The Qinghai-Xizang Plateau （QXP）， recognized as a global climate hotspot and sensitive indicator， profoundly influences regional and global climate and water cycles through complex multi-sphere interactions.This study presents comprehensive review of the current status and data resources of the Qinghai-Xizang’s multi-sphere observation network.Synthesizing multi-source observational data， it comprehensively reviews the key characteristics and mechanisms of the climate “warming and wetting” trend and its multi-sphere responses （atmosphere， cryosphere， hydrosphere， ecosystems）.Key findings reveal an accelerated warming rate， increased precipitation with distinct geographic variations， and subsequent chain reactions including thawing permafrost， melting glaciers， expanding lakes， enhanced vegetation growth， and increased extreme events.However， critical gaps persist in the current observation system， notably sparse coverage in the western Qinghai-Xizang， insufficient multi-sphere synergy， and imperfect data-sharing mechanisms.To address these challenges， future priorities should include expanding western Qinghai-Xizang's monitoring networks， promote low-cost automated instrumentation， enhance multi-source data fusion and model assimilation， and establish secure and standardized data-sharing platforms.This work advances our understanding of the Qinghai-Xizang's climate complexity and provides actionable insights for optimizing climate-environment monitoring systems.

The weather and climate of the Qinghai-Xizang Plateau （QXP） not only exert profound influences on Asian regional climate but also regulate broader Northern Hemisphere climate patterns.Various global atmospheric reanalysis datasets developed abroad have been extensively utilized to characterize the QXP's climate features.In 2020， the China Meteorological Administration released its first-generation global atmospheric reanalysis product （CRA）， which demonstrated superior performance in regions with dense conventional observations， yet its skill over the QXP remains unclear.In this study， we performed an independent evaluation of CRA's upper-air temperature， wind fields， and relative humidity using 686 high-quality， non-assimilated radiosonde profiles from five stations collected during June-August 2014 in the 3rd Qinghai-Xizang Plateau Atmospheric Scientific Experiment.A non-independent assessment was also performed using routine observations from 21 sounding stations across the QXP and adjacent areas.Results were compared with those from ERA-Interim， ERA5， and JRA-55 reanalysis datasets.Non-independent validation results demonstrate that in the troposphere and lower stratosphere， correlation coefficients of temperature and wind speed between CRA and operational radiosonde observations exceed 0.9.Relative to operational radiosondes， CRA temperature bias of 400 hPa and 500 hPa approaching 0 ℃.The near-surface zonal wind RMSE of CRA is about 2.5 m·s^-1， decreasing gradually with altitude to 1.5 m·s^-1 at 100 hPa.Relative humidity RMSE remains below 20% across all altitude layers.Independent validation results indicate that errors in air temperature and wind speed over the eastern Qinghai-Xizang Plateau are generally smaller than those over the western plateau， whereas relative humidity errors are larger in the east.From 600 hPa to 30 hPa， the mean root-mean-square errors （RMSE） of CRA relative to radiosonde for temperature， zonal wind， and meridional wind are 1.38 ℃， 3.19 m·s^-1， and 3.22 m·s^-1 in the western Qinghai-Xizang Plateau， respectively； and 1.16 ℃， 2.65 m·s^-1， and 2.90 m·s^-1 in the eastern Qinghai-Xizang Plateau， respectively.Errors in the western QXP were slightly larger than in the east and exhibited pronounced diurnal variability.Maximum temperature and relative humidity errors below 600 hPa occur in the afternoon.At 500 hPa， peak relative humidity errors appear in the evening.CRA objectively reproduces the QXP’s vertical structures of temperature， wind speed， and relative humidity.Compared to other reanalysis products， CRA’s relative humidity estimates were closest to radiosonde observations， and wind field errors were slightly higher than those of other datasets but did not exceed 0.4 m·s^-1 on average.

Under the influence of global change， drought events have occurred frequently in the originally humid Southwest region since the 21st century， which has inhibited the growth of vegetation in the region to variable degrees and threatened the security of ecological barriers.In this study， we used the Standardized Precipitation Evapotranspiration Index （SPEI） to analyze the frequency and characteristics of extreme drought events in Southwest China from 2001 to 2016.We focused on the 2009 -2010 extreme drought event， which had the longest duration and the widest spatial impact.The Community Land Model version 5 （CLM5.0） was employed to numerically simulate vegetation growth during this extreme drought event.The applicability of the CLM5.0 model for assessing vegetation responses to drought in Southwest China was validated by comparing the simulation results with three remote sensing datasets （GLASS， GIMMS， and GLOBMAP）.Our results revealed that between 2001 and 2016， there were three extreme drought events lasting more than six months in Southwest China， with the most prolonged and severe event occurring in 2009 -2010.The CLM5.0 simulation indicated that during 2009 -2010 extreme drought， CLM5.0 effectively captured the correlation between vegetation and drought， including lagged responses， cumulative effects， as well as resistance and resilience.The intensity of vegetation response to drought decreased from southeast to northwest， with 68.66% of regional vegetation exhibiting a lagged response.Moreover， the lagged response （78.02%） and cumulative effect （89.17%） showed large-area positive correlations with drought， which were consistent with observations from multi-source remote sensing.In terms of simulating the resistance and resilience of different vegetation types to drought， CLM5.0 performed reasonably.Forests exhibited stronger drought resistance compared to shrubs and grasslands， and forests displayed an inverse trend in resistance and resilience.The validation of CLM5.0 model simulations with multi-source remote sensing validation in this study offers a complementary perspective for understanding the multifaceted responses of vegetation to drought in Southwest China， contributing to a more comprehensive assessment and prediction of the impacts of drought on vegetation activities in the region.

