In recent years， rapid urbanization and global warming have led to frequent and severe rainstorm and flood disasters in the Sichuan-Chongqing region.This change will not only have a serious impact on the ecological environment and socio-economic development of the area， but also significantly increase the pressure on urban infrastructure and threaten the safety of people's lives and property.Therefore， it is particularly important to scientifically and accurately analyze the disaster risk of rainstorm and flood in Sichuan-Chongqing region in the past and future.This paper utilized daily precipitation data from 50 selected meteorological stations in the Sichuan-Chongqing region， precipitation data from 5 CMIP6 models， gridded population and economic data under Shared Socioeconomic Pathways （SSPs）， as well as DEM and land use remote sensing data.Firstly， using Taylor diagrams， quantitative indices （S）， and standardized anomaly sequences， the study evaluated the simulation performance of 5 individual CMIP6 models， an equal-weighted aggregation of 5 models （EWA-5）， and unequally-weighted aggregations of 5 models （UEWA-5） for five selected extreme precipitation indices.Then， by building a comprehensive risk assessment model of rainstorm and flood disaster based on disaster risk and vulnerability of disaster bearing body， the study conducted risk assessments， future projections， and comparative analyses of rainstorm and flood disasters during baseline （1995-2014） and future near-term （2025-2044） and long-term （2045-2064） periods under three different climate change scenarios （SSP1-2.6， SSP2-4.5， SSP5-8.5）.Results indicated： （1） The EC-Earth3 model performed best in simulating the five extreme precipitation indices， with correlation coefficients between simulated and observed values of 0.78 for R95p， 0.90 for RX1day， and 0.77 for RX5day.Overall， the simulation performance of UEWA-5 exceeded that of EWA-5.（2） During the baseline period， central Sichuan exhibited high values for the five extreme precipitation indices， followed by eastern Sichuan and Chongqing， while western Sichuan showed lower values.The year 1998 recorded peak values for all five indices， with a maximum single-day precipitation of 86 mm for RX1day and an intensity （SDII） value of 11.3 mm·d^-1.（3） In future periods， the five extreme precipitation indices display a spatial distribution characterized by higher values in central regions and lower values around the periphery.Higher levels of social vulnerability and radiative forcing correlate with larger values of extreme precipitation indices.Comparing the two future periods， values of the indices are larger in the long term， notably with R95p averaging 846.8 mm， an increase of 169.2 mm compared to the near term.（4） During historical periods， areas with higher comprehensive risk of rainstorm and flood disasters were concentrated in central Sichuan and downtown Chongqing.In the two future periods， the high and moderately high-risk areas in central Sichuan are expected to expand， while the moderate-risk areas will shrink.The range of low-risk areas in the western Sichuan Plateau will also decrease， and the risk levels in southern Sichuan and eastern Sichuan-Chongqing border areas will respectively decrease to moderate-low and low-risk zones.Comparing the two future periods， the range of moderately high and moderate-risk areas in central Sichuan is expected to expand， while southwestern Chongqing will transition to a moderate-risk area in the long term.Other regions will generally maintain their original risk levels.Changes in disaster risk levels in the Sichuan-Chongqing region are less pronounced with increasing social vulnerability and radiative forcing， especially in the western Sichuan Plateau and northeastern Sichuan， where changes in disaster risk levels are minimal.The study results can provide important references for reducing disaster risks， enhancing emergency response capabilities， and making scientifically informed decisions for disaster prevention in the Sichuan-Chongqing region.

Drought represents a significant contributing factor to global climate-related disasters.It not only endangers the stability of global ecosystems and biodiversity but also has far-reaching implications for socio-economic development.As global climate change intensifies， so too does the frequency and intensity of droughts.Drought events in ecologically fragile regions not only threaten the availability of water resources but also increase the risk of food insecurity， ecological degradation and social conflict.Nevertheless， despite the growing body of research in this area， there remain significant gaps in our understanding of the spatial and temporal characteristics of drought occurrences over the past four decades， as well as its evolutionary trends under different climate scenarios in the future.This study employs the Standardized Precipitation Evapotranspiration Index （SPEI） and CMIP6 climate change scenarios to analyze the spatial and temporal characteristics of global droughts over the past four decades and to predict the evolution of global droughts under different climate scenarios （SSP1-2.6、 SSP2-4.5、 SSP5-8.5） over the next 80 years.The findings of the study indicate that： （1） During the period between 1980 and 2022， there were notable variations in the spatial and temporal characteristics of global drought across different regions.Globally， approximately 57% of the land area does not exhibit a significant drought trend.However， about 33% of the land shows a persistent aridification trend， particularly in some already arid regions， where the intensity of drought has increased.Conversely， only 10% of the area is becoming wetter， indicating that the regions of the globe that are becoming drier are significantly larger than those that are becoming wetter.This suggests that the aridification process is spreading globally； （2） Over the past four decades， the globe has experienced an arid trend with no significant seasonal differences.However， the arid regions in winter are expanding， accounting for 33.2% of the global land area； （3） Different vegetation cover types exhibit varying responses to drought.Sparsely vegetated areas are more susceptible to drought， while densely vegetated areas tend to be wetter.Furthermore， arid climate zones situated within diverse climatic contexts are confronted with more pronounced drought-related challenges.The largest proportion of severe drought is observed in extreme arid zones， which account for up to 67% of the global land area， this indicates a higher frequency of drought events in drylands； （4） The probability of drought events is predicted to increase significantly in Africa， South America， southeastern Asia， and southern North America， particularly in tropical or warm climatic zones， extreme arid zones， and evergreen broadleaf climate zones.Furthermore， droughts are expected to become more frequent and severe， especially in tropical or very warm climate zones， arid zones， and broadleaf evergreen forest regions.The SSP5-8.5 scenario is projected to have the highest probability and intensity of drought events over the next 80 years， and will be challenged by more frequent and severe droughts.The findings of this study underscore the pervasive and severe nature of the global aridification trend， particularly in the context of climate change， where the frequency and intensity of droughts are projected to increase significantly.This trend not only enhances our comprehension of the risk of drought， but also furnishes an essential point of reference for policymakers， water managers， and the general public.In order to mitigate the potential intensification of droughts in the future， it is imperative that all sectors of society implement more proactive and efficacious measures to promote adaptation and mitigate the challenges posed by droughts.The rational management of water resources， improvements in agricultural irrigation techniques， enhanced ecosystem resilience and the strengthening of monitoring and early warning systems for climate change and droughts will ensure global ecological security and facilitate sustainable socio-economic development.

In an effort to elevate the precision of low-level wind shear forecasting， this paper amalgamates European Centre for Medium-Range Weather Forecasts （ECMWF） fifth-generation reanalysis data （ERA5） and National Centers for Environmental Prediction Final Operational Global Analysis （FNL） reanalysis data， high-resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER GDEM） terrain data， and real-time observational data from Lanzhou Zhongchuan Airport.It employs the Weather Research and Forecasting Model （WRF）， WRF integrated with Computational Fluid Dynamics （CFD）， and Long Short-Term Memory （LSTM） neural network methods to simulate and analyze two wind shear events at Lanzhou Zhongchuan Airport on April 15-16， 2021.The findings reveal that： （1） within grids smaller than 1 kilometer utilizing Large Eddy Simulation （LES）， the WRF model demonstrates superior performance in wind speed simulation for individual stations， yet it falls short when compared to the WRF model combined with Computational Fluid Dynamics （CFD） models in simulating near-surface horizontal wind field wind speeds； （2） concerning the simulation of two low-level wind shears encountered during aircraft landing， both Weather Research and Forecasting Model - Large Eddy Simulation （WRF-LES） and Weather Research and Forecasting Model - Computational Fluid Dynamics （WRF-CFD） models are capable of simulating the first wind shear， however， the second appears to be influenced by the potentially lower wind speed data input into the models， with neither model achieving the threshold for wind speed difference， necessitating further validation in future work； （3） under low wind speed conditions （6 meters per second）， the LSTM-based single-variable wind speed prediction model maintains an average absolute error of approximately 0.59 meters per second， effectively capturing the nonlinear relationship of wind speed changes under various terrain and circulation background conditions.Despite being constrained by WRF errors and incomplete observational elements， multi-variable wind speed prediction can achieve wind speed forecasting with higher computational efficiency and generalization capabilities while ensuring that the average absolute percentage error is less than 6.60%.This paper not only verifies the differences between WRF-CFD and WRF-LES coupling schemes in wind field and low-level wind shear forecasting but also explores the feasibility and accuracy of LSTM-based wind speed prediction， aspiring to offer new perspectives and methods for enhancing wind field simulation accuracy and reducing the time required for detailed wind field simulation.

