been characterized by their substantial computational resource consumption during training and limited incorporation of explicit physical guidance in their modeling frameworks.In contrast,TianXing applies a linear complexity mechanism that ensures proportional scalability with input data size while significantly diminishing GPU resource demands,with only a marginal compromise in accuracy.Furthermore,TianXing proposes an explicit attention decay mechanism in the linear attention derived from physical insights to enhance its forecasting skill.The mechanism can reweight attention based on Earth's spherical distances and learned sparse multivariate coupling relationships,promptingTianXing to prioritize dynamically relevant neighboring features.Finally,to enhance its performance in mediumrange forecasting,TianXing employs a stacked autoregressive forecast algorithm.Validation of the model's architecture is conducted using ERA5 reanalysis data at a 5.625°latitude-longitude resolution,while a high-resolution dataset at 0.25°is utilized for training the actual forecasting model.Notably,the TianXing exhibits excellent performance,particularly in the Z500(geopotential height)and T850(temperature)fields,surpassing previous data-driven models and operational fullresolution models such as NCEP GFS and ECMWF IFS,as evidenced by latitude-weighted RMSE and ACC metrics.Moreover,the TianXing has demonstrated remarkable capabilities in predicting extreme weather events,such as typhoons.展开更多
In recent years,there has been a pronounced increase in the frequency of extreme weather events.To compre hensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomera tion(WUA...In recent years,there has been a pronounced increase in the frequency of extreme weather events.To compre hensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomera tion(WUA),this study utilized meteorological station data,the Mann-Kendall(MK)test,and the Standardized Precipitation-Evapotranspiration Index(SPEI)to quantify the variation trends in heatwaves(HW)and droughts from 1961 to 2020.Then the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services un der various HW,droughts,and HW-drought combination scenarios.The results show that over the past 60 years,the temperature,duration,and frequency of HW have significantly increased in the WUA.Specifically,the high est HW temperature,total HW days,HW frequency,and average HW temperature showed changing trend of+0.17℃/decade,+1.4 day/decade,+0.19 event/decade,and+0.07℃/decade,respectively.The year 2000 was identified as a mutation year for HW,characterized by increased frequency and heightened severity thereafter.The SPEI value exhibited an insignificant upward trend,with 1980 marked as a mutation year,indicating a de creasing trend in drought occurrences after 1980.Heatwaves have a weakening effect on both water yield and climate regulation services,while drought significantly weakened water yield and had a relatively modest effect on climate regulation.During HW-drought composite period,the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years.Besides,as heatwaves intensify,the area of low aggregation for ecosystem services expands,whereas the area of high aggregation decreases.This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.展开更多
Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addi...Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.展开更多
Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was...Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.展开更多
Weather forecasting is crucial for agriculture,transportation,and industry.Deep Learning(DL)has greatly improved the prediction accuracy.Among them,Graph Neural Networks(GNNs)excel at processing weather data by establ...Weather forecasting is crucial for agriculture,transportation,and industry.Deep Learning(DL)has greatly improved the prediction accuracy.Among them,Graph Neural Networks(GNNs)excel at processing weather data by establishing connections between regions.This allows them to understand complex patterns that traditional methods might miss.As a result,achieving more accurate predictions becomes possible.The paper reviews the role of GNNs in short-to medium-range weather forecasting.The methods are classified into three categories based on dataset differences.The paper also further identifies five promising research frontiers.These areas aim to boost forecasting precision and enhance computational efficiency.They offer valuable insights for future weather forecasting systems.展开更多
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support of the National Natural Science Foundation of China(No.52171063).
文摘Weathering steel exhibits excellent corrosion resistance and is widely used in bridges,towers,railways,highways,and other engineering projects that are exposed to the atmosphere for long periods of time.However,before the formation of stable rust layers,weathering steel is prone to liquid rust sagging and spattering,leading to environmental pollution and city appearance concerns.These factors limit the application and development of weathering steel.In this study,a rapid and environmentally friendly method was de-veloped by introducing alloying elements,specifically investigating the role of Sn in the rapid stabilization of rust layers in marine atmo-spheric environments.The rust layer formed on weathering low-alloy steel exposed to prolonged outdoor conditions and laboratory im-mersion experiments was explored using electron probe micro-analyzer(EPMA),micro-Raman,X-ray photoelectron spectroscopy(XPS),and electrochemical measurements.Results showed an optimal synergistic effect between Sn and Cr,which facilitated the accelerated densification of the rust layer.This beneficial effect enhanced the capability of the rust layer to resist Cl^(-)erosion and improved the protec-tion performance of the rust layer.
