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.

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.

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.

An Assessment of Potential Economic Gain from Weather Forecast Based Irrigation Scheduling for Marginal Farmers in Karnataka, Southern State in India

This study is aimed to assess the usefulness of weather forecasts for irrigation scheduling in crops to economize water use. The short-term gains for the farmers come from reducing costs of irrigation with the help of advisory for when not to irrigate because rain is predicted (risk-free because the wrong forecast only delays irrigation within tolerance). Here, a quantitative assessment of saving (indirect income) if irrigation is avoided as rain is imminent (as per forecast), using a five-year archived forecast data over Karnataka state at hobli (a cluster of small villages) level is presented. Estimates showed that the economic benefits to the farmers from such advisories were significant. The potential gain in annual income from such forecast-based irrigation scheduling was of the order of 10% - 15%. Our analysis also indicated that the use of advisory by a small percentage of more than 10 million marginal farmers (landholding < 3 acres) in Karnataka could lead to huge cumulative savings of the order of many crores.

A stamp based exploration framework for numerical weather forecast

Numerical weather simulation data usually comprises various meteorological variables, such as precipitation, temperature and pressure. In practical applications, data generated with several different numerical simulation models are usually used together by forecasters to generate the final forecast. However, it is difficult for forecasters to obtain a clear view of all the data due to its complexity. This has been a great limitation for domain experts to take advantage of all the data in their routine work. In order to help explore the multi-variate and multi-model data, we propose a stamp based exploration framework to assist domain experts in analyzing the data. The framework is used to assist domain experts in detecting the bias patterns between numerical simulation data and observation data. The exploration pipeline originates from a single meteorological variable and extends to multiple variables under the guidance of a designed stamp board. Regional data patterns can be detected by analyzing distinctive stamps on the board or generating extending stamps using the Boolean set operations. Experiment results show that some meteorological phenomena and regional data patterns can be easily detected through the exploration. These can help domain experts conduct the data analysis efficiently and further guide forecasters in producing reliable weather forecast.

数值预报AI气象大模型国际发展动态研究

数值预报是研究地球系统的重要工具,有助于加深科学家对大气、海洋、气候和环境等复杂系统之间相互作用和变化过程的理解,在防灾减灾、气候变化和环境治理等方面发挥着不可或缺的作用。随着模式复杂度和分辨率的提高,传统数值模式在气候变化研究和气候预测方面取得了迅速的进展,但也面临一些挑战,需要得到数据同化、集合耦合、高性能计算和不确定性分析等多方面的支持。而近年来,“AI+气象”的交叉研究在气象领域引起了广泛关注。基于多种深度学习架构的人工智能大模型,依托强大的计算资源和海量的数据进行训练,能够以新的科学范式进行高效数值预报。气象大模型不断涌现,一些科技公司如华为、英伟达、DeepMind、谷歌、微软等,以及国内外高校如清华大学、复旦大学、密歇根大学、莱斯大学等发布了多个涵盖临近预报、短时预报、中期预报和延伸期预报等不同领域的气象大模型。这标志着人工智能与气象领域的交叉融合已经达到新的高度。尽管气象大模型在现阶段取得了较大突破,但其发展仍然面临弱可解释性、泛化能力不足、极端事件预报强度偏低、智能预报结果过平滑、深度学习框架能力需要拓展等诸多挑战。

