The inland arid zone of northwestern China has two climate regimes， monsoon-dominated and westerly-dominated， and is also an area of monsoon-westerly interaction.The study of climate and environment in this region is of great theoretical and practical significance， and it is also a frontier issue that has attracted much attention in global change research.This paper summarized the research progress on the definition of the monsoon zone， the westerly belt and their boundary zones， the progress on the climatic impacts of these two circulations and their interactions as indicated by instrumental measurements and proxies.Future research needs to focus on the westerly-dominated climate regime and its interaction with the monsoon， spatial definition， and driving mechanisms at high resolution and large spatial and temporal scales.These studies can promote research on the response to global change and its dynamic mechanism， and provide scientific support and theoretical basis for regional desertification management and national ecological security guarantee in arid northwestern China.

Based on NCEP/NCAR reanalysis data and the daily precipitation data from 105 meteorological stations in the central and eastern Qinghai-Xizang Plateau from 1982 to 2020， we investigate the spatiotemporal anomalous characteristics and major falling areas of warm season extreme precipitation and typical circulation of large-scale extreme precipitation in the central and eastern Qinghai-Xizang Plateau.The results show that： （1） Total precipitation of central and eastern Tibetan Plateau in the warm season shows statistically significant increasing at the rate of 10.7 mm·（10a）^-1 （P<0.05） during 1982 -2020， but there are obvious interdecadal trend shifts in the late 1990s and late 2000s.The increase in extreme precipitation of central and eastern Qinghai-Xizang Plateau is most prominent after 2009， and the climate tendency rate is of 4~5 times greater than that of during 1982 -2020.In terms of the distribution of spatial climate tendency rates， the trend of extreme precipitation in the southern Qinghai-Xizang Plateau is opposite to that in the central and northern Qinghai-Xizang Plateau in the three periods.The increase of extreme precipitation in the northern Qinghai-Xizang Plateau is the most prominent during 1998 -2009， and the extreme precipitation in the southern Qinghai-Xizang Plateau experiences the interdecadal trend shifts of significant increase， significant decrease and significant increase.（2） The small-scale extreme precipitation in central and eastern Qinghai-Xizang Plateau shows statistically significant decreasing trend （P<0.1）， while the large-scale extreme precipitation exhibits statistically significant increasing trend （P<0.05） during 1982 -2020.According to the locations of precipitation center， the level 4 large-scale extreme precipitation can be divided into three types， that is， northeast type （A type）， southern type （B type） and southeastern type （C type）.（3） The difference of location and intensity of the West Pacific Subtropical High is the main factor， which leads to the difference of water vapor sources and the locations of large-scale extreme precipitation falling areas.When the A-type large-scale extreme precipitation occurs， the West Pacific Subtropical High is anomaly weaker and eastward， which leads to the water vapor mainly transported from the Pacific Ocean and the westerlies.When the B-type occurs， the West Pacific Subtropical High is anomaly stronger and extending westward and southward， consequently， the water vapor mainly transported from the Indian Ocean and the Bay of Bengal.When the C-type occurs， the West Pacific Subtropical High is anomaly stronger， extending from the westward and northward， resulting in the water vapor mainly transported from the northwest Pacific Ocean， the South China Sea and the Bay of Bengal.

Using the observation data of 10 m surface wind speed at 173 meteorological stations in Northwest China from 1979 to 2020 and the ERA-5 reanalysis data， this study investigated the interdecadal variation characteristics of annual and seasonal surface wind speed in Northwest China and displayed the background circulation changes of interdecadal variation in 2003/2004.There are significant regional differences in the trend distribution of surface wind speed during 1979 -2020.The stations of the wind speed with increasing trend were mainly located in central and eastern Gansu， Shaanxi， southwest and northeastern parts of Xinjiang.The others stations generally showed the negative trends of wind speed.The number of the stations with decreasing trends of surface wind speed were obvious larger than that with decreasing trends.The average surface wind speed in the stations with decreasing （increasing） trends were larger （smaller） than that in whole northwest China.The distribution of the annual and four-season leading mode of surface wind speed were generally opposite to the trend distribution from 1979 to 2020.All the PC1s experienced the decadal shift around 2000.The second mode showed positive anomalies over the Ningxia Province and southern Shaanxi Province and negative over others regions.The PC2s both witnessed the decadal shifts in 1987/1988 and 2003/2004.Numerical studies investigated the decadal shift of surface wind speed northwest China in 1987/1988.Therefore， this study focuses on analyzing the possible circulation background of the decadal shift of northwest wind speed in 2003/2004.The composite difference of 500 hPa and 200 hPa geopotential heights between 2004 -2020 and 1988 -2003 presents the meridional dipole mode of Central Asia-Northern Europe in spring， the "Silk Road Pattern" in summer and autumn， the "Scandinavian" mode in winter， and the negative phase of the Arctic Oscillation in annual field.The circulation changes in four seasons and annual averages have their own characteristics.It needs further studies of the physical process of the influences of these circulation factors on the decadal shift of the surface wind speed in northwest China in 2003/2004.

