Vol.31, NO.5, 2020

Display Method:
Observation Characteristics and Synoptic Mechanisms of Typhoon Lekima Extreme Rainfall in 2019
He Lifu, Chen Shuang, Guo Yunqian
2020, 31(5): 513-526. DOI: 10.11898/1001-7313.20200501

Observation characteristics of extreme heavy rainfall with its thermodynamic structure evolution and water vapor transmission of super-typhoon Lekima in 2019 is comprehensively diagnosed and analyzed, in terms of automatic weather station data, FY-2G TBB and the microwave hygrometer channel inversion data of FY-3D MWHSⅡ, radar networking data and NCEP FNL 1° by 1° analysis data. Results show that heavy rain covers most parts of East China when Lekima heading north, its extreme rainfall (process rainfall amount more than 350 mm) occur in eastern Zhejiang and central Shandong, with the maximum rainfall point of 833 mm and 612 mm, respectively. The average rainfall of the whole province ranks first or second in the history of process rainfall in Zhejiang and Shandong, and daily rainfall values of 21 national stations are new historical records. The interaction of the typhoon, subtropical high, mid-latitude westerly trough and the abundant water vapor transport of the strong southeast low-level jet (over 25-35 m·s-1 in speed) along the coast of East China provide favorable environmental conditions for the long-term maintenance of Lekima and the occurrence of extreme heavy rainfall as the typhoon northward. The extreme heavy rainfall in eastern Zhejiang is mainly caused by the development of powerful typhoon body, its deep vertical vortex system (over 60×10-5 s-1 in vorticity) and the strong upward movement breaking through the tropopause, as well as the dense deep convection system (TBB of -80--72 K) with high-efficiency rainfall and latent heat feedback in the eye-wall area of typhoon. The extreme heavy rainfall in the middle part of Shandong is closely related to the evolution of Lekima's asymmetric structure and the invasion of cold air during the typhoon heading northward. The extremity of rainfall comes from the combined action of the long-distance heavy rainfall originated from the easterly inverted trough and a long time "frontal properties" rainfall. The inverted trough frontogenesis, the convergence of southeast low-level jet and the easterly wind provide good dynamic and water vapor conditions for the long-distance rainstorm. Three main spiral rain belts in the north of the typhoon move anticlockwise, new convective systems constantly induce on warm side and merge in the inverted trough area, leading to train effect on the windward slope of terrain in central Shandong. With the continuous invasion of 500 hPa dry and cold air from the lower layer, a θse frontal zone inclining westward with height is formed near 118°E on the west side of typhoon. The warm and humid flow climb causes the second stage of long-time stable rainfall during Lekima's arrival in Shandong and the slow circle round in the Laizhou Bay.

Objective Precipitation Type Forecast Based on ECMWF Ensemble Prediction Product
Dong Quan, Zhang Feng, Zong Zhiping
2020, 31(5): 527-542. DOI: 10.11898/1001-7313.20200502
Ensemble prediction system usually improves forecast skill compared to deterministic model under the same model system. The European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system precipitation type product (PTYPE) is used with the approach of optimal probability threshold (OPT) to produce precipitation type deterministic forecast. The history dataset of winter half year (October to the next March) from 2016-2017 of ECMWF ensemble prediction system and observations of 2515 surface weather stations are used firstly to estimate optimal probability thresholds for different lead times of rain, sleet, snow and freezing rain under criteria of Optimal HSS (OHSS), optimal TS (OTS) and Optimal forecast bias (OB). Then deterministic precipitation type forecast is calculated from the probabilistic forecast of ensemble prediction system, verified by data of 2018 winter half year and compared with the precipitation type forecasts of ECMWF high-resolution deterministic model (HRD) and ensemble prediction system control forecast (CF). It indicates that optimal probability thresholds under three criteria are different. However, optimal thresholds of snow and freezing rain are the largest which are between 80% and 40%, and optimal thresholds of sleet are the smallest which are under 10%. They all decrease with elongating lead times. Optimal thresholds of rain are small which are between 7% and 25%, increasing with elongating lead times. Verification results show that performances of CF are the lowest with the proportion correct between 92% and 91% and HSS between 0.74 and 0.55. The performance of HRD is better than CF with the proportion correct about 93% and HSS between 0.77 and 0.65. OPT based on ensemble prediction system probabilistic forecast improves the forecast skill of precipitation type significantly. The improvement of OHSS is the most significant with the proportion correct about 94% and HSS from 0.81 to 0.68. From the verification of every kind of precipitation types and case analysis, it demonstrates that the performance of HRD and CF for sleet is similar. However, for the other three precipitation types, the performance of HRD is better than that of CF and the performance of OPT is the best. For freezing rain, the forecast bias of CF and HRD is larger than 2 which means too more false alarm. OPT reduces the forecast area and false alarm of freezing rain and improves the performance. HRD and CF forecast less rains and more snows with poor forecast biases. OPT corrects these errors and makes better forecast bias and TS scores for rain and snow. For sleet, the forecast bias of OPT is less than 1 significantly and the TS score is nearly zero which is worse than HRD and CF.
Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model
Jiang Wenjing, Liang Xudong
2020, 31(5): 543-555. DOI: 10.11898/1001-7313.20200503
Nowcasting is mainly based on radar echo or satellite image extrapolation method. However, the prediction ability of extrapolation method decreases with time, because this method cannot describe the physical mechanism during the occurrence, development and extinction of severe convective weather systems. Considering that the prediction ability of numerical model improves with time, the nowcasting system should be based on numerical forecast model. And appropriate data assimilation technology can be used to produce a more accurate initial field, making the integral forecast results closer to the reality. The PFI-4DVar assimilation method (four-dimensional variational technology under physical filter initialization) can filter in the process of assimilation rather than model integration, thus shortening the model spin-up time and getting a more dynamic and physically coordinated analysis field. Therefore, PFI-4DVar assimilation method not only improves model prediction results, but also makes initial field closer to observations, which is very suitable for nowcasting.Using WRF model and WRFDA assimilation system, effects of PFI-4DVar on prediction ability of numerical nowcasting are explored. Through the precipitation case in North China on 11 August 2018, prediction results in control and assimilation tests are discussed. According to ETS scores, the precipitation prediction of assimilation test is closer to the observation compared with control test. The water vapor in assimilated ground and sounding data, the dynamic field in assimilated radar radial wind data and the appropriate cumulus parameterization scheme make the amplitude of divergence in high-level and convergence in low-level in analysis field of assimilation test much stronger than those in background field, thus creating vertical motion. Moreover, the precipitation of assimilation test is mainly caused by process of cumulus.A batch test is carried out on 17 precipitation cases of North China in August 2018. It shows that PFI-4DVar can significantly improve the prediction ability for short precipitation (especially large order precipitation) and timely predict the fall area of heavy rain or rainstorm. After assimilation, ETS scores of 6-hour accumulated precipitation (greater than 25.0 mm) in batch test increase from 0.125 to 0.190, and ETS scores of 6-hour accumulated precipitation (greater than 60.0 mm) increase from 0.016 to 0.081. PFI-4DVar significantly improves the precipitation nowcasting. Calculations are reduced by selecting 12-minute assimilation time window, which greatly saves computational resources. And the time of assimilation test is shortened, ensuring the time efficiency of 6-hour forecast. Therefore, PFI-4DVar can improve and enhance the prediction ability of precipitation nowcasting.
Comparison of Development Mechanisms of Two Cyclones Affecting Northeast China
Gao Songying, Zhao Tingting, Song Lili, Meng Xin, Li Ruihan, Luo Jianyu, Zhang Xu, Pan Xiao
2020, 31(5): 556-569. DOI: 10.11898/1001-7313.20200504
Two cyclones (C304 and C502) generated in the Jianghuai Basin on 3-5 March 2007 and 2-3 May 2016, affect the northeast region in a similar way. However, their intensities are different. The development of C304 is strong whereas that of C502 is explosive. Based on NCEP FNL analysis data and conventional data, their development dynamics are comparatively analyzed, through diagnosis of vorticity advection, temperature advection, moist potential vorticity and frontogenesis function, combining with the circulations of high and low altitudes. The result shows that the vorticity factor and thermal factor play roles in deepening cyclone development and guiding cyclone movement. There are strong cold and warm temperature advections at low attitudes in the process of the strong evolution of C304. High-level positive vorticity advection located above the ground cyclone provides high-level divergence field. During the explosive development of C502, cold and warm advections are inconspicuous. Highly positive vorticity advection is located in front of the high-attitude trough. The development of high-level closed circulation is promoted by the high-level positive vorticity advection. As for C304, strong frontogenesis and frontolyzes symmetrically develop on the lower troposphere, whereas the baroclinicity of C502 is inconspicuous. Positive moist potential vorticities strongly develop in the upper and lower troposphere unusually. C304 vorticity increases mainly in the lower troposphere, however, C502 vorticity increases mainly in the upper troposphere where the high-humidity vortex tongue develops drooping and merges with the troposphere below the positive wet-position vortex column. Two cyclones develop with coexistence of two high-level jets, non-latitude jet and the anticyclonic curved circulation. Southerly and northerly airflows are established in the background of the cyclonic circulation at 850 hPa. C304 is located on the left front of the southerly airstream, and C502 is located between the southerly and northerly airstreams. Under the effect of low-level intensity convergence and high-level intensity scattering, the vertical ascent motion of C304 increases in lower to upper middle troposphere above the ground cyclone center. Under the strong high-level intensity scattering and weak low-level convergence, the vertical ascent movement of C502 occurs on both sides of the ground cyclone center and middle troposphere. Low-level baroclinic forcing is the main starting development mechanism of C304, and high-level vortex downward transmission is the main mechanism of C502 development.