The freeze-thaw cycle of near-surface soil in the perennial permafrost region of the Qinghai-Xizang （Tibetan） Plateau plays a crucial role in regulating water and energy exchange between the soil and the atmosphere.Investigating its spatiotemporal characteristics and response to climate change is essential for understanding the mechanisms driving climate change on the plateau.In this study， we calculated near-surface freeze-thaw parameters-including the start and end times of soil freezing， thawing duration， and freezing duration-across the perennial permafrost region of the plateau from 1980 to 2017 using the Common Land Model 5.0 （CLM5.0）.We further analyzed their spatiotemporal variations and correlations with temperature， precipitation， snow depth， and vegetation index.The results show that： （1） The onset of near-surface soil freezing in the plateau’s permafrost region occurs between September and mid-to-late October， while the thawing period ends between February and May.Semi-humid regions have the longest thawing duration， whereas semi-arid regions have the shortest， with an average difference of 15 days.The freeze-thaw status of permafrost soil on the plateau exhibits significant changes.Except for areas near the Karakoram Mountains， most permafrost regions show a decreasing trend in freezing duration and an increasing trend in thawing duration.The average growth rate of soil thawing duration across the plateau is 2 d·（10a）⁻¹， with the most significant increase observed in semi-humid regions， reaching 4 d·（10a）⁻¹.（2） The freeze-thaw parameters of the plateau's permafrost are associated with geographical factors.In the latitude range of 29°N -36°N and longitude range of 82.5°E -103°E， the thawing duration shows an increasing trend； however， the rate of change decreases in some areas while increasing in others.Additionally， as elevation increases， the growth rate of thawing duration declines.（3） The duration of permafrost thawing is significantly correlated with snow depth， near-surface temperature， precipitation， and vegetation index， though these relationships vary across different climatic regions.Near-surface temperature exhibits a strong positive correlation across all regions， making it the primary driver of freeze-thaw changes.Precipitation and snow depth show positive and negative correlations， respectively， with particularly strong correlations in semi-humid areas.The vegetation index is positively correlated with thaw duration in all regions， with the strongest correlation observed in semi-arid areas.（4） The relationship between thawing duration and seasonal climatic factors varies.Near-surface air temperature exerts a significant influence on the freeze-thaw process at seasonal scales， with the most pronounced impact occurring in spring.Precipitation is positively correlated in summer but negatively correlated in winter.Both snow depth and vegetation index are significantly correlated with thawing duration in semi-arid and semi-humid regions during spring， exhibiting negative and positive correlations， respectively.（5） Near-surface temperature influences the freeze-thaw cycle in the plateau’s perennial permafrost region during both dry and wet seasons.However， the effects of snow depth， precipitation， and vegetation index are more pronounced during the wet season.

Global warming has led to rapid changes in the sea ice of the Antarctic and Arctic， triggering a number of climate feedbacks.Turbulent heat exchange between the polar seas and the air plays an important role in these feedbacks.Solar radiation， as the main energy source at the polar sea surface， is mainly used for sea ice melting and air-sea heat exchange， but the higher albedo of sea ice results in low radiation absorption.Air-sea heat exchange is influenced by temperature and humidity gradients between the sea surface and the atmosphere， with sensible heat dominating at the sea-ice edge and latent heat farther from the sea ice.In the Arctic， air-sea heat exchange is dominated by sensible heat， whereas in the Antarctic it is dominated by latent heat.The air-sea heat fluxes at the north and south polar seas vary seasonally.Sea ice can also inhibit air-sea heat exchange to some extent.Accurate parameterization of the turbulent heat exchange between the sea and the atmosphere in the sea ice regions is crucial for simulating air-sea interactions， however， in situ observations remain extremely rare due to limitations of the polar environment， and accurately representing the turbulent air-sea exchange in polar oceans remains a challenge.In the future， the network of air-sea flux measurements in polar seas should be strengthened， especially in the marginal ice zone， which are necessary and crucial for a deep understanding of the role of air-sea interactions in polar regions on global climate change， and for reducing the uncertainty in climate models.Secondly， the dynamics and thermal properties of ice must be fully considered to optimize the parameterization scheme or develop new models to improve the simulation accuracy.Furthermore， the influence of waves on the air-sea heat exchange in the sea ice region should be clarified to fill the research gaps.Finally， the contribution of the air-sea heat exchange to climate change in the polar regions should be evaluated further to improve the understanding of the role of polar oceans in climate change.

Using effective bias correction methods and transforming non-stationary data to stationary can enhance the scientific accuracy of temperature analysis， allowing for a deeper understanding of its temporal and spatial distribution characteristics and evolution patterns.This study utilizes the ERA5_Land near-surface （2 m） monthly mean temperature observation dataset covering the period from 1970 to 2014.Initially， it employs the Taylor diagram， Taylor index， interannual variability skill score， and rank scoring method to evaluate and select among six climate models from the International Coupled Model Intercomparison Project Phase 6 （CMIP6） and the multi-model ensemble （MME） average models.Subsequently， the superior models are refined using the Delta bias correction method and the Normal distribution matching method.Finally， the study analyzes the temporal and spatial temperature variation characteristics of the Qinghai-Xizang Plateau from 2015 to 2100 under the SSP1-2.6， SSP2-4.5， and SSP5-8.5 scenarios.The results indicate that： （1） Among the six CMIP6 models and the multi-model ensemble （MME） average models analyzed in this study， the EC-Earth3 model demonstrates the most effective performance in simulating temperature.（2） When comparing the Delta bias correction results of the EC-Earth3 model with observational data， the regional averages of the coefficient of determination （R²） and the Nash-Sutcliffe efficiency coefficient （NSE） are 0.992 and 0.983， respectively.After applying the Normal distribution matching method for correction， the regional average values of R² and NSE are 0.990 and 0.978， respectively.This comparison reveals that the Delta bias correction method exhibits superior correction efficacy for the model's monthly temperature.（3） According to the combination of EOF-EEMD， the annual temperature of the first typical field of the three scenarios changes uniformly in the whole region， and there is a common sensitive area of temperature change under SSP1-2.6 and SSP2-4.5 scenarios， that is， the central region of the Qiangtang Plateau.The temperature dynamics in the second typical field reveal a gradual reverse-phase change from the upper reaches of the Zhaqu River to surrounding areas.Under the SSP1-2.6 scenario， the plateau experiences overall cooling in the east and warming in the west.Conversely， under the SSP2-4.5 and SSP5-8.5 scenarios， the plateau initially warms in the east and cools in the west， followed by a subsequent cooling in the east and warming in the west.This study provides a reference for bias correction methods that enhance the accurate application of climate model data in the Qinghai-Xizang Plateau region and offers essential foundational information for a comprehensive assessment of the impacts of temperature changes on water resources， ecosystems， and the environment in this area.

