A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model

A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.

A STUDY OF THE INFLUENCE OF MICROPHYSICAL PROCESSES ON TYPHOON NIDA(2016) USING A NEW DOUBLE-MOMENT MICROPHYSICS SCHEME IN THE WEATHER RESEARCH AND FORECASTING MODEL

The basic structure and cloud features of Typhoon Nida(2016) are simulated using a new microphysics scheme(Liuma) within the Weather Research and Forecasting(WRF) model. Typhoon characteristics simulated with the Liuma microphysics scheme are compared with observations and those simulated with a commonly-used microphysics scheme(WSM6). Results show that using different microphysics schemes does not significantly alter the track of the typhoon but does significantly affect the intensity and the cloud structure of the typhoon. Results also show that the vertical distribution of cloud hydrometeors and the horizontal distribution of peripheral rainband are affected by the microphysics scheme. The mixing ratios of rain water and graupel correlate highly with the vertical velocity component and equivalent potential temperature at the typhoon eye-wall region. According to the simulation with WSM 6 scheme,it is likely that the very low typhoon central pressure results from the positive feedback between hydrometeors and typhoon intensity. As the ice-phase hydrometeors are mostly graupel in the Liuma microphysics scheme, further improvement in this aspect is required.

Artificial Intelligence Based Meteorological Parameter Forecasting for Optimizing Response of Nuclear Emergency Decision Support System

This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.

IMPACT OF VERTICAL RESOLUTION, MODEL TOP AND DATA ASSIMILATION ON WEATHER FORECASTING——A CASE STUDY

The impacts of stratospheric initial conditions and vertical resolution on the stratosphere by raising the model top,refining the vertical resolution,and the assimilation of operationally available observations,including conventional and satellite observations,on continental U.S.winter short-range weather forecasting,were investigated in this study.The initial and predicted wind and temperature profiles were analyzed against conventional observations.Generally,the initial wind and temperature bias profiles were better adjusted when a higher model top and refined vertical resolution were used.Negative impacts were also observed in both the initial wind and temperature profiles,over the lower troposphere.Different from the results by only raising the model top,the assimilation of operationally available observations led to significant improvements in both the troposphere and stratosphere initial conditions when a higher top was used.Predictions made with the adjusted stratospheric initial conditions and refined vertical resolutions showed generally better forecasting skill.The major improvements caused by raising the model top with refined vertical resolution,as well as those caused by data assimilation,were in both cases located in the tropopause and lower stratosphere.Negative impacts were also observed in the predicted near surface wind and lower-tropospheric temperature.These negative impacts were related to the uncertainties caused by more stratospheric information,as well as to some physical processes.A case study shows that when we raise the model top,put more vertical layers in stratosphere and apply data assimilation,the precipitation scores can be slightly improved.However,more analysis is needed due to uncertainties brought by data assimilation.

两种再分析资料和Nudging方法在WRF模式降水模拟中的适用性

采用Grid Nudging(GN)和Spectral Nudging(SN)方法,用再分析资料ERA5和FNL驱动中尺度数值天气预报模式(WRF),探究不同再分析资料和Nudging方法对降水模拟效果的改进效果及机理.对2021年3月15日中国南方地区降水过程设计6组试验进行数值模拟,分析不同试验方案对降水及相关物理量的影响.结果表明,WRF模式能较好地模拟出本次降水事件,进行Nudging驱动后显著提升了降水分布、降水中心落区和降水量的模拟效果.与观测数据综合对比,GN的模拟效果优于SN,尤其是使用ERA5资料结合GN模拟效果最佳,能够准确地模拟出发生在安徽省南部的降水中心以及超过33 mm/d的降水强度.模式结果与两个观测站点记录的降水发生时间和降水强度变化较为一致.GN方法使模式有效提高了西南低空急流的强度,校正了风向,对水汽通量和水汽通量散度的刻画更符合实际情况.

