Current Issue

Vol.35, NO.1, 2024

Articles
Fine Observation Characteristics and Causes of "9·7" Extreme Heavy Rainstorm over Pearl River Delta, China
Chen Xunlai, Xu Ting, Wang Rui, Li Yuan, Zhang Shuting, Wang Shuxin, Wang Mingjie, Chen Yuanzhao
2024, 35(1): 1-16. DOI: 10.11898/1001-7313.20240101
Abstract:
On 7-8 September 2023, the Pearl River Delta experiences an extremely heavy rainstorm, known as "9·7" extreme rainstorm. Multi-source data are comprehensively utilized, including high-density automatic weather station data, sounding data, wind profiler data, Doppler radar data, high-resolution measurements from FY-4B satellite, and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), to analyze the fine precipitation characteristics and causes of this case. Results indicate that the extremely heavy rainstorm is characterized by area of coverage, wide coverage area, long duration, and substantial rainfall. The extremely heavy rainstorm is caused by the combined interaction of 200 hPa upper-level divergence, the middle-level weak guiding flow, the lower-level southwest monsoon, and the residual vortex of Typhoon Haikui (2311). It is generated by the long-term horizontal scale of about 100 km banded mesoscale convective complex, with significant train effect and warm cloud precipitation characteristics. The centroid of intense echoes with an intensity greater than 45 dBZ is located below 4 km during the most intense precipitation stage, while intense echoes with an intensity greater than 30 dBZ can last for up to 21 hours in Shenzhen. In terms of raindrop distribution characteristics extreme rainfall is mainly caused by a high density of small and medium-sized raindrops. When the rainfall intensity exceeds 20 mm·h-1, the size of raindrop particles increases, but the numerical concentration significantly decreases. Results in an increase in raindrop size but a decrease in the number of concentrations. The duration, intensity, and area of extreme rainstorms have a strong correlation with the fluctuation of the low-level jet in the boundary layer and the location of the core area of the jet. Heavy rainfall occurs within 1-2 hours after a rapid strengthening of the low-level jet index. After the low-level jet index decreases, the intensity of heavy precipitation diminishes. Variations in the low-level jet and low-level jet index have significant implications for heavy rainfall. The prolonged presence of Typhoon Haikui residual vortex in the Pearl River Delta is the synoptic-scale cause of this extremely heavy rainstorm. The residence time of the lingering vortex exceeds 16 hours. During that time, the deep boundary layer low-level jet continuously transfers warm water vapor to the lingering vortex. Simultaneously, the water vapor from the western Pacific, carried by the northeast airflow of Typhoon Yunyeung, and the southwest monsoon water vapor transfers through the Bay of Bengal, Indochina Peninsula, and the South China Sea, ultimately results in the formation of a stable mesoscale convergence line near the Pearl River Delta, causing an extremely heavy rainstorm.
Verification and Assessment of "23·7" Severe Rainstorm Numerical Prediction in North China
Zhang Bo, Zhang Fanghua, Li Xiaolan, Hu Yi
2024, 35(1): 17-32. DOI: 10.11898/1001-7313.20240102
Abstract:
During the severe rainstorm in North China from 31 July to 1 August in 2023, CMA-GFS, CMA-EPS, EC-EPS, EC-HR, NCEP-GFS, CMA-TYM, CMA-MESO, and CMA-BJ are tested and evaluated using synoptic verification, threat score (TS), and MODE (method for object-based diagnostic evaluatin). The persistence and intensity of long-term heavy rainfall, as well as the area and intensity of short-term heavy rainfall, are tested and analyzed for their effectiveness over time. Results indicate that the cumulative precipitation predicted by EC-EPS may exceed 100 mm for 14 days in advance, but there is no prediction ability for extreme heavy precipitation above 600 mm. EC-HR forecast for the location of precipitation is generally accurate up to 8 days in advance. In the short term, the daily precipitation intensity forecast by CMA-BJ closely matches the actual situation, indicating its significance in predicting precipitation extremes. The average and maximum precipitation values of CMA-GFS, EC-HR, and NCEP-GFS in the areas with concentrated heavy precipitation are lower than actual values. CMA-GFS doesn't perform very well while EC-HR is closer to the actual situation. CMA-GFS, EC-HR, and NCEP-GFS