The Coupled Model Intercomparison Project （CMIP） provides reliable scientific data for predicting ecology， hydrology and climate under the backdrop of global change.However， there are large biases in current climate models， especially on the Qinghai-Xizang Plateau （QXP）.In this study， we employed Detrended Quantile Mapping （DQM） and Quantile Delta Mapping （QDM） methods to correct and evaluate the precipitation and temperature data of eight CMIP6 models with better simulation performance， utilizing the China Meteorological Forcing Dataset （CMFD）.The results showed that Both methods had corrected the simulation biases of the models， and the correction effects for temperature and precipitation data over the QXP were relatively consistent between the two methods.Then， based on the corrected multi-model ensemble mean （MME） results from QDM method， we analyzed the spatial and temporal variation characteristics of extreme high temperature events， low temperature events， atmospheric dryness and precipitation over the QXP in the early 21^st century （2015 -2057） and later 21^st century （2058-2100）.Under different emission scenarios in the future， extreme high temperature events strengthen， especially in the southeast of the QXP.Extreme high temperature events enhance with the increase of radiation.Extreme low temperature events decrease， with no occurrence in the later 21^st century under high emission scenarios （SSP370 and SSP585）.Under different emission scenarios， precipitation and saturated vapor pressure difference both exhibit a significant increasing trend on the QXP.With global warming， the increase of precipitation does not mitigate atmospheric drought.The atmospheric dryness increases significantly under the future scenarios， especially in summer， at 1.3 to 2 times compared to annual average.

Based on the China snow depth time series data set and high resolution ground meteorological element driven dataset， this study analyzes the spatial and temporal variation of snow depth on the Qinghai-Xizang （Tibetan） Plateau by watershed and elevation gradient during the 1980 -2020 snow season considering different river basins and elevation gradients.Additionally， the study investigates the response of snow depth to climate change in the context of hydrothermal factors.The results show that： （1） Spatial difference in snow depth on the Qinghai-Xizang （Tibetan） Plateau was obvious， showing a distribution pattern of high in the west and low in the east， and greater in the high-altitude mountain areas than in the basin plains， with the average snow depth in the high-altitude mountain areas generally greater than 10 cm.The average snow depth decreased at a rate of 0.25 cm/decade， 64.74% of the regions showed a declining trend， with statistically significant decreases in 29.09% on the Qinghai-Xizang （Tibetan） Plateau during the snow season from 1980 to 2020.（2） There is a clear vertical zonation of snow depth and its trend as influenced by altitude.Below an altitude of 4.2 km， average snow depth increased with elevation.Between 4.2 km and 4.8 km， average snow depth decreased as elevation rises.Above 4.8 km， average snow depth again increased with elevation.A decreasing trend in snow depth was observed across all elevation bands， with the rate of decrease initially increasing and then decreasing with elevation， exhibiting a threshold at approximately 5.0 km.The most rapid decrease in mean snow depth ［3.36 cm·（10a）^-1］occurred in the 5.0~5.2 km elevation band.The interannual variation of mean snow depth exhibited a pronounced altitude-dependent pattern， the rate of snow depth reduction was significantly higher at higher elevations than at lower elevations， especially at 4.8~5.5 km.（3） Climate change on the Qinghai-Xizang （Tibetan） Plateau is ‘warmer and wetter’ overall， but ‘warmer and drier’ in the north-west and south during the snow season from 1980 to 2020.However， there are watershed differences and elevation differences in the response of snow depth to climate change.Specifically， in the Nujiang， Ganges， Amu Darya， and Indus River basins， the warming and aridification of climate conditions have contributed to a reduction in snow depth.Conversely， temperature has a more pronounced effect on snow depth in the Yarlung Tsangpo River， the interior plateau， as well as the Yangtze River basins， the Qaidam Basin， and the Tarim Basin.Additionally， precipitation plays a more significant role in influencing snow depth in the Yellow River， Heihe River basin.In regions with altitudes below 3.5 km， climate conditions characterized by warming and aridification have led to a reduction in snow depth.However， in areas with altitudes above 3.5 km， temperature has a more pronounced influence on snow depth.The altitude-dependent warming of temperature accounts for the altitude-dependent reduction in snow depth.

Utilizing the Anomaly Numerical-correction with Observations （ANO） based on historical observation data and anomaly integration， in conjunction with the ERA5 reanalysis data， a rectification test was conducted on the forecasting products of the Southwest Center WRF-ADAS Real-time Modeling System （SWMS in short）.This study evaluated the efficiency of the ANO method in enhancing short- to medium-term weather forecasts for meteorological quantities during a catastrophic regional heavy rainfall event over the complex topography from June 20 to 25， 2019.Results revealed that the SWMS model exhibited commendable predictability in the middle and upper troposphere， although its accuracy gradually reduced in the lower layers.After post-processing correction using the ANO method， all the predicted variables showed obvious improvements.The average Anomaly Correlation Coefficient （ACC） for the 500 hPa and 700 hPa geopotential height fields within the 72-hour integration increased by a range of 0.1 to 0.2， reaching approximately 0.8， while the 850 hPa geopotential height ACC showed a maximum enhancement of 0.6.Concurrently， the Root Mean Square Error （RMSE） for the corrected 700 hPa and 850 hPa geopotential heights exhibited significant reductions with an average decrease of 24% and 66%， respectively.The correction outcomes for temperature， specific humidity， and horizontal wind also displayed positive effects， which reveal the efficient correcting performance of the ANO method based on historical observation data in rectifying short- to medium-term numerical forecasts of the SWMS model over complex topography.

This study， through comprehensive observations of the radiation field in the Everest National Nature Reserve （hereinafter referred to as "Everest"）， reveals the spatiotemporal variation patterns and potential impacts on the ecological environment and climate.By establishing a meteorological gradient observation network covering diverse ecological environments， including alpine shrubs， alpine wetlands， and alpine desert grasslands， and utilizing advanced radiation observation equipment combined with data quality control and automated processing， the study collected multi-year， continuous， high-precision measurements of four-component radiation fluxes.The results show the following： （1） The net radiation flux at Everest and alpine shrub sites exhibits an annual increase of 0.7 W·m^-2 on an interannual scale； （2） In terms of multi-year monthly average net radiation flux， the values at Everest and alpine shrub sites increase from 15 W·m^-2 in January， peak at 110 W·m^-2 in August， and then decrease to 14 W·m^-2 by December.The multi-year monthly average downward shortwave radiation flux rises from 210 W·m^-2 in January to a maximum of 375 W·m^-2 in June， followed by a significant drop to 230 W·m^-2 in July， remains relatively stable from July to October， then sharply declines from October， reaching a minimum of 200 W·m^-2 in December； （3） The multi-year summer daily average net radiation flux at Everest and alpine shrub sites rises from 0 W·m^-2 at 07:00 （Beijing Time， the same as after）， peaks at 530 W·m^-2 at 13:00， and declines to -110 W·m^-2 at 20:00.The multi-year summer daily average downward shortwave radiation flux increases from 0 W·m^-2 at 07:00， peaks at 860 W·m^-2 at 13:00， and drops back to 0 W·m^-2 at 21:00.In winter， the daily average net radiation flux rises from -120 W·m^-2 at 08:00， reaches a maximum of 370 W·m^-2 at 14:00， then decreases， reaching -120 W·m^-2 again at 20:00.The daily average downward shortwave radiation flux in winter rises from 0 W·m^-2 at 08:00， peaks at 840 W·m^-2 at 14:00， and falls to 0 W·m^-2 by 20:00； （4） There are significant differences in radiation flux among the stations， with the contrast being particularly prominent between the alpine wetland station and the other two stations.The annual average net radiation， annual average downward shortwave radiation， multi-year monthly average net radiation， multi-year monthly average downward shortwave radiation， and the daily average net and downward shortwave radiation in both summer and winter are all higher at the alpine wetland station than at the Everest and alpine shrub stations.The results of this study provide new insights into understanding climate change in high-altitude regions and offer essential data support for developing remote sensing monitoring technology， improving global climate models， and formulating environmental protection strategies in plateau areas.

