Current Issue

Vol.35, NO.4, 2024

Display Method:
Comparison of Two Ice and Snow Storm Processes in China in February 2024
Dong Quan, Chen Boyu, Hu Ning, Kong Linghan, Chen Tao, Wang Jia, Zhang Bo
2024, 35(4): 385-399. DOI: 10.11898/1001-7313.20240401
Two ice and snow storm processes hit middle and eastern China during 31 January to 7 February (Process Ⅰ) and 19 February to 25 February (Process Ⅱ) in 2024, which are the most extreme ice and snow events since 2009. After the long-lasting cryogenic freezing rain and snow weather in the beginning of 2008, studies about freezing rain and snow storm have effectively improved the ability of subjective forecast and objective forecast skills of these kinds of weather. However, the accuracy of forecasting ice, freezing rain, and snow storm cannot meet demands of society. Recently, the capability of new types of observations has significantly advanced, and the surface observation system is more complete. Utilizing these more comprehensive observations to analyze and compare characteristics of these two processes will be beneficial for precious, objective and quantitative forecast of ice and snow storm, ice-accretion for example.
The ice and snow surface observations, reanalysis dataset, and new types of observations, including dual polarization radar and raindrop spectrum, are used to analyze the precipitation, snowfall, ice-accretion and so on. In addition, microphysical characteristics, atmospheric circulations, and stratification features of these two processes are summarized and compared, and causes for differences is researched. Results show that the affected areas, duration, and total amount of precipitation in these two processes are similar. Process Ⅰ is characterized by deeper ice-accretion and snow depth, while Process Ⅱ is characterized by a larger affected area and increased snowfall. The observation of dual polarization radar shows that there are three layers of precipitation drops for Process Ⅰ: An ice crystal layer, melting layer, and liquid layer from top to the bottom. However, there are four layers for Process Ⅱ which are ice crystal layer, melting layer, liquid layer and refrozen layer from top to the bottom. So, for Process Ⅰ, there is no significant refreezing, and the precipitation droplets are mainly supercooled liquid falling through the cold layer near the surface. Process Ⅰ is mainly characterized by freezing rain and deeper ice accretion. During Process Ⅱ, significant refrozen to mixed or solid precipitation near the surface happens which results in more ice pellets which is not good to ice-accretion. On the other hand, the density of ice pellets is larger than that of snow, resulting in thinner snow depth and less ice-acceration for Process Ⅱ. The atmospheric circulation and stratification features indicate that both processes are characterized by cooperation of Siberia high and southern branch trough. However, the lower-level jet and Siberia high of Process Ⅱ are stronger than that of Process Ⅰ, leading to stronger warm layer and cold layer in Process Ⅱ and the colder low level cold layer is the main reason for the significant ice pellet in Process Ⅱ.
Application of Scale-adaptive Dust Emission Scheme to CMA-CUACE/Dust
Zhou Chunhong, Rao Xiaoqin, Sheng Li, Zhang Jian, Lu Jianyan, Lin Jian, Hu Jiangkai, Zhang Bihui, Xu Ran
2024, 35(4): 400-413. DOI: 10.11898/1001-7313.20240402
Sand and dust storms are significant natural disasters which affect East Asia and China in spring, occurring from March to May. Performances of CMA-CUACE/Dust, an operational Asian sand and dust storm numerical forecasting system of CMA since 2006, are analyzed. It’s found that the model overestimates in Central Asia, underestimates in northern Mongolia, and diffuses too quickly in downwind areas far away from the source area, especially in Northern China, Korean Peninsula and Japan, resulting in low peak values or less lingering time there for very extreme sand and dust storm events. A scale-adaptive dust emission scheme is applied by resolving the mean wind speed of the model grid into sectional one which can account for values larger than the mean value by Weibull integration function. This significant aspect is crucial because the dust emission is the third power of the wind in the dust emission scheme. New wind erosion database is also adopted which consists of the updated desertification by using twenty-year surface data and new parameters deduced from site observations in the heart of Gobi Desert, determining the size distribution of the emitted dust together with the soil texture data sampling from main deserts in China.
