Vol.29, NO.6, 2018

Display Method:
Advances of Modern Agrometeorological Service and Technology in China
Hou Yingyu, Zhang Lei, Wu Menxin, Song Yingbo, Guo Anhong, Zhao Xiulan
2018, 29(6): 641-656. DOI: 10.11898/1001-7313.20180601
Agrometeorological operational technology is the foundation and premise of modern agrometeorological service in China. The development of agrometeorological science and technology is always the core for national agrometeorological service. In recent years, national agrometeorological service grows quickly with high quality, and now it covers various fields such as agrometeorological monitoring and assessment, crop yield forecast, agrometeorological disasters monitoring and prediction, meteorological forecasting of disease and pests, and weather forecast for agricultural activities. With the development of agrometeorological technology, more and more numerical products are proposed to support agrometeorological services, e.g., daily, weekly, monthly and yearly products at both stations and grid points. Advances of agrometeorological service and technology are illustrated here, involving fields of agrometeorological monitoring and assessment, crop yield forecast, agrometeorological disasters monitoring and prediction, meteorological forecasting of disease and pests, weather forecast for agricultural activities and agrometeorological service system. Agrometeorological monitoring and assessment includes timely automatic monitoring of soil humidity, assessment of climate suitability based on temperature, precipitation, radiation and integrated functions, quantified assessment of crop growing situation based on three ways (i.e., field observation, remote sensing (RS) monitoring and crop model simulation). Several grades are defined to classify the level of crop growing. Crop yield forecast is mainly upon mathematical statistics methods based on relations between crop yield and key affecting factors, crop model simulation based on growing mechanism process for crops, and RS estimation based on relationships between yield and vegetation index. For some inevitable limitations in each method, multiple methods are integrated to forecast crop yield and the accuracy is generally above 99% during 2008-2017. Agrometeorological disasters monitoring, and prediction are always implemented in some ways, including classification based on single and multiple agrometeorological index, statistical analysis based on field survey, RS monitoring and crop model simulation. Risk analysis is the prerequisite for disasters assessment, involving hazard, vulnerability, sensibility and prevention. Meteorological forecasting of disease and pests currently refers to the linking models of meteorological factors and the occurrence of disease and pests. Key technologies for agricultural activities related weather forecast are the selection of index for key time farming, e.g., planting, flowering and harvest. China Agrometeorological Service System (CAgMSS), in which agrometeorological index, mathematical statistical models, crop growth simulation, RS, GIS, agrometeorological big data and other technologies integrated, is the highlight of national agrometeorological service and extended to provincial agrometeorological institutions. With an increasing demand of modern agricultural development, the meticulous and accurate agricultural meteorological disaster monitoring with risk assessment technology, the integrated technology of crop growth assessment and yield forecast, agricultural climate change impact, big data mining and artificial intelligence technology will become the focus of agrometeorological service in the coming decade.
Index and Loss Estimation of Rain Washing Damage to Early Rice Pollen in Jiangxi Province
Tian Jun, Huo Zhiguo
2018, 29(6): 657-666. DOI: 10.11898/1001-7313.20180602
Rain washing damage to pollen is one of the main agrometeorological disasters of early rice in Jiangxi Province. However, there are few studies on the disaster index and loss estimation model of this disaster. And in routine agrometeorological service, there are no definite and targeted criterion and loss assessment basis of rain washing damage to pollen. Therefore, studies on disaster index and loss estimation mode of rain washing damage to pollen are of great importance to the disaster monitoring, loss assessment and agricultural disasters' insurance management of early rice. Taking the disaster of rain washing damage to pollen in Jiangxi Province as research object, 78 disaster samples of rain washing damage to pollen are picked out based on analysis of long-term (1981-2015) meteorological conditions during the whole growth period of early rice in 14 agrometeorological stations, and historical data about the observation of agrometeorological disasters, diseases and insect pests. Afterwards, index and loss estimation model of rain washing damage to early rice pollen are determined based on correlation analysis, normal distribution and principal component regression method, and verified by independent samples. Results show that the rainfall during heading-flowering stage of early rice has a significant effect on the formation of rain washing damage to early rice pollen. Main and key influence periods are 5 and 3 days before and after the heading-flowering stage, respectively. The daily precipitation 40 mm can be used as the threshold for rain washing damage to pollen in heading-flowering stage of early rice. Based on this index, the number of days with total precipitation exceeding 40 mm and their corresponding accumulative precipitation are counted. When the accumulative precipitation is between 40 mm and 170 mm (light disaster), the yield reduction rate of early rice is generally less than 15%, and the average reduction rate is 10%. When the accumulative precipitation exceeds 170 mm (severe disaster), the yield reduction rate is generally more than 15%, and the average reduction rate is 22%. The grading indexes are detected to be basically consistent with the historical occurrence levels of rain washing damage to early rice pollen. And simulation results of loss estimation model show that simulated early rice yields are highly accordant with the actual yields, the average relative error is 4.3%, and the relative error of 78% data is within 5%. It indicates that the model can be used to simulate and predict the yield reduction rate of early rice when rain washing damages rice pollen.
