Vol.33, NO.5, 2022

Display Method:
Global Research Progress of Drought Indices
Song Yanling
2022, 33(5): 513-526. DOI: 10.11898/1001-7313.20220501
Drought is the most serious meteorological disaster in the world with the heaviest economic loss and the widest range, affecting social economy and people's lives. The impacts of drought are diverse, affecting agriculture, food security, water conservancy and power generation, industry, human and animal health, and so on. In recent decades, drought events occur frequently in China. Coupled with the rapid economic development and population growth, the adverse impact of drought on society and the harm to people's living environment are becoming more and more serious. Therefore, it is of great significance to discuss the global research progress of drought indices for drought research, prevention and drought relief in China. The research on meteorological drought index, agricultural drought index, hydrological drought index, remote sensing drought index and comprehensive drought index in the world are investigated systematically, especially in Europe and the United States, including the source of each drought index, the input parameters for calculation, the ease of use, advantages, disadvantages, and the global use. The progress of drought research in China is also systematically introduced, including some new drought indices or improved drought indices proposed according to the research on the local drought evolution characteristics and risks, especially a series of new industrial or national standards on drought indexes developed. The main problems in drought research is also discussed, including the lack of applicability of drought index, the lack of research on new mechanism-based drought monitoring index and the lack of research on drought prediction and early warning. Therefore, strengthening the research on drought mechanism, carrying out quantitative assessment of drought monitoring and strengthening the application of numerical models in drought prediction and early warning are the key and difficult points of future research. Drought range, degree and trend prediction are of great significance for the selection of national disaster prevention and mitigation measures. Providing drought prediction and early warning information more than one month in advance can provide sufficient time for taking disaster prevention and mitigation measures. Therefore, it is particularly important to study the simulation ability of numerical models to different regions and strengthen the application of numerical models to drought prediction and early warning.
Deviation Distribution Features of CMA-GFS Cloud Prediction
Li Zhe, Chen Jiong, Ma Zhanshan, Lu Huijuan, Hu Jiangkai, Liu Qijun
2022, 33(5): 527-540. DOI: 10.11898/1001-7313.20220502
Clouds play a vital role in weather, climate system and the atmospheric water cycle. The diagnosis and evaluation of numerical model prediction results is important for numerical model research and development. Reasonable diagnosis and evaluation methods can not only provide references for model researchers to optimize model schemes, but also help users understand the performance of model prediction. The cloud characteristics of different regions and seasons should be considered for evaluation because the attributes in different regions are markedly different. The performance and deviation characteristics of the operational CMA-GFS of four seasons are evaluated, based on the reanalysis of ERA5 reanalysis data from March 2021 to February 2022. The frequency bias of cloud occurrence, cloud fraction, integrated cloud hydrometeors from various levels, and the bias and root mean square error of those variables are carefully diagnosed and evaluated via different methods. The deviation characteristics of cloud are emphatically analyzed according to different regions. The possible causes for the significant difference of cloud prediction deviation characteristics at different levels in different regions are preliminarily discussed. The results show that the overall distribution of cloud predicted by CMA-GFS is reasonable, which can describe the meridional peak and valley distribution characteristics of global cloud and reflect the seasonal trend. The cloud amount deviation of high cloud and medium cloud by CMA-GFS is greater than that of low cloud. The root mean square error of cloud amount of high cloud and medium cloud is smaller than that of low cloud. And the model stability for high cloud and medium cloud prediction is also better. The liquid-phased hydrometers integration is mainly negative deviation, and the ice-phased hydrometers integration is mainly positive deviation. The causes of the deviation of cloud predicted by CMA-GFS are different in different regions. In tropical region the deviation is related to the incongruity of convective parameterization and microphysical schemes, while in middle and high latitudes regions the deviations are related to the bias of relative humidity. It also shows that the diagnosis of model cloud features will cover up the actual problems only by a single method, and it needs to be evaluated comprehensively by combining a variety of methods.
