Volume 52,Issue 4,2024 Table of Contents
2024, 52(4):457-467.
DOI: 10.19517/j.1671-6345.20230242
Abstract:
To harness the benefits derived from the high spatial and temporal resolution of vertically continuous wind profile radar observations and exploit its capacity for monitoring atmospheric diffusion conditions during pollution events, this paper calculates parameters such as vertical wind shear, divergence, vorticity, and boundary layer ventilation by utilising 13 sets of wind profile radar data in Hebei Province. The computations are based on the principles of atmospheric dynamics and the dynamic conditions inversion algorithm. By comparing the results with sounding data and analysing them in conjunction with near-surface PM2.5 concentration, the four parameter algorithms are examined. The results show that the variations and characteristics of the inversion products are reasonably represented, effectively reflecting the evolution of atmospheric pollution conditions. However, due to disparities in resolution and detection methods among different observational datasets, substantial discrepancies exist in the results derived from various datasets. Therefore, it is imperative to maintain data consistency when conducting analyses. Taking the regional PM2.5 pollution event in Hebei from 9 to 11 November 2022 as an example, through multi-site joint application, the evolution characteristics of the four products in this process indicate the following: during the pollution accumulation process, vertical wind shear below 3 km decreased from the diagonal to the lower right corner, with most values below 5 m/(s·km). Vorticity and divergence values were mostly within 20×10-5s-1, and less than 15×10-5 s-1 for distances below the 950 hPa isobaric surface, indicating a stable meteorological situation. Boundary layer ventilation was less than 3000 m2/s. Before the pollutant dispersal, vertical wind shear increased to above 10 m/(s·km) throughout the entire layer. The region with wind shear greater than 10 m/(s·km) between 2-3 km height and 0-1 km above the ground showed the strongest sensitivity to pollutant dispersion. Vorticity and divergence above the 850 hPa isobaric surface first increased to above 30×10-5s-1, and when vorticity and divergence above 20×10-5 s-1 extended within the boundary layer, the near-surface PM2.5 concentration rapidly decreased. When boundary layer ventilation reached 4000 m2/s, pollutant concentrations met the standards. The values at downstream stations increased significantly with a noticeable delay along the cold air transmission path, thus multi-station joint analysis could be used for pollution dispersion forecasts at downstream sites. By comparing with the data of airborne sounding and near-surface wind field, the wind profile data had obvious advantages of high temporal and spatial resolution. However, these parameters exclusively responded to atmospheric dynamic conditions and did not account for the evolving principles governing the weather system. The application was most effective when used in conjunction with the prevailing circulation patterns and the diagnostic analysis of the weather system.
To harness the benefits derived from the high spatial and temporal resolution of vertically continuous wind profile radar observations and exploit its capacity for monitoring atmospheric diffusion conditions during pollution events, this paper calculates parameters such as vertical wind shear, divergence, vorticity, and boundary layer ventilation by utilising 13 sets of wind profile radar data in Hebei Province. The computations are based on the principles of atmospheric dynamics and the dynamic conditions inversion algorithm. By comparing the results with sounding data and analysing them in conjunction with near-surface PM2.5 concentration, the four parameter algorithms are examined. The results show that the variations and characteristics of the inversion products are reasonably represented, effectively reflecting the evolution of atmospheric pollution conditions. However, due to disparities in resolution and detection methods among different observational datasets, substantial discrepancies exist in the results derived from various datasets. Therefore, it is imperative to maintain data consistency when conducting analyses. Taking the regional PM2.5 pollution event in Hebei from 9 to 11 November 2022 as an example, through multi-site joint application, the evolution characteristics of the four products in this process indicate the following: during the pollution accumulation process, vertical wind shear below 3 km decreased from the diagonal to the lower right corner, with most values below 5 m/(s·km). Vorticity and divergence values were mostly within 20×10-5s-1, and less than 15×10-5 s-1 for distances below the 950 hPa isobaric surface, indicating a stable meteorological situation. Boundary layer ventilation was less than 3000 m2/s. Before the pollutant dispersal, vertical wind shear increased to above 10 m/(s·km) throughout the entire layer. The region with wind shear greater than 10 m/(s·km) between 2-3 km height and 0-1 km above the ground showed the strongest sensitivity to pollutant dispersion. Vorticity and divergence above the 850 hPa isobaric surface first increased to above 30×10-5s-1, and when vorticity and divergence above 20×10-5 s-1 extended within the boundary layer, the near-surface PM2.5 concentration rapidly decreased. When boundary layer ventilation reached 4000 m2/s, pollutant concentrations met the standards. The values at downstream stations increased significantly with a noticeable delay along the cold air transmission path, thus multi-station joint analysis could be used for pollution dispersion forecasts at downstream sites. By comparing with the data of airborne sounding and near-surface wind field, the wind profile data had obvious advantages of high temporal and spatial resolution. However, these parameters exclusively responded to atmospheric dynamic conditions and did not account for the evolving principles governing the weather system. The application was most effective when used in conjunction with the prevailing circulation patterns and the diagnostic analysis of the weather system.
2024, 52(4):468-476.
DOI: 10.19517/j.1671-6345.20230289
Abstract:
The standard deviation analysis method is crucial for effectively evaluating radar data quality. This study applies the standard deviation analysis to analyse the polarisation parameters of C-band dual-polarisation radar, identifying the main influencing factors and optimising parameter configurations. The improvement effects on the standard deviation of polarisation parameters across typical weather processes are examined through observational experiments. The findings indicate that the accuracy of existing observational modes for C-band polarisation parameters is relatively low, with significant discrepancies in accuracy across different elevation angles. Specifically, the accuracy of polarisation parameter data at 1.5° elevation is higher than that at 0.5°. The dwell time, pulse repetition frequency, and number of pulses directly impact the standard deviation of C-band dual-linear polarisation radar’s polarisation parameters. By optimising the pulse repetition frequency and number of pulses, the accuracy of polarisation standard deviation is enhanced without altering the observational mode within the same dwell time. An analysis of the standard deviation of polarisation parameters during two typical weather processes reveals that, before parameter optimisation, the accuracy of polarisation standard deviation is generally higher during hail processes than in heavy precipitation events. However, post-improvement, the enhancement in the accuracy of polarisation standard deviation for heavy precipitation events is usually superior to that for hail events. The modified observational mode thus improves the accuracy of polarisation parameter data for C-band dual-polarisation radar to a certain extent.
The standard deviation analysis method is crucial for effectively evaluating radar data quality. This study applies the standard deviation analysis to analyse the polarisation parameters of C-band dual-polarisation radar, identifying the main influencing factors and optimising parameter configurations. The improvement effects on the standard deviation of polarisation parameters across typical weather processes are examined through observational experiments. The findings indicate that the accuracy of existing observational modes for C-band polarisation parameters is relatively low, with significant discrepancies in accuracy across different elevation angles. Specifically, the accuracy of polarisation parameter data at 1.5° elevation is higher than that at 0.5°. The dwell time, pulse repetition frequency, and number of pulses directly impact the standard deviation of C-band dual-linear polarisation radar’s polarisation parameters. By optimising the pulse repetition frequency and number of pulses, the accuracy of polarisation standard deviation is enhanced without altering the observational mode within the same dwell time. An analysis of the standard deviation of polarisation parameters during two typical weather processes reveals that, before parameter optimisation, the accuracy of polarisation standard deviation is generally higher during hail processes than in heavy precipitation events. However, post-improvement, the enhancement in the accuracy of polarisation standard deviation for heavy precipitation events is usually superior to that for hail events. The modified observational mode thus improves the accuracy of polarisation parameter data for C-band dual-polarisation radar to a certain extent.
2024, 52(4):477-487.