Comparison on the Precipitation Measurement Between GPM/DPR and CINRAD Radars
Liu Xiaoyang, Li Hao, He Ping, Li Danyang, Zheng Yuanyuan
2018, 29(6): 667-679. DOI: 10.11898/1001-7313.20180603
It is necessary to find out the difference between space-borne and ground-based radar data for evaluating the possibility of combined use of them. 2 neighboring ground-based radars at Taizhou and Changzhou are first checked for data consistence in full resolution and then compared with the DPR radar respectively. Results from the precipitation case on 30 June 2015 show that 2 radars have 0.94 dB bias of mean reflectivity factor on the profile where distances to both radars are equal.To get high temporal and spatial resolution comparisons between DPR and CINRAD, the geometry-matching algorithm is used in vertical where 1-14 DPR range gates could be included in one sample pairs according to the distance to CINRAD radar site. The further away it's from the radar site, the more DPR gates can be included. The grid-matching algorithm is used in horizontal where total 5×5 grids in 1 km resolution are matched with one single DPR range gate. The DPR and CINRAD volume-averaged values are calculated for all such intersecting DPR range gates and 5×5 CINRAD grids. Statistic results on sample pairs show that the mean reflectivity factor biases of DPR radar are -1.2 dB and -1.6 dB for CINRAD Taizhou and Changzhou radars, respectively, and the mean rain rate converted from Z-R relationship are 0.10 mm·h-1 and 0.13 mm·h-1 lower than CINRAD radars', over the same area where the three radars scanned successively within 6 min. When the distance to CINRAD gets longer, the bias between DPR and CINRAD is larger near the top of echo. And the bias in the bright band area is 122 dB larger than the mean bias as well. But the bias has no obvious relevance with distance and height in the other area if beam filling is enough.Attenuation correction and echo coverage over sample cell are among important factors which affect comparison results. Though there is no suitable surface reference, the attenuation correction algorithm for DPR radar over land works and decreases 0.4 dB in mean bias between DPR and CINRAD Taizhou. The maximum correction is only 1.36 dB due to moderate intensity of radar reflectivity factor.The equivalent radar reflectivity factor must be modified for the application of comparing DPR radar to other wavelength radar when the equivalent radar reflectivity factor is greater than 37 dBZ. For the application of combined multi-wavelength (such as DPR and CINRAD), data quality control and clutter identification and elimination are all among direct acting factors.
Thunderstorm Gale Identification Method Based on Support Vector Machine
Yang Lu, Han Feng, Chen Mingxuan, Meng Jinping
2018, 29(6): 680-689. DOI: 10.11898/1001-7313.20180604
A thunderstorm gale recognition model is established using support vector machine based on data of radar and automatic weather stations from Beijing Weather Observatory. Firstly, 18 thunderstorms in Beijing during 2010-2014 are analyzed quantitatively in terms of the statistical method and 9 forecast factors are selected, i.e., the height of the echo top, the maximum albedo, the height of the maximum reflectivity, the total vertical liquid water content, the time rate change of total vertical liquid water content, the total vertical liquid water content density, the height of the maximum reflectivity factor, the storm moving speed and the width of the velocity spectrum. 451 non-high wind samples and 425 high wind samples are selected by matching the time and place of automatic weather stations with the value of the quantitative index of the PUP storm monomer recognition product in all the cases. Secondly, the probability distribution of prediction factors in the wind and non-wind samples are calculated, and relationships with corresponding forecast factors are obtained, and then sample data are normalized by the obtained membership function. Finally, the kernel function and model parameters are established, and the thunderstorm gale recognition model is established using support vector machine. Two typical cases in Beijing are analyzed and tested, one caused by a line thunderstorm which happened on 7 July 2017, and the other caused by an isolated single-cell storm which happened on 19 May 2012. Results show that the identified wind range is consistent with reality, and the hit rate, the false alarm rate and the critical success index are 92.0%, 22.1%, 73.0% and 99.1%, 40.5%, 59.2%, respectively. It will help to improve the accuracy of thunderstorm gale warning and forecasting. However, sometimes multiple thunderstorm cells can be misjudged as one according to these forecast factors. In this case, it is necessary for forecasters to conduct manual intervention in combination with the overall radar base reflectivity and weather conditions, to reduce the misjudgment rate of gale. In the future, long time series radar data should be used to carry out "large sample census" research and an automatic thunderstorm identification system based on weather radar can be established.
