Vol.30, NO.5, 2019

2019, 30(5)
Special Column on Advances in Agrometeorological Science and Technology
The Design and Implementation of China Agricultural Meteorological Service System(CAgMSS)
Wu Menxin, Zhuang Liwei, Hou Yingyu, Mao Liuxi, Wang Jianlin, Lü Houquan, Li Xuan, He Yanbo, Song Yingbo, Guo Anhong, Zhao Xiulan, Qian Yonglan
2019, 30(5): 513-527. DOI: 10.11898/1001-7313.20190501
CAgMSS is an agrometeorological operational service system for users in national and provincial level operational units in China. It fully covers the monitoring, assessment and prediction of agrometeorological operational service, packing 10 main functions such as agrometeorological data analyzing, product processing, meteorological condition assessment, crop yield forecast, disaster degree assessment, agricultural pest meteorological condition prediction, agricultural weather forecast, ecological meteorological assessment, agricultural remote sensing, and crop model simulation. Modern information technologies are applied to realize the whole process such as data collection and management, professional model operation, products processing and distribution. The system realizes normalized management and high efficiency analysis of agrometeorological data based on large-scale relational database technology. It's also highly integrated based on plugin technology and can be used for spatial analysis and high quality rendering of agrometeorological data and product. The system integrates agrometeorological statistics, remote sensing and crop model technology, realizing comprehensive application of multiple data, multiple indicators, and multiple models in crop growth condition assessment. Crop yield meteorological forecast, agrometeorological disaster monitoring and agricultural weather forecast can also be done with high level of quantification, refinement and objectification.CAgMSS has been in use since 2012, and obviously improves the efficiency of agrometeorological operational service work, providing important support for refined and automation of agrometeorological product. Based on the system, a batch of high quality products such as agrometeorological condition assessment, crop yield forecast, foreign agrometeorological condition monitoring, key crop phenology and agricultural activity period meteorological service, agrometeorological disaster assessment are produced and provide important references for guiding the agriculture production and meteorological disaster prevention and reduction. The system is also applied to winter wheat in 14 provinces in 2012. By the year of 2018, some subsystems such as drought monitoring, crop yield forecast and crop model are used in 31 provincial meteorological bureaus.
The Construction and Application of Chinese AgroMeteorological Model(CAMM1.0)
Ma Yuping, Huo Zhiguo, Wang Peijuan, E Youhao, Wu Dingrong, Fang Shibo, Tan Kaiyan, Zhang Yi, Sun Linli, Yang Jianying, Zhao Junfang, Zhou Mengzi, He Di, Xu Jiaxin, Mao Fei, Jiang Chaoyang
2019, 30(5): 528-542. DOI: 10.11898/1001-7313.20190502
In order to develop an agrometeorological model suitable for regional agricultural planting in China, China AgroMeteorological Model version 1.0(CAMM1.0) is established by improving and reconstructing the process and innovative application of existing oversea simulation methods.CAMM1.0 makes several improvements in process of agrometeorological model. It improves crop development process model by using average temperature intensity and soil moisture, improves crop leaf photosynthesis, dry matter distribution and leaf area expansion process model by using soil moisture, expands crop evapotranspiration process model by evaporation ratio method, establishes winter wheat plant height model based on development process. Based on the remote sensing information, the crop irrigation model, data assimilation model, crop growth and assessment model are also constructed. Main functions of CAMM1.0 include real-time crop growth simulation and customized user simulation. The former outputs real-time crop growth state variables, environmental variables and growth evaluation day by day. And the latter can produce customized products. CAMM1.0 can simulate the crop development, photosynthesis and plant height very well. However, the simulation is slightly weak on the process of soil moisture change, and the simulated yield is also slightly low. The assessed trend of summer maize in drought decreasing and waterlogging increasing by CAMM1.0 in Huaihe River Basin is consistent with the observation. Improving the key mechanism of crop growth enhances the response of CAMM1.0 to the environment. The construction of characteristic regional model improves its ability to simulate the growth process of Chinese crops, and realizes the regionalization of the model. The customized operation platform via the Internet is convenient for the agrometeorological application.CAMM1.0 constructs an online real-time operation platform to make the application and extension of the model and further development of the core module more convenient. Some of its sub-modules are constructed by multiple methods, which is more convenient for multi-model integration. The plugin method makes it easy for the model applicating, developing and updating. However, the mechanism of CAMM1.0 is still far from perfect, and the next step is to work on the response of agricultural production to climate change and various meteorological disasters. CAMM1.0 is expected to improve the theoretical level of agrometeorological simulation in China and provide technical solutions for related operational services.