DOI: 10.19517/j.1671-6345.20230243
Abstract:
As the main equipment for atmospheric detection, weather radar plays an important role in meteorological operations, weather early warning, and atmospheric science research. With the upgrading of the S-band dual-polarisation radar, it is very important to improve the precision of radar quantitative precipitation estimation. In this paper, three southwest monsoon and three typhoon precipitation processes over the Shantou region from 2018 to 2022 are selected as the research objects. Hourly rainfall data from the S-band dual-linear polarisation weather radar and 43 automatic weather gauges in Shantou are used to analyse the effectiveness of radar precipitation estimation. To improve the reliability and accuracy of data, the data quality control of radar and rain gauge data is carried out by various means. The data of 92,880 polarisation samples are obtained by matching the station coordinates of the rain gauge with the multi-polarisation parameters of the radar from the time dimension and the space dimension. By means of five error indexes: mean error (ME), relative error (RE), root mean square error (RMSE), standard deviation (STD) and correlation coefficient (ρ), the total samples, sub-rainfall intensity samples and sub-precipitation type samples of PPS, Ryzhkov, CSU-HIDRO and HCA-LIQ radar precipitation estimation algorithms in six precipitation processes are evaluated respectively. By comparing and analysing the error indexes and the stability of the algorithm between the estimated value of radar precipitation and the measured value of rain gauge, the following conclusions are drawn: (1) in the total sample, CSU-HIDRO algorithm and HCA-LIQ algorithm outperform PPS algorithm and Ryzhkov algorithm, with CSU-HIDRO algorithm being the best; (2) CSU-HIDRO algorithm and HCA-LIQ algorithm perform better than PPS algorithm and Ryzhkov algorithm in the samples of southwest monsoon precipitation and typhoon precipitation, with CSU-HIDRO algorithm performing best in the samples of southwest monsoon precipitation, and HCA-LIQ algorithm performing best in typhoon precipitation samples; (3) Ryzhkov algorithm performs best in light rain samples, CSU-HIDRO algorithm outperforms other algorithms in medium rain samples and heavy rain samples, with HCA-LIQ algorithm being next. In a word, both CSU-HIDRO algorithm and HCA-LIQ algorithm adopt the data of raindrop spectrum in South China to improve the fitting of formula parameters, and the effect of quantitative precipitation estimation is better than the other two algorithms in the United States. In practical application, the rainfall intensity and precipitation type can be considered synthetically, and the corresponding precipitation estimation algorithm can be adopted to improve the accuracy of radar quantitative precipitation estimation. At the same time, it also provides reference for the secondary development of radar products and short-term forecast and early warning.
As the main equipment for atmospheric detection, weather radar plays an important role in meteorological operations, weather early warning, and atmospheric science research. With the upgrading of the S-band dual-polarisation radar, it is very important to improve the precision of radar quantitative precipitation estimation. In this paper, three southwest monsoon and three typhoon precipitation processes over the Shantou region from 2018 to 2022 are selected as the research objects. Hourly rainfall data from the S-band dual-linear polarisation weather radar and 43 automatic weather gauges in Shantou are used to analyse the effectiveness of radar precipitation estimation. To improve the reliability and accuracy of data, the data quality control of radar and rain gauge data is carried out by various means. The data of 92,880 polarisation samples are obtained by matching the station coordinates of the rain gauge with the multi-polarisation parameters of the radar from the time dimension and the space dimension. By means of five error indexes: mean error (ME), relative error (RE), root mean square error (RMSE), standard deviation (STD) and correlation coefficient (ρ), the total samples, sub-rainfall intensity samples and sub-precipitation type samples of PPS, Ryzhkov, CSU-HIDRO and HCA-LIQ radar precipitation estimation algorithms in six precipitation processes are evaluated respectively. By comparing and analysing the error indexes and the stability of the algorithm between the estimated value of radar precipitation and the measured value of rain gauge, the following conclusions are drawn: (1) in the total sample, CSU-HIDRO algorithm and HCA-LIQ algorithm outperform PPS algorithm and Ryzhkov algorithm, with CSU-HIDRO algorithm being the best; (2) CSU-HIDRO algorithm and HCA-LIQ algorithm perform better than PPS algorithm and Ryzhkov algorithm in the samples of southwest monsoon precipitation and typhoon precipitation, with CSU-HIDRO algorithm performing best in the samples of southwest monsoon precipitation, and HCA-LIQ algorithm performing best in typhoon precipitation samples; (3) Ryzhkov algorithm performs best in light rain samples, CSU-HIDRO algorithm outperforms other algorithms in medium rain samples and heavy rain samples, with HCA-LIQ algorithm being next. In a word, both CSU-HIDRO algorithm and HCA-LIQ algorithm adopt the data of raindrop spectrum in South China to improve the fitting of formula parameters, and the effect of quantitative precipitation estimation is better than the other two algorithms in the United States. In practical application, the rainfall intensity and precipitation type can be considered synthetically, and the corresponding precipitation estimation algorithm can be adopted to improve the accuracy of radar quantitative precipitation estimation. At the same time, it also provides reference for the secondary development of radar products and short-term forecast and early warning.
2024, 52(4):488-496.
DOI: 10.19517/j.1671-6345.20230104
Abstract:
The International Joint Committee for Guides in Metrology (JCGM) puts forward “Evaluation of measurement data-propagation of distributions using the Monte Carlo method” (MCM) in ISO/IEC Guide 98-3:2008 supplement 1. MCM is a method based on the law of propagation of distribution (LPD). Therefore, compared with the GUM (Guide to the Expression Uncertainty in Measurement) evaluation method, MCM has higher accuracy and reliability when applied to the evaluation of measurement uncertainty. There are two schemes of MCM: one is single batch MCM and the other is adaptive MCM. Since adaptive MCM can effectively save computer resources and is more efficient in evaluation, it is widely used in the evaluation of measurement uncertainty in various fields. To ensure the accuracy of the detection data of the digital barometer, it needs to be calibrated periodically, and to characterise the reliability of the calibration results, and needs to be analysed and evaluated in terms of the measurement uncertainty (referred to as “uncertainty”) of the calibration results. To effectively improve the accuracy of the calibration results of the digital barometer and provide technical support for the credibility and reliability of meteorological observation data, taking the calibration results of PTB220 digital barometer as the research object, the adaptive MCM method is used to evaluate the uncertainty of the measurement results of the digital barometer. The adaptive MCM method is used to compare and verify the GUM method in accordance with the validation scheme given in the ISO/IEC Guide 98-3: 2008 supplement 1. The results show that the deviation of GUM in the uncertainty of the digital barometer exceeds the specified range, i.e., the absolute deviation of the left and right endpoints of the 95% inclusion interval between GUM and MCM uncertainty exceeds the numerical tolerance of 0.005 hPa, which is determined by the effective number of digits of the standard uncertainty, so the GUM method fails to pass the validation. Through the above results, it is indicated that the GUM method may be unsuitable for the verification of the uncertainty of this instrument. The above results indicate that the GUM method may not be suitable for the evaluation of the uncertainty of this instrument.