On August 14， 2018， Typhoon Yagi （2018） moved northward and impacted Shandong Province of China， resulting in widespread rainstorm and heavy rainstorm.The total rainfall caused by the typhoon in Shandong presents a round-shaped distribution.Specifically， on August 14， an outer spiral rainband appeared on the typhoon periphery in southeastern Shandong， bringing short-term heavy rainfall and local heavy rainstorms.Due to the relatively small scale of this rainband， both numerical forecasting models and forecasters face challenges in predicting its rainfall accurately.To study the mechanisms of the outer spiral rainbands of Typhoon Yagi， the characteristics and causes of the spiral rainbands are investigated in this study by using radar data and the observations from ground-based stations， radiosonde stations and aircraft.Numerical experiments are also conducted based on the Advanced Research WRF （Weather Research and Forecasting） model and its Hybrid-3DVAR （three-dimensional variational） data assimilation system.The model adopts 12 km and 4 km one-way nested grids， with 44 vertical layers.The initial ensemble perturbation fields are generated by using a stochastic perturbation method， and the Ensemble Transform Kalman Filter （ETKF） method is used for the bias correction of ensemble forecast， providing flow dependent background errors for the Hybrid-3DVAR assimilation module.Comparative experiments with and without the Aircraft Meteorological Data Relay （AMDAR） data assimilation are conducted by adopting 100% flow-dependent error covariance and by using a 45-minute assimilation time window.The results indicate that the outer spiral rainbands are formed by the merging and development of several linear mesoscale convective systems （MCSs）.The outer spiral rainbands exhibit distinct characteristics of the linear MCSs with leading stratiform precipitation， i.e.， the linear MCSs consist of several convective cells with back-building convection.There are several stronger linear MCSs merging laterally into other linear MCSs.Broad stratiform echoes appear in the front （eastern part） of the linear MCS in its maturity stage， and the convection develops up to 10 km or more.There is a weak-echo transition zone between the strong convective line and the sub-strong stratiform echo region.Short-term heavy rainfall occurs along the linear MCS at the maturity stage.The water vapor of heavy rainfall mainly comes from the near-surface layer （below 850 hPa） around the typhoon， and the water vapor flux convergence is mainly concentrated near the wind field convergence line.Before convection initiation， the middle and lower levels over Shandong are thermally unstable with high temperature and high humidity， and the wind rotates clockwise with height， which favor the development of convective systems.As the typhoon slowly moves northward， downward intrusion of cold air appears at 500 hPa.Below 900 hPa， on the southeast of the typhoon over central Shandong there are local convergence between southwesterly wind and southerly wind， and between southerly wind and southeasterly wind.The convergence-induced dynamic uplift triggers the release of unstable energy， stimulating several local linear MCSs.The MCSs develop northward along the steering flow.The linear MCSs merge and strengthen for several times， and finally the elongated spiral rainbands occur.During the convection lifetime， the updrafts are noticeably stronger than the downdrafts.At the mature stage of the convective systems， dry and cold downdrafts appear in the lower levels in the front of the MCS.Convective systems at the heights above 600 hPa move rapidly eastward with the upper-air steering flow， leading to the gradual weakening and dissipation of the linear MCS.Assimilation of AMDAR can improve the typhoon track and wind field forecasts of the WRF model， as well as the dynamical triggering mechanism of convective systems.Thus， the occurrence of spiral rainbands in the typhoon periphery could be accurately forecasted.Furthermore， central Shandong is a mountainous region， so how does the topography influence the triggering and developing of convective systems？ What are the differences between typhoon outer spiral rainbands and the main body spiral rainbands？ What are the differences between outer spiral rainbands？ These issues deserve further studies.

Based on the hourly precipitation data of 3454 stations with dense space in Sichuan Province and the high-precision grid elevation data， the characteristics of precipitation in flood season in 7 regions of Sichuan Province in recent 10 years were analyzed.The results showed that： （1） There were 3 maximum centers of rainfall in flood season in Sichuan Province： Ya 'an in the southwest of the basin， Anxian in the northwest of the basin and Yanbian in the south of Panxi area.Anxian was the center of heavy rainstorm， and the rainfall in flood season was mainly contributed by the weather process of R ₂₄ ≥100 mm.（2） Affected by the trend of the mountains and the steepness of the terrain， the morphology and isoline gradient of the large value area around the basin had obvious differences.And the larger the accumulated rainfall in flood season， the more the sites were concentrated on the windward slope of the mountains.（3） The degree of night rain gradually weakened from southwest to northeast， among which Panzhihua was the most significant area of night rain in flood season.（4）The daily distribution of R ₂₄ ≥25 mm heavy rainfall was closely related to topography， and the heavy rainfall stations were only distributed in the steep transition zone between the western basin and the plateau.In addition， the percentage of stations with hourly rain intensity ≥50 mm·h^-1 in the rainstorm days in the northwest of the basin was the highest.（5） Compared with persistent heavy rain， the site distribution of persistent heavy rain was more significantly affected by the windward slope topography.

Precipitation plays a critical role in the Earth's hydrological and energy cycles， significantly influencing the biogeochemical cycles and energy exchanges on the land surface.In the ecologically fragile region of the Loess Plateau， the spatial and temporal variability of precipitation has profound implications for both the ecological environment and socioeconomic development.Therefore， study on the spatial and temporal variations of precipitation in the Loess Plateau holds substantial theoretical and practical significance.This study utilizes daily precipitation data from 115 meteorological stations across the Loess Plateau and its surrounding areas， covering the period from 1959 to 2018.By employing methods such as Inverse Distance Weighting （IDW） interpolation and wavelet analysis， the study provides a comprehensive analysis of the spatial and temporal characteristics of precipitation over the past 60 years in the Loess Plateau.The results showed that： （1） The spatial distribution of precipitation in the Loess Plateau exhibits a clear "stepped" pattern， gradually decreasing from southeast to northwest.This distribution highlights a significant gradient where the southeastern regions receive more precipitation than the northwestern regions， with a similar trend of more rainfall in the south compared to the north.Furthermore， localized topography plays a crucial role in modulating precipitation， with higher elevations generally receiving more rainfall.（2） Under the influence of changes in the East Asian monsoon and atmospheric circulation patterns， the spatial distribution of precipitation from 1989 to 2018 differs significantly from that of 1959 to 1988.Specifically， the 200mm and 400mm isohyets have shifted northward， with a notable decrease in precipitation in the southeastern monsoon-dominated areas， while precipitation has increased in the non-monsoon northwestern areas.The monsoon marginal zone of the Loess Plateau is particularly sensitive to monsoon variability.The continuous weakening of the East Asian summer monsoon has diminished the capacity for moisture transport， further exacerbated by El Niño-Southern Oscillation （ENSO） warm events， both of which have contributed to reduced precipitation in the southeast.Conversely， changes in atmospheric circulation have led to increased precipitation in the northwest， resulting in a slight expansion of the semi-humid regions in the area.（3） Over the study period， precipitation in the Loess Plateau exhibits a fluctuating upward trend， indicative of a general tendency towards increased wetness in the region.This suggests a long-term shift towards more humid conditions， which could have significant implications for the region's ecological restoration and water resource management.（4） The interannual variability of precipitation in the Loess Plateau is characterized by oscillations on multiple time scales， specifically at 5-year， 7-year， 11-year， and 43~45-year intervals， with the 5-year cycle identified as the dominant periodicity.

Climate and land-use change are important drivers of variation in carbon storage within terrestrial ecosystems.Investigating the effects of climate and land-use change on carbon storage has practical implications for proposing adaptive management strategies for carbon sequestration in a changing environment.In this study， the InVEST model and the PLUS model were used to evaluate the spatial and temporal dynamics of carbon storage in the water conservation area of the Yellow River under the dual influence of climate and land-use change.The results showed that the land-use in the water conservation area of the Yellow River was dominated by grassland and forest from 1980 to 2020， accounting for 80 % of the total area of the basin， with an increasing trend in the area of forest land， grassland， watershed and construction land， and a decreasing trend in the area of other land-uses.The types of land-use transfer include unused land to grassland， grassland to forest land and cultivated land.From 1980 to 2020， the carbon storage in the water conservation area of the Yellow River showed an overall growth trend.The growth area of carbon storage was mainly located in the western and central regions， increasing by 573.5×10⁶ t， which was closely related to climate warming and humidification and ecological restoration.The urban expansion areas in the central and northern regions are the main areas of carbon storage reduction.In the future， under different land-use scenarios， the area of forest and grassland in the ecological protection scenario will increase significantly.From 2030 to 2050， under SSP119 and SSP245 scenarios， carbon storage will increase by 294.83×10⁶ t and 79.56×10⁶ t， respectively， under natural development scenarios， and carbon storage will increase by 364.8×10⁶ t and 151.95×10⁶ t， respectively， under ecological protection scenarios.Low emission and ecological protection scenarios are favorable for carbon storage increase.In the future， the increase in carbon storage will mainly come from grassland， conversion of unused land into forest and cropland， and conversion of unused land into grassland.The decrease in carbon storage is mainly related to the conversion of forest land into grassland and cropland.It can be seen that protecting forest and grass is an important measure to improve the carbon storage of regional ecosystem.The results can provide a scientific basis for adjusting the land-use structure and carbon sequestration of the ecosystem in the water conservation area of the Yellow River.

