MYJ和YSU方案对WRF边界层气象要素模拟的影响

研究新一代中尺度气象模式WRF中两种大气边界层方案(MYJ,YSU)对沈阳冬季大气边界层结构模拟的影响,重点分析温度层结、低层风场、边界层高度等对污染物扩散有重要影响的气象要素.和观测数据的比对表明WRF基本能够模拟出温度风速的日变化特征,但模拟风速偏大.YSU方案由于模拟的边界层顶卷挟和边界层内混合作用较强,夜间接地逆温强度低于MYJ方案,逆温维持时间比MYJ方案短4小时,同时模拟边界层高度也高于MYJ方案,有利于污染物垂直扩散.边界层高度的3种计算方法中,湍流动能方法计算的边界层高度最高,Richardson数方法次之,位温方法得到的高度最低.Richardson数方法对临界值的选取较敏感.     

晴天及阴天条件下WRF模式中几种边界层参数化方案的对比分析研究

利用WRF模式中5种常用边界层参数化方案(ACM2、YSU、BouLac、MYJ和MYNN2.5)及美国能源部大气辐射观测试验(ARM)寿县综合观测数据(2008年8—12月),对比分析了晴天及阴天条件下,不同参数化方案对典型农田下垫面气象要素及边界层结构的模拟效果,结果表明:(1)模式对于云层状况的模拟,非局地方案ACM2和YSU方案优于局地方案.(2)对于近地层气象要素,晴天和阴天条件下均是ACM2方案对于2 m温度和比湿的模拟效果最好,MYJ方案对于风向风速的模拟效果最好.(3)对于位温及比湿垂直廓线的模拟,晴天和阴天条件下均是非局地方案(ACM2和YSU)对白天的模拟效果优于局地方案;ACM2方案对夜间弱稳定层结和逆湿结构的模拟最优;(4)对于风速垂直廓线的模拟,白天不稳定条件下,晴天条件MYJ方案最优,阴天MYNN2.5方案的模拟效果最好;夜间弱稳定条件下,晴天条件ACM2方案与观测值之间的偏差最小,阴天YSU方案模拟效果最好;(5)总体而言,在对典型农田下垫面进行模拟时,晴天和阴天条件下均是ACM2方案更具优势.     

中国东北半干旱区能量水分循环的同化模拟

使用地球观测系统的中分辨率成像光谱仪（EOS．MODIS）提供的归一化植被指数（NDVI）产品估算植被覆盖度和航天飞机雷达地形测绘任务（SRTM）制成的数字高程模型（DEM）数据遥感产品替换WRF模式默认的植被覆盖度和地形高度,并且利用WRF模式及其先进的三维变分同化系统（WRF．3DVar）循环同化东北半干旱区自动气象站近地面气象要素,对东北半干旱区的温度场、湿度场、风场和能量场的结构及其日变化特征进行了较为细致的模拟研究．通过4组数值模拟试验分别探讨了同化气象要素与改变模式地表参数引起的不同下垫面潜热、感热的分配关系和降水、土壤湿度变化弓f起的地表能量通量模拟效果,并利用通榆站、奈曼站、锦州站、和密云站2009年6-8月的通量观测资料与模拟结果对比检验．结果表明,WRF模式能够较好地模拟出东北半干旱区夏季的近地面温度、风向、净辐射、感热和潜热等要素的变化特征及日变化规律．同化试验（Case2）模拟的近地面气温、相对湿度、风速相比控制性试验（Case1）有所改善;陆面参数试验（Case3）和集合试验（Case4）改善了感热和地表热通量的模拟．WRF模式能较好地模拟出下垫面土壤湿度随时间变化的规律,集合试验（Case4）土壤湿度模拟结果与4个通量站观测值相比无太大差别,但降水的模拟有待改善．本研究利用卫星遥感资料改善模式下垫面陆面参数,利用气象资料同化改善近地面大气要素模拟精度,这是将各种不同空间和时间尺度的多源数据与数值模拟融合的有益尝试．此研究生成的东北地区资料同化数据集可用于气候变化、干旱监测等方面,对深入了解半干旱区气候的形成和维持机理具有重要的意义．     

