Abstract:The terrestrial water cycle has been changing greatly under the impacts of climate warming and human activities in the Yellow River basin that faces a severe water shortage.It is vital to understand the whole picture of water cycle changes,which is essential for achieving efficient use of limited water resources.Given that terrestrial water cycle is a complicated nonlinear system,synergetic mechanism of changes in all the water cycle processes should be taken into account.Moreover,human activities (vegetation cover and water use) must be considered in current water cycle change studies in the Yellow River basin in the Anthropocene,with irrigation being the main sector of human water usages.Greening is happening under the impacts of the ecological construction in the Loess Plateau.River discharge,evapotranspiration,soil moisture,groundwater,and terrestrial water storage are changing as well.And the impacts of revegetation on water remain contentious.Studies show that revegetation has led to a significant increase in evapotranspiration in the Loess Plateau,and most studies support the conclusion that vegetation greening reduces streamflow.Flood irrigation is the main irrigation method in this basin,and this practice would also influence water cycle processes including precipitation,temperature,evapotranspiration,and surface/subsurface water bodies.In this paper,we reviewed the advances in studies on water cycle changes in the Yellow River basin,and discussed future directions.

Abstract:In summer 2022,an extreme drought event occurred in the Yangtze River basin.Based on the WRF-WVT water vapor tracking model,this study simulated the water cycle in the Yangtze River basin under the extreme drought conditions that occurred from June to August 2022,and analyzed the effects of evapotranspiration on local and non-local precipitation in the Yangtze River basin.The results show that the summer drought of 2022 led to a 100—150 mm reduction in land surface water storage in the middle and lower reaches of the Yangtze River from May to August that year.In general,about 45％ of the evapotranspiration in the Yangtze River basin in June—August is formed in local and northern China precipitation,and the evapotranspiration from the Yangtze River basin mainly contributes to local precipitation in June,while the contribution to local and northern China precipitation is approximately equal in July and August.The total amount of local precipitation contributed by evapotranspiration in the Yangtze River basin decreases month by month to 8.2×10⁷ m³ (average 91.2 mm throughout the basin),and the contribution of local evapotranspiration decreases with higher precipitation intensity,while the largest contribution to local precipitation occurs near the Sichuan basin(with the maximum exceeding 40％).The contribution of evapotranspiration from the Yangtze River basin to precipitation in North China increases from June to August,then decreases slightly,with a total of 5.3×10⁷ m³ (with an average of 58.4 mm in North China),and the contribution of evapotranspiration from the Yangtze River basin increases with precipitation intensity.In summer 2022,the contribution of evapotranspiration from the Yangtze River basin to heavy rainfall both locally and in North China was about 12％.

Abstract:Aircraft turbulence poses great danger to safe aircraft operation and affects the comfort of passengers.Therefore,improving the accuracy of aircraft turbulence forecast is of great significance to reducing injury and property loss,and for many years has been the focus of aviation meteorological research.With the enrichment of observation means and progress of science and technology,great progress has been made in the prediction of aircraft turbulence.In this paper,the main methods of aircraft turbulence prediction used throughout the world,especially the latest application of artificial intelligence(AI),are reviewed from the perspectives of both qualitative and quantitative forecast.Then,on this basis,the main problems present in the application of AI to aircraft turbulence prediction and key research directions in the future are summarized as follows:(1)open sharing of aircraft real data,and integration and construction of multi-source turbulence data;(2)interpretability and physical mechanism of aircraft turbulence prediction model built based on AI;and (3)ensemble forecasting of aircraft turbulence based on AI.Finally,the development ideas of the third generation of AI in the field of meteorology are proposed.The future third generation of AI in meteorology may be based on deep learning,integrating mathematical logic reasoning algorithms,and based on “knowledge” (physical mechanisms,experience of forecasters,or basic principles of weather and climatology) and “data” (model data and observation data).That is,it may combine the advantages of the schools of connectionism and symbolism to carry out weather forecasting and climate prediction work.Of course,the third generation of meteorological AI must have characteristics such as higher prediction accuracy,self-learning,and self-adaptation (such as adapting to changes in the rule of meteorological data caused by climate change),and strong explanation in its mechanism.