大型浅水湖泊高时空分辨率风场特征数值模拟研究:以巢湖为例

【目的】大型浅水湖泊的风场是影响湖泊流场、水体富营养化和藻华运移聚集规律的关键因素之一,仅依托气象站点的观测数据难以有效捕捉湖面风场在空间和时间上的快速变化。为精细刻画湖泊高频变化风场,辨识其时空变化特征,【方法】以巢湖为研究对象,综合利用气象观测数据、全球再分析数据集和地理静态数据,通过中尺度天气预报模式和地理空间分析技术,模拟分析巢湖地区2019年高时空分辨率的风场变化过程。【结果】结果显示:巢湖区域风速由东向西、由南向北、由湖面向陆面逐渐减弱;湖面主导风向为东风和偏东风,主导风速为二级至四级,湖面平均风速在11月最大,12月次之,5月最小,不同季节湖面平均风速由大到小依次为冬季、秋季、夏季和春季。【结论】结果表明:巢湖在白天(夜晚)出现冷(暖)湖效应,其风场在早上6时和夜晚23时出现陆风和湖风转换;西半湖受湖陆风和城市热岛影响显著,在夏季会出现明显的环湖湖风锋,其湖风锋穿透内陆距离约4.7~9 km;东半湖受山谷风影响显著,在东南部山脉以东湖区易形成低风速区,在湖口附近受狭管效应易形成高风速区。通过研究成果可进一步认知巢湖区域风场特征,为巢湖水生态环境治理提供技术支撑。

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.

东亚区域人工智能气象大模型预报技巧评估

针对人工智能气象大模型的500 hPa位势高度、2 m气温、10 m风速、降水以及热带气旋路径等,从定性和定量两个角度进行评估。结果表明:从定性角度出发,FuXi、Pangu和GraphCast 3个大模型均会响应热带异常加热,其中Pangu与GraphCast响应强度接近,FuXi响应较弱。从定量角度出发,FuXi整体展现出更高的预报能力,其最大可用预报日数超过9.75 d,Pangu和GraphCast分别为8.75 d和8.5 d。在2 m气温预报中,FuXi的时间异常相关系数为0.48~0.91,Pangu和GraphCast分别为0.43~0.91和0.38~0.83。此外,采用TS(threat score)评分对FuXi和GraphCast降水预报进行评估,FuXi在晴雨、小雨和中雨预报中更具优势,其预报技巧分别为0.22~0.41、0.15~0.24和0.06~0.22,GraphCast在大雨预报中展现更强能力。针对2019年7月29日华北暴雨和2020年8月16—17日乐山暴雨两次极端降水个例进行分析,FuXi和GraphCast均可提前8 d预报降水的空间分布,但在降水量级预报中存在偏差,随着预报时效减小,偏差也逐渐减小。在热带气旋路径预报中,Pangu展现更高精度。

基于LSTM预测模型的应用性能异常检测

目前高性能计算系统规模和复杂性不断增加,应用软件作业性能异常的原因变得更加复杂多样,传统的针对基于监控数据进行人工分析的方法存在效率低下和过分依赖分析人员经验的问题。提出一种基于长短期记忆网络(LSTM)的性能异常检测方法。以天气预报模式WRF为研究对象,首先从历史作业数据中学习出正常性能数据的变化情况,然后通过引入boxplot方法对LSTM模型预测值与实际观测值之间的残差进行统计分析,并将大于下四分位的数据判定为异常,从而实现应用软件作业性能异常的检测。实验结果表明,上述方法不仅可以较好地检测出性能的异常,而且能适用于多种不同类型的数据集。

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.

输电线路气象风险精细化建模及气象灾害的在线预警防御策略

气象因素在输电线路稳定运行中起着重要作用,在评估风险时应予以考虑。为此,提出一种输电线路气象风险预警及防护方法。该方法考虑时空发电预测、设备健康和可靠性评估以及概率负荷预测等方面对气象风险进行精细化建模,并基于气象危害、电网脆弱性和灾后恢复成本提出一种新的风险度量标准。此外,针对负荷中断恢复以及缓解用电拥堵,提出一种气象灾害的在线预警防御策略。最后,在案例中对所提出的方法和策略进行测试分析,其结果可以验证方法和策略的有效性。

基于网格化数值天气预报的区域光伏发电多输出功率预测方法

区域光伏的短期功率预测是省级及以上电网调控中心制定发电计划、提高光伏消纳率的重要基础之一。光伏短期功率预测本质上是构建数值天气预报与实际功率之间的映射模型,为了实现预测精度的提升,利用网格化的数值天气预报,采用残差网络建立区域光伏的多输出预测模型,充分挖掘区域光伏所属空间的气象资源分布与各光伏电站功率的关联关系,实现以网格化数值天气预报为输入的区域各光伏电站的功率预测。以实际运行数据进行仿真,结果表明,本文方法在各光伏电站的功率和总功率2个方面的预测结果均优于现有成熟方法。

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.

