Volume 52,Issue 1,2024 Table of Contents
SHI Yaohui
,
WANG Hailong
,
ZHU Yongchao
,
WU Dongli
,
DING Mingming
,
GOU Qiulei
,
HOU Biao
,
ZHANG Quanjun
,
LIU Cong
,
YANG Dasheng
,
ZHANG Jing
2024, 52(1):1-9.
DOI: 10.19517/j.1671-6345.20230080
Abstract:
Accurate measurement of soil moisture is key for research in multiple disciplines such as meteorology, ecology, hydrology, agriculture, etc. Soil moisture holds important application value in flood forecasting, drought warning, ecological restoration, irrigation management, and soil erosion prevention and control. Currently, soil water monitoring mainly focuses on the point scale (within 1 m2) and remote sensing largescale (research units usually exceed 100 m2). It is necessary to develop soil moisture monitoring technologies that can accurately measure soil moisture content on a small scale and connect with larger-scale soil moisture monitoring technologies such as remote sensing. The Cosmic Ray Neutron Probe (CRNP) is a new method for measuring soil moisture by monitoring the number of neutrons near the Earth’s surface; the measuring radius in the horizontal direction of the CRNP is 130-240 m depending on air humidity etc. The theoretical detection depth of the CRNP is about 12-76 cm. To evaluate the applicability of the CRNP for soil moisture monitoring in different ecosystems, this study, based on the data from the automatic soil moisture stations of the China Meteorological Administration measured by Frequency Domain Reflectometry (FDR), explores the depth of soil moisture monitored by the CRNP in three different ecosystems such as grassland, farmland and woodland during the rainy season (June to September). The results show that the effect monitored by the CRNP in grassland in 0-20 cm is the best, and that for farmland and woodland in 0-30 cm is the best. Compared with soil moisture data monitored by the SMAP (Soil Moisture Active and Passive) satellite remote sensing, the results of soil moisture measurement in farmland and forest ecosystems by the CRNP are less different from those of the FDR; however, for grassland ecosystems, the difference between the results of CRNP and remote sensing monitoring is smaller than that of the FDR, which may be related to the strong heterogeneity of grassland soil. After the occurrence of precipitation events exceeding 5 mm in grasslands, farmland, and woodlands, the soil moisture monitoring values of the CRNP all increase, indicating that this method can sensitively respond to precipitation events. A comparison of changes in soil moisture monitoring values between the CRNP and the automatic stations before and after precipitation events can find that the CRNP is easier to capture the regional water fluctuation changes than the point-scale automatic soil moisture station. The results can provide a scientific basis for the application of the CRNP in soil moisture monitoring in different ecosystems.
Accurate measurement of soil moisture is key for research in multiple disciplines such as meteorology, ecology, hydrology, agriculture, etc. Soil moisture holds important application value in flood forecasting, drought warning, ecological restoration, irrigation management, and soil erosion prevention and control. Currently, soil water monitoring mainly focuses on the point scale (within 1 m2) and remote sensing largescale (research units usually exceed 100 m2). It is necessary to develop soil moisture monitoring technologies that can accurately measure soil moisture content on a small scale and connect with larger-scale soil moisture monitoring technologies such as remote sensing. The Cosmic Ray Neutron Probe (CRNP) is a new method for measuring soil moisture by monitoring the number of neutrons near the Earth’s surface; the measuring radius in the horizontal direction of the CRNP is 130-240 m depending on air humidity etc. The theoretical detection depth of the CRNP is about 12-76 cm. To evaluate the applicability of the CRNP for soil moisture monitoring in different ecosystems, this study, based on the data from the automatic soil moisture stations of the China Meteorological Administration measured by Frequency Domain Reflectometry (FDR), explores the depth of soil moisture monitored by the CRNP in three different ecosystems such as grassland, farmland and woodland during the rainy season (June to September). The results show that the effect monitored by the CRNP in grassland in 0-20 cm is the best, and that for farmland and woodland in 0-30 cm is the best. Compared with soil moisture data monitored by the SMAP (Soil Moisture Active and Passive) satellite remote sensing, the results of soil moisture measurement in farmland and forest ecosystems by the CRNP are less different from those of the FDR; however, for grassland ecosystems, the difference between the results of CRNP and remote sensing monitoring is smaller than that of the FDR, which may be related to the strong heterogeneity of grassland soil. After the occurrence of precipitation events exceeding 5 mm in grasslands, farmland, and woodlands, the soil moisture monitoring values of the CRNP all increase, indicating that this method can sensitively respond to precipitation events. A comparison of changes in soil moisture monitoring values between the CRNP and the automatic stations before and after precipitation events can find that the CRNP is easier to capture the regional water fluctuation changes than the point-scale automatic soil moisture station. The results can provide a scientific basis for the application of the CRNP in soil moisture monitoring in different ecosystems.
2024, 52(1):10-18.
DOI: 10.19517/j.1671-6345.20230026
Abstract:
In order to study the method of measuring precipitation by the DSG5 precipitation weather phenomenon instrument, we compare the detection performance of tipping bucket rain gauge, weighing rain gauge, and the DSG5 precipitation weather phenomenon instrument on precipitation in hail processes. We analyse the characteristics of the precipitation particle spectrum in a hail process. We also discuss the interference of hail on the identification of the DSG5 precipitation weather phenomenon and the method for distinguishing raindrops from hail using raindrop spectrum characteristics. Following this method, we calculate the precipitation formed by raindrops and hail. It is found that hail was easily able to roll into the sampling area from the edge of the DSG5 splash cover or by bouncing on the splash cover, which not only seriously interfered with the judgement of the DSG5 precipitation weather phenomenon but also caused a large error in the precipitation calculated by DSG5. However, the interference particles were generally identified by DSG5 as belonging to the “snow area”. Based on the criterion of DSG5 for different precipitation weather phenomena, the particles that were judged to enter the “snow area” were deleted, and the particles in the “rain with snow” area were merged into the “rain area”. The hail and raindrops in the hail process could be separated. The amount of precipitation formed separately was calculated according to different densities, and then the total precipitation of hail and raindrops was calculated. The calculation results have certain accuracy. The comparative analysis and statistical test of the minute precipitation series of the hail process calculated by the tipping bucket rain gauge, the weighing rain gauge, and the DSG5 show that the minute precipitation series of the process obtained by the tipping bucket rain gauge, the weighing rain gauge, and the DSG5 had good consistency and obeyed the same continuous distribution. Due to the different structures and principles of the equipment, the minute precipitation series obtained by the three methods were also significantly different. The DSG5 could quickly and sensitively detect the start and end times of precipitation, and more accurately reflect the temporal change of precipitation intensity, which could provide accurate precipitation change information for the study of small- and medium-scale weather systems. The tipping bucket rain gauge and the weighing rain gauge had a certain buffer in observing the minute precipitation during the hail process and could not accurately describe the change in the real-time intensity of the minute precipitation. The minute precipitation sequence observed by the weighing rain gauge exhibited an obvious lag during hail processes.
In order to study the method of measuring precipitation by the DSG5 precipitation weather phenomenon instrument, we compare the detection performance of tipping bucket rain gauge, weighing rain gauge, and the DSG5 precipitation weather phenomenon instrument on precipitation in hail processes. We analyse the characteristics of the precipitation particle spectrum in a hail process. We also discuss the interference of hail on the identification of the DSG5 precipitation weather phenomenon and the method for distinguishing raindrops from hail using raindrop spectrum characteristics. Following this method, we calculate the precipitation formed by raindrops and hail. It is found that hail was easily able to roll into the sampling area from the edge of the DSG5 splash cover or by bouncing on the splash cover, which not only seriously interfered with the judgement of the DSG5 precipitation weather phenomenon but also caused a large error in the precipitation calculated by DSG5. However, the interference particles were generally identified by DSG5 as belonging to the “snow area”. Based on the criterion of DSG5 for different precipitation weather phenomena, the particles that were judged to enter the “snow area” were deleted, and the particles in the “rain with snow” area were merged into the “rain area”. The hail and raindrops in the hail process could be separated. The amount of precipitation formed separately was calculated according to different densities, and then the total precipitation of hail and raindrops was calculated. The calculation results have certain accuracy. The comparative analysis and statistical test of the minute precipitation series of the hail process calculated by the tipping bucket rain gauge, the weighing rain gauge, and the DSG5 show that the minute precipitation series of the process obtained by the tipping bucket rain gauge, the weighing rain gauge, and the DSG5 had good consistency and obeyed the same continuous distribution. Due to the different structures and principles of the equipment, the minute precipitation series obtained by the three methods were also significantly different. The DSG5 could quickly and sensitively detect the start and end times of precipitation, and more accurately reflect the temporal change of precipitation intensity, which could provide accurate precipitation change information for the study of small- and medium-scale weather systems. The tipping bucket rain gauge and the weighing rain gauge had a certain buffer in observing the minute precipitation during the hail process and could not accurately describe the change in the real-time intensity of the minute precipitation. The minute precipitation sequence observed by the weighing rain gauge exhibited an obvious lag during hail processes.
