The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central an...The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central and southern regions of North China on 22 May 2017.The microphysical structural characteristics and processes near the embedded convection core and in the stratiform cloud are analyzed comparatively.Particular attention is given to the effect of riming on the microphysical properties near the upper boundary of the melting layer and to the factors influencing riming efficiency.The collaborative observations reveal that the particle size distributions observed near the convection core and in the stratiform region are close,while the particle properties like habit and riming degree are quite different.Above the melting layer,larger plate-like ice particles and supercooled water droplets(D>50μm)are more abundant near the convective core,leading to higher collision efficiencies between ice particles and supercooled water droplets.Larger fluctuation amplitudes of vertical airflow near the convective core also contribute to the increased riming activity and the formation of more heavily rimed particles,such as graupel.Furthermore,in situ measurements from airborne probes also revealed that above the melting layer,the riming process involves two stages:the mass of snow crystals grows as supercooled droplets merge internally without changing size,followed by external freezing that significantly enlarges the crystals.展开更多
Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental cons...Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.展开更多
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr...Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.展开更多
To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this pape...To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network(CNN)-iTransformer-long short-term memory(LSTM)model.First,the rime-optimized random forest regression algorithm(RIME-RF)is employed to perform spatial downscaling on numerical weather prediction(NWP)data,thereby improving its local applicability.Second,a CNN-iTransformer-LSTM hybrid prediction model is constructed.This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data,employs an iTransformer to model the global dependencies among multiple variables,and leverages an LSTM to enhance the learning of short-term temporal dynamic features,thereby achieving efficient collaborative mining of multi-scale features.Finally,experiments are conducted using actual data from a photovoltaic power station in Hebei,China,during various seasons and weather conditions.The results show that the proposed model outperforms the comparison models in terms of the root mean square error(RMSE),mean absolute error(MAE),and R2,maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days.The downscaling process further enhances the prediction performance,verifying the effectiveness and practicality of this method.展开更多
[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the cha...[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.展开更多
Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,...Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.展开更多
This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow r...This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow rime on thousands of trees;conquer the ice and snow wilderness on a snowmobile and start an in depth magical mystery tour in lilin Province.展开更多
In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonder...In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonders of the ice city to the snowcovered wonderland of rime ice.展开更多
The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the tw...The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the two-phase model of air/super-cooled droplets in the Eulerian coordinate system, this paper presents the simulation of the rime ice accretion on the NACA 0012 airfoil. The predicted rime ice shape is compared with those results of measurements and simulations by other icing codes. Also the resulting effects of rime ice on airfoil aerodynamic performance are discussed. Results indicate that the rime ice accretion leads to the loss of the maximum lift coefficient by 26%, the decrease of the stall angle by about 3° and the considerable increase of the drag coefficient.展开更多
Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall eve...Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall event on 20–21 April 2010.The simulation output was compared with precipitation and aircraft observations.The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage.Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles.The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other.However,differences existed in the temporal and spatial evolutions of the precipitation distribution.An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison,while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed.The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles.P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.展开更多
Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polari...Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergenceδderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP)formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42025501)the Natural Science Foundation of Hebei Province(Grant No.D2024304015)+4 种基金the Fundamental Research Funds for the Central Universities,including Grant No.020714380217the Cemac“GeoX”Interdisciplinary Program(Grant No.020714380210)the Open Grants of the Key Laboratory of Radar Meteorology,China Meteorological Administration(Grant No.2023LRM-B05)the Hebei Meteorological Service Scientific Research and Development Project(Grant No.23ky08)the Open Research Program of the State Key Laboratory of Severe Weather(Grant No.2023LASW-A01)。
文摘The process of riming significantly impacts the microphysical characteristics of clouds.This study uses aircraft and radar observation data in stratiform clouds with convection embedded that occurred in the central and southern regions of North China on 22 May 2017.The microphysical structural characteristics and processes near the embedded convection core and in the stratiform cloud are analyzed comparatively.Particular attention is given to the effect of riming on the microphysical properties near the upper boundary of the melting layer and to the factors influencing riming efficiency.The collaborative observations reveal that the particle size distributions observed near the convection core and in the stratiform region are close,while the particle properties like habit and riming degree are quite different.Above the melting layer,larger plate-like ice particles and supercooled water droplets(D>50μm)are more abundant near the convective core,leading to higher collision efficiencies between ice particles and supercooled water droplets.Larger fluctuation amplitudes of vertical airflow near the convective core also contribute to the increased riming activity and the formation of more heavily rimed particles,such as graupel.Furthermore,in situ measurements from airborne probes also revealed that above the melting layer,the riming process involves two stages:the mass of snow crystals grows as supercooled droplets merge internally without changing size,followed by external freezing that significantly enlarges the crystals.