Based on the hourly precipitation and 10 m wind speed data from the European Centre for Medium-Range Weather Forecasts （ECMWF） Fifth Generation Atmospheric Reanalysis dataset from 1979 to 2023， spatiotemporal changes and its corresponding clustering characteristics of compound precipitation and wind speed extremes （PWEs）， and the circulation characteristics in different periods in China were studied by using compound extreme events definition， trend analysis， spatial statistical analysis， and composite analysis.The results showed that PWEs in China were generally more frequent in the east than in the west.Among the PWEs in each subregion， the highest value was found in East China， where the mean value of the frequency and the days were the most in the range of 4~8 times and 4~8 d， and the corresponding area share reached 78.9% and 71.5%， respectively.The overall trend of PWEs from 1979 to 2023 had been decreasing， with the rate of change from 2011 to 2023 being 2.3 times and 3.4 times that of 1979 to 2010.The trend of PWEs from 1979 to 2010 showed an increasing trend in the central and eastern region of Eastern China， the central region of Southwest， and the northern region of Northwest， and the fastest decrease in Central China.From 2011 to 2023， positive trend values were mainly concentrated in the central region of China， and the Eastern China was the region of the fastest growth with rates of 0.96 times and 1.12 d per decade.In contrast， Southern China exhibited a decrease at rates of 0.81 times·（10a）^-1 and -0.77 d·（10a）^-1.The hot spot areas were concentrated on the west side of the Hu Line and coast region from 1979 to 2010， and the distribution of hot spot areas from 2011 to 2023 were consistent with the positive distribution of trend change.In addition， PWEs are the result of the combined effects of the high， middle， and low-level atmospheric layers.The enhancement of atmospheric high-level divergence and the weakening of the jet belt promote the upward movement of the atmosphere and the westward extension of the west Pacific subtropical high.The anomalous easterly wind in the middle atmosphere is conducive to the entry of water vapor from the periphery of the Northwest Pacific subtropical high into the inland regionof China， and the anomalous southeast wind in the low-level atmosphere further promotes the transport of water vapor to the inland region of China.The atmospheric circulation characteristics after 2010 also showed the development of PWEs events towards inland region of China.

Research on the characteristics of carbon and water fluxes and water use efficiency （WUE） in the alpine meadow ecosystem of the Qinghai-Xizang Plateau is of crucial significance for accurately assessing the carbon balance， water cycle， and carbon-water coupling of the alpine grassland ecosystem under the background of climate change.In this study， based on the observation data made using eddy correlation in the alpine meadow on the eastern Qinghai-Xizang Plateau from 2012 to 2017， from the Maqu observation point of the Zoige Wetland Ecosystem Research Station of the Northwest Institute of Eco-Environment and Resources， Chinese Academy of Sciences， we analyzed the changes in carbon and water fluxes and WUE during the growing season.By combining multiple stepwise regression and structural equation modeling， we explored the main driving factors of carbon and water fluxes and WUE during the growing season.The results indicate that： （1） The average annual net ecosystem CO₂ exchange （NEE）， ecosystem respiration （Re）， and total gross primary productivity （GPP） of the Maqu alpine meadow ecosystem over 6 years were -109.7， 798.6， and 908.3 gC·m^-2·a^-1， respectively， showing an overall carbon sink； the annual average evapotranspiration （ET） is 446.5 kg·m^-2·a^-1 the 6-year average water use efficiency （WUE） was 2.0 gC·kg^-1.（2） The daily variations of NEE and GPP during the growing season showed a distinct unimodal pattern， peaking around 14:00 （Beijing Time， the same as after）， while Re showed a relatively flat diurnal pattern， lower slightly at night than during the day； the daily variation of ET exhibited a unimodal pattern， with peak monthly and monthly accumulative values in July； WUE displayed an asymmetric "U" curve with the minimum value at 13:00 -14:00， showing significant daily and monthly variations in July and August.（3） During the growing season， both multiple stepwise regression and structural equation models confirmed the dominant role of temperature in controlling carbon fluxes and radiation in controlling ET.Temperature and radiation were identified as the main influencing factors for WUE during the growing season.

The Qinghai-Xizang Plateau summer monsoon is an important component of the Asian monsoon system， significantly influencing the energy and moisture cycles in the plateau and its surrounding regions.This study uses JRA-55 monthly reanalysis data from 1980 to 2020 and GPCC monthly precipitation data， combined with the Qinghai-Xizang Plateau Monsoon Index.Various statistical methods， including correlation analysis， regression analysis， composite analysis， and dynamic diagnostics， are used in this study.This paper focuses on the impact of the summer monsoon over the Qinghai-Xizang Plateau on water transport， such as precipitation， atmospheric circulation， and water budget.The results show that： （1） When the Qinghai-Xizang Plateau summer monsoon is strong （weak）， precipitation in the central and eastern parts of the plateau increases （decreases）.（2） From the perspective of water vapor transport， when the summer monsoon over the plateau is stronger， there is an anomalous anticyclonic circulation over central India， an anomalous westerly airflow to the south of the plateau， and the water vapor transport over the plateau is primarily dominated by the westerly water vapor transport channel.（3） Analysed in terms of moisture budget， when the Qinghai-Xizang Plateau summer monsoon is strong （weak）， moisture inflow at the southern and western boundaries of the plateau increases （decreases）， while moisture inflow at the northern boundary decreases （increases）， resulting in an increase （decrease） in regional net moisture budget.（4） The impact of the Qinghai-Xizang Plateau summer monsoon on moisture convergence/divergence is mainly driven by the contribution of the wind’s dynamic component， while the thermal component from moisture advection is relatively small.

The seasonal vegetation greening in the Three River Source Region （TRSR） has a profound impact on the regional ecological environment and water resource security.In this study， the moisture drivers of seasonal vegetation greening in the TRSR and their responses to climate change were investigated using multi-source data from 2003 to 2021， through the application of trend analysis， correlation analysis and partial information decomposition （PID） analysis.The results showed that： （1） From 2003 to 2021， the linear trend of the Leaf Area Index （LAI） generally increased in spring， summer and autumn in TRSR， although the environmental conditions varied significantly between seasons.Linear trends in precipitation， soil moisture （SM） and snow cover （SC） all showed an increasing trend in spring and autumn， with insignificant changes in temperature.In summer， linear trends of temperature were slightly increased， but precipitation and SM slightly decreased， as well as insignificant changes in SC.（2） In terms of the effects of moisture driving factors on LAI： correlation analyses results showed that LAI was significantly positively correlated with SM in spring and summer， but not in autumn.The correlation between LAI and SC was weak in all seasons.By introducing the PID analysis method， the nonlinear and synergistic effects of SM and SC on LAI were effectively revealed.The independent information contribution of SC to LAI changes was higher in spring and autumn， making it the main moisture driver in these seasons， while SM contributed more in summer.At the same time， the synergistic effects of SM and SC played an important role in the changes of LAI in all seasons， with the synergistic information contribution exceeding 30% in all seasons.（3） Response of moisture drivers to climate change： correlation analyses results showed that SM was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in spring； SC was significantly positively correlated with precipitation in all seasons and significantly negatively correlated with temperature in both spring and autumn.PID analyses also indicated that precipitation was the main meteorological factor influencing changes in SM and SC across the three seasons， with a higher independent contribution than temperature.However， the synergistic effects of temperature and precipitation on SM and SC in all seasons should not be overlooked.