Abstract:Based on observational and reanalysis data,this study investigated the dominant mode characteristics of interannual variation in the January blocking high frequency over the eastern North Atlantic-Ural region in recent decades,along with their associated key atmospheric background fields.The results show that there are significant inter-monthly differences in the dominant modes of the interannual variation of blocking high frequency in this region during the winter months (December to February) between 1980 and 2019.In December,the dominant mode is manifested as significant in-phase variation of blocking high frequency in the eastern North Atlantic-western Europe region.In January,it shows significant out-of-phase variation of the blocking high frequency between the eastern North Atlantic-western Europe and the Ural region.The dominant mode in February exhibits significant in-phase variation of the blocking high frequency in the eastern North Atlantic-Ural region.Further studies show that the dipole pattern of the blocking high frequency in the eastern North Atlantic-Ural region in January is associated with the dipole variation of the simultaneous atmospheric background field,such as zonal wind,vertical shear of zonal wind,and meridional gradient of potential vorticity.When anomalies of the key atmospheric background fields are negative in the Ural region and positive in the eastern North Atlantic-western Europe region,the blocking high frequency of the two regions respectively increases and decreases,showing a dipole pattern,and vice versa.The dipole pattern of the blocking high frequency and associated background atmospheric circulation anomalies can affect temperature and precipitation in northern Eurasia in January,and can also affect their frequency of their extreme events by modulating horizontal temperature advection,vertical motion and water vapor transport.When the dipole pattern of the blocking high frequency is in the positive phase,the north (south) and south of the Ural region warms (cools),and the frequency of extreme warm and cold events increases (decreases) and decreases (increases),respectively.In northern Scandinavia,which cools,the frequency of extreme warm events decreases.Moreover,the frequency of precipitation and extreme precipitation decreases in the Ural region and northeastern Asia,but increases in parts of continental Europe,and vice versa.In addition,this paper discusses why,among the winter months,the dominant mode of interannual variation of the blocking high frequency exhibits a dipole pattern only in January.This may in part be caused by the inter-monthly differences in the key atmospheric background fields' climatology and variations.

Abstract:Based on the Chapter seven from the Sixth Assessment Report (AR6) contributed by the Intergovernmental Panel on Climate Change (IPCC) Working Group I (WGI),this study interprets the assessment of climate sensitivity from multi-source evidence,including process understanding,instrumental records,paleoclimate data,and emergent constraints.The conclusion is that this evidence gives a combined best estimate of 3 ℃ to equilibrium climate sensitivity (ECS),with a likely range of 2.5—4.0 ℃ and a very likely range of 2.0—5.0 ℃.Meanwhile,for transient climate response (TCR) the best estimate is 1.8 ℃,with a likely range of 1.4—2.2 ℃ and a very likely range of 1.2—2.4 ℃.Compared with previous IPCC reports,the most important innovations in AR6 regarding the estimation of climate sensitivity are that Earth climate system models were not considered as a line of evidence,and that the CMIP6 results were only given for comparison with the combined estimate from other evidence.The average ECS and TCR from CMIP6 were higher than those in CMIP5 and the best estimates in AR6,and the uncertainty range of ECS based on multi-source evidence was effectively narrowed compared with that based on CMIP6.

