对流尺度数值预报中的云物理初始化方法改进及个例试验

通过在云初始化方案中增加由地表感热和潜热通量确定的对流尺度速度作为对流判据,同时增加层云云冰、云水计算方案,改进云分析方法,并基于第2代华东快速更新循环同化模式预报系统,针对2015年4月28日华东强对流个例,进行对比试验,分析了改进的云初始化方案对云分析结果和模式预报效果的影响。试验表明:在云分析中增加对流判据,使得平均40%左右的云分析格点判定为非对流格点,对流格点分布与正的感热通量分布相似,在陆地上有显著日变化。在对流和层云格点判定之后,增加层云云冰、云水计算方案分析层云格点,显著地减小了模式初始场的云冰、云水混合比,有效地减弱了模式积分初始阶段云冰、云水含量的剧烈调整,尤其是在陆地区域。采用改进的云初始化方案进行预报,可以减少模式前1 h和前6 h的降水强度;尤其在个例的循环试验中,强降水中心强度和面积的预报比原方案显著减弱。      

雷达资料同化在局地强对流预报中的应用

采用ARPS模式的资料分析系统ADAS同化多普勒雷达径向速度和反射率因子资料,分析两者对初始场的改进作用,并应用于WRF中尺度模式中对2012年8月21日江西省一次局地强对流过程进行了模拟试验。分析结果表明:（1）ADAS同化系统能够利用雷达径向速度和反射率因子资料有效增加初始场中的中小尺度风场信息和云、水物质含量,并通过湿绝热或非绝热初始化对温度场、湿度场和风场进行调整,使初始场在动力和热力上达到平衡。（2）同化径向速度后对改善模式初始场的动力场有重要贡献,而对大气水凝物和降水的预报影响较小;同化反射率因子的主要作用是调整初始场中的水凝物场和热力场,有效缩短了模式的“ spin-up”时间,明显改进了定量降水预报;同时同化雷达径向速度和反射率因子后,初始场中快速调整出了中小尺度风场水平辐合、垂直运动以及合理的温、湿分布,对3小时内雨带形状、降水落区及定量降水的预报与实况更接近。（3）模拟试验表明,同时同化径向速度和反射率因子能成功模拟出本次对流单体风暴的中β尺度三维空间分布结构及其演变过程,中低层切变线的辐合抬升强迫作用是对流单体风暴组织、发展和维持的主要动力机制之一,对流凝结潜热加热在对流单体风暴的发生发展中发挥了重要作用。因此,雷达资料同化对提高临近数值天气预报的准确率以及对强对流天气系统的模拟能力具有重要意义。     

扩展水凝物控制变量的雷达资料同化对台风“天鸽”数值模拟的影响

利用WRF(Weather Research and Forecast)模式及WRFDA(WRF model data assimilation system)系统,针对2017年台风“天鸽”个例通过同化雷达径向速度(Vr)和反射率因子(RF),研究水凝物控制变量的雷达资料同化对台风分析预报的影响。研究表明：雷达径向速度的直接同化有效地改进了模式初始场中台风涡旋区的中小尺度信息,分析场中产生了气旋性的风场增量,对模式背景场中的台风有显著增强作用。通过在传统控制变量中扩展针对水凝物的控制变量可有效地同化雷达反射率因子资料,对初始场的水物质进行调整,并对随后确定性预报的台风路径和强度都有一定的正效果。此外,相比没有水凝物控制变量的雷达同化试验,加入了水凝物控制变量的雷达资料同化试验降水预报效果更好。这为将我国近海的地基多普勒天气雷达用于台风初始化分析和预报提供了一定的技术支撑和保障。     

