Volume 52,Issue 6,2024 Table of Contents
2024, 52(6):763-774.
DOI: 10.19517/j.1671-6345.20240095
Abstract:
Fengyun-4B is the first operational satellite of the second-generation FengYun geostationary meteorological satellite. The Advanced Geostationary Radiation Imager (AGRI), a multiple channel radiation imager, which adds a low-level water vapour detection channel and an adjusted spectrum range of four channels to improve the quality of observation, is one of the primary payloads onboard FY-4B. As one of the basic quantitative remote sensing products of FY-4B/AGRI, the operational sea surface temperature (SST) derives from the split-window nonlinear SST (NLSST) algorithm in real time. The stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. It degrades not only image quality but also the accuracy of retrieved SSTs. The stripe noise is observed in FY-4B/AGRI SSTs. It is more obvious in the brightness temperature difference (BTD) of the split window data, but the stripe noise is invisible in brightness temperature (BT) images. The stripe noise originates from the relative noise in the BTD. It propagates into SSTs by degrading the atmospheric correction. The bispectral filter approach for removing the stripe noise is applied to FY-4B/AGRI data. The bispectral filter is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. For the assessment of the bispectral filter approach, the retrieved FY-4B/AGRI SST is validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQUAM). Robust statistics are used to assess the impacts of the bispectral filter on SST accuracy. The accuracy and precision of the bispectral filter approach are assessed by determining the robust standard deviation and median bias between FY-4B/AGRI SST and quality-controlled in-situ SST from May to Norember 2023. The matchup space-time window is 4 km and 30 mins from the buoys’ location to the centre of the SST pixel. The validation results demonstrate the effectiveness of the bispectral filter, which reduces stripe noise in the retrieved FY-4B/AGRI SSTs. The image of a bispectral-filtered BTD is clearer than that of an unfiltered BTD. It also improves the accuracy of the SSTs by about 0.04 K to 0.06 K in the robust standard deviation. Furthermore, the bispectral filter approach is based on a simple Gaussian filter and is easy to implement. However, the bispectral filter cannot remove the stripe noise in BT. Such noise should be removed before applying the bispectral filter.
Fengyun-4B is the first operational satellite of the second-generation FengYun geostationary meteorological satellite. The Advanced Geostationary Radiation Imager (AGRI), a multiple channel radiation imager, which adds a low-level water vapour detection channel and an adjusted spectrum range of four channels to improve the quality of observation, is one of the primary payloads onboard FY-4B. As one of the basic quantitative remote sensing products of FY-4B/AGRI, the operational sea surface temperature (SST) derives from the split-window nonlinear SST (NLSST) algorithm in real time. The stripe noise is a common issue in sea surface temperatures (SSTs) retrieved from thermal infrared data obtained by satellite-based multidetector radiometers. It degrades not only image quality but also the accuracy of retrieved SSTs. The stripe noise is observed in FY-4B/AGRI SSTs. It is more obvious in the brightness temperature difference (BTD) of the split window data, but the stripe noise is invisible in brightness temperature (BT) images. The stripe noise originates from the relative noise in the BTD. It propagates into SSTs by degrading the atmospheric correction. The bispectral filter approach for removing the stripe noise is applied to FY-4B/AGRI data. The bispectral filter is a Gaussian filter and an optimal estimation method for the differences between the data obtained at the split window. A kernel function based on the physical processes of radiative transfer has made it possible to reduce stripe and random noise in retrieved SSTs without degrading the spatial resolution or generating bias. For the assessment of the bispectral filter approach, the retrieved FY-4B/AGRI SST is validated against in-situ SST measurements available from in-situ SST Quality Monitor (iQUAM). Robust statistics are used to assess the impacts of the bispectral filter on SST accuracy. The accuracy and precision of the bispectral filter approach are assessed by determining the robust standard deviation and median bias between FY-4B/AGRI SST and quality-controlled in-situ SST from May to Norember 2023. The matchup space-time window is 4 km and 30 mins from the buoys’ location to the centre of the SST pixel. The validation results demonstrate the effectiveness of the bispectral filter, which reduces stripe noise in the retrieved FY-4B/AGRI SSTs. The image of a bispectral-filtered BTD is clearer than that of an unfiltered BTD. It also improves the accuracy of the SSTs by about 0.04 K to 0.06 K in the robust standard deviation. Furthermore, the bispectral filter approach is based on a simple Gaussian filter and is easy to implement. However, the bispectral filter cannot remove the stripe noise in BT. Such noise should be removed before applying the bispectral filter.
2024, 52(6):775-786.
DOI: 10.19517/j.1671-6345.20240030
Abstract:
To achieve pixel-level classification and identification of thunderstorm activity ranges, research on image segmentation technology of meteorological geostationary satellite images is conducted. Taking Guizhou Province and surrounding areas (24°-30°N, 103°-110°E) as an example, the radiation data of Fengyun geostationary satellite water vapour and long-wave infrared channels (6.25-13.5 μm) are selected as features. By integrating ground-based very low frequency/low frequency (VLF/LF) lightning monitoring and spaceborne Lightning Mapping Imager (LMI) data, labelled data is constructed to establish a deep learning dataset. The improved DeepLabv3+ semantic segmentation network along with added training strategies is used to identify the thunderstorm activity range in geostationary satellite images. The research results show that by adopting deep learning training strategies such as data augmentation, adaptive sampling in active learning, Combo Loss combination loss, and Ranger21 optimiser, the impact of thunderstorm small sample data training on network model performance can be effectively reduced, and the problem of data imbalance can be solved. When further comparing different backbone networks, including MobilenetV2, Xception, ResNet_101, ResNet_50, and HRNetV2-48 for feature extraction, it is found that MobilenetV2 has the fastest running speed while ResNet_101 has the best segmentation performance. In addition, by introducing the convolutional block attention module (CBAM) in the encoder and decoder, the model’s ability to learn target features and fuse information at all levels is greatly enhanced. As a result, both pixel accuracy and average intersection are significantly improved, further enhancing the model’s segmentation accuracy. Through extensive ablation experiments on the test dataset comparing SegNet, UNet, FCN, Lraspp, and the original DeepLabv3+ semantic segmentation network model, it is evident that the improved DeepLabv3+ model is superior to all other models. It achieves a pixel average accuracy of 96.82% and an average intersection over union (MIoU) of 76.93%. This not only showcases its superiority but also to a certain extent addresses the problem of high accuracy but low MIoU of the training model on the test set due to imbalanced sample data. This research extends the RGB three-channel data in image recognition to meteorological multi-dimensional data with more than three channels, aiming to mine thunderstorm characteristic information in satellite images more accurately and efficiently and lay a solid foundation for the next step of applying spatio-temporal recurrent neural networks to thunderstorm activity prediction. This study holds great promise for improving our understanding and prediction of thunderstorm activities.
