Vol.27, NO.2, 2016

Display Method:
Numerical Modeling of Hailstorms with AgI Seeding
Lou Xiaofeng, Shi Yu, Lu Guangxian
2016, 27(2): 129-139. DOI: 10.11898/1001-7313.20160201
Cloud numerical simulations are important ways in research of hail processes and hail suppression activities. A 3-D hail model is used to simulate a hailfall case in Beijing on 10 Jun 1996. Series silver iodide (AgI) seeding simulations are designed on seeding height levels, seeding rates and starting seeding times, to get a best seeding scheme which can be used to advise outfield hail suppression operations. The 3-D hail model calculates 27 microphysical processes, which includes condensation, deposition, evaporation, collection, ice nucleation, ice multiplication, melting and freezing, auto conversions of cloud to rain, ice to graupel and graupel to hail. Seeding code is based on cloud chamber results of the mechanism of ice-forming processes by AgI which can be identified as deposition, contact freezing, condensation freezing and immersion freezing nucleation. The total nucleation activities are the sum of contributions from different nucleation modes. Humidity, temperature, cloud droplets concentration and cloud holding time are the main influence factors in AgI nucleation processes. The horizontal domain of the model is 96 km by 96 km with a constant grid increment of 1.2 km, and vertical resolution is 700 m and 20 km high. The time step is 2 s, and sounding data at 0800 BT are used as the initial.In all seeding simulations of different height levels, AgI particles start to nucleate only when they are moved to regions where air temperature is lower than-5℃. If seeding within 2.1-4.9 km height, much more ice nucleation happens, thus resulting in good hail suppression effect. The artificial ice particles make up insufficient natural ice particles. The seeding effect greatly depends on seeding amount. When the amount is less than 5×105 kg-1, hail precipitation is suppressed and rainfall is enhanced. When the amount is bigger than 1×107 kg-1, hail processes are greatly reduced and the rain processes also are weakened. For distributions of updrafts and cloud water, seeding at 12th, 15th, and 18th min, more ice nucleus is nucleated, which makes more graupel particles and better hail suppression effects than other seeding time tests. Among the series of seeding experiments, the best scheme is seeding with 5×106 kg-1 near 5 km height, at the 15th min of simulation, when hail precipitation is decreased about 60% and no much rainfall is lost.
The Experiment of Ice Nucleus Generating Efficiency by Model 37 Silver Iodide Shell
Dang Juan, Su Zhengjun, Fang Wen, Fang Chungang
2016, 27(2): 140-147. DOI: 10.11898/1001-7313.20160202
Model 37 shell is one of the main carrier of silver iodide catalytic agent usedin rainfall enhancement and hail suppression operations. Nucleating effectiveness values of silver iodide filled in shell are important reference to measure the amount of silver iodide catalytic agent using in weather modification operation. Using a 1200 L isothermal cloud chamber and a 20 m3steel plate explosion chamber, the first uniform test of ice nucleus generating efficiency of model 37 silver iodide shells isimplemented by Weather Modification Center of CMA (WMC) from November to December of 2013, and samples chosen randomly from 2 manufacture factories is examined. Uucleation rates of two kinds of sample are detected at eight temperature between-3℃ to-20℃. Results show that fitting values of nucleating effectiveness of two kinds of samples are all in the range of 109-1012/(g·AgI) with the temperature from-6 ℃ to-20℃, and the threshold temperature is-4℃. The nucleating effectiveness of sample 2 is higher than that of sample 1 obviously, and the maximum difference of fitting values between them is 8.4 times at-14℃. The comparison between this experiment and past results given by domestic different experiments is carried out. Results indicate the nucleation rates of sample 1 and sample 2 are higher than the past results at-14℃ above, and they are both 2 to 3 orders of magnitude more effective than the detection values of past experiments at-10℃. To seek cause for this significant difference, a detailed comparative analysis onexperiment condition is carried out. Some techniques applied to the 1200 L isothermal cloud chamber such as temperature controlling, fog making and ice crystals counting are improved, and impacts of those operations are reduced. Meanwhile, larger volume of cloud chamber results in less border effect, and longer time of holding fog in cloud chamber, so it simulates actual atmospheric condition better. Based on above discussions, the testing results of model 37 silver iodide shell nucleation rate are reliable. Furthermore, in view of the great deviation in different experiments, it is very necessary to uniformly test nucleation rate of seeding agent on the same experiment platform.