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.
基金jointly supported by the National Natural Science Foundation of China (Grant Nos.42422502 and 42275038)the China Meteorological Administration Climate Change Special Program (Grant No.QBZ202306)funded by the Met Office Climate Science for Service Partnership (CSSP) China project under the International Science Partnerships Fund (ISPF)。
文摘This past year, 2024, is on track to be the warmest year, joining 2023 as the two hottest years on record. With the exceptional heat, weather and climate extremes were common across the world. In particular, 2024 has seen a remarkable run of extreme precipitation events and resulting impacts. Here, we provide an overview of the most notable extreme events of the year, including extreme precipitation and floods, tropical cyclones, and droughts. The characteristics and impacts of these extreme events are summarized, followed by discussion on the physical drivers and the role of global warming.Finally, we also discuss the future prospects in extreme event studies, including impact-based perspectives, challenges in attribution of precipitation extremes, and the existing gap to minimize impacts from climate extremes.
基金financially supported by the Science and Technology Innovation Foundation of the Command Center of Integrated Natural Resources Survey Center(KC20230002)the China Geological Survey Project(DD20230471,DD20220855 and DD20243282)+1 种基金the National Natural Science Foundation of China(41872100)the National Key R&D Plan(2022YFC2903402)。
文摘Black soil is essential for maintaining regional food security and promoting global agricultural production.Understanding the weathering process of parent material and the accumulation of organic carbon is crucial to comprehending the developmental history and future trends of black soil,especially against the background of large-scale global cultivation and climate change.Although the importance of black soil formation and evolution cannot be ignored,the relevant research is still very scarce.In this study,a typical eight-meter-deep soil core was collected from the Keshan area of the Songnen Plain,Northeast China,where surface black soil developed on paleo-sediments.Using^(14)C dating,the formation age of the black soil was determined.Based on the characteristics of the geochemical composition,grain size and the magnetic susceptibility of the sediments,it was demonstrated that the black soil and its parent material originated from reworked loess.Furthermore,the mass transfer coefficient(τ)of some elements was determined,in order to explore the soil weathering process.By calculating the transported amount of alkaline and alkaline-earth elements,the weathering rate of parent material to black soil was found to be weak,at 0.16 kEq·ha^(-1)·year^(-1).Combining the results of dating and carbon density in the different layers of black soil,the accumulation rate of organic carbon was determined as follows:rapidly increasing in the initial period of 13.2-2.2 ka,reaching its maximum average value of 34.0 g·cm^(-2)·a^(-1)at 2.2-0.8 ka,then showing a decreasing trend with an average value of-77.5 g·cm^(-2)·a^(-1).Compared with regional climate change,Keshan black soil has developed under a colder and wetter climate during the Holocene.Predictably,ongoing global warming may lead to the degradation of black soils in the Songnen Plain,as well as in other regions.Our results will enrich geological knowledge of black soil formation and future evolutionary trends.
