Advanced Search
Article Contents

Numerical Simulation of the Impact of Urban Non-uniformity on Precipitation


doi: 10.1007/s00376-016-5042-1

  • To evaluate the influence of urban non-uniformity on precipitation, the area of a city was divided into three categories (commercial, high-density residential, and low-density residential) according to the building density data from Landsat satellites. Numerical simulations of three corresponding scenarios (urban non-uniformity, urban uniformity, and non-urban) were performed in Nanjing using the WRF model. The results demonstrate that the existence of the city results in more precipitation, and that urban heterogeneity enhances this phenomenon. For the urban non-uniformity, uniformity, and non-urban experiments, the mean cumulative summer precipitation was 423.09 mm, 407.40 mm, and 389.67 mm, respectively. Urban non-uniformity has a significant effect on the amount of heavy rainfall in summer. The cumulative precipitation from heavy rain in the summer for the three numerical experiments was 278.2 mm, 250.6 mm, and 236.5 mm, respectively. In the non-uniformity experiments, the amount of precipitation between 1500 and 2200 (LST) increased significantly. Furthermore, the adoption of urban non-uniformity into the WRF model could improve the numerical simulation of summer rain and its daily variation.
  • 加载中
  • Chen L. X., W. Q. Zhu, and X. J. Zhou, 2000: Characteristics of environmental and climate change in Changjiang Delta and its possible mechanism. Acta Meteorologica Sinica, 14( 2), 129- 140. (in Chinese)9bdea2e50286dcb8dcf496cc8a0801fbhttp%3A%2F%2Fwww.cnki.com.cn%2FArticle%2FCJFDTotal-QXXW200002000.htmhttp://www.cnki.com.cn/Article/CJFDTotal-QXXW200002000.htmCharacteristics of climate change in the Changjiang Delta were analyzed based on the annualmean meteorological data since 1961,including air temperature,maximum and minimum airtemperature,precipitation,sunshine duration and visibility at 48 stations in that area(southernJiangsu and northern Zhejiang),and its adjacent areas(northern Jiangsu,eastern Anhui andsouthern Zhejiang),together with the environmental data.The results indicate that it is gettingwarmer in the Changjiang Delta and cooler in adjacent areas,thus the Changjiang Delta becomes a bigheat island,containing many little heat islands consisting of central cities,in which Shanghai City isthe strongest heat island.The intensity of heat islands enhances as economic development goes up.From the year 1978.the beginning year of reform and opening policy,to the year 1997,the intensityof big heat island of Changjiang Delta has increased 0.5℃ and Shanghai heat island increased 0.8℃.However.since 1978 the constituents of SO 2 ,NO x and TSP(total suspended particles)in theatmosphere,no matter whether in the Changjiang Delta or in the adjacent areas,have all increased,but pH values of precipitation decreased.In the meantime,both sunshine duration and visibility arealso decreased,indicating that there exists a mechanism for climate cooling in these areas.Ouranalyses show that the mechanism for climate warming in the Changjiang Delta may be associatedwith heating increase caused by,economic development and increasing energy consumption.It isestimated that up to 1997 the intensity of warming caused by this mechanism in the Changjiang Deltahas reached 0.8—0.9℃,about 4—4.5 times as large as the mean values before 1978.Since then,the increase rate has become 0. 035℃/a for the Changjiang Delta.It has reached 1.3℃ for Shanghaiin 1997,about 12—13 times as large as the mean values before 1978.This is a rough estimation ofincreasing energy consumption rate caused by economic development.
    Hu X. M., P. M. Klein, and M. Xue, 2013: Impact of low-level jets on the nocturnal urban heat island intensity in Oklahoma Journal of Applied Meteorology and Climatology, 52( 8), 1779- 1802.
    Jauregui E., E. Romales, 1996: Urban effects on convective precipitation in Mexico City. Atmos. Environ., 30( 20), 3383- 3389.10.1016/1352-2310(96)00041-6438a4a3713b008ae952ab443ac6a555dhttp%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2F1352231096000416http://www.sciencedirect.com/science/article/pii/1352231096000416This paper reports on urban-related convective precipitation anomalies in a tropical city. Wet season (May–October) rainfall for an urban site (Tacubaya) shows a significant trend for the period 1941–1985 suggesting an urban effect that has been increasing as the city grew. On the other hand, rainfall at a suburban (upwind) station apparently unaffected by urbanization, has remained unchanged. Analysis of historical records of hourly precipitation for an urban station shows that the frequency of intense (> 20 mm h 611 ) rain showers has increased in recent decades. Using a network of automatic rainfall stations, areal distribution of 24 h isoyets show a series of maxima within the urban perimeter which may be associated to the heat island phenomenon. Isochrones of the beginning of rain are used to estimate direction and speed of movement of the rain cloud cells. The daytime heat island seems to be associated with the intensification of rain showers.
    Li S. Y., H. B. Chen, and W. Li, 2008: The impact of urbanization on city climate of Beijing region. Plateau Meteorology, 27( 5), 1102- 1110. (in Chinese)10.3724/SP.J.1047.2008.000149c0f839f-ad74-4b45-a37b-34a85aec5f68mag4842620082751102ef6054e2706e59d808f1431f034b203chttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-GYQX200805020.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-GYQX200805020.htmThe impact of urban growth on city climate variation is studied using the daily mean data of temperature/velocity and precipitation at 20 meteorological stations from 1970 to 2005.The results show that:(1)In the past 36 years the urban heat island(UHI) area is increasing,the UHI intensity is enhancing,and the UHI centers are evolving from single to several centers.In 2000′s,the maximal UHI intensity is 2.11℃.In the past 36 years the mean temperature in winter increases 0.298℃/10a.(2)The urbanization has made the precipitation show a tendency of uneven distribution.In 1970′s,the precipitation in the west of the city is much,while in the southeast of the city is little;in 1980′s,all the urban zone′s precipitation is little;in 1990′s,the precipitation in both west and south of the city is much,while in the northeast of the city is little;in 2000′s,the little precipitation zone extends from urban district to the southeast.(3)The urban wind speed has a decreasing tendency.The wind speed in 1970′s is 2.49 m·s-1,in 1980′s,is 2.32 m·s-1,in 1990′s,is 2.16 m·s-1,and in 2000′s,is 2.28 m·s-1.In the past 36 years the wind speed decreases 0.05 m·s-1·(10a)-1.(4)The temperature and the population density logarithm have a linear correlation,the correlative coefficient is 0.65;the temperature and the city land area have a linear correlation,the correlative coefficient is 0.6387.
    Liao J. B., X. M. Wang, Y. X. Li, and B. C. Xia, 2011: An analysis study of the impacts of urbanization on precipitation in Guangzhou. Scientia Meteorologica Sinica, 31( 4), 384- 390. (in Chinese)10.1007/s00376-010-1000-52d699413e0b621ba7d614641edd40cb8http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-QXKX201104003.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-QXKX201104003.htmUsing the observation data from 1959 to 2009,the precipitation variation in Guangzhou was studied.We found that the trends of annual total precipitation in suburban Zengcheng station is not obvious,but number of the heavy rain days has increased;the total annual precipitation in Guangzhou fluctuated and there is a slight upward trend with the increasing rate of 10.5 mm/a since 1991.The total precipitation days tend to decrease with the decreasing rate of 7.2 d/10a since 1982,what's more,the heavy rain days and rainfall precipitation levels were significantly rising,the increasing rate of heavy precipitation days is 2.8 d/10a while the growth rate of precipitation load is 2.4%/10a.Urbanization in Guangzhou have caused heavy rain to occur more frequently in Guangzhou.Compared to pre-urbanization,the precipitation in Guangzhou indicated a significant increase since 1991.The contribution rate to the precipitation increase due to urbanization in Guangzhou is 44.7%.
