基于GRAPES全球分析系统的Hybrid-3DVAR混合同化研究

本文基于我国自主研发的GRAPES全球3DVAR同化系统,利用NCEP全球集合预报产品和time-lagged方法,针对膨胀系数、集合样本数和集合权重系数,开展了每日4次、连续一周的GRAPES全球Hybrid-3DVAR混合同化研究。结果表明:所有试验中,集合样本取60个、集合权重取0.5时,得到的混合同化分析和预报误差最小;在该混合同化系统中,在高层也考虑静态背景误差协方差和集合背景误差协方差的耦合,可避免混合同化方案分析场误差在150 hPa及以上过大,并超过3DVAR分析场误差的情况。

利用循环3DVAR改进黄海海雾数值模拟初始场Ⅰ:WRF数值试验

以如何提高黄海海雾数值模拟初始场质量为研究目的，利用WRF模式及其先进的3DVAR同化模块，设计并构建了循环3DVAR同化方案。以2006年3月6～8日的1次大范围黄海海雾过程为研究对象，利用该同化方案进行了一系列WRF数值模拟对比试验。模拟结果显示，循环3DVAR同化方案能有效改进黄海海雾数值模拟初始场质量，主要体现在增加低层大气温度层结构的稳定性与改变大气边界层下层的风场结构，从而导致海雾的模拟结果显著改善。研究结果表明进行海雾数值模拟时，必须高度重视其初始场质量。

利用循环3DVAR改进黄海海雾数值模拟初始场Ⅱ:RAMS数值试验

第一部分的结果显示,利用WRF模式及其循环3DVAR同化方案(WRF循环3DVAR)能有效地提高黄海海雾WRF数值模拟的初始场质量。在此基础上,进一步提出利用WRF循环3DVAR形成的初始场驱动RAMS模式的思路,并以2006年3月6～8日的1次大范围黄海海雾事件为研究对象,设计并实施了一系列RAMS数值模拟对比试验。对试验结果的仔细分析表明,WRF循环3DVAR提供的初始场明显优于RAMS模式自身等熵面客观分析方法生成的初始场,它在动力与物理上非常协调且对模拟结果的改善相当显著。这说明WRF循环3DVAR可以为RAMS模式改进其初始场提供一条切实可行的途径。

Hybrid ETKF-3DVAR方法同化多普勒雷达速度观测资料Ⅰ:模拟资料试验

利用WRF(Weather research and forecasting)模式及模式模拟的资料,采用Hybrid ETKF-3DVAR(ensemble transform Kalman filter-three-dimensional variational data assimilation)方法同化模拟雷达观测资料。该混合同化方法将集合转换卡尔曼滤波(ensemble transform Kalman filter)得到的集合样本扰动通过转换矩阵直接作用到背景场上,利用顺序滤波的思想得到分析扰动场;然后通过增加额外控制变量的方式把"流依赖"的集合协方差信息引入到变分目标函数中去,在3DVAR框架基础下与观测数据进行融合,从而给出分析场的最优估计。试验结果表明,Hybrid ETKF-3DVAR同化方法相比传统3DVAR可以提供更为准确的分析场,Hybrid方法雷达资料初始化模拟的台风涡旋结构与位置比3DVAR更加接近"真实场",对台风路径预报也有明显改进。通过对比Hybrid S试验与Hybrid F试验发现,Hybrid的正效果主要来源于混合背景误差协方差中的"流依赖"信息,集合平均场代替确定性背景场带来的效果并不显著。

GRAPES-3Dvar雷达资料直接同化对江淮暴雨数值预报影响的分析研究

使用具备多普勒雷达资料直接同化功能的GRAPES-3Dvar同化系统,对2007年7月江淮流域连续性暴雨过程中4个个例进行了数值预报试验,结果表明多普勒径向速度以及基本反射率因子的直接同化可以有效改进中尺度降水数值预报的效果。对7月3日雷达资料联合同化以及单独同化对分析和预报影响的进一步分析显示,联合同化能够有效改善初始分析场中的风场和湿度信息,激发模式降水,减小SPIN-UP现象,中尺度系统降水的发生发展以及强度、走势和落区的预报得到较为准确的表征,显著改善了中尺度强降水数值预报效果。径向速度的直接同化主要贡献表现在风场上,能够在初始场中增加气旋性涡旋等中小尺度风信息,改善三维风场分析。反射率直接同化的主要影响体现在湿度场上,改进了初始场中的湿度参数,对降水预报影响明显。