Automatic Identification of Precipitation Cloud Based on Radar Reflectivity Area Spectrum
Yang Youlin, Ji Xiaoling, Zhang Suzhao, Zhu Haibin, Zheng Penghui, Yang Jing
2018, 29(6): 690-700. DOI: 10.11898/1001-7313.20180605
Based on the principle of spectral analysis, the concept and algorithm of radar echo intensity area spectrum are proposed. Stratiform cloud, embedded convective cloud and convective cloud with different nature are investigated. Their parameter characteristics of total area, spectral shape, spectral peak value, spectral mid-value, spectral width and strong echo area(where the echo intensity exceeds 40 dBZ), basic precipitation echo area(where the echo intensity exceeds 20 dBZ) of the echo intensity area spectrum are analyzed using radar intensity data of Yinchuan Doppler weather radar. According to characteristic parameters of precipitation cloud area spectrum with different properties of radar echo intensity, a technical method is established to identify precipitation cloud types based on radar echo intensity area spectrum. The percentage of strong echo area in the total area of echo and the percentage of basic precipitation echo area in the total area of echo are used as main factors to distinguish precipitation cloud types, and the discriminant index of precipitation clouds of different types are given, such as stratiform cloud, embedded convective cloud and convective cloud and so on, based on characteristic parameters of radar echo intensity area spectrum. Meanwhile, an automatic recognition models of precipitation cloud type based on radar echo are established, and the automatic classification of precipitation cloud types based on radar echo intensity area spectrum is realized. The model is used to judge the precipitation type of 6 strong precipitation cases from 2016 to 2017. All of 6 strong precipitation processes are accurately identified, including 2 times as convective precipitation and 4 times as mixed cloud precipitation. Discriminant results are satisfied. It is better to reflect the type of precipitation cloud and verifies the feasibility of the identification method. And it is also a great significance for further intelligent analysis of precipitation properties, automatic monitoring of heavy precipitation and refined quantitative precipitation estimation.
A Visibility Estimation Method Based on Digital Total-sky Images
Lu Tianshu, Yang Jun, Deng Min, Du Chuanyao
2018, 29(6): 701-709. DOI: 10.11898/1001-7313.20180606
The proposed visibility estimation method is a curve fitting algorithm, which establishes a relation between the image's atmospheric transmittance and atmospheric visibility. Firstly, the total-sky image is captured by the digital total-sky image visibility experimental platform. The core unit of this platform is a digital camera equipped with a fisheye lens, and the camera is placed vertically towards the sky. The platform can collect a total-sky image at specified time interval, and then the original image is transferred to a computer for image processing. In particular, the total-sky image needs to be converted into a panoramic image using an image calibration algorithm, and the panoramic image contains most of the near-surface image information. Next, the panoramic image is used to compute the visibility. Some visibility-related image features are extracted from the panoramic image firstly. The image's atmospheric transmittance can be calculated using dark channel prior theory. The relationship between the atmospheric transmittance and atmospheric visibility can be established by curve fitting method, and the initial visibility estimate model based on total-sky images is achieved. The model can be improved by combining a number of field experiments. Finally, the retrieved visibility is calculated by importing the real-time total-sky image into the model.Results show that the basic trend of visibility data from total-sky visibility estimation model is consistent with that of the forward scattering visibility meter through the comparative test and calculating correlation coefficients. The trend is most noticeable in low or medium visibility. However, as the visibility increases, the consistency decreases because of more fluctuation. As the forward scattering visibility meter used to establish the model whose measuring range is from 0 to 35 km, estimate model results are generally less than the measurement of forward scattering visibility meter especially when the visibility is high. In general, the basic trend of visibility data of total-sky visibility estimation model is consistent with that of the forward scattering visibility meter when the global atmospheric light is well-distributed and there is no underexposure or overexpose. The correlation coefficient between results of two methods is close to 1, which also means that the consistency between the two methods is good. In addition, the image features used in this method do not depend on a certain point in the image, nor are they limited to a certain range of visual distance. At the same time, there is no need to use manually set target or to fix a particular building, which makes it easier for observers to select the appropriate direction to measure visibility accurately. The proposed method has advantages of high measurement accuracy and large sampling range and can be used as a supplementary observation method of the traditional forward scattering visibility meter.