To achieve pixel-level classification and identification of thunderstorm activity ranges, research on image segmentation technology of meteorological geostationary satellite images is conducted. Taking Guizhou Province and surrounding areas (24°-30°N, 103°-110°E) as an example, the radiation data of Fengyun geostationary satellite water vapour and long-wave infrared channels (6.25-13.5 μm) are selected as features. By integrating ground-based very low frequency/low frequency (VLF/LF) lightning monitoring and spaceborne Lightning Mapping Imager (LMI) data, labelled data is constructed to establish a deep learning dataset. The improved DeepLabv3+ semantic segmentation network along with added training strategies is used to identify the thunderstorm activity range in geostationary satellite images. The research results show that by adopting deep learning training strategies such as data augmentation, adaptive sampling in active learning, Combo Loss combination loss, and Ranger21 optimiser, the impact of thunderstorm small sample data training on network model performance can be effectively reduced, and the problem of data imbalance can be solved. When further comparing different backbone networks, including MobilenetV2, Xception, ResNet_101, ResNet_50, and HRNetV2-48 for feature extraction, it is found that MobilenetV2 has the fastest running speed while ResNet_101 has the best segmentation performance. In addition, by introducing the convolutional block attention module (CBAM) in the encoder and decoder, the model’s ability to learn target features and fuse information at all levels is greatly enhanced. As a result, both pixel accuracy and average intersection are significantly improved, further enhancing the model’s segmentation accuracy. Through extensive ablation experiments on the test dataset comparing SegNet, UNet, FCN, Lraspp, and the original DeepLabv3+ semantic segmentation network model, it is evident that the improved DeepLabv3+ model is superior to all other models. It achieves a pixel average accuracy of 96.82% and an average intersection over union (MIoU) of 76.93%. This not only showcases its superiority but also to a certain extent addresses the problem of high accuracy but low MIoU of the training model on the test set due to imbalanced sample data. This research extends the RGB three-channel data in image recognition to meteorological multi-dimensional data with more than three channels, aiming to mine thunderstorm characteristic information in satellite images more accurately and efficiently and lay a solid foundation for the next step of applying spatio-temporal recurrent neural networks to thunderstorm activity prediction. This study holds great promise for improving our understanding and prediction of thunderstorm activities.
2024, 52(6):787-796.
DOI: 10.19517/j.1671-6345.20230364
Abstract:
In this paper, the relationship between the data at all levels formed by DSG5 in a rain-snow transition weather process and its identification process of weather phenomena are analyzed. The variation characteristics of the precipitation particle spectrum and meteorological elements such as air temperature, ground temperature and grass surface temperature in the process of rain-snow transition are studied. The ECMWF model data and dual-polarization Doppler radar data are used to explore the weather background of rain-snow transition and the spatial distribution of precipitation particles in different phases. It is found that: Before the start of precipitation, a cold air layer formed at the bottom of the warm air layer below 2 km above the station due to the influence of the backflow cold air, and the snowflakes in the upper layer experienced different degrees of melting after falling into the warm air layer. With the enhancement of the backflow cold air, the cold air layer near the ground gradually became thicker, the temperature became colder, and the warm layer gradually became thinner. The station experienced three main precipitation stages: raindrops, raindrops plus ice particles, and ice particles plus snowflakes. The phase change of precipitation particles over the station was clearly reflected in the spatial distribution of the correlation coefficient of the dual-polarisation Doppler radar. In the process of changing from rain to snow, the scale spectrum of surface precipitation particles obviously widened, and the velocity spectrum obviously narrowed. The raindrop scale time spectrum and velocity time spectrum better reflected the changes in the phase state of precipitation particles. The distribution of parameters such as the correlation coefficient of dual-polarisation Doppler radar and the changes of air temperature, ground temperature, and grass surface temperature assisted DSG5 in judging the precipitation weather phenomenon. In general, the identification results of DSG5 precipitation weather phenomenon were consistent with the analysis results of dual-polarisation Doppler radar and other observation data. To avoid interference, the particle number threshold was adopted by DSG5, which made the start time of precipitation judged by DSG5 lag by 5 minutes, the end time of precipitation advance by 6 minutes, and the total duration of precipitation judged by DSG5 was obviously shorter. According to the continuity of the precipitation process and radar echo, the time periods before and after the precipitation process identified by DSG5 were included in the corresponding weather process, and the identification results of DSG5 were optimised to overcome the problem that the total precipitation time of DSG5 was too short.
In this paper, the relationship between the data at all levels formed by DSG5 in a rain-snow transition weather process and its identification process of weather phenomena are analyzed. The variation characteristics of the precipitation particle spectrum and meteorological elements such as air temperature, ground temperature and grass surface temperature in the process of rain-snow transition are studied. The ECMWF model data and dual-polarization Doppler radar data are used to explore the weather background of rain-snow transition and the spatial distribution of precipitation particles in different phases. It is found that: Before the start of precipitation, a cold air layer formed at the bottom of the warm air layer below 2 km above the station due to the influence of the backflow cold air, and the snowflakes in the upper layer experienced different degrees of melting after falling into the warm air layer. With the enhancement of the backflow cold air, the cold air layer near the ground gradually became thicker, the temperature became colder, and the warm layer gradually became thinner. The station experienced three main precipitation stages: raindrops, raindrops plus ice particles, and ice particles plus snowflakes. The phase change of precipitation particles over the station was clearly reflected in the spatial distribution of the correlation coefficient of the dual-polarisation Doppler radar. In the process of changing from rain to snow, the scale spectrum of surface precipitation particles obviously widened, and the velocity spectrum obviously narrowed. The raindrop scale time spectrum and velocity time spectrum better reflected the changes in the phase state of precipitation particles. The distribution of parameters such as the correlation coefficient of dual-polarisation Doppler radar and the changes of air temperature, ground temperature, and grass surface temperature assisted DSG5 in judging the precipitation weather phenomenon. In general, the identification results of DSG5 precipitation weather phenomenon were consistent with the analysis results of dual-polarisation Doppler radar and other observation data. To avoid interference, the particle number threshold was adopted by DSG5, which made the start time of precipitation judged by DSG5 lag by 5 minutes, the end time of precipitation advance by 6 minutes, and the total duration of precipitation judged by DSG5 was obviously shorter. According to the continuity of the precipitation process and radar echo, the time periods before and after the precipitation process identified by DSG5 were included in the corresponding weather process, and the identification results of DSG5 were optimised to overcome the problem that the total precipitation time of DSG5 was too short.
2024, 52(6):797-806.
DOI: 10.19517/j.1671-6345.20230430
Abstract:
With the rapid development of numerical weather prediction services, the resolution and forecasting lead time of meteorological models have significantly improved, leading to an exponential growth in the volume of forecast data output. As a national meteorological model research and operational centre, CMA Earth System Modeling and Prediction Center (CEMC) currently produces daily gridded data outputs of 0.76 TB, with an annual output reaching 155.12 TB. Given the enormous data volumes, researchers’ preferences for data access are evolving. Wagemann predicts that future scientific users increasingly prefer cloud platforms or other interfaces for data access rather than solely relying on downloads. To address these issues, this paper proposes a lightweight distributed parallel processing framework for gridded data management, aiming to streamline data management processes and enhance data access speed. The core design philosophy revolves around leveraging search engine technology for rapid metadata retrieval and gridded data decoding techniques for efficient data acquisition. To mitigate performance penalties from repetitive decoding, the framework decodes gridded data files once and supports multiple retrievals and extractions, significantly accelerating data access. Additionally, it supports cross-platform data access, facilitating easier data acquisition for researchers. The framework adopts a three-tier architecture: the data layer stores data, the algorithm layer implements core search and cataloguing algorithms, and the business layer interfaces directly with user needs. The framework implements crucial functions such as gridded data cataloguing, extraction, and clipping. During cataloguing, users invoke the cataloguing interface and input parameters (e.g., original data file paths, index names, index types), and the system automatically parses file metadata and generates indexes. For data extraction, users call the retrieval interface with specific parameters to obtain designated data. Moreover, the framework supports precise extraction of specified latitudinal and longitudinal data segments by configuring cropping parameters. It reduces decoding time by creating indexes based on binary storage characteristics, utilises an inverted index value-id model for rapid data location retrieval, enhances processing performance through GlusterFS shared storage and Celery distributed message queues, and ensures efficient and stable data transmission using gRPC technology for C/S communication. Practical tests and applications demonstrate the framework’s exceptional performance in handling massive meteorological data. Notably, it successfully processes petabyte-scale gridded data during the Beijing Winter Olympics meteorological support services, significantly improving data access efficiency. Additionally, the framework supports flexible processing and scalable upgrades for various file formats to meet diverse user needs. By integrating advanced search engine technology, gridded data decoding methods, and a distributed cluster framework, the platform not only enables rapid data retrieval and efficient access but also satisfies researchers’ urgent demand for cross-platform data access. As meteorological data continues to grow, this platform holds significant potential to play a pivotal role in various fields, offering more robust data support for weather forecasting, scientific research, and operational applications.