After evaluation for the strongest sand and dust episode of 13-17 in March 2021 in East Asia in the past decade, and the consistent run in the same operational environment from 1 March to 31 May in 2023, it is found that the updated CMA-CUACE/Dust effectively improve disadvantages such as overestimation in Central Asia, underestimation in northern Mongolia, and rapid dissipation in China. The predicted peak dust concentration of the extreme episode closely matches observations in China both in the source area and in Shanghai after a 4-day transportation. Threat score (TS) of three-month forecast run also indicates that the improved model shows good consistency and continuity in forecast results across various forecast lengths. TS for 1-5 days with different forecast lengths is significantly higher than that of the previous operational system and surpass those of Korean dust model—ADAM (the Asian Dust Aerosol Model). Furthermore, the missing rate is significantly reduced, while the false alarm rate remains almost unchanged. TS for episodes beyond the level of sand and storms are all above 0.3, with some exceeding 0.5. All these findings show that the improved model performs much better than the previous one.
Application of Topographic Impact Horizontal Correlation Model to CMA-MESO System
Zhuang Zhaorong, Li Xingliang, Wang Ruichun, Gao Yudong
2024, 35(4): 414-428. DOI: 10.11898/1001-7313.20240403
The impact of near-surface observations on analysis and forecasting in complex terrain is studied by introducing the role of terrain in the background error horizontal correlation model. Observation information propagates isotropically on the model level in the height-based terrain-following coordinates since the background error horizontal correlation in CMA-MESO 3DVar system is characterized by an isotropic Gaussian correlation model. However, in the near-surface layer with complex topography, the propagation of observation information is blocked by mountain ranges, and thus its background error covariance is inhomogeneous and anisotropic, and furthermore, the propagation of observation information should vary with topography. The background error horizontal correlation coefficients in complex terrain are computed using NMC method by National Meteorological Center of USA. Results show that the blocking of large terrain causes the background error horizontal correlation coefficients to decrease more rapidly across mountain ranges, where the near-surface wind field is more localized than the temperature and humidity fields, with smaller horizontal correlation characteristic length scales, and the wind field information propagates over a closer distance. Based on the actual statistical structure, a Gaussian correlation model that includes effects of terrain height and terrain gradient is constructed, and the newly constructed horizontal correlation model accurately characterizes the decrease after mountain ranges are blocked. In CMA-MESO 3DVar analysis, the impact of terrain on the propagation of observational information is effectively incorporated by including a terrain height error term in the background error level correlation model. Idealized experiments show that the horizontal correlation modeling scheme that considering the terrain height error term allows the observation information to propagate anisotropically with the effect of observation information significantly diminishing across large terrain and the analysis increments more reasonable. Results of a forecast experiment for a heavy precipitation process in northern China indicate that the correlation modeling scheme that varying with the terrain height propagates the anisotropy of the ground observation information and weakens the analytical increment near the ground with large terrain, and thus makes a slightly biased and positive contribution to the precipitation forecast neutrality. Results of a 5-day hourly cycle rapid updating analysis and forecast for precipitation processes in East China show that the horizontal correlation modeling scheme with terrain elevation makes a positive contribution to 10-m wind field at the ground level and the precipitation forecast within 24 hours.