基于WRF-LES模式的大气边界层近地风场精细化模拟研究

台风等极端气象灾害对工程结构安全造成严重威胁,研究近地面大气边界层精细化模拟对于土木工程具有重要应用价值.数值天气预报系统(WRF)中的大涡模拟(LES)模块具有参数方案多、精度高等优点,适用于近地面风场精细化模拟,但数值天气预报-大涡模拟(WRF-LES)精细化模拟效果与参数设置密切相关.寻求适用于精细化模拟近地面风场的参数设置,选用WRF-LES模式中的几种次网格模型和空间差分格式,采用较细密的网格分辨率,进行理想大气边界层模拟.对比平均风速剖面、湍流强度剖面和功率谱等风场特性,讨论关键参数对近地面风场模拟精度的影响,确定合适的参数设置.研究表明:对次网格模型,非线性回波散射和各向异性(NBA1)模型可有效改善近地面风场模拟精度;对网格方案,在计算域底部不均匀加密垂直网格可更好地描述近地面风场空间分布特征,有效减小计算资源;对空间差分格式,偶数阶差分相较奇数阶差分格式可捕获更小尺度湍流结构.所提出的WRF-LES模式参数方案,可为精细化模拟近地面风场和台风边界层提供技术参考.

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.

Numerical Simulation of the Heavy Rainfall in the Yangtze-Huai River Basin during Summer 2003 Using the WRF Model

In this study, a 47-day regional climate simulation of the heavy rainfall in the Yangtze-Huai River Basin during the summer of 2003 was conducted using the Weather Research and Forecast （WRY） model. The simulation reproduces reasonably well the evolution of the rainfall during the study period＇s three successive rainy phases, especially the frequent heavy rainfall events occurring in the Huai River Basin. The model captures the major rainfall peak observed by the monitoring stations in the morning. Another peak appears later than that shown by the observations. In addition, the simulation realistically captures not only the evolution of the low-level winds but also the characteristics of their diurnal variation. The strong southwesterly （low-level jet, LLJ） wind speed increases beginning in the early evening and reaches a peak in the morning; it then gradually decreases until the afternoon. The intense LLJ forms a strong convergent circulation pattern in the early morning along the Yangtze-Huai River Basin. This pattern partly explains the rainfall peak observed at this time. This study furnishes a basis for the further analysis of the mechanisms of evolution of the LLJ and for the further study of the interactions between the LLJ and rainfall.

Validation of WRF model on simulating forcing data for Heihe River Basin

The research of coupling WRF （Weather Research and Forecasting Model） with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA （China Meteorological Administration） and hourly meteorological observation data from seven sites of WATER （Watershed Airborne Telemetry Experimental Research） are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE （Mean Bias Error） of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were -0.19 ℃, -4.49 hPa, 4.08% and 0.92 m/s, the average RMSE （Root Mean Square Error） of them were 2.11 ℃, 5.37 hPa, 9.55% and 1.73 m/s, and the average R （correlation coefficient） of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were-0.16 ℃,-6.62 hPa,-5.14%, 0.26 m/s, 33.0 W/m^2 and-6.44 W/m^2, the average RMSE of them were 2.62 ℃, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m^2 and 53.5 W/m^2, and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained： （1） regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; （2） the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; （3） both wind speed and rainfall simulated from WRF model did not agree well with observation data.

基于WRF模式分析高垂直分辨率对环渤海辐射雾模拟的适用性研究

研究普遍认为高垂直分辨率数值天气预报模式能改善雾形成阶段的预报能力,而对雾发展与消散阶段的模拟能力是否改善存在较多争论。为探讨高垂直分辨率模式对环渤海地区辐射雾模拟的适用性,利用卫星资料和观测资料,采用击中率(POD)、虚报率(FAR)和预报技巧(ETS)3个检验指标,评估了不同垂直分辨率模式对环渤海大雾过程各阶段的模拟效果。结果表明:模式上层设置较高的垂直分辨率对雾模拟影响极小,而低层设置较高的垂直分辨率总体上能改善雾形成时间、持续时长和雾层厚度的模拟能力,但雾区的模拟高度依赖于大雾分布形态。对零散非均一分布大雾过程,高垂直分辨率模式看似改善了POD,但却伴随着FAR的上升和ETS的下降;对均一分布的大范围雾过程,高垂直分辨率模式能同时改善各检验指标,有效改善此类雾预报。

Improvement and Evaluation of the Latest Version of WRF-Lake at a Deep Riverine Reservoir