As one of the most sensitive natural elements in response to climate change， snow cover has a significant effect on the Earth's surface radiation balance and water cycle.The global snow cover area is approximately 46×10⁶ km² and 98% of the snow cover distributed in the Northern Hemisphere.Due to its distinctive radiative properties （high surface albedo） and thermal characteristics （low thermal conductivity）， changes in snow cover play a crucial role in the energy balance and water cycle between land and the atmosphere.In the context of global warming， the snow cover in the Northern Hemisphere has been decreasing in recent decades， especially in the spring.Therefore， the capabilities of CMIP6 （Coupled Model Intercomparison Project Phase 6） data to simulate the snow cover area were evaluated based on observational data and the future changes in snow cover were also assessed using a multi-model average in this study.By using the snow cover products from the National Oceanic and Atmospheric Administration/National Climatic Data Center （NOAA/NCDC） as reference data， the Taylor skill scoring， relative deviation， and other methods were applied to evaluate the spring snow cover （SCF） data in the Northern Hemisphere from the International Coupled Model Comparison Project Phase 6 （CMIP6） during 1982 -2014.The ensemble average of the top three models was further selected to predict the spatiotemporal variation characteristics of SCF under different emission scenarios from 2015 to 2099， providing insights into the modeling capabilities of CMIP6 and future changes in SCF.During the historical period （1982 -2014）， SCF was characterized by high coverage at high latitudes and low coverage at low latitudes， with high-altitude regions such as Tibetan Plateau and eastern Asia having higher snow coverage than those at the same latitudes.Overall， 68.37% of the regions in the Northern Hemisphere showed a decreasing trend in SCF， while 31.63% of the regions showed an increasing trend in SCF.Most CMIP6 models overestimated SCF in the Tibetan Plateau region compared to the reference data.In addition， most models simulated larger areas with a decreasing trend in SCF than those evaluated by the reference data and underestimated SCF in March， April， and May.Various models exhibited differing abilities to simulate SCF， with NorESM2-MM， CESM2， BBC-CSM2-MR， NorESM2-LM， and CESM2-WACCM demonstrating superior capabilities.The Multi-Model Ensemble Mean （MME） consistently outperformed individual models， closely aligning with observational data.There were significant differences in the ability of the CMIP6 models to simulate the spatial distribution， inter-annual variation trends， and intra-annual variations of SCF in the Northern Hemisphere.At the end of the 21st-century （2067 -2099）， SCF in the Northern Hemisphere exhibited a decreasing trend in most areas， which intensifies with increasing emission intensity.The changes in SCF were relatively consistent under different emission scenarios before 2040.SCF maintains a steady state under the SSP1-2.6 scenario， showed a slight decreasing trend under the SSP2-4.5 scenario， and showed a significant decreasing trend under the SSP5-8.5 scenario after 2040.

In this study， the improvements of lake dynamic module parameters in the literature were added to WRF-Lake （WRF4.4.1） at first， then six microphysical schemes， five cumulus convection schemes and two boundary layer schemes were selected.A total of 60 WRF-Lake simulations with different parameterization schemes were carried out from July 5 to 13， 2008 in the Nam Co Lake area.Sensitivity experiments were conducted to comparatively analyze the effects of different parameterization scheme combinations on atmospheric boundary layer variables.The "ranking method" was employed to comprehensively evaluate the simulation capabilities of different parameterization schemes in the summer atmospheric boundary layer over Nam Co Lake.The results indicated that the model captures the overall spatial and temporal distribution characteristics of the summer average two-meter temperature in Nam Co.However， the simulated values of the two-meter temperature over the lake were higher than the land surface data.Due to the selection of cumulus convection parameterization schemes and the impact of model performance， there was significant differentiation in the simulation effects of precipitation among experimental groups， leading to varying degrees of overestimation of daily precipitation.The daily average variations of latent heat fluxes showed the best correlation with observational values， while sensible heat and wind direction exhibited relatively good performance， and wind speed showed the least satisfactory results.Overall， comprehensive analysis of the simulation capabilities of each experimental group for the summer atmospheric boundary layer over Nam Co Lake revealed that Scheme 58 （SBU-Tiedtke-MYNN3） performed the best in simulations of 2 m temperature， daily precipitation， 10 m wind fields， and surface heat fluxes.The RMSE value for two-meter temperature and daily precipitation was 2.33 °C and 10.48 mm， respectively.The correlation coefficient for the daily average variation of 10 m wind speed was -0.41， and the ratio of standard deviations was 0.94.The correlation coefficient for the daily average variation of 10 m wind direction was 0.59， and the ratio of standard deviations was 0.73.The correlation coefficient for the daily average variation of sensible heat flux was 0.94， and the ratio of standard deviations was 1.89.The correlation coefficient for the daily average variation of latent heat flux was 0.89， and the ratio of standard deviations was 0.91.Therefore， it is recommended to use the aforementioned grid parameterization scheme for simulating the summer atmospheric boundary layer over the region of Lake Nam Co.