雷达资料同化对一次飑线过程的模拟影响

雷达资料同化能够改善强对流天气的预报,但是不同的模式方案配置会得到不同的结果。本文针对中国南部2018年3月4日一次飑线过程,以全球预报模式GFS分析场和预报场为背景场,采用中尺度区域气象预报模式ARPS 3DVAR系统同化多普勒雷达径向速度,用云分析处理反射率数据,考虑同化间隔、频次、云分析中不同参数调整,采用1 h同化窗口,设计不同同化方案,最后用WRF模式进行预报,研究雷达资料同化对飑线系统触发及发展机制的影响。结果表明,同化间隔过短时,由于模式热动力变量没有平衡产生虚假回波,同化间隔过长时,系统触发和发展的特征普遍偏弱;采用12 min间隔同化得到了最好的初始场,并且同化频次越高得到的降水预报结果越好。此外,ARPS云分析能大大改善初始场,减少模式自调整时间,其中湿度调整、温度调整、雨水调整及水汽调整对系统动力过程和水凝物初始场分布都有较大的影响,而垂直速度相关参数调整影响较小。     

云分析方法对浙江省一次强对流天气数值模拟的应用初探

基于云分析方法,利用WRF-ARW模式设计一套快速更新循环同化方案,对2019年3月21至22日发生在浙江省的一次强对流天气过程进行模拟研究。通过设计两组对比试验,对云分析同化雷达反射率效果进行诊断分析。在控制试验中,模拟降水落区和降水量与实况差别较大,而采用云分析方法同化雷达反射率（CA-DA）的模拟试验中,降水模拟效果相对较好,雨带明显东移,强度与实况接近。初步结果表明,利用云分析同化雷达反射率因子可以改进模式初始场的风、温度、水汽场以及水凝物等信息,进而缩短模式spin-up时间,提高降水落区及强度的预报。     

FY-4A闪电资料在对流天气数值预报中的影响研究

利用GRAPES云分析系统,对2019年8月4—5日发生在山西省中北部的太行山区域强对流降水过程进行了分析和数值试验,重点分析引入FY-4A的闪电成像仪(lightning mapping imager event,LMIE)资料对模式计算的雷达反射率、云微物理变量和降水预报的影响。试验结果表明:加入LMIE资料计算得到的雷达回波更接近实测雷达回波,并且对云水、云冰和雪等云微物理要素进行了调整,使发生闪电区域的云微物理要素的含量显著增加,云微物理要素的极值中心一般与闪电活跃区域分布一致。云信息初始化可以有效提高24 h以内的降水预报准确率,减弱数值模式的spin-up现象;加入LMIE资料后,可以进一步提高1～12 h降水预报准确率。     

实时天气背景依赖的反射率因子间接同化及多暴雨个例试验

为避免直接同化时反射率非线性观测算子线性化带来的线性近似误差问题,目前许多研究和业务中还常采用间接同化方式来同化雷达反射率因子,其通过背景场温度判定水凝物类型及比例。基于一种实时天气背景依赖的雷达反射率因子间接同化方案,进行了4次暴雨过程（2次强对流,2次锋面）的循环同化及预报试验。结果表明:对于强对流暴雨个例,相对于传统温度判定方案,天气背景依赖方案的温度预报误差更小、降水预报评分更高,而对于锋面过程区别不明显;进一步机理分析表明,对于强对流暴雨个例,由于背景依赖方案在同化反射率因子时引入了实时天气背景信息,使得分析场水凝物结构能够更好表征实际对流特征且与其它模式变量更为协调,进而改善了模式预报的热、动力及水汽条件,从而改善了降雨预报效果;而锋面暴雨由浅对流过程占主导,水凝物以低层的雨水为主导,冰相水凝物对于该过程的影响较小,由于两种方案反演的雨水结构和量级均相似,因此降雨预报差异较小。     