基金financially supported by the Technological Agency of the Czech Republic (TAČR), Joint Grant No SS 02030031 ARAMISby the long-term strategic development financing of the Institute of Computer Science of the Czech Academy of Sciences (RVO 67985807)
文摘Rime ice is an effective winter ambient air pollution accumulator.Due to its higher ion content as compared to snow it is a non-negligible contributor to atmospheric deposition fluxes with potential environmental consequences,particularly in mountain regions.Here we explore spatio-temporal patterns of rime formation as a proxy for the propensity of individual sites to form rime ice.We present the recent time trends in rime ice occurrence and thickness measured by 23 professional meteorological stations in the Czech Republic in 2002–2023.In an exploratory data analysis,we found high year-to-year variability in rime occurrence and thickness at all sites.According to the annual mean number of hours with rime detected,the stations situated at the highest altitudes are significantly different(higher)from the rest of the sites.The highest rime hour and thickness records by far were observed at the LYSA station in the Beskydy(Beskid)Mts situated at the exposed mountaintop and highly elevated above the surrounding terrain.For advanced statistical modelling of rime thickness,we used two generalised additive models that account for long-term trends(potentially nonlinear),seasonal and daily variability.In an expanded model we further considered the effect of the North Atlantic Oscillation(NAO)index.All the parameters included in the models proved to be statistically significant,although the strength of their effect differed.Factors affecting the rime formation(meteorology and terrain)are strongly site-specific and identification of the significance of individual influencing factors remains a challenging task for our future research.Here,we explore a rare long-term rime record with detailed temporal resolution from multiple uniformly measured sites,which significantly enhances our understanding of rime formation.Additionally,the rime record is from a temperate zone,where rime forms only during a small part of the year.
基金the 2023 Liaoning Institute of Science and Technology Doctoral Program Launch Fund(2307B29),covering aspects such as data collection and publication of the paper。
文摘Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
文摘To enhance the accuracy of short-term photovoltaic power output prediction and address issues such as insufficient spatial resolution of meteorological forecast data and weak generalization ability of models,this paper proposes a prediction method that integrates spatial downscaling meteorological data with a convolutional neural network(CNN)-iTransformer-long short-term memory(LSTM)model.First,the rime-optimized random forest regression algorithm(RIME-RF)is employed to perform spatial downscaling on numerical weather prediction(NWP)data,thereby improving its local applicability.Second,a CNN-iTransformer-LSTM hybrid prediction model is constructed.This model utilizes a CNN as a spatial feature extractor to capture local patterns in meteorological data,employs an iTransformer to model the global dependencies among multiple variables,and leverages an LSTM to enhance the learning of short-term temporal dynamic features,thereby achieving efficient collaborative mining of multi-scale features.Finally,experiments are conducted using actual data from a photovoltaic power station in Hebei,China,during various seasons and weather conditions.The results show that the proposed model outperforms the comparison models in terms of the root mean square error(RMSE),mean absolute error(MAE),and R2,maintaining high prediction accuracy and stability even under complex weather conditions such as overcast and rainy days.The downscaling process further enhances the prediction performance,verifying the effectiveness and practicality of this method.
文摘[Objective]Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production.This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature.[Methods]A dynamic prediction model for crop canopy temperature was developed based on Long Short-Term Memory(LSTM),Variational Mode Decomposition(VMD),and the Rime Ice Morphology-based Optimization Algorithm(RIME)optimization algorithm,named RIME-VMD-RIME-LSTM(RIME2-VMDLSTM).Firstly,crop canopy temperature data were collected by an inspection robot suspended on a cableway.Secondly,through the performance of multiple pre-test experiments,VMD-LSTM was selected as the base model.To reduce crossinterference between different frequency components of VMD,the K-means clustering algorithm was applied to cluster the sample entropy of each component,reconstructing them into new components.Finally,the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM,enhancing the model's prediction accuracy.[Results and Discussions]The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)(0.3601 and 0.2543°C,respectively)in modeling different noise environments than the comparator model.Furthermore,the R2 value reached a maximum of 0.9947.[Conclusions]This model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.
基金supported by the National Natural Science Foundation of China[grant number 51775020]the Science Challenge Project[grant number.TZ2018007]+2 种基金the National Natural Science Foundation of China[grant number 62073009]the Postdoctoral Fellowship Program of CPSF[grant number GZC20233365]the Fundamental Research Funds for Central Universities[grant number JKF-20240559].