With the rapid development of numerical weather prediction services, the resolution and forecasting lead time of meteorological models have significantly improved, leading to an exponential growth in the volume of forecast data output. As a national meteorological model research and operational centre, CMA Earth System Modeling and Prediction Center (CEMC) currently produces daily gridded data outputs of 0.76 TB, with an annual output reaching 155.12 TB. Given the enormous data volumes, researchers’ preferences for data access are evolving. Wagemann predicts that future scientific users increasingly prefer cloud platforms or other interfaces for data access rather than solely relying on downloads. To address these issues, this paper proposes a lightweight distributed parallel processing framework for gridded data management, aiming to streamline data management processes and enhance data access speed. The core design philosophy revolves around leveraging search engine technology for rapid metadata retrieval and gridded data decoding techniques for efficient data acquisition. To mitigate performance penalties from repetitive decoding, the framework decodes gridded data files once and supports multiple retrievals and extractions, significantly accelerating data access. Additionally, it supports cross-platform data access, facilitating easier data acquisition for researchers. The framework adopts a three-tier architecture: the data layer stores data, the algorithm layer implements core search and cataloguing algorithms, and the business layer interfaces directly with user needs. The framework implements crucial functions such as gridded data cataloguing, extraction, and clipping. During cataloguing, users invoke the cataloguing interface and input parameters (e.g., original data file paths, index names, index types), and the system automatically parses file metadata and generates indexes. For data extraction, users call the retrieval interface with specific parameters to obtain designated data. Moreover, the framework supports precise extraction of specified latitudinal and longitudinal data segments by configuring cropping parameters. It reduces decoding time by creating indexes based on binary storage characteristics, utilises an inverted index value-id model for rapid data location retrieval, enhances processing performance through GlusterFS shared storage and Celery distributed message queues, and ensures efficient and stable data transmission using gRPC technology for C/S communication. Practical tests and applications demonstrate the framework’s exceptional performance in handling massive meteorological data. Notably, it successfully processes petabyte-scale gridded data during the Beijing Winter Olympics meteorological support services, significantly improving data access efficiency. Additionally, the framework supports flexible processing and scalable upgrades for various file formats to meet diverse user needs. By integrating advanced search engine technology, gridded data decoding methods, and a distributed cluster framework, the platform not only enables rapid data retrieval and efficient access but also satisfies researchers’ urgent demand for cross-platform data access. As meteorological data continues to grow, this platform holds significant potential to play a pivotal role in various fields, offering more robust data support for weather forecasting, scientific research, and operational applications.
GUO Wenxin
,
LI Rong
,
YU Wanrong
,
LI Jianqiang
,
ZHENG Yu
,
CHEN Xiaojian
,
LIU Xin
,
LIU Sichen
,
NIU Liumin
,
YANG Jie
,
CHE Huizheng
2024, 52(6):807-815.
DOI: 10.19517/j.1671-6345.20240021
Abstract:
In the realm of meteorological forecasting, the integration of various nowcasting extrapolation methods is critical for enhancing accuracy and reliability. This paper introduces an innovative method for radar echo extrapolation called Rolling Fusion (RF), specifically designed to improve radar composite reflectivity (CREF) extrapolation. RF represents a novel synthesis of Optical Flow (OF) and Deep Learning (DL) methodologies, targeting the enhancement of nowcasting weather predictions. Central to the RF approach is RFNet, a sophisticated tool that employs a two-layer convolutional neural network. This network is optimised using Particle Swarm Optimisation (PSO), a computational methodology inspired by the social behaviour of birds and fish. PSO is particularly valuable in refining the network’s parameters to tackle the prevalent issue of CREF intensity imbalance, which can skew forecasting results. By optimising these parameters, RFNet ensures a balanced and accurate representation of various intensity levels, crucial for predicting severe weather conditions. The training process for RFNet is meticulously structured, utilising 10 steps of CREF data extrapolated from both OF and DL methods to anticipate the subsequent 10 steps. This dynamic approach not only enables high accuracy in nowcasting predictions but also enhances training efficiency by using the initially trained RFNet as a pre-trained model for further training cycles. This layered training process reduces computational demands, making the system both time-efficient and resource-efficient. Empirical results from this study reveal that RFNet effectively mitigates common drawbacks associated with deep learning predictions, specifically intensity attenuation and echo structure blurring. These enhancements allow RFNet to provide clearer and more accurate forecasts. Performance assessments across various intensity thresholds from 20 to 50 dBz demonstrate the method’s robustness. At lower thresholds, such as 20 and 30 dBz, RFNet and DL exhibit comparable performance, both of which surpass the capabilities of OF. In these scenarios, RFNet’s advanced integration of methodologies ensures superior forecasting precision. At a 40 dBz threshold, DL initially excels within the first 30 minutes of forecast duration. However, RFNet outperforms DL beyond this timeframe, highlighting its strength in extended forecasting scenarios. Notably, at the 50 dBz threshold, RFNet displays a significant performance advantage over both DL and OF, maintaining superior forecasting ability for up to 42 minutes. This capability is particularly valuable in predicting high-intensity weather events, where rapid changes necessitate agile and accurate forecasting models. Additionally, the research indicates a trend where RFNet’s extrapolation performance improves as CREF intensity surpasses 40 dBz. This improvement underscores the system’s adaptability and effectiveness in handling severe weather conditions, ultimately contributing to more reliable and actionable nowcasting weather forecasts.
In the realm of meteorological forecasting, the integration of various nowcasting extrapolation methods is critical for enhancing accuracy and reliability. This paper introduces an innovative method for radar echo extrapolation called Rolling Fusion (RF), specifically designed to improve radar composite reflectivity (CREF) extrapolation. RF represents a novel synthesis of Optical Flow (OF) and Deep Learning (DL) methodologies, targeting the enhancement of nowcasting weather predictions. Central to the RF approach is RFNet, a sophisticated tool that employs a two-layer convolutional neural network. This network is optimised using Particle Swarm Optimisation (PSO), a computational methodology inspired by the social behaviour of birds and fish. PSO is particularly valuable in refining the network’s parameters to tackle the prevalent issue of CREF intensity imbalance, which can skew forecasting results. By optimising these parameters, RFNet ensures a balanced and accurate representation of various intensity levels, crucial for predicting severe weather conditions. The training process for RFNet is meticulously structured, utilising 10 steps of CREF data extrapolated from both OF and DL methods to anticipate the subsequent 10 steps. This dynamic approach not only enables high accuracy in nowcasting predictions but also enhances training efficiency by using the initially trained RFNet as a pre-trained model for further training cycles. This layered training process reduces computational demands, making the system both time-efficient and resource-efficient. Empirical results from this study reveal that RFNet effectively mitigates common drawbacks associated with deep learning predictions, specifically intensity attenuation and echo structure blurring. These enhancements allow RFNet to provide clearer and more accurate forecasts. Performance assessments across various intensity thresholds from 20 to 50 dBz demonstrate the method’s robustness. At lower thresholds, such as 20 and 30 dBz, RFNet and DL exhibit comparable performance, both of which surpass the capabilities of OF. In these scenarios, RFNet’s advanced integration of methodologies ensures superior forecasting precision. At a 40 dBz threshold, DL initially excels within the first 30 minutes of forecast duration. However, RFNet outperforms DL beyond this timeframe, highlighting its strength in extended forecasting scenarios. Notably, at the 50 dBz threshold, RFNet displays a significant performance advantage over both DL and OF, maintaining superior forecasting ability for up to 42 minutes. This capability is particularly valuable in predicting high-intensity weather events, where rapid changes necessitate agile and accurate forecasting models. Additionally, the research indicates a trend where RFNet’s extrapolation performance improves as CREF intensity surpasses 40 dBz. This improvement underscores the system’s adaptability and effectiveness in handling severe weather conditions, ultimately contributing to more reliable and actionable nowcasting weather forecasts.