The International Joint Committee for Guides in Metrology (JCGM) puts forward “Evaluation of measurement data-propagation of distributions using the Monte Carlo method” (MCM) in ISO/IEC Guide 98-3:2008 supplement 1. MCM is a method based on the law of propagation of distribution (LPD). Therefore, compared with the GUM (Guide to the Expression Uncertainty in Measurement) evaluation method, MCM has higher accuracy and reliability when applied to the evaluation of measurement uncertainty. There are two schemes of MCM: one is single batch MCM and the other is adaptive MCM. Since adaptive MCM can effectively save computer resources and is more efficient in evaluation, it is widely used in the evaluation of measurement uncertainty in various fields. To ensure the accuracy of the detection data of the digital barometer, it needs to be calibrated periodically, and to characterise the reliability of the calibration results, and needs to be analysed and evaluated in terms of the measurement uncertainty (referred to as “uncertainty”) of the calibration results. To effectively improve the accuracy of the calibration results of the digital barometer and provide technical support for the credibility and reliability of meteorological observation data, taking the calibration results of PTB220 digital barometer as the research object, the adaptive MCM method is used to evaluate the uncertainty of the measurement results of the digital barometer. The adaptive MCM method is used to compare and verify the GUM method in accordance with the validation scheme given in the ISO/IEC Guide 98-3: 2008 supplement 1. The results show that the deviation of GUM in the uncertainty of the digital barometer exceeds the specified range, i.e., the absolute deviation of the left and right endpoints of the 95% inclusion interval between GUM and MCM uncertainty exceeds the numerical tolerance of 0.005 hPa, which is determined by the effective number of digits of the standard uncertainty, so the GUM method fails to pass the validation. Through the above results, it is indicated that the GUM method may be unsuitable for the verification of the uncertainty of this instrument. The above results indicate that the GUM method may not be suitable for the evaluation of the uncertainty of this instrument.
2024, 52(4):497-507.
DOI: 10.19517/j.1671-6345.20230202
Abstract:
Building an open real-time monitoring system for integrated meteorological services and comprehensively improving the operational quality and efficiency of meteorological services is an important task for the development of meteorological informatization. The scope of meteorological integrated business monitoring includes multiple business fields such as forecast, prediction, observation, information, and public services. The monitoring function covers multiple aspects such as meteorological data, meteorological business applications, and infrastructure resources. The monitoring data have a wide range, diverse content, and intensive transmission, with a massive expansion in quantity and capacity. Establishing a standardised access mechanism for monitoring data has become a key factor in ensuring that complex and diverse monitoring data can be efficiently, completely, and accurately collected into the real-time monitoring system, and achieving integrated monitoring for meteorological comprehensive service. Upon the CIMISS-MCP monitoring data framework, this article designs a scalable monitoring data content model based on standardised data classification. Firstly, based on the content and application characteristics of the monitoring data, the meteorological business integrated monitoring data is divided into three categories: event information, status information, and indicator data, and their meanings are defined separately. Secondly, for each category of monitoring data, by analysing and summarising the business connotation of massive monitoring information, the necessary attribute items are extracted, forming a universal model composed of data identification domain and data attribute domain. Thirdly, considering the significant specificity of data attribute domain content due to the differences in monitoring objects and monitoring requirements, attribute items are defined for typical data domain content of event information required for core monitoring functions such as meteorological data full process monitoring, meteorological business application task monitoring, and centralised alarm monitoring. This model adopts various scalability elements in the design of general and typical attributes, which has good universality and scalability characteristics. It is widely applied in the construction of the “Meteorological Integrated Real-time Monitoring System” of the China Meteorological Administration, achieving the generation, collection, storage, calculation, service, and publication display of massive monitoring data, and well supporting various functions of the meteorological integrated monitoring business. At the same time, it is widely applied in the localisation and functional expansion of business monitoring systems in various provincial meteorological data centres and shows positive benefits. The design and application of this model effectively ensure the efficient and stable operation of meteorological information business, laying a solid data foundation for service-oriented and intelligent operation and maintenance of meteorological big data centres in the era of intelligent meteorology.
Building an open real-time monitoring system for integrated meteorological services and comprehensively improving the operational quality and efficiency of meteorological services is an important task for the development of meteorological informatization. The scope of meteorological integrated business monitoring includes multiple business fields such as forecast, prediction, observation, information, and public services. The monitoring function covers multiple aspects such as meteorological data, meteorological business applications, and infrastructure resources. The monitoring data have a wide range, diverse content, and intensive transmission, with a massive expansion in quantity and capacity. Establishing a standardised access mechanism for monitoring data has become a key factor in ensuring that complex and diverse monitoring data can be efficiently, completely, and accurately collected into the real-time monitoring system, and achieving integrated monitoring for meteorological comprehensive service. Upon the CIMISS-MCP monitoring data framework, this article designs a scalable monitoring data content model based on standardised data classification. Firstly, based on the content and application characteristics of the monitoring data, the meteorological business integrated monitoring data is divided into three categories: event information, status information, and indicator data, and their meanings are defined separately. Secondly, for each category of monitoring data, by analysing and summarising the business connotation of massive monitoring information, the necessary attribute items are extracted, forming a universal model composed of data identification domain and data attribute domain. Thirdly, considering the significant specificity of data attribute domain content due to the differences in monitoring objects and monitoring requirements, attribute items are defined for typical data domain content of event information required for core monitoring functions such as meteorological data full process monitoring, meteorological business application task monitoring, and centralised alarm monitoring. This model adopts various scalability elements in the design of general and typical attributes, which has good universality and scalability characteristics. It is widely applied in the construction of the “Meteorological Integrated Real-time Monitoring System” of the China Meteorological Administration, achieving the generation, collection, storage, calculation, service, and publication display of massive monitoring data, and well supporting various functions of the meteorological integrated monitoring business. At the same time, it is widely applied in the localisation and functional expansion of business monitoring systems in various provincial meteorological data centres and shows positive benefits. The design and application of this model effectively ensure the efficient and stable operation of meteorological information business, laying a solid data foundation for service-oriented and intelligent operation and maintenance of meteorological big data centres in the era of intelligent meteorology.
2024, 52(4):508-518.
DOI: 10.19517/j.1671-6345.20230328
Abstract:
In order to meet the requirements of meteorological departments for efficient management and sharing of meteorological government administration data, solve the problems of difficult communication, sharing and collaboration of data, and promote the integration of meteorological government administration data into the national integrated government big data system, China Meteorological Administration designs and builds the meteorological department’s first centralised and unified government administration data centre, which is “one level of concentration and four levels of application,” relying on the national-level meteorological proprietary cloud platform, and directly supporting meteorological government administration and business applications. This article analyses the current status, existing problems and construction needs of meteorological department government administration data, introduces the core architecture design, main business functions, the key information flow, the government administration data classification and hierarchical data system based on the characteristics of meteorological government administration data, key implementation technologies, and application effectiveness of the system. The implementation of the system adopts a series of advanced information technologies, including support for multi-source heterogeneous data aggregation, policy-based data cleansing and integration governance, globally unified data service and interface management, full-text search based on the open-source ElasticSearch engine, message-driven management application collaboration, data synchronisation in the master and standby centres, etc. The system started construction in 2019, and after more than two years of research and development, it completes the development of system software functions, establishes a data collection, data storage and data update mechanism, provides unified data sharing services, and continues to carry out the aggregation and governance of government administration data resources. It builds data applications such as office management, forecasting, human resources management, technology management, forecaster team portraits, full-text retrieval, business collaboration, comprehensive management decision-making analysis, etc. As the system is put into business applications, government administration data of the meteorological department are standardised, managed and effectively utilised, establishing a data resource directory of 12 first-level categories, 72 second-level categories, and 357 types of government administration data, completing the collection and storage of 237 types of data, releasing a total of 30 data interfaces in 20 categories, implementing 72 types of government administration data sharing applications within the meteorological department. Master data such as personnel, institutions, administrative divisions, seasonal holidays, and public roles provide real-time data services for more than 200 applications of the meteorological department. As a digital base for the digital transformation and upgrading of government administration of the meteorological department, the system provides “one-stop” government data management and sharing application services for the four-level meteorological departments at the national, provincial, city and county levels after its completion, and plays an essential role in effectively supporting China Meteorological Administration’s office management, scientific decision-making and performing public functions.
