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

In this paper， the BCC_AVIM land model and encrypted observation data are used to evaluate the simulation performance of different soil stratification schemes on soil temperature and humidity.The results indicate that the encryption scheme has the best simulation performance.In the BCC_AVIM land model， the soil layer is defined as ten layers and linearly interpolate the depth of each adjacent node to obtain a new 20-layer scheme， referred to as the encryption scheme in this paper.The soil stratification scheme with 20 soli layers in the reference model CLM5.0 is also applied to the BCC_AVIM land model， which is called CLM5.0 scheme in this paper.Comparing the simulation results of the soil stratification scheme before and after improvement with the encrypted observation data， it shows that： （1） The encrypted observation data and the original observation data can well reflect the changing trend of soil temperature and humidity at the whole vertical level， but for the soil freezing period， the encrypted observation data can better depict the temperature change of shallow soil and the humidity change of deep soil.（2） For the soil temperature and humidity during the freezing period， the simulation performance of the encryption scheme and CLM5.0 scheme show improvements compared with the original scheme.The encryption scheme is closest to the measured temperature and humidity data’s numerical values and amplitude changes.Furthermore， the encryption scheme is more reasonable in determining freezing time in the soil’s shallow and middle layers.（3） For the soil temperature and humidity during ablation， the simulation performance of the encryption scheme and CLM5.0 scheme are enhanced compared with the original scheme.After the shallow and middle soil layers enter the ablation period， the simulated and measured data of soil temperature and humidity by the encryption scheme are closer to the numerical values and curve trends.

The source of the Yellow River is an important water conservation area in the Yellow River Basin， and it is of great significance to study the influence of different soil stratification on the simulation results of freeze-thaw process and to improve the model's simulation ability of water-heat transport process for the study of freeze-thaw process in the alpine region.In this paper， we use the observation data from Mado station in the Yellow River source area as the forcing field to drive the land surface model CLM5.0 （Community Land Model） to simulate in Mado station， and use the three improved soil layering schemes of CLM5.0 to simulate the influence of different soil layering on the soil freezing and thawing process， and compare the simulation results with the observations to analyze the influence of the improved layering schemes on the land surface model.CLM5.0 in the Yellow River source area in the process of freezing and thawing on the soil temperature and humidity simulation ability to improve the effect of the following conclusions： （1） adjusted three soil layering scheme on the simulation of soil temperature at Mardo station has a better effect of the improvement of the simulation of the three programs in the 30-layer program simulation of the best effect of the simulation， simulated values and the average correlation coefficient with the observed value reaches 0.954， the average root-mean-square error of 3.334 ℃ （2） The simulation effect of the three adjusted soil layering schemes on soil moisture at Mado station is also improved more significantly， which can accurately capture the seasonal changes of soil moisture in each layer in a whole year， affected by precipitation， the simulated values are not sufficiently relevant to the simulation of the troughs of the measured values， and the best simulation effect is achieved in the 30-layer scheme among the three schemes， with an average correlation coefficient of 0.770， and an average root-mean-square error of The average correlation coefficient is 0.770， and the average root mean square error is 0.039 m³·m^-3； （3） For the simulation of the initial day of freezing and the initial day of ablation， the adjusted three different soil layers have obvious effects on the simulation of the freezing period and the ablation period， and the simulated initial day of freezing and the initial day of ablation of the shallow layer are in line with the observed values， while the simulation of the initial day of freezing and the initial day of ablation of the deeper layer is somewhat biased， with delays compared with the observed values， and the period of ablation is more persistent.