Cloud Parameter Characteristics of Three Strengthening Convective Systems During Downhill Processes in Beijing
Yang Yiya, Lei Lei, Zhong Jiqin, Zhai Liang, Jing Hao, Guo Rui
2024, 35(4): 429-443. DOI: 10.11898/1001-7313.20240404
Based on FY-4A geostationary satellite multi-channel data, mesoscale convective systems (MCSs) during three downhill processes of convection strengthening (1 July 2021, 12 June 2022 and 4 August 2022) in Beijing are identified by temperature threshold method. Precipitation data from Beijing-Tianjin-Hebei automatic weather stations, along with data from CMA-BJ mesoscale numerical model, are also utilized to analyze cloud characteristics of MCSs in formation and mature stages. Results indicate that the area of the convective cloud increases slowly, but the brightness temperature gradient is high, the brightness temperature decreases rapidly, the lowest temperature is below -65 ℃ and the maximum temperature variation rate in the area is -40 ℃·(15 min)-1 in the formation stage of MCSs. The maximum bright temperature gradient area corresponds to the inflow and water vapor convergence area, and it is located on the side of the cloud movement direction. In the mature stage, the area of the convective cloud increases rapidly. The number of short-time heavy precipitation stations is the largest, and the rainfall intensity is higher when the area is the largest. The brightness temperature is maintained at a relatively low value, but the area and amplitude of the temperature variation zone, as well as the brightness temperature gradient in the mature stage are smaller than that in the formation stage. The brightness temperature difference between water vapor brightness temperature and infrared brightness temperature can represent convective cloud development intensity, showing characteristics of slow fluctuation increase-fast increase-stable maintenance over time which indicate stages of MCSs. In the view of kinematic characteristics of the cloud cluster, it can be seen that strongly convergent and strong upward motion are located in the inflow region, the cloud top height increases rapidly, the infrared brightness and water vapor brightness temperature decreases rapidly, and the brightness temperature is close to 0 ℃ in the initial stage. In the mature stage, the cloud top height, infrared brightness and water vapor brightness temperature stay a relatively stable state, strong updraft and downdraft coexist, there is obvious outflow within the upper air, and the convective area increases to the maximum. The downdraft in the cloud body creates a strong outflow ahead of the low-level movement, leading to the extreme wind on the ground. The convergence of environmental wind and outflow strengthens the updraft movement in front of the MCS, which is conducive to the formation of new cells and the strengthening of MCS. Above results can reveal the development stage of MCSs and provide reference for determining whether MCSs in Beijing are strengthening or weakening during the downhill processes, as well as the potential area of severe weather on the surface.
Agro-climatic Zoning of Oiltea Camellia in China Based on Climate-land Integrated Impacts
Sun Guanghui, Duan Juqi, Li Junru, Liao Yaoming
2024, 35(4): 444-455. DOI: 10.11898/1001-7313.20240405
In recent years, China has accelerated the development of oiltea camellia industry and promoted the expansion of oiltea camellia cultivation nationwide, necessitating a refined agricultural climate zoning for oiltea camellia cultivation across the country. Considering soil factors is crucial for enhancing the precision of agricultural climate zoning for oiltea camellia. Therefore, based on the selection of potential climate factors affecting oiltea camellia cultivation distribution, as well as land conditions such as slope and soil thickness, dominant climate factors are analyzed using maximum entropy (MaxEnt) model and ArcGIS technology. It provides a refined climate zoning of oiltea camellia cultivation in China based on the joint effect of climate and land, identifying the potential for expansion and offering a scientific basis for the planning and implementation of oiltea camellia expansion. Results indicate the dominant climate factors affecting oiltea camellia cultivation distribution in China are the average temperature in January, accumulated temperature above or equal to 10 ℃, consecutive days with minimum temperature equal to or below -10 ℃, and annual cumulative precipitation. Taking into account the impact of slope and soil thickness on climatic suitability analysis, the climate zoning for oiltea camellia is divided into four suitability levels: The most suitable, more suitable, suitable region, and unsuitable region. The area for the most suitable region is 3.003×107 hm2, more suitable region is 2.858×107 hm2, and suitable region is 1.458×107 hm2.The precision of regional climate zoning for oiltea camellia cultivation is enhanced, reducing the suitable planting area for oiltea camellia by two-thirds compared to those not considering land factors. Suitable cultivation areas for oiltea camellia in China are in the region south of Yangtze River, especially from Sichuan Basins to the Qinling Mountains to the south of Huai River, and the east of Yungui Plateau to the coastal areas. Compared to the current planting area and range, oiltea camellia in China has significant potential for expansion. The potential planting boundaries, considering the joint effect of climate and land, tend to be further north. It suggests significant potential for expansion in the distribution of oiltea camellia in China. Therefore, when planning the layout of oiltea camellia cultivation, in addition to considering major planting areas such as Hunan, Jiangxi, Guangxi and Hubei, provinces with significant expansion potential such as Yunnan, Sichuan, Guangdong and Chongqing should also be taken into account, and their planting areas should be appropriately increased.