    Lowry W. P., 1998: Urban effects on precipitation amount. Progress in Physical Geography, 22( 4), 477- 520.10.1177/0309133398022004031b82f4fa6d18efe156423aa277d9e2dfhttp%3A%2F%2Fdialnet.unirioja.es%2Fservlet%2Farticulo%3Fcodigo%3D453977http://dialnet.unirioja.es/servlet/articulo?codigo=453977Major reviews of urban effects on local climate, extending from Kratzer in 1937 through to Landsberg in 1981, have dealt primarily with radiation, temperature, wind, and air quality. To a much lesser extent they have examined moisture-related elements including humidity, cloud, precipitation, and storminess. Selecting air temperature to represent the former group and precipitation amount to represent the latter, the author asserts that, because of the intrinsic physical differences between them, there are necessarily important differences in the methods to be used for their proper observation, analysis, presentation, and interpretation pertaining to urban effects. The principal differences are based in the fact that temperature is continuous in both time and space, whereas precipitation is continuous in neither. The author maintains that because of these differences, urban climatologists have had much greater success in specifying and explaining urban effects on temperature than on precipitation amount. Further, he makes the case that, lack of recognition that methods used for the study of urban effects on temperature are too often inappropriate for study of urban effects on precipitation amount, has led to a state of affairs where there remains basic uncertainty about the specification of urban effects on precipitation amount, and even greater uncertainty about their explanation. In making that case, the author includes 1) an historical perspective, 2) a critical evaluation of methods, 3) an overview of the status of urban precipitation climatology, and 4) recommendations concerning future research.
    Miao S. G., F. Chen, Q. C. Li, and S. Y. Fan, 2011: Impacts of urban processes and urbanization on summer precipitation: A case study of heavy rainfall in Beijing on 1 August 2006. Journal of Applied Meteorology and Climatology, 50( 4), 806- 825.10.1007/s13143-014-0016-70a1c7909-2aee-4694-9658-3eee0bfcfd8b0825dd9bf898e5debb8e206546d89a31http%3A%2F%2Flink.springer.com%2F10.1007%2Fs13143-014-0016-7refpaperuri:(2053229b2072a9fd72ff8134e3006e55)http://link.springer.com/10.1007/s13143-014-0016-7Weather and climate changes caused by human activities (e.g., greenhouse gas emissions, deforestation, and urbanization) have received much attention because of their impacts on human lives as well as scientific interests. The detection, understanding, and future projection of weather and climate changes due to urbanization are important subjects in the discipline of urban meteorology and climatology. This article reviews urban impacts on precipitation. Observational studies of changes in convective phenomena over and around cities are reviewed, with focus on precipitation enhancement downwind of cities. The proposed causative factors (urban heat island, large surface roughness, and higher aerosol concentration) and mechanisms of urban-induced and/or urban-modified precipitation are then reviewed and discussed, with focus on downwind precipitation enhancement. A universal mechanism of urban-induced precipitation is made through a thorough literature review and is as follows. The urban heat island produces updrafts on the leeward or downwind side of cities, and the urban heat island-induced updrafts initiate moist convection under favorable thermodynamic conditions, thus leading to surface precipitation. Surface precipitation is likely to further increase under higher aerosol concentrations if the air humidity is high and deep and strong convection occurs. It is not likely that larger urban surface roughness plays a major role in urbaninduced precipitation. Larger urban surface roughness can, however, disrupt or bifurcate precipitating convective systems formed outside cities while passing over the cities. Such urban-modified precipitating systems can either increase or decrease precipitation over and/or downwind of cities. Much effort is needed for in-depth or new understanding of urban precipitation anomalies, which includes local and regional modeling studies using advanced numerical models and analysis studies of long-term radar data.
    Rosenfeld D., 2000: Suppression of rain and snow by urban and industrial air pollution. Science, 287( 5459), 1793- 1796.10.1016/j.ijfoodmicro.2014.11.023107103021af0481b2ecbe261f403a76bf5bd1c8ehttp%3A%2F%2Fwww.ncbi.nlm.nih.gov%2Fpubmed%2F10710302%3Fdopt%3DAbstracthttp://www.ncbi.nlm.nih.gov/pubmed/10710302?dopt=AbstractOur method for the analysis of quantitative microbial data shows a good performance in the estimation of true prevalence and the parameters of the distribution of concentrations, which indicates that it is a useful data analysis tool in the field of QMRA.
    Shepherd J. M., H. Pierce, and A. J. Negri, 2002: Rainfall modification by major urban areas: Observations from spaceborne rain radar on the TRMM satellite. J. Appl. Meteor., 41( 7), 689- 701.10.1175/1520-0450(2002)041<0689:RMBMUA>2.0.CO;25607a620-c68e-4cb9-acd3-6041f87f23b60b54b7b2e4dc8fcc409170accd48091fhttp%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013125690%2Frefpaperuri:(16e55b4179ade8c71ec65990efcaf437)http://ci.nii.ac.jp/naid/10013125690/Data from the Tropical Rainfall Measuring Mission (TRMM) satellite's precipitation radar (PR) were employed to identify warm-season rainfall (1998-2000) patterns around Atlanta, Georgia; Montgomery, Alabama; Nashville, Tennessee; and San Antonio, Waco, and Dallas, Texas. Results reveal an average increase of about 28% in monthly rainfall rates within 30-60 km downwind of the metropolis, with a modest increase of 5.6% over the metropolis. Portions of the downwind area exhibit increases as high as 51%. The percentage changes are relative to an upwind control area. It was also found that maximum rainfall rates in the downwind impact area exceeded the mean value in the upwind control area by 48%-116%. The maximum value was generally found at an average distance of 39 km from the edge of the urban center or 64 km from the center of the city. Results are consistent with the Metropolitan Meteorological Experiment (METROMEX) studies of St. Louis, Missouri, almost two decades ago and with more recent studies near Atlanta. The study establishes the possibility of utilizing satellite-based rainfall estimates for examining rainfall modification by urban areas on global scales and over longer time periods. Such research has implications for weather forecasting, urban planning, water resource management, and understanding human impact on the environment and climate.
    Skamarock W.C., Coruthors, 2008: A description of the advanced research WRF version 3. NCAR Tech. Note NCAR/ TN-475+STR,88 pp.10.5065/D68S4MVH6e1e8ed5238484bf7e6021f9957054e6http%3A%2F%2Fwww.researchgate.net%2Fpublication%2F244955031_A_Description_of_the_Advanced_Research_WRF_Version_2http://www.researchgate.net/publication/244955031_A_Description_of_the_Advanced_Research_WRF_Version_2The development of the Weather Research and Forecasting (WRF) modeling system is a multiagency effort intended to provide a next-generation mesoscale forecast model and data assimilation system that will advance both the understanding and prediction of mesoscale weather and accelerate the transfer of research advances into operations. The model is being developed as a collaborative effort ort among the NCAR Mesoscale and Microscale Meteorology (MMM) Division, the National Oceanic and Atmospheric Administration's (NOAA) National Centers for Environmental Prediction (NCEP) and Forecast System Laboratory (FSL), the Department of Defense's Air Force Weather Agency (AFWA) and Naval Research Laboratory (NRL), the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma, and the Federal Aviation Administration (FAA), along with the participation of a number of university scientists. The WRF model is designed to be a flexible, state-of-the-art, portable code that is an efficient in a massively parallel computing environment. A modular single-source code is maintained that can be configured for both research and operations. It offers numerous physics options, thus tapping into the experience of the broad modeling community. Advanced data assimilation systems are being developed and tested in tandem with the model. WRF is maintained and supported as a community model to facilitate wide use, particularly for research and teaching, in the university community. It is suitable for use in a broad spectrum of applications across scales ranging from meters to thousands of kilometers. Such applications include research and operational numerical weather prediction (NWP), data assimilation and parameterized-physics research, downscaling climate simulations, driving air quality models, atmosphere-ocean coupling, and idealized simulations (e.g boundary-layer eddies, convection, baroclinic waves).*WEATHER FORECASTING