models all provide inadequate forecasts for the persistence of heavy rainfall. However, EC-HR has a relative advantage in predicting persistent precipitation 8 days in advance. TS of CMA-BJ is highest for precipitation forecasts above 50 mm and 100 mm. EC-HR and CMA-TYM precipitation forecasts above 50 mm are relatively stable. From the daily MODE results of the precipitation concentration period from 29 July to 31 July, it is evident that EC-HR exhibits a northward predictive characteristic, while the prediction of CMA-BJ is slightly southward. The forecasting ability of CMA-GFS is insufficient, and forecasts from NCEP-GFS and CMA-MESO are not stable. The high-pressure system in North China has a significant impact on the precipitation. EC-HR model forecasts the formation and reinforcement of a 500 hPa high-pressure system 3 to 4 days earlier than CMA-GFS and NCEP-GFS models. It also surpasses both in predicting the precise location and strength of intense precipitation. Additionally, EC-HR model predicts the emergence of a 925 hPa low-pressure trough and a low-level jet 7 days in advance. However, it underestimates the intensity of the trough and jet system, with the actual location being to the west to north. CMA-GFS and NCEP-GFS underestimate the impact of the Taihang Mountains on easterly winds, leading to significantly lower precipitation forecasts. The analysis of the deviation in the 36 h precipitation forecast for 30 July also shows that EC-HR has weak predictions for low-level wind fields, trough positions, and convective precipitation, resulting in a weak intensity of heavy precipitation and a west-north precipitation area.
Wind Lidar Applicability in Low Visibility Weather in Qingdao
Yan Shen, Shi Xiaomeng, Fu Gang, Chen Qingfeng, Li Yuwei
2024, 35(1): 33-44. DOI: 10.11898/1001-7313.20240103
Abstract:
Utilizing data of Doppler wind lidar and L-band radiosonde system installed at Qingdao National Basic Meteorological Observing Station from April 2021 to December 2022, their detection capability and accuracy under low visibility weather conditions are compared and evaluated, specifically in terms of detection height, horizontal wind speed and wind direction, using data obtained from L-band radiosonde system as the reference standard. During non-precipitation weather, when visibility exceeds 10000 m, the wind lidar demonstrates a stable average maximum detection height of approximately 1200 m. The root mean square errors of the wind speed and direction fluctuate around 1.2 m·s-1 and 25°, respectively. However, when the visibility drops below 10000 m, the detection height and accuracy of the wind lidar are influenced by the level of interference, particularly in different visibility and relative humidity ranges. In situations where visibility is below 1000 m, the decrease in atmospheric visibility is attributed to increased water vapor content in the air, with relative humidity consistently exceeding 95%. This high humidity significantly interferes with laser transmission in the atmosphere, resulting in an average maximum detection height of less than 400 m. The correlation between horizontal wind speed and direction decreases to 0.91 and 0.94, and the root mean square error increases to 1.4 m·s-1and 42.7°, respectively. When the visibility ranges between 1000 m and 10000 m, the wind lidar's detection capability varies with the water vapor content in the atmosphere. In cases where relative humidity is below 90%, meeting the criteria for hazy days, the decrease in atmospheric visibility is primarily due to increased aerosol particle content. Under these conditions, the average maximum detection height remains stable above 1200 m. The correlation coefficients for horizontal wind speed and direction are as high as 0.97 and 0.98, with root mean square errors of about 1.1 m·s-1 and 22.3°, respectively. These results are comparable to the detection capability demonstrated under high visibility weather conditions. As relative humidity increases, the impact of water vapor attenuation on laser transmission starts to affect the detection height and accuracy of the wind lidar to varying degrees. When the relative humidity exceeds 95%, the average maximum detection height influenced by water vapor decreases to below 400 m. The correlation coefficient of wind speed decreases to 0.94, with a corresponding increase in the root mean square error to 1.5 m·s-1, and the accuracy of wind direction remains relatively stable under these conditions.