Quality Factors and Processing Algorithm for Wind Profiling Radar Data
Gao Zhuyu, Ruan Zheng, Wei Ming, Ge Runsheng, Liu Ruiting
2016, 27(2): 148-159. DOI: 10.11898/1001-7313.20160203
In recent years, wind profiling radar (WPR) network in China is under rapid development. To take advantage of the network measurements in weather analysis and numerical prediction, it's of great significance to make full aware of quality factors and improve the current processing algorithm for WPR data.Many factors affect the quality of horizontal wind data detected by WPR, especially system error and meteorological background. According to five-beam WPR, a new method for examining system error from radar Doppler measurements is proposed. As for meteorological background, wind filed is assumed homogenous when it is detected by WPR, and the accuracy of horizontal wind data will decline when the assumption is not satisfied. During the period of precipitation, scattering caused by raindrops is much stronger than turbulence detected by WPR. And the assumption of homogenous wind breaks down easily for the cause that fall terminal velocity of precipitation particles changes rapidly in space when convective precipitation happens, which is a significant problem for WPR data quality control algorithm.However, two independent wind profiles can be measured with a five-beam WPR and differences between measured zonal winds and meridional winds can reflect errors caused by the inhomogeneity of wind field. In order to reduce such errors, all observations are examined to make sure data detected under circumstances where the wind filed is extremely inhomogenous are deleted. Besides, different averaging methods, such as consensus average and simple average, used to calculate hourly averaged winds also affect the accuracy of it and comparisons are conducted on two averaging methods.Combined with 10 radars of Guangdong WPR network, evaluation of the new methods for processing basic data is analyzed from March to May in 2014. Results show that 10 radars in Guangdong WPR network, including 8 boundary radars (LC), 1 troposphere radar Ⅰ(PA) and 1 troposphere radar Ⅱ(PB), meet the designed requirements respectively in terms of the maximum height of credible data in clear air, which is 3 km for LC radar, 6 km for PB radar and 10 km for PA radar. Furthermore, there are no large system errors in 10 radars except that the examining consequence is unsatisfactory during 1-2 km for PA radar. It is necessary to consider the atmospheric inhomogeneities that may cause great errors especially when it rains heavily, and consensus averaged wind is superior to simple averaged wind in median and high heights. Therefore, an improved algorithm according to examination of atmospheric inhomogeneities and consensus average is proposed to obtain hourly averaged winds. It is proved that winds obtained from the improved algorithm show better representation than the currently used data during precipitation, as the stand deviation of differences between two independent measured zonal wind values and meridional wind values are both close to 1 m·s-1.