对流涡度矢量和湿涡度矢量在暴雨诊断分析中的应用研究

利用对流涡度矢量(CVV, (ζa×θe)/ρ)和湿涡度矢量(MVV, (ζa×θv)/ρ)对华北地区一次大范围的大到暴雨天气过程进行了诊断研究.结果表明,CVV和MVV垂直分量与云和降水密切相关,暴雨区区域平均和垂直积分的CVV和MVV垂直分量与云中水凝物混合比的相关系数分别为0.92和0.95,与降水率的相关系数分别为0.71和0.47.云中水凝物分为液态水凝物和固态水凝物,虽然同态水凝物的含量相对较少,但它在云的变化中起着更重要的作用.滞后相关分析表明,降水率的峰值到来之前4-5 h云中水凝物含晕最少,降水率的峰值到来之后1-2 h,云中水凝物含量最多.云中水凝物与降水率的滞后相关系数主要是由液态水凝物与降水率的滞后相关系数决定的,只有约1/4的滞后相关系数是由固态水凝物与降水率的滞后相关系数贡献的.CVV和MVV的垂直分量与云中水凝物具有非常好的相关性(同时及滞后),与降水率的相关也较好,可以代表云和对流系统的发展.CVV和MVV垂直分量的局地变化超前降水率3 h左右,可以作为降水发生的先兆,对降水的预报有潜在的意义.CVV垂直分量的局地变化与降水率的相关系数大于MVV垂直分量的局地变化与降水率的相关系数,因此在实际预报中可以利用CVV垂直分量的局地变化来估计未来降水的变化.     

华南区域高分辨率模式中不同雷达回波反演技术方案的比较试验

结合一次比较典型的华南前汛期暴雨过程,对目前常用的几种雷达回波反演技术进行比较,试图得到一种适用于华南区域高分辨率模式的技术方案。首先比较了两种不同的水凝物反演技术对短期降水预报的影响,发现基于暖雨假设构造的经验公式反演得到的云微物理量进行Nudging以后预报降水比实况偏弱,而利用LAPS系统可以反演出高层的固态粒子,这些云粒子通过冰相过程可以产生强度与实况相当的降水。仅仅引入初始场的凝结物只对第一小时降水预报改进比较明显。通过对两种不同的水汽反演技术进行比较,发现1D+3D方法反演得到的水汽场并不适用于高分辨率模式,按该方法调整初始场水汽之后反而导致降水预报效果变差,而根据雷达回波调整水汽饱和的方法却可以有效提高前6小时的降水评分,这种改进主要来自降水漏报现象的减少。总体来说,结合利用LAPS系统反演云物理量以及水汽饱和调整方法是一套比较适合华南区域高分辨率模式的技术路线。     

CINRAD/CD雷达反射率因子同化对中尺度数值模式云微物理量场调整的分析

以ARPS模式及其资料三维同化系统ARPS3DVAR和复杂云分析模块为研究平台,使用贵阳多普勒雷达的体扫观测资料,分析了雷达反射率因子资料对云微物理量场的同化问题及雷达资料质量控制对同化的影响.结果表明,数值试验中两个不同的云分析方案都能同化出合理的云水场和水凝物场,同时因为水凝物场的调整,位温场和垂直速度场也得到了很好的响应;与没有使用云分析方案的试验比较,使用了云分析方案同化雷达资料的试验,在初始时刻能够调整出一个合理的云微物理量场,减少模式的热启动时间;而没有使用云分析方案的试验在2～3 h后才能调整出一个位置有较大偏差的水凝物场,雷达反射率资料的同化在强对流风暴的模拟中起到了一个非常关键的作用.     