Atmospheric Dynamics Analysis and Simulation of the Migration of Fall Armyworm
Guo Anhong, Wang Chunzhi, Deng Huanhuan, Yuan Fuxiang, He Liang, Zhang Lei
2022, 33(5): 541-554. DOI: 10.11898/1001-7313.20220503
After the invasion of fall armyworm (Spodoptera frugiperda) in China at the end of 2018 or at the beginning of 2019, it spreads rapidly and becomes a seasonal migrating pest that seriously threatens the maize production in China. The long-distance migration of adult fall armyworm is closely related to the seasonal changes of atmospheric circulation in East Asia. The atmospheric circulation and low layer wind dynamic condition that influence the migration of fall armyworm in 2019-2021 are analyzed, and 4 typical weather processes beneficial to the migration are selected to simulate the migration path and landing point with Hybrid Single-particle Lagrangian Integrated Trajactory(HYSPLIT) model. The results show that, during the northward migration of fall armyworm in spring and summer, the strength of the southwest airflow is different due to the varying strength, location and westward extension of the Northwestern Pacific subtropical high(WPSH) in each year, and therefore the low layer wind driving the migration of fall armyworm to transition region and main corn planting area is different. The earlier onset of the South China Sea summer monsoon in May of 2019 is conducive to the early migration from South China into the middle and lower reaches of the Yangtze. June is a key stage for the fall armyworm migration to northern summer maize area, and further spreading to the spring maize area in North China. The west ridge point positions of WPSH from June to July varies in different year, and leads to different northernmost landing position of the pests, which is attributed to the south airflow carrying fall armyworm on the western side of the WPSH. The dynamic conditions of low layer wind during August to September are different in 2019-2021, which vital to the migration to main spring maize producing areas in Northeast China. In 4 simulation cases, 3-weather-process simulations are effectively monitored and verified, indicating the migration path and landing point of fall armyworm. However, in January of 2022, there are some misreports landing point in Guizhou and Fujian, though the southwest low level jet is favorable. HYSPLIT model is effective in the flight trajectory simulation, but it has some uncertainty in the migration distance, time and landing point due to biological characteristics, topography, microclimate, etc. In future, the simulation model should be improved by combination of real-time radar monitoring and other means.
Weather Conditions and Cloud Microphysical Characteristics of an Aircraft Severe Icing Process
Wang Zelin, Zhou Xu, Wu Junhui, Li Baiping, Lin Yujie, Yan Wenhui, Zhang Ying
2022, 33(5): 555-567. DOI: 10.11898/1001-7313.20220504
Based on the aircraft measurements on 28 February 2021, combined with ERA5 reanalysis data and sounding data, the weather background and the characteristics of cloud structure of a severe icing case on the aircraft are analyzed. The severe icing process is induced by the joint influence of high-level trough, low-level shear line, low-level jet and cold front. The ERA5 reanalysis data show that the maximum value area of supercooled water is mainly distributed at the height of 700 hPa to 600 hPa on the warm side of the front area and the ambient temperature is -4 to -12℃, accompanied by an upward movement of -0.2 to -0.8 Pa·s-1. The sounding data show that the cloud system is distributed in multiple layers. There is a deep dry layer between the upper ice crystal cloud and the lower supercooled water cloud. The temperature in the aircraft detection area is -9 to -3℃ and the dew-point spread is 0℃, which are favorable for icing. During the icing process, the air temperature is -8 to -5℃. Aircraft measurements show that there is abundant supercooled water in clouds. The average liquid water content by cloud particles probe is 0.35 g·m-3, and the maximum value is 0.7 g·m-3. The average liquid water content by total water content measurement probes is 0.5 g·m-3, the maximum value is 0.85 g·m-3, and for 11 minutes the liquid water content is larger than 0.45 g·m-3. The average median volume diameter of cloud particles is 20.3 μm, and the number concentration of cloud particles is 149.3 cm-3 on average. The number concentration of cloud particles tends to increase from low level to high level and vice versa for the median volume diameter of cloud particles. Finally, the conditions with different icing intensity that the King-air 350 aircraft may encounte during weather modification work in the cloud are discussed. The calculation shows that the King-air 350 aircraft carries out observation research or weather modification tasks when the liquid water content in the cloud is higher than 0.04 g·m-3, 0.15 g·m-3 and 0.45 g·m-3, it may encounter light, moderate, and severe icing, under certain conditions.