Evaluation Model of Yellow Peach Climatic Quality Rating in Hilly Mountainous Areas
Wang Tianying, Li Minhua, Wu Zhongchi, Huang Anfeng, Yang Changshun, Yang Pinling, Wang Tianke
2024, 35(4): 456-466. DOI: 10.11898/1001-7313.20240406
The study of evaluation indexes for yellow peach climate quality and its meteorological factor model can provide technical support to ensure high-quality production and facilitate rural revitalization. Taking “Jinxiu” variety of yellow peach as the research object, based on the yellow peach quality observations from 221-1300 m altitude and temperature and rainfall data from 13 meteorological stations near orchards at the middle section of Luoxiao Mountains and the west side of Xuefeng Mountain during 2019-2023, a climatic quality evaluation index for yellow peach and meteorological factorial regression model for its quality elements are constructed by using the methods of weighted summation, Pearson’s correlation, regression analysis and multiple covariance analysis, and examined with independent samples. Effects of different altitudes and harvest dates on the climatic quality ratings of yellow peaches are further investigated based on the constructed model. Results show that the main meteorological influencing factors for yellow peach soluble solids content (SS) is the average air temperature 80 d before harvest, for titratable acid content (AT) is the total rainfall 40 d before harvest, and for fruit shape index (IS) are the average air temperature from 1 May to 10 June, total rainfall from 1 May to 10 June, the average air temperature 10 d before harvest and total rainfall 10 d before harvest. Mean absolute error between the simulated and measured values of SS, AT, and IS of validation samples is 0.397%, 0.093%, and 0.010, respectively, and the root mean square error is 0.072%, 0.014%, and 0.001, respectively, and r is 0.649 (p=0.05), 0.718 (p=0.01), and 0.957 (p=0.01), respectively. The simulated quality ratings for 75% of validation samples match the actual climatic quality ratings, while 25% differs by 1 level. Simulation based on the constructed model reveals that the total frequency of superior and excellent quality in the study area shows an increasing and then decreasing trend with both the elevation and the harvesting period, among which the best quality is found in the mid-high elevation areas of 600-820 m or the harvest from 31 July to 10 August. Fruits harvested in high elevation areas above 1300 m or harvested from 21 August to 31 August appear to have a high frequency of lower quality.
Vertical Activity Characteristics of Pleonomus Canaliculatus in Winter Wheat and Summer Maize Rotation Fields
Zhao Huarong, Ren Sanxue, Qi Yue, Zhang Ling, Tian Xiaoli, Yang Chao, Hu Lili
2024, 35(4): 467-479. DOI: 10.11898/1001-7313.20240407
Based on the stratified survey data of Pleonomus canaliculatus in the soil of winter wheat and summer maize rotation field in North China Plain, the vertical activity of Pleonomus canaliculatus in the soil of winter wheat and summer maize rotation fields, the correlation between meteorological conditions and farmland planting management are observed and analyzed, and effects of Pleonomus canaliculatus damage on the yield of winter wheat are analyzed. By combining the insect population weight index with population density index, characteristics of the harm-dormancy activity of Pleonomus canaliculatus are investigated comprehensively in different soil layers. Results show that in the winter wheat and summer maize rotation growing season, there are 3 harm and 3 dormant periods, 3 harm periods appear in the winter wheat regreening-jointing period, the summer maize seedling period and the autumn seedling period of winter wheat, and 3 dormant periods appear in winter wheat overwintering period, winter wheat ripening-harvesting period and summer maize filling-ripening period. Among 3 harm periods, winter wheat regreening-jointing period is the most serious, which could lead to serious yield reduction of winter wheat. Winter is warmer, and spring temperature is warmer early, so Pleonomus canaliculatus exhibits characteristics of going down late and coming up early, which shortens the dormant period in winter and prolong the harmful activity period. Soil temperature, moisture, and the relationship between food and source affect the damage, dormancy, and feeding activities of Pleonomus canaliculatus. The suitable soil moisture content for it is about 15% to 18%, and the suitable soil temperature is 14 to 18 ℃. In summer, Pleonomus canaliculatus may enter dormancy or reduce activity due to lack of food sources or high temperatures and humidity of soil, Pleonomus canaliculatus can enter the dormancy or reduced activity. The analysis of winter wheat yield reduction caused by Pleonomus canaliculatus damage shows that the yield reduction rate is increased by 5.1% with an increase of 10 m-2 in insect population density or with an increase of 1.0 g·m-2 in insect weight. Results provide reference for agricultural production in North China to address climate change and scientifically manage farm to avoid diseases and pests.