GRAPES三维云初始场形成及在短临预报中的应用

围绕GRAPES_Meso的云初始场形成,以ARPS模式云分析方案为基础,优化诊断后应用我国风云二号静止气象卫星云产品、多普勒天气雷达三维组网拼图产品等观测资料结合模式背景信息,根据云热力-动力学原理及观测试验经验关系等,对云初始场的信息进行分析并通过松弛逼近同化方法实现对云内微物理信息同化应用。GRAPES_Meso中采用优化后的云初始场方案,水平分辨率为0.03°×0.03°和0.1°×0.1°的1个月（2014年7月15日-8月14日）连续试验和个例分析结果显示:云初始场形成方案能够分析出飑线等天气系统的云系和云内微物理变量特征。从模拟云图看,包含云初始场信息的GRAPES_Meso的云系的形态特征和分布范围短时临近预报结果更为准确。云初始场信息同化应用后,在1 h的时间尺度上,即可预报出与实况更为接近的降水;0~12 h时间范围内对降水均有积极的影响,可满足短时临近预报的需求,降水量级略偏大。批量连续试验（水平分辨率为0.03°×0.03°和0.1°×0.1°）的各个量级降水ETS（equitable threat score）评分都显著提高。     

基于雷达资料的云分析在冰雹云短时预报中的应用

通过ARPS/ADAS云分析系统将雷达反射率因子信息引入到模式的初始场中,并结合中尺度数值模式模拟研究了2008年6月23日发生在北京周边地区的一次冰雹天气过程。结果表明:在初始场中引入雷达反射率因子信息之后,改进了初始场中水凝物信息和温度场,使得初始场更加符合实际大气状况;与未进行云分析试验比较,云分析之后,模式模拟对于雹云产生位置、强度以及移动路径有明显改进,其中3h之内的模拟效果最好;降水降雹提早1h发生,并且降水降雹的峰值也提前出现,明显减少模式spin-up时间。     

NUMERICAL EXPERIMENTS AND ANALYSIS OF DIGITAL FILTER INITIALIZATION FOR WRF MODEL

Initialization and initial imbalance problem were discussed in the context of a three-dimensional variational data assimilation system of the new generation “Weather Research and Forecasting Model”. Several options of digital filter initialization have been tested with a rain storm case. It is shown that digital filter initialization, especially diabatic digital filter initialization and twice digital filter initialization, have effectively removed spurious high frequency noise from initial data for numerical weather prediction and produced balanced initial conditions. For six consecutive intermittent data assimilation cycles covering a 3-day period, mean initialization increments and impact on forecast variables are studied. DFI has been demonstrated to provide better adjustment of the hydrometeors and vertical velocity, reduced spin-up time, and improved forecast variables quantity.     

Assimilation of Radar and Cloud-to-Ground Lightning Data Using WRF-3DVar Combined with the Physical Initialization Method——A Case Study of a Mesoscale Convective System

Radar data, which have incomparably high temporal and spatial resolution, and lightning data, which are great indicators of severe convection, have been used to improve the initial field and increase the accuracies of nowcasting and short-term forecasting. Physical initialization combined with the three-dimensional variational data assimilation method(PI3 DVar_rh) is used in this study to assimilate two kinds of observation data simultaneously, in which radar data are dominant and lightning data are introduced as constraint conditions. In this way, the advantages of dual observations are adopted. To verify the effect of assimilating radar and lightning data using the PI3 DVar_rh method, a severe convective activity that occurred on 5 June 2009 is utilized, and five assimilation experiments are designed based on the Weather Research and Forecasting(WRF) model. The assimilation of radar and lightning data results in moister conditions below cloud top, where severe convection occurs; thus, wet forecasts are generated in this study.The results show that the control experiment has poor prediction accuracy. Radar data assimilation using the PI3 DVar_rh method improves the location prediction of reflectivity and precipitation, especially in the last 3-h prediction, although the reflectivity and precipitation are notably overestimated. The introduction of lightning data effectively thins the radar data, reduces the overestimates in radar data assimilation, and results in better spatial pattern and intensity predictions. The predicted graupel mixing ratio is closer to the distribution of the observed lightning,which can provide more accurate lightning warning information.     

