Simulating Urban Flow and Dispersion in Beijing by Coupling a CFD Model with the WRF Model

The airflow and dispersion of a pollutant in a complex urban area of Beijing, China, were numerically examined by coupling a Computational Fluid Dynamics （CFD） model with a mesoscale weather model. The models used were Open Source Field Operation and Manipulation （OpenFOAM） software package and Weather Research and Forecasting （WRF） model. OpenFOAM was firstly validated against wind-tunnel experiment data. Then, the WRF model was integrated for 42 h starting from 0800 LST 08 September 2009, and the coupled model was used to compute the flow fields at 1000 LST and 1400 LST 09 September 2009. During the WRF-simulated period, a high pressure system was dominant over the Beijing area. The WRF-simulated local circulations were characterized by mountain valley winds, which matched well with observations. Results from the coupled model simulation demonstrated that the airflows around actual buildings were quite different from the ambient wind on the boundary provided by the WRF model, and the pollutant dispersion pattern was complicated under the influence of buildings. A higher concentration level of the pollutant near the surface was found in both the step-down and step-up notches, but the reason for this higher level in each configurations was different： in the former, it was caused by weaker vertical flow, while in the latter it was caused by a downward-shifted vortex. Overall, the results of this study suggest that the coupled WRF-OpenFOAM model is an important tool that can be used for studying and predicting urban flow and dispersions in densely built-up areas.

Move a Tropical Cyclone with 4D-Var and Vortex Dynamical Initialization in WRF Model

Previous studies showed that 4 D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4 D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4 D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously,and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4 D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation,which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4 D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper.

The Impacts of Topography on Spatial and Temporal Rainfall Distribution over Rwanda Based on WRF Model

The impact of topography on heavy rainfall during two rain seasons was investigated in order to explain their mechanisms on rainfall distribution over Rwanda. Weather Research and Forecasting (WRF-ARW) model was used to study two historical cases of heavy rainfall which took place over Rwanda during two rain seasons, March to May (MAM) and September to December (SOND), from April 7 to 9, 2012 (for MAM) and from October 29 to 31, 2012 (during SOND). The control experiment was done with actual topography, whereas sensitivity experiment was carried out with topography reduced by half. Results show that rainfall distribution over Rwanda significantly changes when topography is reduced. The reduction in topography leads to a decrease in rainfall amounts in both MAM and SOND seasons, with varying magnitudes. This reveals the importance of orography in determining rainfall amounts and distribution over the region. The accumulated rainfall amount from WRF underestimate or overestimate rain gauge stations data by region and by season, but there is good agreement especially in altitude below 1490 m and above 1554 m during April and October respectively. The results may motivate modelling carters to further improve parameterization schemes in the mountainous regions.

Numerical Simulations of Heavy Rainfall over Central Korea on 21 September 2010 Using the WRF Model

On 21 September 2010, heavy rainfall with a local maximum of 259 mm d-1 occurred near Seoul, South Korea. We examined the ability of the Weather Research and Forecasting （WRF） model in reproducing this disastrous rainfall event and identified the role of two physical processes： planetary boundary layer （PBL） and microphysics （MPS） processes. The WRF model was forced by 6-hourly National Centers for Environmental Prediction （NCEP） Final analysis （FNL） data for 36 hours form 1200 UTC 20 to 0000 UTC 22 September 2010. Twenty-five experiments were performed, consisting of five different PBL schemes--Yonsei University （YSU）, Mellor-Yamada-Janjic （MYJ）, Quasi Normal Scale Elimination （QNSE）, Bougeault and Lacarrere （BouLac）, and University of Washington （UW）--and five different MPS schemes--WRF Single- Moment 6-class （WSM6）, Goddard, Thompson, Milbrandt 2-moments, and Morrison 2-moments. As expected, there was a specific combination of MPS and PBL schemes that showed good skill in forecasting the precipitation. However, there was no specific PBL or MPS scheme that outperformed the others in all aspects. The experiments with the UW PBL or Thompson MPS scheme showed a relatively small amount of precipitation. Analyses form the sensitivity experiments confirmed that the spatial distribution of the simulated precipitation was dominated by the PBL processes, whereas the MPS processes determined the amount of rainfall. It was also found that the temporal evolution of the precipitation was influenced more by the PBL processes than by the MPS processes.