2024, 52(1):19-26.
DOI: 10.19517/j.1671-6345.20230076
Abstract:
To address the measurement data errors caused by environmental differences between the three-cup wind speed sensor under metrological verification conditions and observation scenarios, we commit to studying the authenticity, accuracy, and consistency of air flow rate measurement standards in the process of value transmission. We provide ideas and reference indicators for the evaluation of new product types of the new generation three-cup wind speed sensor as a measuring instrument. According to the measurement method of the cup anemometer and the verification regulation of the wind speed and direction sensor of the Automatic Weather Station, and adding the automatic turntable system composed of the angle encoder as the main body in the experiment, we design the experiment of measuring the performance level of the three-cup wind speed sensor in the non-horizontal wind field. By adjusting the tilt angle of the three-cup wind speed sensor in the wind tunnel test section, simulating its measurement state in the natural non-horizontal wind field, we synchronously collect the standard indicated wind speed of the wind tunnel, the measured wind speed of the three-cup wind speed sensor, and their corresponding tilt angle, calculate the error of the displayed values, and use statistical methods such as variance analysis, trend analysis, correlation analysis, and linear regression analysis, to study the indication error of the three-cup wind speed sensor under different tilt angles, and obtain the corresponding relationship between the indication error of the three-cup wind speed sensor in the wind tunnel test section and the measured wind speed and tilt angle. We also study the differences in the influence of the rising and falling movements of the near-ground air on the display value of the three-cup wind speed sensor. We propose the measurement performance indicators of the three-cup wind speed sensor in non-horizontal wind fields, establish a connection between the experimental data of the standard measurement laboratory and the observation data of the meteorological ground station, and fill the gap in this research direction of meteorological wind speed measurement. We study the regression relationship between the measured wind speed, standard wind speed, and tilt angle of the three-cup wind speed sensor in a non-horizontal wind field, and propose a magnitude transfer correction algorithm for the data of the three-cup wind speed sensor in a measuring environment in a non-horizontal wind field. We explore the algorithm design direction of the next-generation three-cup wind speed sensor, provide ideas for its type evaluation as a measuring instrument, and lay the groundwork for future research on similar application scenarios.
To address the measurement data errors caused by environmental differences between the three-cup wind speed sensor under metrological verification conditions and observation scenarios, we commit to studying the authenticity, accuracy, and consistency of air flow rate measurement standards in the process of value transmission. We provide ideas and reference indicators for the evaluation of new product types of the new generation three-cup wind speed sensor as a measuring instrument. According to the measurement method of the cup anemometer and the verification regulation of the wind speed and direction sensor of the Automatic Weather Station, and adding the automatic turntable system composed of the angle encoder as the main body in the experiment, we design the experiment of measuring the performance level of the three-cup wind speed sensor in the non-horizontal wind field. By adjusting the tilt angle of the three-cup wind speed sensor in the wind tunnel test section, simulating its measurement state in the natural non-horizontal wind field, we synchronously collect the standard indicated wind speed of the wind tunnel, the measured wind speed of the three-cup wind speed sensor, and their corresponding tilt angle, calculate the error of the displayed values, and use statistical methods such as variance analysis, trend analysis, correlation analysis, and linear regression analysis, to study the indication error of the three-cup wind speed sensor under different tilt angles, and obtain the corresponding relationship between the indication error of the three-cup wind speed sensor in the wind tunnel test section and the measured wind speed and tilt angle. We also study the differences in the influence of the rising and falling movements of the near-ground air on the display value of the three-cup wind speed sensor. We propose the measurement performance indicators of the three-cup wind speed sensor in non-horizontal wind fields, establish a connection between the experimental data of the standard measurement laboratory and the observation data of the meteorological ground station, and fill the gap in this research direction of meteorological wind speed measurement. We study the regression relationship between the measured wind speed, standard wind speed, and tilt angle of the three-cup wind speed sensor in a non-horizontal wind field, and propose a magnitude transfer correction algorithm for the data of the three-cup wind speed sensor in a measuring environment in a non-horizontal wind field. We explore the algorithm design direction of the next-generation three-cup wind speed sensor, provide ideas for its type evaluation as a measuring instrument, and lay the groundwork for future research on similar application scenarios.
2024, 52(1):27-36.
DOI: 10.19517/j.1671-6345.20220520
Abstract:
Ultrasonic anemometers are widely acclaimed for their non-rotating parts, rapid response, and high precision in measuring wind speed and direction. However, the traditional ultrasonic anemometers with a large physical footprint, substantial power requirements, and considerable cost impede their extensive utilisation. To overcome these challenges and facilitate the widespread adoption of ultrasonic anemometers, this study presents a cutting-edge design featuring a domestically produced Field Programmable Gate Array (FPGA) integrated with Finite Impulse Response (FIR) filters and crosscorrelation detection algorithms. The FPGA-based ultrasonic anemometer design offers clearly noticeable advantages. By reducing the distance between transducers to 80 mm and limiting the sensing region to just 1/10 of traditional ultrasonic anemometers, this compact configuration allows for accurate calibration in some provincial-level wind tunnels. Rigorous wind tunnel tests are conducted to evaluate the performance of the ultrasonic anemometers, demonstrating the instrument’s capability to measure wind speed and direction up to 50 times per second. In terms of measurement accuracy, the performance of this ultrasonic anemometer is tested in a wind tunnel, demonstrating its capability to meet specific measurement requirements for both low and high wind speeds. For wind speeds from 0 to 5 m/s, the maximum measurement error remains within ±0.3 m/s. In the range of 5 m/s to 20 m/s, the maximum error is ±0.5 m/s. For wind speeds between 20 m/s and 30 m/s, the maximum error is ±5%. Under stable wind conditions, the maximum error for wind direction measurement is within ±1°. Additionally, the power consumption of the ultrasonic anemometer is significantly reduced to 0.2 W, only 1/20 of the consumption of traditional ultrasonic anemometers with Digital Signal Processors (DSP). The integration of FPGA technology, FIR filters, and cross-correlation detection algorithms into the ultrasonic anemometer design contributes remarkably to improvements in size, cost, and power consumption compared to conventional DSP-based ultrasonic anemometers. The compact size and decreased power requirements make the FPGA-based ultrasonic anemometer an ideal choice for applications necessitating compactness, cost-effectiveness, and energy efficiency. Its potential for widespread utilisation in diverse fields is highly promising. The presented FPGA-based ultrasonic anemometer design surmounts the limitations associated with traditional models. Its compactness, reduced power consumption, and cost-effectiveness make it a compelling solution for accurate wind speed and direction measurements in various practical applications. Comprehensive wind tunnel tests substantiate its precision and reliability, bolstering its potential for widespread adoption and utilisation in diverse fields.