基于累加气候概率的FSS检验方法对多模式短时暴雨预报的评估

为深入认识数值天气模式强降水精细化预报性能,本文以短时强降水多发的浙江省2019年5到10月降水为例,采用分数技巧评分(Fractions Skill Score,简称FSS)邻域检验方法,评估了6个业务模式短时降水预报准确性,重点探讨了各模式短时暴雨预报能力及天气背景的影响。结果表明:(1)基于站点降水的累加气候概率,确定了短时小雨、中雨、大雨、暴雨和大暴雨的预报技巧评分阈值各为0.583、0.522、0.506、0.502和0.500,改进并实现了FSS方法对长时间序列各等级降水预报技巧尺度的综合评估。(2)只有上海中尺度区域数值预报业务系统和浙江中尺度区域数值预报业务系统的暴雨预报平均评分达到预报技巧,相应技巧尺度为159、159和183 km;这3个产品共有约6成预报达到技巧评分,其技巧尺度累积频率从3 km至183 km可增幅近50%,这种尺度选择性评价可为不同尺度下产品应用提供参考。(3)不同天气背景下各模式预报性能差异明显。台风类、梅雨类和弱天气尺度强迫类短时暴雨预报的最优模式分别是欧洲中期天气预报中心全球预报模式、上海中尺度区域数值预报业务系统和浙江中尺度区域数值预报业务系统,各技巧尺度为27、99和135 km,模式产品使用中需分类区别对待。

基于ARIMA模型对定西天气数据的分析与预测

由于天气对农业生产、水资源管理和自然灾害预防等具有重要影响,文章采用ARIMA模型来实现对天气的有效预测。通过利用ACF和PACF图粗略确定ARIMA模型的参数,最终确定最优模型:ARIMA(1,1,1)为日最低气温模型,其残差序列自相关函数与偏自相关函数基本落在95%置信区间内;同时Ljung-Box Q统计结果表明残差不存在相关关系(P>0.05),即残差为白噪声,满足随机性假设;最终计算误差(日最低气温)RMSE、MAPE、MAE分别为2.63、1.22%、2.06,预测结果良好,为定西天气的预测提供了可行的方案。

考虑数值天气预报系统的风电功率预测方法研究

随着全球能源需求的增长和环境保护的压力,风能作为一种清洁、可再生的能源,得到了广泛关注,然而,风电的间歇性和波动性给电网的稳定运行带来了挑战。文章通过分析数值天气预报系统的特点和工作原理,结合风电功率预测的需求,提出了一种基于数值天气预报的风电功率预测方法,并通过虚拟案例分析验证其有效性和可靠性,结果表明,文章所提方法的预测精度明显优于传统方法。该研究为风电场的运行调度和电力系统的稳定运行提供了科学依据和有力支持,具有广泛的应用前景。

GNSS PWV典型季风气候特征奇异谱分析

为了进一步研究利用全球卫星导航系统(GNSS)大气可降水量(PWV)分析不同气候类型特征,进行GNSS PWV典型季风气候特征奇异谱分析:选取中国中东部地区2016—2021年部分连续运行参考站(CORS)数据,提出将全球气压和温度(GPT(GPT3_1、GPT3_5))模型、欧洲中期天气预报中心第五代大气再分析数据集(ERA5)模型3种大气模型分别与GNSS解算的对流层总延迟(ZTD)数据融合获取的PWV值进行对比分析,得出中国3种典型季风气候类型GNSS PWV最优大气模型;然后提出利用奇异谱分析(SSA)法分解重构出GNSS PWV时间序列,从而基于GNSS PWV分析不同季风气候类型特征。结果表明,中国3种典型季风气候类型条件下ERA5模型精度较优,选择ERA5模型为最优大气模型,重构后的GNSS PWV变化趋势能够很好地反映出3种典型季风气候类型的特征;因此GNSS PWV可应用于气候特征分析。

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.