基于Hybrid EnSRF-En3DVar的雷达资料同化研究

基于WRF模式构建了Hybrid En SRF-En3DVar同化系统,该系统使用En SRF方案直接更新集合扰动。利用构建的同化系统针对台风"桑美"分别进行集合协方差权重敏感性试验和同化雷达不同观测资料的敏感性试验。集合协方差权重敏感性试验发现:当集合协方差权重分别为0.25、0.5和0.75时,同化效果优于3DVar试验,其中0.75的集合协方差权重试验得到了分析场的最优估计;当集合协方差权重为1.0时,分析场最差。同化雷达不同观测资料的敏感性试验表明,联合同化雷达径向风及反射率能有效改善大气湿度场和风场,但对风场的改善效果不如仅同化雷达径向风好。将En SRF集合扰动更新方案与扰动观测方案综合分析发现,扰动观测方案集合离散度较小,计算代价大,En SRF方案优于扰动观测方案。

SVD-En3DVar方法同化多普勒雷达速度观测资料Ⅰ.模拟资料试验

利用WRF模式及模式模拟的资料,开展了利用SVD-En3DVar(基于集合和SVD技术的三维变分同化方法)方法同化雷达径向速度资料的试验。由于雷达观测经常出现大面积空缺,同化时引入了一种局地化方法避免远距离虚假相关的影响。试验着重研究了不同的初始扰动样本产生方法以及不同的样本积分时间对同化结果的影响。提出了一种为预报集合提供初始扰动场的新方法,这一方法将温度和比湿的伪随机扰动场当作观测增量,通过3DVar(three-di mensional variational technique)系统生成所有变量的初始扰动场。试验表明,用这种方法给出的初始扰动样本各个变量间有较好的协调性,积分后扰动不会快速衰减,可以减少模式调整的时间,达到缩短同化循环时间窗的目的。同化雷达径向风资料后对12小时内的温度,湿度和水平风的预报都有所改进,对降水的预报也有一定改进。

利用WRF-3DVAR同化常规观测资料对近地层风速预报的改进试验

利用WRF 3DVAR同化系统和Evenson提出的方法在分析时刻添加扰动形成扰动型初始场,并在此基础上同化常规观测资料(包括固定站点的探空观测和常规地面观测),通过比较WRF3DVAR系统对整层大气的初始(分析)场及预报场的改进,检验了同化常规观测资料后WRF模式对研究区域内近地层风速的预报效果。结果表明,同化常规观测资料对初始场有改进,且对低层大气风场和温度场的改进较FNL资料明显;GFS同化常规观测资料后生成的初始场和FNL资料提供的初始场对风速和温度的预报在不同气压层存在不同的预报时效,低层(1000 hPa)风速和温度预报在前36 h改善明显,而在较高的500 hPa和200 hPa上风速预报在前36 h改进明显,温度预报则只在前12 h得到了改善;无论是采用FNL资料还是同化常规观测资料作为初始场,对近地层风速预报均在前36 h有改进,表明对于近地面风速这种短时间内变化较大的变量,同化与之时间和空间上较为接近的资料可能改善其预报效果。