Snow cover is an important component of the cryosphere.In recent years， climate warming has led to a reduction in snow cover area.This change may cause uneven distribution of water resources and a decline in biodiversity， thereby affecting local life， economic development and ecological environment.Qinghai Lake is the largest inland lake in China.In recent years， its water level has changed rapidly.The runoff into the lake is affected by the snow cover and its changes in the basin.However， the characteristics， changes and causes of the influence of the snow cover in the Qinghai Lake Basin are still unclear.Based on the temperature and precipitation data from the Moderate-resolution Imaging Spectroradiometer （MODIS） daily cloudless 500 m snow area product Dataset and the China Meteorological Forcing Dataset （CMFD）， This paper studies the spatial and temporal variation characteristics and influencing causes of snow cover in the Qinghai Lake Basin.The results show that： （1） There is a good correspondence between the distribution of the average annual snow cover frequency from 2000 to 2020 and the altitude.As the altitude decreases， the snow cover frequency also decreases accordingly.It is simultaneously affected by the average annual temperature and the annual precipitation.Among them， the areas significantly affected by the average annual temperature in a partial correlation are mainly distributed in the northern and eastern parts of Qinghai Lake， and the areas significantly affected by the annual precipitation in a partial correlation are mainly distributed in the middle and upper reaches of the Buha River in the middle of the Qinghai Lake Basin.（2） From 2001 to 2017， precipitation increased in the Qinghai Lake Basin and the Qilian Mountains region.However， due to the increase in the average annual temperature and the decrease in the annual snowfall in the two regions， the snow cover area decreased.（3） The intra-annual variations of snow cover area in the Qinghai Lake Basin and the Qilian Mountains region are relatively similar， both showing a double-peak fluctuation feature.However， the reduction in snow cover area from April to July and the increase from August to January of the following year in the Qinghai Lake Basin are both greater than those in the Qilian Mountains region.From January to March， the snow cover in the Qilian Mountains region decreased， while that in the Qinghai Lake Basin increased， which corresponds well to the lake effect after the melting of Qinghai Lake.

A comprehensive analysis was conducted to examine the processes and impacts of water level variations in Qinghai Lake under changing climatic conditions.The study utilized monthly mean water level data from 1959 to 2017， sourced from the Qinghai Lake Basin， in conjunction with meteorological and climate variables derived from the ERA5 reanalysis dataset developed by the European Centre for Medium-Range Weather Forecasts （ECMWF）.Several large-scale atmospheric circulation indices were also incorporated to investigate their influence on lake dynamics.This integrated dataset enabled a systematic assessment of the dominant climatic drivers and facilitated the development of predictive models to simulate future water level changes.To identify the most relevant influencing factors， the Random Forest （RF） algorithm was employed to perform feature selection and importance ranking.This process allowed for an evaluation of the relationship between feature relevance and model performance.Subsequently， a comparative analysis was undertaken using five machine learning models： RF， Support Vector Machine （SVM）， Multi-layer Perceptron （MLP）， Long Short-Term Memory （LSTM） networks， and Multiple Linear Regression （MLR）.The models were trained and validated to simulate monthly water level fluctuations and to assess the influence of model complexity and temporal learning ability on predictive accuracy.The analysis revealed that key drivers of Qinghai Lake water levels include the North Atlantic Oscillation （NAO）， Atlantic Multidecadal Oscillation （AMO）， Niño 3.4 index， relative humidity at 400 hPa， 450 hPa， and 100 hPa （RH400， RH450， RH100）， precipitation， temperature at 1000 hPa （T1000）， vertical wind velocity at 1000 hPa （W1000）， and longwave radiation （LW）.Among the models tested， the LSTM network exhibited superior performance due to its ability to capture complex nonlinear and sequential dependencies in the data.When the ten most significant features were selected， the LSTM model achieved a Pearson correlation coefficient （R） of 0.95， Nash-Sutcliffe Efficiency （NSE） of 0.96， Normalized Root Mean Square Error （NRMSE） of 0.14， and Kling-Gupta Efficiency （KGE） of 0.87.The MLP model demonstrated the second-best performance， while RF and SVM yielded comparable but slightly lower results.MLR performed the worst， reflecting its limitations in modeling nonlinear and temporal relationships.Projections based on the LSTM model indicate that the water level of Qinghai Lake is likely to rise by approximately 2.55 m between 2017 and 2030.This anticipated increase reflects the continuing influence of climate change and underscores the importance of adaptive water resource management strategies in plateau lake regions.The findings offer a reliable methodological framework for modeling and forecasting hydrological changes in alpine lake systems under future climate scenarios.

As China’s “Central Water Tower” and vital ecological barrier， the Qinling Mountains’ temperature variability plays an important role in regional water conservation， ecosystem stability， and regional climate regulation.To evaluate the performance of statistically downscaled and bias-corrected Global Climate Models （GCMs） dataset （NEX-GDDP-CMIP6） in simulating observed temperature changes and further to project the future temperature variability over the Qinling Mountains， this study analyzes 8 NEX-GDDP-CMIP6 models against the CN05.1 observational dataset.The assessment focuses on the models’ ability to replicate observed annual mean temperature patterns， spatial trends， and temporal variability from 1961 to 2014.Furthermore， future temperature changes under the four Shared Socioeconomic Pathway （SSP） scenarios are projected for the period 2015 -2100.The results demonstrate that 8 models effectively capture the observed spatial pattern， warming trends distribution and interannual variability， with corresponding correlation coefficients of 0.90~0.92， 0.51~0.77， and 0.46~0.57 for 1961 -2014， respectively.The multi-model ensemble mean （MME） outperforms individual models， with correlation coefficients of 0.92， 0.65 and 0.74 for the three metrics.The MME indicates a persistent warming trend over the Qinling Mountains， with the stronger warming under the higher SSP scenarios.The warming trends are projected increase at 0.10 ℃·（10a）^-1 （SSP1-2.6）， 0.26 ℃·（10a）^-1 （SSP2-4.5）， 0.42 ℃·（10a）^-1 （SSP3-7.0）， and 0.57 ℃·（10a）^-1 （SSP5-8.5） for 2015 -2100.Notably， the warming exhibit altitudinal， zonal， and meridional dependencies， intensifying with higher elevation， latitude， and longitude.Relative to the reference period （1995 -2014）， the annual mean temperature is projected to increase by 0.65~0.97 ℃ in the near-term （2021 -2040）， 1.37~2.0 ℃ in the mid-term （2041 -2060）， and 1.39~4.46 ℃ by the end-century （2081 -2100） under the four SSP scenarios.The temperature changes are temporally consistent across the North and South Slopes over the Qinling Mountains and following with the entire regional average.However， the North slope warms more rapidly than the South slope， particularly under high-emission scenarios （e.g.， SSP5-8.5）， where North slope warming accelerates markedly.These findings provide critical insights for climate adaptation and ecological management in the Qinling Mountains.