Potential Skill Map of Predictors Applied to the Seasonal Forecast of Summer Rainfall in China
Liu Boqi, Zhu Congwen
2020, 31(5): 570-582. DOI: 10.11898/1001-7313.20200505
Anomalous summer rainfall in China is affected by many factors, whose complex interaction restricts the predictability of Chinese summer rainfall (CSR). The predicting skill of the state-of-the-art dynamic models on the CSR is still limited, leaving challenges in developing objective statistical predicting methods. A method for searching potential predicting skill of predictors (i.e., potential skill map, PSM) is proposed, which can be used to select predictors automatically based on the PSM, and a new automatic statistical prediction model of the CSR is established.Compared with traditional linear correlation analysis, the PSM using the cross-validation concept not only reflects the potential predicting skills of predictors on predictands, but is free from effects of extreme events. It is completely based on real-time statistical predicting procedure, which aims to find sufficient conditions for predictands in logical. The PSM is an important supplement to the traditional correlation coefficient map. They work together to provide potential predictors with necessary and sufficient conditions. The predictor automatic selector takes advantage of the idea of ensemble forecasting. It selects predictors with the most significant potential forecasting skill from the PSM, and then generates final forecast products by averaging a large number of predicting members. The year-by-year automatic selection of the predicators is thus realized. This solution doesn't rely on subjective experiences of foreasters, and also provides a new way to further investigate the predictability of the interannual variability of the East Asian summer monsoon. This new automatic statistical prediction model of the CSR based on the PSM and the predictor automatic selector shows a high reforecast skill for the CSR. In the 21-year reforecasting experiment from 1999 to 2019, predictors in the previous autumn and winter seasons are used to predict the CSR. Results show an average symbol agreement rate of 60% and the mean anomaly correlation coefficient of 0.436 between the reforecast and the observed CSR. As to the predicting skill (PS) score in the National Climate Center, the reforecast CSR reaches 71.00 in average. After variance correcting, the PS score further increases to 82.10, which is much higher than predicting skills of current dynamical models. It is noteworthy that the reforecast experiment in the present uses the first 12 multiple regression coefficients and EOF modes of the CSM, of which the first 4 multiple regression coefficients and EOF modes play a dominant role in the overall distribution of the CSM. By contrast, higher-order modes could further improve the reforecast skill by increasing the diversity of the reforecasting CSM, which represent their potential physical implications.
Aerosol Optical Properties and Radiative Effects During a Pollution Episode in Beijing
Liang Yuanxin, Che Huizheng, Wang Hong, Peng Yue, Zhang Yangmei, Tao Fa
2020, 31(5): 583-594. DOI: 10.11898/1001-7313.20200506
Based on continuous observations of aerosol optical properties from sun-photometer and PM2.5 concentration, the variation of aerosol optical depth, single scattering albedo and asymmetry factor during a pollution episode in Beijing from 25 January to 28 January in 2018 are analyzed. Combined with Raman-Mie Lidar vertical detection, the vertical variation of aerosol extinction coefficient is analyzed in detail. Based on ground-based observations, using a shortwave radiative transfer model, the shortwave radiative heating rates under the clear sky background during the pollution episode are calculated. Results show that under clean condition (25 January 2018), the average daily PM2.5 concentration is 19.00 μg·m-3, aerosol optical depth at 440 nm is 0.13, single scattering albedo is 0.87, and the extinction coefficient of aerosol is less than 0.10 km-1. During the pollution episode (26-27 January 2018), the average daily PM2.5 concentration is 83.21 μg·m-3, aerosol optical depth is 2.48, single scattering albedo increases to 0.94, the main aerosol extinction layer height increases to 3.00 km and the mean extinction coefficient of the whole layer is 0.43 km-1. The aerosol layer can heat the atmosphere evidently, the magnitude of radiative heating rates by aerosol depends on distribution of the aerosol in the vertical direction, and the heating rate under the concentrated heating layer decreases rapidly. Under clean condition, extinction coefficient is less than 0.1 km-1 which causes the shortwave radiative heating rate of aerosol layer within 10.00 K·day-1. During the pollution episode, the strong heating effect in the middle and upper aerosol layers (1.50-3.00 km) where the average shortwave radiative heating rate reaches 13.89 K·day-1, while the lower aerosol layer (within 1.50 km) has a weak heating effect, and the average shortwave radiative heating rate within 1.50 km is only 0.99 K·day-1. Heating rate accuracy is affected by single scattering albedo, the increased aerosol scattering ability would weaken the heating effect on the atmosphere, and the heating rate in pollution condition is more sensitive to changes of aerosol scattering ability. With the mean extinction coefficient of the whole layer being 0.43 km-1, the increase of single scattering albedo from 0.87 to 0.94 cause the heating rate of the upper and middle aerosol layers decreases by 3.74 K·day-1, while the heating rate of the lower aerosol layer increases by 0.81 K·day-1 on 27 January 2018.