SVD-En3DVar方法同化多普勒雷达速度观测资料Ⅱ.实际资料试验

文章的第I部分（徐道生等,2011）将基于SVD（singular value decomposition）技术和预报集合的三维变分同化方法（SVD-En3DVar）用于同化模拟的雷达速度观测资料,试验表明,通过3DVar（threedi mensional variational technique）方法产生预报集合的初始扰动场,可以缩短SVD-En3DVar中预报样本的积分时间,同化对改进暴雨的短期预报有一定好处。本文进一步将这一方法用于同化实际观测资料。选择2008年6月华南地区和2003年7月江淮地区的两个暴雨个例进行同化试验,并将其与WRF-3DVar（3DVar based onthe weather researchforecasting model）的同化结果进行比较。结果表明,同化雷达径向风资料以后,在模式初始场中包含了更多的中小尺度信息。对于使用了13部雷达资料的第一个个例,经SVD-En3DVar同化以后对18小时内每6小时一次的累计降水预报都有所改进,而WRF-3DVar的同化效果则不明显。对于只同化1部雷达观测资料的第二个个例,WRF-3DVar和SVD-En3DVar方法同化以后对前6小时的降水预报都有所改进,但对于第6~18小时的降水预报,两种方法都没有改进。

自动站资料在WRF 3DVAR中的同化敏感性试验

为了了解国家级地面自动观测站不同观测要素对数值预报作用,利用WRF三维变分同化系统对自动站资料的不同观测要素开展同化&预报试验。两个月的数值预报检验结果表明:1)只同化自动站温度观测对位势高度、温度和相对湿度场预报的影响作用最大,且主要为负作用,而同化气压或风向风速观测对上述要素预报影响作用比同化温度小,但会改善位势高度预报和部分温度预报,对相对湿度预报改善作用不明显;2)对于降水预报,同化风向风速方案的评分结果最差,同化温度的方案次之,同化气压的方案可以改善24h降水预报结果。由此看出用WRF 3DVAR同化地面观测资料时,气压观测十分重要,同时也说明要想用好地面观测资料,让其为数值预报提高发挥最大作用仍需开展很多研究工作。

CMA-TYM混合En3DVar方案的设计和初步试验

基于CMA-TYM的3DVar系统,发展了利用扩展控制变量引入流依赖背景误差协方差(集合扰动成员统计表达)的混合En3DVar同化方案。测试显示,单点台风中心气压数据同化会引起风场非对称增量形成,以及导致传统3DVar方案认为不相关的湿度增量出现。台风个例试验表明,混合En3DVar方案能提取台风内零散观测资料信息,并按实际台风动力特征和分布区域向四周传播,从而影响分析场中台风涡旋强度、结构。同时,与3DVar方案相比,混合En3DVar方案对提高台风路径、强度预报效果明显。

GRAPES Hybrid-3DVar对台风苏迪罗的数值试验

为了把反映天气形势变化的背景误差协方差引入到变分分析系统中来提高分析质量,本文在GRAPES区域三维变分框架的基础上通过扩展控制变量方法实现动态与静态背景误差协方差耦合,建立混合三维变分分析系统(GRAPES Hybrid-3DVar)。通过控制变量扰动产生的集合样本进行单点观测分析试验验证Hybrid-3DVar及其局地化方案的合理性,并针对台风苏迪罗进行实际观测资料同化和数值预报试验,结果表明:用集合样本描述的背景误差协方差是随着天气流型变化的,动力场和质量场的离散度在台风中心处最大,因而混合同化的分析增量包含更多细微结构和中小尺度信息;其分析和24 h内预报要素质量优于3DVar,24 h内降水强度和落区预报也更准确,混合同化分析改善了3DVar分析的降水空报问题;同时混合同化分析的24 h内台风路径预报也最接近实况,台风强度预报在48 h之内都比3DVar更接近观测。

Effect of Length Scale Tuning of Background Error in WRF-3DVAR System on Assimilation of High-Resolution Surface Data for Heavy Rainfall Simulation

We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.

NUMERICAL EXPERIMENTS ON MULTI-LEVEL STATISTICAL ESTIMATION OF DYNAMIC BALANCE CONSTRAINTS IN GRAPES-3DVAR

This paper further explores the estimating and expressing of dynamic balance constraints using statistical methods in GRAPES-3DVAR(Version GM). Unlike the single-level scheme which only considers the coupling between mass and wind at one level, the multi-level scheme considers the coupling between their vertical profiles and calculates the balanced mass field at each layer using the rotational wind at all model levels. A reformed ridge regression method is used in the new scheme to avoid the multicollinearity problem and reduce the noises caused by unbalanced mesoscale disturbances. The results of numerical experiments show that the new scheme can get more reasonable vertical mass field, reduce the magnitude of the adjustment by the initialization, and improve the potential temperature analysis performance. Furthermore, the results of forecast verification in January(winter) and July(summer) both confirm that the new scheme can significantly improve the temperature forecast accuracy and bring slight positive effects to the pressure and wind forecast.