The Ya'an area has a typical "windward slope" and "trumpet" topography， the observation and study of cloud-precipitation in the Ya'an area has always been one of the hot issues of mountain meteorology in China.In this paper， using the observation data of Ka-band millimeter-wave cloud radar， K-band micro-rain radar and DSG4 laser raindrop spectrometer during the autumn rainy season in Ya'an area in 2023， the two types of cloud-precipitation in Ya'an during the autumn rainy period are studied and compared.The results show that （1） the frequency of warm cloud-precipitation （WCP） is higher than that of cold cloud-precipitation （CCP） during the autumn rainy period in Ya'an， but the intensity of precipitation is generally weaker and the cumulative rainfall is less.（2） In terms of vertical structure and macroscopic features， there are obvious differences between WCP and CCP： the radar echo reflectivity factor Z high-frequency region of CCP has a wider distribution and stronger echo， and the magnitude of the slope of Z high-frequency region at different heights reflects the difference between the ice-phase particles' growth mechanism and rate and that of WCP's liquid cloud raindrops' touch-and-go growth rate； CCP's linear depolarization ratio， LDR， increases abruptly near the zero-degree layer due to the melting of ice-phase particles.The mean Doppler velocity V_m has a smaller value in the high-frequency region and the velocity spectral width W has a larger value in the high-frequency region， suggesting a larger mean droplet scale and a wider concentration distribution.In terms of macroscopic parameters， the cloud base and cloud top of the CCP are significantly higher， the cloud layer is thicker， and the distribution of values is more dispersed.（3） In terms of microscopic characteristics， the raindrop spectra of the two types of cloud-precipitation are different with altitude： the concentration of small raindrops in the CCP increases and then decreases with decreasing altitude due to the melting of ice-phase particles and raindrops merging， whereas that in the WCP fluctuates due to the effects of evaporation， merging， water vapor transport， and updraft lifting altitude.The medium- and large-sized raindrop concentrations of both types of cloud-precipitation increase with decreasing altitude on the whole.In terms of the differences in raindrop concentrations at different heights， above 455 m， the concentration of raindrops of all grain sizes in WCP is almost lower than that in CCP； below 455 m， the concentration of small and large raindrops in WCP is higher than that in CCP， and the concentration of medium raindrops is lower.（4） Comparison of the raindrop spectra under different precipitation intensities showed that when the precipitation is weak， the WCP had lower concentrations of raindrops of all particle sizes than the CCP， but when the precipitation reached a certain intensity （rainfall intensity R \ge5 mm·h^-1）， the WCP produced more small raindrops and a small number of larger raindrops.The few extreme values of Z， LDR， V_m and W of WCP are larger than those of CCP at altitudes below 3 km due to the synergistic effect of low-altitude rapids and topographic uplift in the region， which makes the small WCP raindrops repeatedly touch and form medium-to-large raindrops in the re-uplift airflow.

To solve the difficult problem of forecasting the location and time of heavy rain under the background of Central Asian low vortex.using conventional observation data， satellite， radar and ERA5 0.25°×0.25° reanalysis data， to analyze the mesoscale characteristics and atmospheric instability， and reveal the convection triggering and maintenance mechanisms from June 30 to July 3， 2024， and find that there are two rainstorm centers in this weather event， whose triggering mechanisms are significantly different.The rainstorm in Bozhou is a short-term heavy rainfall caused by strong convection.It is affected by the eastward movement of short-wave disturbances split from the bottom of the Central Asian trough， the northward movement of warm moist air carried by southwest winds in the middle layer， and the eastward infiltration of cold air guided by east winds in the lower layer.Warm moist air climbs along the cold cushion， and there is a convectively unstable layer in the upper air of Bozhou.Surface mesoscale convergence lines are the main triggering factors， and middle γ scale convective systems continue to generate and merge to enhance middle β scale convective systems to produce heavy rainfall， and the convection has a high-quality heart structure.The rainstorm of Tianshan Mountain area and its northern slope are characterized by mixed rainfall， with long-lasting precipitation.The Central Asian trough weakens and moves eastward， while the southwest airflow at the middle level guides warm and humid air northward.At the same time， the northwest jet stream at the lower level brings cold air southward， leading to the convergence of cold and warm air.Before and in the early stages of precipitation， the atmosphere is affected by convective instability， which accumulates unstable energy for the generation of precipitation.In the later stages， the atmosphere shifts to conditional symmetric instability， which maintains and enhances precipitation.The frontogenesis of cold fronts in the middle and lower layers of the troposphere is the main triggering factor of heavy rainfall.The upward movement of the secondary circulation of the front and the occurrence and development of convection are closely related， and the front disturbance and cumulus convection have a positive feedback effect.Multiple medium β scale convection systems move eastward one after another to produce heavy rainfall， and the convection has a low quality center and is warm cloud precipitation.This study reveals the triggering and maintenance mechanisms of short-term severe convective rainstorms and mixed rainstorms in northern Xinjiang under the background of the Central Asian low vortex by refining their types， providing key references for the refined forecasting of rainstorms in northern Xinjiang.