Beijing Regional Environmental Meteorology Prediction System and Its Performance Test of PM2.5 Concentration
Zhao Xiujuan, Xu Jing, Zhang Ziyin, Zhang Xiaoling, Fan Shuiyong, Su Jie
2016, 27(2): 160-172. DOI: 10.11898/1001-7313.20160204
Beijing Regional Environmental Meteorology Prediction System (BREMPS) is established by coupling BJ-RUC, WRF-Chem and preferred visibility parameterization scheme. The performance test with observations in 2014 and Asia-Pacific Economic Cooperation (APEC) period shows that BREMPS has a good forecasting ability for two important elements in air quality and haze forecasting in Beijing and surrounding area, PM2.5 concentration and visibility. Correlation coefficients of PM2.5 between forecasted and observed values reaches above 0.6 at most sites, and even reaches above 0.8 at some sites in Beijing. Forecasted values generally underestimate the PM2.5 concentrations with a regional average normalized mean bias of-15%. The forecast performance shows slight decrease after 24 forecasted hours. Comparing with the regional average, the forecast performance is best in Beijing urban area and northern of Hebei. The forecasted PM2.5 concentration agrees well with the observation in Beijing area. Correlation coefficients of PM2.5 concentrations between forecasted and observed values in 48 forecasted hours are about 0.77 in urban area. The normalized mean bias is generally in a range of-26%. The correlation coefficient in rural area is higher than that in urban area. However, the mean bias is also higher in rural area. That is probably attributed to the inaccuracy of the emission information in these areas. The forecast performance is better in spring, autumn and winter, during which the correlation coefficient between forecasted and observed values mostly ranges from 0.7 to 0.9, and the normalized mean bias is within 20%. The forecasted visibility is closer to automatic measurements than artificial observations. Forecasted values are in good agreement with observations during sustained low visibility synoptic processes. For the hourly visibility lower than 10 km, the accuracy of forecast is 77%, which decreases with the reduction of visibility and reaches 40% when the visibility is lower than 2 km. BREMPS shows good forecast performance during the APEC period, when the temporal evolutions of AQI and visibility, and spatial distribution of PM2.5 concentrations are well forecasted, providing strong support for the environmental meteorology forecast service.
Flash Cell Identification, Tracking and Nowcasting with Lightning Data
Zhou Kanghui, Zheng Yongguang, Lan Yu
2016, 27(2): 173-181. DOI: 10.11898/1001-7313.20160205
Lightning, accompanying with convective storms in the whole lifecycle, can reflect the development of storms effectively. The national lightning detection network makes it possible to get lightning location data instantly all over China, which would be highly valuable in convective system monitoring. A new method for flash cell identification, tracking and nowcasting is proposed. Using cloud-to-ground lightning location data over China, a new cluster algorithm of fast searching and density peaks identifying, is utilized to recognize the flash cells by clustered flashes. Time and area distribution characteristics of flashes are used in identification. Second, Kalman filtering is used to track the moving path of cells, considering cell spitting and merging conditions. Finally, based on the previous path, the linear moving path in next 60 min is predicted with Kalman filtering.lightning location data in 2013 are analyzed by this method. Doppler radar data are applied to evaluate its performance, which proves its effectiveness on identification and track for thunderstorm split and merge. The overall performance is as better as TITAN (Thunderstorm Identification, Tracking, Analysis and Nowcasting) for thunderstorm nowcasting in 60 minutes, and even better from some aspects. The probability of detection of nowcasting for 10 min is about 0.7, about 0.6 for 30 min and 0.2 for 60 min, respectively. The probability of detection and critical success index decrease dramatically with time, and the false alarm rate increases rapidly in 60 min. All of those mean that the linear nowcasting would be more reliable in near time, and it is meaningful in 0-60 min forecast.One case is analyzed in detail, which shows that flashes only appear in deep convective systems that usually companies with severe weather. Furthermore, flashes disappears obviously in the dissipative stage of storms, which would be an indicator for predicting the end of convective systems.