基金Project(42202318)supported by the National Natural Science Foundation of ChinaProject(252300421199)supported by the Natural Science Foundation of Henan Province,ChinaProject(2024JJ6219)supported by the Hunan Provincial Natural Science Foundation of China。
文摘The undrained mechanical behavior of unsaturated completely weathered granite(CWG)is highly susceptible to alterations in the hydraulic environment,particularly under uniaxial loading conditions,due to the unique nature of this soil type.In this study,a series of unconfined compression tests were carried out on unsaturated CWG soil in an underground engineering site,and the effects of varying the environmental variables on the main undrained mechanical properties were analyzed.Based on the experimental results,a novel constitutive model was then established using the damage mechanics theory and the undetermined coefficient method.The results demonstrate that the curves of remolded CWG specimens with different moisture contents and dry densities exhibited diverse characteristics,including brittleness,significant softening,and ductility.As a typical indicator,the unconfined compression strength of soil specimens initially increased with an increase in moisture content and then decreased.Meanwhile,an optimal moisture content of approximately 10.5%could be observed,while a critical moisture content value of 13.0%was identified,beyond which the strength of the specimen decreases sharply.Moreover,the deformation and fracture of CWG specimens were predominantly caused by shear failure,and the ultimate failure modes were primarily influenced by moisture content rather than dry density.Furthermore,by comparing several similar models and the experimental data,the proposed model could accurately replicate the undrained mechanical characteristics of unsaturated CWG soil,and quantitatively describe the key mechanical indexes.These findings offer a valuable reference point for understanding the underlying mechanisms,anticipating potential risks,and implementing effective control measures in similar underground engineering projects.
基金supported by the National Natural Science Foundation of China(Grant Nos.42150204 and 2288101)supported by the China National Postdoctoral Program for Innovative Talents(BX20230045)the China Postdoctoral Science Foundation(2023M730279)。
文摘A nonlinear multi-scale interaction(NMI)model was proposed and developed by the first author for nearly 30 years to represent the evolution of atmospheric blocking.In this review paper,we first review the creation and development of the NMI model and then emphasize that the NMI model represents a new tool for identifying the basic physics of how climate change influences mid-to-high latitude weather extremes.The building of the NMI model took place over three main periods.In the 1990s,a nonlinear Schr?dinger(NLS)equation model was presented to describe atmospheric blocking as a wave packet;however,it could not depict the lifetime(10-20 days)of atmospheric blocking.In the 2000s,we proposed an NMI model of atmospheric blocking in a uniform basic flow by making a scale-separation assumption and deriving an eddyforced NLS equation.This model succeeded in describing the life cycle of atmospheric blocking.In the 2020s,the NMI model was extended to include the impact of a changing climate mainly by altering the basic zonal winds and the magnitude of the meridional background potential vorticity gradient(PVy).Model results show that when PVy is smaller,blocking has a weaker dispersion and a stronger nonlinearity,so blocking can be more persistent and have a larger zonal scale and weaker eastward movement,thus favoring stronger weather extremes.However,when PVy is much smaller and below a critical threshold under much stronger winter Arctic warming of global warming,atmospheric blocking becomes locally less persistent and shows a much stronger westward movement,which acts to inhibit local cold extremes.Such a case does not happen in summer under global warming because PVy fails to fall below the critical threshold.Thus,our theory indicates that global warming can render summer-blocking anticyclones and mid-to-high latitude heatwaves more persistent,intense,and widespread.
文摘The China Meteorological Administration(CMA)said that in the last five years,China has made big improvements in its weather services.This includes better weather forecasts and ways to protect people from disasters.
文摘I.Circle the correct options.1.Nancy is picking what to wear to school on Wednesday.She goes outside and feels the air.Nancy decides to wear a coat.What is the weather like?A.Cool and sunny.C.Cool and cloudy.B.Warm and sunny.D.Warm and cloudy.2.It was cool and sunny in the morning when Nancy went to school.Now it is the afternoon.She doesn't need her coat anymore.How did the weather change?
文摘Nature is amazing!Sometimes you don’t need a weather App to predict rain or shine-you just need nature!Feel the heat:listen to crickets Want to know the outside temperature?Listen to the crickets!Count how many times a cricket chirps in 15 seconds and add 40.That’s the temperature in Fahrenheit(华氏温度,1℉≈0.56℃).Do it several times and find the average.