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

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

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

High-quality precipitation is an important precondition to conduct the study of ecohydrology and climate change in mountain area.However， complicated terrain and scarce and uneven ground observation stations make the understanding of spatiotemporal variations characteristics of precipitation in the Ailao Mountain Area unclear.In this study， GWR （Geographical Weighted Regression） model is used to downscale GSMaP-Gauge precipitation data with 0.1° spatial resolution to 30 m.After validating the accuracy of downscaled precipitation data by using monthly meteorological stations data， the monthly precipitation dataset from 2000 to 2020 in the Ailao Mountain Area is developed.Based on this dataset， the long-term （2000 -2020） spatiotemporal variations characteristics of precipitation at both annual and monthly scales in the study area are illustrated.The results showed that （1） the accuracy of downscaled GSMaP-Gauge precipitation by GWR model was reliable （R ²=0.77， Bias=-0.01）， with the significant improvement of spatial details.（2） Spatially， the annual precipitation amount increased from north to south， and it first increased and then decreased with the rising of elevation in the Ailao Mountain Area.Temporally， there was obvious dry and wet seasons in the study area from 2000 to 2020， with the precipitation amount from May to September accounting for 74.74% of the annual precipitation.And precipitation amount first increased and then decreased from February to December， with the maximum occurred in July.（3） From the aspect of spatiotemporal change， the annual average precipitation amount decreased from 2001 to 2020， only 24.19% area was at an increasing trend concentrated at the southeastern Ailao Mountain Area.As for different months， precipitation amount was significantly increased in January and significantly decreased in May， while the change trends of precipitation in the other months were insignificant.This study has found that GWR downscaling method is valid to obtain high-resolution precipitation data， which is also an effective pathway to clarify the spatiotemporal characteristics of precipitation， and to provide key and basic data for ecohydrological process study and regional water resources management in mountain area.

The effective assimilation of observation data in the slope areas of the Qinghai-Xizang （Tibetan） Plateau and typhoon systems has a significant impact on the capabilities of weather forecasting in China， with background error being a key factor affecting the performance of data assimilation.The purpose of this study is to gain a deeper understanding of the characteristics of background errors in conventional control variables and hydrometeor control variables within convective systems on the slopes of the Qinghai-Xizang （Tibetan） Plateau and typhoon systems， so as to develop the data assimilation scheme which is applicable to the convective systems on the slopes of the Qinghai-Xizang （Tibetan） Plateau and typhoon systems.This study employs the Ensemble Transform Kalman Filter （ETKF） and the hybrid Ensemble-Variational data assimilation method to update the ensemble perturbation and the ensemble mean respectively to produce convective-scale ensemble forecast samples with 80 ensemble members and 4-kilometers resolution.This study focuses on cases of convective weather from the northeast slope of the Qinghai-Xizang （Tibetan） Plateau in mid-August 2022 and Typhoon "Meihua"， the 12th typhoon of 2022.Multivariate background error covariances， including those for multiphase hydrometeor and vertical velocity， were computed through physical transformation， vertical transformation， and horizontal transformation.Analyses of the spatial error characteristics， including the eigenvalues， eigenvectors， and characteristic length scales were conducted， and the results of the analyses indicate that background errors are more pronounced in the case of convective system on the slope of the Qinghai-Xizang （Tibetan） Plateau in comparison to the typhoon system.At the same time， the simulation of hydrometeor variables and vertical velocity is less precise in the case of convective system on the slope of the Qinghai-Xizang （Tibetan） Plateau compared to the typhoon system.In the context of data assimilation for these comparable convective systems， the analysis tends to be more aligned with observations and less so with the background， this feature highlights the necessity for high-quality and comprehensive observations in the slope areas of the Qinghai-Xizang （Tibetan） Plateau.In addition， the atmospheric characteristics and the horizontal scale of hydrometeor variables and vertical velocity in the case of convective system on the slope of the Qinghai-Xizang （Tibetan） Plateau display smaller and more localized features compared with those observed in the typhoon system.Moreover， hydrometeor control variables and the vertical velocity exhibit smaller horizontal scales and more pronounced localized characteristics compared to conventional control variables， therefore potentially leading to the influence range of observations and information related to hydrometeor control variables in the slope areas of the Qinghai-Xizang （Tibetan） Plateau relatively limited in the context of the subsequent assimilation analysis.