Abstract:In this study,using data from 840 observed stations and ERA5 reanalysis,the decadal variation characteristics of the nationwide cold wave frequency in the cold season over the past 63 years (1960—2022) were analyzed.The statistical results show the following:against the background of global warming,the decreasing trend of cold wave frequency reversed in 2012,then showed a significant increasing trend from 2012 to 2022.During the latter period,the Ural Mountain blocking frequency was significantly negatively correlated with the occurrence frequency of nationwide cold waves.The Ural Mountain blocking high inhibits the occurrence and development of high-altitude jet streams,weakens the zonal circulation of the atmosphere,and blocks the southward movement of cold air masses in the Arctic,in turn reducing the frequency of cold waves.At the same time,the Ural Mountain blocking high favors weakening the surface 2 m temperature gradient over mid-to-high-latitude Eurasia.The frequent occurrence of Ural Mountain blocking often leads to more weather-scale phenomena of Arctic warming and mid-latitude cooling.In addition,Arctic warming effect itself also has a significant negative correlation with China’s cold wave frequency.Arctic warming reduces the temperature difference between mid-to-high latitudes,and weakens the westerly belt,thus also weakening the transport of cold air.Over the past 11 years,the Ural Mountain blocking frequency has shown a significant decreasing trend.Correspondingly,the zonal gradient of potential vorticity on isentropic surfaces has a linear increasing trend,which is unfavorable for the maintenance of blocking highs.Along with this,the Arctic warming effect also shows a weakening trend.This atmospheric circulation configuration is conducive to China's increase of cold wave frequency in recent years.

Abstract:Based on the observed monthly temperature data of 160 stations in China and NCEP-DOE reanalysis (R-2) data from 1979 to 2017,the variation characteristics of spring East Atlantic/West Russia (EATL/WRUS) teleconnection pattern and its relationship with spring temperature anomalies in China are investigated in this paper.Results show that the positive (negative) phase of spring EATL/WRUS pattern presents a “+-+” (“-+-”) zonal wave train over 500 hPa from North Atlantic to middle and high latitudes of Eurasia.The main active centers of the wave train are located in West Europe,near the Urals and south to the Lake Baikal,respectively.In the vertical direction,spring EATL/WRUS pattern is featured by a significant quasi-barotropic structure.There is a significant positive correlation between spring EATL/WRUS pattern anomaly and the spring temperature anomalies in Northwest China.During the positive (negative) phase years of spring EATL/WRUS pattern,there are negative (positive) anomaly of geopotential height and anomalous cyclonic (anticyclonic) circulation near the Urals,as well as positive (negative) anomaly of geopotential height and anomalous anticyclonic (cyclonic) circulation south to the Lake Baikal,which weakens (strengthens) the cold air affecting Northwest China,leading to an increase (decrease) of spring temperature in Northwest China.

Abstract:Severe extreme rainstorm events occurred in Hebei Province from July 29 to August 1,2023.The 3-day maximum cumulative precipitation at meteorological observation stations reached 1003.4 mm (Liangjiazhuang,Lincheng,and Xingtai),breaking historical records at many stations in Beijing and Hebei Province.Using observation data,such as high,surface,cloud maps,and Doppler radar,along with ERA5 reanalysis data,this study conducted a preliminary analysis of the extremities and causes of this rainstorm.The results reveal that this extreme rainstorm process resulted from mid- and low-latitude multiscale atmospheric systems involving the high,middle,and low layers of the troposphere,superimposed by topography:(1) The eastward movement of the mid-latitude continental high pressure and the northwestward direction of the western Pacific subtropical high formed a stable northwest-southeast high-pressure dam in northern Hebei.Typhoon “Doksuri” slowed down and stagnated over Henan when blocked by the high-pressure dam,resulting in prolonged typhoon vortex precipitation.(2) The northward movement of water vapor carried by Typhoon “Doksuri” and the southward transport of water vapor by Typhoon “Khanun” on the southern side of the sub-high provided the necessary moisture for extreme rainstorms in Hebei.The convergence of the northeast- and southeast-bound jets during the weakening of the northern typhoon,combined with the uplifting terrain of Taihang Mountain,formed a strong dynamic mechanism for extreme precipitation.(3) In the middle part of Taihang Mountain,heavy precipitation was concentrated from the night of July 29th to the night of July 30th,mainly due to the weakening of the spiral rain belt of the typhoon remnant,the uplift of the east-to-northeast jet on the windward slope,and enhanced terrain convergence.Heavy precipitation exceeding 250 mm occurred on the windward slope at elevations of 300—800 m in the eastern foothills of Taihang Mountain.(4) In the northern part of Taihang Mountain,the precipitation period was longer,mainly concentrated from the night of July 29th to the night of July 31st,with the larger area experiencing heavy precipitation exceeding 250 mm.This included the windward slope at elevations of 300—600 m in the eastern foothills of the northern mountain and the adjacent eastern plain.However,the precipitation intensity on the windward slope was lower than in the middle part of Taihang Mountain,while the plain areas experienced higher precipitation intensity.Heavy precipitation in the northern region was primarily caused by the convergence and topographic effects of the northeast and southeast jet streams running parallel to the mountain,with more convective precipitation in the shallow areas and plains on the eastern side of the mountain.(5) The physical fields of water vapor,dynamics,and heat during this precipitation event exhibited strong extremes,with significant deviations ranging from 2σ to 6σ.