基金funded by the National Natural Science Foundation of China(Nos.42173059 and 41991322)。
文摘Enhanced silicate weathering(ESW)is a geoengineering method aimed at accelerating carbon dioxide(CO_(2))removal(CDR)from atmosphere by increasing the weathering flux of silicate rocks and minerals.It has emerged as a promising strategy for CDR.Theoretical studies underscore ESW’s substantial potential for CDR and its diverse benefits for crops when applied to croplands.However,the well-known significant discrepancies in silicate weathering rates between laboratory and field conditions introduce uncertainty in CDR through ESW.By compiling data from recent literature,we calculated and compared CDR efficiency(t CO_(2)t^(-1)_(silicate)ha^(-1)y^(-1))observed in mesocosm experiments and field trials.The findings indicate that CDR efficiencies in field trials are comparable to or exceeding that observed in mesocosm experiments by 1-3 orders of magnitude,particularly evident with wollastonite application.The hierarchy of CDR efficiency among silicates suitable for ESW is ranked as follows:olivine≥wollastonite>basalt>albite≥anorthite.We suggest the potential role of biota,especially fungi,in contributing to higher CDR efficiencies observed in field trials compared to mesocosm experiments.We further emphasize introducing fungi known for their effectiveness in silicate weathering could potentially enhance CDR efficiency through ESW in croplands.But before implementing fungal-facilitated ESW,three key questions need addressing:(i)How does the community of introduced fungi evolve over time?(ii)What is the long-term trajectory of CDR efficiency following fungal introduction?and(iii)Could fungal introduction lead to organic matter oxidation,resulting in elevated CO_(2)emissions?These investigations are crucial for optimizing the efficiency and sustainability of fungal-facilitated ESW strategy.
基金National Natural Science Foundation of China(U2242203, 42305164, 42175105)Key Innovation Team of China Meteorological Administration (CMA2023ZD08)Science and Technology Research Project of Guangdong Meteorological Service (GRMC2023M31)。
文摘Precise high-temperature weather forecasts are essential, as heatwaves are increasing in frequency under the ongoing climate change. Land-surface schemes have been demonstrated to be crucial to numerical weather predictions.However, few studies have explored the impact of land surface schemes on short-range high-temperature weather forecasts via operational numerical weather prediction models. To evaluate the impact of the soil thermal process on high-temperature weather forecasts, we coupled the soil thermal process of the state-of-the-art Common Land Model(CoLM) with the South China operational numerical weather prediction model(CMA-TRAMS) and compared the coupled model with the original CMA-TRAMS, which incorporated the Simplified Model for land Surface(SMS). Contrast experiments based on two versions of CMA-TRAMS were conducted for the year 2022 when persistent extreme heatwaves were observed in Central-East China. The results are as follows:(1) Short-range high-temperature weather forecasts were sensitive to soil thermal process schemes. The original CMA-TRAMS clearly underestimated the summertime near-surface air temperature(T2m) over almost all areas of China, whereas the CoLM led to a reduction of the negative biases by approximately 0.5°C.(2) The more accurate initial soil temperatures and the deeper soil structure used in the CoLM test contributed to actual predictions of soil heat flux, soil temperature, and T2m. Nevertheless, the SMS test failed to capture upward heat transport from deeper to shallower soil layers at night due to the shallow soil structure and lower accuracy of the bottom and initial soil temperatures.(3) Higher soil temperatures resulted in increased near-surface moisture and cloud cover in the CoLM test, which led to the warmer soil and further mitigated the cold biases of T2m through reduced longwave and shortwave radiation losses at the land surface.
基金supported by Science and Technology Projects of Jiangsu Province(No.BE2022003)Science and Technology Projects of Jiangsu Province(No.BE2022003-5).
文摘Contemporary power network planning faces critical challenges from intensifying climate variability,including greenhouse effect amplification,extreme precipitation anomalies,and persistent thermal extremes.These meteorological disruptions compromise the reliability of renewable energy generation forecasts,particularly in photovoltaic(PV)systems.However,current predictive methodologies exhibit notable deficiencies in extreme weather monitoring,systematic transient phenomena analysis,and preemptive operational strategies,especially for cold-wave weather.In order to address these limitations,we propose a dual-phase data enhancement protocol that takes advantage of Time-series Generative Adversarial Networks(TimeGAN)for temporal pattern expansion and the K-medoids clustering algorithm for synthetic data quality verification.In order to better extract the spatiotemporal features of the model input simultaneously,we develop a hybrid neural architecture integrating Convolutional Neural Networks with Long Short-Term Memory modules(CNN-LSTM).To avoid the problem of hyperparameters getting trapped in local optimal solutions,we use the Whale Optimization Algorithm(WOA)algorithm to obtain the global optimal solution by simulating the hunting of humpback whales,further enhancing the generalization ability of the model.Experimental validation demonstrates performance improvements,with the proposed model achieving 30%higher prediction accuracy compared to Genetic Algorithm-Backpropagation Neural Network(GA-BPNN)and Radial Basis Function-Support Vector Regression(RBF-SVR)benchmarks,promoting the renewable energy prediction in data-constrained extreme weather scenarios for future power networks.