Intercomparison of different physics schemes in the WRF model over the Asian summer monsoon region

WRF模式对包含西太平洋、东印度洋和中国大陆的大区域气候模拟能力的提高对东亚夏季风气候的预测非常重要。本文的研究目的是找出合理的物理方案组合来模拟中国大陆、东印度洋和西太平洋这一特大区域的降水和温度。通过模拟2008年夏季气候并与观测进行对比分析,主要比较了Mellor–Yamada–Janjic(MYJ)和Yonsei University(YSU)边界层方案、WSM3和WSM5微物理过程方案、Betts–Miller–Janjic(BMJ)和Tiedtke积云参数化方案对这一区域的气候模拟结果。研究表明:各种物理方案的组合对于温度空间分布的模拟与观测类似;边界层方案的选择对降水影响较大,MYJ方案比YSU方案对降水的模拟效果要好;积云方案Tiedtke对雨带分布,特别是赤道辐合带(ITCZ)降水模拟更加合理。

Analysis of Wind Power Assessment Based on the WRF Model

Assessing wind energy is a key step in selecting a site for a wind farm. The accuracy of the assessment is essential for the future operation of the wind farm. There are two main methods for assessing wind power: one is based on observational data and the other relies on mesoscale numerical weather prediction(NWP). In this study, the wind power of the Liaoning coastal wind farm was evaluated using observations from an anemometer tower and simulations by the Weather Research and Forecasting(WRF) model, to see whether the WRF model can produce a valid assessment of the wind power and whether the downscaling process can provide a better evaluation. The paper presents long-term wind data analysis in terms of annual, seasonal, and diurnal variations at the wind farm, which is located on the east coast of Liaoning Province. The results showed that, in spring and summer, the wind speed, wind direction, wind power density, and other main indicators were consistent between the two methods. However, the values of these parameters from the WRF model were significantly higher than the observations from the anemometer tower. Therefore, the causes of the differences between the two methods were further analyzed. There was much more deviation in the original material, National Centers for Environmental Prediction(NCEP) final(FNL) Operational Global Analysis data, in autumn and winter than in spring and summer. As the region is vulnerable to cold-air outbreaks and windy weather in autumn and winter, and the model usually forecasted stronger high or low systems with a longer duration, the predicted wind speed from the WRF model was too large.

A COMPARATIVE STUDY OF TWO LAND SURFACE SCHEMES IN WRF MODEL OVER EASTERN CHINA

Two land surface models, Community Land Model (CLM3.5) and NOAH model, have been coupled to the Weather Research and Forecasting (WRF) model and been used to simulate the precipitation, temperature, and circulation fields, respectively, over eastern China in a typical flood year (1998). The purpose of this study is to reveal the effects of land surface changes on regional climate modeling. Comparisons of simulated results and observation data indicate that changes in land surface processes have significant impact on spatial and temporal distribution of precipitation and temperature patterns in eastern China. Coupling of the CLM3.5 to the WRF model (experiment WRF-C) substantially improves the simulation results over eastern China relative to an older version of WRF coupled to the NOAH-LSM (experiment WRF-N). It is found that the simulation of the spatial pattern of summer precipitation in WRF-C is better than in WRF-N. WRF-C also significantly reduces the summer positive bias of surface air temperature, and its simulated surface air temperature matches more closely to observations than WRF-N does, which is associated with lower sensible heat fluxes and higher latent heat fluxes in WRF-C.