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

In recent years， Southwestern China， including Yunnan， Guizhou， Sichuan， and Chongqing， has been frequently hit by flood disasters caused by climate change， resulting in severe casualties and enormous property losses.The occurrence of these disasters is closely related to abnormal precipitation.Although traditional statistical methods and atmospheric models have achieved certain effectiveness in precipitation forecasting， effective approaches for dealing with the complex spatiotemporal characteristics of precipitation data are still lacking.With the development of machine learning technology， the convolutional long short-term memory network （ConvLSTM）， which integrates convolutional neural networks （CNN） and long short-term memory networks （LSTM）， has shown outstanding performance in addressing spatiotemporal sequence problems， particularly in the field of precipitation forecasting.In order to more accurately predict the summer precipitation in the southwestern region of China for the next year （short-term climate prediction of precipitation）， this study constructed a dataset by integrating global sea surface temperature and precipitation data in Southwestern China.The ConvLSTM was used for training and named SST-ConvLSTM.This model not only captures the spatiotemporal characteristics in real precipitation data but also learns some information from global sea surface temperature data， thereby enhancing the accuracy of short-term climate prediction of precipitation.The results show that compared to ConvLSTM that does not consider sea surface temperature and a traditional atmospheric model， SST-ConvLSTM model has significant advantages in short-term climate prediction of summer precipitation in Southwestern China.（1） Numerically， the predictions of the SST-ConvLSTM model are closest to the real precipitation data， with similar trend changes.In contrast， both ConvLSTM and the traditional atmospheric model show certain deviations in their predictions.（2） Spatially， the SST-ConvLSTM model also performs well.Its predictions are consistent with the spatial distribution of real precipitation data and accurately reflect the spatial distribution of precipitation.（3） In model evaluation， three evaluation metrics were used to assess the performance of the SST-ConvLSTM model.The results show that the SST-ConvLSTM model performs well in all evaluation metrics and achieves the best scores.These findings provide important references and insights for future research on precipitation prediction in Southwestern China.

The turbulent transport characteristics of momentum and scalar over the canopy were studied by using the three levels （20 m， 38 m and 56 m） turbulence data measured at the 60m forest micro meteorological tower in southern Sichuan from May 1 to June 30， 2021.Coherent structure is the main form of turbulent motion， which is composed of updraft （ejection） and downdraft （sweep）.In this paper， the quadrant analysis method is used to analyze the boundary of the roughness sublayer， roughness sublayer and constant flux layer above the forest canopy and the ejection-sweep motion characteristics of the constant flux layer， including the difference of ejection and sweep contribution to flux， the difference between momentum and scalar transport， and the difference between different scalar （T， q， CO₂） transport.The results show that under unstable and stable conditions， the ejection dominates the scalar transport at all three levels， while the sweep is the main eddy current motion for scalar transport above the roughness sublayer under neutral conditions.For the momentum flux， under unstable conditions， the ejection dominates at all three levels.Under stable conditions， the ejection dominates at the roughness sublayer and the constant flux layer， while at the boundary of the roughness sublayer and the constant flux layer， the effect of the sweep is greater than that of the ejection.Under neutral conditions， the flux contribution of the sweep is greater than that of the ejection except for the roughness sublayer.The third-order cumulant expansion method （CEM） can more accurately express the flux contribution caused by ejection and sweep， while the incomplete cumulant expansion method （ICEM） is poor in simulating the temperature at the boundary of roughness sublayer and constant flux layer.Through the calculation of transmission efficiency， the difference between momentum and scalar transmission is further quantified.The turbulent transfer efficiency of momentum decreases with increasing instability， while the heat transfer efficiency is the opposite.Atmospheric stability is an important factor controlling momentum and scalar transfer， and the transfer efficiency of water vapor is less affected by atmospheric stability.Under the condition of strong instability， the heat transfer efficiency is more effective than other scalar transfer.