2024, 52(6):816-829.
DOI: 10.19517/j.1671-6345.20230431
Abstract:
In order to gain a deeper understanding of the statistical characteristics and forecasting focuses of extreme thunderstorm gusts with a magnitude of 11 or above that occur in Shandong under the background of cold vortices and cause disasters, the paper analyses the large-scale circulation background, radar characteristics, vertical wind shear, and parameter features related to the intensity potential and momentum downward transmission of extreme thunderstorm winds. This is done by analysing the maximum hourly wind speed of automatic precipitation data, MICAPS data, radar wind profile, North China radar, lightning positioning data and NCEP reanalysis data during 2013-2021, through statistical methods, weather diagnosis, and synthetic analysis, etc. The main conclusions are as follows: (1) The extreme thunderstorm gale events above grade 11 are divided into 6 categories: low trough type, northwest airflow type, transverse trough type, upper cold vortex type, subtropical high marginal type and warm shear type. (2) There are five types of thunderstorms that produce extreme thunderstorm wind: bow echo, multi-cell storm, squall line, supercell and ordinary thunderstorm, among which bow echo and multi-cell storm are the main forms, accounting for 63% of the total number of occurrences. Squall lines and supercells are also forms of extreme thunderstorm winds, accounting for 17% and 13% respectively. (3) The middle front of the bow echo is the region with a high probability of extreme thunderstorm and gale. The right front of the moving direction of the multi-cell storm, the front of the squall line and the right front of the moving direction of the supercell are also the key areas of the extreme thunderstorm and gale. (4) When the extreme thunderstorm and gale occur, the top height of the echo is usually above 15 km. The extreme thunderstorm gale appears in the front or right front of the moving direction of the high centre of the Echo Top. (5) When the strong hourly pressure change over 2.5 hPa appears on the ground, the cold pool centre is lower than the surrounding atmosphere, and the environmental wind field in the middle and lower troposphere is obviously strengthened, the moving direction of the storm should be combined, to determine in advance the direction of the storm ahead, especially the right front, whether there is the possibility of extreme thunderstorm gale.
In order to gain a deeper understanding of the statistical characteristics and forecasting focuses of extreme thunderstorm gusts with a magnitude of 11 or above that occur in Shandong under the background of cold vortices and cause disasters, the paper analyses the large-scale circulation background, radar characteristics, vertical wind shear, and parameter features related to the intensity potential and momentum downward transmission of extreme thunderstorm winds. This is done by analysing the maximum hourly wind speed of automatic precipitation data, MICAPS data, radar wind profile, North China radar, lightning positioning data and NCEP reanalysis data during 2013-2021, through statistical methods, weather diagnosis, and synthetic analysis, etc. The main conclusions are as follows: (1) The extreme thunderstorm gale events above grade 11 are divided into 6 categories: low trough type, northwest airflow type, transverse trough type, upper cold vortex type, subtropical high marginal type and warm shear type. (2) There are five types of thunderstorms that produce extreme thunderstorm wind: bow echo, multi-cell storm, squall line, supercell and ordinary thunderstorm, among which bow echo and multi-cell storm are the main forms, accounting for 63% of the total number of occurrences. Squall lines and supercells are also forms of extreme thunderstorm winds, accounting for 17% and 13% respectively. (3) The middle front of the bow echo is the region with a high probability of extreme thunderstorm and gale. The right front of the moving direction of the multi-cell storm, the front of the squall line and the right front of the moving direction of the supercell are also the key areas of the extreme thunderstorm and gale. (4) When the extreme thunderstorm and gale occur, the top height of the echo is usually above 15 km. The extreme thunderstorm gale appears in the front or right front of the moving direction of the high centre of the Echo Top. (5) When the strong hourly pressure change over 2.5 hPa appears on the ground, the cold pool centre is lower than the surrounding atmosphere, and the environmental wind field in the middle and lower troposphere is obviously strengthened, the moving direction of the storm should be combined, to determine in advance the direction of the storm ahead, especially the right front, whether there is the possibility of extreme thunderstorm gale.
2024, 52(6):830-840.
DOI: 10.19517/j.1671-6345.20230368
Abstract:
Convective extreme gale is one of the main types of severe convective weather that causes meteorological disasters. It has the characteristics of strong locality, short life history, and difficulty in forecasting. In recent years, extreme gale events occur frequently in China, causing serious economic losses to the society and public. However, the characteristics, impact systems, and causes of extreme gale vary in different regions. At present, there are many studies on the background of extreme gales in the Sichuan Basin, but research on convective potential and Doppler radar characteristics is still relatively limited. Therefore, it is necessary to conduct detailed research on typical cases of extreme gales that occur. On 11 April 2022, a rare extreme gale process occurred in Sichuan Basin with maximum wind speed reaching 37.4 m/s. During this period, there were also hail and short-time heavy rain. To explore the causes of this extreme gale process, this paper conducts research and analysis on circulation situation, convective environmental conditions, and meso-small scale system characteristics by using surface hourly meteorological observation data, upper-air observation data, Doppler radar products, and black body temperature data from the FY-2H satellite, providing reference for future forecasting of extreme gale weather in the Sichuan Basin. The conclusions are as follows: (1) This extreme gale process was triggered by the dry and cold air entering the basin, and the forced uplift of the ground convergence line strengthened the storm weather; the unstable stratification featuring “upper dry and lower humid”, a large temperature lapse rate, vertical wind shear with moderate intensity, and high CAPE value, all provided favourable convective environmental conditions for this extreme gale process, (2) The severe weather of this process mostly occurred during the period of severe development and consolidation of the Mesoscale Convective System, and was located in the high-value area of TBB gradient in front of the convective cloud clusters and near the low-value centre of TBB, (3) The main factors causing extreme strong wind on the ground included the cold pool effect when the squall line passed through, and the strong pressure gradient and density current, (4) The front inflow and the rear inflow of the storm together with the strong mid-altitude radial convergence were favourable conditions for the emergence of extreme strong wind; the sharp decrease of the reflectivity factor core and the maximum vertically integrated liquid water implied the humid critical strike flow caused by a sharp drop in temperature in the system, which increased the intensity of strong wind.