    Song J., J. P. Tang, and J. N. Sun, 2009: Simulation study of the effects of urban canopy on the local meteorological field in the Nanjing area. Journal of Nanjing University (Natural Sciences), 45( 6), 779- 789. (in Chinese)10.1360/972008-2143a6c0f887befc66dd8aab329f074ec80ehttp%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-NJDZ200906008.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-NJDZ200906008.htmNumerical experiments were made to investigate the effects of urban canopy on local meteorological field in Nanjing area,from July 17th to July 18th,2005,by using the Weather Research and Forecasting Model(WRF).Three types of surface conditions were employed in the simulations: the wrf-no urban case,in which the natural surface was chosen;the wrf-ucm case,in which the urban surface was chosen but the Urban Canopy Model(UCM) was not used;the wrf+ucm case,in which the urban surface was chosen,and the UCM was employed.The results of numerical experiments were compared with the data of the field observations.This study shows that the temperature at 2 m in the wrf+ucm case is slightly lower than that in the wrf-ucm case,and both are higher than that in the wrf-no urban case.Meanwhile,the sensible heat flux of the urban surface with canopy is similar to that of urban surface without canopy in the daytime,while the former is slightly higher than the latter during the night.The values in the two cases are obviously higher than in the case with natural surface in the area.However,the latent heat flux in the wrf+ucm case is lower than that in the wrf-ucm case,and both are much lower than that in the wrf-no urban case.That is to say,urban ground makes the urban fields drier than the natural ones.On the other hand,the effects of urban canopy influence the air flow in the area,which makes the horizontal wind be reduced over the city,and consequently the vertical motion is enhanced.It seems that this influence is more obvious during the night than in the daytime.
    Song Y. Q., H. N. Liu, X. Y. Wang, N. Zhang, and J. N. Sun, 2014a: The influence of urban heterogeneity on the surface energy balance and characters of temperature and wind. Journal of Nanjing University (Natural Sciences), 50( 6), 810- 819. (in Chinese)
    Song Y. Q., H. N. Liu, Y. Zhu, and X. Y. Wang, 2014b: Numerical simulation of urban heterogeneity's influence on urban meteorological characteristic. Plateau Meteorology, 33( 6), 1579- 1588. (in Chinese)10.7522/j.issn.1000-0534.2013.00080b831a731-2894-4551-8b54-079b61be6255mag4842620143361579The Nanjing city was divided into three types according the building density: Commercial, Hi-dens Res(High intensity residential), Low-dens Res(Low intensity residential). The influence of urban heterogeneity on urban meteorological characteristic in Nanjing was researched by WRF model. The results show that: After considering the effect of the urban heterogeneity, the spatial distribution of temperature, the urban heat island, the relative humidity and the winds exhibit are more complex in the urban region. In the simulations of urban canopy, urban heterogeneity has obvious effects on the heat island and other meteorological characters.The simulated mean heat island intensity, dry island intensity and decrease of wind of city will decrease 0.02℃, 0.2% and 0.11 m&#183;s<sup>-1</sup>. But the maximum heat island intensity and dry island intensity of city will increase 0.28℃ and 1.51%. In the city of considering heterogeneity, the spaial distribution variances of urban heat island, dry island and decrease of wind will increase 0.06, 2.08 and 0.28.
    Sun J. S., B. Yang, 2008: Meso- scale torrential rain affected by topography and the urban circulation. Chinese Journal of Atmospheric Sciences, 32( 6), 1352- 1364. (in Chinese)10.3878/j.issn.1006-9895.2008.06.105208fcfa-da3b-481c-8734-ddc4e916ae8c4825320083269Some theoretical features of meso-β scale torrential rain, which are caused by joint action of topography and the urban heat island, are gained by mesoscale dynamic meteorology theory and scale analysis. Using observation datasets with high spatial-temporal resolution based on auto-weather station network and wind profile data from two profilers which are located at different positions, most of the theoretical features are confirmed by three cases which occurred in Beijing in the summer of 2006. The results indicate that (1) the temperature gradient in front of mountains, mainly caused by the urban heat island, is able to engender a relatively isolated vertical wind shear near the windward slope, and the shear is much more important to grow, develop and maintain the mesoscale convective system. The closer the mountain is to urban areas, the stronger the temperature gradient in front of mountains is, and the local stronger vertical wind shear is easy to be at the position. On the other hand, the response time of strong vertical wind shear depends on the intensity of temperature gradient. (2) Once stronger convective precipitation begins on the windward slope, the positive feedback between rainfall intensity and horizontal wind velocity toward the windward slope will appear, and the process is an essential condition to form meso-β scale torrential rain. (3) The stronger the terrain grade is, the stronger ascending motion will be forced and the smaller horizontal-scale mesoscale weather system will be stirred; in front of smoother topography, however, the mesoscale system at a relatively larger horizontal scale is easy to be formed. (4) generally, most of the mesoscale torrential rain processes, which are caused by joint influence of topography and thermodynamic urban circulation, should occur in front of mountains in the evening or the early morning.
    Sun J. S., H. Wang, L. Wang, F. Liang, Y. X. Kang, and X. Y. Jiang, 2006: The role of urban boundary layer in local convective torrential rain happening in Beijing on 10 July 2004. Chinese J. Atmos. Sci., 30( 2), 221- 234. (in Chinese)98bef774-50b0-4007-a50b-7def9d36e94e76505b89b766b97d046d5acb398cfb99http%3A%2F%2Fen.cnki.com.cn%2Farticle_en%2Fcjfdtotal-dqxk200602004.htmrefpaperuri:(3750b9c219257f7d898babd9e9205148)http://en.cnki.com.cn/article_en/cjfdtotal-dqxk200602004.htmAn isolated mesoscale convective torrential rain which happened in Beijing urban on 10 July 2004("7.10") made a great traffic trouble because of serious inundation cross the urban areas and caught various social attention.The triggering mechanism of the convective torrential rain and the reason that the downpour occurred only in urban center are studied by analyzing a large number of observation data sets,such as observational data with high spatial-temporal resolution based on auto-weather station network,Doppler radar observational products,available vertical distribution of wind detected by a boundary wind profiler,TBB data from GOES and conventional weather observational data sets.Based on a simple mesoscale theoretical analysis and detail observational investigation,the spatial structure of the weather system is proposed.The research results indicate that(1) the local vapor condition and the large-scale vapor transportation are favorable during the torrential rain.However,the large-scale descending area has been keeping inhibition during the weather event,and this is a great difference between the isolated meso-scale convective storm system(MCSS) and other mesoscale torrential rain events which happen in regional precipitation;(2) The convective activities in Beijing area are closely related to gravity wave.At the initial stage of "7.10" local torrential rainfall,the local convective instable energy is possible to be triggered by gravity wave which is motivated by the stronger convective activities in Laishui and Yixian counties of Hebei Province to the southwest of Beijing and a series of relatively isolated meso- scale convection cells (MCCs),which appear to be linear,develop in Beijing.Finally,a meso- scale convective storm system is organized by urban mesoscale convergence line.The MCSS not only causes the heaviest rain intensity in the urban center,but also excites gravity wave and brings forth the similar meso- scale convection cells again.When the meso- scale convection cells are reorganized,a meso- scale convective storm system reappears in the urban areas.However,the second heaviest rain intensity is debilitated obviously compared with the former MCSS because the local instable energy have been released partly during the first precipitation period;(3) prior to the torrential rain,a mesoscale convergence line can be observed not only in urban surface but also in total boundary layer above,which plays a key role in organizing the isolated meso- scale convection cells(MCCs).The research confirms that the thermodynamic forcing caused by the temperature difference between urban areas and suburbs,is a fundamental factor in the development of the convergence line.On the other hand,because of the thermodynamic difference between urban areas and suburbs,the vertical wind shear is strengthened in the urban center,and the horizontal flow in lower layer is accelerated in suburbs,in other words,the thermodynamic forcing is advantageous to keeping the stronger convergence motion toward central convection area and providing enough compensated moisture current around a relative large field.