Studying the impact of valley winds on terrain precipitation is crucial for gaining a deeper understanding of the mechanism of precipitation formation under complex terrain conditions.Based on GPM/DPR data from 2014 to 2021， the connected domain method was used to identify the summer terrain precipitation system in the Ili River Valley.Combined with the 10 m surface wind data from ERA5， the precipitation process was divided into valley wind type and mountain wind type.The spatiotemporal distribution， vertical structure， and macro and micro characteristics of precipitation in these two types of "trumpet mouth" terrain were compared and analyzed.The results show that valley wind precipitation is concentrated on the windward slopes of the southern and eastern foothills of the valley， with the precipitation period mainly from noon to evening （12:00 -20:00 Beijing time， same as after）.Mountain wind precipitation is more abundant in the valley plain， with more precipitation occurring from night to morning （01:00 -06:00）.The average wind speed of valley wind （0.79 m·s^-1） is 6.8% higher than that of mountain wind （0.74 m·s^-1）.The average near surface precipitation rate （R） and rain top height （STH） of valley wind and mountain wind precipitation are 1.32 mm·h^-1， respectively 1.15 mm·h^-1， 5.90 km， 5.72 km， statistics show a positive correlation between STH and R； Under the influence of uphill winds， the R， STH， mass weighted average diameter （D _m）， and particle number concentration （dBN _w） of valley wind precipitation increase under the influence of terrain uplift， reaching a maximum at an altitude of 2 -3 km.The upwelling airflow formed on the windward slope promotes the condensation and coalescence of cloud droplets into raindrops； The average dBN _w of valley wind precipitation （33.5） is nearly 3% smaller than that of mountain wind precipitation （34.5）， while the average Dm of the former （1.63 mm） is 18.1% larger than that of the latter （1.38 mm）.Due to the lower radar reflectivity factor of mountain winds compared to valley winds in the liquid phase region below 0 ℃， when raindrops descend to the dry layer near the ground， the large droplets break and evaporate， resulting in more dBN _w and smaller D _m.Valley winds affect the macroscopic structure and microphysical processes of terrain precipitation.In future research on identifying the potential of terrain cloud artificial precipitation enhancement and numerical simulation of fine structure of precipitation in mountainous areas， attention should be paid to the role and dynamic mechanism of valley winds.

In traditional contingency tables， the hit and false alarm events are given equal weights when calculating the precipitation TS score.The weight of missed precipitation events with different amounts that satisfy the threshold conditions is also the same.At the same time， the frequency calculation method for hit events of different precipitation amounts within the threshold range is also the same.However， these methods lack precision in the verification of high-resolution grid precipitation forecasts.In fact， the impact of differences in observed precipitation values， even when predictions hit the threshold， may be completely different.Additionally， the impact of differences in the forecasting and observation of precipitation values in missed precipitation events may also vary greatly.The article proposes a precipitation impact forecast score based on the comprehensive consideration of precipitation hit rate， missed alarm ratio， and precipitation amount."Impact" is defined as the characterization of potential consequences that may result from forecast hits or misses on actual precipitation occurrences.For hit and missed events， impact factors are defined by taking the logarithm of observed precipitation and the logarithm of the difference between observed and forecasted precipitation.Based on this， equivalent impacts （A_I and C_I） of hit and missed events are accumulated at the spatiotemporal scale.The scoring solely considering the impact of C_I is defined as a sub-item of the Impact Threat Scoring （ITS）.The precipitation scoring that takes into account the combined effects of A _I and C_I is defined as the ITS score.Analysis shows that ITS₀ assigns dynamic weights based on the degree of difference in missed precipitation， allowing for a clear distinction of the impact level of missed events.On the other hand， ITS rewards the accurate prediction of heavy precipitation.The larger the hit precipitation values and the smaller the difference between the forecasted and observed values in missed events， the larger the ITS score.These factors result in a better ability to depict the potential consequences on actual precipitation.

