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.

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

Enhancing the ability of the WRF model in simulating a large area covering the West Pacific Ocean, China's Mainland, and the East Indian Ocean is very important to improve prediction of the East Asian monsoon climate. The objective of this study is to identify a reasonable configuration of physical parameterization schemes to simulate the precipitation and temperature in this large area. The Mellor-Yamada-Janjic （MYJ） and Yonsei University （YSU） PBL schemes, the WSM3 and WSM5 microphysics schemes, and the Betts-Miller-Janjic （BMJ） and Tiedtke cumulus schemes are compared through simulation of the regional climate of summer 2008. All cases exhibit a similar spatial distribution of temperature as observed, and the spatial correlation coefficients are all higher than 0.95. The cases combining MY J, WSM3/WSM5, and BMJ have the smallest biases of temperature. The choice of PBL scheme has a significant effect on precipitation in such a large area. The cases with MYJ reproduce a better distribution of rain belts, while YSU strongly overestimates the precipitation intensity. The precipitation simulated using WSM3 is similar to that using WSM5. The BMJ cumulus scheme combined with the MYJ PBL scheme has a smaller bias of precipitation. However, the Tiedtke scheme reproduces the precipitation pattern better, especially over the ITCZ.

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.

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.

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.

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.

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.

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.

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.

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.

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).

Improving the Forecasts of Coastal Wind Speeds in Tianjin,China Based on the WRF Model with Machine Learning Algorithms

Characterized by sudden changes in strength,complex influencing factors,and significant impacts,the wind speed in the circum-Bohai Sea area is relatively challenging to forecast.On the western side of Bohai Bay,as the economic center of the circum-Bohai Sea,Tianjin exhibits a high demand for accurate wind forecasting.In this study,three machine learning algorithms were employed and compared as post-processing methods to correct wind speed forecasts by the Weather Research and Forecast(WRF)model for Tianjin.The results showed that the random forest(RF)achieved better performance in improving the forecasts because it substantially reduced the model bias at a lower computing cost,while the support vector machine(SVM)performed slightly worse(especially for stronger winds),but it required an approximately 15 times longer computing time.The back propagation(BP)neural network produced an average forecast significantly closer to the observed forecast but insufficiently reduced the RMSE.In regard to wind speed frequency forecasting,the RF method commendably corrected the forecasts of the frequency of moderate(force 3)wind speeds,while the BP method showed a desirable capability for correcting the forecasts of stronger(force>6)winds.In addition,the 10-m u and v components of wind(u_(10)and v_(10)),2-m relative humidity(RH_(2))and temperature(T_(2)),925-hPa u(u925),sea level pressure(SLP),and 500-hPa temperature(T_(500))were identified as the main factors leading to bias in wind speed forecasting by the WRF model in Tianjin,indicating the importance of local dynamical/thermodynamic processes in regulating the wind speed.This study demonstrates that the combination of numerical models and machine learning techniques has important implications for refined local wind forecasting.

基于CMIP6耦合WRF的黄河上游复合干旱热浪事件演变规律

复合干旱热浪事件较传统极端气候事件破坏性更强,近年来在全球范围内发展迅速,黄河上游作为气候敏感区受其影响尤其突出,刻画其特征并分析未来可能气候条件下的演变趋势对事件防控有重要意义。本文提出了一种基于第六次国际耦合模式比较计划CMIP6耦合天气预报研究模式WRF的未来气象数据动力降尺度方法。识别了黄河上游不同情景下的复合干旱热浪事件及其特征,揭示了复合事件与单一事件的区别,分析了复合干旱热浪事件的未来演变规律。结果表明:(1)历史期、SSP245和SSP585情景下复合干旱热浪事件较单一事件的温度升高3.8%、13.1%、13.5%,干旱指数降低5.8%、2.6%、2.6%,极端特征更加显著。(2)SSP245情景下复合干旱热浪事件特征呈西南高、东北低的空间分布形式,而在SSP585情景下以北部、东部区域分布最高。(3)未来各情景下区域整体复合干旱热浪事件特征呈显著上升趋势,其中SSP585的上升趋势高于SSP245。