A PRELIMINARY STUDY ON THE 3DVAR ASSIMILATION OF THE AMSU-A DATA IN SPACE-TIME MULTISCALE ANALYSIS SYSTEM

Assimilating satellite radiances into Numerical Weather Prediction(NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA's STMAS(Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data.Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5-11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0-6h precipitation forecast significantly.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

Altimetric Data Assimilation by EnOI and 3DVAR in a Tropical Pacific Model:Impact on the Simulation of Variability

When altimetric data is assimilated, 3DVAR and Ensemble Optimal Interpolation （EnOI） have different ways of projecting the surface information downward. In 3DVAR, it is achieved by minimizing a cost function relating the temperature, salinity, and sea level. In EnOI, however, the surface information is propagated to other variables via a stationary ensemble. In this study, the differences between the two methods were compared and their impacts on the simulated variability were evaluated in a tropical Pacific model. Sea level anomalies （SLA） from the TOPEX/Poseidon were assimilated using both methods on data from 1997 to 2001 in a coarse resolution model. Results show that the standard deviation of sea level was improved by both methods, but the EnOI was more effective in the central/eastern Pacific. Meanwhile, the SLA evolution was better reproduced with EnOI than with 3DVAR. Correlations of temperature with the reanalysis data increased with EnOI by 0.1 0.2 above 200 m. In the eastern Pacific below 200 m, the correlations also increased by 0.2. However, the correlations decreased with 3DVAR in many areas. Correlations with the independent TAO profiles were also compared at two locations. While the correlations increased by up to 0.2 at some depths with EnOI, 3DVAR generally reduced the correlations by 0.1 0.3. Though both methods were able to reduce the model-data difference in climatological sense, 3DVAR appears to have degraded the simulated variability, especially during E1 Nifio-Southern Oscillation events. For salinity, similar results were found from the correlations. This tendency should be considered in future SLA assimilations, though the comparisons may vary among different model implementations.

WRF-3DVAR同化GPS掩星资料对强对流天气预报的影响

利用WRF三维变分同化系统,同化GPS掩星折射率资料,对2012年4月30日的强对流天气进行同化试验,分析同化折射率资料对于我国春季大范围强对流天气模拟的影响。结果表明:通过同化GPS掩星观测资料,可以明显调整和改善分析场的风、温度和湿度,其中,温度场的调整集中在我国北方地区850hPa以上的高度层;水汽的调整以中低层大气负的调整为主,最大可达-0.89g/kg;中纬度地区的高层风场都有明显的改善。青藏高原以风场调整为主,增加了西北风的分量,温度场也有改善。从模式结果对比发现,同化GPS掩星折射率资料,对高原稀疏的云团以及锋面云系有更好地描述;由于较准确地描述了低层的风场信息,模式对成都双流机场区域的小时降水量模拟也有较好效果。

Analyses and Forecasts of a Tornadic Supercell Outbreak Using a 3DVAR System Ensemble

As part of NOAA＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

测风塔资料在风电场风速预报中的应用研究

以2023年4月18日发生在甘肃省的一次大风天气过程为例,选取GFS(全球预报系统)0.25°×0.25°再分析资料,基于WRF模式及其三维变分同化系统(WRF-3DVAR),利用测风塔资料和常规观测资料进行数值同化试验,分析测风塔资料和常规观测资料同化对改进数值模式初始场的影响及其后续预报的机制。结果表明:同化试验能有效地改进初始场,使用常规观测资料和测风塔资料可有效地修正初始场的10 m风场、2 m温度场,改善预报效果;通过对单点的近地层风速进行检验,发现同化测风塔资料和常规观测资料对近地层风速的改进效果最佳,同化常规观测资料的改进效果次之,且同化观测资料对模式预报具有时效性,不同高度上的风速预报效果均在前36 h有明显的改善。