Indicator Construction of Spring Low-temperature Disaster Affecting Winter Wheat of Huang-Huai-Hai Based on Meta-analysis
Li Meixuan, Huo Zhiguo, Kong Rui, Jiang Mengyuan, Mi Qianchuan
2024, 35(1): 45-56. DOI: 10.11898/1001-7313.20240104
Abstract:
The low temperature disaster in spring is one of the main agro-meteorological disasters affecting the yield and quality of winter wheat by affecting the development process and physiological function, resulting in yield reduction. In order to clarify the quantitative relationship between spring low temperature stress and winter wheat yield and its components in Huang-Huai-Hai Region, an indicator is constructed based on yield reduction rate. Based on 1924 sets of experimental data and control data in 34 retrieved literatures, effects of low temperature stress on wheat yield and its components at green-up stage, jointing stage, booting stage and heading-flowering stage are analyzed by Meta-analysis. Using the minimum temperature and accumulated cold of the process as identification factors, the critical thresholds of 0, 10% and 30% of yield reduction rate are determined by using the Youden's Index to establish and verify the low temperature disaster grade indicator. Results show that the yield and its components of winter wheat are jointly affected by the intensity and duration of low temperature to different extent in different developmental stages. The yield and all its components decrease significantly under low temperature stress, and the sensitivity of panicle number per plant and grain number per panicle to low temperature stress is greater than that of thousand kernel weight. The low temperature disaster grade indicators are constructed according to the yield reduction rate of (0, 10%], (10%, 30%], (30%, 100%]. Taking the minimum temperature (unit: ℃) of the process as identification factor, ranges for low temperature disaster grade (Ⅰ, Ⅱ, Ⅲ) during the green-up stage are [-5.0, -2.0), [-8.5, -5.0), <-8.5; during the jointing period, they are [-1.0, 3.0), [-2.5, -1.0), <-2.5; during the booting stage, they are [1.1, 5.1), [-3.0, 1.1), <-3.0. With the accumulated cold (unit: ℃·h) of the process as identification factor: Indicators during the green-up stage are [-216.1, -72.0), [-360.0, -216.1), <-360.0; during the jointing stage, they are [-41.0, -1.2), [-66.0, -41.0), <-66.0; during the booting stage, they are [-101.6, -16.8), [-169.3, -101.6), <-169.3; during the heading to flowering period, they are [-38.5, -19.6), [-93.8, -38.5), <-93.8. The accuracy of the indicator constructed with process accumulated cold volume is higher than that of process minimum temperature in all growth stages, indicating that the identification factor (accumulated cold of the process) based on the comprehensive influence of low temperature intensity and duration of the process could better characterize the severity of winter wheat suffering from low temperature disaster.
High Temperature Heat Damage Grade Index of Tea Plants and Its Distribution Characteristics in Southern Yangtze River and South China
Li Xin, Wang Peijuan, Tang Junxian, Wang Qi, Li Yang, Huo Zhiguo
2024, 35(1): 57-67. DOI: 10.11898/1001-7313.20240105
Abstract:
With the trend of global climate change, it is important to study the high temperature heat damage of tea plants and analyze the spatial and temporal distribution characteristics to warn damage early and reduce production losses. An index is established based on daily maximum air temperature and historical heat damage disaster records at 510 meteorological stations over tea regions in Southern Yangtze River and South China from 1961 to 2022, to determine and verify the extent of high temperature heat damage, using methods of disaster inversion and K-means clustering analysis method. The spatial and temporal distribution characteristics of high temperature heat damage are analyzed. The total days with moving average of 14-consecutive-day maximum temperature above 34.5 ℃ are statistically analyzed, and for mild, moderate and severe high temperature heat damage, the value is in the range of 1-17 d, 18-38 d and above 38 d, respectively. The accuracy rate of complete compliance with the validation sample is 73.9%, and the accuracy rate of basic compliance is 91.3%. The total number of heat damage on tea plants in Southern Yangtze River and South China shows fluctuating changes from 1961 to 2022. The total number of heat damage on tea plants in tea regions of Southern Yangtze River and South China is the lowest in 1999 and 1997, respectively, while numbers are the highest in 2021 for both tea regions. Compared to tea regions of Southern Yangtze River, there are more high temperature heat damages in South China, especially mild high temperature heat damages. Moreover, the number of high temperature heat damage on tea plants in South China shows a significant increasing trend in the past 62 years, but the trend of changes in the number of high temperature heat damage on tea plants in most tea regions of Southern Yangtze River is not significant.