    Wu X., X. Y. Wang, X. N. Zeng, and L. Xu, 2000: The effect of urbanization on short duration precipitation in Beijing. Journal of Nanjing Institute of Meteorology, 23( 1), 68- 72. (in Chinese)10.1142/S175882511200130076967d4b5c54e92cbd0ef69d08739b67http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-NJQX200001010.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-NJQX200001010.htmHour precipitation from AWS in urban and suburb of Beijing is analysed to study the effects of urbanization on short duration precipitation.Results show that hour precipitation can be fitted to logarithm Weibull distribution and that the enhancement of rainfall due to urbanization is remarkable in downwind under moderate/heavy short duration precipitation process,and that the increase of probability and intensity of torrential rain is significant in urban center.
    Zhang C. L., S. G. Miao, Q. C. Li, and F. Chen, 2007: Impacts of fine-resolution land use information for Beijing on a summer, severe rainfall simulation. Chinese Journal of Geophysics, 50( 5), 1373- 1382. (in Chinese)10.1002/cjg2.1136ba5b0ff1-3750-42dc-8a93-6f603c25e717f7a22f1b6f1dfd036655e5c9ec5d3af6http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fcjg2.1136%2Fpdfrefpaperuri:(ab8770c39e864ce201c7e9b5c7406362)http://en.cnki.com.cn/Article_en/CJFDTotal-DQWX200705011.htmUsing the land use data around Beijing in 2000 with the resolution of 500 m,we updated the U.S.Geological Survey global land use classification data for numerical weather model,in which there are 25 types with 30 s lat-lon equidistant grids(1 km resolution).And then by 24-hour numerical experiments with the MM5V3.6 coupled with Noah LSM system,two domain two-way nested with the resolution of 10-3.3 km,we investigated the impact of fine-resolution land use information incorporation on a summer severe rainfall in Beijing.Analyses show that,the new land use data can not only represent better the real characteristic of underlying surface around Beijing area,especially the rapid expanding of urban/built-up areas since 1990s',but also help to correct the unreasonable classification Savanna in the original USGS data for the middle-latitudes of Asia data as the deciduous broadleaf.Furthermore,numerical experiments prove that incorporation of the fine-resolution land use information has a significant impact on the short-range severe rainfall weather event.For the intensity and location of major rainfall centers,their difference ranges of 12 h rainfall amount are beyond 30 km,and the relative difference of the maximum rainfall amount reaches up to 30%.One important interaction mechanism between urban underlying surface and atmosphere is also revealed,that is,the urban expansion reduces natural vegetation cover,and then it can help to decrease ground evaporation and local water vapor supply,enlarge surface sensible heat flux,deepen PBL height and enhance the mixing of water vapor.Hence it is not conducive to the occurrence of the rainfall.
  • [1] MIAO Yucong, LIU Shuhua, CHEN Bicheng, ZHANG Bihui, WANG Shu, LI Shuyan, 2013: Simulating Urban Flow and Dispersion in Beijing by Coupling a CFD Model with the WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1663-1678.  doi: 10.1007/s00376-013-2234-9
    [2] MIAO Yucong, LIU Shuhua, ZHENG Hui, ZHENG Yijia, CHEN Bicheng, WANG Shu, 2014: A Multi-Scale Urban Atmospheric Dispersion Model for Emergency Management, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 1353-1365.  doi: 10.1007/s00376-014-3254-9
    [3] Ui-Yong BYUN, Jinkyu HONG, Song-You HONG, Hyeyum Hailey SHIN, 2015: Numerical Simulations of Heavy Rainfall over Central Korea on 21 September 2010 Using the WRF Model, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 855-869.  doi: 10.1007/s00376-014-4075-6
    [4] Dongmei XU, Feifei SHEN, Jinzhong MIN, Aiqing SHU, 2021: Assimilation of GPM Microwave Imager Radiance for Track Prediction of Typhoon Cases with the WRF Hybrid En3DVAR System, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 983-993.  doi: 10.1007/s00376-021-0252-6
    [5] SUN Jianhua, ZHAO Sixiong, XU Guangkuo, MENG Qingtao, 2010: Study on a Mesoscale Convective Vortex Causing Heavy Rainfall during the Mei-yu Season in 2003, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1193-1209.  doi: 10.1007/s00376-009-9156-6
    [6] Jun WANG, Jinming FENG, Qizhong WU, Zhongwei YAN, 2016: Impact of Anthropogenic Aerosols on Summer Precipitation in the Beijing-Tianjin-Hebei Urban Agglomeration in China: Regional Climate Modeling Using WRF-Chem, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 753-766.  doi: 10.1007/s00376-015-5103-x
    [7] Xinguan DU, Haishan CHEN, Qingqing LI, Xuyang GE, 2023: Urban Impact on Landfalling Tropical Cyclone Precipitation: A Numerical Study of Typhoon Rumbia (2018), ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-022-2100-8
    [8] LIU Huizhi, LIANG Bin, ZHU Fengrong, ZHANG Boyin, SANG Jianguo, 2003: A Laboratory Model for the Flow in Urban Street Canyons Induced by Bottom Heating?, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 554-564.  doi: 10.1007/BF02915498
    [9] HU Wei, ZHONG Qin, 2010: Using the OSPM Model on Pollutant Dispersion in an Urban Street Canyon, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 621-628.  doi: 10.1007/s00376-009-9064-9
    [10] Ning ZHANG, Yunsong DU, Shiguang MIAO, 2016: A Microscale Model for Air Pollutant Dispersion Simulation in Urban Areas: Presentation of the Model and Performance over a Single Building, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 184-192.  doi: 10.1007/s00376-015-5152-1
    [11] Lin Naishi, Zhou Zugang, Zhou Liufei, 1998: An Analytical Study on the Urban Boundary Layer, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 258-266.  doi: 10.1007/s00376-998-0044-2
    [12] Xiaojuan LIU, Guangjin TIAN, Jinming FENG, Bingran MA, Jun WANG, Lingqiang KONG, 2018: Modeling the Warming Impact of Urban Land Expansion on Hot Weather Using the Weather Research and Forecasting Model: A Case Study of Beijing, China, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 723-736.  doi: 10.1007/s00376-017-7137-8
    [13] WANG Gengchen, BAI Jianhui, KONG Qinxin, Alexander EMILENKO, 2005: Black Carbon Particles in the Urban Atmosphere in Beijing, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 640-646.  doi: 10.1007/BF02918707
    [14] HE Yuting, JIA Gensuo, HU Yonghong, and ZHOU Zijiang, 2013: Detecting urban warming signals in climate records, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1143-1153.  doi: 10.1007/s00376-012-2135-3
    [15] Liu Huizhi, Sang Jianguo, Zhang Boyin, Johnny C.L. Chan, Andrew Y.S.Cheng, Liu Heping, 2002: Influences of Structures on Urban Ventilation:A Numerical Experiment, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 1045-1054.  doi: 10.1007/s00376-002-0063-3
    [16] Jae-Jin KIM, Do-Yong KIM, 2009: Effects of a Building's Density on Flow in Urban Areas, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 45-56.  doi: 10.1007/s00376-009-0045-9
    [17] MIAO Shiguang, JIANG Weimei, 2004: Large Eddy Simulation and Study of the Urban Boundary Layer, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 650-661.  doi: 10.1007/BF02915732
    [18] QIU Jinhuan, YANG Jingmei, 2008: Absorption Properties of Urban/Suburban Aerosols in China, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 1-10.  doi: 10.1007/s00376-008-0001-0
    [19] JIANG Yujun, LIU Huizhi, SANG Jianguo, ZHANG Boyin, 2007: Numerical and Experimental Studies on Flow and Pollutant Dispersion in Urban Street Canyons, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 111-125.  doi: 10.1007/s00376-007-0111-0
    [20] TAO Jun, CHENG Tiantao, ZHANG Renjian, CAO Junji, ZHU Lihua, WANG Qiyuan, LUO Lei, and ZHANG Leiming, 2013: Chemical composition of PM2.5 at an urban site of Chengdu in southwestern China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1070-1084.  doi: 10.1007/s00376-012-2168-7