Application of WRF Model for Vehicular Pollution Modelling Using AERMOD

Vehicular pollution is becoming significant in urban areas because of increasing population. This is at ground level, so it gives high population exposure. In this study, Chembur, which is the most polluted area in Mumbai city due to industrial and vehicular sources, is selected for vehicular pollution modeling using AMS/EPA Regulatory Model (AERMOD). Meteorological parameters, land use surface characteristics and source emission data are collected as required by AERMOD. The results of modelling depend upon reliability of input data and meteorological data has a vital role in the performance of the model. Generally, temporally and spatially interpolated meteorological data is used in modeling. This is generally collected from nearby meteorological station but this causes inaccuracy of the results. In this paper, the Weather Research and Forecasting (WRF) model has been used to generate onsite data on nine meteorological parameters. The modeling of six roads of Chembur has been performed using above meteorological data. This approach gives good results of traffic modeling. The results of AERMOD are compared with observed air quality which has contribution from all sources in the region and relative contribution of vehicular sources identified.

Comparison of WRF Model Physics Parameterizations over the MENA-CORDEX Domain

We investigated the performance of 12 different physics configurations of the climate version of the Weather, Research and Forecasting (WRF) Model over the Middle East and North Africa (MENA) domain. Possible combinations among two Planetary Boundary Layer (PBL), three Cumulus (CUM) and two Microphysics (MIC) schemes were tested. The 2-year simulations (December 1988-November 1990) have been compared with gridded observational data and station measurements for several variables, including total precipitation and maximum and minimum 2-meter air temperature. An objective ranking method of the 12 different simulations and the selection procedure of the best performing configuration for the MENA domain are based on several statistical metrics and carried out for relevant sub-domains and individual stations. The setup for cloud microphysics is found to have the strongest impact on temperature biases while precipitation is most sensitive to the cumulus parameterization scheme and mainly in the tropics.

A NUMERICAL STUDY OF TROPICAL DEEP CONVECTION USING WRF MODEL

The Weather Research Forecast model (WRF) configured with high resolution and NCEP 1°×1° reanalysis data were used to simulate the development of a tropical deep convection over the Tiwi Islands,northern Australia,and to investigate the sensitivity of model results to model configuration and parameterization schemes of microphysical processes.The simulation results were compared with available measurements.The results show that the model can reproduce most of the important characteristics of the observed diurnal evolution of the convection,including the initiation of convection along the sea-breeze front,which is then reinforced by downdraft outflows,merging of cells and the formation of a deep convective system.However,further improvement is needed to simulate more accurately the location and the time for initiation of the deep convective system.Sensitivity tests show that double-nesting schemes are more accurate than the non-nesting schemes in predicting the distribution and intensity of precipitation as far as this particular case is concerned.Additionally,microphysical schemes also have an effect on the simulated amount of precipitation.It is shown that the best agreement is reached between the simulation results and observations when the Purdue Lin scheme is used.

Study of Weak Intensity Cyclones over Bay of Bengal Using WRF Model

Numerical simulations of four weak cyclonic storms [two cases of pre-monsoon cyclones: Laila (2010), Aila (2009) and two cases of post-monsoon cyclones: Jal (2010), SCS (2003)] are carried out using WRF-ARW mesoscale model. Betts-Miller-Janjic (BMJ) as cumulus parameterization (CP) scheme, Yonsei University(YSU) planetary boundary layer (PBL) scheme and WRF single moment 6 class (WSM6) microphysics (MP) scheme is kept same for all the cyclone cases. Three two-way interactive nested domains [60 km,20 kmand6.6 km] are used with initial and boundary conditions from NCEP Final Analysis data. The model integration is performed to evaluate the track, landfall time and position as well as intensity in terms of Central Sea Level Pressure (CSLP) and Maximum Surface Wind speed (MSW) of the storm. The track and landfall (time and position) of almost all cyclones are well predicted by the model (except for SCS cyclone case) which may be because of the accurate presentation of the steering flow by CP scheme. Irrespective of season, the intensity is overestimated in all the cases of cyclone, mainly because of the lower tropospheric and mid-tropospheric parameters are overestimated. YSU PBL scheme used here is responsible for the deep convection in and above PBL. Concentration of frozen hydrometeors at the mid-tropospheric levels and thus the latent heat released during auto conversion of hydrometeors is also responsible for overestimation of intensity.