Climatic Prediction of Spring Sowing Period in the Middle and Lower Reaches of the Yangtze Based on DERF2.0
Zhang Daquan, Wang Yongguang
2016, 27(2): 182-190. DOI: 10.11898/1001-7313.20160206
Based on the hindcast data of the second generation monthly dynamic extended range forecast model (DERF2.0) of National Climate Center and historical spring sowing data, combined with NCEP/NCAR reanalysis data, correlation analysis is conducted between historical spring sowing data and reanalysis data and model hindcast data, respectively. Regions with anomalies correlation coefficient (ACC) passing significant test are defined as key circulation zones, and the overlapping areas where both anomalies correlation coefficients passing the significant test are selected as key influencing factors of climatic conditions of spring sowing period in the middle and lower reaches of the Yangtze. Using historical time series of selected factors of certain circulation variables, e.g., geopotential height of 200, 500 hPa and 700 hPa, zonal and meridional wind of 850 hPa as predictors, favorable, unfavorable and continuous unfavorable days of spring sowing period in the middle and lower reaches of the Yangtze as predictors, utilizing optimal subset regression method (OSR), a model interpretation scheme of climatic conditions of sowing period prediction is established. The performance of the model interpretation scheme with different lead time are evaluated and analyzed. Meanwhile, the predictive skill of typical years with unfavorable climatic conditions is tested. Hindcast test of predictive scheme exhibits considerable overall predictive skill on both favorable and unfavorable days of spring sowing period. Predictive results of different lead time indicate that the prediction performance of unfavorable and continuous unfavorable days grows better as the lead time shortens. Moreover, hindcast result with lead time equals 0 shows that the model interpretation scheme not only simulates the annual variations well, but also illustrates certain predictive ability of decadal change of climatic conditions of spring sowing period. In the operational prediction of climatic conditions of spring sowing period, rolling forecast result of model interpretation scheme should be utilized to achieve better predictive performance. Since the model interpretation scheme does not give climatic conditions of spring sowing period directly, each variables of model output should be considered. In order to test the predictive skill of typical years with unfavorable climatic conditions, five years with typical unfavorable climatic conditions of spring sowing period (with continuous unfavorable days exceeding 10 days) are selected and verification results indicate that since the 1980s, the integrated result of scheme is approximately the same with observation, which exhibits considerable predictive skill.
Integrated Technology of Yield Dynamic Prediction of Winter Wheat in Shandong Province
Qiu Meijuan, Song Yingbo, Wang Jianlin, Wu Dingrong, Liu Ling, Liu Jiandong
2016, 27(2): 191-200. DOI: 10.11898/1001-7313.20160207
Using winter wheat yield and growth data of 17 prefecture-level city, daily meteorological data from 1980 to 2011, and daily 20 cm depth soil moisture data of 14 representative meteorological stations from 1992 to 2011, methods for dynamic prediction of winter wheat yield are established in 4 regions of Shandong Province, considering historical meteorological influence index for bumper or poor harvest of crop yield, key meteorological factors influence index, the climatic suitability influence index and the WOFOST crop growth model, respectively. A newly developed statistical method, cluster analysis of statistical test (CAST), which divides planting areas of winter wheat in Shandong Province into four regions. These four methods are used to predict yield of winter wheat in regions of Shandong Province from 2004-2011. An integrated prediction method is established in which the weight coefficients of each method is determined based on the prediction accuracy, and the prediction method with accuracy lower than 90.0% in each period is removed.The comparison result shows the prediction accuracy in each region and period of four single yield prediction method is very unstable and has a large fluctuation range. Forecast results of the historical meteorological influence index for bumper or poor harvest of crop yield are relatively good in region of C1 and C3. The accuracy of key meteorological factor influence index in region C1 and C2 is relatively consistent, while not quite stable in region C3. The prediction accuracy of the climatic suitability influence index generally is more than 80%. And the prediction accuracy of WOFOST in four regions all reaches 90.0%, except for certain instability and fluctuation. Through integrating these methods, the accuracy in each region and each period is significantly improved, which is generally above 95.0%, and the prediction result is stable. Therefore, the integrated prediction method could overcome shortcomings of the single forecast method, and it is more suitable for application.
Cloud Type Identification Based on Macro and Micro Properties of Clouds from MODIS
Wu Xiao, You Ran, Wang Minyan, Gu Junxia
2016, 27(2): 201-208. DOI: 10.11898/1001-7313.20160208
Satellite cloud type product has been operationally processed in China National Satellite Meteorological Center (NSMC) for many years. But due to causes of instruments on board and methods used for cloud type identification, this product still needs improving. In 2011, American scientists proposed a new method to classify cloud types in NPOESS (national polar-orbiting operational environmental satellite system) cloud products algorithm theoretical basis documents. This method uses the satellite derived cloud optical thickness product, cloud effective radius product, cloud top height product, cloud phase product, a set of characteristic values of cloud optical thickness, cloud effective radius, cloud top height, and cloud phase for 6 cloud types to calculate distances between satellite data and characteristic parameters of 6 cloud types. Finally, a minimum distance is obtained, and the corresponding cloud type is derived.Using MODIS data, the minimum distance cloud type identification method is combined with multiple-threshold method, and cloud type identification experiments are carried out. By incorporating methods into software, and using cloud optical thickness product, cloud effective radius product, cloud top height product, cloud phase product, cloud top temperature product, and brightness temperature product of MODIS as inputs of the software, cloud type identification results are outputted for years of 2008 and 2013. Results are compared with ground cloud type observations, and two series are more than 60% consistent. Also, pictures combining satellite derived cloud types and ground hourly precipitation amount observations reflect that analyzed cumulonimbus and nimbostratus are reasonably in the zone of raining. Because the cloud optical thickness can largely reveal the water content in clouds and the vertical thickness of clouds, this cloud type identification method captures raining clouds effectively.