Evaluation and Quality Mark of Radiosonde Geopotential Height of L-band Radar
Lei Yong, Guo Qiyun, Qian Yuan, Cao Xiaozhong
2018, 29(6): 710-723. DOI: 10.11898/1001-7313.20180607
Using analysis data of NCEP FNL and forecast data of GRAPES_GFS as background fields, the error analysis of geopotential height(sounding height) data of Beijing sounding station are obtained from observation residuals, average deviations, standard deviation, probability density distributions, kurtosis coefficients, skewness coefficients, correlation coefficient and root mean square error. According to assessment results, data quality is marked, and parameters are solved according to results of the quality mark. Test results show whether based on NCEP FNL or GRAPES_GFS, the error of the sounding height is basically within ±5 dagpm, and the absolute value of observation residuals increases with the decrease of air pressure. Observation residuals below 100 hPa is basically within ±3 dagpm. Observation residuals are mostly negative at the top of 100 hPa. The average deviation, standard deviation, probability density distribution, kurtosis coefficient, skewness coefficient, correlation coefficient and root mean square error are analyzed and evaluated from characteristics and distribution characteristics of the seasonal error, all of which show that the quality of data at height of detection potential is good, and each parameter is close to their optimal state. However, at the high level (10-30 hPa), the average deviation and standard deviation show obviously that the evaluation result of GRAPES_GFS is better than that of NCEP FNL, and the other parameters are basically the same and the difference is small. The average deviations plus standard deviation of two times is selected as the suspicious threshold value of the potential height at a single moment, and average deviation plus standard deviation is selected as the error threshold of the potential height. This choice is not only meaningful in mathematical statistics, but also shows that the threshold value is based on the background field error feature and self-adaptive threshold value, which can help to find out the true error point for correction.
Comparison of Brightness Temperature of Multi-type Ground-based Microwave Radiometers
Mao Jiajia, Zhang Xuefen, Wang Zhicheng, Yang Rongkang, Pan Xuguang, Ji Chengli, Guo Ran
2018, 29(6): 724-736. DOI: 10.11898/1001-7313.20180608
Ground-based microwave radiometer (MWR) can detect temperature and humidity profiles continuously and steadily, which compensate the shortcoming of the conventional sounding because of the long observation time interval. As a result, it is very helpful to explore the thermal process evolution of meso-scale synoptic system. At present, many types of ground-based MWR are developed at home and abroad. They are of different technical systems and their suitability for wide operational use is much concerned in scientific research institutions and management departments.The error of MWR product includes the contribution of both algorithm and hardware system, which is hard to distinguish. Therefore, to evaluate the observation performance of hardware system of the MWR, the brightness temperature of MWR is directly compared in this experiment. Using observations of 4-type radiometers and operational sounding data at the testbed of China Meteorological Adminatration from January 2016 to March 2018, and the simulated brightness temperature based on forward calculation from sounding data of MonoRTM as the reference, the accuracy of radiometers in different weather and seasons is compared and analyzed.Results show that the accuracy of brightness temperature of the domestic radiometer is similar to that of the imported radiometer. The observed brightness temperature of 4 radiometers are well related with simulated brightness temperature, and correlation coefficients basically are above 0.9, reaching a significant level of 0.001. Under clear sky conditions, the average of mean square root between the observed and simulated brightness temperature of four radiometers is 2.08-3.75 K. And the MWR-G shows the smallest error of brightness temperature, whose average deviation of each channel is 1.08 K, and the root mean square error is 2.08 K. The brightness temperature errors are minimum in winter and maximum in summer. Under precipitation conditions, the effectiveness of the brightness temperature observation of MWR is obviously reduced.Certainly, there are also some errors in sounding data itself. And it is difficult to completely avoid the drifting problem of sounding balloon, although a variety of ground-based remote sensing methods are used to assist the identification. It suggests to develop and apply calibration system with high accuracy and high stability, to ensure the accurate measurement of the radiometer. In addition, best observation mode of MWR during precipitation, and the material selection, replacement and maintenance of the radome need to be tested and verified, to expand the effective detection range of MWR.