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 21 April 2015
Manuscript revised: 29 December 2015
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Numerical Simulation of the Impact of Urban Non-uniformity on Precipitation

  • 1. School of Atmospheric Sciences, Nanjing University, Nanjing 210093
  • 2. Dalian Meteorological Station, Dalian 116000

Abstract: To evaluate the influence of urban non-uniformity on precipitation, the area of a city was divided into three categories (commercial, high-density residential, and low-density residential) according to the building density data from Landsat satellites. Numerical simulations of three corresponding scenarios (urban non-uniformity, urban uniformity, and non-urban) were performed in Nanjing using the WRF model. The results demonstrate that the existence of the city results in more precipitation, and that urban heterogeneity enhances this phenomenon. For the urban non-uniformity, uniformity, and non-urban experiments, the mean cumulative summer precipitation was 423.09 mm, 407.40 mm, and 389.67 mm, respectively. Urban non-uniformity has a significant effect on the amount of heavy rainfall in summer. The cumulative precipitation from heavy rain in the summer for the three numerical experiments was 278.2 mm, 250.6 mm, and 236.5 mm, respectively. In the non-uniformity experiments, the amount of precipitation between 1500 and 2200 (LST) increased significantly. Furthermore, the adoption of urban non-uniformity into the WRF model could improve the numerical simulation of summer rain and its daily variation.

1. Introduction
  • Urbanization takes place at an exceptionally rapid rate in China. The areas of cities in China, particularly in the Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta urban agglomeration regions, have continued to grow. The process of urbanization has altered the natural surface and resulted in increases in anthropogenic heat and pollutant emissions, which inevitably have impacts on urban meteorological environments. Problems such as "urban heat islands" (UHIs), "dry islands", "rain islands" and "turbid islands", have arisen in association with urbanization.