Located in the temperate monsoon climate zone of northeast China， dominated by coniferous and broad-leaved mixed forest the Lesser Khingan Mountains has the largest and most complete primary forest of Korean pine in Asia， which plays an important role in regulating the regional climate.In order to explore the sensible heat flux （H） and latent heat flux （LE） characteristics of the coniferous and broad-leaved mixed forest in the Lesser Khingan Mountains and the regulation effect of environmental， biological factors on H and LE， H and LE at 40 m and 50 m altitude of Wuying National Climate Observatory from 2007 to 2023 were studied by using the eddy correlation method， and a structural equation model was constructed to analyze the influencing factors in detail.The results show that： The interannual variation of H and LE in the coniferous and broad-leaved mixed forest in the Lesser Khingan Mountains showed a decreasing trend， but the variation trend was not completely consistent.The annual average of H and LE at 40 m height was 19.84±1.83 W·m^-2 and 29.39±2.93 W·m^-2， respectively.The annual average of H and LE at 50 m altitude was 22.71±1.29 W·m^-2 and 31.76±1.07 W·m^-2， respectively.The peak of H appeared in April， the secondary peak in October， and the peak of LE appeared in July， in which LE was greater than H from May to September， indicating that the energy conversion was dominated by latent heat exchange in May to September， and by sensible heat exchange in other months.The 30-min scale energy closure rate was 49%， the monthly energy closure rate ranged from 32 to 61%， in which the growing season and non-growing season were 53% and 38%， respectively， and the daily scale energy closure rate was 52%.The structural equation model showed that the heat transfer process of the coniferous and broad-leaved mixed forest in the Lesser Khingan Mountains was mainly limited by energy.Net radiation had a positive effect on H and LE， while air temperature， vapor pressure deficit， soil volumetric water content and leaf area index had opposite effects on H and LE.Climate change has a complex regulatory mechanism on H and LE.The complex interannual variation of H and LE can be partially explained by constructing structural equation models for different years.

Hongjiannao Lake is the largest desert freshwater lake in China.In recent decades， the area of the lake has sharply decreased.The evaporation of the lake surface is the main factor consuming its water volume.Therefore， this paper aims to reveal the characteristics of evaporation changes and the mechanism of the driving factors.Currently， most studies on Hongjiannao Lake directly use or convert the evaporation data observed at meteorological stations， which have many missing and discontinuous data， and do not qualitatively and quantitatively analyze the meteorological factors influencing the evaporation changes of Hongjiannao Lake.To address these issues， this paper uses the data converted from meteorological stations， calculates the evaporation using the FAO （P-M） formula， and simulates the evaporation using the CLM-LISSS model to obtain the evaporation data of Hongjiannao Lake.Through comparison with the converted evaporation data from meteorological stations， it is found that the evaporation values and correlations simulated by the CLM-LISSS model are closer to the actual situation than the results calculated by the FAO （P-M） formula.The evaporation simulation results based on the preferred model showed that the average annual value of simulated evaporation of Hongjiannao lake from 1980 to 2018 was 1004.56 mm， and the M-K mutation test did not find the mutation year， and the overall trend was significantly upward （3.01 mm·a^-1）.The meteorological factors that have significant positive correlation with evaporation are air temperature， wind speed and downward long-wave radiation， and their correlation with evaporation and their own change trend pass the significance test of 95%.The sensitivity coefficient of evaporation to meteorological factors and the contribution of each meteorological factor to evaporation change were quantitatively analyzed by the formula calculation method and the perturbation analysis method of climate state respectively.The meteorological factors with greater contribution obtained by the two methods were significantly consistent with the correlation， and they were all air temperature， wind speed and downward long-wave radiation.However， the contribution ranking obtained by these two methods is slightly different and the contribution values of each factor are significantly different.This is mainly due to the fact that the change of evaporation is only caused by the change of a single factor， which reduces the influence of other driving factors， and effectively reduces the error between the change value of evaporation trend and the contribution sum of meteorological factors， from 128.40 mm （109.40%） to 56.83 mm （48.42%）.The perturbation analysis of climate state is superior to the formula calculation method in both mechanism and error.The results show that the contribution of meteorological factors to evaporation changes from large to small are downward long-wave radiation （71.47%）， temperature （59.83%）， wind speed （41.00%）， air pressure （1.54%）， downward short-wave radiation （-3.00%） and specific humidity （-22.43%）.

In order to study the turbulent coherent structure characteristics under the complex underlying surface of forests， three-layer turbulence data set up at 1.33 times， 2.53 times， and 3.86 times canopy degrees in the artificial forest area of Mount Si E in Leshan City， Sichuan Province （canopy height of 15 m） in May 2021 were used to observe the turbulent characteristics of the rough sublayer， rough sublayer， and constant flux layer boundaries， as well as the constant flux layer.Based on the observation data mentioned above， three coherent structural modes of different scales at different heights were extracted using the Complementary Empirical Mode Decomposition （CEEMD） algorithm.The flux contribution of coherent structures was quantified， and the main feature quantities of coherent structures were analyzed and studied.The results indicate that the CEEMD algorithm can extract coherent structural modes of different scales， and the modes exhibit typical coherence.In terms of the contribution of coherent structures to flux， there are differences at different heights.The flux contributions to sensible heat， latent heat， and momentum are 21%， 17%， and 11% at 20 m， 13%， 11%， and 7% at 38 m， and 12%， 10%， and 6% at 56 m， respectively.This indicates that at three heights， the transport efficiency of scalar fluxes such as sensible and latent heat flux by coherent structures is greater than that of momentum flux， and the flux contribution of coherent structures is the largest on the 20 m rough sublayer， which decreases with increasing observation height.The contribution of coherent structures to flux varies with different flux types and layer types under different stable layer structures.The maximum flux contribution of coherent structures to the transport of sensible heat flux at three heights occurs under neutral conditions， followed by stable and unstable conditions.For latent heat flux， the maximum flux contribution occurs under stable conditions at all three heights， followed by neutral and unstable conditions.The results of the flux contribution to the cleaning and spraying processes indicate that the spraying process dominates at higher heights （38 m and 56 m）， while the cleaning process plays a dominant role as the observation height decreases to approach the rough sub layer.Finally， the relationship between the slope strength， stability parameters， and friction velocity of coherent structural structures was studied.For both horizontal and vertical wind， the slope strength of both increases with the increase of friction velocity， but there are differences in the performance of stability parameters.The slope strength of horizontal wind is maximum under neutral conditions and decreases with the increase of stability and instability.The slope strength of the vertical wind is maximum under unstable conditions and decreases with the increase of stability as the coherent vertical motion is suppressed.The research conclusion of this article provides a statistical understanding of the flux contribution and coherent motion characteristics of coherent structures under complex underlying surfaces， and supports the subsequent study of turbulent structures.