The WRF-lake vertically one-dimensional(1D)water temperature model,as a submodule of the Weather Research and Forecasting(WRF)system,is being widely used to investigate water-atmosphere interactions.But previous applications revealed that it cannot accurately simulate the water temperature in a deep riverine reservoir during a large flow rate period,and whether it can produce sufficiently accurate heat flux through the water surface of deep riverine reservoirs remains uncertain.In this study,the WRF-lake model was improved for applications in large,deep riverine reservoirs by parametric scheme optimization,and the accuracy of heat flux calculation was evaluated compared with the results of a better physically based model,the Delft3D-Flow,which was previously applied to different kinds of reservoirs successfully.The results show:(1)The latest version of WRF-lake can describe the surface water temperature to some extent but performs poorly in the large flow period.We revised WRF-lake by modifying the vertical thermal diffusivity,and then,the water temperature simulation in the large flow period was improved significantly.(2)The latest version of WRF-lake overestimates the reservoir-atmosphere heat exchange throughout the year,mainly because of underestimating the downward energy transfer in the reservoir,resulting in more heat remaining at the surface and returning to the atmosphere.The modification of vertical thermal diffusivity can improve the surface heat flux calculation significantly.(3)The longitudinal temperature variation and the temperature difference between inflow and outflow,which cannot be considered in the 1D WRF-lake,can also affect the water surface heat flux.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis(SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting(WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters(P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters(P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10(the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.

WRF、EC和T639模式在福建沿海冬半年大风预报中的检验与应用

基于福建省冬半年沿海和港湾岛屿自动站的逐时极大风观测资料和WRF（Weather Research and Forecast）、EC（European Centre for Medium-Range Weather Forecasts）细网格以及T639（TL639L60）三种模式预报的10 m风场资料，将模式预报的风向风速与观测资料进行对比检验，结果表明：福建省沿海冬半年大风的盛行风向以东北风为主，大风的时空分布极为不均，沿海风力的脉动性、跳跃性、局地性突出。从三种模式对风速风向的模拟效果来看， WRF和EC细网格的预报效果较好，有可参考性， T639可参考性不高。对于风速，模式预报结果相比实况极大风速偏小，港湾岛屿代表站风速的平均绝对误差均小于沿海代表站，预报平均误差由沿海向内陆逐渐减小，由中部向南北逐渐减小。风向相比风速的预报效果要差， WRF和EC细网格的风向预报误差在45°-50°，有一定的参考意义；港湾岛屿代表站风向的平均绝对误差大于沿海代表站，以浮标站的误差最大。当观测风速出现7级及以上风速时，若对大风进行分级检验，则较低风速的预报平均绝对误差小于较高风速；风向预报的平均绝对误差也大大降低，且误差都在45°以内，具有良好的参考性。

中尺度WRF数值模式系统本地化业务试验

利用中尺度WRF数值模式及WRF三维变分同化系统,在对比试验的基础上,选取了适合本地的积云过程、微物理过程和辐射过程的方案组合;选择了NCEP/GFS作为模式的背景场;统计计算了以云南为中心的区域背景误差协方差并替换了三维变分同化系统中原有的背景误差协方差;同时,考虑模式底层高度与地面观测站高度的差异,进行了地面资料地形订正。通过上述试验研究,建立了本地化的中尺度WRF数值预报业务系统,该系统能较好地刻画本地下垫面的动力和热力状况,预报能力有明显改善。

WRF3.0参数化敏感性及集合预报试验

为了研究WRF模式中的参数化方案对暴雨数值模拟的影响及物理过程集合预报在WRF降水集合预报中的适用性,利用中尺度WRF3.0数值模式,将模式中的物理过程参数化方案进行组合,构造了20个集合预报成员,对2003年7月8、9日发生在江淮地区的一次降水过程进行了集合预报试验。得到:集合成员之间存在一定的不确定性,不确定性随降水量级增大而增大,最终维持在一个较高的水平;通过定量比较发现,对于0.1 mm量级降水和25 mm以上量级降水,积云对流参数化方案对降水的影响要大于行星边界层方案对降水的影响;而对于10 mm量级降水,行星边界层方案对降水的影响要大于积云参数化方案对降水的影响。对20个预报成员进行了集合平均及降水概率预报试验,结果表明,集合平均的结果要比各个成员的稳定、可靠;概率预报能够提供一些有利于降水预报的信息。