Ultrasonic anemometers are widely acclaimed for their non-rotating parts, rapid response, and high precision in measuring wind speed and direction. However, the traditional ultrasonic anemometers with a large physical footprint, substantial power requirements, and considerable cost impede their extensive utilisation. To overcome these challenges and facilitate the widespread adoption of ultrasonic anemometers, this study presents a cutting-edge design featuring a domestically produced Field Programmable Gate Array (FPGA) integrated with Finite Impulse Response (FIR) filters and crosscorrelation detection algorithms. The FPGA-based ultrasonic anemometer design offers clearly noticeable advantages. By reducing the distance between transducers to 80 mm and limiting the sensing region to just 1/10 of traditional ultrasonic anemometers, this compact configuration allows for accurate calibration in some provincial-level wind tunnels. Rigorous wind tunnel tests are conducted to evaluate the performance of the ultrasonic anemometers, demonstrating the instrument’s capability to measure wind speed and direction up to 50 times per second. In terms of measurement accuracy, the performance of this ultrasonic anemometer is tested in a wind tunnel, demonstrating its capability to meet specific measurement requirements for both low and high wind speeds. For wind speeds from 0 to 5 m/s, the maximum measurement error remains within ±0.3 m/s. In the range of 5 m/s to 20 m/s, the maximum error is ±0.5 m/s. For wind speeds between 20 m/s and 30 m/s, the maximum error is ±5%. Under stable wind conditions, the maximum error for wind direction measurement is within ±1°. Additionally, the power consumption of the ultrasonic anemometer is significantly reduced to 0.2 W, only 1/20 of the consumption of traditional ultrasonic anemometers with Digital Signal Processors (DSP). The integration of FPGA technology, FIR filters, and cross-correlation detection algorithms into the ultrasonic anemometer design contributes remarkably to improvements in size, cost, and power consumption compared to conventional DSP-based ultrasonic anemometers. The compact size and decreased power requirements make the FPGA-based ultrasonic anemometer an ideal choice for applications necessitating compactness, cost-effectiveness, and energy efficiency. Its potential for widespread utilisation in diverse fields is highly promising. The presented FPGA-based ultrasonic anemometer design surmounts the limitations associated with traditional models. Its compactness, reduced power consumption, and cost-effectiveness make it a compelling solution for accurate wind speed and direction measurements in various practical applications. Comprehensive wind tunnel tests substantiate its precision and reliability, bolstering its potential for widespread adoption and utilisation in diverse fields.
2024, 52(1):37-44.
DOI: 10.19517/j.1671-6345.20230012
Abstract:
With the rapid development of the Internet, the learning resources available to meteorological staff as learners are greatly enriched. Information overload leads to difficulties in retrieving suitable online learning resources; learners also have different learning needs in different environments and sequential access modes. However, existing recommendation systems, such as collaborative filtering and content-based recommendation, only involve two types of entities: items and users. They do not consider contextual information such as learners’ learning objectives and knowledge levels, as well as different sequential access patterns to learning resources, resulting in low accuracy in recommendation results. This paper proposes a hybrid recommendation algorithm that combines context awareness, sequential pattern mining, and collaborative filtering algorithms to recommend learning resources for learners. The hybrid recommendation algorithm includes three main steps: (1) integrating contextual information into the recommendation process using a contextual pre-filtering algorithm, (2) calculating learner similarity based on contextualised data and predicting the evaluation of learning resources, (3) generating the first N recommendations for the target learner, applying the GSP algorithm to the results, and filtering the final recommendations based on the learner’s sequential access patterns. In hybrid recommendation algorithms, context awareness is used to integrate contextual information about learners, such as knowledge level and learning objectives; sequential pattern mining is used to mine weblogs to discover learners’ sequential access patterns; collaborative filtering is used to calculate predictions and generate recommendations for targeted learners based on contextual data and sequential access patterns of learners. This hybrid recommendation algorithm incorporates contextual characteristics and learners’ sequential access patterns into the recommendation process to achieve improved personalised recommendation. When calculating the similarity between learners and learning items, the contextual characteristics of learners are taken into account; combining multiple recommendation techniques helps alleviate data sparsity problems. Experimental comparisons have shown that this recommendation algorithm is significantly superior to other recommendation algorithms in terms of recall, accuracy, and F1, especially when the neighbourhood value is 25. The hybrid recommendation algorithm is applied to the Yunzhipei intelligent teaching management system, with a user satisfaction rate of 93.7%, achieving a good application effect. In later stages, hybrid recommendation algorithms will be applied to the search and recommendation of electronic documents and institutional trees, providing assistance to meteorological employees in recommending accurate reference documents; it can also be combined with ElasticSearch to relocate and valuemine heterogeneous data, enhancing the value of business and management historical data.
With the rapid development of the Internet, the learning resources available to meteorological staff as learners are greatly enriched. Information overload leads to difficulties in retrieving suitable online learning resources; learners also have different learning needs in different environments and sequential access modes. However, existing recommendation systems, such as collaborative filtering and content-based recommendation, only involve two types of entities: items and users. They do not consider contextual information such as learners’ learning objectives and knowledge levels, as well as different sequential access patterns to learning resources, resulting in low accuracy in recommendation results. This paper proposes a hybrid recommendation algorithm that combines context awareness, sequential pattern mining, and collaborative filtering algorithms to recommend learning resources for learners. The hybrid recommendation algorithm includes three main steps: (1) integrating contextual information into the recommendation process using a contextual pre-filtering algorithm, (2) calculating learner similarity based on contextualised data and predicting the evaluation of learning resources, (3) generating the first N recommendations for the target learner, applying the GSP algorithm to the results, and filtering the final recommendations based on the learner’s sequential access patterns. In hybrid recommendation algorithms, context awareness is used to integrate contextual information about learners, such as knowledge level and learning objectives; sequential pattern mining is used to mine weblogs to discover learners’ sequential access patterns; collaborative filtering is used to calculate predictions and generate recommendations for targeted learners based on contextual data and sequential access patterns of learners. This hybrid recommendation algorithm incorporates contextual characteristics and learners’ sequential access patterns into the recommendation process to achieve improved personalised recommendation. When calculating the similarity between learners and learning items, the contextual characteristics of learners are taken into account; combining multiple recommendation techniques helps alleviate data sparsity problems. Experimental comparisons have shown that this recommendation algorithm is significantly superior to other recommendation algorithms in terms of recall, accuracy, and F1, especially when the neighbourhood value is 25. The hybrid recommendation algorithm is applied to the Yunzhipei intelligent teaching management system, with a user satisfaction rate of 93.7%, achieving a good application effect. In later stages, hybrid recommendation algorithms will be applied to the search and recommendation of electronic documents and institutional trees, providing assistance to meteorological employees in recommending accurate reference documents; it can also be combined with ElasticSearch to relocate and valuemine heterogeneous data, enhancing the value of business and management historical data.
2024, 52(1):45-54.
DOI: 10.19517/j.1671-6345.20230049
Abstract:
Meteorological satellites play an increasingly important role in meteorological observation, disaster prevention and mitigation, and weather forecasting. Direct reception, satellite broadcasting, and website download are common methods for obtaining satellite data. A number of high-speed data ground satellite direct reception stations have been built in some regions of each province across China to receive and process various satellite data products such as FY-3 and FY-4 in real-time. Currently, the satellite direct-receiving data are stored on different devices, which poses significant inconvenience for data sharing applications. Convenient and efficient data access with hierarchical security management is urgently needed. Although satellite data product files can be read and accessed through the Tianqing unified data service interface, the files need to be downloaded time after time and stored onto a local disk directory for further processing. This traditional service mode has disadvantages of low time efficiency and short time sequences, which can not meet the demand of big data applications such as artificial intelligence for long time series product files. Therefore, there is an urgent need to build a unified big data file sharing system. A data lake can store any type of massive data and has good scalability. Various data files centralisedly stored can be directly accessed through the data lake engine for analysis and mining. Consequently, a data lake can reduce the repeated storage and migration of data files and significantly improve the support capability in big data applications. So, in this paper, we propose a convenient and efficient access and security hierarchical management scheme for satellite multi-source data using a data lake. We introduce data lake technology to build a provincial meteorological data lake system based on the meteorological big data cloud platform with a set of unified national standards. A substantial volume of raw data and processed products from FY-3 and FY-4 satellite ground receiving stations are being aggregated into the data lake, enabling a unified data sharing service. The technical challenges such as massive multisource heterogeneous data storage, data management, and data sharing service are being partially resolved to a certain extent. Through the unified authority control technology, we achieve the hierarchical permission management of data access, ensuring the stability and security in data applications. Furthermore, the unified file directory service technology facilitates fast and convenient retrieval of long-time series files, providing significant support for the service applications and satellite data mining in the era of big data.