Convective extreme gale is one of the main types of severe convective weather that causes meteorological disasters. It has the characteristics of strong locality, short life history, and difficulty in forecasting. In recent years, extreme gale events occur frequently in China, causing serious economic losses to the society and public. However, the characteristics, impact systems, and causes of extreme gale vary in different regions. At present, there are many studies on the background of extreme gales in the Sichuan Basin, but research on convective potential and Doppler radar characteristics is still relatively limited. Therefore, it is necessary to conduct detailed research on typical cases of extreme gales that occur. On 11 April 2022, a rare extreme gale process occurred in Sichuan Basin with maximum wind speed reaching 37.4 m/s. During this period, there were also hail and short-time heavy rain. To explore the causes of this extreme gale process, this paper conducts research and analysis on circulation situation, convective environmental conditions, and meso-small scale system characteristics by using surface hourly meteorological observation data, upper-air observation data, Doppler radar products, and black body temperature data from the FY-2H satellite, providing reference for future forecasting of extreme gale weather in the Sichuan Basin. The conclusions are as follows: (1) This extreme gale process was triggered by the dry and cold air entering the basin, and the forced uplift of the ground convergence line strengthened the storm weather; the unstable stratification featuring “upper dry and lower humid”, a large temperature lapse rate, vertical wind shear with moderate intensity, and high CAPE value, all provided favourable convective environmental conditions for this extreme gale process, (2) The severe weather of this process mostly occurred during the period of severe development and consolidation of the Mesoscale Convective System, and was located in the high-value area of TBB gradient in front of the convective cloud clusters and near the low-value centre of TBB, (3) The main factors causing extreme strong wind on the ground included the cold pool effect when the squall line passed through, and the strong pressure gradient and density current, (4) The front inflow and the rear inflow of the storm together with the strong mid-altitude radial convergence were favourable conditions for the emergence of extreme strong wind; the sharp decrease of the reflectivity factor core and the maximum vertically integrated liquid water implied the humid critical strike flow caused by a sharp drop in temperature in the system, which increased the intensity of strong wind.
2024, 52(6):841-849.
DOI: 10.19517/j.1671-6345.20230332
Abstract:
In order to fully utilize the capabilities of the densely distributed Global Navigation Satellite System (GNSS) network, this study has developed a comprehensive near real-time GNSS retrieval system for analyzing Zenith Total Delay (ZTD) and Horizontal Gradients (HG). This system is specifically applied to investigate the evolution characteristics of ZTD and HG during the heavy rainfall event on 7 May 2017 in Guangzhou. The analysis incorporates gridded precipitation products from the China Meteorological Administration and ERA5 reanalysis data, ensuring a robust comparison and validation of the results. The study’s findings reveal that the temporal variations in ZTD and HG had the potential to capture early indicators of precipitation, offering valuable insights into the atmospheric conditions preceding rainfall events. Specifically, the increase in ZTD values was observed to coincide with the onset of precipitation, suggesting that ZTD could serve as a precursor to heavy rainfall. Meanwhile, the horizontal gradients (HG) exhibited distinct directional patterns that correlated with the movement and intensity of rainfall, indicating that HG not only reflected the presence of precipitation but also provided information on its spatial distribution and dynamics. Furthermore, the vector characteristics of HG were found to reveal certain critical features related to rainfall, such as the direction of moisture convergence and the intensity of localised convective activity. Areas with high Horizontal Gradient (HG) delay tended to have higher Integrated Water Vapor (IWV) and were more likely to experience precipitation. In summary, the spatiotemporal characteristics of ZTD and HG, as derived from the GNSS network, offered significant potential for improving short-term precipitation forecasting, particularly for extreme weather events such as heavy rainfall. By providing early warning signals and detailed insights into the evolving atmospheric conditions, these GNSS-derived parameters served as a valuable auxiliary tool for meteorologists and researchers engaged in the nowcasting of severe weather. The integration of ZTD and HG data into existing forecasting models could have enhanced the accuracy and lead time of predictions, thereby contributing to better preparedness and risk mitigation efforts in regions prone to sudden and intense precipitation events.
In order to fully utilize the capabilities of the densely distributed Global Navigation Satellite System (GNSS) network, this study has developed a comprehensive near real-time GNSS retrieval system for analyzing Zenith Total Delay (ZTD) and Horizontal Gradients (HG). This system is specifically applied to investigate the evolution characteristics of ZTD and HG during the heavy rainfall event on 7 May 2017 in Guangzhou. The analysis incorporates gridded precipitation products from the China Meteorological Administration and ERA5 reanalysis data, ensuring a robust comparison and validation of the results. The study’s findings reveal that the temporal variations in ZTD and HG had the potential to capture early indicators of precipitation, offering valuable insights into the atmospheric conditions preceding rainfall events. Specifically, the increase in ZTD values was observed to coincide with the onset of precipitation, suggesting that ZTD could serve as a precursor to heavy rainfall. Meanwhile, the horizontal gradients (HG) exhibited distinct directional patterns that correlated with the movement and intensity of rainfall, indicating that HG not only reflected the presence of precipitation but also provided information on its spatial distribution and dynamics. Furthermore, the vector characteristics of HG were found to reveal certain critical features related to rainfall, such as the direction of moisture convergence and the intensity of localised convective activity. Areas with high Horizontal Gradient (HG) delay tended to have higher Integrated Water Vapor (IWV) and were more likely to experience precipitation. In summary, the spatiotemporal characteristics of ZTD and HG, as derived from the GNSS network, offered significant potential for improving short-term precipitation forecasting, particularly for extreme weather events such as heavy rainfall. By providing early warning signals and detailed insights into the evolving atmospheric conditions, these GNSS-derived parameters served as a valuable auxiliary tool for meteorologists and researchers engaged in the nowcasting of severe weather. The integration of ZTD and HG data into existing forecasting models could have enhanced the accuracy and lead time of predictions, thereby contributing to better preparedness and risk mitigation efforts in regions prone to sudden and intense precipitation events.
LI Wantong
,
ZHANG Zhilong
,
LI Xiaoxia
,
WANG Xinlong
,
WU Jianxun
,
LIU Yuan
,
SHI Jing
,
ZHANG Haonan
2024, 52(6):850-857.
DOI: 10.19517/j.1671-6345.20230424
Abstract:
Lakes are important parts of land water resources, and their surface water temperatures are the most sensitive and rapid response to climate and environmental changes. An accurate understanding of their changing characteristics is highly important for analysing and understanding global climate change and improving the ecological environment of lakes. In July 2022, the Meteorological Observation Centre of China Meteorological Administration and the National Satellite Meteorological Centre jointly deploy drift buoys at the China Radiometric Calibration Site of remote sensing satellite (CRCS) to carry out the Fengyun Satellite “lake-sea” cooperative observation experiment. Two sets of drift buoys are deployed on the line from Haixin Mountain to the southeast of Lake Qinghai. In this paper, we use data from the drift buoy from 10 July to 30 September, 2022, combined with data from four sets of automatic meteorological stations on the land around Lake Qinghai, to analyse the characteristics and correlations between the changes of water temperature and air temperature in Lake Qinghai, and the differences in the characteristics of the changes of air temperature on the lake and land. To ensure the reasonableness of the test data, quality control of the test data is conducted, including spatial consistency checks, climatological threshold checks and temporal consistency checks, and a total of 13488 sets of water temperature data and 20232 sets of air temperature data are screened out. The analysis results of the above data indicate that there is a certain correlation between water temperature and air temperature in Lake Qinghai, the correlation coefficient of the minute value is 0.71, and the correlation coefficient of the daily mean value is 0.73. The diurnal variations of water temperature and air temperature are unimodal, and the rising and falling stages are basically the same; however, the diurnal variations of water temperature are obviously smaller than those of air temperature, and the decreasing time is earlier than that of air temperature. The diurnal variations in lake and land air temperatures exhibit a certain off-peak phenomenon, the peak land air temperature is advanced, and the valley value is lagging. The factors influencing the correlation between air and water temperatures are diverse and are related not only to subsurface characteristics and the distance from the observation location but also to solar and surface radiation. The contributions of multisource observational data, such as total radiation, shortwave radiation, cloud remote sensing products and high-resolution satellite observations, to the difference between air and water temperatures are analysed during the day and night and in different seasons in future work. The study of the difference between air and water temperatures can provide technical support for analysing heat exchanges between the lake and the atmosphere of Lake Qinghai.