Thermal Characteristics over Eurasia in January-March and Its Relationship with Precipitation of China
Yan Hongming, Wang Ling, Li Rui
2016, 27(2): 209-219. DOI: 10.11898/1001-7313.20160209
Seasonal changes of thermal differences between the sea and the land (land-sea thermal contrast) is a key influence factor to monsoon formation, strength change, onset and retreat. Land thermal condition significantly influences atmospheric circulation at high and low level, monsoon activities and climatic anomalies. Being the largest land of the world, effects of Eurasian continent on global climate are more complicated and important, especially considering the heat source seasonal changes of the Tibetan Plateau.Based on NCEP/NCAR reanalysis data and monthly data of 160 meteorological stations in China from 1979 to 2011, the thermal characteristics over Eurasian continent are investigated. Results show that the climatic variability of surface air temperature (SAT) displays obvious difference in different regions and seasons. The SAT variability is significantly larger in south Eurasia than that in north Eurasia, and is the biggest in winter and the weakest in summer. Temporal and spatial characteristics of SAT over Eurasian continent from January to March are emphatically investigated, and it's found thermal changes are just opposite with positive (negative) anomalies in south Eurasia and negative (positive) anomalies in south Eurasia.According to the variation characteristics of temporal coefficient of the first empirical orthogonal function (EOF-PC1), 8 positive and 8 negative thermal contrast years are chosen, and the relationship between changes of the thermal variation over Eurasian continent and precipitation in China is further studied using methods of correlation and composite analysis. Results show that this thermal contrast is not only closely connected with precipitation in China from January to March, but also connected with the precipitation in the following summer. The thermal index is shown to be positively correlated with the accumulative precipitation in South China, Southwest China and middle reaches of the Huang River from January to March, and the rainfall amount of the middle and lower reaches of the Yangtze in the late summer.Finally, possible ways of thermal anomalies from January to March associated with precipitation in China are investigated. It indicates that the thermal contrast between south Eurasia and north Eurasia is closely related with AO, the east Asian trough, and the upper level jet stream in east Asia at the same time. Besides, the south Asia high, the upper level jet stream in east Asia, and Asian monsoon are also possible linkage ways between thermal contrast and the climate of China.