Construction of Air-sounding-profile System Based on Foundation-remote-sensing Equipment
Lin Xiaomeng, Wei Yinghua, Zhang Nan, Wang Yanchun
2022, 33(5): 568-580. DOI: 10.11898/1001-7313.20220505
Radiosonde data are indispensable for severe convective weather forecast because they can not only reflect the temperature and humidity structure and dynamic characteristics of local atmosphere, but also indicate the favorable conditions of convection initiation and development. However, the conventional radiosonde cannot capture local environmental parameters instantly due to insufficient space layout and low detection frequency, and the drift of sounding balloon worsens the representativeness of data. Acquiring accurate high-resolution data of temperature, humidity and horizontal wind is in great need. Combining ground-based remote sensing data and automatic weather station (AWS) data, including data of wind profile radar and AWS at Xiqing Station and radiometer at Tieta Station, a Foundation-remote-sensing Air-sounding-profile System (FAS) is constructed based on the strength of specific humidity profile inversion method and WPR-HW method. The inversion results of FAS is verified using European Centre for Mudium-Range Weather Forecasts reanalysis (ERA5) through 10 severe convective weather cases.The comparison shows that the inversion data of specific humidity and convective available potential energy (CAPE) from FAS has good consistency with ERA5 data. The correlation coefficient is 0.93, the root mean square error is 1.4 g·kg-1, the mean absolute deviation is 1.06 g·kg-1, and the mean relative deviation is 11.22% for 130 groups of specific humidity between FAS and ERA5 data. The correlation coefficient is 0.84, and the mean relative deviation is 35% for CAPE in 65 groups. The comparison test verifies the credibility of FAS data (correlation coefficients of specific humidity and CAPE both passing the test of 0.01 level). The time resolution of FAS is much higher. The mean interval time between Beijing radiosonde's occurrence time and severe convection's occurrence time is 7 h 18 min, while FAS's time resolution is just a few minutes. As a result, the mean value of CAPE detected by Beijing radiosonde is 322 J·kg-1, which is too low to indicate local convective potential in the afternoon or evening, while the mean value by FAS is 1451.88 J·kg-1, which can describe the change of atmospheric state during severe convection. FAS has high practical value in short-term forecast, as it can be used to determine the presence of convective potential and distinguish severe convective weather types timely through the configuration of convective parameters. The FAS can also capture elaborate thermodynamic structure of meso-scale and micro-scale weather system before the occurrence of severe convection. The FAS can significantly improve the lead time and accuracy of forecast.
Filling in the Dual Polarization Radar Echo Occlusion Based on Deep Learning
Yin Xiaoyan, Hu Zhiqun, Zheng Jiafeng, Zuo Yuanyuan, Huangfu Jiang, Zhu Yongjie
2022, 33(5): 581-593. DOI: 10.11898/1001-7313.20220506
Radar beam blockage is an important error source that affects the quality of weather radar data. The S-band dual-polarization radar in Guangzhou has multi-azimuth occlusion at low elevation and is partially occluded at high elevation. Based on deep learning methods such as convolutional neural network, two echo filling networks, i.e., VEF(vertical echo-filling) and HEF(horizontal echo-filling) are constructed. Based on this architecture, echoes from the unblocked area are used to construct training datasets and fill the reflectivity ZH and differential reflectivity ZDR in the occlusion area. For the area with only 0.5° elevation occlusion, multi-modal modeling is carried out based on VEF architecture by using 3D data from multiple upper elevations, radial directions and gates. Considering that the radar beam broadens with distance and to avoid the influence of the melting layer, the radar beam is divided into four sections according to the oblique distance of 0.5° elevation, and the vertical echo-filling model is trained respectively. For the area with high occlusion elevation, multi-mode modeling is carried out based on HEF architecture using the data of multiple adjacent radial directions and gates with the same elevation. According to the number of occlusion radial, two types of horizontal echo-filling models, three radials echo-filling model and five radials echo-filling model are constructed respectively. Finally, the models are evaluated by three cases and three indicators:Explained variance, mean absolute error and correlation coefficient. The maximum explained variance of ZH vertical echo-filling model is 0.91, the minimum mean absolute error is 1.72 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR vertical echo-filling model is 0.87, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.92. The maximum explained variance of ZH horizontal fill model is 0.92, the minimum mean absolute error is 1.69 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR horizontal echo-filling model is 0.92, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.96. The deep learning echo-filling model can be used to correct the echoes of Guangzhou S-band dual-polarization radar occlusion area effectively, and the quality of weather radar data is improved.