Abstract:The sudden subway disaster resulting from exceptionally heavy rainfall in Zhengzhou,Henan Province,on July 20,2021,marked one of the most significant casualties the city had experienced in recent years.This incident arose from a combination of objective factors,such as intense rainstorms and unexpected accidents,and subjective factors,including a lack of risk awareness and inadequate emergency response mechanisms.This paper begins by identifying key scenario elements within the evolution of the “Zhengzhou Rainstorm Subway Disaster Event” and constructing the scenario evolution process of the event through scenario analysis.Building upon this foundation,we create a scenario deduction model for the “Zhengzhou Rainstorm Subway Disaster Event” using Bayesian network.Expert scoring methods are employed to calculate the conditional probabilities of network nodes,and Netica software is used to compute the state probabilities of disaster scenarios nodes.Finally,we assess and deduce the probabilities of personnel fatalities and subway damage under varying levels of rainstorm disasters,different emergency response activities,and distinct surrounding environmental conditions.The objective is to extract insights,pinpoint vulnerabilities,and provide valuable references for risk prevention and response measures in anticipation of and protection against events similar to the “Zhengzhou Rainstorm Subway Disaster”.

Abstract:This study focuses on the generation of high-resolution wind forecasts over East China and its surrounding regions (110°—130°E,20°—40°N) for the period from January to April 2020,utilizing wind forcast data from the European Centre for Medium-Range Weather Forecasts (ECMWF),the Mesoscale component of the Global and Regional Assimilation and Prediction Enhanced System (GRAPES-Meso),the Global Forecast System (GFS) of the National Centers for Environmental Prediction (NCEP),and the Global Forecast System of the Global and Regional Assimilation and Prediction System (GRAPES-GFS).Various interpolation techniques,including bilinear interpolation,inverse distance weighted interpolation,kriging interpolation,and cubic spline interpolation,were employed to create downscaling forecasts spanning from 0 to 72 hours.These high-resolution forecasts aim to cater to the specific needs of airports and their terminal areas.Furthermore,this study encompasses multimodel ensemble forecasts of high-resolution wind fields.The results reveal that inverse-distance weighted interpolation outperforms other interpolation schemes for horizontal wind forecast interpolation.Leveraging the augmented complex extended Kalman Filter (ACEKF) for multimodel ensemble forecasts substantially reduces root-mean-square errors (RMSEs) in wind field predictions.Notably,whether concerning surface winds or high-level winds,the ACEKF forecasts exhibit significant superiority compared bias-removed ensemble mean (BREM) forecasts and individual models,as evidenced by lower RMSEs.Examining wind forecasts at three prominent airports in East China—Shanghai,Qingdao,and Xiamen—reveals that ACEKF forecasts not only feature reduced RMSEs compared to BREM,ECMWF,and GRAPES-GFS forecasts but also display consistent performance across varying altitudes.This heightened forecast stability distinguishes ACEKF forecasts from BREM and individual model forecasts.