海陆风及沿海风速廓线在风电场风速预报中的应用

为了建立沿海风功率预报系统,本文探讨了中国沿海风电场风速预报问题,并利用数值模式RAMS对海陆风进行了模拟研究.发现海陆风发生时,海风和陆风阶段风速廓线存在较大差异,海风阶段风速的垂直切变明显小于陆风阶段.海陆风发生时,风速会呈现有规律的变化,即海风和陆风分别有两个时段：风速增加时段和风速减少时段.在为沿海风电场提供风速预报时,当模式预报到海陆风发生时,可以利用海陆风的这种特点,使用统计方法对预报出的风速进行有效的订正.并发现即使没有海陆风发生,当风向为海洋吹向陆地时,风速随高度的垂直切变同样小于陆地吹向海洋的时段.利用统计方法根据不同风向时风速廓线的特性,把数值模式计算高度上的预报结果,精确地插值到风机涡轮高度,会很大程度上减少风速预报的误差及风功率预报环节的误差.     

关中平原麦田干热风过程陆气交换特征的数值模拟

干热风是常发生在中国北方冬小麦产区的高温、低湿、风速较大,并伴随有强热量交换和水分蒸发过程的农业气象灾害.当其发生时如果不能保证作物水分的供给,将严重影响产量.因此,开展干热风发生机理的准确模拟与预报,对指导采取适时的农田管理措施,保障小麦产量至关重要.本文应用中尺度气象模式WRF(Weather Research and Forecasting)输出结果驱动北京大学陆面模式PKULM(Peking University Land Model)的模拟方法,结合气象台站观测与风云3A卫星遥感反演的叶面积指数数据,对关中平原2008-2013六年间干热风过程中农田生态系统温度、湿度、风速、感热通量、潜热通量等陆气交换特征进行了数值模拟.准确模拟出了干热风发生期间的大气温度、相对湿度、风速、土壤温度、土壤含水量及冠层感热通量、总感热通量、冠层潜热通量和总潜热通量等关键要素特征,揭示了本地区干热风的两种形成机制.模拟结果表明:WRF对干热风过程的温度场、湿度场与风场模拟比较准确,PKULM对于植物在干热风过程中、特别是气孔关闭阶段的感热、潜热通量的模拟明显优于WRF-Noah LSM(Noah Land Surface Model)的模拟效果.模拟结果与相应气象要素的观测值数值接近、变化趋势一致,在实际干热风发生时间段内的各要素模拟值均达到干热风指标,因此使用本模式系统模拟预报干热风灾害是可行的.     

两种边界层参数化方案模拟热带气旋Megi(2010)路径差异的机理分析

本文利用中尺度数值模式WRF,分别采用YSU和MYJ两种边界层参数化方案对2010年超强台风Megi的移动路径进行了模拟,研究了热带气旋(TC)路径模拟对边界层方案的敏感性,并从模拟TC尺度差异所造成的影响角度揭示了模式边界层方案影响TC路径的机理.结果表明:由于两种方案对边界层垂直混合作用过程的描述不同,两个试验模拟的低层水汽垂直输送存在差异.相对于能很好模拟出Megi路径的MYJ方案,YSU方案模拟的TC外围螺旋雨带更活跃,造成TC尺度增大,引起TC中心北侧外围气压梯度和径向风速增加,使得由副高向TC中心输送更多的质量,造成副高异常减弱,从而导致由副高主导的引导气流发生改变,最终使得采用YSU方案模拟的Megi路径出现提前转向.     