An Objective Prediction Model for Tropical Cyclone Genesis in the Northwest Pacific
Zheng Qian, Gao Meng
2022, 33(5): 594-603. DOI: 10.11898/1001-7313.20220507
At present, the maximum predictable time of tropical cyclone using numerical model is limited to 2 weeks. Statistical forecasting methods have substantial advantages in mining the potential value of massive meteorological and oceanographic observations, surpassing the limit of numerical forecast, and providing a new way to solve the bottlenecks of tropical cyclone forecasts. A novel statistical prediction scheme is proposed for tropical cyclone annual frequency and genesis location in the Northwest Pacific. The effect of large-scale meteorological factors including sea surface temperature, the geopotential height, the humidity, the vorticity, the wind shear, the Nio3.4 index, the QBO index and the SO index on the annual frequency of tropical cyclone in Northwest Pacific are considered. Correlations between the annual frequency of tropical cyclone and the large-scale environmental variables are analyzed and 14 highly correlated predictors are selected to predict tropical cyclone frequency. The least absolute shrinkage and selection operator method is used to select 8 factors from 14 initial predictors. Then, a prediction model based on random forest is established using training samples (1979-2015) for calibration and testing samples (2016-2020) for validation. In addition, the impact of environmental conditions including the vorticity, the wind shear, the humidity, the potential intensity, the sea surface temperature anomaly and the Nio3.4 index on the formation location of tropical cyclone is also investigated. The stepwise regression algorithm is used to choose a set of independent predictive variables by an automatic procedure. The local Poisson regression is performed on training datasets using count data inside data circles whose size is determined by the method of likelihood cross validation maximation. The seasonality of tropical cyclone genesis location is added to Poisson model. Results show that the random forest model presents a major variation and trend of tropical cyclone annual frequency though there are some deviations from the fitted data. The rank importance of influence indicates the primary effect of sea surface temperature and secondary effect of atmospheric variables on tropical cyclone frequency, which further reveals the applicability of the random forest model. The local Poisson regression model predicts where the tropical cyclone is most likely to occur. This model performs well when tropical cyclone occurs in the region of the Philippine and has some deviation in some months when tropical cyclone occurs in the region of the South China Sea. This model has good performance in predicting tropical cyclone genesis location but is weak in predicting abnormal situations. Finally, these two models are used to simulate tropical cyclone genesis activity in 1979-2020. The distribution of simulated tropical cyclone genesis points is consistent with the observations. This new prediction scheme can provide support for tropical cyclone risk analysis.
Peripheral Cloud System Structure and Precipitation Characteristics of Typhoon Bebinca(1816)
Mao Zhiyuan, Fu Danhong, Huang Yanbin, Li Guangwei, Ao Jie, Cai Xingfu
2022, 33(5): 604-616. DOI: 10.11898/1001-7313.20220508
Based on radar and raindrop disdrometer observations, the evolution of microphysical features and raindrop size distribution of the precipitation in different peripheral cloud system structures during Typhoon Bebinca(1816) affecting Hainan Island are compared at Haikou and Tunchang stations. The results show that, convective cloud system in Haikou area strengthens with the development of precipitation cloud system, while in Tunchang area stratiform cloud precipitation is dominant, which causes some differences between raindrop size distributions. During the entire precipitaion process, the average raindrop size distribution of Haikou and Tunchang stations are unimodal spectrum, the number concentration of 1 mm raindrops at Tunchang Station is larger, while there are more large raindrops at Haikou Station. The raindrops observed at Haikou and Tunchang stations are mainly with a diameter of less than 1 mm, and the raindrop number concentration accounts for over 50% of the total concentration, but its contribution to the rain intensity is 3.7% and 17.15%, respectively. The raindrops with a diameter of 1-3 mm at Tunchang Station contributes the most to the rain intensity, reaching 79.84%. The raindrop number concentration of Haikou Station decreases with the increase of raindrop diameter, but its contribution to the rainfall intensity increases, and the contribution of raindrops with diameter greater than 3 mm to the total rainfall intensity reaches 56.61%. Comparing the evolution of microphysical parameters, the characteristic parameter curve of Haikou Station is unevenly distributed in time, showing paroxysmal heavy precipitation, while the characteristic parameter curve of Tunchang Station has little fluctuation, and the precipitation is smaller, uniform, and continuous than that of Haikou Station. In the time evolution of raindrop size distribution, Haikou Station is always in the form of single peak with a wider spectrum, while at Tunchang Station it is mainly single peak, with fewer double peaks. When the rainfall intensity increases, the raindrop size distribution at both stations increases in the diameter range of 1 mm, the spectrum width expands rapidly, and the large-scale raindrops will increase in number, especially the large scale raindrops with a diameter of more than 3 mm at Haikou Station. However, the increase in the number concentration of small-scale raindrops does not lead to an increase in rainfall intensity. The raindrop size distribution conforms to Gamma distribution, and the slope parameter and shape parameter comply with binomial relationship.