Using aerosol scattering and absorption coefficient observations in Chengdu city from March 2021 to February 2022， combined with EAC-4， MERRA-2 reanalysis data， and the libRadtran radiative transfer model， the aerosol optical parameters in different seasons and the radiative effects of total aerosols and absorbing carbonaceous aerosols were investigated.The results showed significant seasonal variations in aerosol optical parameters： the single scattering albedo （SSA） at 550 nm reached its maximum value in winter （0.91±0.02）， while it was the lowest in spring （0.84±0.04）.The asymmetry factor （ASY） at 550 nm followed the order of summer > winter > spring > autumn.The mean total aerosol optical thickness （AOD） was higher in spring and winter （about 0.77）， but lower in summer and autumn （about 0.50~0.53）.Further analysis of the optical depth of light-absorbing carbonaceous aerosols revealed that black carbon （BC） contributed the most in winter， whereas the brown carbon （BrC） reached its peak in spring.The radiative transfer model calculation indicated that the annual average shortwave radiative forcing induced by total aerosols at the surface （BOA）， top of the atmosphere （TOA）， and within the atmosphere （ATM） was -107.21±42.49 W·m^-2， -32.10±20.40 W·m^-2， and 75.10±40.16 W·m^-2， respectively.These indicated there were an overall cooling effect at the surface but a warming effect within the atmosphere for aerosol.Notably， BC exhibited distinct seasonal variations in radiative forcing： TOA forcing of BC was the highest in winter （7.18±1.59 W·m^-2） and lowest in autumn （4.48±1.49 W·m^-2）.Additionally， BC contributed 31.3% of the annual mean atmospheric radiative forcing， highlighting its significant warming effect.In contrast， the radiative forcing of BrC and its proportion relative to total aerosols were higher in spring and lower in winter.

In order to reduce the rate of flight diversion and return caused by low visibility， this study has established a short-term and nowcasting model of low visibility using ground observation data and the upper-air and surface forecast data of the ECMWF at Jingdezhen Airport based on machine learning algorithms.Comparing the evaluation indicators， the results find that the XGBoost and LightGBM machine learning algorithms outperform the SVM machine learning algorithm in nowcasting of the airport low visibility.A detailed comparison of the evaluation metrics was conducted both before and after feature screening in the same machine learning algorithms.The study highlights that feature screening significantly boosts the effectiveness of both models.Furthermore， the SHAP （SHapley Additive exPlanations） method elucidates the contribution of each feature to the LightGBM model's output.The main conclusions are as follows： （1） The machine learning models established by LightGBM and XGBoost perform well in airport low visibility forecasting， with the AUC reaching up to 0.98， and the F1_score for the prediction of current low visibility and the low visibility in the next one hour can reach up to 0.92.（2）Data cleaning and feature screening is conducive to improving the prediction accuracy of the XGBoost algorithm for low visibility in the next hour， according to the principle that "feature engineering in machine learning requires features to be mutually independent".Moreover the LightGBM model with feature screening has a lower false negative rate than the LightGBM model without feature selection when forecasting the current and future one-hour low visibility.For the forecast of the current low visibility， the LightGBM_24_0h model is the best.For the forecast of low visibility in the next one hour， the XGBoost_24_1h model is the best.And feature selection has a greater improvement on the performance of the XGBoost algorithm.（3） The splitting times and SHAP values are used respectively to analyze the feature importance of the LightGBM algorithm model.It shows that under different feature importance criteria， nine features， namely the measured relative humidity， air temperature， wind， sea level pressure at the airport， and the relative humidity at 1000 hPa， vertical velocity and divergence at 925 hPa， and divergence at 850 hPa predicted by ECMWF， are more important for the prediction of low visibility at the airport.And divergence， as an input feature of the machine learning model， can greatly improve the performance of the machine learning model.（4） When explaining feature importance based on SHAP values， the cumulative proportion of the top ten feature importance accounts for 80%.This indicates that in the nowcasting of low visibility at Jingdezhen Airport where fog is the main factor， the LightGBM model can output prediction results according to key forecast factors，.And when forecasting whether the low visibility in the next one hour will continue， more attention should be paid to the changes in 850 hPa divergence， 1000 hPa relative humidity， airport sea level pressure and wind direction.