Evaluating Sensitivity to Different Options and Parameterizations of a Coupled Air Quality Modelling System over Bogotá, Colombia. Part I: WRF Model Configuration

Meteorological inputs are of great importance when implementing an air quality prediction system. In this contribution, the Weather Research and Forecast (WRF-ARW) model was used to compare the performance of the different cumulus, microphysics and Planet Boundary Layer parameterizations over Bogotá, Colombia. Surface observations were used for comparison and the evaluated meteorological variables include temperature, wind speed and direction and relative humidity. Differences between parameterizations were observed in meteorological variables and Betts-Miller-Janjic, Morrison 2-moment and BouLac schemes proved to be the best parameterizations for cumulus, microphysics and PBL, respectively. As a complement to this study, a WRF-Large Eddy Simulation was conducted in order to evaluate model results with finer horizontal resolution for air quality purposes.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

基于雷达估测降雨及WRF-Hydro模型的典型山洪模拟研究

受复杂地形与基础气象水文资料缺乏限制,山区小尺度流域的水文预警预报技术较为薄弱,利用高分辨率雷达观测资料驱动分布式水文模型是提高山区小流域洪水预报性能的有效途径之一。本文以位于重庆中部的山区小流域二河流域为研究区域,开展基于雷达估测降雨数据的WRF-Hydro模型在山区小流域的山洪模拟研究,以评估雷达估测降雨的水文应用效果和WRF-Hydro模型在山区小流域的适用性。选取流域内典型的暴雨洪水过程,利用S波段的多普勒天气雷达的估测降雨数据驱动WRF-Hydro模型,并结合新安江模型进一步对比分析模拟效果。研究结果表明:(1)在二河流域,采用雷达估测降雨数据驱动WRF-Hydro模型,可以较好地模拟洪水过程、洪水流量以及峰现时间,纳什效率系数高于0.65,克林-古普塔效率系数高于0.50,相关系数高于0.85。(2)将WRF-Hydro模型与新安江模型进行比较分析,在二河流域,WRF-Hydro模型的模拟效果优于新安江模型,纳什系数差值0.03,相关系数差值为0.04,进一步表明WRF-Hydro模型在山区小流域较优的洪水模拟性能。总体而言,基于雷达估测降雨数据的WRF-Hydro模型在二河流域表现出了良好的模拟洪水的性能,可进一步在类似小尺度山区流域进行应用研究。

基于CALMET-WRF耦合的复杂地形下导线覆冰的精细化数值模拟研究

利用耦合了WRF模式的CALMET模型,对2022年1月5—10日山西省南部中条山区导线覆冰事件的天气背景场进行了数值模拟,在评估了WRF和CALMET对气象要素的模拟效果的基础上,分别利用WRF和CALMET模拟的气象场驱动Makkonen覆冰模型,对本次导线覆冰过程进行了数值模拟,得到了如下结论:1)相比于WRF模式的模拟结果,CALMET降尺度后的气象场能更符合实际地形影响下近地面温度场和风场的分布规律,其模拟的近地面低温区(气温<0℃)范围较WRF模拟范围更大。2)CALMET的气温均方根误差整体较WRF模式减小0.5~1℃,相关系数由0.5~0.8提升至0.6~0.85;风速的均方根误差较WRF减少了1 m/s,相关系数较WRF提升0.2,说明WRF模式结合CALMET模拟气象场更加接近真实观测结果。3)利用CALMET降尺度场驱动覆冰模型能较好地反映微尺度地形下电线积冰的时空分布特征,各杆塔模拟的覆冰厚度偏差较WRF显著减小了2 mm,且降低了模式对覆冰启动的滞后时间。