边界层参数化方案对边界层热力和动力结构特征影响的比较

本文利用高分辨率中尺度WRF模式,通过改变边界层参数化方案进行多组试验,评估该模式对美国北部森林地区边界层结构的模拟能力,同时比较了五种不同边界层参数化方案模拟得出的边界层热力和动力结构.结果表明：除个别方案外,配合不同边界层方案的WRF模式都能成功模拟出白天对流边界层强湍流混合特征和夜间稳定边界层内强逆温、逆湿和低空急流等热力和动力结构.非局地YSU、ACM2方案在白天表现出强的湍流混合和卷夹,相比于局地MYJ、UW方案,模拟的对流边界层温度更高、湿度更低、混合层高度更高、感热通量更大,更接近实际观测,这表明在不稳定层结下考虑非局地大涡输送更为合理,但局地方案在风速和风向的预报上存在一定优势.TEMF方案得到的白天局地湍流混合强度为所有方案中最弱,混合层难以发展,无法体现对流边界层内气象要素垂直分布均匀的特点.对于夜间稳定边界层的模拟,不同参数化方案之间的差异较小,但是YSU方案在一定程度上高估了机械湍流,导致局地湍流混合偏强,从而影响了其对稳定边界层的模拟能力.     

气溶胶辐射效应对城市边界层影响的数值模拟研究

近年来,伴随污染和城市化进程的加剧,气溶胶辐射效应对城市边界层的影响日益显著.文章以北京地区一次冬季污染过程为例,采用中尺度数值预报模式,在优化辐射方案中气溶胶垂直廓线的基础上验证了模式的有效性,分析了气溶胶对辐射和边界层的影响过程,最后通过敏感性试验探究了气溶胶、城市化和气象要素间的相互关系.结果表明:(1)优化后的模式可以较好地模拟北京地区温度场、湿度场、风场的分布特征.(2)气溶胶在大气中通过削减到达地面的入射短波辐射使地表温度降低,通过对辐射的吸收或后向散射作用,使高层温度升高,温度场的变化使层结稳定性增强,从而减少近地层的能量输送,使边界层高度下降.(3)随着气溶胶光学厚度的增加,乡村地区最易变为稳定层结,郊区较易变为稳定层结,城区最难变为稳定层结,且气溶胶辐射效应、城市下垫面以及二者的共同作用是影响城市边界层气象要素变化的主要原因.     

基于WRF数值模拟的夏季强对流测定方法研究

在固定时间内使WRF数值模式的预报和观测间距实现最小化,是实时准确测定夏季强对流的重要依据,因此研究一种基于WRF数值模拟的夏季强对流测定方法.通过分析河南省2018年7月2日至7月23日闪电定位资料和降水资料,统计夏季强对流天气中降水强度与闪电频数关系,将二者进行线性拟合,根据拟合结果将夏季强对流天气中闪电频数反演至降水强度,并将反演后的降水强度通过四维变分同化资料方法,同化至WRF数值模拟中,获取最终WRF极小化代价函数,实现夏季强对流天气实时准确测定.通过该方法对河南省某机场进行WRF数值模拟研究发现,模拟结果的水平风速、垂直速度、位温与扰动气压变化明显,可准确测定该机场的夏季强对流天气,具有可靠性.     

一次冰雹过程的惯性重力波观测及数值模拟

使用高灵敏度的电容式微压波传感器对1998年4月11日16时发生在贵州省普定县的一次降冰雹过程的重力波进行观测,利用WRF（Weather Research and Forecast）中尺度模式对这一过程进行数值模拟,使用Morlet小波方法对模拟结果进行分析,得出这一过程中惯性重力波的分布和变化规律,并分析急流、地形及切变线对惯性重力波的影响.观测发现：在降冰雹前,每隔1~4小时出现一次短周期重力波阵性增强的现象.数值模拟结果显示：在低空降冰雹前几个小时有强的短周期重力波出现,其中周期较长的出现早、存在时间长,周期较短的出现晚、存在时间短;强的低空急流和风速垂直切变触发对流或湍流的发生和加强,对流或湍流又激发了80~200 min的短周期重力波;短周期重力波更容易向垂直方向传播,而长周期重力波倾向于水平方向传播.长周期重力波在降冰雹后周期有明显变短现象,随高度越加明显.由地形形成的重力波在最高山峰上空振幅最大.     

Numerical simulation and data assimilation of the water-energy cycle over semiarid northeastern China

The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas.     