Characteristics of the Forecast Jumpiness Based on TIGGE Data
Guo Huanhuan, Duan Mingkeng, Zhi Xiefei, Hu Hangfei, Zhao Huan
2016, 27(2): 220-229. DOI: 10.11898/1001-7313.20160210
Based on 500 hPa geopotential height, 850 hPa temperature and the mean sea level pressure forecasts from ECMWF, NCEP and CMA in TIGGE datasets, characteristics of the forecast jumpiness for the control and ensemble-mean forecasts and the comparison of their characteristics conducted using Jumpiness index and other different forecast jumps: The flip, flip-flop, flip-flop-flip and so on. Results indicate that in terms of the period-average forecast jumpiness features, the period-average jumpiness indices increase with the forecast range in agreement with the practical experience that forecasts are usually more consistent at short forecast ranges. And for ensemble prediction system, the ensemble-mean forecast is less jumping than its corresponding control forecast, especially at long forecast ranges, which indicates that the forecast jumpiness could be reduced using the ensemble prediction method. And both for the control forecast and ensemble-mean forecast, the forecast jumpiness of ECMWF is lower. In frequency statistics of the forecast jumpiness, frequencies of the flip, flip-flop and flip-flop-flip are in descending order. For these three types of forecast jumps, the frequency of ensemble-mean forecast is significantly lower than that of the control forecast especially at long forecast ranges. It indicates that the ensemble-mean forecast is less jumping than its corresponding control forecast, which also shows that the forecast jumpiness could be reduced using the ensemble prediction method. The frequency variation of parallel flip, parallel flip-flop and parallel flip-flop-flip indicates that the control forecast and ensemble-mean forecast have large difference at long forecast ranges. And the correlation coefficient of their Jumpiness indices also confirms this conclusion. At last, the sensitivity of the forecast jumpiness to areas, time and parameters are presented. Results show that the period-average forecast jumpiness has a strong sensitivity to the area and parameter. And the sensitivity of the control forecast to the area and parameter is stronger than that of ensemble-mean forecast. The smaller the studied area is, the larger the period-average forecast jumpiness becomes, which indicates that the forecast jumpiness intensity is stronger. As the weather and climate characteristics of the selected areas are not the same, the period-average forecast jumpiness is different. For different variables, the period-average forecast jumpiness is also various. The period-average Jumpiness index of mean sea level pressure is the maximum, the result of 500 hPa geopotential height is the second, and the minimum result is 850 hPa temperature. That is to say, the forecast jumpiness intensity of temperature is lower than geopotential height results. And the frequency of different forecast jumps and the difference of the jumpiness between control forecast and ensemble-mean forecast show little sensitivity to the choice of the area, time and parameter.
Quality Control and Analysis of in Situ Soil Moisture Data in Yunnan
Lan Ying, Zheng Youfei, Duan Changchun, Yin Jifu, Wu Rongjun, Huang Tunan
2016, 27(2): 230-238. DOI: 10.11898/1001-7313.20160211
Soil moisture is a variable that plays a crucial role in the energy and mass exchange between the atmosphere and land, and it is often used as an environment factor and process parameter in meteorology researches. However, due to the diversity of climatological conditions and differences in measurement setup, the quality of the soil moisture measurement is highly variable, which may have a significant impact on the data accuracy. Therefore, appropriate quality characterization is desired.Based on soil moisture data of 37 stations of Yunnan in 2010-2014, the spectrum-based approaches are used to eliminate 3 kinds of abnormal data. The constant, spike and noisy, caused by saturation of the signal and unresponsive sensors, are screened out by procedures analysis on the time series. The data integrality is shown not good, especially at some stations in the northwest of Yunnan Province, where the instruments are newly set up. After the first instability period, the data quality gradually improves. Among all abnormal values, the proportion of constant is up to 97% in all stations. The distribution of stations containing more spike shows less distinct pattern.Compared to the related meteorological data, different soil types show different responses to the precipitation. Sandy soil shows quicker reaction to the precipitation, and the change of soil moisture is more significant as well. As the rainfall intensity increases, the rise of the moisture increases too. Loam soil shows continuous rise in the few hours after the rainfall, but the variation is rather weaker, and the deeper soil moisture changes relatively smoothly. Clayey soil has a weak relationship with precipitation, and the soil moisture descends more quickly in the deeper layer than other soil type. It's found that the rainfall intensity and the soil moisture rise are not simply positively correlated, the soil moisture rises less after downpour, and these responses need further research.