WRF模型驱动的网格新安江模型及其应用

为提升输入网格新安江模型的降雨和蒸发数据的时空分布准确度,完善水文循环过程,并为进一步实现WRF模型与网格新安江模型的耦合提供基础,构建了由WRF驱动的网格新安江模型。首先,采用逐步订正法将WRF预报降雨与雨量站降雨融合来获取WRF融合降雨;然后将WRF预报气象数据输入网格新安江模型中并采用彭曼公式计算单元网格小时蒸发能力;最后由WRF融合降雨和彭曼公式蒸发能力驱动网格新安江模型在湿润的屯溪流域进行洪水模拟预报。结果表明:①WRF融合降雨具有较高精度且具有精细空间分布。相较于WRF预报降雨,WRF融合降雨与实测降雨的相关性(RR≥0.99)和拟合度(NSE≥0.98)更高,雨峰误差(-8.1%~3.5%)和雨量误差(-2.0%~6.7%)均明显减小。在空间分布上,WRF融合降雨具有比站点插值降雨更复杂的空间信息,信息熵(SE)显著增加(30.4%~48.2%),并包含了WRF降雨和站点插值降雨的降雨中心。②彭曼公式蒸发能力不仅呈现出逐小时变化规律,且与降雨过程密切相关。在空间分布上,彭曼公式蒸发能力与海拔密切相关,在中高程地区最大,低高程地区次之,而在高程较高地区最小。③WRF驱动的网格新安江模型具有较大的洪水预报潜力。相较于使用WRF预报降雨驱动网格新安江模型,由WRF融合降雨和彭曼公式蒸发能力驱动的网格新安江模型在屯溪流域的洪水预报精度明显提高,预报洪水的NSE均在0.90以上,洪量、洪峰和峰现时间合格率均达到100%。

基于WRF模式的四川省凉山州地区风能资源可开发区域研究

利用MERRA2再分析数据驱动WRF模式,对四川凉山州地区2020年全年进行风资源模拟分析,并用凉山州地区典型测风塔数据对模拟结果进行检验,并进行详细地风资源分析,再根据风电场开发8%基准内部收益率反推可开发风能资源的区域分布。结果表明:凉山州大部分地区100 m高度年平均风速在5 m/s以上,风速极大值一般位于山脊,凉山州风能最好的区域主要集中在会东县和宁南县。凉山州典型区域内均表现出受西南季风影响的特征,即冬、春季节风大,夏、秋季节风小,主风向呈强西南风状态,且风功率密度变化规律与风速的变化规律基本一致。凉山州山地区域可开发风能资源的平均风功率密度临界值为258 W/m^(2),这些区域主要集中在会理、会东、宁南、布拖、木里和盐源县境内。可开发区域分布图对指导凉山州地区风能开发提供科学参考。

WRF模式对翁源一次特大暴雨过程的模拟分析

采用中尺度数值模式WRF对2010年5月6日翁源发生的特大暴雨过程进行数值模拟研究,结果表明:模式成功地模拟出翁源24 h累计积雨量和前2个强降水阶段的落区和量级,但在第3阶段出现漏报;充沛的水汽含量、持续的水汽输送、强烈的水汽辐合、深厚的湿层以及850 hPa高能舌和中尺度能量锋的存在为强降水提供了良好的水汽和热力条件;低层西南气流明显减弱,中高层吹西北风,翁源处于水汽辐散区,热力条件转差是第3阶段模拟效果偏差的重要原因。该次过程对流云发展旺盛,伸展高度高,具有暖云层剖面结构,属于积云为主的暖区暴雨过程。

WRF模式在三峡库区气象模拟中的研究进展

近几年中尺度数值大气模式WRF迅速发展,其应用越来越广。本文概述了中尺度天气数值模拟影响的关键问题,从物理参数化方案、驱动数据和合理的空间尺度三个方面介绍WRF模式在气象领域的发展和应用,阐明WRF模式在中尺度模拟的过程中的普遍性和优越性。最后通过对WRF模式在国内的应用现状进行归纳与梳理,以及对模式未来发展趋势的展望,对WRF模式在三峡库区的应用进行探讨,为WRF模式在三峡库区的气象模拟研究提供必要的参考。

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.