The Construction and Application of Assessing Index to Crop Growing Condition
Zhang Lei, Hou Yingyu, Zheng Changling, Liu Wei, He Liang, Guo Anhong, Cheng Lu
2019, 30(5): 543-554. DOI: 10.11898/1001-7313.20190503
It is generally accepted that crop growing assessment can reveal its temporary condition and response to weather and climate when crop growing condition is assessed in a reasonable and effective way. To address this, normal field observation and remote sensing monitoring are the major techniques to quantify the crop growing condition. However, limited by their time-efficiency and uncertainties in algorithm, there are some deficiencies within them. Crop model, as an alternative way, is proposed to detect crop growing condition, with the advantage of its better mechanism and timeliness. Currently, WOFOST and ORYZA2000 are widely used to simulate the growth of winter wheat, spring maize, summer maize, single-season rice and double-season rice. Derived from WOFOST and ORYZA2000, the daily outputs, i.e., the development stage, leaf area index and total aboveground production, are simulated from 2001. Three outputs are selected as impacted variables for crop growing, and quantified through membership function. In the initial stage of crop growth, development stage, leaf area index and total aboveground production generate comprehensive effect, and they are weighted aggregated to a integrated index by the respective weight of 0.3, 0.3 and 0.4 according to expert scoring method. In the later stage, development stage and total aboveground production are the major factors influencing crop condition, and they are aggregated to a integrated index by the weight derived from their relative relationship with dry weight of storage organs. The assessing index is keeping well with experimental observation and remote sensing monitoring, implying its effectivity in evaluation service. The crop growing condition is assessed on any day during the growing season, and the corresponding daily integrated index is built in datasets under Crop Growth Simulating and Monitoring System in China (CGMS-China). According to daily integrated index, crop growing condition is divided into 5 levels, i.e., better, good, normal, bad and worse. Based on CGMS-China, daily crop growing condition is illustrated in the spatial distribution, which can distinguish the regional or local distinction of condition. Moreover, assessing index is spatially aggregated to the index at the province scale, which is a base quantity for comparing the provincial crop growing condition, corresponding to the assessing scale of yield prediction in agrometeorological services. Under the typical weather conditions, assessing index is efficient in specific regions and even local stations. For example, impacts of high temperature and drought from 21 July to 10 August in 2018 are well performed in the assessing index change at spatial and temporal scales. The assessing index for crop growing assessment based on crop model can provide more accurate and quantifier outputs, fitting with the demand of modern agriculture and agrometeorological service.
The Applicability of Mechanism Phenology Models to Simulating Apple Flowering Date in Shaanxi Province
Wu Dingrong, Huo Zhiguo, Wang Peijuan, Wang Jinghong, Jiang Huifei, Bai Qinfeng, Yang Jianying
2019, 30(5): 555-564. DOI: 10.11898/1001-7313.20190504
China's apple growing area and production rank first in the world, and thus apple is one of important economical crops in China. Meteorological disaster occurring in apple critical phenology stage is one of the main disasters impacting yield and quality, especially in the dominant planting provinces such as Shaanxi. Accurate forecasting on flowering date in Shaanxi can provide scientific support for taking applicable defensive management and improving the ability to resist meteorological disasters, and therefore benefit to apple yield and quality. Taking apple flowering stage as an example, the applicability of 4 typical phenology models is evaluated, including Sequential Model (SM), Parallel Model (PM), Deepening Rest Model (DRM), and Thermal Time Model (TTM). There are 4 apple planting divisions in Shaanxi Province. In each division, there are two phenology observation sites. Internal validation and cross validation (Leave One Out Cross Validation) of 4 models are done using sites with longer observations, while shorter record sites are used to evaluate the effect of model extrapolation application. In 4 divisions, 4 sites performing internal validation and cross validation are Xunyi, Luochuan, Liquan and Baishui, respectively, while four sites to conduct extrapolation application are Changwu, Baota, Fengxiang and Tongchuan, respectively. Model performance is assessed according to the root mean square error (RMSE) of modelled flowering date. Internal validation results show that optimal models are different in different sites and generally TTM and SM give similar accuracy (3.30 d). Cross validation also verifies and there is no particularly prominent model. The average RMSE for all four models is 4.52 d. TTM is then extrapolatively applied to other sites with two methods (extrapolation based on values in a single site, and extrapolation based on average values of 4 sites). The accuracy of both methods is higher than that of similar studies abroad (10.0 d), while the accuracy of extrapolation based on values in a single site (5.90 d) is higher than that of extrapolation based on average values of 4 sites (7.21 d). Considering the complexity and simulation accuracy, TTM is recommended to be used to simulate the flowering period in each apple planting division in Shaanxi Province.