El Ni?o-Southern Oscillation （ENSO） is the most prominent interannual climate mode over the tropical Pacific， which is characterized by a periodic and phase-locked evolution of sea surface temperature anomalies （SSTA）.From the perspective of ENSO cycle， this study objectively classified the SSTA evolution of ENSO from 1961 to 2021 into two results using K-means clustering method （KMA）： 3 or 5 types of ENSO cycle.When it is classified into 3 types， the basic characteristics of ENSO cycle are warm-developing， warm-decaying， and cold-persistence.When it is classified into 5 types， the discrepancies of intensity and zonal distribution of the development and decay processes between super-strong and normal events are highlighted.To further explain these discrepancies， this study employed a KMA considering the Principal Component Analysis （Empirical Orthogonal Function）.Based on the two EOF leading modes which reflects the zonal symmetric and asymmetric development modes， the zonally symmetric and asymmetric development processes of the ENSO cycle are divided.Combined with the KMA clustering analysis， it is further found that zonally asymmetric development mode together with the zonally consistent development mode jointly lead to the zonal asymmetric development speed of ENSO cycle.Reconstruction of the zonally homogeneous and asymmetric evolution modes of ENSO reveals that wind and thermocline thickness anomalies may be key factors controlling the zonal asymmetric evolution of SSTA.This study objectively classified different types of ENSO evolution， providing reference for climate dynamics and impacts of ENSO diversity.

The summer precipitation in eastern China has significant interdecadal variaitons， which can impact the spatiotemporal variability of drought and floods as well as people’s living.Hence， it is important to understand and predict the interdecadal variations of summer precipitation in eastern China.The Coupled Model Intercomparison Project Phase 6（CMIP6） can help to understand the changes in climatic factors and predict their future changes.What is the capability of CMIP6 models in simulating the interdecadal variations of summer precipitation in eastern China？ What are the potential reasons？ In order to understand the above questions， this study evaluated the capability of CMIP6 models in simulating the interdecadal variations of summer precipitation in eastern China， using the CN05.1 observational data， ERA5 reanalysis data， NOAA sea surface temperature （SST） data and the output of historical experiments from 30 CMIP6 models.The results indicate that during 1961 -2014， the summer precpitation over eastern China underwent two notable interdecadal variations， which occurred in the mid-1970s and early-1990s.During these two interdecadal variations， the simutaneously enhanced/weakened Western Pacific Subtropical High （WPSH） and South Asian High（SAH） as well as the interdecadal change in tropical Pacific SSTs induce changes in the winds and divergence at the 850-hPa and 200-hPa pressure levels over southern China （18°N -30°N， 105°E -122°E）.The associated interdecadal changes in water vapor flux， meridional circulation and atmospheric stability in lower troposphere led to interdecadal changes in summer precipitation in eastern China.Although the CMIP6 models can well simulate the climatology of summer precipitation in eastern China， only 5 out of 30 models have relatively good capability in simulating the aforementioned two interdecadal variations in summer precipitation， which have taylor scores larger than 0.7， while the other models have relatively poor skill.In addition， the best multi-model ensemble （BMME） means show better skills in simulating these two interdecadal variations in summer precipitation than individual models.This is because the BMME can well simulate the simultaneous change in WPSH and SAH as well as the change in tropical Pacific SSTs， which leads to a good simulation of the meridional circulation over eastern China， resulting in a good simulation of the summer precipitation anomalies south of the Yangtze River.