Meteorological satellites play an increasingly important role in meteorological observation, disaster prevention and mitigation, and weather forecasting. Direct reception, satellite broadcasting, and website download are common methods for obtaining satellite data. A number of high-speed data ground satellite direct reception stations have been built in some regions of each province across China to receive and process various satellite data products such as FY-3 and FY-4 in real-time. Currently, the satellite direct-receiving data are stored on different devices, which poses significant inconvenience for data sharing applications. Convenient and efficient data access with hierarchical security management is urgently needed. Although satellite data product files can be read and accessed through the Tianqing unified data service interface, the files need to be downloaded time after time and stored onto a local disk directory for further processing. This traditional service mode has disadvantages of low time efficiency and short time sequences, which can not meet the demand of big data applications such as artificial intelligence for long time series product files. Therefore, there is an urgent need to build a unified big data file sharing system. A data lake can store any type of massive data and has good scalability. Various data files centralisedly stored can be directly accessed through the data lake engine for analysis and mining. Consequently, a data lake can reduce the repeated storage and migration of data files and significantly improve the support capability in big data applications. So, in this paper, we propose a convenient and efficient access and security hierarchical management scheme for satellite multi-source data using a data lake. We introduce data lake technology to build a provincial meteorological data lake system based on the meteorological big data cloud platform with a set of unified national standards. A substantial volume of raw data and processed products from FY-3 and FY-4 satellite ground receiving stations are being aggregated into the data lake, enabling a unified data sharing service. The technical challenges such as massive multisource heterogeneous data storage, data management, and data sharing service are being partially resolved to a certain extent. Through the unified authority control technology, we achieve the hierarchical permission management of data access, ensuring the stability and security in data applications. Furthermore, the unified file directory service technology facilitates fast and convenient retrieval of long-time series files, providing significant support for the service applications and satellite data mining in the era of big data.
2024, 52(1):55-65.
DOI: 10.19517/j.1671-6345.20220483
Abstract:
The hourly precipitation data from the densified automatic stations over Hainan Island from April to September between 2017 and 2021 and the fifth generation global reanalysis data of the European Centre for Medium-range Weather Forecasts (ECMWF) with an interval of 1 h and a spatial resolution of 0.25°×0.25° are used to classify the circulation configuration of short-term heavy rainfall over Hainan Island. Furthermore, the synoptic diagnostic analysis method is used to synthetically analyse the environmental fields and physical fields of the short-term heavy rainfall processes, and the characteristics of the temporal and spatial distribution, circulation situation, and key environmental parameters of the short-term heavy rainfall over Hainan Island under various weather patterns are analysed. The results show that: (1) The short-term heavy rainfall over Hainan Island has an obvious diurnal variation characteristic, showing a single peak type, mainly occurring from 15:00 to 19:00. (2) The circulation patterns of short-term heavy rainfall over Hainan Island mainly include the South China Sea Trough, the South China Coast Trough, Southwest Trough, and cold front pattern. (3) The South China Sea Trough, South China Coast Trough, Southwest Trough, and cold front pattern account for 37%, 31%, 16%, and 16%, respectively. The cold front pattern mainly appears in April and May. The South China Sea Trough and South China Coast Trough mainly appear in July, August, and September, and the Southwest Trough may appear in other months except September. (4) The spatial distribution of short-term heavy precipitation varies significantly under different weather patterns. The South China Sea Trough type high-frequency stations are mainly concentrated in the northeast inland of Hainan Island, while the South China Coast Trough type high-frequency stations are mainly concentrated in the northern inland and western parts of Hainan Island. The Southwest Trough is mainly concentrated in the area north of Wuzhishan; the cold front pattern mostly occurs in the northern region of Hainan Island. (5) The humidity conditions of the South China Sea Trough and the South China Coast Trough patterns are good, with a high amount of unstable energy and weak vertical wind shear. The unstable energy for the Southwest Trough is high, with the humidity condition being average and the vertical wind shear weak. The cold front pattern has an obvious characteristic of being drier above and wetter below, and the wind speed difference (0～6 km) is the largest, with 75% quantile greater than 10 m/s. However, the unstable energy of the cold front pattern is the lowest.
The hourly precipitation data from the densified automatic stations over Hainan Island from April to September between 2017 and 2021 and the fifth generation global reanalysis data of the European Centre for Medium-range Weather Forecasts (ECMWF) with an interval of 1 h and a spatial resolution of 0.25°×0.25° are used to classify the circulation configuration of short-term heavy rainfall over Hainan Island. Furthermore, the synoptic diagnostic analysis method is used to synthetically analyse the environmental fields and physical fields of the short-term heavy rainfall processes, and the characteristics of the temporal and spatial distribution, circulation situation, and key environmental parameters of the short-term heavy rainfall over Hainan Island under various weather patterns are analysed. The results show that: (1) The short-term heavy rainfall over Hainan Island has an obvious diurnal variation characteristic, showing a single peak type, mainly occurring from 15:00 to 19:00. (2) The circulation patterns of short-term heavy rainfall over Hainan Island mainly include the South China Sea Trough, the South China Coast Trough, Southwest Trough, and cold front pattern. (3) The South China Sea Trough, South China Coast Trough, Southwest Trough, and cold front pattern account for 37%, 31%, 16%, and 16%, respectively. The cold front pattern mainly appears in April and May. The South China Sea Trough and South China Coast Trough mainly appear in July, August, and September, and the Southwest Trough may appear in other months except September. (4) The spatial distribution of short-term heavy precipitation varies significantly under different weather patterns. The South China Sea Trough type high-frequency stations are mainly concentrated in the northeast inland of Hainan Island, while the South China Coast Trough type high-frequency stations are mainly concentrated in the northern inland and western parts of Hainan Island. The Southwest Trough is mainly concentrated in the area north of Wuzhishan; the cold front pattern mostly occurs in the northern region of Hainan Island. (5) The humidity conditions of the South China Sea Trough and the South China Coast Trough patterns are good, with a high amount of unstable energy and weak vertical wind shear. The unstable energy for the Southwest Trough is high, with the humidity condition being average and the vertical wind shear weak. The cold front pattern has an obvious characteristic of being drier above and wetter below, and the wind speed difference (0～6 km) is the largest, with 75% quantile greater than 10 m/s. However, the unstable energy of the cold front pattern is the lowest.
2024, 52(1):66-75.
DOI: 10.19517/j.1671-6345.20220499
Abstract:
By employing the monthly average precipitation from 87 stations in Guangxi in June and NCEP/NCAR reanalysis data, the correlation between the interannual increment of monthly precipitation in Guangxi in June and the 500 hPa geopotential height field in the previous period from 1960 to 2021 is under investigation. Selecting the precursor signals that impact the precipitation anomaly in Guangxi in June occurs as part of this investigation. An ensemble forecasting model of the interannual increment of monthly precipitation, constructed by combining the fuzzy neural network and entropy metric method, is in continual operation. The cross-check of the prediction model from 1960 to 2013 and the independent sample test from 2014 to 2021 happen regularly. Results display a relative high prediction accuracy of the model, with a correlation coefficient of 0.93 between the predicted and actual values of the interannual increments of the return sample, passing the significance test of α=0.001. There is a return-year homogeneity rate of 87.5%, a fitted mean absolute error of 26.64 mm, and a fitted mean relative error of 9.06 %. This model is more stable than the prediction model built by the traditional stepwise regression method. For this reason, the entropy metric-fuzzy neural network ensemble prediction model sees better prospects for operational forecasting of short-term climate drought and flood trends.