文摘Parameter extraction of photovoltaic(PV)models is crucial for the planning,optimization,and control of PV systems.Although some methods using meta-heuristic algorithms have been proposed to determine these parameters,the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases.The unstable results will affect the reliable operation and maintenance strategies of PV systems.In response to this challenge,an improved rime optimization algorithm with enhanced exploration and exploitation,termed TERIME,is proposed for robust and accurate parameter identification for various PV models.Specifically,the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity.Meanwhile,a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth.The TERIME algorithm is applied to estimate the optimal parameters of the single diode model,double diode model,and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France,Photo Watt-PWP 201 and S75.According to the statistical analysis in 100 runs,the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions.All of our source codes are publicly available at https://github.com/dirge1/TERIME.
文摘This is an in-depth journey to experience the ice and snow of Changbai Mountain.In these few days,you will explore Changbai Mountain and enjoy powder skiing;gallop on the ski trail;watch the stunning wonders of snow rime on thousands of trees;conquer the ice and snow wilderness on a snowmobile and start an in depth magical mystery tour in lilin Province.
文摘In the vast tand of Northeast China,we are slowly starting an ultimate journey of the ice and snow carnival and exploration of folk customs.This is a magic kingdom of ice and snow ,from the meticulously crafted wonders of the ice city to the snowcovered wonderland of rime ice.
文摘The roughness effect based on the wall function method is introduced into the numerical simulation of the rime ice accretion and the resulting effect on the aerodynamic performance of the airfoil. Incorporating the two-phase model of air/super-cooled droplets in the Eulerian coordinate system, this paper presents the simulation of the rime ice accretion on the NACA 0012 airfoil. The predicted rime ice shape is compared with those results of measurements and simulations by other icing codes. Also the resulting effects of rime ice on airfoil aerodynamic performance are discussed. Results indicate that the rime ice accretion leads to the loss of the maximum lift coefficient by 26%, the decrease of the stall angle by about 3° and the considerable increase of the drag coefficient.
基金supported by the National Key Research and Development Program of China (Grant No. 2018YFC1507900)the National Natural Science Foundation of China (Grant Nos. 41575131, 41530427 and 41875172)
文摘Using the Weather Research and Forecasting(WRF)model with two different microphysics schemes,the Predicted Particle Properties(P3)and the Morrison double-moment parameterizations,we simulated a stratiform rainfall event on 20–21 April 2010.The simulation output was compared with precipitation and aircraft observations.The aircraft-observed moderate-rimed dendrites and plates indicated that riming contributed significantly to ice particle growth at the mature precipitation stage.Observations of dendrite aggregation and capped columns suggested that aggregation coexisted with deposition or riming and played an important role in producing many large particles.The domain-averaged values of the 24-h surface precipitation accumulation from the two schemes were quite close to each other.However,differences existed in the temporal and spatial evolutions of the precipitation distribution.An analysis of the surface precipitation temporal evolution indicated faster precipitation in Morrison,while P3 indicated slower rainfall by shifting the precipitation pattern eastward toward what was observed.The differences in precipitation values between the two schemes were related to the cloud water content distribution and fall speeds of rimed particles.P3 simulated the stratiform precipitation event better as it captured the gradual transition in the mass-weighted fall speeds and densities from unrimed to rimed particles.
基金jointly funded by the Northwest Regional Weather Modification Capacity Building Project of the China Meteorological Administration(Grant No.ZQC-R18209)the National Natural Science Foundation of China(Grant Nos.41875172 and 42075192)。
文摘Three cases of microphysical characteristics and kinematic structures in the negative temperature region of summer mesoscale cloud systems over the eastern Tibetan Plateau(TP)were investigated using X-band dual-polarization radar.The time-height series of radar physical variables and mesoscale horizontal divergenceδderived by quasi-vertical profiles(QVPs)indicated that the dendritic growth layer(DGL,-20°C to-10°C)was ubiquitous,with large-value zones of K_(DP)(specific differential phase),Z_(DR)(differential reflectivity),or both,and corresponded to various dynamic fields(ascent or descent).Ascents in the DGL of cloud systems with vigorous vertical development were coincident with large-value zones of Z_(DR),signifying ice crystals with a large axis ratio,but with no obvious large values of K_(DP),which differs from previous findings.It is speculated that ascent in the DGL promoted ice crystals to undergo further growth before sinking.If there was descent in the DGL,a high echo top corresponded to large values of K_(DP),denoting a large number concentration of ice crystals;but with the echo top descending,small values of K_(DP)formed.This is similar to previous results and reveals that a high echo top is conducive to the generation of ice crystals.When ice particles fall to low levels(-10℃to 0℃),they grow through riming,aggregation,or deposition,and may not be related to the kinematic structure.It is important to note that this study was only based on a limited number of cases and that further research is therefore needed.