    Many studies have been conducted on these phenomena, and important progress has already been accomplished. However, the urban effect on precipitation is relatively complicated. Potential mechanisms of the effects of cities on precipitation include the dynamic actions of urban buildings and thermodynamic actions of UHIs altering the flow field characteristics, the impervious surfaces of urban areas affecting the evaporation process and influencing land-surface water vapor transfer, and urban air pollution (e.g., the direct and indirect effects of aerosols) affecting radiation and cloud microphysical processes. Studying the urban effect on precipitation has become a frontier of focus for the atmospheric sciences, with a large number of studies devoted to the subject (Jauregui and Romales, 1996; Lowry, 1998; Li et al., 2008). (Liao et al., 2011) analyzed the pattern of variation in precipitation in Guangzhou using daily observed precipitation data from 1959 to 2009 and found that the number of heavy rain days is increasing. The study conducted by (Zhang et al., 2007) determined that urban expansion reduces the coverage of natural vegetation, which further reduces surface evaporation and local water vapor supply. Meanwhile, it increases the boundary layer height and enhances the mixing of atmospheric water vapor, ultimately decreasing the amount of precipitation. (Sun et al., 2006) analyzed the formation mechanism and the role of the urban boundary layer in a relatively independent meso-β-scale convective rainstorm system in Beijing. The observational study conducted by (Chen et al., 2000) determined that precipitation in the Yangtze River Delta region increased in response to urbanization, while the temperature in surrounding regions decreased. Based on mesoscale weather dynamics theory and a scale analysis method, (Sun and Yang, 2008) found that a meso-β-scale rainstorm was affected by the interaction of the terrain and UHIs. (Shepherd et al., 2002) analyzed the distribution of summer precipitation in several cities in the U.S. from 1998 to 2000 using TRMM satellite data. They reported a 28% increase in the mean monthly precipitation at locations 30-60 km from cities in the downwind direction, and an approximate 5.6% increase in urban areas. (Rosenfeld, 2000) suggested that urbanization and industrial pollution would result in increased precipitation and snowfall in downstream locations. And finally, the results of the study conducted by (Wu et al., 2000) demonstrated that the most significant urban effect attributed to the thermal and dynamic effects of cities was the increase in short-term precipitation.

    A high-resolution numerical simulation is a common and effective tool for studying the effects of cities on precipitation. As a result of its exceptional performance, the WRF model has been widely applied in the field of urban meteorology. Details on the WRF model can be found in (Skamarock et al., 2008). The WRF model classifies cities into three categories: commercial, high-density residential (hi-dens res), and low-density residential (low-dens res). The buildings in commercial cities are the tallest, whereas the buildings in low-dens res cities are the shortest. In most cases, when using WRF (the version in this study is 3.3.1) to simulate urban meteorology, a city is determined to belong to one of the aforementioned categories based on its building density (Song et al., 2009). However, due to the non-uniformity of urban density, all three types of urban area exist within a city. Therefore, it is difficult to conclude that a city belongs solely to one of these categories. (Hu et al., 2013) considered urban non-uniformity in a study on low-level jets. For the city of Nanjing, there are numerous skyscrapers in the downtown area, i.e., in Gulou and Xinjiekou, which is commercial. However, the south of the city is close to the Qinhuai River, which belongs to the hi-dens res category. And the eastern part, i.e., Xianlin, belongs to the low-dens res category. Categorizing a city into only one classification may cause relatively large deviation in the model. Some studies (Song et al., 2014a, b) show that the surface energy balance, temperature and wind are significantly influenced by urban non-uniformity, but there have been few studies on the link between urban non-uniformity and precipitation. Building dynamics, UHIs and aerosols have influences on precipitation. In addition, the former two affect each other. When urban non-uniformity is considered, urban dynamic parameters change, which also leads to a change in the UHI effect. Therefore, urban non-uniformity is an important factor and should be considered in precipitation simulation. In the present study, Nanjing was recategorized based on the characteristics of different areas of the city, and the potential effect of the urban non-uniformity on precipitation was investigated.

2. Methods
  • The WRF model system is composed of a new-generation mesoscale forecasting model and assimilation system, jointly established in 1997 by the Mesoscale & Microscale Meteorology Division of the NCAR, the Environmental Modeling Center at the NCEP, the Forecast Research Division of the Forecast Systems Laboratory, and the Center for Analysis and Prediction of Storms at the University of Oklahoma. The WRF model system has been applied extensively.

    The urban canopy model (UCM) in the WRF model was used in the present study. The UCM includes the following features: (1) streets with 2D structures are parameterized to calculate their thermal characteristics; (2) the shadows and reflections of buildings are considered; (3) the courses of streets and the daily variation of the solar elevation angle are considered; (4) the thermal effects of road surfaces, wall surfaces and roofs are differentiated.

  • Three experiments (A, B and C) were designed for the present study. In experiment A, the non-uniformity of the city was considered; different areas of the city were classified into three categories: commercial, hi-dens res, and low-dens res. In experiment B, the city was considered to be uniform (hi-dens res). And in experiment C, the effect of a non-urban environment was considered; the original land surface types of the city were replaced by irrigated cropland and pasture.

    The city classification was mainly based on its building density (Song et al., 2014a, Fig. 1). The building densities were obtained by statistically analyzing the 25-m resolution land surface type data developed by Landsat satellites. When the building density was less than 0.3, the area was defined as low-dens res; when it was greater than or equal to 0.45, the area was defined as commercial. The distributions of the land surface types in the third level of the model for experiments A and B can be found in Song et al. (2014a, Fig. 2). The commercial area accounted for 23.7% in the non-uniform category, while the hi-dens res and low-dens categories accounted for 41.2% and 35.1%, respectively. However, in the uniform category, the city area was completely hi-dens res. The land types of the central and peripheral areas of Nanjing change when the spatial variation is considered. Even though the height of the city decreases, the non-uniform distribution of the city increases.

    Compared to a uniform city, the mean values of sensible heat, UHI and friction velocity are less in a non-uniform city. However, the extreme values are larger (Song et al., 2014a). This demonstrates that a non-uniform city provides weaker land-surface forcing, but enhances the land-surface forcing turbulence corresponding to a uniform city. Obviously, the enhancement of precipitation is due to the urban non-uniformity rather than the mean land-surface forcing.

    Figure 1.  Observed and simulated monthly mean precipitation at Nanjing station.

    Figure 2.  Spatial distribution of summer accumulated precipitation (mm) in the Nanjing region: (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison. The wind fields in the figure are the mean wind fields at 10 m for rainy summer days (m s$^-1$).

  • A three-level nested grid with a two-way nesting experiment was used for the simulation. The central longitude and latitude of the domain were 32.1004°N and 118.8986°E, respectively. The outer domain was 900 km × 900 km, with 9-km horizontal grid spacing; the middle domain was 303 km × 303 km, with 3-km spacing; and the innermost domain was 101 km × 101 km, with 1-km spacing. The model top was set at 100 hPa and there were 27 vertical layers. The simulation period was from 0000 UTC 1 January 2011 to 1800 UTC 31 December 2011, and the model ran month by month. The 1°× 1° resolution NCEP data were used as the boundary conditions, which were forced every 6 h. The result was output every hour. The model parameterization can be found in Song et al. (2014a, Table 1). The Multi-layer Building Environment Model, Noah land-surface, Monin-Obukhov, and unified Noah land-surface schemes were chosen in this study.