Expressway Pavement Temperature Forecast Based on LSTM and Prior Knowledge
Xiong Guoyu, Zu Fan, Bao Yunxuan, Wang Kexin
2024, 35(1): 68-79. DOI: 10.11898/1001-7313.20240106
Abstract:
The variation of road surface temperature along highways is a crucial indicator for traffic meteorological conditions and constitutes a significant focus in the research on meteorological disasters related to transportation. Accurate forecast of pavement temperature, timely issuance of pavement condition warnings, and alerting relevant personnel to take defensive measures are of paramount importance for ensuring the safety of people's lives and property. Observations from 4 expressway meteorological stations along Nanjing City Ring Expressway and the corresponding ERA5-land reanalysis data from 2019 to 2022 are analyzed. Utilizing feature engineering techniques that consider the daily and seasonal temperature variations as well as temperature trends, a long-short-term memory (LSTM) neural network model, incorporating prior knowledge, is established for multi-step pavement temperature forecasting at 10 min intervals for the next 3 hours. The models are validated under different scenarios including extreme high and low pavement temperature conditions. They are further transferred and applied to 5 additional meteorological stations to investigate the model universality. This approach addresses the challenge of pavement temperature forecasting for stations with limited historical data due to new construction or equipment maintenance. Results indicate that the incorporation of prior knowledge facilitates a more comprehensive consideration of environmental influences by maximizing the feature extraction capabilities of LSTM. All forecasting performance metrics of the model exhibit significant improvements, with the accuracy exceeding 85%. As the forecast lead time extends, the enhancement in various forecast metrics becomes more pronounced, reaching a maximum accuracy improvement of 36%. The model accurately predicts the occurrence time and extremities of extreme low temperatures, but it exhibits relatively weaker capabilities in forecasting extreme high temperatures, with approximately 1 h advance in occurrence time and an underestimation of about 4 ℃. Despite this generally lower forecasting efficacy, the model still provides valuable information. When applying models to forecast pavement temperatures at other meteorological stations, the accuracy exceeds 62%. The forecast performance is better for short lead times, with the accuracy surpassing 80%. The underlying surface type plays a crucial role in the selection of different models. The suburban station model performs relatively optimally for urban meteorological stations and suburban meteorological stations, while the rural station model performs relatively optimally for rural meteorological stations.
Operational Systems
Design and Application of Algorithm Intensive Environment for CMA Big Data and Cloud Platform
Huo Qing, He Wenchun, He Lin, Gao Feng, Chen Shiwang, Xu Yongjun
2024, 35(1): 80-89. DOI: 10.11898/1001-7313.20240107
Abstract:
With the advancement of meteorological services, various product processing systems and supporting data management systems have been developed for different business systems. However, this has led to the problem of system non-intensification. The lack of intensification in meteorological services can result in inconsistent data standards and make the operation and maintenance more challenging, which can lead to significant waste in investment due to duplicate data storage and inconsistent data caused by untimely synchronization. Moreover, the lack of information and technology hinders the integration of upstream and downstream businesses. The intensive development of meteorological business systems and the reform of "cloud+end" technology system are important measures to achieve high-quality development of meteorological business. China Meteorological Administration proposed to build a "cloud+end" technology system in 2020. CMA Big Data and Cloud Platform serves as the cloud, while the meteorological business system is the end, clarifying the positioning of CMA Big Data and Cloud Platform as a key foundational technology platform. The data processing line (DPL) is an intensive environment for meteorological algorithms. It addresses business needs such as efficient and stable processing of data products, data sharing and collaboration among business systems, and efficient and intensive business applications. To achieve this goal, algorithm libraries and task control functions are established by utilizing technologies such as integrated digital and computing, efficient task scheduling, visual process arrangement, and containers. The algorithm library facilitates the standardization, unified management and sharing of algorithms. Task control supports multiple scheduling strategies with high reliability and fault tolerance, enabling efficient and stable scheduling operations for algorithms. All functions mentioned above are in the form of interfaces for the application frontend. At the same time, based on the meteorological business comprehensive monitoring system (referred to as Tianjing) enables automatic collection of algorithm operation status and detection of abnormal alarms. Since its operation in 2021, the processing assembly line has facilitated the real-time operation of 202 business systems nationwide, resulting in a performance improvement of 1-10 times and a significant increase in efficiency. It plays an important supporting role in improving the operational efficiency of business systems, enhancing their collaboration, accelerating the process of "cloud+end" business technology system reform, and promoting the intensive development of meteorological business.