On August 13-14， 2022， an extreme rainstorm event occurred in Yuzhong region of Gansu Province， northeast of the Qinghai-Xizang Plateau.Accumulated daily precipitation reached 130.6 mm and the maximum hourly precipitation was 36.6 mm， breaking the heaviest daily precipitation records of the region and causing serious social impact and economic losses.Based on the data of surface minute observation and high altitude observation， Lanzhou Doppler radar and ECMWF Reanalysis v5（ERA5）， by analyzing the observation characteristics， environmental conditions， topographic effects and instability mechanism of the two heavy precipitation stages in this extreme rainstorm， the results show that： （1） The rainstorm was caused by the convergence of weak cold air brought by the shortwave trough in the westerlies and warm and humid air outside the subtropical high in the Longzhong area.The 700 hPa shear line provided the dynamic lifting conditions， and the surface cold front provided the triggering conditions.（2） The radar reflectance factor in the rainstorm process was characterized by persistent strong echoes accompanied by "backward propagation"， low-level jets and obvious convergence.In the second stage， the echo top height behind the cold front was similar to that in the first stage， but the scope was larger and the structure was more compact， and the convective cloud development was more vigorous.（3） The water vapor conditions of the rainstorm were abundant.In the first stage， there was significant convective instability due to strong convergence and upward movement at the lower level and high convective effective potential energy.In the second stage， the upward movement was weakened， the convective effective potential energy was 0， and the dynamic and convective instability conditions were weak.（4） The release of unstable energy triggered by cold front baroclinic frontogenesis was the main triggering mechanism of precipitation in the first stage.After the transit of the cold front， the precipitation in the second stage was formed by the combination of terrain， frontogenic secondary circulation and instability.Since the heavy precipitation after the summer cold front was not common in the northeast part of the Qinghai-Xizang Plateau， forecasters tended to ignore such kind of rainstorms.Therefore， we need to strengthen monitoring and early warning of such rainstorm events.

Climate comfortableness is the key factor that has impact in many fields， such as residents’ life quality， tourism development， and urban planning layout.The Universal Thermal Climate Index （UTCI is currently the most important and effective way to evaluate climate comfortableness at the international level.In-depth research on the climate comfortableness of the Yellow River Basin can not only fill the gap in the study of climate comfortableness in the Yellow River Basin area but also supplement a comprehensive understanding.Based on the results of climate zoning， the Yellow River Basin is divided into six sub-regions Using the reanalyzing data of ERA 5， the spatial distribution and temporal change of climate comfortableness in the Yellow River Basin from 1979 to 2022 were analyzed and discussed with the adoption of UTCI.The results show as follows： （1） From an overall perspective， the annual average UTCI value of the Yellow River Basin is 2.8 ℃， with a comfortable grade of coolness.The UTCI value is mostly in the cold zone and comfortable zone.The distribution of hot zone is relatively less.There is a large difference in UTCI distribution among the internal regions.Region I has a relatively longer duration of low temperature， and the area， with mild cold stress （coolness） and stronger cold stress （uncomfortable coldness）， is larger.Region II is mostly in the cold zone.Region III and Region IV are relatively close， and are dominated by “comfortableness” and “coolness”.The UTCI values in region V and region VI are at a higher level， but most areas are still in the comfortable zone.（2） In terms of the seasons， the four regions of III， IV， V， and VI have relatively extensive comfortable zones in spring and autumn， the overall comfortable area will be expanded in summer across the entire Yellow River basin， and the cold and uncomfortable zone will become dominant in winter， while the overall comfortable zone will be shrinked across the entire Yellow River basin.（3） The average UTCI in China as a whole has shown an overall upward trend from 1979 to 2022， with a change rate of 0.4 ℃·（10a）^-1.The range of change in sub-regions is 0.14~0.85 ℃·（10a）^-1.The annual UTCI change in the Yellow River basic shows a significant feature of west-high-east-low and north-high-south-low in the spatial distribution.（4） The level of climate comfortableness， taken as a whole， is mainly in the comfortable and slightly uncomfortable categories.The number of days in each of the six comfortable levels is as follows： 24 days （cold discomfort）， 126 days （slightly cold， discomfort）， 59 days （cool）， 131 days （comfort）， 19 days （slightly hot discomfort）， and 6 days （hot discomfort）.Region I and Region II have not been affected by the discomfort caused by heat.However， regions in the Yellow River basin， and regions of III， IV， V， and VI， are affected by slightly hot discomfort and the average duration in the slightly hot discomfort zone throughout the year is 19 days， 23 days， 24 days， 46 days， and 60 days respectively.

Soil water-heat dynamics are pivotal in influencing regional hydrological processes.Understanding the dynamics of soil thermal and moisture changes during freezing and thawing processes is essential for assessing water balance in high-altitude regions.This study utilizes meteorological and soil water-heat observational data from a typical shallow mountainous catchment in the Qilian Mountains to simulate the water-heat dynamics of subalpine shrub meadow soil using the SHAW model， analyzing the changes in water balance during the soil freezing and thawing process.The results indicate that the SHAW model effectively simulates the temporal and vertical variations in soil temperature and moisture in subalpine shrub meadow soils.The findings demonstrates that the Nash-Sutcliffe Efficiency （NSE） for simulated soil temperature at various depths exceeded 0.88， with ae correlation coefficient （R） greater than 0.97and a Root Mean Square Error （RMSE） less than 1.89 ℃.For soil moisture， the correlation coefficient （R） was greater than 0.94， NSE was greater than 0.88.and the RMSE was less than 0.05 m³·m⁻³.Overall， the simulation of soil temperature is more accurate than that of soil moisture， especially in deeper soil layers.The soil freezing and thawing periods， delineated by temperature profiles， revealed a distinct unidirectional freezing and thawing characteristic of the subalpine shrub meadow soil， with the longest duration in the complete freezing period and the shortest in the freezing period.The trends in temperature and moisture across the soil profile exhibit a "U" shape， indicating higher soil temperatures and moisture during the thawing period compared to the freezing period， with significant fluctuations in surface soil moisture and relative stability at deeper layers.The water balance characteristics are significantly varied across different soil freezing and thawing periods.During the freezing period， the precipitation input is 4.28 mm， with the main expenditure of water is deep percolation at 9.06 mm.In the complete freezing period， the precipitation input is 28.69 mm， with the main expenditure of water is surface runoff at 17.90 mm.During the thawing period and the complete thawing period， the precipitation input is 106.29 mm and 207.31 mm respectively， with the major water output through evapotranspiration， where plant transpiration accounted for 78.11% and 71.54% respectively.The soil moisture shows a negative balance during the freezing and complete thawing periods， indicating a net loss of moisture.Conversely， the soil moisture exhibits a positive balance during the complete freezing and thawing periods， signifying a net increase in moisture.This study may provide empirical data and theoretical support for the formation and transformation of water resources in the Qilian Mountain region.