By employing the monthly average precipitation from 87 stations in Guangxi in June and NCEP/NCAR reanalysis data, the correlation between the interannual increment of monthly precipitation in Guangxi in June and the 500 hPa geopotential height field in the previous period from 1960 to 2021 is under investigation. Selecting the precursor signals that impact the precipitation anomaly in Guangxi in June occurs as part of this investigation. An ensemble forecasting model of the interannual increment of monthly precipitation, constructed by combining the fuzzy neural network and entropy metric method, is in continual operation. The cross-check of the prediction model from 1960 to 2013 and the independent sample test from 2014 to 2021 happen regularly. Results display a relative high prediction accuracy of the model, with a correlation coefficient of 0.93 between the predicted and actual values of the interannual increments of the return sample, passing the significance test of α=0.001. There is a return-year homogeneity rate of 87.5%, a fitted mean absolute error of 26.64 mm, and a fitted mean relative error of 9.06 %. This model is more stable than the prediction model built by the traditional stepwise regression method. For this reason, the entropy metric-fuzzy neural network ensemble prediction model sees better prospects for operational forecasting of short-term climate drought and flood trends.
2024, 52(1):76-89.
DOI: 10.19517/j.1671-6345.20230040
Abstract:
The snow capital of China in the Altai Mountains, which is located in the northernmost part of Altay Prefecture in Xinjiang, experiences many Warm-Sector Snowstorms in winter due to the frequent southward movement of the polar front area. These snowstorm disasters sometimes cause more harm to the national economy and people’s lives than rainstorms, often resulting in huge losses to transportation, animal husbandry, electricity, etc. Meanwhile, in order to improve the service level of ice and snow tourism forecast in Xinjiang, it is necessary to actively explore the characteristics of water vapour during the winter snowstorms in the Altai Mountains. Based on the solid precipitation data and NCEP/NCAR reanalysis data of the Altai Mountains, the circulation background and water vapour characteristics of three snowstorms in the Altai Mountains in 2021 are analysed using synoptic diagnosis and different water vapour analysis methods. The results indicate that: (1) The three snowstorm processes are typical of the warm region of northern Xinjiang. The snowstorms mainly occur in the divergence area on the right side of the southwest (by the west) jet axis at the upper level, the southwest (by the west) frontal area at the bottom of the polar vortex, the convergence area in front of the outlet area of the southwest jet at the lower level, and the overlap zone of the surface decompression and temperature rise. (2) The Euler’s method analysis shows that the water vapour in this area mainly comes from the Atlantic Ocean and its coast. The western boundary of the Altai Mountains is a water vapour input, and the eastern and southern boundaries are water vapour outputs. The water vapour input in the middle and lower layers is closely related to the quantity of snowstorms. The convergence area of water vapour flux divergence is located in the lower troposphere. (3) The HYSPLIT’s (Lagrange) method analysis shows that water vapour mainly come from the Arctic Ocean and Europe, followed by Central Asia and Canada, significantly differing from the above conclusion; the water vapour in the lower troposphere makes a significant contribution to the overall snowstorm area. (4) The contribution model of water vapour for the snowstorm process in the Altai Mountains is constructed. When the water vapour of 700 hPa and above reaches the key area from the source, it mainly enters the snowstorm area from the west (southwest) path. When the water vapour of 700 hPa and below reaches the key area, it primarily enters the snowstorm area from the southeast path when the circulation is appropriate, but the contributions from the west (southwest) and northwest paths cannot be ignored; water vapour mainly accumulates in the lower troposphere and converges and rises.
The snow capital of China in the Altai Mountains, which is located in the northernmost part of Altay Prefecture in Xinjiang, experiences many Warm-Sector Snowstorms in winter due to the frequent southward movement of the polar front area. These snowstorm disasters sometimes cause more harm to the national economy and people’s lives than rainstorms, often resulting in huge losses to transportation, animal husbandry, electricity, etc. Meanwhile, in order to improve the service level of ice and snow tourism forecast in Xinjiang, it is necessary to actively explore the characteristics of water vapour during the winter snowstorms in the Altai Mountains. Based on the solid precipitation data and NCEP/NCAR reanalysis data of the Altai Mountains, the circulation background and water vapour characteristics of three snowstorms in the Altai Mountains in 2021 are analysed using synoptic diagnosis and different water vapour analysis methods. The results indicate that: (1) The three snowstorm processes are typical of the warm region of northern Xinjiang. The snowstorms mainly occur in the divergence area on the right side of the southwest (by the west) jet axis at the upper level, the southwest (by the west) frontal area at the bottom of the polar vortex, the convergence area in front of the outlet area of the southwest jet at the lower level, and the overlap zone of the surface decompression and temperature rise. (2) The Euler’s method analysis shows that the water vapour in this area mainly comes from the Atlantic Ocean and its coast. The western boundary of the Altai Mountains is a water vapour input, and the eastern and southern boundaries are water vapour outputs. The water vapour input in the middle and lower layers is closely related to the quantity of snowstorms. The convergence area of water vapour flux divergence is located in the lower troposphere. (3) The HYSPLIT’s (Lagrange) method analysis shows that water vapour mainly come from the Arctic Ocean and Europe, followed by Central Asia and Canada, significantly differing from the above conclusion; the water vapour in the lower troposphere makes a significant contribution to the overall snowstorm area. (4) The contribution model of water vapour for the snowstorm process in the Altai Mountains is constructed. When the water vapour of 700 hPa and above reaches the key area from the source, it mainly enters the snowstorm area from the west (southwest) path. When the water vapour of 700 hPa and below reaches the key area, it primarily enters the snowstorm area from the southeast path when the circulation is appropriate, but the contributions from the west (southwest) and northwest paths cannot be ignored; water vapour mainly accumulates in the lower troposphere and converges and rises.
10 Analysis of Radar Echoes Layered Structure Characteristics of Convective Cells in Weining, Guizhou
2024, 52(1):90-103.
DOI: 10.19517/j.1671-6345.20220490
Abstract:
In this paper, 111 convective cells (35 hailstorm cells and 76 thunderstorm cells) in Weining are selected to analyse the radar echoes’ layered structure of convective cells in the vertical direction. The polarization parameters of the convective cloud and the phase distribution characteristics are analysed combined with the observation of dual-polarisation weather radar, preliminarily revealing that hailstorm cells have the characteristics of rapid development and small diameter of hail. The results are as follows: (1) The typical radar echo characteristic quantities of convective cells in Weining are basically consistent; the differences in macroscopic characteristic quantities are small, with the life cycle being about 100 min, and the maximum reflectivity factor mainly located within the height difference ±2.0 km from the 0 ℃ layer. Hailstorm cells are stronger than thunderstorm cells and exhibit rapid development, longer duration, and greater stability. (2) The ratio of the mean value of the maximum reflectivity factor of hail cloud echo greater than that of the 0 ℃ layer is only 28.6%; the hail diameter ≥10 mm accounts for only 8.3%, and the time for convective cells to develop into hail cloud cells is only 30 min, which is generally unfavourable for the formation of large hail. The size of hail corresponds to the spreading height of strong echo above the melting layer. The size of hail varies with the change of 45 dBz and 55 dBz echo development altitude; the higher the altitude, the larger the hail. (3) In general, there is little difference between hailstorm and thunderstorm, making it difficult to completely distinguish hailstorms. The X-band dual polarisation weather radar can reflect the type of precipitation particles and basically accords with the characteristics of the shape, size, and phase distribution of precipitation particles. It is necessary to introduce dual-polarisation radar observations, which is of great significance to improve the accuracy and lead time of hail recognition. However, the transformation characteristics of precipitation phase particles in the critical transition region should be considered, especially the interaction of Zhh, Zdr,KDP, ρHV, and other polarisations in the phase transition of particles. (4) The atmosphere in Weining is unstable, and the lowlevel humidity conducive to convective weather formation reaches more than 83%. However, the vertical wind shear is weak, and the unstable energy is low; only 20% of cases have unstable stratification and possess strong unstable energy. In addition, the melting effect in the falling process of medium and small hail makes it easier to produce small hailstones in Weining.