Special Column on China Weather-Modification Field Experiment
Rainfall Enhancement and Fog Dissipation Experiments in Wuling Mountain in 2020 Using Artificial Strong Sound Wave
Sun Yue, Xiao Hui, Feng Qiang, Zhang Yun, Shu Weixi, Fu Danhong, Yang Huiling
2024, 35(1): 90-102. DOI: 10.11898/1001-7313.20240108
Abstract:
Low-frequency sound wave is a new type of operational approach that has the potential for enhancing rainfall and dissipating fog. To investigate the impact of this type of equipment, field operations and observational experiments are conducted in Wuling Mountain from August to September 2020. Wuling Mountain is located at Chengde of Hebei outside the northeastern boundary of Beijing. The main peak of the Yanshan Mountains is renowned for its foggy summers with an altitude of 2118 m. In the experiment, a prototype of an electronic acoustic low-frequency strong sound wave device is used. This device has a maximum sound pressure level of 155 dB. Meanwhile, observation instruments such as a disdrometer, visibility meter, fog droplet spectrometer, and automatic weather station with an ultrasonic anemometer are deployed. These instruments are used to obtain the background conditions and to monitor macro and micro changes during rainfall enhancement and fog dissipation operations for evaluating the effectiveness.In two typical cases with an obvious defogging effect, within 2 to 3 minutes after the start of the operation, the number of droplets smaller than 10 μm decreased, while the number of droplets larger than 10 μm increased. Subsequently, the size of the droplets on most scales decreased significantly, resulting in improved visibility. Within a span of 10 minutes, visibility could increase from less than 100 m to a maximum of 1000 m within 10 min. The relationship between wind speed, wind direction, and the dissipation effect of fog shows that cases with a noticeable defogging effect occur when the average wind speed is less than 1.5 m·s-1 and the wind direction causes the fog to pass through the near side of the influence range of the sound wave device, while cases with an average wind speed greater than 2 m·s-1 hardly show any change in visibility trends. Results, which align with the experimental expectations, are observed during an operation on a convective cloud precipitation when the surface mean wind speed is 1.4 m·s-1. In this case, the rainfall intensity increased rapidly from 0.3 mm·h-1 to more than 7 mm·h-1 within 3 min of operation, and large raindrops with rapid occurrence but short duration is observed. In other rainfall enhancement experimental cases, the average wind speed exceeded 3 m·s-1 during the operation period, and no clear and consistent evidence of increased rainfall is observed, which may be affected by the high wind speeds and only one single observation point.