Rainfall Intensity and Raindrop Spectrum for Different Parts in Landing Typhoon Matmo
Lin Wen, Lin Changcheng, Li Bailiang, Zheng Wenjun, Hu Lunshan, Yu Yongjiang
2016, 27(2): 239-248. DOI: 10.11898/1001-7313.20160212
During typhoon Matmo passage over Fujian from 23 Jul to 25 Jul in 2014, it passes through two disdrometer sites: Pingnan and Youxi. Pingnan site locates in eastern Fujian that represents the heavy rain region of typhoon Matmo, and Youxi site represents the middle path region of typhoon Matmo moving. Thus, microphysical characteristics of raindrop size distribution in different parts of typhoon Matmo are studied through the PARSIVEL2 disdrometer measurements at these two sites. The evolution of raindrop size distribution parameters reveals different segments of the storm, that the fluctuation of rainfall intensity in the right part is stronger than those in the middle. Heavy rainfalls happen in right front side, rear side of outer rainband and residual cloud. There is showery in the outer rainband in which rainfall intensity fluctuate frequently. Approaching to the central region of typhoon Matmo, the precipitation becomes more continuous and rainfall intensity changes more smoothly. In the right side rainband, the spectral width of raindrop size distribution changes from wide to narrow, the concentration of small rain drops increases at first and then decreases, and concentration of large rain drops decreases gradually. In the middle path of typhoon Matmo, the concentration of small rain drops increases first and then decreases, but spectral width of raindrop size distribution and concentration of large rain drops suddenly rise for difference. The evaluation of raindrop concentration and liquate water has some certain relations to the changed rainfall intensity, but degrees of their changes are not in tune. When rainfall intensity is less than 10 mm·h-1, a large number of small droplets contribute to the precipitation. The contribution to rainfall intensity by large number of small raindrops is higher in the right side rainband than in the middle moving path. In the central region of typhoon Matmo, the contribution by high concentration of small raindrops to the rainfall intensity is higher than in front side and rear side rainband. On the contrary, when rainfall intensity is higher than 10 mm·h-1, the heavy rainfall in front outer rainband and residual cloud are the direct appearances by concentration growing of large droplets. The precipitation at Pingnan is more unstable than that at Youxi, so more fiercely collision broken processes lead to raindrop concentration repeating with droplets growing in right part of typhoon Matmo. Parameters of μ and λ meet the linear function both at 2 sites. Linear fit functions can be used to reduce Gamma distribution function, getting good results. The μ and λ have wide distributions in the region less than 10 mm·h-1. However, when rainfall intensity is more than 10 mm·h-1, parameters of μ and λ decrease with rainfall intensity increasing, and vary with rainfall intensity related to the region and precipitation types.
The Relationship Between Urban Spatial Morphology Parameters and Urban Heat Island Intensity Under Fine Weather Condition
Zhang Hailong, Zhu Shanyou, Gao Yang, Zhang Guixin
2016, 27(2): 249-256. DOI: 10.11898/1001-7313.20160213
Urban heat island (UHI) phenomenon has a significant negative impact on economic development, urban climate, human life and health. An important cause for UHI is that intense buildings block the radiative transfer and exchange between urban canopies. Building distribution patterns and heights, which affect the formation and intensity of urban heat island, can be described with urban morphological parameters including frontal area index (FAI) and sky view factor (SVF).With high spatial resolution, 3D building data of Adelaide central city, Australia, SVF and FAI are estimated and their relationships with urban heat island intensity (UHII) under fine weather conditions are analyzed. Sky view factor is a commonly-used morphological parameter that describes the degree to which the sky is obscured by building block. Compared with sky view factor calculated from fish-eye photos, SVF is very consistent with the former, with the correlation coefficient of 0.97, which can be used to analyze the change of urban heat island intensity in different seasons. Frontal area index is calculated by weighted average method of all seasons with observations of wind speed and direction in Adelaide airport. Relationships between SVF and UHII, FAI and UHII in different times of various seasons and their influences are analyzed.A high negative linear relationship is found between SVF and UHII at night time under fine weather conditions, and significant positive linear relationship is found during daytime, especially in the afternoon of spring, autumn and winter. A logarithmic relationship is found between FAI and UHII appears at night and morning time, while at daytime they are linearly related. The applicability of the correlation between SVF and UHII at different search radius is higher than that of FAI. At the scale of 200 m, at night time of spring and winter, the correlation of FAI and UHII is greater than that of SVF and UHII, while the correlation between SVF and UHII is greater at daytime of autumn. For the other moments, there are little difference between correlation of FAI to UHII and that of SVF to UHII.