In this paper, 111 convective cells (35 hailstorm cells and 76 thunderstorm cells) in Weining are selected to analyse the radar echoes’ layered structure of convective cells in the vertical direction. The polarization parameters of the convective cloud and the phase distribution characteristics are analysed combined with the observation of dual-polarisation weather radar, preliminarily revealing that hailstorm cells have the characteristics of rapid development and small diameter of hail. The results are as follows: (1) The typical radar echo characteristic quantities of convective cells in Weining are basically consistent; the differences in macroscopic characteristic quantities are small, with the life cycle being about 100 min, and the maximum reflectivity factor mainly located within the height difference ±2.0 km from the 0 ℃ layer. Hailstorm cells are stronger than thunderstorm cells and exhibit rapid development, longer duration, and greater stability. (2) The ratio of the mean value of the maximum reflectivity factor of hail cloud echo greater than that of the 0 ℃ layer is only 28.6%; the hail diameter ≥10 mm accounts for only 8.3%, and the time for convective cells to develop into hail cloud cells is only 30 min, which is generally unfavourable for the formation of large hail. The size of hail corresponds to the spreading height of strong echo above the melting layer. The size of hail varies with the change of 45 dBz and 55 dBz echo development altitude; the higher the altitude, the larger the hail. (3) In general, there is little difference between hailstorm and thunderstorm, making it difficult to completely distinguish hailstorms. The X-band dual polarisation weather radar can reflect the type of precipitation particles and basically accords with the characteristics of the shape, size, and phase distribution of precipitation particles. It is necessary to introduce dual-polarisation radar observations, which is of great significance to improve the accuracy and lead time of hail recognition. However, the transformation characteristics of precipitation phase particles in the critical transition region should be considered, especially the interaction of Zhh, Zdr,KDP, ρHV, and other polarisations in the phase transition of particles. (4) The atmosphere in Weining is unstable, and the lowlevel humidity conducive to convective weather formation reaches more than 83%. However, the vertical wind shear is weak, and the unstable energy is low; only 20% of cases have unstable stratification and possess strong unstable energy. In addition, the melting effect in the falling process of medium and small hail makes it easier to produce small hailstones in Weining.
2024, 52(1):104-115.
DOI: 10.19517/j.1671-6345.20220530
Abstract:
In order to enhance the daily application of high-resolution new detection data and improve the analysis and prediction abilities of forecasters for weak precipitation weather processes, at the same time, for the sake of developing superior meteorological support capacities for important activities or major events, new detection data such as microwave radiometer, laser wind radar, Doppler radar, phased array radar, ground densified observation data, ERA5 reanalysis data, and multimodel numerical prediction data are used to analyse a forecast error that occurred during a weak precipitation event in Xi’an urban area on 24 April 2022. The result shows that (1) Both the global numerical models (CMA-GFS, ECMWF, NCEP) and mesoscale numerical models (CMA-Beijing, CMA-MESO, CMA-TYM) predicted significant precipitation in Xi’an urban area, mainly due to the high prediction of low-level relative humidity. (2) Based on the analysis of various new types of detection data, poor humidity conditions in the near-surface layer and low absolute water vapour content in the middle layer were found, leading to the conclusion that dry and warm air in the middle layer could not be conducive to cloud formation and rainfall. Moreover, weak vertical upward movement with very dry lower layers caused the raindrops to evaporate during the falling process, making it impossible for raindrops to fall to the ground in Xi’an urban area. (3) The reason for the poor humidity conditions in the near-surface layer of the urban area of Xi’an was the urban dry heat island effect and the low-level dry warm advection transport. Moreover, after the precipitation cloud group climbed the Qinling Mountains, its humid air adiabatically sank to the urban area, causing an increase in temperature and a decrease in humidity, resulting in the formation of a relatively deep dry layer in the urban area. Even if there were raindrops falling, stronger evaporation would occur due to the above conditions, which was also one of the important reasons for the lack of precipitation in the urban area of Xi’an. (4) The main reason for the lack of successful subjective prediction correction ability was that forecasters lacked the skills to revise key precipitation elements such as boundary layer water vapour, dynamic uplift conditions, or some other elements that were beneficial for precipitation. Furthermore, there was insufficient research on the dry heat island effect of big cities like Xi’an or the terrain impact on weak precipitation in the Qinling Mountains.
In order to enhance the daily application of high-resolution new detection data and improve the analysis and prediction abilities of forecasters for weak precipitation weather processes, at the same time, for the sake of developing superior meteorological support capacities for important activities or major events, new detection data such as microwave radiometer, laser wind radar, Doppler radar, phased array radar, ground densified observation data, ERA5 reanalysis data, and multimodel numerical prediction data are used to analyse a forecast error that occurred during a weak precipitation event in Xi’an urban area on 24 April 2022. The result shows that (1) Both the global numerical models (CMA-GFS, ECMWF, NCEP) and mesoscale numerical models (CMA-Beijing, CMA-MESO, CMA-TYM) predicted significant precipitation in Xi’an urban area, mainly due to the high prediction of low-level relative humidity. (2) Based on the analysis of various new types of detection data, poor humidity conditions in the near-surface layer and low absolute water vapour content in the middle layer were found, leading to the conclusion that dry and warm air in the middle layer could not be conducive to cloud formation and rainfall. Moreover, weak vertical upward movement with very dry lower layers caused the raindrops to evaporate during the falling process, making it impossible for raindrops to fall to the ground in Xi’an urban area. (3) The reason for the poor humidity conditions in the near-surface layer of the urban area of Xi’an was the urban dry heat island effect and the low-level dry warm advection transport. Moreover, after the precipitation cloud group climbed the Qinling Mountains, its humid air adiabatically sank to the urban area, causing an increase in temperature and a decrease in humidity, resulting in the formation of a relatively deep dry layer in the urban area. Even if there were raindrops falling, stronger evaporation would occur due to the above conditions, which was also one of the important reasons for the lack of precipitation in the urban area of Xi’an. (4) The main reason for the lack of successful subjective prediction correction ability was that forecasters lacked the skills to revise key precipitation elements such as boundary layer water vapour, dynamic uplift conditions, or some other elements that were beneficial for precipitation. Furthermore, there was insufficient research on the dry heat island effect of big cities like Xi’an or the terrain impact on weak precipitation in the Qinling Mountains.
2024, 52(1):116-123.
DOI: 10.19517/j.1671-6345.20220508
Abstract:
Ground-air temperature difference is the main contributor to surface sensible heat flux. The change of ground-air temperature difference effectively reflects the change characteristics of surface sensible heat flux, which profoundly impacts atmospheric circulation, weather changes, agricultural production, and the ecological environment. Over the past half-century, Shijiazhuang’s climate is warming significantly, with the Urban Heat Island effect being obvious. Concurrently, the local climate and environmental effects are brought about by urbanisation and are also very significant. Studying the variation characteristics of the ground-air temperature difference can deepen our understanding of changes in the near-surface thermal environment in Shijiazhuang, providing a scientific reference for guiding agricultural production, as well as protecting and improving the urban ecological environment. Based on the daily ground air temperature, 0 cm ground temperature, and precipitation data from 17 national meteorological stations in Shijiazhuang from 1972 to 2021, the variation characteristics of the ground-air temperature difference in Shijiazhuang are analyzed. The results show that: (1) The ground-air temperature difference in Shijiazhuang increasingly rises from January, reaching a maximum of 5.0 ℃ in May, then begins to fall, reaching a minimum of -0.8℃ in December; The ground-air temperature difference is negative in January, November and December, and positive in other months. The average ground-air temperature difference in spring, summer and autumn is positive, with the maximum in summer and greater in spring than in autumn; The average ground-air temperature difference in winter is mainly negative. (2) The multi-year average ground-air temperature difference in Shijiazhuang is between 1.6~2.6 ℃, averaging at 2.1 ℃. Generally, the ground-air temperature difference in the east is larger than that in the west. (3) In recent 50 years, the annual average ground-air temperature difference in Shijiazhuang exhibits a significant decreasing trend, the rate of change is -0.14 ℃/10a. The ground-air temperature difference in summer, autumn, and winter in Shijiazhuang decreases significantly, with the most potent trend in summer. The decreasing trend of ground-air temperature difference in urban and nearly rural stations in Shijiazhuang is more evident. The conclusion of this article is of reference significance for scientifically understanding the changes in the urban ecological environment of Shijiazhuang.