Ridge Regression Prediction Model for Temperatures of South China in May
Han Pucheng, Ji Zhongping
2024, 35(4): 480-492. DOI: 10.11898/1001-7313.20240408
The temperature variability of South China in May is investigated, identifying precursor signals in sea surface temperatures (SST) and exploring the potential physical processes influencing these variations. A ridge regression prediction model has been developed. The analysis reveals that during years with anomalously high (low) temperature in May, there are observed anticyclonic (cyclonic) circulations over the Ural Mountains and East Asia, along with anomalous cyclonic (anticyclonic) circulations near Lake Baikal. These conditions weaken (strengthen) the East Asian meridional circulation, reducing (intensifying) cold air activity. Concurrently, the subtropical high abnormally extends westward (retreats eastward) in the South China region, while the southwest winds weaken (strengthen).
The key precursor SST signals for temperature anomalies in May are identified, primarily from the North Atlantic tripole pattern in the preceding winter and the basin-wide variability pattern in the Indian Ocean. Among these, SST signal of the North Atlantic Ocean shows the strongest correlation. When the North Atlantic Ocean SST precursor signal is in a positive (or negative) phase, it influences the meridional circulation to weaken (or strengthen) and reduces (or intensifies) cold air activity through the Eurasian teleconnection wave train. Simultaneously, the subtropical high extends westward (or retreats eastward) in South China, resulting in higher (or lower) temperature.
The multivariate ridge regression prediction model for temperature in May, developed using precursor signals from the preceding winter, demonstrates good fitting results and predictive capability for anomalous years. The model’s performance is validated through various statistical tests, including mean squared error (MSE) and correlation coefficients, which demonstrate its robustness and accuracy in predicting temperature anomalies in May. Results indicate that the ridge regression model offers a significant advantage over traditional multiple linear regression models in this context. The model’s predictive power is particularly remarkable in capturing the overall trends and variations of temperature in May, although it exhibits some limitations in predicting extreme values. The research provides valuable insights into the climate dynamics of South China and offers a reliable tool for enhancing the accuracy of short-term climate forecasts in the region, and underscores the importance of considering large-scale climate signals, such as the North Atlantic Tripole and Indian Ocean Basin-wide variability, in the development of predictive models for regional climate anomalies. By incorporating these signals, the model can better account for the complex interactions between different climate systems, leading to more accurate and reliable forecasts. This approach not only enhances our understanding of the factors influencing temperature in South China in May, but also provides a framework for future research and operational forecasting efforts aimed at mitigating impacts of climate variability.
Spectral Correction Impacts of Lightning from Tall Buildings on Channel Temperature Inversion
Wang Xuejuan, Hua Leyan, Wang Binghao, Xu Weiqun, Lü Weitao, Chen Lüwen, Wu Bin, Qi Qi, Ma Ying, Yang Jing
2024, 35(4): 493-501. DOI: 10.11898/1001-7313.20240409
During the lightning spectral observation, the spectral intensity is significantly reduced due to instrumental factors and other factors. The spectral intensity attenuation significantly affects the accuracy of temperature calculations. Temperature, as a fundamental parameter, is inextricably linked to other parameters within the lightning discharge channel, and accurate determination of the plasma temperature is crucial for gaining insights into the dynamic and physical processes of the discharge. Up to now, there have been no detailed and definitive reports on the influence of instrumental response on tall building lightning spectroscopy and temperature diagnostics.