Wind is one of the most important meteorological conditions in previous Winter Olympics， and it is the primary factor that affects the mountain events for Beijing Winter Olympics.Understanding the fine distribution law of wind can provide important theoretical basis for track construction， wind forecast and prevention measures.Using hourly observation data from surface automatic weather stations at different altitudes in Yanqing mountain area of Beijing Winter Olympics from December 2017 to March 2022， this study investigated the characteristics of local wind field during winter and early spring （Mar， Paralympics period） under complex terrain， focusing on comparing the frequency of wind speeds and directions， as well as the diurnal and seasonal variations.Firstly， all stations were grouped into four categories using the K-Means clustering algorithm， and Groups 1 to 4 represent the low-elevation Yangqing suburb area， the northeastern foothills transition area， the southwestern transition area and the high-elevation mountain top area， respectively.Subsequently， fine-grained characteristic analysis was conducted on each group separately.Results show that： （1） The frequency of strong winds is closely related to the altitude， with higher altitudes generally having a higher frequency of strong winds.In Groups 1~2 （altitude below 1000 m）， the frequency of light winds （≤3.3 m·s^-1） exceeds 80%， while the proportion of strong winds （≥10.7 m·s^-1） is 0%.In Group 3 （above 1000 m）， the frequency of light winds decreases to below 75%， and strong winds occasionally occur for less than 1%.In Group 4 （above 1800 m）， there is a significant shift in the wind speed frequency distribution， with the frequency of strong winds increasing to above 10%， which is much higher during winter compared to early spring.（2） There are significant local variations in the distribution characteristics of wind directions.Group 4 is primarily dominated by large-scale winter monsoonal circulation， resulting in a prevailing northwesterly wind， with rare concurrence of other wind directions.Groups 1~3 are influenced by a combination of large-scale circulation， valley wind circulation and underlying surface conditions， leading to different frequencies for each wind direction.（3） The diurnal variation exhibits contrasting characteristics between high and low elevations.Groups 1~3 show lower wind speeds at night and higher wind speeds during the day， while Group 4 shows a reserved pattern and an obvious small wind “window period” in midday.Groups 1~3 exhibit distinct daily transitions in wind direction， occurring after sunrise and sunset， whereas Group 4 does not show any diurnal change.（4） From a seasonal perspective， there are significant local differences between early spring and winter.Compared to winter， Group 2 exhibits a daytime wind speed increase in early spring， and Group 3 exhibits a nighttime decrease， while Group 4 exhibits a significant decrease in wind speeds throughout the day.Wind directions in early spring are relatively more variable， with an evident increase in northeasterly winds in Group 1， a delay of about 3 hours in the transition of valley wind circulation in Group 2， and an increase in southwesterly winds in Groups 3~4.This study contributes to a deeper comprehension of the fine-scale spatiotemporal patterns of near-surface local wind fields within complex terrains， and can offer crucial background clues for Winter Olympics and small-scale mountainous meteorological monitoring and forecasting.

This study aims to improve the accuracy of simulating soil hydrothermal processes on the Qinghai-Xizang Plateau by introducing a novel soil stratification method combined with an integrated freeze-thaw gravel parameterization scheme.The region's unique topography and complex climate pose challenges for conventional numerical models in achieving precise simulations.The proposed scheme incorporates freeze-thaw parameterization， gravel parameterization， and refined vertical soil discretization， offering a more comprehensive representation of the soil characteristics and terrain complexity specific to the Qinghai-Xizang Plateau.To evaluate the effectiveness of the scheme， the BCC-CSM atmospheric circulation model， provided by the National Earth System Modeling Center， was used for testing.The results demonstrate that integrating freeze-thaw and gravel parameterization significantly improves the representation of soil hydrothermal distributions， especially during the winter and at greater soil depths.By refining the soil stratification to 20 and 30 layers， the simulations of soil temperature and moisture have been further enhanced.The 30-layer stratification yields the most accurate outcomes， followed closely by the 20-layer configuration.This approach notably reduces bias and root mean square error in soil temperature simulations， particularly in the central and western regions of the Qinghai-Xizang Plateau， with better performance in winter compared to summer.While soil moisture simulation accuracy lags behind temperature results， the stratification refinement reduces errors， particularly in shallow soil layers.The enhanced stratification also improves the correlation between simulated values and CRA data， strengthening the alignment between simulation and observation， especially in the central and western parts of the plateau.This research provides new insights into soil hydrothermal processes on the Qinghai-Xizang Plateau and offers critical methodology and technical support for future climate simulations and predictions.Moreover， the proposed integrated scheme holds significant potential for simulating soil hydrothermal processes in other plateau regions and may be applied across a wide range of fields.

The most significant meteorological component is temperature， and weather forecasting relies heavily on how accurately temperatures are predicted.This study uses a linear non-graded regression method to rectify the inaccuracies in temperature forecasts induced by terrain variation in the 2 m temperature hourly forecast product of the mesoscale numerical model （China Meteorological Administration Guangdong， CMA-GD）， and use the one-dimensional Kalman filtering method and the double-weighted moving average method to correct the results.The accuracy of the hourly distribution exhibits a diurnal variation feature， and the model terrain height deviation is linearly negatively connected with the temperature error mean value， according to the results.The daytime correction impact is superior than the nighttime correction effect following the ungraded regression method.recorrecting using the best time frame for mathematical correction techniques （15 days for the Kalman method and 20 days for the mean method）.It is discovered that the mean method's re-correction effect outperforms the Kalman methods， and that the correction effect is more pronounced during the day than at night.Summer and autumn have a better re-correction impact than winter and spring， with some negative correction effects in spring and little difference between the two techniques in the latter.In the former， the mean value method outperforms the Kalman method.There are eight stations with negative correction following the ungraded regression method， but no negative correction stations follow the mathematical correction methods.Therefore the northern region typically experiences a better corrective impact than the southern region.The fraction of correction magnitude for both MAE and ACC is positively correlated with a binomial connection.The terrain deviation correction method has the least slope and restricted correction effect， while the mean value approach has the best correlation and largest slope.An error assessment was conducted in the middle part of Poyang Lake Plain and the south Zhejiang-Fujian hilly region.The peak error value in the former was lower than that in the latter， and the correction amplitude at the peak was smaller.After correction， the MAE decreased by 25.1% and 19.8%， respectively.From November 2022 to January 2023， during frequent cold air intrusions， the MAE in the middle part of the Poyang Lake Plain decreased by 13.5%.With corrected forecast errors oscillating around the zero axis and a noticeable improvement in systematic positive errors， the model significantly overestimates the temperature forecast for high mountain areas.The temperature forecast errors oscillate with the smallest amplitude from August to October and the largest amplitude in spring and winter.Taking the warming process （May 1-6， 2022） and the strong cooling process （November 28-December 3， 2022） as examples， the corrected MAE decreased by 18.2% and 16.0%， respectively， indicating that the method has achieved stable correction effects during transitional weather.This composite method has good stability， strong forecast correction ability， easy to promote.

This study evaluates the simulation capabilities of 16 models with varying resolutions from the High-Resolution Model Intercomparison Project （HighResMIP） in reproducing warm-season （May to September） precipitation over the eastern slope of the Qinghai-Xizang Plateau， using the CN05.1 dataset as observational reference.Through comparative analysis of outputs from multiple models against observational data， this study elucidates model strengths and limitations in capturing spatiotemporal variability， precipitation intensity， and terrain-related mechanisms.Results indicate that high-resolution climate models demonstrate reasonable accuracy in simulating annual and warm-season precipitation spatial patterns， though notable inter-model discrepancies persist.Three models （CMCC-CM2-HR4， CMCC-CM2-VHR4， FGOAL-f3-H） successfully replicate the observed increasing trend in annual precipitation， while others exhibit stable or decreasing trends.Persistent model deficiencies emerge in simulating precipitation frequency and intensity： all models systematically underestimate light precipitation events （<1 mm∙d^-1） while overestimating heavy precipitation frequency （>4 mm∙d^-1）.Medium-to-low-resolution models show a systematic phase lag of approximately 30 days in diurnal precipitation cycles compared to observations.In contrast， the high-resolution models performed better in simulating precipitation frequency than the medium low resolution group.Based on comprehensive evaluation of temporal distribution， frequency characteristics， and model skill scores， the ECWMF model demonstrates superior performance， whereas the FGOAL-f3-H model exhibits significant negative biases.