Ground-air temperature difference is the main contributor to surface sensible heat flux. The change of ground-air temperature difference effectively reflects the change characteristics of surface sensible heat flux, which profoundly impacts atmospheric circulation, weather changes, agricultural production, and the ecological environment. Over the past half-century, Shijiazhuang’s climate is warming significantly, with the Urban Heat Island effect being obvious. Concurrently, the local climate and environmental effects are brought about by urbanisation and are also very significant. Studying the variation characteristics of the ground-air temperature difference can deepen our understanding of changes in the near-surface thermal environment in Shijiazhuang, providing a scientific reference for guiding agricultural production, as well as protecting and improving the urban ecological environment. Based on the daily ground air temperature, 0 cm ground temperature, and precipitation data from 17 national meteorological stations in Shijiazhuang from 1972 to 2021, the variation characteristics of the ground-air temperature difference in Shijiazhuang are analyzed. The results show that: (1) The ground-air temperature difference in Shijiazhuang increasingly rises from January, reaching a maximum of 5.0 ℃ in May, then begins to fall, reaching a minimum of -0.8℃ in December; The ground-air temperature difference is negative in January, November and December, and positive in other months. The average ground-air temperature difference in spring, summer and autumn is positive, with the maximum in summer and greater in spring than in autumn; The average ground-air temperature difference in winter is mainly negative. (2) The multi-year average ground-air temperature difference in Shijiazhuang is between 1.6~2.6 ℃, averaging at 2.1 ℃. Generally, the ground-air temperature difference in the east is larger than that in the west. (3) In recent 50 years, the annual average ground-air temperature difference in Shijiazhuang exhibits a significant decreasing trend, the rate of change is -0.14 ℃/10a. The ground-air temperature difference in summer, autumn, and winter in Shijiazhuang decreases significantly, with the most potent trend in summer. The decreasing trend of ground-air temperature difference in urban and nearly rural stations in Shijiazhuang is more evident. The conclusion of this article is of reference significance for scientifically understanding the changes in the urban ecological environment of Shijiazhuang.
2024, 52(1):124-131.
DOI: 10.19517/j.1671-6345.20220424
Abstract:
Based on data from ultraviolet radiation observations, traditional meteorological observations, and ambient air quality observations in Guangzhou from 2019 to 2021, we analyse the variation characteristics of ultraviolet radiant intensity in Guangzhou, and the correlations between ultraviolet radiation intensity and meteorological factors as well as ambient air quality factors. We select seven meteorological and ambient air quality factors significantly linked to the intensity of ultraviolet radiation in Guangzhou as characteristic elements for the input ultraviolet radiation intensity fitting model. We further use the Gradient Boosting Decision Tree (GBDT) algorithm to establish a fitting model for the city’s ultraviolet radiation intensity. The results demonstrate that: (1) The ultraviolet radiant intensity in Guangzhou has obvious seasonal and daily variation characteristics. The seasonal variation of ultraviolet radiation intensity in Guangzhou is characterized by high levels in summer and autumn, and low levels in winter and spring. In 2020 and 2021, the maximum value of ultraviolet radiation intensity in Guangzhou occurs in July, while in 2019, the maximum value of ultraviolet radiation intensity in Guangzhou appeared in September. The minimum ultraviolet radiation intensity in Guangzhou between 2019 and 2021 occurred in March, with the lowest intensity 15.9 W·m-2 in 2020. Daily variations in ultraviolet radiation intensity in Guangzhou typically show low in the morning and evening levels and a high at noon level, with ultraviolet radiation intensity reaching its daily maximum around 12 o’clock. (2) The meteorological and ambient air quality factors significantly correlated with the intensity of ultraviolet radiation in Guangzhou are air temperature, visibility, total cloud cover, relative humidity, solar altitude angle, ozone concentration, and nitrogen dioxide concentration. (3) The ultraviolet radiation intensity model in Guangzhou has a good fitting effect. The coefficient of determination for the training data set and the test data set of the ultraviolet radiation intensity fitting model in Guangzhou are 0.93 and 0.80, respectively. Moreover, the RMSE of the training and testing set data for the ultraviolet radiation intensity model in Guangzhou is 2.7 and 4.9 W·m-2 respectively. The estimate accuracy of ultraviolet radiation intensity levels in Guangzhou is 75%, one level difference in ultraviolet radiant levels accounts for 21%, and two level difference in ultraviolet radiant levels accounts for 4%.
Based on data from ultraviolet radiation observations, traditional meteorological observations, and ambient air quality observations in Guangzhou from 2019 to 2021, we analyse the variation characteristics of ultraviolet radiant intensity in Guangzhou, and the correlations between ultraviolet radiation intensity and meteorological factors as well as ambient air quality factors. We select seven meteorological and ambient air quality factors significantly linked to the intensity of ultraviolet radiation in Guangzhou as characteristic elements for the input ultraviolet radiation intensity fitting model. We further use the Gradient Boosting Decision Tree (GBDT) algorithm to establish a fitting model for the city’s ultraviolet radiation intensity. The results demonstrate that: (1) The ultraviolet radiant intensity in Guangzhou has obvious seasonal and daily variation characteristics. The seasonal variation of ultraviolet radiation intensity in Guangzhou is characterized by high levels in summer and autumn, and low levels in winter and spring. In 2020 and 2021, the maximum value of ultraviolet radiation intensity in Guangzhou occurs in July, while in 2019, the maximum value of ultraviolet radiation intensity in Guangzhou appeared in September. The minimum ultraviolet radiation intensity in Guangzhou between 2019 and 2021 occurred in March, with the lowest intensity 15.9 W·m-2 in 2020. Daily variations in ultraviolet radiation intensity in Guangzhou typically show low in the morning and evening levels and a high at noon level, with ultraviolet radiation intensity reaching its daily maximum around 12 o’clock. (2) The meteorological and ambient air quality factors significantly correlated with the intensity of ultraviolet radiation in Guangzhou are air temperature, visibility, total cloud cover, relative humidity, solar altitude angle, ozone concentration, and nitrogen dioxide concentration. (3) The ultraviolet radiation intensity model in Guangzhou has a good fitting effect. The coefficient of determination for the training data set and the test data set of the ultraviolet radiation intensity fitting model in Guangzhou are 0.93 and 0.80, respectively. Moreover, the RMSE of the training and testing set data for the ultraviolet radiation intensity model in Guangzhou is 2.7 and 4.9 W·m-2 respectively. The estimate accuracy of ultraviolet radiation intensity levels in Guangzhou is 75%, one level difference in ultraviolet radiant levels accounts for 21%, and two level difference in ultraviolet radiant levels accounts for 4%.
2024, 52(1):132-140.
DOI: 10.19517/j.1671-6345.20230004
Abstract:
Aircraft icing is an important and dangerous weather condition that affects flight safety. Accurate diagnosis of aircraft icing is of great significance to the operation and safety of civil aviation, especially with the advancement of airworthiness certification of domestic large aircraft; the diagnosis of aircraft icing potential attracts the attention of many departments such as civil aviation and meteorology. In this paper, referring to the improved CIP (Current Icing Potential) index, the fuzzy logic method is used to increase the correlation between the vertical velocity and the liquid water content in the cloud affecting aircraft icing, and optimize the aircraft icing potential equation to obtain the SCIP (Simplified Current Icing Potential) index. The index gives a relatively accurate aircraft icing potential directly based on atmospheric layer data and has the characteristics of a high hit rate and strong practicability in the diagnosis of aircraft icing. The aircraft icing potential diagnostic indexes are compared and validated, using the China Civil Aviation Aircraft reports from 28 February 2021 to 31 December 2021. The icing potential index is applied to reanalysis data to simulate historical icing events. Compared with the improved CIP index, the SCIP index shows better Receiver Operating Characteristic Curve (ROC) and True Skill Score (TSS). The results also show that increasing vertical velocity and cloud liquid water content in the aircraft ice accumulation potential diagnosis algorithm are crucial to avoid the warning of air alarms. However, for different degrees of ice accumulation, the improved CIP index and SCIP index show a consistent diagnostic effect, indicating that the SCIP index plays an important role in the diagnosis of aircraft ice accumulation potential but cannot judge the ice accumulation intensity. In order to further evaluate the influence of increasing vertical velocity and cloud liquid water content in the aircraft ice accumulation potential index, reanalysis data are used to calculate the aircraft ice accumulation potential of an individual case of aircraft ice formation in Shaanxi Province on 16 March 2020, indicating that increased upward vertical velocity and larger cloud liquid water content provide better conditions for aircraft ice accumulation under the condition of low cloud top temperature. The SCIP index makes up for the shortcomings of the improved CIP algorithm in the diagnosis of aircraft ice accumulation potential under this weather condition, indicating that the increase of these two factors is of great significance to improve the diagnosis effect of ice accumulation potential.