The Cause of Night Clear Air Echo of S-band Weather Radar in Beijing
Teng Yupeng, Chen Hongbin, Ma Shuqing, Li Siteng, Wu Dongli, Zhou Yan
2020, 31(5): 595-607. DOI: 10.11898/1001-7313.20200507
S-band weather radar can often detect a large number of clear air echoes at night. However, there are different views on the mechanism of clear air echoes. According to characteristics of biological migration, combined with L-band radiosonde data and Beijing S-band weather radar data from 1 March to 18 October in 2018, changes of clear air echo reflectivity factor in different seasons and wind directions are analyzed to discuss causes of clear air echo. Firstly, characteristics of time variation of clear air echo are analyzed. The intensity of clear air echo in Beijing increases after sunset, weakens before sunrise, and changes little at night. The intensity of echo gradually increases from March to the middle of May, then weakens from July to the first ten days in August, and maintains at a high level since September. In vertical profiles, the fluctuation of high-level echoes, up to the altitude of 2 kilometers, is larger than that of low-level echoes, with an obviously seasonal variation. Secondly, by comparing the relationship between the wind direction and radar data in different periods of time, it is found that the echo is not consistent with the biological activity regularity. The entropy of radar data gray level co-occurrence matrix is also calculated, and there is no rule to follow the biology. Therefore, it is considered that the turbulence is the dominant cause of night clear air echo in Beijing. And then, radiosonde data show that the temperature lapse rate and wind shear are consistent with the seasonal variation of combined reflectivity factor at the altitude from 1 to 2 km. Finally, possible influence factors of clear air echo are inferred based on some phenomena observed.
Comparison of Reflectivity Factor of Dual Polarization Radar and Dual-frequency Precipitation Radar
Jiang Yinfeng, Kou Leilei, Chen Aijun, Wang Zhenhui, Chu Zhigang, Hu Hanfeng
2020, 31(5): 608-619. DOI: 10.11898/1001-7313.20200508
To find the root cause of the difference between spaceborne radar and ground-based radar data, their similarities and differences are quantitatively analyzed using GPM (Global Precipitation Measurement Mission) DPR (dual-frequency precipitation radar) and C-band dual-polarization radar (CDP) at Nanjing University of Information Science & Technology with respect to reflectivity factor classification of hydrometeor types by spatial-temporal matchup. The comparison reveals a high correlation of 0.86 between reflectivity factor detected by GPM DPR and CDP from 2015 to 2017 and a small root mean square error(RMSE) of 3.33 dB after attenuation correction and band correction, and the correlation passes the test of 0.001 level. The band correction formulas for detecting different hydrometeors reflectivity factor in C- and Ku-band are fitted by T-matrix method, applied the formula of dry snow to dry snow and graupel, applied the formula of wet snow is applicable to wet snow and rain hail, applied the formula of water to moderate rain, applied big drop and heavy rain and the band correction formula of ice to ice crystal. Band correction is carried out for different hydrometeors echoes after attenuation correction, the echo consistency of wet snow, graupel, big drops and moderate rain is well, and the correlation coefficient is over 0.85, the RMSE is less than 4 dB and echo differences of wet snow, graupel, big drops and moderate rain are small. The echo correlation coefficient of dry snow is relatively less than 0.8 due to the complex shape of dry snow which leads to difference between horizontal and vertical directions of CDP and difference between Mie scattering simulation and actual situation of dry snow, and further study on simulation of dry snow reflectivity factor is deserved. Due to the detection resolution of DPR and insufficient effective irradiation volume of CDP, the echo correlation coefficient of heavy rain and ice crystal is less than 0.4, and the reflectivity factor of heavy rain and ice crystal detected by DPR is less than CDP. The difference of reflectivity factor between DPR and CDP is mainly caused by dry snow, heavy rain and ice crystal. The amount of band correction is less than the amount of attenuation correction, then attenuation is the main factor. Band correction improves the matching situation on the basis of attenuation correction. NS mode and HS mode in DPR are different. NS mode can detect high reflectivity factor and is sensitive to strong echo, but is weak in detecting small reflectivity factor, while HS mode can detect small reflectivity factor and is sensitive to weak echo, but is weak in detecting high reflectivity factor.