Long-term Meteorological Prediction Model on the Occurrence and Development of Rice Leaf Roller Based on Atmospheric Circulation
Wang Chunzhi, Zhang Lei, Guo Anhong, Li Xuan, Liu Wei, Zhuang Liwei, Lu Minghong, Lü Houquan, Bao Yunxuan
2019, 30(5): 565-576. DOI: 10.11898/1001-7313.20190505
To understand the possible influencing mechanism of atmospheric circulation on the occurrence and development of rice leaf roller in China, relationships between atmospheric circulation characteristic indices and ratios of the occurrence area of rice leaf roller in China are fully analyzed from 1980 to 2016. 74 atmospheric circulation characteristic indices and their combinations are analyzed by factor puffing. Results show that 46 indices of these atmospheric circulation characteristic ones have significant influences on the ratio of occurrence area of rice leaf roller, and main influencing periods are from July to September, as well as last July to March. Indices of subtropical high category are most influential, followed by polar vortex category, circulation category, trough category and then others. Among 46 significant atmospheric circulation characteristic factors, 27 subtropical high factors and 10 polar vortex factors, accounting for 59% and 22% of the total, respectively, are the main factors influencing the ratio of the occurrence area of rice leaf roller. 10 key atmospheric circulation characteristic indices that directly influence the ratio of occurrence area of rice leaf roller are determined, and 7 of them have great change at 4 occurrence levels of rice leaf roller as light, partially light, partially severe and severe. 9 prediction models for ratios of the occurrence area of rice leaf roller are established to predict at the beginning of January and March to October. The hindcast of 9 models from 1980 to 2014 are good and accuracies in extending prediction years of 2015-2016 are 86.6%, 90.5%, 91.8%, 93.4%, 93.4%, 94.0%, 94.0%, 94.3% and 95.4%, respectively. Key atmospheric circulation characteristic factors represent climate background for the occurrence area of rice leaf roller very well in China. In the rice-planted area the atmospheric circulation influences the temperature, precipitation, etc., and thus affects the ratio of occurrence area of rice leaf roller. The ratio of the occurrence area of rice leaf roller in dry-warm and wet-warm years is usually larger than that in dry-cold years.