In the spring and summer of 2022， the spatio-temporal variance of rainfall in Qingyang， Gansu Province， was conspicuously manifest.Regional and staged drought and flood disasters occurred alternately， and extreme weather phenomena emerged frequently and profoundly.Relying on precipitation observation， circulation index data， and sophisticated correlation analysis methods， this paper meticulously analyzes the precipitation characteristics and underlying potential causes of the abrupt change of drought and flood conditions in Qingyang during 2022.The results are elaborated as follows： （1） The spring and summer precipitation in Qingyang City in 2022 demonstrated remarkable and distinct features.It involved a prolonged duration of drought， an incredibly rapid transformation between drought and flood， and extreme precipitation that surpassed the historical extremes with remarkable magnitude.From March to June， the precipitation in numerous counties was less than 50%.Intriguingly， the precipitation in July suddenly turned out to be 41.0% more than that in the same period.The maximum daily rainfall recorded in the regional torrential rain on July 15 was an astonishing 373.1 mm， shattering the historical records.Such a rapid and dramatic change in the precipitation pattern is highly uncommon and rarely observed.（2） The precipitation of each county from March to May exhibited a significantly positive correlation with the ONI index， while a notably negative correlation with ONI was evident in July.During La Nina years， the position of the Western Pacific subtropical high shifted northward.Due to high temperatures and scarce rainfall in spring， Qingyang City became prone to extreme precipitation in summer.In the spring of 2022， the La Nina event began to intensify with considerable force， which exerted a profound and significant influence on the sudden and drastic change of drought and flood conditions in Qingyang City.（3） From March to mid-May， the Arctic Oscillation （AO） index was strikingly higher than the historical average.The weakened cold air activity led to an overall reduction in precipitation.From mid-July to mid-August， the negative phase of AO collaborated seamlessly with the strong subtropical high， facilitating the concentrated occurrence of heavy rain.（4） From March to June and August 2022， the 500 hPa at the mid-high latitude in Eurasia presented a distinctive "two trough and one ridge" circulation pattern.Despite the robustness of the Western Pacific subtropical high， the airflow convergence zone did not encompass Qingyang.Conspicuously， in July， the 500 hPa transformed into a "two ridges and one trough" pattern.The vigorous cold air and the intense water vapor transportation conspired to increase the frequency of heavy precipitation， ultimately resulting in the occurrence of extreme precipitation.

In order to construct a grid spatiotemporal continuous three-dimensional wind products integrating wind retrieved by radar， combining ERA5 reanalysis wind field which used as the first-guess field and three-dimensional wind field retrieved by dual-radar networking which used as the observed field based on optimal interpolation technique.The error statistics of the ERA5 reanalysis and radar retrieved wind fields are defined based on the data of six precipitation processes in the Zhejiang region and the error structures were employed to compute the weights， the combined hourly three-dimensional wind field with 0.25°×0.25° resolution were obtained as the final results which checked and evaluated with the second radio-sounding wind data.The results show that： Compared with the ERA5 wind field， the accuracy of the combined wind field is improved， root mean square error is reduced by 6% （U component）， 16% （V component）； the correlation coefficient is increased by 0.02 （V component）.The combined wind field has effectively corrected the rather smaller wind field shown by the reanalysis data near the eye of typhoon Lekima， filled the blind area of radar retrieval wind field， and formed a complete and accurate wind field product， corrected the large deviation of the typhoon location with ERA5 wind field.Combined with the small-scale wind information retrieved by radar， the combined wind field reflects the original inconspicuous convergence characteristics of ERA5 wind field， and improves the application value of data.

In the context of climate change， accurately simulating soil moisture using land surface process models holds significant importance for weather forecasting， agricultural production， and hydrological processes.This study utilized meteorological observation data from the Arou site in the upper reaches of the Heihe River as the driving data for the Noah-MP model to conduct soil moisture simulation experiments， aiming to assess the soil moisture simulation performance of the Noah-MP model in the alpine mountainous area of the upper reaches of the Heihe River.Without considering uncertainties in model parameters and driving data， arbitrary combinations of the parameterization schemes for different physical processes of the Noah-MP model were made.A soil moisture multi-parameterization ensemble simulation experiment encompassing 17， 280 different combination schemes was designed.The Natural Selection sensitivity analysis method was employed to analyze the sensitivity of shallow soil moisture simulation results to the parameterization schemes and further quantify the uncertainty range of the simulation results of the soil moisture multi-parameterization ensemble.The results of this research indicate that the Noah-MP model can be applied to simulate soil moisture in the alpine mountainous area of the upper reaches of the Heihe river basin.The model demonstrates relatively high accuracy in simulating shallow soil moisture， and the simulated soil moisture change trends are generally consistent with the observed data.This consistency suggests that the Noah-MP model is well-suited for capturing the dynamics of shallow soil moisture in these regions.However， the simulation accuracy for deep soil moisture is relatively poor， with the simulated soil moisture change trends showing considerable deviations from the observed data.This suggests that there are still challenges in accurately modeling moisture dynamics at greater soil depths， potentially due to the complexity of subsurface hydrological processes in cold and mountainous environments.The analysis also reveals that shallow soil moisture simulation results are sensitive to the parameterization schemes of four physical processes： supercooled liquid water in frozen soil， frozen soil permeability， partitioning precipitation into rainfall and snowfall， and the first-layer snow or soil temperature time scheme.Among these， the parameterization scheme of frozen soil permeability is particularly sensitive， indicating that it plays a crucial role in determining the accuracy of the simulation results.During the soil freeze-thaw cycle in the alpine mountainous area of the upper reaches of the Heihe River， the simulation results of soil moisture during the freezing period showed increased sensitivity to parameterization schemes， making the selection of the parameterization scheme for the soil freezing process the main factor contributing to the uncertainty of the simulation results of the soil moisture multi-parameterization ensemble.