WRF模式不同微物理方案对青海高原夏季一次降水过程模拟差异的初步探讨

为了解不同微物理方案对青海高原地区夏季降水过程模拟的影响,利用WRF模式和NCEP再分析资料,选取Lin方案、WSM6方案和New Thompson方案等3种微物理过程参数化方案,模拟了青海高原地区2017年夏季一次典型降水过程,并结合探空、降水等观测资料对模拟结果进行了探讨。结果表明:WRF模式3种微物理参数化方案对温度廓线的模拟表现出较好的一致性,且均与实况接近,但对不同区域相对湿度廓线的模拟能力有明显差异;3种微物理参数化方案均能模拟出本次降水的主要走向、雨带及强降水中心,但模拟的雨带和强降水中心位置较实况偏东且局部雨量偏大;WSM6方案对降水空间分布的模拟与实况最接近,WSM6和New Thompson方案模拟的降水偏差均略小于Lin方案;3种方案降水模拟结果中,小雨TS评分最高,其次是中雨,对大雨模拟的可信度均较差;降水模拟小雨TS评分最高的是New Thompson方案,为0.60,WSM6方案次之,Lin方案较差,为0.43;对中量降水模拟,WSM6方案略优于其他两个方案;降水模拟New Thompson和WSM6方案整体优于Lin方案。

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。

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%。

WRF/Grid-XAJ双向耦合系统构建及其暴雨洪水模拟应用

为解决陆气双向耦合中气象与水文模型不易匹配的问题,基于质量守恒原理,以土壤含水量为纽带,构建高效的WRF/Grid-XAJ双向耦合(双耦)系统。通过屯溪流域2场洪水事件分析发现:模型权重参数可定量评估水文-气象模型耦合的相容性;降水同化后,双耦系统的降水峰值模拟精度稍好于WRF模型(误差在±5%内);双耦较单向耦合(单耦)系统能更准确地反映土壤含水量;降水同化前,双耦和单耦系统低估了洪水过程;同化后两者的模拟结果提升且与Grid-XAJ模型接近(三者纳什效率系数ENS>0.85),其中双耦系统洪峰模拟效果最好(误差在±11%内),说明WRF/Grid-XAJ双耦系统在暴雨洪水模拟预报方面有较好的应用潜力,为水文-气象模型双向反馈建模提供了新思路。

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模式多参数化方案对东南亚低纬高原陆气耦合强度的模拟评估

东南亚低纬高原是全球陆气耦合的热点地区之一,其陆气相互作用对气候、水文和环境均具有重要的影响。本研究采用均匀抽样方法,结合WRF模式中多参数化方案,开展了48组数值模拟试验,通过优选参数化方案组合,对该地区陆气耦合强度及其相关变量进行了模拟评估。研究表明:(1)从48组模拟试验集合中可发现,对于近地面或地表的气温、比湿、向下长波、向上长波和土壤温度,集合模拟能力较好;对于近地面或地表的风速、降水、感热通量、潜热通量、向下短波和向上短波,集合模拟可较好反映各变量的变化特征;但是对于地表的土壤湿度,集合模拟能力较差。对于近地面或地表的风速、降水、潜热通量、向下短波、向上短波、土壤温度和土壤湿度,不同组合间模拟差异较小;但是对于地表的感热通量,不同组合间模拟差异较大。(2)根据等权重平均Taylor评分获得的最优参数化方案组合可以提升对于近地面或地表的气温、比湿、向下短波、向上短波、向下长波、向上长波和土壤温度的模拟能力,但对于近地面或地表的风速、降水、感热通量、潜热通量和表层土壤湿度提升效果不明显。(3)最优参数化方案组合可以合理地反映陆气耦合的空间特征和时间变化,但模拟的耦合强度较参考值偏弱,主要与潜热通量和向下短波辐射模拟能力较差有关。