South China （SC） is one of the regions with the most annual precipitation in China.Under the background of global warming， there had been significant changes in precipitation at regional and scale levels， the area of dry- and wet-season precipitation was expanding， and regional extreme precipitation events showed a significant upward trend in SC， but the changes and impacts of different types of precipitation were not the same.To better understand how different types of precipitation in SC respond to global warming， this paper investigated the characteristics of convective precipitation （CP） and large-scale precipitation （LSP） in SC based on ERA5 reanalysis precipitation dataset from 1960 to 2022 using linear correlation， trend analysis and wavelet analysis.The results showed that： （1） SC was dominated by LSP in winter while CP in other seasons.（2） CP and LSP in SC showed an increasing trend in winter， but most of CP showed a decreasing trend in other seasons.CP in SC had a relatively significant 2~4 years cycle from the 1980s to the beginning of the 21st century in winter， as well as there was an interdecadal characteristic of shifting from more to less in the 1990s to the beginning of the 21st century in spring， but the significant cyclical variations were mainly found in the period before the 1990s in autumn.In all seasons except winter， the changes in LSP were consistent with CP.（3） The total precipitation （TP） from April to October in Guangdong and Guangxi was mainly CP （CP accounts for about 65% of TP）， with the largest proportion in August （71.8% in Guangdong and 69.0% in Guangxi）.On the other hand， the proportion of CP in the first rainy season （up to 80% in May） was significantly higher than in the second rainy season in Hainan.Additionally， the proportion of CP remained at about 50% even in autumn and winter.（4） The diurnal variation intensity of CP and LSP was strongest in Hainan while the intensity of CP was the weakest in Guangxi.The peak periods of the strongest precipitation in Guangdong， Guangxi， and Hainan occured respectively from 13:00 to 17:00 （Beijing time， the sameas followed）， 15:00 to 17:00， and 14:00 to 16:00.The LSP peaked between 09:00 and 17:00 in Guangdong， and it started to strengthen from 04:00 and weakened in the afternoon， with the strongest period around 10:00 in Guangxi， for Hainan， the strongest period was 12:00 -17:00 before August， but 15:00 -16:00 after late September.In conclusion， the characteristics of precipitation varied with different seasons and types.Therefore， it was necessary to continue considering the impact of different types of precipitation in future research.

Based on the daily minimum temperature station data provided by the National Meteorological Information Centre from 1980 to 2022， the month-by-month reanalysis data of the NCEP/NCAR， and the monthly Sea Surface Temperature （SST） data from the NOAA， by using EOF， simple linear regression and T-N wave flux methods， the main anomalous spatial and temporal characteristics of winter cold wave frequency in the southern China are studied， and the influence mechanisms of atmospheric circulation and winter Atlantic Sea Surface Temperature Anomaly （SSTA） on it are also analyzed.The results show that： （1） The large value areas of winter cold wave frequency are mainly located in the eastern and central of the southern China， with an approximately "inverse C" distribution.There are three main frequency anomalous modes， namely， regionally consistent anomaly， north-south antiphase anomaly and tripole anomaly patterns according to the EOF analysis， among which the regionally consistent anomaly reflects the overall anomalous spatial and temporal characteristics of the winter cold wave frequency in the southern China.（2） The negative phase of the North Atlantic Oscillation （NAO）， the strong Caspian Sea - Tibetan Plateau ridge and the East Asian Trough located to the north and to the east， the weak in the north and strong in the south of the Siberian High， the strong temperate jet and the weak subtropical jet are the key circulation systems affect the winter cold wave frequency in the southern China.The cold air pool is located in the Western Siberia.The high and low level circulation systems cooperate together to make the cold air from Western Siberia move southward to the vicinity of the Caspian Sea， and then transport eastward along the northern side of the Tibetan Plateau， then move southward into the southern China along the eastern side of the Tibetan Plateau， resulting in the increase of the winter cold wave frequency in the whole southern China.（3）In winter， the “+”“-”“+” tripolar SSTA in the North Atlantic can stimulate the -NAO atmospheric circulation anomalies through the exchange of heat fluxes between air and sea and the Rossby wave energy anomalies.The Rossby wave energy propagates from the North Atlantic to East Asia along the south and north two paths， and stimulates the corresponding anomalous waves， which enhance the key circulation systems in the north and south affecting the cold wave frequency in the southern China.When the North Atlantic SSTA exhibits an inverse "C" anomaly in spring， and there is a trend of developing into a “+”“-”“+” tripolar pattern in summer and autumn， the winter cold wave frequency in the southern China can be predicted to more.