Based on multiple observations， 16778 strong winds with speed equal to or greater than 17 m·s^-1 from 125 national meteorological stations over Yunnan from 2013 to 2021 are divided into 8 types： thermal low winds， thunderstorm winds， local winds， cold-air winds， winds before southern trough， winds under 500 hPa westerly jet， typhoon winds and winds after plateau trough.The characteristics of spatio-temporal distribution and wind direction/speed of all types of winds except for the local winds are explored.The most frequent strong winds over Yunnan are thermal low winds， followed by thunderstorm winds.The strong winds are mainly observed over the northwest， central， and eastern parts of Yunnan.Except for thunderstorm winds， the wind directions of strong winds could be determined by the causes of their occurrence.The wind speeds mainly range from level 7 to level 9， and the distribution of wind speeds which are equal to or greater than level 10 shows strong discontinuity.Even though the frequencies of thunderstorm winds and winds before southern trough are less than that of thermal low winds， the two types of winds have a higher ability to cause extremely strong winds.The annual variation of occurring times shows that the winds mainly occur in winter and spring， with the most frequency in March.The annual variation of occurring times of thunderstorm winds shows a bimodal structure with a main peak in April and a sub peak in August.Meanwhile， the annual variation of occurring days of thunderstorm winds also shows a bimodal structure， the occurring days of the sub peak in August are comparable to that of the main peak in April， indicating that thunderstorm winds occurring in spring are relatively organized， while thunderstorm winds occurring in summer are relatively scattered.For the diurnal variation of occurring times， the thermal low winds mainly occur in daytime， with the highest occurring frequency at 15:00 to 15:59 （Beijing time， the same below）.The thunderstorm winds and winds before southern trough mainly occur in afternoon， with the highest occurring frequency at 16:00 to 17:59.The cold-air winds mainly occur from the afternoon to the early morning， with the highest occurring frequency at 18:00 to 19:59.The winds under 500 hPa westerly jet have two periods with high frequencies， afternoon and early morning， and more winds occur in the afternoon.The typhoon winds mainly occur in daytime， with the highest frequency occurring at 16:00 to 17:59.The winds after plateau trough mainly occur in daytime and the beginning of the night， and more winds occur at 15:00 to 16:59.

Based on conventional meteorological observation data， ERA5 reanalysis data， lightning location data， and doppler weather radar data， taking Henan province that is the most representative region in central plains as the study area， significant elevated convection cases during the cold season （October to April of the following year）from 2010 to 2021 were classified according to instability mechanisms and the radar echo characteristics under different mechanisms were compared and analyzed.The results show as follows： （1） Elevated convection during the cold season in Henan Province are classified into four categories based on unstable mechanisms： conditionally instability， conditionally symmetric instability， mixed conditionally instability and conditionally symmetric instability and frontal secondary circulation triggering.Among them， conditionally instability class accounts for the largest proportion， followed by conditionally symmetric unstable instability and mixed class， while frontal secondary circulation triggering class accounts for a smaller proportion.（2） The radar echoes of elevated convection during the cold season are mainly large-scale stratiform mixed echoes.The intensity center of individual elevated convection exceeds 40 dBZ and the maximum echo top height is between 6 to 10 km.A ring-shaped or banded echo is observed at high elevation angles near the freezing layer.（3） In the radar echoes of conditionally instability class， the strongest center （55~60 dBZ） and the highest development height and echo top height （8~12 km） are observed， with the highest probability of hail.The mixed class echoes have a wide range and a large center intensity （40~55 dBZ） and the development height of convection is related to the dominant instability type， with echo top height reaching 8-10 km.The echoes of conditionally symmetric instability class and frontal secondary circulation triggering class are similar in structure， showing stripe or patchy features， with weaker center intensity （30~45 dBZ）， uniform echo texture， development height of echoes up to 3 km and maximum echo top height of 6~8 km.Convection cells propagate along the thermal wind direction， without new cell propagation or merging phenomena， and can form ice-phase particles and thunderstorms， but the probability of hailstorm is relatively low.（4） The radial velocity field shows discontinuity between the upper and lower wind fields， with a northeast or northeast jet stream below 850 hPa and a strong southwest jet stream above 700 hPa reflecting well the meteorological characteristics of warm and humid airflow ascending along the cold pad； the low-level zero-velocity line shows an "S" shaped bend and obvious positive and negative velocity centers form a typical "eye" structure， with the frontal secondary circulation class being the most significant， followed by the conditionally symmetric instability class.