Establishment and analysis of a High-Resolution Assimilation Dataset of the water-energy cycle in China

For better prediction and understanding of land-atmospheric interaction, in-situ observed meteorological data acquired from the China Meteorological Administration (CMA) were assimilated in the Weather Research and Forecasting (WRF) model and the monthly Green Vegetation Coverage (GVF) data, which was calculated using the Normalized Difference Vegetation Index (NDVI) of the Earth Observing System Moderate-Resolution Imaging Spectroradiometer (EOS-MODIS) and Digital Elevation Model (DEM) data of the Shuttle Radar Topography Mission (SRTM) system. Furthermore, the WRF model produced a High-Resolution Assimilation Dataset of the water-energy cycle in China (HRADC). This dataset has a horizontal resolution of 25 km for near surface meteorological data, such as air temperature, humidity, wind vectors and pressure (19 levels); soil temperature and moisture (four levels); surface temperature; downward/upward short/long radiation; 3-h latent heat flux; sensible heat flux; and ground heat flux. In this study, we 1) briefly introduce the cycling 3D-Var assimilation method and 2) compare results of meteorological elements, such as 2 m temperature and precipitation generated by the HRADC with the gridded observation data from CMA, and surface temperature and specific humidity with Global Land Data Assimilation System (GLDAS) output data from the National Aeronautics and Space Administration (NASA). We find that the simulated results of monthly 2 m temperature from HRADC is improved compared with the control simulation and has effectively reproduced the observed patterns. The simulated special distribution of ground surface temperature and specific humidity from HRADC are much closer to GLDAS outputs. The spatial distribution of root mean square errors (RMSE) and bias of 2 m temperature between observations and HRADC is reduced compared with the bias between observations and the control run. The monthly spatial distribution of surface temperature and specific humidity from HRADC is consistent with the GLDAS outputs over China. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations, and the simulated results could be used in further research on the long-term climatic effects and characteristics of the water-energy cycle over China.     

Distributions-oriented wind forecast verification by a hidden Markov model for multivariate circular–linear data

Winds from the North–West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 h in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency. In the context of distributions-oriented forecast verification, we propose a comprehensive model-based inferential approach to investigate the ability of the WRF system to forecast the local wind speed and direction allowing different performances for unknown weather regimes. Ground-observed and WRF-forecasted wind speed and direction at a relevant location are jointly modeled as a 4-dimensional time series with an unknown finite number of states characterized by homogeneous distributional behavior. The proposed model relies on a mixture of joint projected and skew normal distributions with time-dependent states, where the temporal evolution of the state membership follows a first order Markov process. Parameter estimates, including the number of states, are obtained by a Bayesian MCMC-based method. Results provide useful insights on the performance of WRF forecasts in relation to different combinations of wind speed and direction.     

The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation,Columbia River basin,Canada

Glaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best.     

太湖富营养化条件下影响蓝藻水华的主导气象因子

利用2004-2018年卫星遥感解译的太湖蓝藻水华信息构建蓝藻综合指数,采用随机森林机器学习算法分析同期气象因子与蓝藻水华综合指数的关系,定量评估影响蓝藻水华的主要气象因子特征变量的重要性度量和贡献率.结果表明,在光、温、水、风等主要气象要素中,气温对蓝藻水华综合指数起着主导的作用,其次是风速和降水,日照时间的影响或可忽略.其中气温条件中重要性度量最大的是年平均气温,其次是冬、春季节的平均气温;风速因子中影响较大的是7月份的平均风速;水分条件中主导因子是9月累计降水量.优选的随机森林模型模拟值与实际蓝藻水华综合指数的变化趋势基本一致,拟合优度为0.91,通过0.01显著性检验,随机森林模型模拟效果较好.用随机森林模型模拟值对太湖蓝藻水华分等级评估,模型模拟精度达到了86.7%,其中5个重度等级年份模型模拟结果完全一致,中度等级的6个年份模型模拟值有5年与之相符,中度以上等级的模拟精度达90.9%,模型能够反映气象因子对蓝藻水华综合指数的综合影响,对中、重度蓝藻水华的模拟效果更好.随机森林模型有助于理解富营养化状态下影响蓝藻水华的主导气象因子,利用气象因子的可预测性可以促进蓝藻水华预测预警能力的提升.     