This study examines observational data of the lower atmosphere during the summer seasons at the Antarctic Zhongshan Station from November 2018 to February 2019 and December 2019.It explores the profile characteristics and diurnal variations of meteorological elements， as well as inversions and jets at the station.The wind analysis reveals that the station experiences predominant winds from the east to northeast directions.Wind speeds increase sharply with altitude， peaking at approximately 1.2 kilometers in the morning and 0.8 kilometers in the afternoon， with lower speeds observed later in the day， possibly due to the development of downslope winds and enhanced turbulent mixing from thermal convection.Low-level jets are mostly concentrated in the speed range of 8~12 m·s^-1 and at altitudes between 800~1600 m， with multiple layers of jets identified.Temperature observations show the presence of a superadiabatic layer near the surface， with potential temperature gradually rising above this layer up to 3000 m， indicating a stable atmospheric structure.Further analysis of inversion layers demonstrates significant diurnal variations， with thicker and closer-to-ground layers in the morning and thinner but more intense layers in the afternoon， influenced by solar radiation and turbulence intensity.Multiple inversion layers corresponding to multiple jets are also observed， highlighting the connection between inversions and wind shear induced by jets.Humidity analysis indicates a decrease in specific humidity with altitude， particularly pronounced below 250 m.Specific humidity levels are lower in the afternoon compared to the morning up to 250 m.The study of humidity stratification reveals that the heights of maximum gradient changes in specific humidity coincide with the heights of inversion layers and low-level jets.

Based on the observation time series data of Guizhou DSG1 precipitation phenomenon instrument from 2018 to 2023， the particle number distribution and scale spectrum characteristics of rain， snow and hail three types precipitation were compared and analyzed， and an integrated determination algorithm for precipitation phenomenon type identification was established based on the particle number， particle spectral width， and particle plurality， and the applicability of the algorithm was evaluated.The specific conclusions are： （1） The diameter spectrum widths of rain， snow， and hail droplets are concentrated in the ranges of 1~8 mm， 1~12 mm， and 5~12 mm， respectively.The velocity spectra are concentrated in the ranges of 3~15 m∙s^-1， 3~5 m∙s^-1， 12~15 m∙s^-1， and the particle plurality velocities are 4.4 m∙s^-1， 1.1 m∙s^-1 and 4.4 m∙s^-1.respectively.The rain and snow precipitation types can be effectively recognized by the particle falling velocities.（2） The percentages of rain particles in the raindrop and hail drop spectrum accounted for 50.1% and 64.3%， and the number of snow particles in the snowdrop spectrum accounted for 70.2%， which exceeded half of the total number of particles.The percentage of hail particles in the hail droplet spectrum is 0.19%， which is significantly higher than the short-term heavy precipitation （0.005%）.（3） Particles with particle diameters greater than 3 mm and particle velocities of less than 5 m∙s^-1 mainly exist in the process of snowfall.Particles with particle diameters greater than 5 mm and particle velocities greater than 10 m∙s^-1 mainly exist in the process of hailstorms and short-term heavy precipitation.Increasing the velocity limit can improve the accuracy of hail particle recognition.（4） By evaluating the integrated determination algorithm for precipitation phenomenon type recognition， the accuracy of single precipitation type recognition reaches more than 95%， and the false alarm rate of hail is only 1.7%， which can effectively reduce the cases of misrecognition as hail in short-term heavy precipitation.

This study aims to investigate the microphysical structure and hydrometeor conversion processes of convective clouds in the Yushu region of the Tibetan Plateau （referred to as the Plateau）.Using the WRF mesoscale numerical forecast model combined with observational data from the Yushu region in Qinghai during the summer of 2019， we analyzed a summer convective precipitation event in the Yushu area.The results show： （1） The 24-hour cumulative precipitation simulated by WRF is similar to the observed precipitation at the Yushu station.The spatial and temporal distribution of simulated precipitation echoes is generally consistent with Ka-band millimeter-wave cloud radar detection results， indicating the reliability of the simulation results.（2） Particles of different phases in precipitation clouds show distinct vertical distribution structures.The maximum centers of solid hydrometeors are all at relatively high altitudes， with cloud ice's maximum center being the highest at around 200 hPa.The maximum center of liquid hydrometeors is at 500 hPa.Water vapor's maximum center is at the lowest height， below 500 hPa， and its maximum value appears earlier than other particles.（3） In cloud microphysical conversion processes， cloud water makes the largest contribution to precipitation.Water vapor forms snow， graupel， and other hydrometeors through deposition.Ice-phase particles transform into graupel and snow particles through processes such as aggregation， Bergeron process， collection， and collision-coalescence.As they descend， ice-phase particles melt and combine with cloud water， accelerating the conversion of cloud water to rainwater.

Based on daily observations from ground-level stations and daily NCEP/NCAR reanalysis dataset， the circulation anomalies of the winter persistent regional haze events for the years 1980 -2021 in Guanzhong region of Shaanxi and their precursor signal are analyzed.The winter persistent regional haze events in Guanzhong region is found to be closely connected with a pronounced atmospheric teleconnection pattern from the North Atlantic to Euraisa.A positive North Atlantic Osciilation （NAO+） phase and a positive East Atlantic/West Russia（EA/WR+） phase are observed as important part of this teleconnection pattern， the NAO+ pattern act as the origin of the atmospheric transmission.Approximately 75% of the NAO positive phase days from 15 to 5 days before the occurrence of persistent regional haze events are greater than 5 days， with a daily NAO index greater than or close to 0 and an average intensity greater than 0.There is a clear EATL/WRUS+ pattern in the Eurasian continent at 500 hPa three days prior to and after the occurrence day of persistent regional haze events in Guanzhong during winter.On the 1^ST to 6^th day， a clear positive geopotential height anomaly extends from Northeast China to the Sea of Japan at 850 hPa， which reflects the positive anomaly pressure gradient from the Korean Peninsula to central China accompanied with stronger southeast wind in Guanzhong.The stronger southeast wind anomaly intensifies the convergence in Guanzhong region and Southern Shaanxi， and transport the aerosol from central and eastern China to Shaanxi Province.

Using the hourly precipitation data of 80 national stations in Heilongjiang Province， the NCEP/ NCAR and the EC-ERA5 reanalysis data， the diurnal variation characteristics of heavy precipitation （hourly precipitation≥5 mm） by Northeast Cold Vortex （NECV） during the warm season （from May to September） from 1981 to 2022 were analyzed.Multiple typical cases at four times a day were selected for synthesis， which was used to eliminate the influence of intensity change caused by the generation and extinction process of a single case system， and analyze the reasons for the diurnal variation characteristics of heavy precipitation.The results showed that： （1） the heavy rainfall in the warm season was concentrated in June to August， and the heavy rainfall occurred mostly in July， and the maximum precipitation was the heaviest in June and August.The high frequency region of heavy precipitation was located in the southeast quadrant of NECV， the lower frequency could be found in the northeast quadrant.（2） The central position and intensity of NECV corresponding to the large-scale heavy precipitation had obvious diurnal variation characteristics： NECV was stronger at night， and its location was north and west.During the day， NECV was weak， and its position was south to east.The large range of heavy rainfall coupled with the high and low air jet stream appeared： the strong rainfall area was located in the upper jet stream core right back or left front side， the lower jet stream front and left front side.The distribution of the high and low air jet showed significant diurnal variation： the strongest in the afternoon and the best dynamic conditions； the upper level jet stream is weakest at night， and the corresponding upper level divergence conditions are weakest.The southerly air vapor transport was the main at night； During the day， the water vapor transport of the southwest air stream was significantly enhanced， and in the afternoon， the water vapor transport of the southwest air stream was mainly.（3） The wide range of heavy precipitation was corresponded to strong cyclonic activities， and there was little difference in the central positions of cyclones among different times.Both the cyclone intensity and the surface dew point temperature had significant diurnal variation characteristics.The high frequency area of heavy precipitation generally appeared in the large pressure gradient area of the cyclone center and its north or east side， corresponding to the larger dew point temperature.（4） The high frequency of heavy precipitation was related to the distribution of local topography.The mesoscale vertical circulation at night played a more prominent role in the magnitude and high frequency distribution of heavy precipitation on the east side of NECV.The cold air at night was more active， the spatial gradient of nighttime precipitation was larger， and the heavy precipitation was more localized and affected by terrain more significantly.The effect of topography on increased precipitation in daytime was not obvious.The amount of water on the leeward slope was generally greater than that on the windward slope.