Based on the spectral analysis of a lightning return stroke channel spectrum, the spectral is corrected by accounting for the instrumental response. Then, the spectral structure and line intensities before and after correction are compared and analyzed. Nitrogen ionized (NII) lines in the visible region and neutral oxygen (OI) lines in the near-infrared region are selected for temperature calculations using the multi-line method. The influence of spectral correction on the temperature analysis of the tall building lightning return stroke channel is investigated. Results show that after correction, the intensity of spectral lines is significantly enhanced. In particular, the spectral line structure in the visible region changes significantly, while the spectral line structure in the near-infrared region changes little. The continuum radiation in the visible region of the corrected tall building lightning spectrum is significantly enhanced, which is different from the results of natural cloud-to-ground lightning spectra after considering the instrumental response correction. Due to the significant enhancement of the continuum radiation intensity in the visible region resulting from the spectral correction, the continuum radiation intensity should be subtracted when using NII lines in the visible region to calculate the tall building lightning temperature. In this case, the coefficient of fitted line and the calculation accuracy increases, while the average temperature decreases by 4660 K compared to that before correction. Conversely, since the original continuum radiation intensity of tall building lightning spectra in the near-infrared region is relatively low, the spectral correction has little effect on the continuum spectrum intensity. Therefore, after spectral correction, when using OI lines in the near-infrared region to calculate the temperature, the determination coefficient of the linear fitting increases, resulting in improved fitting performance and an increase of 1540 K in the average temperature.
Design and Application of Meteorological Algorithm Scheduling Framework Based on Data Perception Technology
Huo Qing, He Wenchun, Gao Feng, Chen Shiwang, Xu Yongjun, Wang Qi
2024, 35(4): 502-512. DOI: 10.11898/1001-7313.20240410
The generation efficiency of data products depends on both the computational efficiency and the startup efficiency of algorithms. In meteorological operations, algorithms are typically data-driven, meaning they are initiated immediately upon data arrival to accelerate the generation time of data products. Therefore, data-driven meteorological services urgently require an efficient task scheduling framework to achieve the goal of starting and running algorithms as soon as data arrive, and to improve the generation efficiency of meteorological data products. CMA Big Data and Cloud Platform, referred to as Tianqing and led by National Meteorological Information Center began nationwide business operations in December 2021. The data processing line (DPL), as the core function of Tianqing, enables the unified management and centralized scheduling of meteorological algorithms. DPL has established various task scheduling capabilities, including timer-triggered scheduling, sequential scheduling, data-arrival scheduling, and manual scheduling. Among these methods, data-arrival scheduling based on data reporting status realizes the algorithm to start immediately after data reporting, greatly improving the startup time of meteorological algorithms and the generation time of meteorological data products.
Core functions of data-arrival scheduling include data state awareness components, task scheduling execution components, task scheduling post-processing components, and configuration management. Among these components, the data state perception component realizes real-time analysis of the reporting status of various meteorological data in the sky engine, and sends scheduling messages to the task scheduling execution component when the data reporting rate meets scheduling requirements. The task scheduling execution component combines the necessary resource information for the algorithm and computing nodes optimization, generate task scheduling instructions, and implement algorithm startup execution. The post-processing phase of task scheduling includes gathering and updating algorithm execution status, calculation node status, and sending of alarm information for algorithm execution abnormalities. Configuration management supports configuring data-aware scheduling parameters on the front-end page.
Real-time analysis of data status and scheduling enables efficient task scheduling that starts and runs the algorithm as soon as data are reported. The scheduling delay is significantly reduced, compared to the original schedule, from 3784 ms to 11 ms. Data-arrival scheduling, as the core capability of CMA Big Data and Cloud Platform, is deployed and operated in real-time in provinces and regions. Currently, the system supports the efficient scheduling of 19 core business algorithms at the national level, with a total of approximately 6.67×105 daily scheduling tasks and an average scheduling delay of 31 ms. Efficient scheduling is achieved with 14 algorithms supported at the provincial level, encompassing a total of approximately 8×104 daily scheduling times and an average scheduling delay of 156 ms. In addition, data aware scheduling achieves seamless integration of upstream and downstream algorithms in meteorological services, providing a solution to eliminate the problem of disconnection between meteorological services and improve collaboration between meteorological services.