Abstract:In this study,based on the hourly temperature fusion data collected by the Land Surface Data Assimilation System (CLDAS) of the China Meteorological Administration,the respective performances of the ECMWF and CMA-MESO-3km models for hourly temperature forecast in Gansu Province were tested and evaluated,and the low-frequency moving average correction algorithm (LPSC) was used to correct the models’ systematic errors.In addition,the hourly temperature forecast effects of the two revised models and SCMOC were statistically compared.The results show the following:1) The ECMWF and CMA-MESO-3km models have relatively stable systematic errors in the hourly temperature forecast of Gansu Province,while the forecast accuracy at night is significantly lower than that in the daytime,which is mainly characterized by significantly higher forecast at night and weak negative deviation in the daytime.2) The systematic error of the hourly temperature forecast of the ECMWF and CMA-MESO-3km models in Gansu Province can be effectively improved with the LPSC algorithm,and the correction effect is significant.After revision,the forecast accuracies of ECMWF and CMA-MESO-3km are respectively improved by 20.24 and 20.25％,and the mean error is reduced to within ±0.3 ℃.The spatial distribution also showed that the mean error provincewide was reduced to within ±2 ℃.3) The comparison test of similar products show that the forecast effects of hourly temperature of the revised ECMWF and CMA-MESO-3km were better than that of SCMOC on the province as a whole.The forecast accuracies were respectively 20.65 and 13.55％ higher than that of SCMOC,and the mean absolute error was significantly lower than that of SCMOC at each time.The spatial distribution of the skill score shows that the revised ECMWF is positive in most places throughout the province,more than 80％ of which is in the southern mountainous area of Jiuquan.However,there are some differences in the forecast effect of the revised CMA-MESO-3km,which are mainly reflected in central and southeastern of Gansu Province,with weak positive technique predominating in winter and spring,and basically negative technique in summer and autumn.The results of the special application show that special attention should be paid to using this method for transforming weather processes.

Abstract:Convective initiation (CI) in the atmosphere is an important symptom of severe convective weather and the focus of severe convective weather forecast and early warning,which has been paid more and more attention in recent years.This paper reviews the research progress of CI at home and abroad,mainly including the definition of CI,mature CI algorithm based on radar data and satellite data,local space-time distribution characteristics of CI,and main factors affecting CI.On this basis,the future research of CI is prospected in order to deepen the understanding of CI and provide reference for the short-term and imminent forecast and early warning of severe convective weather.

Abstract:Northern China experienced four sandstorms or severe sandstorms in spring 2021, contrasting with just one event in the corresponding period of 2022. Utilizing air quality and multi-source meteorological data spanning 2015 to 2022, we applied the Lamb Jenkinson classification and Mann-Whitney U test methods to analyze similarities and differences in the sand source areas' conditions and meteorological factors during the spring of 2021 and 2022. Our findings reveal that the sand and dust weather (SDW) in northern China is frequently categorized into NW-N (cyclone type) and E-NE (high-pressure type), with the NW-N type leading to higher PM₁₀ extreme values and a broader range of high concentrations. In terms of meteorological factors, synoptic conditions favorable for SDW in spring 2022 occur more frequently, with the differences in daily PM₁₀ concentration predominantly associated with the NW-N type when compared to spring 2021. The frequency of NW-N type events and cyclone intensity remains comparable between the two periods, along with similar dynamic uplift conditions conducive to SDW are similar. Regarding sand source area conditions, the soil temperature in Mongolia's sand source area displayed a “cold before and warm after” pattern in the pre-winter of 2021, resulting in an early peak of snowmelt and other water content. In addition, a widespread decrease in precipitation and a relatively strong cyclone in Mongolia's sand source area in March contributed to the high incidence of sand and dust in spring 2021. Conversely, during the pre-winter of 2022, the soil temperature in Mongolia's sand source area followed a “warm before and cold after” trend, leading to a delayed peak of water content and soil moisture content during the snowmelt period. These conditions, characterized by thicker and moisture soil, were less conducive to sand formation. Therefore, the disparities in Mongolian sand source area conditions represent the primary factor behind the significant differences in SDW between the two periods.