In order to meet the requirements of meteorological departments for efficient management and sharing of meteorological government administration data, solve the problems of difficult communication, sharing and collaboration of data, and promote the integration of meteorological government administration data into the national integrated government big data system, China Meteorological Administration designs and builds the meteorological department’s first centralised and unified government administration data centre, which is “one level of concentration and four levels of application,” relying on the national-level meteorological proprietary cloud platform, and directly supporting meteorological government administration and business applications. This article analyses the current status, existing problems and construction needs of meteorological department government administration data, introduces the core architecture design, main business functions, the key information flow, the government administration data classification and hierarchical data system based on the characteristics of meteorological government administration data, key implementation technologies, and application effectiveness of the system. The implementation of the system adopts a series of advanced information technologies, including support for multi-source heterogeneous data aggregation, policy-based data cleansing and integration governance, globally unified data service and interface management, full-text search based on the open-source ElasticSearch engine, message-driven management application collaboration, data synchronisation in the master and standby centres, etc. The system started construction in 2019, and after more than two years of research and development, it completes the development of system software functions, establishes a data collection, data storage and data update mechanism, provides unified data sharing services, and continues to carry out the aggregation and governance of government administration data resources. It builds data applications such as office management, forecasting, human resources management, technology management, forecaster team portraits, full-text retrieval, business collaboration, comprehensive management decision-making analysis, etc. As the system is put into business applications, government administration data of the meteorological department are standardised, managed and effectively utilised, establishing a data resource directory of 12 first-level categories, 72 second-level categories, and 357 types of government administration data, completing the collection and storage of 237 types of data, releasing a total of 30 data interfaces in 20 categories, implementing 72 types of government administration data sharing applications within the meteorological department. Master data such as personnel, institutions, administrative divisions, seasonal holidays, and public roles provide real-time data services for more than 200 applications of the meteorological department. As a digital base for the digital transformation and upgrading of government administration of the meteorological department, the system provides “one-stop” government data management and sharing application services for the four-level meteorological departments at the national, provincial, city and county levels after its completion, and plays an essential role in effectively supporting China Meteorological Administration’s office management, scientific decision-making and performing public functions.
2024, 52(4):519-534.
DOI: 10.19517/j.1671-6345.20230217
Abstract:
Using data from 373 meteorological stations in the Western China region and ERA5 reanalysis data from 1981 to 2022, this study employs methods such as Mann-Kendall, correlation, and composites to analyse the characteristics of autumn rainfall changes in Western China after entering a new climatic phase, and explores the atmospheric circulation and sea temperature anomaly characteristics. The results indicate that over the past 32 years, the overall trend of Western China autumn rain significantly increases, with an increase in precipitation of approximately 30.4 mm, at a rate of about 9.5 mm per decade. Spatially, most areas show an increase post-shift, with significant increases primarily located in most of the Sichuan Basin, western Hunan, southern Shanxi, and southern Ningxia regions. Circulation field analysis reveals that since 2011, the blocking high near the western coast of Europe to the Ural Mountains and the Sea of Okhotsk area (hereafter referred to as the Okhotsk high) strengthens. The Western Pacific Subtropical High (hereafter referred to as the West Pacific Sub-high) also intensifies and extends westward. The southwesterly winds turning through the South China Sea from the subtropical region of the Western Pacific, the easterlies from the Northwest Pacific region, and the southwesterly winds from the Indian Ocean through the Bay of Bengal all strengthen. These factors collectively contribute to an increasing trend in water vapour transported to Southwestern, providing favourable conditions for the transition from lesser to greater autumn rainfall. Sea surface field analysis indicates that during the earlier spring and summer, as well as the concurrent autumn seasons, sea temperatures in the Indian Ocean transition from cold to warm, while sea temperatures in the central Pacific shift from warm to cold. These shifts prompt adjustments in convective activity and meridional circulation across the mid-low latitudes. These also lead to a shift in the West Pacific Sub-high from weak to strong, markedly extending westward, enhancing the intensity of water vapour transported from its western side and the Bay of Bengal to Southwestern. Anomalous warming in the Northwest Pacific Ocean, by stimulating meridional teleconnection wave trains, strengthens the Okhotsk high. Elevated sea temperatures in the western Atlantic increase convective activity from the Arabian Sea to the Bay of Bengal, which, through downstream propagation of teleconnection wave trains, facilitates the transport of water vapour from the Bay of Bengal to Southwestern. The combined sea temperature changes in these regions thus intensify the water vapour transport from the western side of the West Pacific Sub-high and the Bay of Bengal to Southwestern, consequently leading to an increase in autumn rainfall from lesser to greater amounts.
Using data from 373 meteorological stations in the Western China region and ERA5 reanalysis data from 1981 to 2022, this study employs methods such as Mann-Kendall, correlation, and composites to analyse the characteristics of autumn rainfall changes in Western China after entering a new climatic phase, and explores the atmospheric circulation and sea temperature anomaly characteristics. The results indicate that over the past 32 years, the overall trend of Western China autumn rain significantly increases, with an increase in precipitation of approximately 30.4 mm, at a rate of about 9.5 mm per decade. Spatially, most areas show an increase post-shift, with significant increases primarily located in most of the Sichuan Basin, western Hunan, southern Shanxi, and southern Ningxia regions. Circulation field analysis reveals that since 2011, the blocking high near the western coast of Europe to the Ural Mountains and the Sea of Okhotsk area (hereafter referred to as the Okhotsk high) strengthens. The Western Pacific Subtropical High (hereafter referred to as the West Pacific Sub-high) also intensifies and extends westward. The southwesterly winds turning through the South China Sea from the subtropical region of the Western Pacific, the easterlies from the Northwest Pacific region, and the southwesterly winds from the Indian Ocean through the Bay of Bengal all strengthen. These factors collectively contribute to an increasing trend in water vapour transported to Southwestern, providing favourable conditions for the transition from lesser to greater autumn rainfall. Sea surface field analysis indicates that during the earlier spring and summer, as well as the concurrent autumn seasons, sea temperatures in the Indian Ocean transition from cold to warm, while sea temperatures in the central Pacific shift from warm to cold. These shifts prompt adjustments in convective activity and meridional circulation across the mid-low latitudes. These also lead to a shift in the West Pacific Sub-high from weak to strong, markedly extending westward, enhancing the intensity of water vapour transported from its western side and the Bay of Bengal to Southwestern. Anomalous warming in the Northwest Pacific Ocean, by stimulating meridional teleconnection wave trains, strengthens the Okhotsk high. Elevated sea temperatures in the western Atlantic increase convective activity from the Arabian Sea to the Bay of Bengal, which, through downstream propagation of teleconnection wave trains, facilitates the transport of water vapour from the Bay of Bengal to Southwestern. The combined sea temperature changes in these regions thus intensify the water vapour transport from the western side of the West Pacific Sub-high and the Bay of Bengal to Southwestern, consequently leading to an increase in autumn rainfall from lesser to greater amounts.
2024, 52(4):535-548.
DOI: 10.19517/j.1671-6345.20230241
Abstract:
In order to improve the understanding of the characteristics and mechanisms of typhoon remote rainstorms within the subtropical high, a remote rainstorm process triggered by Typhoon Mangosteen in 2018 is analysed using multi-source observation and ERA5 reanalysis data. The results show that: (1) The rainstorm occurred within a strong convective and unstable environment characterised by high temperature, high humidity, and high energy within the 500 hPa subtropical high. The rainstorm area was located at the north end of the typhoon trough at the lower level, within the southerly wind speed convergence area, and to the right side of the upper jet stream inlet area. (2) The lower-level warm and humid advection provided sufficient water vapour and energy, promoting the development and maintenance of convective unstable stratification. Strong upper-level divergence coupled with lower-level convergence induced a strong upward motion, which provided favourable dynamic conditions for the rainstorm. (3) The mesoscale convective system exhibited three evolution stages: the organisation and establishment of banded convection, the development and movement of supercells in the south, the weakening of convection in the north, and the development of new convection in the south, resulting in extreme short-term heavy precipitation (exceeding 100 mm/h) during two stages. (4) The low-level North China high combined with the typhoon trough and the offshore high successively enhanced the low-level convergence and maintained the shear line (surface convergence line), which was conducive to the development and maintenance of convection. The typhoon trough acted as the trigger and organiser of the initial convective zone, and the outflow of the thunderstorm cold pool played a pivotal role in the development and movement of supercells. (5) The divergence and suction force of the upper jet stream in the mid-level convergence, the development of positive vorticity and low trough, and the enhancement of vertical wind shear in the rainstorm area were conducive to the development of the southern convective zone. During typhoon activity at the low latitudes, there was a typhoon trough at the lower level of the 500 hPa subtropical high, and the convergence area at the northern end of the typhoon trough was the focus of long-range rainstorm forecasts.