Accurate forecasting of daily maximum （T _max） and minimum （T _min） temperatures is essential for meteorological operational， as enhanced precision safeguards socioeconomic stability in agriculture， transportation and public health.To address pronounced systematic biases in numerical weather prediction models over complex terrain-particularly regions with heterogeneous underlying surfaces-this study develops an advanced deep learning framework integrating geographic feature clustering.The study focuses on China’s Hunan Province， characterized by a distinctive concave-shaped， three-tiered topography encompassing mountains， hills， basins， and plains.A baseline convolutional neural network （CNN） was constructed using ECMWF forecast fields， high-resolution CLDAS （China Land Data Assimilation System） reanalysis data， and multi-dimensional geographic variables （elevation， slope， aspect， terrain roughness index）.Three comparative experiments rigorously evaluated terrain-processing efficacy： Method 1 （K-means clustered geographic variables delineating topographic regimes）， Method 2 （Conventionally standardized non-clustered geographic variables）， and Method 3 （Geographic variable exclusion as terrain-agnostic control）.Validation confirmed Method 1's superiority， achieving 24-hour mean absolute error （MAE） reductions of 4.7% for T _max and 9.4% for T _min relative to Method 3， while improving forecast skill by 2.5% （T _max） and 1.4% （T _min） compared to Method 2.These statistically significant gains validate the benefits of explicit terrain feature clustering.Building on this approach， the CNN-Terrain Correction （CNN-TC） model was developed for 72-hour T_max/T _min predictions.CNN-TC framework delivers transformative improvements.Versus ECMWF outputs， T _max MAE decreased by 23.5%~37.3% and T _min MAE by 20.8%~26.9% across 24~72 hour lead times.Compared to operational SCMOC products， errors reduced 18.7%~27.6% （T _max） and 26.8%~32.3% （T _min）.Critically， the model compresses spatial error dispersion， narrowing 24-hour MAE ranges from 1.2~5.8 ℃/0.8~5.9 ℃ （ECMWF） to a stable 0.9~1.7 ℃/0.8~1.7 ℃ for T _max/T _min-demonstrating breakthrough operational stability.Monthly verification confirmed persistent superiority， with T _max MAE reductions of 5.6%~59.1% and T _min improvements of 6.3% to 47.8% relative to ECMWF.During the November 2022 cold-air outbreak， the model captured intricate spatiotemporal cooling patterns， outperforming ECMWF and SCMOC with over 30% error reduction， underscoring its capability in extreme weather.This study verifies that deep learning combined with terrain clustering effectively mitigates systematic biases over complex terrain.The CNN-TC framework establishes a robust solution for refined meteorological services in mountainous regions.Cross-regional implementation requires localized hyperparameter optimization and cluster retraining to address spatial heterogeneity in climate regimes and surface properties.

For the general problem of rainstorm under complex terrain， the use of isobaric coordinates can not accurately analyze and predict the intensity and movement of mountain rainstorm due to the intersection of the lower troposphere isobaric surface and the ground.In order to improve the rainstorm prediction level under complex terrain conditions， it is necessary to develop a set of rainstorm prediction methods under the terrain following coordinate system， which can not only directly reflect the terrain impact in physical quantities， but also avoid the differences in dynamic analysis of different regions caused by terrain in local rectangular coordinates or isobaric coordinates.Taking the second-order moist potential vorticity in the ensemble dynamic factor heavy rainfall forecasting method as an example， this paper studies and attempts dynamic heavy rainfall forecasting under the terrain-following coordinate system.It derives the mathematical form of the second-order moist potential vorticity in the terrain following coordinate system， and then explores the physical information contained in the second-order moist potential vorticity in the terrain following coordinate system and the possibility of improving the precipitation forecast through case analysis and comparison with the second-order moist potential vorticity in the isobaric coordinate system.Furthermore， the analog method is introduced to conduct forecast verification on quantitative precipitation forecasting using the second-order moist potential vorticity in the terrain-following coordinate system.The verification results show that in the ETS score of summer precipitation forecast in 2016 and 2017， The score of precipitation similarity correction method based on topographic second-order moist potential vorticity is higher than that of original forecast precipitation of GFS model.

This paper analyzes the spatial and temporal characteristics of winter precipitation variability in China and its relationship with SST anomalies in the Indian and Pacific Oceans using precipitation data observed at meteorological stations from 1961 to 2021， ERA5 reanalysis data and SST data.The results show that from 1961 to 2021， winter precipitation in the southeastern coastal areas of China， where the interannual variability of winter precipitation is the largest， has an increasing trend ［6.7 mm·（10a）^-1］.The interannual variability of winter precipitation in the lower reaches of the Yangtze River is second， but the increase in precipitation is the most significant ［9.6 mm·（10a）^-1］.The increase rates of winter precipitation in these two regions are higher than the national average ［1.5 mm·（10a）^-1］.Winter precipitation in China is closely related to SST anomalies in the Indo-Pacific.Specifically， SST anomalies in the Equatorial Central and Eastern Pacific （ECEP） and the Indian Ocean （IO） both have a significant lead positive correlation with winter precipitation in China， but their spatial distribution is different.The ECEP SST anomalies in summer and autumn are significantly positively correlated with winter precipitation in the southeastern coastal areas of China， and the IO SST anomalies in winter enhance the positive correlation between precipitation in the southeastern coastal areas of China and the ECEP SST anomalies.The positive correlation between the IO SST anomalies and winter precipitation in the southeastern coastal areas only appears in autumn， while the positive correlation with winter precipitation in northern China already appears in spring.The impact of the winter IO SST anomalies on winter precipitation in northern China is independent of the ECEP SST.The different evolutions of the ECEP SST anomalies in summer and autumn， that is， the different evolutions of El Niño and La Niña， have different effects on China's winter precipitation.If the summer El Niño continues to autumn， China's winter precipitation is higher in 78% of the years； if La Niña appears and develops in autumn， China's winter precipitation is lower in 83% of the years.The La Niña events that occur in summer and continue to autumn and the El Niño events that occur in autumn and continue to winter have weak indicative significance for China's winter precipitation.In the persistent El Niño （developing La Niña）， the 850 hPa circulation of ECEP is characterized by abnormal easterly （westerly） winds， which stimulates abnormal cyclonic （anticyclonic） circulation in the South China Sea.The southeastern coast of China is affected by abnormal southwesterly （northeasterly） winds， and the water vapor from the South China Sea is abnormally more （less）.The water vapor is characterized by convergence （divergence） anomalies， and the precipitation in the region is more （less）.The IO SST anomaly stimulates the abnormal circulation in the northwest Pacific， affects the winter circulation system north of China and the water vapor transported from east to west， and thus regulates China's winter precipitation.Therefore， the prediction of China's winter precipitation needs to comprehensively consider the ECEP SST anomaly and the IO SST anomaly， especially the evolution of the SST in the equatorial central and eastern Pacific in summer and autumn.