基金supported in part by the Meteorological Joint Funds of the National Natural Science Foundation of China under Grant U2142211in part by the National Natural Science Foundation of China under Grant 42075141,42341202+2 种基金in part by the National Key Research and Development Program of China under Grant 2020YFA0608000in part by the Shanghai Municipal Science and Technology Major Project(2021SHZDZX0100)the Fundamental Research Funds for the Central Universities。
文摘In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu-Weather,FengWu,and FuXi have emerged as promising alternatives for numerical weather prediction in weather forecasting.However,these models have been characterized by their substantial computational resource consumption during training and limited incorporation of explicit physical guidance in their modeling frameworks.In contrast,TianXing applies a linear complexity mechanism that ensures proportional scalability with input data size while significantly diminishing GPU resource demands,with only a marginal compromise in accuracy.Furthermore,TianXing proposes an explicit attention decay mechanism in the linear attention derived from physical insights to enhance its forecasting skill.The mechanism can reweight attention based on Earth's spherical distances and learned sparse multivariate coupling relationships,promptingTianXing to prioritize dynamically relevant neighboring features.Finally,to enhance its performance in mediumrange forecasting,TianXing employs a stacked autoregressive forecast algorithm.Validation of the model's architecture is conducted using ERA5 reanalysis data at a 5.625°latitude-longitude resolution,while a high-resolution dataset at 0.25°is utilized for training the actual forecasting model.Notably,the TianXing exhibits excellent performance,particularly in the Z500(geopotential height)and T850(temperature)fields,surpassing previous data-driven models and operational fullresolution models such as NCEP GFS and ECMWF IFS,as evidenced by latitude-weighted RMSE and ACC metrics.Moreover,the TianXing has demonstrated remarkable capabilities in predicting extreme weather events,such as typhoons.
基金supported by the National Natural Science Foundation of China(Grants No.42371354,42375129,42371115)the Fundamental Research Funds for National Universities,China Uni-versity of Geosciences,Wuhan.
文摘In recent years,there has been a pronounced increase in the frequency of extreme weather events.To compre hensively examine the impact of extreme weather on ecosystem services within the Wuhan Urban Agglomera tion(WUA),this study utilized meteorological station data,the Mann-Kendall(MK)test,and the Standardized Precipitation-Evapotranspiration Index(SPEI)to quantify the variation trends in heatwaves(HW)and droughts from 1961 to 2020.Then the Integrated Valuation of Ecosystem Services and Trade-offs(InVEST)model was employed to evaluate and compare the differences in water yield and climate regulation ecosystem services un der various HW,droughts,and HW-drought combination scenarios.The results show that over the past 60 years,the temperature,duration,and frequency of HW have significantly increased in the WUA.Specifically,the high est HW temperature,total HW days,HW frequency,and average HW temperature showed changing trend of+0.17℃/decade,+1.4 day/decade,+0.19 event/decade,and+0.07℃/decade,respectively.The year 2000 was identified as a mutation year for HW,characterized by increased frequency and heightened severity thereafter.The SPEI value exhibited an insignificant upward trend,with 1980 marked as a mutation year,indicating a de creasing trend in drought occurrences after 1980.Heatwaves have a weakening effect on both water yield and climate regulation services,while drought significantly weakened water yield and had a relatively modest effect on climate regulation.During HW-drought composite period,the average monthly water yield showed a notable discrepancy of 60 mm compared to humid years.Besides,as heatwaves intensify,the area of low aggregation for ecosystem services expands,whereas the area of high aggregation decreases.This study provides a preliminary understanding of the impact of urban extreme weather on urban ecosystem services under changing climatic conditions.