Based on observational data， it is evident that fog occurrence in Eastern China is notably higher during winter compared to other months.Spatial analysis of climatological statistics reveals a distinct heterogeneity， dividing Eastern China into northern and southern regions based on fog day patterns.Furthermore， winter can be subdivided into early （November and December） and late （January and February） periods， characterized by varying trends in fog occurrence and interregional correlations.Examining the relationship between sea surface temperature （SST） and fog days across both southern and northern China throughout winter， significant influences emerge.During early winter， fog days in both regions are markedly impacted by SST anomalies in the tropical Pacific.Conversely， in late winter， while the north Atlantic SST exerts a considerable influence on fog days in northern China， southern China continues to be strongly affected by tropical Pacific SST.Analyzing atmospheric circulation patterns reveals distinct mechanisms driving fog occurrences in different seasons.In early winter， warm SST anomalies in the tropical Pacific drive northward movement of the Western North Pacific Subtropical High， resulting in anticyclonic anomalies over northern Japan and weakened Siberian High， leading to increased fog days in both southern and northern areas of Eastern China.Conversely， in late winter， weakened influence from the tropical Pacific shifts the anomalous southerly winds only over southern China， while the north Atlantic SST anomalies induce anomalous southeasterly winds over northern areas， enhancing moisture transport from the north Pacific， with relatively stable atmospheric structure， thus significantly increasing fog days over northern China.

In order to solve the problem of the traditional remote sensing drought index focuses on the monitoring of a single response factor and lacks a complete analysis of drought.In this paper， we selected TVDI， RVI， PDI， and GVMI daily products estimated from remote sensing data as independent variables， and MCI calculated from meteorological data at the adjacent moments of satellite transit as dependent variables， and uses the Random Forest Regression （RFR） model to construct a integrated remote sensing drought monitoring model.The results show that the accuracy of RFR model is better than that of the Ordinary Least Squares （OLS） model in bothtraining data and test data.The R value of the RFR training data is 0.97， the RMSE is 0.33， the R value of the RFR test data is 0.90， and the RMSE is 0.53.The R value of the OLS training data is 0.78， the RMSE value is 0.73， the R value of the OLS test data is 0.76， and the RMSE value is 0.79.The comparisons of RFR and OLS model in R and RMSE show that the RFR model is superior than the OLS model in the characterization of regional drought.In the application of drought monitoring in Southwest China in 2022， the RFR results are consistent with the spatiotemporal distribution of the MCI index， which can better characterize the spatial and temporal dynamics of the regional drought， reflecting the practicality of the RFR model in the actual drought monitoring process.However， the accuracy of RFR model is related to the number of regional stations and the spatial distribution of stations， and the accuracy of the RFR model is higher in areas with a large number of stations and uniform distribution of stations.

In order to achieve further improvement in the forecast accuracy of station temperatures and enhance the forecast capability for extreme temperatures， this study establishes a 24-hour national station daily maximum （minimum） temperature forecast model for Jiangxi Province based on the LightGBM machine-learning algorithm and the MOS forecast framework by using the surface observation data of 91 national stations in Jiangxi Province and the upper-air and surface forecast data of the ECMWF model from 2017 to 2019.The results of the 2020 evaluation show that the LightGBM model daily maximum （minimum） temperature forecast is consistent with the observed trend， and the annual average forecast is better than that of three numerical models， ECMWF， CMA-SH9 and CMA-GFS， two machine learning products， RF and SVM， and subjective revision products.In terms of the spatial and temporal distribution of forecast errors， the model's daily maximum （minimum） temperature forecast errors in winter and spring are slightly larger than those in summer and autumn； the daily maximum temperature forecast errors show the spatial distribution characteristics of "larger in the south and smaller in the north， and larger in the periphery than in the centre"， while the opposite is true for the daily minimum temperatures.In terms of important weather processes， the LightGBM model has the best prediction effect among the seven products in the high temperature process； in the strong cold air process， the LightGBM model is still better than the three numerical model products and the other two machine-learning models， but the prediction effect of the daily minimum temperature is not as good as that of the subjective revision products.After a simple empirical correction for the low-temperature forecast error in the strong cold air process， the model low-temperature forecast effect is close to that of the subjective revision product.The model significance analysis shows that the recent surface observation features also contribute to the model construction， and the results can be used as a reference for model improvement and temperature forecast product development.At present， the LightGBM model temperature forecast products have been applied to meteorological operations in Jiangxi Province.