Singular value decomposition analysis of spatial relationships between monthly weather and air pollution index in China

Spatial patterns are important features for understanding regional air quality variability. Statistical analysis tools, such as empirical orthogonal function (EOF), have been extensively used to identify and classify spatial patterns. These tools, however, do not directly reveal the related weather conditions. This study used singular value decomposition (SVD) to identify spatial air pollution index (API) patterns related to meteorological conditions in China, one of world’s regions facing catastrophic air pollution. The monthly API and four meteorological variables (precipitation, surface air temperature, humidity, and wind speed) during 2001–2012 in 42 cities in China were used. The two leading SVD spatial patterns display the API anomalies with the same sign across China and opposite signs between northern and southern China, respectively. The meteorological variables have different relationships with these patterns. For the first pattern, wind speed is the most important. The key regions, where the correlations between the API field and the wind speed’s SVD time series are significant at the 99% confidence level, are found nationwide. Precipitation and air temperature are also important in the southern and northern portions of eastern China, respectively. For the second pattern, the key regions occur mainly in northern China for temperature and humidity and southern China for wind speed. Air humidity has the largest contribution to this pattern. The weather-API relationships characterized by these spatial patterns are useful for selecting factors for statistical air quality prediction models and determining the geographic regions with high prediction skills.     

北京城市化对夏季大气边界层结构及 降水的月平均影响

为克服针对一次或几次天气过程研究城市化对边界层结构及降水影响的局限性,尝试研究北京城市化对夏季大气边界层结构及降水的月平均影响,本文首先总结了2006年8月份的主要天气过程,分析了气象站观测的10 m高度风速、2 m高度气温、2 m高度比湿和24 h降水的月平均分布特征,然后利用WRF/Noah/UCM模拟系统,进行了该月30个个例的高分辨率数值模拟及检验分析,并通过多组不同城市化情景的敏感性试验对比分析了城市化对夏季大气边界层结构及降水的月平均影响.研究表明:本文所用对高分辨率数值模拟结果进行月平均的方法可以较合理地模拟出城市化对大气边界层结构及降水的影响,并再现观测到的各站风频差异.8月份,北京城市化对气温的影响高度白天约为800 m,近地面气温升高1℃以上;夜间约为200 m,对近地面气温的影响达到最大(1.4℃以上).白天,城市化使城市及下风向的一些区域风速略有减小;夜间,城市及周边区域200 m以下风速明显减小,且在100 m左右高度处风速减小最明显,减小达0.8 m/s以上.城市化白天使700 m以下比湿减小,近地面处减小达1.2g/kg以上,夜间使近地面空气比湿略有减小.城市化对城市区域平均降水量的影响随城市发展的不同阶段而不同.初步模拟分析表明, 北京城市化已使上风向区域以及城区三环以内降水量减少,海淀和昌平降水明显增加.     

Modelling of meteorological conditions at an urban scale for the PUMA winter campaign

For the winter 2000 campaign of the Pollution of Urban Midlands Atmosphere project, observation and numerical modelling of meteorological conditions over the West Midlands conurbation, UK, was undertaken. Modelling was performed using the regional atmospheric meteorological system (RAMS). This paper presents a comparison of modelled and observed wind and temperature for 25 and 26 January 2000. The RAMS model uses two nested grids with a mesh size of 2 km for the inner grid which is embedded in the outer grid with a mesh size of 8 km. Statistical evaluation of the model results against the observational data of wind speed, direction and temperature at 10 m was conducted. In general, the modelling results are in a reasonable agreement with observation. The statistical evaluation suggests that model performance is poorer for the inner grid than the outer grid as the model uncertainties (mainly mean bias) transfer from the outer to inner one. The low indices of agreement of temperature and wind are mainly associated with the systematic root-mean-square-difference values. For temperature, the systematic bias may also be affected by representation of cloud amount by the model. For wind, the model tends to have a poor performance for calm conditions, as under a stable anti-cyclonic situation local wind patterns associated with topography may develop, although the topography of the region is relatively flat. The results for the inner grid reveal some subtle spatial patterns at a scale smaller than 10 km near hills and valleys with differences in elevation of a few hundred metres.