基金the Science and Technology Project of State Grid Fujian Electric Power Co.,Ltd.(Project No.B31300240001)with the project title“Research on Key Technologies for Load Forecasting and Regulation Capability Evaluation of Regional Power Grid Taking into AccountWide Area Distributed New Energy Access”.
文摘Grid-supplied load is the traditional load minus new energy generation,so grid-supplied load forecasting is challenged by uncertainties associated with the total energy demand and the energy generated off-grid.In addition,with the expansion of the power system and the increase in the frequency of extreme weather events,the difficulty of grid-supplied load forecasting is further exacerbated.Traditional statistical methods struggle to capture the dynamic characteristics of grid-supplied load,especially under extreme weather conditions.This paper proposes a novel gridsupplied load prediction model based on Convolutional Neural Network-Bidirectional LSTM-Attention mechanism(CNN-BiLSTM-Attention).The model utilizes transfer learning by pre-training on regular weather data and fine-tuning on extreme weather samples,aiming to improve prediction accuracy and robustness.Experimental results demonstrate that the proposed model outperforms traditional statistical methods and existing machine learning models.Through comprehensive experimental validation,the attention mechanism demonstrates exceptional capability in identifying and weighting critical temporal features across different timescales,which significantly contributes to enhanced prediction performance and stability under diverse weather conditions.Moreover,the proposed approach consistently exhibits strong generalization capabilities across multiple test cases when applied to different regional power grids with distinct operational patterns and varying load characteristics,showcasing its practical adaptability to real-world scenarios.This study provides a practical solution for enhancing grid-supplied load forecasting capabilities in the face of increasingly complex and unpredictable weather patterns.
基金supported by the National Natural Science Foundation of China(Grant Nos.U2242213,U2142213,42305167,42175105)。
文摘Systematic bias is a type of model error that can affect the accuracy of data assimilation and forecasting that must be addressed.An online bias correction scheme called the sequential bias correction scheme(SBCS),was developed using the6 h average bias to correct the systematic bias during model integration.The primary purpose of this study is to investigate the impact of the SBCS in the high-resolution China Meteorological Administration Meso-scale(CMA-MESO)numerical weather prediction(NWP)model to reduce the systematic bias and to improve the data assimilation and forecast results through this method.The SBCS is improved upon and applied to the CMA-MESO 3-km model in this study.Four-week sequential data assimilation and forecast experiments,driven by rapid update and cycling(RUC),were conducted for the period from 2–29 May 2022.In terms of the characteristics of systematic bias,both the background and analysis show diurnal bias,and these large biases are affected by complex underlying surfaces(e.g.,oceans,coasts,and mountains).After the application of the SBCS,the results of the data assimilation show that the SBCS can reduce the systematic bias of the background and yield a neutral to slightly positive result for the analysis fields.In addition,the SBCS can reduce forecast errors and improve forecast results,especially for surface variables.The above results indicate that this scheme has good prospects for high-resolution regional NWP models.
基金supported by Key Laboratory of Smart Earth(KF2023ZD03-05)CMA Innovative and Development Program(CXFZ.20231035)+2 种基金National Key R&D Program of China(No.2021ZD0111902)National Natural Science Foundation of China(Nos.62472014,U21B2038)the Scientific and Technological Project of China Meteorological Administration(CMAJBGS202505).
文摘Weather forecasting is crucial for agriculture,transportation,and industry.Deep Learning(DL)has greatly improved the prediction accuracy.Among them,Graph Neural Networks(GNNs)excel at processing weather data by establishing connections between regions.This allows them to understand complex patterns that traditional methods might miss.As a result,achieving more accurate predictions becomes possible.The paper reviews the role of GNNs in short-to medium-range weather forecasting.The methods are classified into three categories based on dataset differences.The paper also further identifies five promising research frontiers.These areas aim to boost forecasting precision and enhance computational efficiency.They offer valuable insights for future weather forecasting systems.