Accumulated Temperature Stability of Spring Maize and Its Application to Growth Period Forecast
Wang Jingxuan, Guo Jianping, Li Rui
2019, 30(5): 577-585. DOI: 10.11898/1001-7313.20190506
Northeast China is the largest spring maize production area in China and plays a vital role in ensuring food security. Temperature is an important environmental factor affecting agricultural production, especially for mid-high latitudes. Accumulated temperature, as a measure of heat, can be used to estimate the growth rate of crops, and the advance or delay of the growth period will affect the accumulation of dry matter in crops. Therefore, accurate forecast of maize growth period can promote current farming systems and management measures to ensure spring maize yield. As one of the most commonly used accumulated temperature calculation methods, the active accumulated temperature is refered to the accumulation of the average daily temperature over a period of time above a certain threshold, which is widely used in phenological period forecasting, agrometeorological disaster assessment, introduction of new varieties, and agro-climatic thematic analysis and zoning. The active accumulated temperature required for the growth period of the crop is not a constant. The relationship between crop development speed and temperature is not linear. Affected by the crop variety and environmental factors, the active accumulated temperature reflects the instability to influence application effect. Therefore, it is of great significance to modify the existing accumulated temperature models and improve the stability of accumulated temperature for better application. Based on the growth and development of spring maize, 5 agrometeorological stations in Northeast China, Hailun, Dunhua, Changling, Kuandian and Zhuanghe are selected to comprehensively analyze the meteorological factors affecting the stability of accumulated temperature and to revise the widely used active accumulated temperature model. After evaluating its effect, the revised model is applied to the growth period forecast of spring maize. Results show that due to its important role in affecting the stability of the accumulated temperature, the temperature is the key factor considered in the model revision. The revised model improves its stability and reduces variation coefficients in the emergence-heading period and the heading-maturation period by 0.42% and 1.42%, respectively. Using data in 1981-2010 for hindcast and data in 2011-2017 for forecast test, compared with the original active accumulated temperature model, the forecast error in revised model during the mature period is reduced by 3.78 d and 1.1 d. The revised model does not improve the forecast of the heading period.
Effects of Topographic Perturbation on the Precipitation Distribution in Sichuan
Wang Chengxin, Gao Shouting, Ran Lingkun, Chen Yueli
2019, 30(5): 586-597. DOI: 10.11898/1001-7313.20190507
Terrain characteristics can be accurately represented in spectrum space. Terrain spectra can quantitatively reflect effects of topographic dynamic forcing on the atmosphere. The one-dimensional weighted-average spatial spectral analysis method is used to explore topographic forcing on precipitation distribution in Sichuan. Results indicate that spectral distributions of terrain and winter precipitation in zonal direction present a typical resonance coupling pattern, while that of terrain and precipitation in other seasons drifts toward the smaller scale. In meridional direction, spectral distributions of terrain and precipitation in each season present the large-scale drift pattern. Different patterns are probably relevant to the change of circulations. In winter, due to strong zonal circulation and weak meridional circulation, atmospheric fluctuations caused by zonal topographic forcing show the most significant impact on precipitation. After that season, the zonal circulation weakens gradually in agreement with the decrease of zonal topographic forcing while the meridional flow enhances, leading to the increase of the damping of the zonal wind disturbance caused by terrain, and the pattern transforms from resonance to drift. Summer rainfall is produced by interaction among different scale systems, and terrain is one of the most important factors. The maximum topographic spectral energy in zonal direction is about an order of magnitude larger than that in meridional direction, implying that effects of topographic dynamic forcing are zonally stronger than that in meridional direction. Values of meridional and zonal topographic characteristic scales are 296.8 km and 475.8 km, respectively, which reflects the characteristic of the mesoscale topographic forcing coincident with the frequent mesoscale systems in Sichuan. The peak of the precipitation spectral energy in summer is about two orders of magnitude larger than that in winter and one order of magnitude larger than that in spring or autumn, and the characteristic scale in summer is about 150 km smaller than that in winter. It illustrates that the intensity of the zonal topographic dynamic forcing in summer is significantly increased when the scale of precipitation systems decreases, which explains the high frequency of mesoscale convective precipitation, and implies the significant impact of topographic dynamic forcing on atmosphere as well. The strongest summer precipitation in Sichuan is located at Ya'an, where larger-scale topographic perturbation is more significant than other region in Sichuan. The terrain spectra and summer precipitation spectra in meridional direction are phase-locked in identical wavelength (37.1 km), implying the critical role of terrain on the occurrence of heavy rainfall, and the effect of topographic dynamic forcing in meridional direction is dominant.