Variational assimilation of satellite cloud water/ice path and microphysics scheme sensitivity to the assimilation of a rainfall case

Hydrometeor variables (cloud water and cloud ice mixing ratios) are added into the WRF three-dimensional variational assimilation system as additional control variables to directly analyze hydrometeors by assimilating cloud observations. In addition, the background error covariance matrix of hydrometeors is modeled through a control variable transform, and its characteristics discussed in detail. A suite of experiments using four microphysics schemes (LIN, SBU-YLIN, WDM6 and WSM6) are performed with and without assimilating satellite cloud liquid/ice water path. We find analysis of hydrometeors with cloud assimilation to be significantly improved, and the increment and distribution of hydrometeors are consistent with the characteristics of background error covariance. Diagnostic results suggest that the forecast with cloud assimilation represents a significant improvement, especially the ability to forecast precipitation in the first seven hours. It is also found that the largest improvement occurs in the experiment using the WDM6 scheme, since the assimilated cloud information can sustain for longer in this scheme. The least improvement, meanwhile, appears in the experiment using the SBU-YLIN scheme.     

Use of precipitation data for diabatic initialization to improve the tropical analysis of divergence and moisture

Summary First, we review the present status of diabatic initialization used for numerical weather prediction and conclude that the deficiency of diabatic initialization mostly stems from the shortcoming in evaluating diabatic heating rates accurately, particularly the release of latent heat by cumulus convection. This indicates the need to adjust the initial conditions for physical processes, and Krishnamurti and his colleagues introduced in 1984 the concept of physical initialization. Since cumulus convection is most sensitive to input data among many physical processes, the adjustment of atmospheric input data to a prediction model to produce desired initial precipitation rates is referred to as cumulus initialization.In this article we describe a general approach to diabatic initialization with a special emphasis on cumulus initialization. We present the results of forecasting experiments with a version of the NCAR Community Climate Model (CCM) to demonstrate the efficacy of a cumulus initialization procedure to ameliorate the spinup problern of precipitation. Finally, we discuss application of the present methodology of cumulus initialization for a stability-dependent mass-flux cumulus parameterization of CCM2 to pave the way to complete the diabatic normal mode initialization package for CCM2. Note that the present cumulus initialization scheme can be used to assimilate into the atmospheric analysis of the tropics the precipitation rates estimated by satellite radiometric imagery data.With 8 FiguresThe National Center for Atmospheric Research is sponsored by the National Science Foundation     

ON USE OF LHN METHOD TO ASSIMILATE THE INTENSIFIED SURFACE PRECIPITATIONS FOR GRAPES_MESO MODEL INITIALIZATION

The quantitative precipitation forecast (QPF) in very-short range (0-12 hours) has been investigated in this paper by using a convective-scale (3km) GRAPES_Meso model. At first, a latent heat nudging (LHN) scheme to assimilate the hourly intensified surface precipitation data was set up to enhance the initialization of GRAPES_Meso integration. And then based on the LHN scheme, a convective-scale prediction system was built up in considering the initial “triggering” uncertainties by means of multi-scale initial analysis (MSIA), such as the three-dimensional variational data assimilation (3DVAR), the traditional LHN method (VAR0LHN3), the cycling LHN method (CYCLING), the spatial filtering (SS) and the temporal filtering (DFI) LHN methods. Furthermore, the probability matching (PM) method was used to generate the QPF in very-short range by combining the precipitation forecasts of the five runs. The experiments for one month were carried out to validate the MSIA and PM method for QPF in very-short range. The numerical simulation results showed that: (1) in comparison with the control run, the CYCLING run could generate the smaller-scale initial moist increments and was better for reducing the spin-up time and triggering the convection in a very-short time; (2) the DFI runs could generate the initial analysis fields with relatively larger-scale initial increments and trigger the weaker convections at the beginning time (0-3h) of integration, but enhance them at latter time (6-12h); (3) by combining the five runs with different convection triggering features, the PM method could significantly improve the QPF in very-short range in comparison to any single run. Therefore, the QPF with a small size of combining members proposed here is quite prospective in operation for its lower computation cost and better performance.