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.

Statistical Time Series Forecasting Models for Pandemic Prediction

COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.

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.

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.

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%。

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.

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.

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-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.

Forecasting solar still performance from conventional weather data variation by machine learning method

Solar stills are considered an effective method to solve the scarcity of drinkable water.However,it is still missing a way to forecast its production.Herein,it is proposed that a convenient forecasting model which just needs to input the conventional weather forecasting data.The model is established by using machine learning methods of random forest and optimized by Bayesian algorithm.The required data to train the model are obtained from daily measurements lasting9 months.To validate the accuracy model,the determination coefficients of two types of solar stills are calculated as 0.935and 0.929,respectively,which are much higher than the value of both multiple linear regression(0.767)and the traditional models(0.829 and 0.847).Moreover,by applying the model,we predicted the freshwater production of four cities in China.The predicted production is approved to be reliable by a high value of correlation(0.868)between the predicted production and the solar insolation.With the help of the forecasting model,it would greatly promote the global application of solar stills.

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.

基于WRF模拟的中国西北河谷城市夏季的大气边界层特征

为提高河谷地形气象场的模拟效果,利用第5代再分析资料(ERA5)和全球再分析资料(FNL)作为初始场,以天水市为研究对象,驱动中尺度天气预报模式比较对西北河谷城市边界层模拟的适用性,分析西北河谷城市夏季的大气边界层特征.结果表明, ERA5模拟的天水市主城区近地面温度、近地面风速、风向以及相对湿度与观测值的相关性更好,尤其是近地面风速和风向,分别比FNL模拟的结果提升25.4%和70.0%.天水市主城区的气象场空间分布呈明显的城市热岛效应和山谷风环流,相对开阔的麦积区城市热岛效应更强;白天发生的降水会弱化谷风环流和热岛效应,河谷内及周边风速均较小.天水市主城区夏季近地面温度与风速呈正相关,与相对湿度呈负相关,大气边界层高度呈现明显的日变化,大气层结稳定.

Analysis of a Cold Wave Process in Jiujiang and Its Numerical Model Forecast

The cold wave weather process in Jiujiang in the early spring of February 2020 was analyzed.The results show that the establishment of blocking high near Lake Baikal and the rapid southward of cold air after accumulation resulted in the cold wave weather accompanied by strong cooling,hale and rain(snow)weather in Jiujiang.Before the cold wave broke out,the ground warmed up significantly,which was also one of thermal conditions for this cold wave weather.Water vapor conditions were abundant at middle and low levels;at 850 hPa,temperature dropped by 12-14℃during February 14-15,and-4℃isotherm appeared in the southern part of central Jiangxi,which is a favorable condition for rain(snow)in most areas of Jiujiang.

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.

Comparisons of Three-Dimensional Variational Data Assimilation and Model Output Statistics in Improving Atmospheric Chemistry Forecasts

Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.

A STATISTICAL MODEL FOR PREDICTION OF INTENSITY AND FREQUENCY OF TROPICAL CYCLONES MAKING LANDFALL ON CHINA

Based on NCEP/NCAR reanalysis data and Yearbook of China landfalling tropical cyclones(hereafter CLTC) from 1949 to 2008,correlation between CLTC frequency/intensity and 500 hPa height field and sea surface temperature(SST) fields are investigated and employed for TC statistical prediction.A prediction model for yearly and monthly intensity and frequency of CLTC is established with binomial curve fitting by choosing the gridpoints with high correlation coefficients as composite factors.Good performance of the model in experiments shows that the model could be used in routine forecast.

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR)is of urgent demand for the local economic and societal development.This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously SINTEX-F).The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands,initialized from 1 March.In addition,the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature(SST)anomalies,but the individual members show great discrepancies,indicating large uncertainty in the forecasts.Furthermore,the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982-2020,albeit with underestimated magnitudes.The Weather Forecast and Research(WRF)downscaling hindcast experiments with a finer resolution of 30 km,which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method,display improved predictions of the extreme summer precipitation anomalies to some extent.However,the performance of the downscaling predictions is highly dependent on the global model forecast skill,suggesting that further improvements on both the global and regional climate models are needed.

WRF-Hydro大气-陆面-水文耦合模式应用研究综述

在人类活动加重气候变暖的背景下,极端水文气象事件发生概率增加。数值模式作为研究水循环和极端水文事件的有效工具,已在全球范围内得到广泛应用。为深入理解气候变化背景下全球陆地水循环时空演变规律,揭示大气-陆面-水文互馈机制,大气-陆面-水文耦合过程模拟研究已成为国际大气、水文等学科研究的热点之一。本文首先回顾和梳理了大气-陆面-水文耦合模式的发展历程,阐明了大气-陆面-水文耦合模式WRF-Hydro(Weather Research and Forecasting Model Hydrological modeling system)的优势,并系统总结了WRF-Hydro模式的主要敏感性参数分析及模式在对地表径流、土壤湿度、能量水分循环以及相关大气和水文过程等方面的应用。最后探讨WRF-Hydro大气-陆面-水文耦合模式未来发展趋势,提出应着眼于发展有效的尺度转换方案、完善参数化方案以及开展流域内大气、水文变量时空分布高分辨率模拟等方面,以期系统提升耦合模式对大气、陆面过程及水文过程的刻画能力。