The Influence of Soil Relative Moisture on Dry-hot Wind Disaster of Winter Wheat
Shang Ying, Huo Zhiguo, Zhang Lei, Li Jianyong, Wu Li, Fan Yuxian, Wu Dingrong, Wang Chunzhi, Liu Hongju
2019, 30(5): 598-607. DOI: 10.11898/1001-7313.20190508
Based on daily and hourly meteorological data, layered soil moisture data and disaster data, the dry-hot wind disasters of winter wheat accompanying high temperature and low humidity are studied in North China and Huanghuai Region through historical disaster inversion, normal distribution test, soil moisture treatment and independent t-test. These disaster samples are classified:Class A samples are not affected by the relative humidity of soil, while Class B samples are affected by the relative humidity of soil. Thresholds of soil relative humidity on dry-hot wind disaster of wheat are determined according to independence of samples in two groups, and verified by random samples. Relative humidity values of the whole and layered soil layers subject to normal distribution. The relative humidity of Sample A and Sample B in each soil layer is independent. Thresholds of relative humidity affecting dry-hot wind disaster are 58%-65% for the whole soil layer and 56%-75% for layered soil. The mean value of the whole layer is approximately 60% and increases with the depth of the soil layer. The coincidence rate of relative humidity threshold of each soil layer is between 72.5% and 85%with average value about 80%, which could reasonably reflect the influence of soil relative humidity on dry-hot wind disaster of wheat. For convenience in application, 60% of the soil relative humidity in depth of 10-20 cm layer is selected as the critical threshold to determine the influence of soil relative humidity on the dry-hot wind disaster of northern winter wheat, being significant when it is greater than or equal to 60% and ignoral when less than 60%. The conformance rate of independent samples is 82.5%. Results provide a scientific basis for quantifying effects of soil relative humidity on the dry-hot wind disaster of winter wheat.
Spatial-temporal Variation and Zoning of Rain-washing Damage to Early Rice Pollen in Jiangxi Province
Tian Jun, Huo Zhiguo, Liu Dan, Yang Jun
2019, 30(5): 608-618. DOI: 10.11898/1001-7313.20190509
Rain-washing damage to pollen is one of the main agrometeorological disasters of early rice in Jiangxi. Meanwhile, the rice cropping areas in Jiangxi have very complex terrains, and the agroclimatic conditions are variable, and therefore the coincidence period of flowering period and wet season vary in different regions. Considering those above, based on observations of 81 meteorological stations and 14 rice stations in the early rice planting areas of Jiangxi from 1981 to 2017, the spatial-temporal variation and zoning of rain-washing damage to early rice pollen in Jiangxi are analyzed by empirical orthogonal function(EOF) rotated empirical orthogonal function(REOF).Results show that the occurrence frequency of rain-washing damage to early rice pollen in Jiangxi is generally high in northeast and low in southwest, while in northern Jiangxi, it is high along the south part and low on both sides. The high value area is in northern Pingxiang, southern Yichun, Xinyu, Nanchang, northern Fuzhou and northeastern Jiangxi, with the occurrence frequency more than 60%. The low value area is in Ganzhou and southwestern Ji'an, with the occurrence frequency less than 40%. Mild disasters occur all over the Province, and its frequency and range are increasing since 1992. Severe disasters mainly occur in northeastern Jiangxi, and there are two active periods and two inactive periods. According to results of EOF and REOF analysis, the high value areas (the absolute value is not less than 0.5) of the first five rotated principal components cover almost the whole planting area of the early rice in Jiangxi, and high value areas of five components are in the south part of northern Jiangxi, central Jiangxi, northeastern Jiangxi, southern Jiangxi and the north part of northern Jiangxi in turn, which means that the rain-washing damage to early rice pollen in Jiangxi can be divided into five subareas as above. Among those subregions, northeastern Jiangxi is in the high-risk area of severe rain-washing damage to pollen, the south part of northern Jiangxi is in the high-risk area of mild rain washing-damage to pollen, central Jiangxi and the north part of northern Jiangxi are the sub-high risk area, and southern Jiangxi is the low risk area.