The effects on surface air temperature series are different or more by urbanization with different levels， in order to clarify this difference in Central China， based on the daily air temperature data of 268 national meteorological stations of Central China during 1964 -2023， the big city stations， general city stations and national basic/reference stations were selected， meanwhile the reference stations were selected by the methods of Empirical Orthogonal Function （EOF） and adjacent station selection.Then the calculation formulas of urbanization bias， contribution of urbanization bias and urbanization bias correction were constructed.The effects of urbanization on annual and seasonal average temperature， average maximum and minimum temperature series of big city， general city and national basic/reference stations were comparably analyzed in Central China in 1964 -2023 and 1979 -2023， and then the urbanization biases of the annual and seasonal temperature series in above stations were corrected.The results showed that： in the two periods， the annual average temperature， annual average maximum and minimum temperature in city and basic/reference stations increased by urbanization， and the urbanization biases of the three temperatures in above stations in 1979 -2023 were more than those in 1964 -2023， but the contributions of urbanization bias decreased in big city and basic/reference stations.As far as average temperature was concerned， the effect of urbanization on annual average minimum temperature was significantly higher than that in annual average maximum temperature in city and basic/reference stations in the two periods； As far as season was concerned， it was the most significant effect on winter warming by urbanization in 1964 -2023， while it decreased obviously in 1979 -2023； As far as different level stations were concerned， it was the most significant effect of urbanization on big city stations in 1964 -2023， while the contribution of urbanization bias of general city stations was 5.6% higher than that in big city stations in 1979 -2023， the urbanization biases of the annual average temperature in national basic/reference stations were 0.040~0.041 ℃·（10a）^-1 in the two periods.After urbanization bias correction， the warming trends of annual average temperature， annual average maximum and minimum temperature reduced 0.044 ℃·（10a）^-1， 0.010 ℃·（10a）^-1， 0.070 ℃·（10a）^-1 respectively in Central China in 1964 -2023； The areas with the most significant decrease of warming trend were the central east of Henan Province which were the areas with the most significant urbanization in Central China， as the rapid development of urban， the impacts on climate and environment should be put specially attention.

Accurate heating load forecasting is crucial for enhancing the efficiency of district heating systems and improving indoor comfort in buildings.This study takes Tianjin， a major city in northern China， as a case study.Based on hourly heating load and meteorological data from the heating season in 2021 -2022， the impacts of comprehensive meteorological factors， such as temperature， wind speed， relative humidity， and solar radiation， on heating load is analyzed.An efficient short-term heating load prediction model is constructed using the Nonlinear Autoregressive with Exogenous Inputs （NARX） neural network algorithm.The results show that the hourly heating load that displays significant diurnal and monthly variations， has a notably negative correlation with temperature， weakly negative correlation with solar radiation， while the relationships with humidity and wind speed vary depending on the season.Compared to the prediction model considering only temperature， the model incorporating temperature， wind speed， relative humidity， and solar radiation together has better prediction performance， reducing the relative error by approximately 1.4%.By comparing the forecast results with the LSTM neural network prediction model， the NARX model significantly enhances prediction accuracy with decreasing the relative error by about 3.6%.