The large-scale development and utilization of wind and solar power are critical for achieving power system decarbonization and "Dual Carbon" （carbon peak and carbon neutrality） goals. While northern China boasts abundant wind-solar resources， the Yarlung Zangbo River Basin （YZRB） presents a uniquely advantageous setting for large-scale renewable energy base construction. Its exceptionally rich hydropower potential provides a vital foundation of flexible generation capacity and a vast， natural storage medium through reservoir regulation， significantly enhancing the economic viability of integrating variable wind and solar power compared to regions reliant solely on complementary generation or artificial storage. However， the inherent variability， intermittency， and uncertainty of wind and solar power pose substantial challenges to the structural transformation of power systems， impacting grid stability and dispatch optimization. To accurately characterize the resource potential within this strategic basin， this study conducts a comprehensive spatiotemporal analysis of wind and solar resources across the YZRB. This study utilizes the high-resolution near-surface meteorological forcing dataset for the Third Pole region （TPMFD）， offering long-term （1979 -2023）， high spatiotemporal resolution surface meteorological data essential for detailed assessment. Analysis reveals a distinct "higher in the west， lower in the east" spatial pattern for both wind speed and solar radiation intensity across the basin. Crucially， temporal trend analysis identifies a complex， two-phase evolution over the 45-year period （1979 -2023）， characterized by significant trend reversals. Wind speed exhibited a pronounced declining trend from 1979 to 2006， followed by a robust increasing trend from 2007 to 2023. Conversely， solar radiation showed a significant increasing trend between 1979 and 2009， which reversed into a clear decreasing trend from 2010 to 2023. This non-stationarity in resource availability has profound implications for long-term energy project planning and performance modeling. Spatial analysis further identifies Lhasa City， Shannan City， and Shigatse City as possessing superior wind and solar resource endowments relative to other basin areas. These regions are thus highlighted as optimal core zones for deploying future integrated hydro-wind-solar （HWS） complementary system， where hydropower's inherent flexibility can effectively balance wind-solar variability， maximizing system efficiency， reliability， and economic returns. This study underscores the significant potential of the YZRB for large-scale， low-carbon energy systems based on HWS integration. Realizing this potential necessitates focused research addressing key uncertainties. Future work must prioritize： （1） Advanced projection and analysis of future wind-solar resource trends under evolving climate scenarios to inform resilient infrastructure planning； （2） Detailed assessment of the frequency， intensity， and duration of compound wind-solar extreme events （e.g.， concurrent low-wind and low-sunlight periods）， which represent major risks to system security； and （3） Development and application of sophisticated integrated optimization models for the holistic configuration， scheduling， and dispatch of HWS resources across the basin and their integration with the wider grid. Addressing these research imperatives is essential for unlocking the YZRB's full potential to contribute significantly to China's energy transition and global decarbonization objectives.

Accurate retrieval of raindrop size distributions （DSDs） based on dual-polarization radar can provide substantial data for the study of precipitation microphysical properties on a large scale.In order to further improve DSDs retrieval accuracy， this study proposes a new double-moment normalization method based on the sixth and seventh moments （M6M7 method）， comparing it with the third and sixth moments method （M3M6 method） and the constrained Gamma model DSD retrieval method （C-G method） from three perspectives： overall results， different rainfall intensities， and different rainfall particle sizes.Utilizing data from six rainfall events observed by dual-polarization radar and surrounding disdrometers at Heyuan station between May and June 2022， the retrieval results of each algorithm were analyzed.The results demonstrate that during light rain （0 mm·h^-1<R≤5 mm·h^-1）， the M6M7 method exhibits the smallest parameter biases among the three methods.As rainfall intensity increases， biases for most parameters （except for the increase of liquid water content （LWC） and rainfall rate （R）） remain relatively stable， with M6M7 consistently showing the lowest biases across different particle sizes and minimal fluctuation with the increase of particle size.Compared with M6M7 method， the M3M6 method incorporates specific differential phase shift on propagation （K_dp ） for retrieval.Although K_dp is noise-sensitive， it has good quality in heavy rain （R>30 mm·h^-1）， resulting in smaller estimation biases for intense rainfall events and a decreasing trend in bias with larger particle sizes （excluding LWC and R）.For moderate rain （5 mm·h^-1<R≤30 mm·h^-1）， the C-G method shows small median deviations yet significant fluctuations in certain parameters.With the increase of rainfall intensity and particle size， its biases shows a trend of first decreasing and then increase， accompanied by pronounced relative bias instability.Comprehensive evaluation results demonstrate that the M6M7 method consistently maintains median deviations approaching 0 across all DSD parameters， while exhibiting significantly tighter error fluctuation ranges.In marked contrast， both the M3M6 method and C-G method display substantially wider bias variability， with their error distributions spanning broader numerical ranges and demonstrating less stable performance characteristics.The newly proposed M6M7 method technique demonstrates advantages over traditional approaches， exhibiting enhanced comprehensive retrieval capabilities with regard to both accuracy and stability， particularly excelling in light-to-moderate rainfall with consistent accuracy.The M3M6 method proves more effective for heavy rain and storms， while the C-G method demonstrates unstable retrieval characteristics.The final section demonstrates the retrieval performance of the algorithm integrating both M6M7 and M3M6 methods， verifying its capability to further improve raindrop size distribution retrieval accuracy.