Icing Potential Index of Aircraft Icing Based on Fuzzy Logic
Qi Chen, Jin Chenxi, Guo Wenli, Gan Lu, Zhao Delong, Lu Xu, Wu Shuai, Li Heiping
2019, 30(5): 619-628. DOI: 10.11898/1001-7313.20190510
Aircraft icing, a cumulative hazard, is one of the major weather hazards affecting aviation. It reduces aircraft efficiency by increasing weight, reducing lift, decreasing thrust, and increasing drag. Icing also seriously impairs aircraft engine performance and causes false indication on flight instruments, loss of radio communications and failures of control panel, brakes, and landing gear. Therefore, the prediction of aircraft icing is one of the key research focuses.In order to establish an aircraft icing potential index with more reasonable threshold and easy to adopt, 372 aircraft icing cases and corresponding observation data from 2014 to 2017 provided by Beijing Weather Modification Office are analyzed based on fuzzy logical principles. The membership function of temperature and relative humidity derived from those data analysis is used to calculate the initial possibility of icing. On this basis, the membership function representing the influence of vertical velocity and cloudiness on the initial possibility of icing is determined using the national pilot reports (PIREPs) in 2016 and corresponding ERA5 reanalysis data to screen different forms of membership functions. Based on membership functions of temperature, relative humidity, vertical velocity and cloudiness, the icing potential index (Ip) can be calculated by using output from numerical weather prediction model.According to ERA5 reanalysis data, 61 icing cases and 45 non-icing cases are used to test the effectiveness of Ip. Results show that the accuracy, missing alarm rate and false alarm rate of Ip are 80.2%, 9.4% and 10.4%. Compared with the commonly used icing index (Ic), the accuracy of Ip is better, the missing alarm and false alarm reduce significantly. However, it should be noted that the difference between aircraft type and flight speed of different aircraft icing cases in this study is not discussed, and it is assumed that effects of vertical velocity and cloud cover on the initial possibility of icing are independent, which need further study.In summary, the established icing potential index (Ip) based on fuzzy logical principles is efficient and feasible, and provides information for pilots to avoid high-risk areas of icing in the air. Combining with the regional numerical weather prediction model, it can output the possibility of icing in certain areas under certain meteorological conditions and provide reference for pilots to avoid high-risk areas of icing in the air.
A Statistical Study of Brunt-vaisala Frequency with Second-level Radiosonde Data in China
Li Fangfang, Chen Qiying, Wu Hongkun
2019, 30(5): 629-640. DOI: 10.11898/1001-7313.20190511
Based on the second-level sounding data of high vertical resolution in China from June 2014 to May 2017, time and space distribution characteristics of brunt-vaisala frequency in China are analyzed. Results show that the distribution of atmospheric brunt-vaisala frequency increases with height, data of lower stratosphere is larger than the troposphere, and the brunt-vaisala frequency remains constant in the vertical direction in the troposphere and low stratosphere. The brunt-vaisala frequency in the troposphere is greatly affected by the topography, and gradually increases from west to east with the change of longitude, with a small value area in the plateau region. The brunt-vaisala frequency in the low stratosphere is less affected by the topography and mainly changes with latitude, and it's greater in the southern region than that in the northern region. The brunt-vaisala frequency of the transition layer varies greatly with height. The southern part of lower transition layer changes faster with height than the northern part. The middle and southern parts of the upper transition layer change faster with height than the northern part. The brunt-vaisala frequency in the transition layer increases with latitude. The brunt-vaisala frequency doesn't change significantly with seasons at 5-10 km height and low stratosphere, but in the transition layer between troposphere and stratosphere (10-18 km), the seasonal change is significant. It changes most significantly in winter, less significantly in spring and autumn, and minimally in summer. Below 5 km, the seasonal variation is obvious, and it changes the most in winter. The brunt-vaisala frequency below 5 km in the northern region troposphere shows annually variation characteristics, and the peak value is in winter. The brunt-vaisala frequency doesn't change significantly with time in the stratosphere of north and south regions, and changes little with time in the troposphere in north and south regions. The brunt-vaisala frequency shows annually variation characteristics in the lower troposphere over the northern region, with peaks appearing in the winter, and there is also a one-year periodic variation in the transition layer, the peak is in winter and the valley is in summer. The brunt-vaisala frequency changes at the same height of the southern region and the northern region are similar in the transition layer. There is an annual change, the peak is in winter and the valley is in summer, but the central value of the transition layer in the southern region is smaller than the central value in the northern region. In the transition layer, the influence of the brunt-vaisala frequency with the height on the gravity-wave momentum flux is considered. The brunt-vaisala frequency and wind speed calculated by second-level sounding data are finely changed, and the change of atmospheric stability can be grasped more accurately than the conventional sounding.