In order to improve the understanding of the characteristics and mechanisms of typhoon remote rainstorms within the subtropical high, a remote rainstorm process triggered by Typhoon Mangosteen in 2018 is analysed using multi-source observation and ERA5 reanalysis data. The results show that: (1) The rainstorm occurred within a strong convective and unstable environment characterised by high temperature, high humidity, and high energy within the 500 hPa subtropical high. The rainstorm area was located at the north end of the typhoon trough at the lower level, within the southerly wind speed convergence area, and to the right side of the upper jet stream inlet area. (2) The lower-level warm and humid advection provided sufficient water vapour and energy, promoting the development and maintenance of convective unstable stratification. Strong upper-level divergence coupled with lower-level convergence induced a strong upward motion, which provided favourable dynamic conditions for the rainstorm. (3) The mesoscale convective system exhibited three evolution stages: the organisation and establishment of banded convection, the development and movement of supercells in the south, the weakening of convection in the north, and the development of new convection in the south, resulting in extreme short-term heavy precipitation (exceeding 100 mm/h) during two stages. (4) The low-level North China high combined with the typhoon trough and the offshore high successively enhanced the low-level convergence and maintained the shear line (surface convergence line), which was conducive to the development and maintenance of convection. The typhoon trough acted as the trigger and organiser of the initial convective zone, and the outflow of the thunderstorm cold pool played a pivotal role in the development and movement of supercells. (5) The divergence and suction force of the upper jet stream in the mid-level convergence, the development of positive vorticity and low trough, and the enhancement of vertical wind shear in the rainstorm area were conducive to the development of the southern convective zone. During typhoon activity at the low latitudes, there was a typhoon trough at the lower level of the 500 hPa subtropical high, and the convergence area at the northern end of the typhoon trough was the focus of long-range rainstorm forecasts.
2024, 52(4):549-559.
DOI: 10.19517/j.1671-6345.20230280
Abstract:
Using the data from conventional meteorological observation stations, sounding data, and S-band Doppler and dual-polarization radar data from Qingdao, the weather background and radar echo characteristics of a convective hail event at Qingdao Airport on the afternoon of July 8, 2023 are analyzed. The results show that the hail event occurred under the combined action of a strong westerly wind belt at 500 hPa and a cold vortex trough, with strong local characteristics. The entire atmosphere had strong instability, with obvious characteristics of dryness in the upper layer and wetness in the lower layer. The vertical wind shear intensity in the lower layer was moderate, while the vertical wind shear intensity in the deep layer was strong. The wind vector difference between 0 and 6 km reached 21 m/s, and the temperature difference between high and low altitudes was large. The height at wet bulb temperature 0 ℃ was 4 km. The radar echoes had high-rise strong echoes, bounded weak echo areas, echo hangings, and three-body scattering structures. At the same time, there was a clear mesocyclone structure at an altitude of 4-8 km in the middle layer, with a diameter of about 4 km and a thickness of about 3.2 km in the convergence layer. There was a velocity divergence in the upper layer. These were all typical radar characteristics of hailstorms. The large values of C-VIL and VIL density could well correspond to hail particles, and the C-VIL mutation could also be used as a reference index for forecasting hail and strong winds. The C-VIL jump occurred 30 minutes before the hail at Qingdao Airport, and the sudden drop occurred 7 minutes before the strong wind at Qingdao Airport. During this hail process, the dual-polarisation radar parameters ZDR and ρhv could effectively distinguish hail and strong precipitation, while the KDP large value area corresponded to heavy rain or melted hail. It was necessary to combine other parameters to further determine the particle morphology. This hail process showed obvious ZDR and KDP column structures, with the ZDR column higher than the KDP column. The ZDR column corresponded to strong upward motion, which corresponded to the location of the mesocyclone on the velocity diagram, while the KDP column corresponded to a more obvious downdraft. The analysis results can provide some reference for short-term and imminent forecasts of hail at Qingdao Airport, and also have some instructive significance for the practical application of dual-polarisation radar products.
Using the data from conventional meteorological observation stations, sounding data, and S-band Doppler and dual-polarization radar data from Qingdao, the weather background and radar echo characteristics of a convective hail event at Qingdao Airport on the afternoon of July 8, 2023 are analyzed. The results show that the hail event occurred under the combined action of a strong westerly wind belt at 500 hPa and a cold vortex trough, with strong local characteristics. The entire atmosphere had strong instability, with obvious characteristics of dryness in the upper layer and wetness in the lower layer. The vertical wind shear intensity in the lower layer was moderate, while the vertical wind shear intensity in the deep layer was strong. The wind vector difference between 0 and 6 km reached 21 m/s, and the temperature difference between high and low altitudes was large. The height at wet bulb temperature 0 ℃ was 4 km. The radar echoes had high-rise strong echoes, bounded weak echo areas, echo hangings, and three-body scattering structures. At the same time, there was a clear mesocyclone structure at an altitude of 4-8 km in the middle layer, with a diameter of about 4 km and a thickness of about 3.2 km in the convergence layer. There was a velocity divergence in the upper layer. These were all typical radar characteristics of hailstorms. The large values of C-VIL and VIL density could well correspond to hail particles, and the C-VIL mutation could also be used as a reference index for forecasting hail and strong winds. The C-VIL jump occurred 30 minutes before the hail at Qingdao Airport, and the sudden drop occurred 7 minutes before the strong wind at Qingdao Airport. During this hail process, the dual-polarisation radar parameters ZDR and ρhv could effectively distinguish hail and strong precipitation, while the KDP large value area corresponded to heavy rain or melted hail. It was necessary to combine other parameters to further determine the particle morphology. This hail process showed obvious ZDR and KDP column structures, with the ZDR column higher than the KDP column. The ZDR column corresponded to strong upward motion, which corresponded to the location of the mesocyclone on the velocity diagram, while the KDP column corresponded to a more obvious downdraft. The analysis results can provide some reference for short-term and imminent forecasts of hail at Qingdao Airport, and also have some instructive significance for the practical application of dual-polarisation radar products.
2024, 52(4):560-570.
DOI: 10.19517/j.1671-6345.20230219
Abstract:
In order to thoroughly comprehend the types, characteristics, and patterns of weather systems leading to maize lodging disasters in Jilin Province, an analytical approach employing historical disaster data, maize development period data, and ERA5 reanalysis data is adopted. Utilising typical case analysis methods, we seek to scrutinise and summarise the weather systems associated with maize lodging disasters in Jilin Province. The outcomes indicate that four predominant types of weather systems contribute significantly to maize lodging in the region: typhoon, northeast cold vortex, low-level shear line, and upper trough. During the jointing tasselling period, maize lodging is predominantly influenced by the northeast cold vortex, while during the tasselling milking period, the primary influencing factors are the low-level shear line and upper trough. The milking ripening period is chiefly affected by the northward movement of typhoons. Typhoons emerge as the most severe weather system causing maize lodging in Jilin Province, exhibiting extensive impacts, with the trajectory of their movement playing a pivotal role in determining the extent of crop lodging disasters. Typically, maize lodging disasters triggered by typhoons occur predominantly on the western or northern peripheries of their paths and on the northern flank of the associated surface low-pressure system. The second most influential weather system is the northeast cold vortex. Maize lodging disasters instigated by the cold vortex primarily manifest in the southeast quadrant of its influence, where conditions such as water vapour, dynamics, heat, and convective instability are most pronounced. The low-level shear line and upper trough exhibit a limited influence range. The occurrence site of maize lodging induced by the low-level shear line is contingent upon the positioning of the low-level shear line or convergence line. Maize lodging disasters attributed to upper-level troughs typically manifest in the frontal region of the upper-level trough, proximal to the ground cold front.