Aircraft icing is an important and dangerous weather condition that affects flight safety. Accurate diagnosis of aircraft icing is of great significance to the operation and safety of civil aviation, especially with the advancement of airworthiness certification of domestic large aircraft; the diagnosis of aircraft icing potential attracts the attention of many departments such as civil aviation and meteorology. In this paper, referring to the improved CIP (Current Icing Potential) index, the fuzzy logic method is used to increase the correlation between the vertical velocity and the liquid water content in the cloud affecting aircraft icing, and optimize the aircraft icing potential equation to obtain the SCIP (Simplified Current Icing Potential) index. The index gives a relatively accurate aircraft icing potential directly based on atmospheric layer data and has the characteristics of a high hit rate and strong practicability in the diagnosis of aircraft icing. The aircraft icing potential diagnostic indexes are compared and validated, using the China Civil Aviation Aircraft reports from 28 February 2021 to 31 December 2021. The icing potential index is applied to reanalysis data to simulate historical icing events. Compared with the improved CIP index, the SCIP index shows better Receiver Operating Characteristic Curve (ROC) and True Skill Score (TSS). The results also show that increasing vertical velocity and cloud liquid water content in the aircraft ice accumulation potential diagnosis algorithm are crucial to avoid the warning of air alarms. However, for different degrees of ice accumulation, the improved CIP index and SCIP index show a consistent diagnostic effect, indicating that the SCIP index plays an important role in the diagnosis of aircraft ice accumulation potential but cannot judge the ice accumulation intensity. In order to further evaluate the influence of increasing vertical velocity and cloud liquid water content in the aircraft ice accumulation potential index, reanalysis data are used to calculate the aircraft ice accumulation potential of an individual case of aircraft ice formation in Shaanxi Province on 16 March 2020, indicating that increased upward vertical velocity and larger cloud liquid water content provide better conditions for aircraft ice accumulation under the condition of low cloud top temperature. The SCIP index makes up for the shortcomings of the improved CIP algorithm in the diagnosis of aircraft ice accumulation potential under this weather condition, indicating that the increase of these two factors is of great significance to improve the diagnosis effect of ice accumulation potential.
2024, 52(1):141-150.
DOI: 10.19517/j.1671-6345.20220523
Abstract:
The short-time rainstorm caused by small and mesoscale weather systems, which has characteristics such as short duration and strong disaster potential. The short-time rainstorms are always the focus of meteorological research and the challenge in weather forecasting. Currently, FY-4A satellite products have a few applications in research and service, especially in the analysis of short-term rainstorms. The application and analysis of satellite cloud images in weather reports are still mainly based on judgment and qualitative extrapolation, lacking quantitative proximity prediction indicators of short-term rainstorms. Based on FY-4A satellite product data and ground precipitation observation data from 2018 to 2021, we study the analysis of short-term rainstorm weather processes in the flood season, which occurs from May to September in Ningxia. By using correlation analysis, box-plot and extreme value statistics methods, we evaluate the monitoring and early warning indicators of FY-4A satellite products in the weather process of short-term rainstorms. The results show that: (1) FY-4A satellite data can serve as not only qualitative indicators but also quantitative indicators for short-term rainstorms monitoring and early warning after processing with methods such as correlation analysis, box plot, and extreme value statistics. (2) Thirteen products of the FY-4A satellite have a good correlation with the sample sequence of short-term heavy precipitation, including Black Body Temperature (TBB), Convective Inception (CIX), Cloud Mask (CLM), Cloud Phase (CLP), Cloud Type (CLT), Cloud Top Height (CTH), Cloud Top Pressure (CTP), Cloud Top Temperature (CTT), Total Precipitable Water (TPW), Quantitative Rainfall Rate Estimation (QPE), Cloud Effective Radius (CER), Cloud Liquid Water Path (LWP) and Tropopause Folding Uppermost Height (TZD), which can serve as monitoring and early warning indicators. We set the criteria that short-term heavy rainfall will occur when nine out of thirteen product indicators meet the standards, which can be optimized and adjusted through operational practices. (3) Among the thirteen products, there are four key indicators, which are Convective Inception (CIX), Cloud Mask (CLM), Cloud Phase (CLP), and Cloud Type (CLT), and three auxiliary indicators, which are Cloud Top Height (CTH), Cloud Effective Radius (CER), and Cloud Liquid Water Path (LWP), while the other products are numerical discriminant indicators. (4) After improvement based on the evaluation, the True Skill Statistic (TS) increases by 5.4%, the false reporting rate decreases by 2.7%, and the miss reporting rate decreases by 1.9%. The study of monitoring and early warning indicators using FY-4A satellite multi-channel product data is an experimental trial and has great application value, especially in the application of quantitative indicators to intelligent discrimination and automatic alarm. This study holds significant importance for improving the early warning recognition and advance warning capabilities of short-term heavy precipitation.
The short-time rainstorm caused by small and mesoscale weather systems, which has characteristics such as short duration and strong disaster potential. The short-time rainstorms are always the focus of meteorological research and the challenge in weather forecasting. Currently, FY-4A satellite products have a few applications in research and service, especially in the analysis of short-term rainstorms. The application and analysis of satellite cloud images in weather reports are still mainly based on judgment and qualitative extrapolation, lacking quantitative proximity prediction indicators of short-term rainstorms. Based on FY-4A satellite product data and ground precipitation observation data from 2018 to 2021, we study the analysis of short-term rainstorm weather processes in the flood season, which occurs from May to September in Ningxia. By using correlation analysis, box-plot and extreme value statistics methods, we evaluate the monitoring and early warning indicators of FY-4A satellite products in the weather process of short-term rainstorms. The results show that: (1) FY-4A satellite data can serve as not only qualitative indicators but also quantitative indicators for short-term rainstorms monitoring and early warning after processing with methods such as correlation analysis, box plot, and extreme value statistics. (2) Thirteen products of the FY-4A satellite have a good correlation with the sample sequence of short-term heavy precipitation, including Black Body Temperature (TBB), Convective Inception (CIX), Cloud Mask (CLM), Cloud Phase (CLP), Cloud Type (CLT), Cloud Top Height (CTH), Cloud Top Pressure (CTP), Cloud Top Temperature (CTT), Total Precipitable Water (TPW), Quantitative Rainfall Rate Estimation (QPE), Cloud Effective Radius (CER), Cloud Liquid Water Path (LWP) and Tropopause Folding Uppermost Height (TZD), which can serve as monitoring and early warning indicators. We set the criteria that short-term heavy rainfall will occur when nine out of thirteen product indicators meet the standards, which can be optimized and adjusted through operational practices. (3) Among the thirteen products, there are four key indicators, which are Convective Inception (CIX), Cloud Mask (CLM), Cloud Phase (CLP), and Cloud Type (CLT), and three auxiliary indicators, which are Cloud Top Height (CTH), Cloud Effective Radius (CER), and Cloud Liquid Water Path (LWP), while the other products are numerical discriminant indicators. (4) After improvement based on the evaluation, the True Skill Statistic (TS) increases by 5.4%, the false reporting rate decreases by 2.7%, and the miss reporting rate decreases by 1.9%. The study of monitoring and early warning indicators using FY-4A satellite multi-channel product data is an experimental trial and has great application value, especially in the application of quantitative indicators to intelligent discrimination and automatic alarm. This study holds significant importance for improving the early warning recognition and advance warning capabilities of short-term heavy precipitation.
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.