Operational Systems
Implementation and Application of BCC CMIP6 Experimental Data Sharing Platform
Ma Qiang, Yan Jinghui, Wei Min, Xin Xiaoge, Zhang Li, Zhang Fang, Wu Tongwen
2022, 33(5): 617-627. DOI: 10.11898/1001-7313.20220509
The experimental data of ongoing CMIP6 (Coupled Model Intercomparison Project Phase 6) are widely used to study the mechanism of climate change and provide technical support for the assessment report of the Intergovernmental Panel on Climate Change (IPCC). With more types of model experiments and more complex climate model, the amount of CMIP experimental data are also increasing rapidly. Therefore, Beijing Climate Center (BCC) has established Earth System Grid Federation (ESGF) data node to share experimental data of BCC CMIP6.BCC has three latest versions of models to participate in the project through model development in recent years. The hardware of the platform adopts a distributed storage architecture and is deployed in the demilitarized zone (DMZ) of China Meteorological Administration, which provides a strong guarantee for its network access rate and security. The data processing module mainly checks the integrity, processes the original model output and adopts the climate model output rewriter (CMOR) software to standardize the format. Thematic real-time environmental distributed data services data server is used for local storage management and data sharing, publishing metadata to ESGF index node for unified data retrieval. The data storage directory adopts hierarchical management structure with self-describing information to realize hierarchical and classified storage of different elements in different experiments. To ensure the security of data sharing, the platform is optimized based on ESGF security framework in addition to physically adding replica storage, and the needs of easy access are also considered.Totally, 190 TB experimental data of BCC CMIP6 have been released and shared since the establishment of the platform. The platform has provided important technical support for BCC to participate in the CMIP6, and it has also supported scientific research in the fields of climate change simulation and prediction, weather and climate extremes, global warming and human activities.Subsequent work will provide continuous data services to the CMIP and can be extended to other related model comparison programs. It is also important to further improve the capabilities of customized data sharing services.
Design and Implementation of Meteorological Disaster Risk Management System
Li Ying, Wang Guofu
2022, 33(5): 628-640. DOI: 10.11898/1001-7313.20220510
China is one of the countries with the most serious meteorological disasters in the world. Reducing disaster losses and mitigating disaster risks is important for improving social governance and enhancing people's welfare, and it is also the fundamental goal of meteorological services. As a non-engineering measure for disaster prevention and mitigation, Meteorological Disaster Risk Management System (MDRMS) provides users with reference for decision making and is one of the most effective tools for mitigating meteorological disaster risks. In order to effectively reduce the risk of meteorological disasters and meet the urgent needs of service, China National Climate Center has designed and built MDRMS. MDRMS provides decision-makers and other stakeholders with professional services with four sub-systems:Big data application sub-system, model algorithm sub-system, online analysis sub-system, and comprehensive operation sub-system.From the view of application and function, MDRMS realizes the functions of disaster monitoring and identification, disaster impact assessment, risk assessment, risk prediction, risk zoning, and disaster information services for major meteorological disasters such as rainstorm, typhoon, drought, high temperature and low temperature. A series of products are also established.From the view of design and construction, the key technologies adopted are generic and the operation is intuitive and friendly. The system is built with key technologies such as big data fusion based on spatial-temporal matching, Web-GIS, distributed spatial data storage, micro service, and multi-tenant. The application of new technologies significantly improves the access efficiency and application capability of the system to multi-source, heterogeneous and massive data related to meteorology disaster risk management and enhances the user experience.From the view of deployment and openness, MDRMS has better integration, openness and scalability. It is deployed in China Meteorological Administration, ensuring access, personalized configuration, and service customization for internal users at four levels:National, provincial, municipal, and county levels, as well as access for external users at some product levels.The implementation of MDRMS shows that it has good operational capability and development prospect, which promotes the objective development of meteorological disaster risk management operation and enhances disaster prevention and mitigation decision-making service capability. In the future, the system will be improved following the principle of intensification, integration, and objectification. It will be integrated with the meteorological big data cloud platform in terms of data environment, product interfaces and algorithm functions, and provincial versions and mobile versions will be vigorously developed, focusing on creating an application ecosystem for MDRMS, providing richer and more practical meteorological disaster risk management products for users at all levels, and further playing an important role of information technology in meteorological disaster risk management.