Based on reanalysis data， multi-source observation data， and Wenshan Doppler radar data， a study was conducted on the circulation background， environmental potential， evolution process， and formation mechanism of a EF0 tornado that occurred in Guangnan County， Wenshan Prefecture， Yunnan Province， in the afternoon of May 31， 2021.The results showed that： （1） The tornado occurred in the western flow at edge of subtropical anticyclone， the 700 hPa wind speed convergence region， and the 850 hPa shear line.There was a dry and cold air intrusion in the middle atmosphere， and having the necessary thermal and water vapor conditions for a tornado to occur.However， the dynamic conditions were not conducive to the occurrence of a tornado， that is， the low-level and deep vertical wind shear were significantly weaker than that of non-supercell tornadoes in the central and eastern regions of China.（2） Higher ground temperatures， water vapor boundaries on cloud images， and mesoscale convergence lines jointly triggered convective storms in southeastern Yunnan Province.Affected by complex terrain， the convective cells appeared to be split and strengthened， and continued to advance towards the Guangnan County， which has a higher elevation in the northwest.（3） The possible mechanism for the formation of the tornado is that the high ground temperature in the Guangnan Basin created convective instability， and the cold pool outflow below the convective cells on the eastern side of the basin entered the basin and expanded westward.The intersection of warm and cold air caused new cells to begin triggering along the convergence shear line， and the temperature difference between the cold pool and the surrounding environment below it was conducive to the formation of the tornado.Subsequently， the convective cells developed into a narrow and long shape along the terrain on the western side of the basin， then an initial vortex formed in the lower level of the storm.The topographic forcing enhanced the uplift motion above the initial vortex， causing it to tilt and stretch while increasing vorticity， ultimately forming this tornado.

This study utilizes a high-resolution emission inventory and the WRF-CMAQ modeling system to analyze the temporal and spatial evolution of tropospheric NO? vertical column density （VCD） derived from TROPOMI satellite data. It also provides a preliminary assessment of the uncertainty in the NO? emission inventory for the Sichuan Basin in 2019. The findings reveal elevated tropospheric NO? VCD in areas with intense anthropogenic activity， including the Chengdu Plain， southern Sichuan's urban clusters， and Chongqing， while the central Sichuan Basin remains relatively clean. Seasonal variations， influenced by both meteorological conditions and anthropogenic emissions， show significantly higher NO? VCD in winter and spring compared to summer and autumn. A comparison between the WRF-CMAQ model and TROPOMI satellite data for January 2019 indicates strong agreement in cleaner regions， though TROPOMI reports notably higher NO? VCD in high-emission cities such as Chengdu and Chongqing， suggesting that the emission inventory may underestimate NO? emissions in megacities. This work underscores the need for stringent NO? emission controls in major cities， such as Chengdu and Chongqing， while also emphasizing the urgency of enhancing emission controls in medium-sized cities across the Sichuan Basin.

By using the large eddy simulation （LES） version of the Weather Research and Forecasting （WRF） model， vertical structure and the feature of turbulent transportation of shallow-convective-cloud-topped atmospheric convective boundary layer was simulated in addition to the idealized dry convective boundary layer on oceanic surface.Numerical convergence of LES model was analyzed with the model results in different resolutions.The results showed that the dry convective boundary layer was vigorous.Approximately 1.15 km of the planetary boundary layer height was simulated with the LES in different resolutions.Vertical uniform structure of the averaged potential temperature （θ）， mixing ratio of water vapor （ q_v） and horizontal wind （u and v） were shown in the idealized dry convective boundary layer but with large vertical gradients in the near surface layer and the top of the boundary layer.Higher resolution model resolved more detailed structure of convective bubbles within the dry convective boundary layer， larger variance of potential temperature in the entrainment layer， vertical uniform distribution of the averaged quantities within the simulated mixed layer extending more closing to the surface， and the errors in LES model under coarse resolution were mainly concentrated in the lower boundary layer and near the inversion layer.The higher resolution LES model showed larger resolved sensible heat flux while the total flux remained.In the case of shallow-convective-cloud-topped boundary layer， obvious different boundary layer structure was displayed in comparison with that of the dry convective boundary layer.Conditionally unstable layer existed over the mixing layer， and mixing layer height dropped.Vertical profiles of averaged θ， q_v， u and v showed similar structure in the mixing layer as that in the convective boundary layer.The averaged meteorological quantities in the lower mixing layer were uniformly distributed in vertical direction.In the cloudy layer， however， positive heat flux and vertical turbulent kinetic energy appeared.Negative heat flux was observed from the top of mixing layer to the lower cloudy layer， which reflected the weak inverse temperature and entrainment at top of mixing layer.Lower-resolution model simulated more deviations of the top of temperature inversion， and mean wind velocity and fluxes near surface.The large eddy simulation model tended to converge at 40 m resolution in the vigorously developing dry convective boundary layer case while it converged at 30 m resolution in the shallow-convective-cloud-topped boundary layer.The atmospheric boundary layer with lower mixing layer height needs to be simulated using a higher resolution LES model.