In order to thoroughly comprehend the types, characteristics, and patterns of weather systems leading to maize lodging disasters in Jilin Province, an analytical approach employing historical disaster data, maize development period data, and ERA5 reanalysis data is adopted. Utilising typical case analysis methods, we seek to scrutinise and summarise the weather systems associated with maize lodging disasters in Jilin Province. The outcomes indicate that four predominant types of weather systems contribute significantly to maize lodging in the region: typhoon, northeast cold vortex, low-level shear line, and upper trough. During the jointing tasselling period, maize lodging is predominantly influenced by the northeast cold vortex, while during the tasselling milking period, the primary influencing factors are the low-level shear line and upper trough. The milking ripening period is chiefly affected by the northward movement of typhoons. Typhoons emerge as the most severe weather system causing maize lodging in Jilin Province, exhibiting extensive impacts, with the trajectory of their movement playing a pivotal role in determining the extent of crop lodging disasters. Typically, maize lodging disasters triggered by typhoons occur predominantly on the western or northern peripheries of their paths and on the northern flank of the associated surface low-pressure system. The second most influential weather system is the northeast cold vortex. Maize lodging disasters instigated by the cold vortex primarily manifest in the southeast quadrant of its influence, where conditions such as water vapour, dynamics, heat, and convective instability are most pronounced. The low-level shear line and upper trough exhibit a limited influence range. The occurrence site of maize lodging induced by the low-level shear line is contingent upon the positioning of the low-level shear line or convergence line. Maize lodging disasters attributed to upper-level troughs typically manifest in the frontal region of the upper-level trough, proximal to the ground cold front.
2024, 52(4):571-582.
DOI: 10.19517/j.1671-6345.20230249
Abstract:
This article introduces a novel method that draws on big data thinking, treating the weather system as a comprehensive entity in which the interactions of the high, middle, and low-level atmospheres, as well as the influences of static, thermal, and dynamic conditions, are considered. It utilises a novel approach to comprehensive similarity assessment through situation field analysis, using derived data from numerical weather models and reanalysed grid data of various meteorological elements as its fundamental characteristics. The approach begins by employing the machine learning Principal Component Analysis (PCA) method to condense the features of the original grid field data, making it adaptable to the resource processing capabilities of conventional business platforms. Subsequently, the derived dimensional feature data of different meteorological elements at various spatial levels are normalised to ensure a balanced effect when participating in similarity calculations. The constructed sample-derived feature factor matrix, suitable for comprehensive weather similarity analysis, undergoes calculation of the similarity distance for each feature dimension among the samples. Based on the variance contribution rates of the initial field information contained in the data from different “principal component” dimensions, different weights are assigned to the similarity distance results of each dimension, yielding a comprehensive similarity distance. Finally, using the K-Nearest Neighbours (KNN) algorithm, the method provides the most comprehensive similar sequence in the historical weather situation database for the target sample, thus upgrading and improving traditional methods of similar weather forecasting. This method provides a multi-element and multi-level “stereoscopic” comprehensive similarity, aiding forecasters in better understanding the structure and evolution of weather systems and, consequently, more accurately assessing the possible occurrence of related weather phenomena. Comparative analysis and testing applications indicate that the results of comprehensive similarity analysis are superior to traditional “sliced” similarity analysis, which only targets single meteorological elements or altitude levels, particularly in terms of matching critical weather system positions and strength features. It resolves issues such as inconsistent results of similar weather situation analysis for different “slices” and poor forecast stability. This method provides more direct and efficient assistance in weather analysis and forecasting and holds promising prospects for refined meteorological forecasting services. In several instances of extreme precipitation meteorological forecasting services in the Guangxi region since 2023, this method achieves significant application effectiveness.
This article introduces a novel method that draws on big data thinking, treating the weather system as a comprehensive entity in which the interactions of the high, middle, and low-level atmospheres, as well as the influences of static, thermal, and dynamic conditions, are considered. It utilises a novel approach to comprehensive similarity assessment through situation field analysis, using derived data from numerical weather models and reanalysed grid data of various meteorological elements as its fundamental characteristics. The approach begins by employing the machine learning Principal Component Analysis (PCA) method to condense the features of the original grid field data, making it adaptable to the resource processing capabilities of conventional business platforms. Subsequently, the derived dimensional feature data of different meteorological elements at various spatial levels are normalised to ensure a balanced effect when participating in similarity calculations. The constructed sample-derived feature factor matrix, suitable for comprehensive weather similarity analysis, undergoes calculation of the similarity distance for each feature dimension among the samples. Based on the variance contribution rates of the initial field information contained in the data from different “principal component” dimensions, different weights are assigned to the similarity distance results of each dimension, yielding a comprehensive similarity distance. Finally, using the K-Nearest Neighbours (KNN) algorithm, the method provides the most comprehensive similar sequence in the historical weather situation database for the target sample, thus upgrading and improving traditional methods of similar weather forecasting. This method provides a multi-element and multi-level “stereoscopic” comprehensive similarity, aiding forecasters in better understanding the structure and evolution of weather systems and, consequently, more accurately assessing the possible occurrence of related weather phenomena. Comparative analysis and testing applications indicate that the results of comprehensive similarity analysis are superior to traditional “sliced” similarity analysis, which only targets single meteorological elements or altitude levels, particularly in terms of matching critical weather system positions and strength features. It resolves issues such as inconsistent results of similar weather situation analysis for different “slices” and poor forecast stability. This method provides more direct and efficient assistance in weather analysis and forecasting and holds promising prospects for refined meteorological forecasting services. In several instances of extreme precipitation meteorological forecasting services in the Guangxi region since 2023, this method achieves significant application effectiveness.
2024, 52(4):583-591.
DOI: 10.19517/j.1671-6345.20230251
Abstract:
With global warming, the occurrence of extreme weather and climate events, coupled with the rapid development of the economy and society, the problem of water resource shortages in Erhai Lake is becoming increasingly serious. To increase precipitation in the Erhai Lake basin, the meteorological department often carries out artificial rainfall enhancement operations and establishes artificial rainfall enhancement test areas in the Erhai Lake basin to conduct artificial rainfall enhancement effect evaluation research, so as to improve the ability of artificial rainfall enhancement operations. According to the weather situation of the rainfall enhancement operation day, this paper selects 819 daily precipitation data points of the test area and the contrast area in the Erhai Lake basin from 2005 to 2014 (November to February in winter and June to September in summer), and 79 rainfall enhancement operation data points from 2015 to 2020. The artificial rainfall enhancement operations in the Erhai Lake basin are subject to statistical and physical tests, and the following conclusions are drawn. (1) The statistical effect test of the artificial rainfall enhancement operations in the Erhai Lake basin is carried out by using the regional regression numerical simulation evaluation programme. The rainfall enhancement efficiency of stratus and mixed cloud is 16.57% and 20.09% respectively, and passes the significance test at the significance level α≤0.05. (2) The physical examination of the rainfall enhancement effect of the stratus cloud precipitation on 31 January 2020 and the mixed cloud precipitation on 13 August 2020 is conducted by using the new generation of weather radar. The results show that the maximum echo intensity and the average vertical integral liquid water content (VIL) of the stratus cloud target cloud body in the experimental area decreases slightly after the operation, and the echo top height changes little. The maximum echo intensity, the average VIL and the echo top height of the mixed cloud target cloud body increase rapidly. The ground rainfall intensity is significantly enhanced after the two types of rainfall enhancement operations. (3) The physical examination of the above two processes is conducted by using the laser raindrop spectrometer. The results show that the rainfall intensity and the number concentration of the stratus cloud and the mixed cloud rainfall enhancement operations in the experimental area begin to increase about 10-15 minutes after the operation. The diameter of the maximum number concentration changes from 0.437 mm to 0.562 mm, the spectral width increases, and the peak type changes from single peak to bimodal structure. The characteristics of the raindrop spectrum in the contrast area do not change obviously during the same period, indicating that the rainfall enhancement operation intensifies the condensation of water vapour in the cloud and produces more raindrops, and at the same time, it touches and produces larger scale raindrops.