Lakes are important parts of land water resources, and their surface water temperatures are the most sensitive and rapid response to climate and environmental changes. An accurate understanding of their changing characteristics is highly important for analysing and understanding global climate change and improving the ecological environment of lakes. In July 2022, the Meteorological Observation Centre of China Meteorological Administration and the National Satellite Meteorological Centre jointly deploy drift buoys at the China Radiometric Calibration Site of remote sensing satellite (CRCS) to carry out the Fengyun Satellite “lake-sea” cooperative observation experiment. Two sets of drift buoys are deployed on the line from Haixin Mountain to the southeast of Lake Qinghai. In this paper, we use data from the drift buoy from 10 July to 30 September, 2022, combined with data from four sets of automatic meteorological stations on the land around Lake Qinghai, to analyse the characteristics and correlations between the changes of water temperature and air temperature in Lake Qinghai, and the differences in the characteristics of the changes of air temperature on the lake and land. To ensure the reasonableness of the test data, quality control of the test data is conducted, including spatial consistency checks, climatological threshold checks and temporal consistency checks, and a total of 13488 sets of water temperature data and 20232 sets of air temperature data are screened out. The analysis results of the above data indicate that there is a certain correlation between water temperature and air temperature in Lake Qinghai, the correlation coefficient of the minute value is 0.71, and the correlation coefficient of the daily mean value is 0.73. The diurnal variations of water temperature and air temperature are unimodal, and the rising and falling stages are basically the same; however, the diurnal variations of water temperature are obviously smaller than those of air temperature, and the decreasing time is earlier than that of air temperature. The diurnal variations in lake and land air temperatures exhibit a certain off-peak phenomenon, the peak land air temperature is advanced, and the valley value is lagging. The factors influencing the correlation between air and water temperatures are diverse and are related not only to subsurface characteristics and the distance from the observation location but also to solar and surface radiation. The contributions of multisource observational data, such as total radiation, shortwave radiation, cloud remote sensing products and high-resolution satellite observations, to the difference between air and water temperatures are analysed during the day and night and in different seasons in future work. The study of the difference between air and water temperatures can provide technical support for analysing heat exchanges between the lake and the atmosphere of Lake Qinghai.
2024, 52(6):858-868.
DOI: 10.19517/j.1671-6345.20230404
Abstract:
Based on the lightning monitoring data from the Chinese ADTD (Advanced TOA and Direction system) lightning locator network and the NCEP (National Centers for Environmental Prediction) reanalysis data from 2010 to 2019, this analysis combines weather verification indicators such as TS scores and forecast deviations to provide the physical characteristics of environmental conditions related to lightning in different regions in China. The characteristics of lightning activity show a pattern of more lightning in the south and less in the north, with the highest lightning density in South China, followed by the eastern parts of Jiangnan and the eastern regions of Southwest China. Regarding monthly frequency variations, except for June, which sees a higher occurrence of lightning in South China, August records the highest lightning frequency nationwide and in most regions. In terms of diurnal variation, cloud-to-ground (CG) lightning activity is most concentrated in the afternoon, with the daily peak at 16:00. The analysis of environmental conditions indicates that thermal environmental characteristics significantly indicate lightning occurrence, and thermal-related physical quantities have the best indicative significance for lightning occurrence, followed by water vapour physical quantities. The indicative role of dynamic-related characteristic quantities is not significant. Among them, the K index is the best index nationwide, with an optimal threshold of 37 ℃. For different regions, there are significant differences in the conditions of low-level water vapour, mid-level water vapour saturation, cold air intensity, and temperature difference between high and low levels. Comprehensive statistics show that regions with better low-level water vapour conditions are more sensitive to the K index and have relatively higher thresholds, such as northern China, the Yangtze River Basin, and southern China. For mid to high latitude regions, atmospheric instability is more sensitive to lightning occurrence, so indices such as BLI and LI perform better. The water vapour condition is a necessary factor for lightning occurrence, but its indicative significance for lightning cannot be used alone as a basis for lightning prediction. It should be determined by combining atmospheric circulation and other environmental field characteristics. Dynamic effects are deemed a necessary condition for the development of severe convective weather, although their ranking in the verification of dynamic indicators is relatively lower, failing to accurately reflect their importance for lightning occurrence. The lightning forecasting in China mainly relies on the determination of the location of lightning occurrence. It is necessary to combine the atmospheric circulation situation and environmental water vapour, dynamics, and thermal conditions to determine the area and possibility of thunderstorm occurrence, and then measure the accuracy of the forecast through weather verification indicators. These findings provide an objective reference for understanding and forecasting lightning activity in different regions in China.
Based on the lightning monitoring data from the Chinese ADTD (Advanced TOA and Direction system) lightning locator network and the NCEP (National Centers for Environmental Prediction) reanalysis data from 2010 to 2019, this analysis combines weather verification indicators such as TS scores and forecast deviations to provide the physical characteristics of environmental conditions related to lightning in different regions in China. The characteristics of lightning activity show a pattern of more lightning in the south and less in the north, with the highest lightning density in South China, followed by the eastern parts of Jiangnan and the eastern regions of Southwest China. Regarding monthly frequency variations, except for June, which sees a higher occurrence of lightning in South China, August records the highest lightning frequency nationwide and in most regions. In terms of diurnal variation, cloud-to-ground (CG) lightning activity is most concentrated in the afternoon, with the daily peak at 16:00. The analysis of environmental conditions indicates that thermal environmental characteristics significantly indicate lightning occurrence, and thermal-related physical quantities have the best indicative significance for lightning occurrence, followed by water vapour physical quantities. The indicative role of dynamic-related characteristic quantities is not significant. Among them, the K index is the best index nationwide, with an optimal threshold of 37 ℃. For different regions, there are significant differences in the conditions of low-level water vapour, mid-level water vapour saturation, cold air intensity, and temperature difference between high and low levels. Comprehensive statistics show that regions with better low-level water vapour conditions are more sensitive to the K index and have relatively higher thresholds, such as northern China, the Yangtze River Basin, and southern China. For mid to high latitude regions, atmospheric instability is more sensitive to lightning occurrence, so indices such as BLI and LI perform better. The water vapour condition is a necessary factor for lightning occurrence, but its indicative significance for lightning cannot be used alone as a basis for lightning prediction. It should be determined by combining atmospheric circulation and other environmental field characteristics. Dynamic effects are deemed a necessary condition for the development of severe convective weather, although their ranking in the verification of dynamic indicators is relatively lower, failing to accurately reflect their importance for lightning occurrence. The lightning forecasting in China mainly relies on the determination of the location of lightning occurrence. It is necessary to combine the atmospheric circulation situation and environmental water vapour, dynamics, and thermal conditions to determine the area and possibility of thunderstorm occurrence, and then measure the accuracy of the forecast through weather verification indicators. These findings provide an objective reference for understanding and forecasting lightning activity in different regions in China.
2024, 52(6):869-878.