Based on MERRA-2 reanalysis data and the dust records at meteorological stations from 1980 to 2020， we have defined the Westerly Jet stream Position Index （JPI） and provided preliminary insights into the impact of north-south movement of westerly jet stream on dust activity in the Taklamakan Desert （TD）.By conducting a comparative analysis of the dust mixing ratio， frequency of dust events， and the atmospheric circulation fields during periods characterized by northward and southward shifts in the jet stream， we have derived the following conclusions： （1） The position of the westerly jet stream in spring exhibits obvious fluctuation between different years， and the interannual variation trend between different months is also different.The meridional movement is mainly caused by the changes of the north-south temperature gradient over the TD.When the temperature decreases in the southern regions and increases in the northern regions， the north-south temperature gradient decreases， causing the jet stream to shift northward.（2） The spring dust activity in the TD exhibits a significant correlation with the positional changes of the westerly jet stream.The dust mixing ratio within the atmospheric column experiences substantial increases （decreases） as the jet stream shifts towards the north （south）.The difference in dust mixing ratio is greater in the lower layers than in the middle to upper layers.（3） The frequency of dust events in the TD during spring is closely correlated with the north-south movement of the jet stream.The northward shift of the jet stream leads to an elevation in the average occurrence of floating dust， blowing sand， and dust storms throughout each month in spring.The difference in the number of dust weather days caused by the positional shift of the jet stream is more significant in March and May compared to April.Especially episodes of floating dust， there is an average difference of up to 3 days between northward and southward shifts of the jet stream in May.（4） The northward migration of the westerly jet stream during spring alters the atmospheric circulation fields and exerts a profound influence on the emission and transportation of dust in the hinterland of TD.The abnormal anticyclone dominates the middle and upper atmosphere of the Tibetan Plateau and TD， intensifying the westerly jet stream over the desert and augmenting downward momentum transfer.Additionally， abnormal easterly winds manifest in the lower atmosphere， leading to an increase in the surface wind speed.These new findings will be helpful for understanding the formation mechanisms of spring dust activities in the TD and provide scientific basis and reference for climate change in the arid regions of Northwest China.

Under the background of global warming， the temperature has increased frequently in the southwest， and the ecosystem in the southwest is vulnerable and sensitive to climate change in the past few decades.The southwest region is an important carbon sink area in China.Monitoring and simulation of vegetation variations is of great significance for an in-depth understanding of the carbon cycle mechanism and promoting sustainable economic development.Leaf Area Index （LAI） and Gross Primary Productivity （GPP）， as indicators of vegetation health and ecosystem stability， can be used to quantify vegetation studies and characterize dynamic changes of vegetation.Vegetation dynamic Model is one of the important means to study vegetation growth and change.Community Land Model （CLM） is one of the earliest land model with the function of vegetation dynamic simulation， the most developed and widely used land model in the world.Model evaluation is an indispensable part of model development， which provides a basis for model development and improvement.This study uses the Community Land Model version5 （CLM5） to simulate and analyze the spatial and temporal variations of the leaf area index （LAI） and total primary productivity （GPP） in the southwest region across 2000 -2016， and compare it with multiple sets of remote sensing data to evaluate LAI and GPP simulations of CLM5 in the southwest.The results showed that CLM5 could well simulate the seasonal variation of LAI and GPP in southwest China， but overestimated LAI in growing season.The CLM5 can reasonably simulate LAI of temperate deciduous broadleaf shrubs， LAI， GPP of alpine C3 meadow and GPP of C3 meadow.CLM5 could capture the spatial distribution pattern of LAI and GPP in the southwest， which is decreasing from southeast to northwest， but CLM5 overestimates LAI in the whole southwest region， especially in the karst landform area of Guizhou.Contrary to the overestimation of LAI simulation， CLM5's overall simulation of GPP in Southwest China is low， especially in Yunnan province.In addition， CLM5 has a poor simulation of LAI and GPP trend in the southwest.Especially in most parts of Yunnan， remote sensing data mainly shows an upward trend， while CLM5 simulation shows a downward trend.In a word， CLM5 can simulate the seasonal change and spatial distribution of LAI and GPP in the southwest， but the simulation of the trend in some areas of Yunnan and Guizhou is poor， and more in-depth parametric schemes for the development of farmland in Sichuan Basin， Yunnan forests， and karst vegetation in Guizhou are needed to improve the simulation.