With global warming, the occurrence of extreme weather and climate events, coupled with the rapid development of the economy and society, the problem of water resource shortages in Erhai Lake is becoming increasingly serious. To increase precipitation in the Erhai Lake basin, the meteorological department often carries out artificial rainfall enhancement operations and establishes artificial rainfall enhancement test areas in the Erhai Lake basin to conduct artificial rainfall enhancement effect evaluation research, so as to improve the ability of artificial rainfall enhancement operations. According to the weather situation of the rainfall enhancement operation day, this paper selects 819 daily precipitation data points of the test area and the contrast area in the Erhai Lake basin from 2005 to 2014 (November to February in winter and June to September in summer), and 79 rainfall enhancement operation data points from 2015 to 2020. The artificial rainfall enhancement operations in the Erhai Lake basin are subject to statistical and physical tests, and the following conclusions are drawn. (1) The statistical effect test of the artificial rainfall enhancement operations in the Erhai Lake basin is carried out by using the regional regression numerical simulation evaluation programme. The rainfall enhancement efficiency of stratus and mixed cloud is 16.57% and 20.09% respectively, and passes the significance test at the significance level α≤0.05. (2) The physical examination of the rainfall enhancement effect of the stratus cloud precipitation on 31 January 2020 and the mixed cloud precipitation on 13 August 2020 is conducted by using the new generation of weather radar. The results show that the maximum echo intensity and the average vertical integral liquid water content (VIL) of the stratus cloud target cloud body in the experimental area decreases slightly after the operation, and the echo top height changes little. The maximum echo intensity, the average VIL and the echo top height of the mixed cloud target cloud body increase rapidly. The ground rainfall intensity is significantly enhanced after the two types of rainfall enhancement operations. (3) The physical examination of the above two processes is conducted by using the laser raindrop spectrometer. The results show that the rainfall intensity and the number concentration of the stratus cloud and the mixed cloud rainfall enhancement operations in the experimental area begin to increase about 10-15 minutes after the operation. The diameter of the maximum number concentration changes from 0.437 mm to 0.562 mm, the spectral width increases, and the peak type changes from single peak to bimodal structure. The characteristics of the raindrop spectrum in the contrast area do not change obviously during the same period, indicating that the rainfall enhancement operation intensifies the condensation of water vapour in the cloud and produces more raindrops, and at the same time, it touches and produces larger scale raindrops.
2024, 52(4):592-600.
DOI: 10.19517/j.1671-6345.20230196
Abstract:
Gale is one of the main meteorological disasters affecting the safety of heavy haul railway transportation. Therefore, carrying out a risk assessment of gale disasters has certain guiding significance for preventing disasters and reducing damages in railway departments. This paper takes Shuozhou-Huanghua railway as a typical section of the heavy haul railway. First, the risk assessment system of gale disasters in typical sections along the heavy haul railways is constructed based on the four criteria levels such as the disaster-inducing factors of gale, environmental sensitivity of gale disasters, vulnerability of gale disaster-bearing bodies, and the capability of disaster prevention and mitigation, as well as the 17 indicator layers such as the number of blue level alarms for gale, the number of yellow level alarms for gale, the number of orange level alarms for gale, the number of red level alarms for gale, frequency of strong crosswind, altitude, terrain, the number of tracks, the number of trains, station level, allowable speed of lines, bridges, tunnels, presence of wind barriers, strong wind warning capabilities, the number of emergency personnel, and emergency rescue equipment and materials. Then, the analytic hierarchy process and entropy weight method are used to determine the weight coefficients of the above four criteria levels and the above 17 indicator layers of emergency, and finally, the comprehensive risk zoning of wind disasters in typical sections of heavy-duty railways is obtained. The results show that the railway sections from Shenchi South station to Ningwu West station, from Huanghua South station to Huanghua Port station, from Beigang station to Dagang station, and Shengang station are the highest risk sections of gale disasters along the Shuozhou-Huanghua railway. The railway sections from Longgong station to Diliudeng station, and from Huanghua East station to Yangsanmu station are the second highest risk sections of gale disasters along Shuozhou-Huanghua railway. The railway sections from Houwen station to Sanji station, from Xingtang station to Quyang station, from Dingzhou West station to Dingzhou East station, from Anguo station to Boye station, from Lixian station to Litianmu station, and from Douzhuangzi station to Guozhuangzi station are medium high risk sections of gale disasters along Shuozhou-Huanghua railway. To sum up, it can be seen that the comprehensive risk level of gale disasters along Shuozhou-Huanghua railway sections in the western mountainous, hilly areas and eastern coastal areas is the highest.
Gale is one of the main meteorological disasters affecting the safety of heavy haul railway transportation. Therefore, carrying out a risk assessment of gale disasters has certain guiding significance for preventing disasters and reducing damages in railway departments. This paper takes Shuozhou-Huanghua railway as a typical section of the heavy haul railway. First, the risk assessment system of gale disasters in typical sections along the heavy haul railways is constructed based on the four criteria levels such as the disaster-inducing factors of gale, environmental sensitivity of gale disasters, vulnerability of gale disaster-bearing bodies, and the capability of disaster prevention and mitigation, as well as the 17 indicator layers such as the number of blue level alarms for gale, the number of yellow level alarms for gale, the number of orange level alarms for gale, the number of red level alarms for gale, frequency of strong crosswind, altitude, terrain, the number of tracks, the number of trains, station level, allowable speed of lines, bridges, tunnels, presence of wind barriers, strong wind warning capabilities, the number of emergency personnel, and emergency rescue equipment and materials. Then, the analytic hierarchy process and entropy weight method are used to determine the weight coefficients of the above four criteria levels and the above 17 indicator layers of emergency, and finally, the comprehensive risk zoning of wind disasters in typical sections of heavy-duty railways is obtained. The results show that the railway sections from Shenchi South station to Ningwu West station, from Huanghua South station to Huanghua Port station, from Beigang station to Dagang station, and Shengang station are the highest risk sections of gale disasters along the Shuozhou-Huanghua railway. The railway sections from Longgong station to Diliudeng station, and from Huanghua East station to Yangsanmu station are the second highest risk sections of gale disasters along Shuozhou-Huanghua railway. The railway sections from Houwen station to Sanji station, from Xingtang station to Quyang station, from Dingzhou West station to Dingzhou East station, from Anguo station to Boye station, from Lixian station to Litianmu station, and from Douzhuangzi station to Guozhuangzi station are medium high risk sections of gale disasters along Shuozhou-Huanghua railway. To sum up, it can be seen that the comprehensive risk level of gale disasters along Shuozhou-Huanghua railway sections in the western mountainous, hilly areas and eastern coastal areas is the highest.
2024, 52(4):601-609.