    Figure 3.  Frequency of summer (June, July and August) precipitation in the Nanjing region (%): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison.

3. Results
  • The precipitation for Jiangsu province in the summer of 2011 ranged from 247.1 mm (Guanyun) to 1243.6 mm (Jiangyin), mainly concentrated in the southern part of Jiangsu and over the Yangtze River. The mean was 731.2 mm, which was approximately 50% of the annual value. Figure 1 compares the simulated and observed total monthly precipitation (mm) in the three experiments. The surface data from a representative station in Nanjing (58238; coordinates: 31.93°N, 118.90°E) were selected as the observation. The observed annual precipitation amount in 2011 was 989.2 mm. The simulated precipitation amount for the station in Nanjing, based on the three experiments, was 810.4 mm, 723.2 mm and 625.7 mm, respectively. In winter, spring and fall, the precipitation simulated in the non-uniform, uniform and non-urban experiments was relatively close to the observation. However, in summer, the accumulated precipitation simulated in the uniform and non-urban experiments was significantly lower than observed, whereas that simulated in the non-uniform experiment was closest to the observation. The error of the monthly mean accumulated precipitation for 2011 was 14.9 mm in the non-uniform experiment, which was lower than that of the uniform experiment. Thus, the simulation accuracy regarding urban precipitation can be effectively increased when urban non-uniformity is considered in the WRF model.

    Compared with uniform experiments, the accumulated precipitation for the 12 months of the non-uniform experiments increased by 0.68%, -0.33%, 0.50%, -2.40%, -1.13%, 14.51%, -6.81%, 2.42%, -0.31%, -0.45%, -2.41% and -0.52%, respectively. For summertime (June, July, August), the values were 14.51%, -6.81% and 2.42%, respectively. The differences among the three experiments were greatest in summer because the amount of precipitation is largest in this season. Therefore, the effect of urban non-uniformity on summer precipitation (June, July and August) is mainly discussed hereafter. Figure 2 presents the spatial distribution of the simulated accumulated precipitation in summer in the Nanjing region. It shows a mean northeasterly wind field on rainy days. The wind speed significantly decreased in the urban area, especially centrally in the non-uniform experiment. Compared with the uniform experiment, the airflows also converged at the center in the non-uniform experiment (Fig. 2d). The simulated summer mean accumulated precipitation amount for the entire region was 423.09 mm, 407.40 mm and 389.67 mm in experiments A, B and C, respectively. There was a significant difference among the simulations, and the non-uniform experiment result was the highest. Compared with the non-urban experiment, the existence of the city resulted in a significant increase in precipitation in the urban area and downstream locations. The distribution patterns of the summer precipitation were significantly different in the non-uniform and uniform experiments. Precipitation increased more significantly in the urban areas and the southeast in the non-uniform experiment, whereas it decreased in downstream areas. Besides, the maximum precipitation amount was greater in the non-uniform experiment.

    Figure 4.  Intensity of summer precipitation in the Nanjing region (mm h$^-1$): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison. The wind fields in the figure are the mean wind fields at 10 m for rainy summer days (m s$^-1$).

    Figure 5.  Daily variation of summer precipitation in the Nanjing region: (a) observation data from Nanjing station; (b) simulation results.

    Figure 6.  Spatial distribution of accumulated precipitation on 20 July 2011, in Nanjing (mm): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison. The wind fields in the figure are the mean wind fields at 10 m on 20 July 2011 (m s$^-1$).

    Figure 3 presents the spatial distributions of the simulated summer precipitation frequency (the ratio of total precipitation to summertime precipitation) in the Nanjing region. Compared to the non-urban experiment (experiment C), the precipitation frequency was higher in the urban areas of experiments A and B. In general, the presence of the city resulted in an increasing trend for the precipitation frequency in the urban area. Also, it increased in the southeastern direction of the city, but decreased, to a certain extent, in the northwestern direction.

    Figure 4 presents the spatial distributions of the simulated summer precipitation intensity (the ratio of accumulated summer precipitation to total precipitation) in the Nanjing region. The distributions were relatively similar to the accumulated summer precipitation. The mean intensity of the precipitation simulated in the non-uniform, uniform and non-urban experiments was 1.37 mm h-1, 1.33 mm h-1 and 1.26 mm h-1, respectively.

    Figure 7.  Daily mean friction velocity on 20 July 2011 across the Nanjing region (m s$^-1$): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison.

    Figure 5a presents the observed accumulated precipitation every 6 h at Nanjing station. The data indicate that the occurrence of precipitation was greatest between 0200 and 0800 (LST), slightly lower between 0800 and 1400 (LST) and between 1400 and 2000 (LST), and lowest between 2000 and 0200 (LST). Figure 5b presents the daily variation of the summer mean accumulated precipitation for the entire Nanjing region. Compared to the observation, the lowest precipitation simulated by the three experiments occurred at night, while the highest occurred in the morning. In the morning, the precipitation was lowest in the non-uniform experiment. However, in the afternoon, the precipitation in the uniform and non-urban experiments was much lower than in the morning, while the non-uniform experiment results were much higher and equivalent to the simulations in the morning. Clearly, the results of the non-uniform experiment were better. Therefore, urban non-uniformity can significantly increase convective precipitation in summer afternoons, and improve the model's performance in terms of the pattern of daily precipitation.

    In the present study, light, moderate and heavy rain were defined as daily precipitation from 0.02 to 10 mm, from 10 to 20 mm, and greater than 20 mm, respectively. The effects of the three experiments on these three grades of rain were also compared. There was no significant difference between the spatial distribution of accumulated precipitation simulated in the three experiments for light and moderate rain (data not shown). The mean accumulated precipitation for light rain simulated in experiments A, B and C was 73.5 mm, 79.9 mm and 75.3 mm, respectively. And for moderate rain, the values were 71.5 mm, 76.9 mm and 77.9 mm, respectively. However, there were significant differences among the accumulated precipitation amounts for heavy rain in the three experiments: 278.2 mm (experiment A); 250.6 mm (experiment B), and 236.5 mm (experiment C). Hence, the effect of urban non-uniformity on precipitation was primarily manifested during heavy precipitation events in summer.

  • Figure 6 shows the distribution of simulated precipitation for an event that occurred on 20 July 2011 in the Nanjing region. The precipitation and its intensity were smallest in the non-urban experiment and largest in the non-uniform experiment. In addition, the majority of precipitation was distributed in and around the urban area. For both the precipitation range and its intensity, the results of the uniform experiment (experiment B) were between those of the non-urban and non-uniform experiments. The mean accumulated precipitation simulated in the three experiments was 1.24 mm, 0.93 mm and 0.23 mm, respectively.