The Applicability of Two Statistical Downscaling Methods to the Qinling Mountains
Liu Jiayi, Deng Lijiao, Fu Guobin, Bai Hongying, Wang Jun
2018, 29(6): 737-747. DOI: 10.11898/1001-7313.20180609
The Qinling Mountains is not only the dividing line of northern China and southern China, but also the dividing line between monsoon climate of medium latitudes and subtropical monsoon climate in China, i.e., the dividing line between China's warm temperate and subtropical regions. It also has abundant natural resources because of its special geographical location and complex climate environment. In the context of global warming, impacts of climate change on forest ecosystems in the Qinling Mountains are of great significance. Global climate model which is widely used in large-scale climate simulation studies, cannot be applied in this region due to low resolution. Statistical downscaling model can be used to provide local-scale daily temperature and precipitation for studying climate change impacts of this region. Different statistical downscaling models have different principals, as well as different predictors. Therefore, it is necessary to compare different downscaling models and to select more appropriate downscaling model to obtain reasonable simulation results. Focusing on the future daily mean temperature and precipitation for the Qinling Mountains, the multiple linear regression and the ridge regression downscaling approaches based on ASD (automated statistical downscaling) model are implemented. Outputs from the general circulation model (MPI-ESM-LR) under RCP4.5 and RCP8.5 scenarios are analyzed. Simulation results of two statistical downscaling approaches during calibration and validation periods are analyzed and future climate change projections in periods of 2006-2040, 2041-2070 and 2071-2100 are generated. During the calibration and validation periods, both statistical downscaling approaches perform well in simulating the mean temperature and precipitation. However, the multiple linear regression perform better than the ridge regression, and the mean of simulated temperature is better than that of precipitation. Both statistical downscaling approaches project an increase for the mean temperature and its magnitudes depending on the emission scenarios, i.e., RCP8.5 resulting in a higher temperature than RCP4.5. The annual precipitation would slightly decrease but not statistically significantly, while the seasonal distribution of annual precipitation will change, a slightly increase in spring and a decrease in other seasons, especially in summer. In summary, the multiple linear regression is more suitable for statistical downscaling research in the Qinling Mountains.
Meso-scale Convective Characteristics of "7·22" Extreme Rain in the West Mountainous Area of Fujian
Feng Jinqin, Liu Ming, Cai Jing
2018, 29(6): 748-758. DOI: 10.11898/1001-7313.20180610
An extreme severe rain occurred in the west mountainous area of Fujian on 22 July 2015, with the precipitation of 254.9 mm for 6 hours and the maximum total precipitation of 295.5 mm. Using conventional observations, automatic weather station data, satellite data, wind-profiling radar and CINRAD-SA Doppler radar data, this extreme severe precipitation is analyzed focusing on the environmental conditions and structure characteristics of the meso-scale convective system (MCS). Results show that predominate influencing systems are the low-level shear line and the upper trough between subtropical high and the anticyclone over Yunnan and Guangxi. The reinforcement of unsteady convective stratification, decrease in the level of lifting condensation and free convection, high atmospheric precipitable water and weak vertical wind shear over the rainstorm-hit area are all favorable to the development of MCS. The initial convective cloud develops on the edge of Guangdong and Fujian. Convective cells develop strongly with the favorable meso-scale environmental conditions. At the stage of northeast development, convective cells are born in the north of MCS and move southeast. Many northwest and southeast short echo bands come into being one after another. MCS moves northeastward under actions of cell advection and storm propagation. At the stage of quasi-stationary, the strong temperature gradient area locates in the middle of MCS. The north and south cloud clusters weaken rapidly. The structure of MCS changes from training line and adjoining stratiform MCS to back-building and quasi-stationary MCS in the stage of development. Back-building and quasi-stationary MCS results in the extreme severe rain. The northwest airflow on high level increases and extends to low level. The inflow in front of MCS on low level is strengthened and extends to high level. The cold air intrusion at high level and reinforcement of southwest jet with wind velocity convergence at low level over the rainstorm-hit area lead to the development of MCS. The effect of the trumpet-shaped topography strengthens the southwest airflow on the boundary layer. A small cyclonic eddy generates in the north of Liancheng and is closely related to the southwest airflow within the boundary layer blocked by mountain. When the northwest convective cell moves in, the convective cell strongly develops. The northeast-southwest back-propagating MCS rarely moves because directions of cell advection and storm propagation are in confrontation. The back-propagating MCS causes obvious train effect, which brings about extreme severe rain.