DOI: 10.19517/j.1671-6345.20230205
Abstract:
In order to better consolidate the results of the creation of “China’s Natural Oxygen Bar” in Yichun, promote the green and healthy development of Yichun’s forest recreation and tourism industry, and help transform the value of ecological resources in the Lesser Khingan Mountains region, this paper formulates a data quality control method to analyse and evaluate the characteristics of the daily changes of anion concentration in the hinterland of Yichun based on the monitoring data on the anion concentration in the air at 13 stations in Yichun. The annual, seasonal, monthly and diurnal variation of anion concentration in Yichun, the hinterland of the Lesser Khingan Mountains, are analysed and evaluated. The results show that the annual average negative oxygen ion concentration in Yichun is generally higher than that in most areas in China, showing a spatial distribution of high in the mountains and low in the plains, and increasing with the latitude and longitude and the forest cover, showing a growing trend from the south to the north. Seasonal change characteristics are not significant; the overall trend is the highest in summer, followed by winter and spring, and the lowest in autumn, while the counties and districts show obvious local characteristics. Negative oxygen ion concentration has a daily change pattern, showing U-shaped bimodal characteristics, with nighttime significantly higher than daytime. Monthly changes are not significant, with the peak occurring in July-August and the lowest values in January and April. Negative oxygen ion concentrations in 90 percent of the county-level administrative areas reach the level of Class I, and the negative oxygen ion concentrations at the three types of monitoring stations basically reach the level of freshness and above.
In order to better consolidate the results of the creation of “China’s Natural Oxygen Bar” in Yichun, promote the green and healthy development of Yichun’s forest recreation and tourism industry, and help transform the value of ecological resources in the Lesser Khingan Mountains region, this paper formulates a data quality control method to analyse and evaluate the characteristics of the daily changes of anion concentration in the hinterland of Yichun based on the monitoring data on the anion concentration in the air at 13 stations in Yichun. The annual, seasonal, monthly and diurnal variation of anion concentration in Yichun, the hinterland of the Lesser Khingan Mountains, are analysed and evaluated. The results show that the annual average negative oxygen ion concentration in Yichun is generally higher than that in most areas in China, showing a spatial distribution of high in the mountains and low in the plains, and increasing with the latitude and longitude and the forest cover, showing a growing trend from the south to the north. Seasonal change characteristics are not significant; the overall trend is the highest in summer, followed by winter and spring, and the lowest in autumn, while the counties and districts show obvious local characteristics. Negative oxygen ion concentration has a daily change pattern, showing U-shaped bimodal characteristics, with nighttime significantly higher than daytime. Monthly changes are not significant, with the peak occurring in July-August and the lowest values in January and April. Negative oxygen ion concentrations in 90 percent of the county-level administrative areas reach the level of Class I, and the negative oxygen ion concentrations at the three types of monitoring stations basically reach the level of freshness and above.
2024, 52(4):610-618.
DOI: 10.19517/j.1671-6345.20230274
Abstract:
The single-factor analysis and multi-factor comprehensive evaluation delineation method are used to evaluate the geo-disaster prone area, and the rainfall distribution characteristics and the frequency of geo-disasters are combined to determine the geo-disaster meteorological early warning area. Taking the towns and streets as early warning units, and the effective rainfall and activated rain intensity of geological disasters as the disaster-causing factors, the box plot analysis method is used to determine the rainfall threshold index value of all levels of landslide geological hazard. The method of typical heavy rainfall process combined with disaster reverse check and historical typical case back is used to test the rainfall threshold of geological disaster warning. From the statistical analysis of geological disaster census cases in Zhuzhou from 2011 to 2021, it is found that the geological disasters in Zhuzhou are mainly landslide disasters, followed by collapse, and small-scale geological disasters account for 98% of the total. The occurrence period of geological disasters in Zhuzhou is consistent with the rainy season, and landslide geological disasters are mainly distributed in Youxian County and Chaling County, and collapse disasters are mainly distributed in Lusong District and Hetang District. More than 80% of towns and streets in the city have experienced geological disasters. Geological disasters in Zhuzhou are closely related to rainfall, and heavy rain or more is the main factor for the formation of geological disasters in Zhuzhou. When the rain intensity is more than 20 mm/h, the risk of geological disasters in Zhuzhou is relatively high, and the risk of geological disasters induced by heavy rain intensity more than 50 mm/h is great. The critical value of effective rainfall for geological disasters in Liling is 61.9 mm, and the critical rainfall in other counties (cities, districts) is less than 50 mm, and the effective rainfall is more than 80 mm when more than 80% of geological disasters occur. The geological disaster meteorological early warning area of Zhuzhou can be divided into key early warning area A, sub-key early warning area B and general early warning area C. Each early warning area contains N early warning units of towns or streets. The effective rainfall and activated rain intensity are taken as the disaster factors. Using the box plot analysis method, it is clear that the disaster probability of 25% is the yellow warning rainfall threshold, the disaster probability of 50% is the orange warning rainfall threshold, and the disaster probability of 75% is the red warning rainfall threshold. The blue, yellow, orange and red warning rainfall thresholds of landslide-type geological disasters in different meteorological warning zones of Zhuzhou are determined. It is proved that the threshold index of early warning rainfall with effective rainfall as the disaster factor has a good application effect in practical early warning, while the threshold index of early warning rainfall with activated rainfall intensity as the disaster factor is lower and has a larger false alarm rate, so it should not be used alone in practical early warning.
The single-factor analysis and multi-factor comprehensive evaluation delineation method are used to evaluate the geo-disaster prone area, and the rainfall distribution characteristics and the frequency of geo-disasters are combined to determine the geo-disaster meteorological early warning area. Taking the towns and streets as early warning units, and the effective rainfall and activated rain intensity of geological disasters as the disaster-causing factors, the box plot analysis method is used to determine the rainfall threshold index value of all levels of landslide geological hazard. The method of typical heavy rainfall process combined with disaster reverse check and historical typical case back is used to test the rainfall threshold of geological disaster warning. From the statistical analysis of geological disaster census cases in Zhuzhou from 2011 to 2021, it is found that the geological disasters in Zhuzhou are mainly landslide disasters, followed by collapse, and small-scale geological disasters account for 98% of the total. The occurrence period of geological disasters in Zhuzhou is consistent with the rainy season, and landslide geological disasters are mainly distributed in Youxian County and Chaling County, and collapse disasters are mainly distributed in Lusong District and Hetang District. More than 80% of towns and streets in the city have experienced geological disasters. Geological disasters in Zhuzhou are closely related to rainfall, and heavy rain or more is the main factor for the formation of geological disasters in Zhuzhou. When the rain intensity is more than 20 mm/h, the risk of geological disasters in Zhuzhou is relatively high, and the risk of geological disasters induced by heavy rain intensity more than 50 mm/h is great. The critical value of effective rainfall for geological disasters in Liling is 61.9 mm, and the critical rainfall in other counties (cities, districts) is less than 50 mm, and the effective rainfall is more than 80 mm when more than 80% of geological disasters occur. The geological disaster meteorological early warning area of Zhuzhou can be divided into key early warning area A, sub-key early warning area B and general early warning area C. Each early warning area contains N early warning units of towns or streets. The effective rainfall and activated rain intensity are taken as the disaster factors. Using the box plot analysis method, it is clear that the disaster probability of 25% is the yellow warning rainfall threshold, the disaster probability of 50% is the orange warning rainfall threshold, and the disaster probability of 75% is the red warning rainfall threshold. The blue, yellow, orange and red warning rainfall thresholds of landslide-type geological disasters in different meteorological warning zones of Zhuzhou are determined. It is proved that the threshold index of early warning rainfall with effective rainfall as the disaster factor has a good application effect in practical early warning, while the threshold index of early warning rainfall with activated rainfall intensity as the disaster factor is lower and has a larger false alarm rate, so it should not be used alone in practical early warning.
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