Attenuation effects in X-band weather radar significantly constrain its detection accuracy.Traditional attenuation correction methods typically rely on empirical formulas with limited parameters and poor generalization capability， leading to significant uncertainty in the correction results.In recent years， deep learning algorithm with powerful nonlinear fitting capacity has emerged as a promising technical approach to overcome the limitations of conventional methods.Based on the Transformer's underlying framework， this study develops an X-band radar attenuation correction architecture named as XCORnet.Utilizing the observational data from the upgraded polarimetric S-band Next Generation Weather Radar （CINRAD/SAD） at Beijing Daxing in 2023 -2024 flood season as truth， the horizontal reflectivity （Z _H） and differential reflectivity （Z _DR） measurements in corresponding range bins from the Beijing Fangshan X-band dual-polarization radar （XPOL） are spatially and temporally matched.These matched XPOL observations of Z _H， Z _DR along with specific differential phase （K _DP） are incorporated to construct the AI training dataset， in which 2642624 samples for Z _H and 2605583 samples for Z _DR， respectively.The dataset is partitioned with 80% for training and 20% for testing.Within the XCORnet framework， the models with K _DP as the primary feature input for Z _H and Z _DR attenuation correction are trained， respectively.And then， two XCORnet-based models are evaluated by means of the test set.The results demonstrate that the AI-based model outperforms traditional methods significantly.For Z _H correction， the ratio bias （BIAS） of XPOL to SAD are from original 0.875 to model-based correction 0.972， surpassing the empirical formula-based 0.901.The root mean square error （RMSE） are reduced from original 8.693 to model-based 5.811 dB with a 33.15% improvement， whereas the empirical formula only reduced it to 6.820 dB （with a 21.54% improvement）.For Z _DR correction， the BIAS are from original 0.862 to model-based 1.141， outperforming the over-correction of empirical formula-based （1.273）.The RMSE decreased from original 1.679 to model-based 0.972 dB （with a 42.10% improvement）， compared to the empirical formula-based reduction only 1.382 dB （with a 17.69% improvement）.For Z _H correction， the mean absolute error （MAE） improves from 6.292 to 4.222 dB （with a 32.89% improvement）， whereas the empirical formula only reduces it to 5.113 dBZ （with a 18.73% improvement）.For Z _DR correction， the MAE decreases from original 1.271 to model-based 0.697 dB （with a 45.16% improvement）， compared to the empirical formula-based reduction only to 1.008 dB （with a 20.69% improvement）.The MAE with model correction also showed distinct advantages over that with traditional methods.Three cases further validate the stability and generalization capability of AI-based correction.

Assimilation of thinned radar radial wind data can help improve the model's forecasting capability for short-term precipitation.However， the thinning method affects the distribution characteristics of the radar radial wind super-observations（SOs）， which in turn affects the assimilation and prediction results.This study investigates the impact of radar radial wind thinning method on rainfall forecasting through sensitive experiments.Based on a heavy rain event in North China， two experimental groups were conducted， employing radar radial wind SOs with varying grid resolutions （by altering radial spacing or azimuthal interval）.The results show that changing the radial spacing or azimuthal interval of super-observation box alters the extremes of the SOs and their locations， thereby influencing both the intensity and position of the jet stream.The radial spacing additionally affects the jet height by influencing the altitude at which the extremes of the SOs occur， and it has a relatively significant impact on the quantity of data obtained and the analysis error.The assimilation of radial wind SOs with different grid resolution similarly adjusts the wind field， which can increase the cyclone of the shear line in central and southern Hebei and the southerly component of the low-level jet in central Shandong.The grid resolution of the SOs mainly affects the curvature of the cyclonic shear and the intensity of the southerly jet.For precipitation forecasting， assimilation radar radial wind SOs can improve the overall performance of precipitation forecasts for the first 6 h of the model， particularly in capturing heavy precipitation events exceeding 25 mm.Meanwhile， it provides better scores for 24 h forecasts of light and moderate rainfall and can restrain some false precipitation forecasts.When a higher resolution of radial wind SOs is adopted， the forecasting performance for heavy precipitation within 12 h improves significantly.The threat score， false alarm ratio， and probability of detection （POD） of precipitation are more sensitive to changes in radial spacing during thinning， while variations in azimuthal interval have a relatively more pronounced impact on forecasting bias.

Gridded precipitation， potential evapotranspiration （PET）， and land surface temperature （LST） datasets provide valuable alternatives for hydrologic modeling in ungauged areas.However， their performance in specific regions requires further investigation.This study evaluates six gridded precipitation products （CHIRPS， CMORPH， GPM， GSMaP， MSWEP， and PERSIANN）， three PET products （GLDAS， GLEAM， and ERA5-Land）， and one LST product （MOD11A1） in the Taolai River Basin.The effects of different precipitation and PET inputs on actual evapotranspiration （AET） and soil moisture were also analyzed.The results indicate that runoff simulations vary significantly depending on the precipitation product used.GPM and MSWEP perform best， achieving Nash-Sutcliffe efficiency （NSE） values of 0.58 and 0.54， respectively， during the validation period.These two products are reliable alternatives for precipitation sources in data-scarce scenarios.In contrast， the selection of PET products exhibits limited impact on runoff simulations， with the NSE values ranging from 0.61 to 0.74.If runoff simulation accuracy is the primary concern， the GLDAS， GLEAM， and ERA5-Land products are suitable substitutes.Additionally， when surface temperature data are unavailable， the MOD11A1 product achieves an NSE of 0.61 during the validation period.For scenarios with no meteorological observations， combining GPM or MSWEP precipitation data with any of the three PET products and MOD11A1 yields NSE values exceeding 0.53 in both calibration and validation periods.This study confirms that gridded datasets can partially mitigate hydrological simulation challenges in data-scarce regions， while recommending expanded applicability assessments across more basins to enhance reliability.