DOI: 10.19517/j.1671-6345.20230370
Abstract:
Based on comprehensive data spanning from 2004 to 2021, encompassing 125 typhoon disaster events within China, and in conjunction with wind and rain observation data collected from ground meteorological stations as well as synchronous social and economic statistical data, this study develops a robust model for assessing the direct economic losses of typhoon disasters. This model takes into account causative factors, receptor characteristics, and disaster prevention and reduction capacity. On the basis of this model, we design a quantitative analysis method to conduct an in-depth exploration and quantitative analysis of the dominant factors influencing the change in direct economic losses caused by typhoon disasters in China. The research findings indicate that during the observation period from 2004 to 2021, overall direct economic losses resulting from typhoon disasters in China exhibit a significant year-on-year decreasing trend. Simultaneously, there are discernible signs of weakening in the wind and rain intensity associated with these typhoons in China. Taking 2012 as the cut-off point (the year when real-time correction technology for ensemble forecast of typhoon paths is officially put into use), our study finds that prior to this year, the typhoon wind index is identified as the most significant factor contributing to economic losses from these disasters during the observation period from 2004 to 2021; however, its influence decreases significantly after 2012, becoming the smallest contributing factor to the direct economic losses of typhoon disasters during the observation period from 2012 to 2021. Compared with the observation period from 2004 to 2011, there is a notable increase in contributions to the direct economic losses of typhoon disasters in China from factors related to typhoons after 2012, such as the typhoon rain index, regional GDP, typhoon intensity forecast error, and drainage pipe density factor. Particularly noteworthy is our identification of improved accuracy in forecasting typhoon intensity along with substantial increases during the observation period from 2012 to 2021 regarding drainage pipe density being the dominant impact factor driving down direct economic losses resulting from typhoon-related disasters. This study not only reveals differences over different research periods regarding dominant influencing factors on direct economic losses caused by Chinese typhoon disasters but also emphasises strengthening development and application of technologies for forecasting typhoons alongside improving infrastructure like drainage systems can effectively reduce their impact on society’s economy. These findings provide crucial references for formulating more scientific and efficient strategies aimed at addressing typhoon-related disasters.
Based on comprehensive data spanning from 2004 to 2021, encompassing 125 typhoon disaster events within China, and in conjunction with wind and rain observation data collected from ground meteorological stations as well as synchronous social and economic statistical data, this study develops a robust model for assessing the direct economic losses of typhoon disasters. This model takes into account causative factors, receptor characteristics, and disaster prevention and reduction capacity. On the basis of this model, we design a quantitative analysis method to conduct an in-depth exploration and quantitative analysis of the dominant factors influencing the change in direct economic losses caused by typhoon disasters in China. The research findings indicate that during the observation period from 2004 to 2021, overall direct economic losses resulting from typhoon disasters in China exhibit a significant year-on-year decreasing trend. Simultaneously, there are discernible signs of weakening in the wind and rain intensity associated with these typhoons in China. Taking 2012 as the cut-off point (the year when real-time correction technology for ensemble forecast of typhoon paths is officially put into use), our study finds that prior to this year, the typhoon wind index is identified as the most significant factor contributing to economic losses from these disasters during the observation period from 2004 to 2021; however, its influence decreases significantly after 2012, becoming the smallest contributing factor to the direct economic losses of typhoon disasters during the observation period from 2012 to 2021. Compared with the observation period from 2004 to 2011, there is a notable increase in contributions to the direct economic losses of typhoon disasters in China from factors related to typhoons after 2012, such as the typhoon rain index, regional GDP, typhoon intensity forecast error, and drainage pipe density factor. Particularly noteworthy is our identification of improved accuracy in forecasting typhoon intensity along with substantial increases during the observation period from 2012 to 2021 regarding drainage pipe density being the dominant impact factor driving down direct economic losses resulting from typhoon-related disasters. This study not only reveals differences over different research periods regarding dominant influencing factors on direct economic losses caused by Chinese typhoon disasters but also emphasises strengthening development and application of technologies for forecasting typhoons alongside improving infrastructure like drainage systems can effectively reduce their impact on society’s economy. These findings provide crucial references for formulating more scientific and efficient strategies aimed at addressing typhoon-related disasters.
2024, 52(6):879-889.
DOI: 10.19517/j.1671-6345.20230282
Abstract:
With the rapid and intensive development of high-tech industrial development zones, they become more sensitive to environmental factors, especially extreme climate events. Under the dual influences of global warming and urbanisation, extreme rainfall events occur frequently in cities, and urban inland inundation poses a significant threat to development zones. Using digital elevation model data, land use data, remote sensing image data, POI data, and measured precipitation data, a one-dimensional and two-dimensional hydrodynamic-pipe network coupling inland inundation model for Jianhu High-tech Zone is constructed. The one-dimensional hydrodynamic layer includes river centre lines, river sections, flow boundaries, and open boundaries, with a total of 68 river sections set up at an average interval of 260 metres. Through grid subdivision, the surface elevation data are assigned to grid nodes to build a gridded two-dimensional terrain model, simulating the evolution of two-dimensional hydrodynamic forces. The grid subdivision is based on research needs, with grid refinement in areas near the river channel and sparse grid in areas away from the river channel to improve model simulation accuracy and efficiency. A total of 8345 triangular grids are divided. After parameter calibration, the model achieves visual numerical simulation of inland inundation conditions. The model is tested using two measured rainfall processes. The model simulation results are consistent with the actual inland inundation conditions. Based on this, the evolution process and distribution characteristics of inland inundation under extreme rainstorm conditions are analysed. Based on the constructed urban inland inundation numerical simulation model, the surface water depth and spatial distribution characteristics of inland inundation under five-year, ten-year, twenty-year, thirty-year, fifty-year, and one-hundred-year return periods are simulated, with severe inland inundation, moderate inland inundation, and general inland inundation classified. The distribution map of inland inundation grades is drawn. The results show that under different return periods, the water depth in the high-tech zone mainly distributes between 0.1 and 0.25 metres, accounting for about 50% of the total inland inundation risk area. Under the scenario of a 100-year return period, commercial residential buildings are mostly affected by heavy rainfall, accounting for 38.4% of the affected area. Transportation facilities and public facilities for healthcare are the next, accounting for 28.5% of the affected area. Public facilities for science and education, culture, and companies are less affected, accounting for 14.2% and 19.8% of the affected area respectively. The evaluation results can provide a scientific reference for the construction and flood prevention planning of high-tech zones.
With the rapid and intensive development of high-tech industrial development zones, they become more sensitive to environmental factors, especially extreme climate events. Under the dual influences of global warming and urbanisation, extreme rainfall events occur frequently in cities, and urban inland inundation poses a significant threat to development zones. Using digital elevation model data, land use data, remote sensing image data, POI data, and measured precipitation data, a one-dimensional and two-dimensional hydrodynamic-pipe network coupling inland inundation model for Jianhu High-tech Zone is constructed. The one-dimensional hydrodynamic layer includes river centre lines, river sections, flow boundaries, and open boundaries, with a total of 68 river sections set up at an average interval of 260 metres. Through grid subdivision, the surface elevation data are assigned to grid nodes to build a gridded two-dimensional terrain model, simulating the evolution of two-dimensional hydrodynamic forces. The grid subdivision is based on research needs, with grid refinement in areas near the river channel and sparse grid in areas away from the river channel to improve model simulation accuracy and efficiency. A total of 8345 triangular grids are divided. After parameter calibration, the model achieves visual numerical simulation of inland inundation conditions. The model is tested using two measured rainfall processes. The model simulation results are consistent with the actual inland inundation conditions. Based on this, the evolution process and distribution characteristics of inland inundation under extreme rainstorm conditions are analysed. Based on the constructed urban inland inundation numerical simulation model, the surface water depth and spatial distribution characteristics of inland inundation under five-year, ten-year, twenty-year, thirty-year, fifty-year, and one-hundred-year return periods are simulated, with severe inland inundation, moderate inland inundation, and general inland inundation classified. The distribution map of inland inundation grades is drawn. The results show that under different return periods, the water depth in the high-tech zone mainly distributes between 0.1 and 0.25 metres, accounting for about 50% of the total inland inundation risk area. Under the scenario of a 100-year return period, commercial residential buildings are mostly affected by heavy rainfall, accounting for 38.4% of the affected area. Transportation facilities and public facilities for healthcare are the next, accounting for 28.5% of the affected area. Public facilities for science and education, culture, and companies are less affected, accounting for 14.2% and 19.8% of the affected area respectively. The evaluation results can provide a scientific reference for the construction and flood prevention planning of high-tech zones.