    Figure 7 presents the spatial distributions of mean daily friction velocity on 20 July 2011. The friction velocity in the urban area was significantly greater than that in the suburban area. When urban non-uniformity was considered, the friction velocity exhibited a more complicated spatial distribution. The friction velocity increased obviously in the central urban area and also increased over downstream locations, even where the friction velocity was relatively low. Though the overall urban building height decreased, the non-uniformity of the urban distribution increased in the non-uniform city. Overall, the roughness increased, resulting in significant disturbances in the flow field across the urban area.

    Figure 8.  Daily mean vertical velocity profile on 20 July 2011 across the Nanjing region (cm s$^-1$) [vertical wind vector (w) $\times 25$; the blue lines at the bottom of (a, b, d) represent the region of the city]: (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison. The blue boxes reflect the regions that have significant updrafts. The wind fields in the figure are the mean wind fields at the vertical direction on 20 July 2011 (m s$^-1$).

    Figure 9.  Daily mean water vapor flux divergence at 850 hPa on 20 July 2011 across the Nanjing region (10$^-6$ kg m$^-2$ s$^-2$): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison. The red box reflects the region that has significant water vapor divergence.

    Figure 10.  Daily mean vertical velocity at 700 hPa on 20 July 2011 across the Nanjing region (m s$^-1$): (a) non-uniform experiment; (b) uniform experiment; (c) non-urban experiment; (d) non-uniform-uniform comparison.

    Figure 8 presents the mean daily vertical velocity profiles in the y direction that passed through the center of the domain on 20 July 2011. Compared to experiments B and C, there was a significant updraft in the north of the city in experiment A (see the areas within the blue rectangles in Figs. 8a and d), which corresponded very well to the location of the precipitation.

    Figure 9 presents the spatial distributions of daily mean water vapor flux divergence at 850 hPa on 20 July 2011. For experiment A, there was significant water vapor divergence in the precipitation area (the area within the red rectangle in Fig. 9a). The water vapor flux divergence in the precipitation was -0.268× 10-6, -0.055× 10-6 and -0.045× 10-6 kg m-2 s-2 in experiments A, B and C, respectively. The region of water vapor divergence within the precipitation area for the three experiments was 608 km2, 554 km2 and 543 km2, respectively. Hence, water vapor divergence was the most intense and covered the largest area in the non-uniform experiment.

    Figure 10 presents the distributions of daily mean vertical velocity at 700 hPa on 20 July 2011. Compared to the other two experiments, there was significant upward movement in the precipitation area in experiment A, and the areas of positive vertical velocity were more concentrated. The mean upward velocity within the precipitation area at 700 hPa was 1.1 cm s-1, 0.1 cm s-1 and -0.5 cm s-1 for experiments A, B and C, respectively. In addition, the area of upward movement was 553 km2, 427 km2 and 268 km2, respectively. Hence, the upward velocity and area of upward movement was relatively fast and large, respectively, at 700 hPa in experiment A. The upward velocity and area of upward movement in experiment B were the second fastest and largest, respectively. In experiment C, the mean vertical movement was downward, and the area of upward movement was the smallest among the three experiments.

    Mechanistically, the urban impact on precipitation involves dynamic, thermodynamic and chemical effects. The dynamic effects involve increases in surface roughness and enhancements to the drag and lift effects on the airflow. Thermodynamic effects encompass changes in the surface energy balance and the impact of the UHI on the structure of the urban boundary layer. And chemical effects mainly relate to artificial increases in the influence of aerosols on the microstructure of clouds——otherwise known as "aerosol indirect effects". In this study, the setup of the WRF model did not include chemical processes. Therefore, the impact of urban non-uniformity on precipitation was restricted to the other two aspects: thermodynamic and dynamic effects. Compared to the uniform city, the mean UHI intensity and heat flux of the non-uniform city were lower. Figure 11 shows the diurnal variation of the UHI intensity in the non-uniform and uniform experiments, indicating that the diurnal variation of the UHI could not explain the difference in the diurnal variation of precipitation between these two urban experimental setups (Fig. 5b). We believe that in this simulation, the influence of the UHI was not the main reason for the increased precipitation in the non-uniform city. Certainly, however, the UHI may play an important role in the increase of precipitation compared with the non-urban experiment (Miao et al., 2011).

    In the experiments carried out in this work, the total volume of buildings in the non-uniform and uniform setups was 6.41 km3 and 6.07 km3, respectively. The volume in the non-uniform experiment was 5.6% higher, which was close to the percentage increase of precipitation (3.85%) in summer. However, heavy rain in summer increased by 11%——much more than the increase in building volume in the non-uniform experiment. This shows that the increase in summer precipitation was due to two aspects, the increase in buildings and the urban non-uniformity, and the effect of urban non-uniformity on convective precipitation was much greater than that on non-convective precipitation.

    Figure 11.  Daily variation of the summer UHI in the Nanjing region.

4. Conclusions
  • In the present study, sensitivity simulations using the WRF model were conducted to investigate the effect of urban non-uniformity on precipitation in Nanjing in 2011. The main findings can be summarized as follows:

    (1) The effect of urban non-uniformity on precipitation was relatively small in winter, spring and fall, but relatively large in summer. The precipitation simulated in the non-uniform experiment was the most comparable to observations, implying that consideration of urban non-uniformity can significantly improve model performance in terms of urban summer precipitation.

    (2) Urbanization will result in increases of total accumulated precipitation, precipitation intensity and precipitation frequency in urban areas, and this effect is further increased when urban non-uniformity is considered. The accumulated summer precipitation was 423.1 mm, 407.4 mm and 389.7 mm in the non-uniform, uniform and non-urban experiments, respectively. Therefore, the amount of precipitation simulated in the non-uniform experiment was largest.

    (3) The simulated contribution of precipitation to heavy rain (daily accumulated precipitation >20 mm) in the non-uniform experiment was significantly higher. The summer mean accumulated precipitation for heavy rain was 278.19 mm, 250.61 mm and 236.54 mm in the three experiments, respectively. The effect on light rain and moderate rain was relatively small.

    (4) When urban non-uniformity was considered, the precipitation in the morning decreased, but the precipitation between 1500 and 2200 (LST) increased significantly. The pattern of the daily variation was closest to observations in the non-uniform experiment.

    (5) The effect of urban non-uniformity on precipitation is mainly realized through increased land surface roughness and surface friction velocity, which in turn increase the low-level water vapor divergence and enhances the mean upward velocity, promoting an increase in heavy precipitation in the afternoon.

    It is important to note that in investigating the effect of urban non-uniformity on precipitation in this study, the urban non-uniformity was represented by only three categories. Furthermore, the dynamic and thermodynamic effects relating to urban non-uniformity were not separated. This will be the next step in our continuing research.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return