The Outbreak and Damage of the Pleonomus Canaliculatus in Wheat Field Under the Background of Climate Change
Ren Sanxue, Zhao Huarong, Qi Yue, Tian Xiaoli, Yang Chao, Hu Lili
2020, 31(5): 620-630. DOI: 10.11898/1001-7313.20200509
In recent years, with the large-scale implementation of conservation tillage measures and crop straw crushing in North China, the winter wheat and summer corn are planted in two crops per year, creating a favorable environment for feeding and habituating for the Pleonomus canaliculatus. As the temperature in autumn, winter and spring of Gucheng Station in Hebei Province alternates between cold and warm from 2018 to 2019, the minimum temperature is significantly higher, inducing the explosive occurrence of the Pleonomus canaliculatus in the wheat field. According to the investigation of spring wheat field excavation, the maximum density of insect population is 144 heads·m-2, the maximum weight of insect population is 18.764 g·m-2. Among 58 investigation points, densities of 57 points exceed 5 heads·m-2, which calls for control measures. The density of insects in the jointing-harvest period is the highest during the booting period, followed by the jointing period, and that of the harvest period is the lowest. The oldest larvae have a maximum length of 34.68 mm, and a maximum width of 4.9 mm, 4.68 mm longer and 0.90 mm wider comparing to existing record respectively. The density of insect populations in the continuous cropping winter wheat and summer maize gramineous crops is 35.3 to 40.4 heads·m-2, which is significantly higher than that of soybean, corn, and winter wheat recreation grounds. The peanut and spring corn lands are more than 5 times higher than the soybean insect population density, and the weight of insect population is more than 10 times higher. Yield measurement in mature wheat fields shows the grain yield is reduced by 36.8%. When the insect population density increases by 10 heads·m-2, grain yield decreases by 4.824%. When insect population weight increases by 1 g·m-2, grain yield reduction increases by 3.871%, and 10% increase of plant pest will make the grain yield reduction rate increase by 11.587%.
Climate Suitability Regionalization of Pecan Based on MaxEnt Model
Cheng Jinxin, Duan Changchun, Yan Shengjie
2020, 31(5): 631-640. DOI: 10.11898/1001-7313.20200510
The maximum entropy model (MaxEnt) is an effective tool for agricultural climate suitability regionalization due to its objective, quantitative characteristics and good performance. Many representative planting sites in the study area is required by this method as statistical samples. However, the current scale of pecan planting in Yunnan Province is relatively small, and the spatial distribution of planting sites is too scarce to meet the model construction requirements. Therefore, it is difficult to obtain reliable results by directly applying this model to the climatic suitability zoning of pecan.Based on MaxEnt model and Geographic Information System (GIS), an improved method of agricultural climate suitability regionalization is proposed. Regionalization of climate suitability for pecan crop in Yunnan Province of China is carried out based on local climate data combined with data from 274 pecan planting sites in the contiguous United States. Results show that the dominant factors affecting the climate suitability of pecan crop are as follows: The average temperature in July, annual average temperature, extreme minimum temperature of 30 years, annual amount of precipitation, amount of precipitation from March to May, annual accumulated sunshine hours and accumulated sunshine hours from April to May. The MaxEnt Model based on climate data from 274 pecan planting sites in the contiguous United States has high accuracy in corresponding areas. In order to apply this model in Yunnan properly, a reliability factor is introduced to improve the climate suitability index by means of modeling areas as well as the deviations of climate factors of training samples. Then based on this improved climate suitability index, the climate suitability of pecan plantation in Yunnan Province is divided into 4 grades: Optimum, suitable, sub-suitable and unsuitable, among which the areas for the optimum and suitable areas are distributed in subtropics areas and tropical marginal areas, with abundant heat resources, relatively sufficient sunshine hours and favorable chilling condition in winter. Due to the complex terrain and climatic conditions in Yunnan Province, regionalization results are substantially fragmented.