WRF模式分辨率对新疆异常降雨天气要素模拟的影响

利用新一代中尺度气候数值模式WRF对中国西北干旱典型地区——新疆2000年10月的异常降雨事件进行模拟,主要研究了不同水平分辨率、垂直分辨率以及不同积云参数化过程对气温、降雨和土壤温度模拟的影响。结果表明:不同分辨率的模拟基本上均模拟出了地面气温的分布特征,且随着水平分辨率的提高,模式模拟能力显著提高,对地面气温值、分布范围的模拟渐趋合理,同时模式对于地形引起的温度分布变化的模拟更加趋近实际。水平分辨率、垂直分辨率的提高同样改善了模式对降水的模拟能力,分辨率的提高不仅改进了模式对降雨分布区模拟的精度,也增强了对于地形引起的降雨变异的模拟能力。在土壤温度模拟上,不同分辨率的试验均能模拟出土壤温度的分布特征,较高水平分辨率有利于描述土壤温度分布细节,但更容易出现"数值点风暴"现象。

山区风场的WRF/CALMET模式模拟

为更精确地模拟地形热力引起的山区局地风场,采用美国国家大气研究中心(NCAR)的中尺度天气模式WRF对某山谷工业园区的大气流场进行了模拟,然后采用诊断风场模块CALMET对模拟结果进行小尺度(格距0.1 km)修正.模拟中,WRF模式采用三重嵌套网格方案,最内层网格格距为1.0 km.结果表明:WRF模式的模拟结果与实测值相差甚大,而WRF/CALMET耦合模式的模拟结果与实测值接近,因此采用WRF/CALMET耦合模式模拟山区大气流场比较符合实际.

雷暴中雷电活动与WRF模式微物理和动力模拟量的对比研究

利用WRF模式模拟发生在成都地区的典型雷暴天气过程,得到相应雷电活动过程中微物理和动力输出场,将其与雷电监测定位网所探测到的地闪资料进行对比分析,在电荷分离的微物理学基础上讨论了WRF(Weather Research and Forecasting)模式输出的不同微物理及动力因子与地闪的相关性。结果表明:-10°C到-20°C之间的电荷分离区域内,冰晶粒子与霰粒子质量混合比最大值与地闪频数随时间变化趋势基本保持一致。在雷电活动中后期,霰、冰晶及雪晶粒子最大值位置与地闪密度大值中心位置对应性较好,空间上均能指示地闪发生区域。最大上升速度与风暴相对螺旋度可以指示地闪频数变化,风暴相对螺旋度空间上可指示地闪密度大值中心。模拟结果表明WRF模式微物理及动力输出场可以指示地闪活动的发生时间和位置,表现了日益成熟WRF模式进行雷电数值预报与研究的潜能。

WRF模式在南京数值天气预报中的应用

利用最新版本的WRFV3.1进行了南京地区的高分辨率的数值天气预报,对预报结果进行了细致的评估,结果表明:模式很好的预报出了温度、相对湿度及风场的逐日变化特征。0～24 h预报的平均温度和实况基本一致,预报的击中率为0.73;预报的平均相对湿度较实况偏高了0.3%,击中率为0.74;模式预报的风场跟实况较为吻合,对风速增大、减小的趋势预报非常准确。另外,模式对层云降水预报较好,能很好的把握了降水的强度及空间变化,但对积云强降水预报,结果不理想,降水范围、强度均偏小,持续时间偏短。

美国WRF模式的进展和应用前景

RF(WeatherResearchForecast)模式系统是美国气象界联合开发的新一代中尺度预报模式和同化系统 ,2 0 0 4年 6月对外发布了第二版和三维变分同化系统。这个模式采用高度模块化、并行化和分层设计技术 ,集成了迄今为止在中尺度方面的研究成果。模拟和实时预报试验表明 ,WRF模式系统在预报各种天气中都具有较好的性能 ,同时实现在线完全嵌套大气化学模式 ,不仅具有较好的天气预报水平 ,而且具有预报空气质量的能力 ,具有广阔的应用前景。