2024, 52(6):890-897.
DOI: 10.19517/j.1671-6345.20230410
Abstract:
Based on the field experiment method of agricultural meteorology, two main cultivars: Laoshan Datian spring tea population and Longjing 43, are selected for research. Using two consecutive years of observations from 2022-2023, 17 test samples and 68 replicates are collected for Laoshan Datian spring tea population and Longjing 43 to detect their biochemical components such as caffeine, amino acids, tea polyphenols, and phenol ammonia ratio. We establish the climate evaluation indicators for Laoshan spring tea by conducting correlation analysis and regression analysis of each biochemical component with the daily average meteorological data of temperature, sunshine, and relative humidity of 1-20 days before tea picking. The results show that: (1) There is a significant correlation between caffeine, amino acids, tea polyphenols, and phenol ammonia ratio of the two varieties of Laoshan spring tea and meteorological factors of 1-20 days before tea picking. The correlations pass the 0.05 and 0.01 significance tests, respectively. The main meteorological factors affecting the different biochemical components are basically constant; however, different meteorological factors have different primary times of action. (2) We establish an optimal regression model for tea polyphenols, amino acids, phenol ammonia ratio, and meteorological factors. The results of the forecasting equations show that: the average prediction accuracies of polyphenols, amino acids, and phenol ammonia ratio of tea from Laoshan Datian spring tea population are 88.5%, 94.6% and 96.4%, respectively; and those of polyphenols, amino acids, and phenol ammonia ratio of Longjing 43 are 88.6%, 92.9% and 97.5%, respectively. (3) We further establish the climate evaluation indicators for Laoshan spring tea: by conducting the K-means clustering analysis on phenol ammonia ratio and amino acid samples, four grades of Laoshan spring tea have been classified. Climate quality evaluation indicators for Laoshan spring tea are established based on the corresponding phenol ammonia ratio meteorological indicators for each grade. Combined with the forecasting equation of Laoshan Datian spring tea population and Longjing 43, we can determine the different levels of climate quality of Laoshan tea by predicting the threshold of phenol ammonia ratio. The purpose of this study is to provide technical support for the evaluation of spring tea climate quality in Laoshan, which is very important and highly practical. At the same time, it is aimed at the Laoshan tea industry, which is conducive to improving the competitiveness and adding value of spring tea. This helps to contribute to rural revitalisation.
Based on the field experiment method of agricultural meteorology, two main cultivars: Laoshan Datian spring tea population and Longjing 43, are selected for research. Using two consecutive years of observations from 2022-2023, 17 test samples and 68 replicates are collected for Laoshan Datian spring tea population and Longjing 43 to detect their biochemical components such as caffeine, amino acids, tea polyphenols, and phenol ammonia ratio. We establish the climate evaluation indicators for Laoshan spring tea by conducting correlation analysis and regression analysis of each biochemical component with the daily average meteorological data of temperature, sunshine, and relative humidity of 1-20 days before tea picking. The results show that: (1) There is a significant correlation between caffeine, amino acids, tea polyphenols, and phenol ammonia ratio of the two varieties of Laoshan spring tea and meteorological factors of 1-20 days before tea picking. The correlations pass the 0.05 and 0.01 significance tests, respectively. The main meteorological factors affecting the different biochemical components are basically constant; however, different meteorological factors have different primary times of action. (2) We establish an optimal regression model for tea polyphenols, amino acids, phenol ammonia ratio, and meteorological factors. The results of the forecasting equations show that: the average prediction accuracies of polyphenols, amino acids, and phenol ammonia ratio of tea from Laoshan Datian spring tea population are 88.5%, 94.6% and 96.4%, respectively; and those of polyphenols, amino acids, and phenol ammonia ratio of Longjing 43 are 88.6%, 92.9% and 97.5%, respectively. (3) We further establish the climate evaluation indicators for Laoshan spring tea: by conducting the K-means clustering analysis on phenol ammonia ratio and amino acid samples, four grades of Laoshan spring tea have been classified. Climate quality evaluation indicators for Laoshan spring tea are established based on the corresponding phenol ammonia ratio meteorological indicators for each grade. Combined with the forecasting equation of Laoshan Datian spring tea population and Longjing 43, we can determine the different levels of climate quality of Laoshan tea by predicting the threshold of phenol ammonia ratio. The purpose of this study is to provide technical support for the evaluation of spring tea climate quality in Laoshan, which is very important and highly practical. At the same time, it is aimed at the Laoshan tea industry, which is conducive to improving the competitiveness and adding value of spring tea. This helps to contribute to rural revitalisation.
2024, 52(6):898-904.
DOI: 10.19517/j.1671-6345.20230374
Abstract:
To solve the deterioration problem of the reflectance, differential reflectance and other parameters when the X-band phased array weather radar is interfered with by metallic lightning rods and metallic down-conductor systems, the segmented metal foil array is used as the guiding structure of the down-conductor system, and a new type of wave-transmitting lightning rod is designed in combination with a high-performance composite rod, successfully solving the problem of wave transmittance and lightning current: a continuous plasma channel is formed by the lightning electric field, which can be used as a lightning flow path; the radar reflectivity is greatly reduced by the discontinuous special metal foil. In order to verify the protection and transmission capability of the wave-transparent lightning rod, tests are carried out: (1) the test of the high voltage attachment point and impulse discharge capacity, the average breakdown threshold of the wave-transparent lightning rod is 4.45 kV/cm, and the impulse discharge capacity is greater than 150 kA (±10%, 10/350 μs), which meets the requirements of the second level lightning protection stipulated in the GB50057 standard; (2) the field installation test, the test results show that the influence of the new type of beam on radar reflectance and differential reflectance is greatly reduced. The new wave-transparent lightning rod can greatly reduce the negative influences of the lightning protection system on radar detection performance and can be further applied and popularised in direct lightning protection of similar phased array radar.
To solve the deterioration problem of the reflectance, differential reflectance and other parameters when the X-band phased array weather radar is interfered with by metallic lightning rods and metallic down-conductor systems, the segmented metal foil array is used as the guiding structure of the down-conductor system, and a new type of wave-transmitting lightning rod is designed in combination with a high-performance composite rod, successfully solving the problem of wave transmittance and lightning current: a continuous plasma channel is formed by the lightning electric field, which can be used as a lightning flow path; the radar reflectivity is greatly reduced by the discontinuous special metal foil. In order to verify the protection and transmission capability of the wave-transparent lightning rod, tests are carried out: (1) the test of the high voltage attachment point and impulse discharge capacity, the average breakdown threshold of the wave-transparent lightning rod is 4.45 kV/cm, and the impulse discharge capacity is greater than 150 kA (±10%, 10/350 μs), which meets the requirements of the second level lightning protection stipulated in the GB50057 standard; (2) the field installation test, the test results show that the influence of the new type of beam on radar reflectance and differential reflectance is greatly reduced. The new wave-transparent lightning rod can greatly reduce the negative influences of the lightning protection system on radar detection performance and can be further applied and popularised in direct lightning protection of similar phased array radar.
External LinksChina Meteorological AdministrationCMA Meteorological Observation CentreChinese Academy of Meteorological SciencesBeijing Meteorological ServiceNational Satellite Meteorological Center
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.