Comprehensive Evaluation of Rainfall Enhancement of Gas Cannon in Anhui Province
Yang Huiling, Sun Yue, Xiao Hui, Cao Yanan, Feng Liang, Feng Qiang, Shu Weixi, Zhu Mingjia
2024, 35(1): 103-117. DOI: 10.11898/1001-7313.20240109
Abstract:
Gas cannon is a new type of equipment used for rainfall enhancement operating which comprehensively utilizes the influence of shock waves, sound waves, and catalysts to interfere with and catalyze local weather. At present, the use of gas cannons to conduct artificial weather operations in China is still in the experimental stage. Based on multi-source observations from dual-polarization weather radar, rain gauges and other equipment, the rainfall enhancement effect and the possible physical mechanism are comprehensively analyzed for 81 gas cannon operation cases in Anhui Province from 2021 to 2023. Observations of typical cases show that the effect of rainfall enhancement is better when the gas cannon is operated prior to the onset of rainfall, accompanied by an increase in the horizontal reflectivity factor ZH and the differential reflectivity ZDR, and the decrease in the co-polarization correlation coefficient ρhv. However, the effectiveness is poor when the operation is after the start of rainfall. It is observed that the cloud undergoes significant changes primarily in the sub-zero layer following the use of warm cloud catalyst, and the cloud changes rapidly but effects are short-lived. On the other hand, when a cold cloud catalyst is used, the cloud undergoes obvious changes in both the warm cloud region and the cold cloud region with a greater effecting range and longer duration of effects. This may be attributed to the impact of the cold cloud catalyst on the ice phase microphysical processes within the cloud. The increase in radar velocity spectrum width (SW) during the operation of a gas cannon may be caused by the increase in air vortex. Statistical results of hourly rainfall enhancement show that the number of cases of significant rainfall enhancement from the gas cannon is slightly higher than that of significant rainfall reduction. Among the three different types of operation timing, the rainfall enhancement effect is best for Type 2 (rainfall operation at the beginning). The significance of rainfall enhancement is negatively correlated with the duration of the operation, while the duration of the operation is negatively correlated with the increment of ZDR. Excessive sowing can lead to a reduction in rainfall. The significance of rainfall enhancement is negatively correlated with the amount of rainfall in the affected area prior to the operation. After the beginning of rainfall, the operational effectiveness of the gas cannon is poor. The rainfall enhancement is positively correlated with ZH, as well as with middle and low-level wind speed and wind shear. However, the enhancement of rainfall is negatively correlated with high-level wind speed. The high wind speed in the middle and high levels is not conducive to enhancing the rainfall through gas cannon operation. These results provide physical evidence for the effect of a gas cannon on cloud microphysical structure and rainfall.
Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement
Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai
2024, 35(1): 118-128. DOI: 10.11898/1001-7313.20240110
Abstract:
As an important part of weather modification operation, the scientific effectiveness evaluation of artificial rainfall enhancement has gradually attracted attentions of government and public. In order to evaluate effects of artificial rainfall enhancement objectively and quantitatively, combing linear fitting, polynomial regression, spline regression and 3 other machine learning methods including decision tree, support vector machine and neural network, the relationship model between the rainfall in the target area and the contrast area is established based on rainfall data and operation information of recent 10 years in Fujian. Different historical regression statistical test schemes of rainfall enhancement effects are compared and analyzed, aiming to further optimize the best natural rainfall estimation algorithm based on alterable contrast area with statistical method, which can provide reference for the assessment of artificial rainfall enhancement effects. Results show that historical rainfall data samples are mainly concentrated in the weak rainfall grade. Using multiple regression methods (linear regression, polynomial regression and spline regression), the piecewise statistics of rainfall data does not significantly improve the linear regression model between two regions, and its root mean square error (RMSE) is generally higher than the statistical results. By comparing various machine learning and linear regression models, it is found that CNN and quomial regression perform relatively well when the regional average surface rainfall is taken as the statistical variable, with the determination coefficient of CNN being 0.516 and RMSE being 1.097 mm. Each statistical model is greatly improved after six root square transformations of rainfall data. The performance of the model established by CNN method is relatively optimal, with determination coefficient R2 up to 0.658 and RMSE only , followed by SVM statistical model based on sixth-order root square transform data, with R2 being 0.41 and RMSE being . In order to further overcome the time asynchronization and uneven spatial distribution of rainfall in the two regions, the convolutional neural network CNN optimizers (RMSP, ADAM and SGD) are used to establish the contrast-target region rainfall relationship model based on the grid data of natural rainfall plane. Comparison results show that the ADAM optimizer model is the best with the RMSE of 0.61 mm, and its ability to estimate natural rainfall in the affected area is enhanced, by which method the disturbance of the heavy rainfall center will be reduced.