Based on the hourly data of total irradiance observed by 26 ground radiation stations in Henan Province， the paper verified and analyzed the applicability of FY-4A inverted total irradiance products in Henan Province， and The Probability Density Function Matching Method was used to correct the systematic error of the FY-4A total irradiance product.The results showed that： （1） The total irradiance of FY-4A was greater than that of ground observation， and the average error of the two was larger in southern Henan and smaller in northern Henan.Moreover， from the perspective of time evolution， the relative error of the two was smaller in the winter half year and larger in the summer half year， this was related to the influence of meteorological elements such as cloud cover， relative humidity of atmosphere and different underlying surfaces on the total irradiance retrieved by FY-4A.（2） The average error of total irradiance of FY-4A satellite and ground observation varied with different irradiance levels， showing a non-independent systematic error as a whole， that was， it overestimated low irradiance and underestimated high irradiance.（3） The Probability Density Function Matching Method had a good correction ability for the total irradiance of FY-4A， and it was better to establish a revised model on an annual basis in summer to correct the error， and in autumn and winter， it was better to establish a revised model with the season as the time scale.

In order to evaluate the potential of wind， solar radiation and precipitation in guaranteeing the development of clean energy comprehensively， this study analyzed the temporal and spatial changes in climate variables and resources of wind speed， solar radiation and precipitation in Qinghai Province throughout the year and four seasons from 1961 to 2021 based on the observation data of daily 10-meter-height wind speed， sunshine duration and precipitation at 51 meteorological stations.The results are as follows： （1） The annual average wind speed， solar radiation and precipitation in Qinghai Province are 2.67 m·s^-1， 6084.2 MJ·m^-2， and 299.7 mm， respectively.Wind speed tends to be higher in the western regions and lower in the east， which exceeds 3 m/s and reaches the standard for wind energy resource development in western Qinghai.The annual solar radiation in the entire Province exceeds 5040 MJ·m^-2， and reaches the "very rich" level according to China Solar energy GB Standards.The solar radiation of Qaidam Basin is at its highest abundance level， which is ideal for solar energy resource development.Precipitation generally decreases from southeast to northwest.The resources of wind， solar radiation， and precipitation in Qinghai Province exhibits seasonal complementarity， characterized by a pattern of “strong winds， good sunlight， and less water in spring， whereas weak winds， good sunlight， and abundant water in summer”.（2） Under climate change， the annual average wind speed and total solar radiation in Qinghai Province show a significant decrease at rates of 0.16 m·s^-1·10a^-1and 29.04 MJ·m^-2·（10a）^-1， respectively.The western and central parts of Haixi are most affected by these changes， but the wind speed and solar radiation still remain within the acceptable range for wind energy and photovoltaic resource development.Meanwhile， precipitation increases significantly at a rate of 8.85 mm·（10a）^-1， with the largest increase observed in western Yushu， eastern Haixi and northern Guoluo.The most significant decrease in wind speed is observed in spring， while summer solar radiation decreases at the fastest rate but with a substantial increase in precipitation.The changes in solar radiation and precipitation could be ascribed to the increased cloud cover in this region.（3） The changes in the areas where wind speed and solar radiation meet suitable development standards in Qinghai Province are not significant with reduced variabilities， which could ensure the stable development of clean energy.Wind energy resources in western Qinghai （such as Tanggula Mountains）， solar energy resources in the Qaidam Basin， and water energy resources in the three major river basins of the Yangtze River， Yellow River， and Lancang River have great potential for development.Overall， the results provide a theoretical foundation for the development of a balanced clean energy system encompassing wind， solar and hydropower.This contributes to achieving national "dual carbon" goals and enhancing the high-quality development of Qinghai Province.

Mountainous winds exhibit strong intermittent， fluctuating， and non-stationary characteristics due to the influence of terrain， resulting in poor observation quality， which makes conventional quality control methods unable to effectively improve their observation quality.To address this issue， a quality control method （VCG） based on variational mode decomposition， convolutional neural networks， and deep learning of gated cyclic units is constructed， and a particle swarm optimization strategy and wind power reconstruction model are introduced to comprehensively improve the quality of observation data.To verify the effectiveness of this method， 10 minute wind speed and direction data of target wind turbines in six complex mountainous wind farms in Jiangxi Ganzhou， Sichuan Guangyuan， Anhui Wuhu， Hubei Huangshi， Henan Pingdingshan， and Guangxi Hezhou in 2016 was quality controlled by VCG and compared with single machine learning method， spatial regression method （SRT）， and inverse distance weighting method （IDW）.The results indicate that VCG method is suitable for quality control of observed wind data in mountainous wind farms， and has a higher error detection rate for suspicious data compared to conventional methods； The controlled data can better restore the observed background field and have a lower error rate when applied to the power generation evaluation business of wind farms； And it has the characteristics of strong terrain adaptability.