利用WRF3D-Var同化多普勒雷达反演风场试验研究

为了将C波段雷达风场资料更好地应用于数值预报模式中,利用两步变分法反演多普勒雷达风场资料,并处理成标准的常规探空资料,以WRF模式及其三维变分同化系统为平台,针对2013年6月19日发生在天水的一次强暴雨过程进行同化雷达反演风的试验研究.试验结果表明:同化雷达反演风场后,对降水预报的改进能维持12h,尤其同化雷达反演风场后3~9h效果非常显著;0~3h作用不是很明显;9~12h预报具有一定的正作用.另外,循环同化比同化一次效果好,但并不是同化次数越多越好.因此,同化C波段雷达反演风场后,对降水预报具有一定的正作用.     

Impact of AWS observations in WRF-3DVAR data assimilation system: a case study on abnormal warming condition in Odisha

An abnormal warming condition with 3?C5?°C rise in temperature above its normal value was observed in the Indian state of Odisha during 12?C16 November 2009. This study aims at examining the impact of additional weather observations obtained from the automatic weather stations (AWS) installed in the recent past on the numerical simulation of such abnormal warming. AWS observations, such as temperature at 2?m (T2m), dew point temperature at 2?m (Td2m), wind vector at 10?m (speed and direction), and sea level pressure (SLP) have been assimilated into the state-of-the-art Weather Research and Forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). Six sets of experiments have been conducted here. There is no data assimilation in the control experiment, whereas AWS and radiosonde observations have been assimilated in rest of the five experiments. The model integrations have been made for 72?h in each experiment starting from 0000 UTC November 12 to 0000 UTC November 15, 2009. Assimilation experiments have also been performed to assess the impact of individual surface parameters on the model simulations. Impact of AWS observations on model simulation has been examined with reference to the control simulation and quantified in terms of root-mean-square error and forecast skill score for temperature, sea level pressure, and relative humidity at three selected stations Bonaigarh, Brahmagiri, and Nuapada in Odisha. Results indicate improvements in the surface air temperature and SLP simulations in the timescale of 72?h at all the three stations due to additional weather data assimilation into the model. Improvements in simulation are significant up to 24?h. The assimilation of additional wind fields significantly improved the temperature simulation at all the three stations. The simulated SLP has also improved significantly due to the assimilation of surface temperature and moisture.     

多普勒天气雷达风场反演技术研究进展

多普勒天气雷达是研究中小尺度天气系统的重要工具，但却只能提供风场的径向速度，因而必须通过风场反演技术来求解二维或三维风场。详细分析了各种单部、多部和多基地多普勒天气雷达风场反演技术及其优缺点，同时指出了应重点研究的方向。从反演结果来看，基于变分法的反演技术明显优于其它方法，这是今后的一个主要研究方向；利用四维同化理论，在数值模式初始场中使用反演数据，是反演技术应用的重点。     

Assimilation of Doppler weather radar observations in a mesoscale model for the prediction of rainfall associated with mesoscale convective systems

Obtaining an accurate initial state is recognized as one of the biggest challenges in accurate model prediction of convective events. This work is the first attempt in utilizing the India Meteorological Department (IMD) Doppler radar data in a numerical model for the prediction of mesoscale convective complexes around Chennai and Kolkata. Three strong convective events both over Chennai and Kolkata have been considered for the present study. The simulation experiments have been carried out using fifth-generation Pennsylvania State University-National Center for Atmospheric Research (PSU-NCAR) mesoscale model (MM5) version 3.5.6. The variational data assimilation approach is one of the most promising tools available for directly assimilating the mesoscale observations in order to improve the initial state. The horizontal wind derived from the DWR has been used alongwith other conventional and non-conventional data in the assimilation system. The preliminary results from the three dimensional variational (3DVAR) experiments are encouraging. The simulated rainfall has also been compared with that derived from the Tropical Rainfall Measuring Mission (TRMM) satellite. The encouraging result from this study can be the basis for further investigation of the direct assimilation of radar reflectivity data in 3DVAR system. The present study indicates that Doppler radar data assimilation improves the initial field and enhances the Quantitative Precipitation Forecasting (QPF) skill.     

Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009

This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ _e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields.     

机载多普勒天气雷达及应用研究进展

机载多普勒天气雷达由于其灵活机动性,在台风、暴雨等灾害性天气系统中尺度三维精细结构研究中发挥着重要作用.对机载多普勒天气雷达技术及其资料应用进行了概要性综述,主要从机载多普勒天气雷达发展历程、4种主要机载多普勒雷达技术特点、雷达天线扫描策略、单多普勒雷达风场反演技术、双多普勒雷达风场反演技术、雷达资料同化以及目标观测等方面进行阐述和分析;着重讨论了应用中需要解决的问题.最后,指出发展具有快速扫描和双偏振功能的机载相控阵多普勒雷达是机载天气雷达的发展方向,它可以获取高时空分辨率的探测数据,能够对云和降水系统的三维精细动力结构、热力结构以及微物理结构等进行综合研究.     

Assimilation of Indian Doppler Weather Radar observations for simulation of mesoscale features of a land-falling cyclone

In this paper, impact of Indian Doppler Weather Radar (DWR) data, i.e., reflectivity (Z), radial velocity (Vr) data individually and in combination has been examined for simulation of mesoscale features of a land-falling cyclone with Advance Regional Prediction System (ARPS) Model at 9-km horizontal resolution. The radial velocity and reflectivity observations from DWR station, Chennai (lat. 13.0°N and long. 80.0°E), are assimilated using the ARPS Data Assimilation System (ADAS) and cloud analysis scheme of the model. The case selected for this study is the Bay of Bengal tropical cyclone NISHA of 27–28 November 2008. The study shows that the ARPS model with the assimilation of radial wind and reflectivity observations of DWR, Chennai, could simulate mesoscale characteristics, such as number of cells, spiral rain band structure, location of the center and strengthening of the lower tropospheric winds associated with the land-falling cyclone NISHA. The evolution of 850 hPa wind field super-imposed vorticity reveals that the forecast is improved in terms of the magnitude and direction of lower tropospheric wind, time, and location of cyclone in the experiment when both radial wind and reflectivity observations are used. With the assimilation of both radial wind and reflectivity observations, model could reproduce the rainfall pattern in a more realistic way. The results of this study are found to be very promising toward improving the short-range mesoscale forecasts.     

A brief summary of Dr. G. V. Rao’s research interests

A brief summary of Dr. G. V. Rao's research interests is presented. Many of his earlier studies were in conjunction with the summer Monsoon Experiment of 1979 (MONEX-79). These included: 1) the structure of the Somali jet based on aerial observations; 2) sea-level air trajectories over the equatorial Indian Ocean; 3) structural features of the east African low-level flow; 4) effects of Indian Ocean surface temperature anomaly patterns on the summer monsoon circulations; 5) structures of the monsoon low-level flow over the Arabian Sea; 6) characteristics and momentum-flux budgets of the Arabian Sea convective bands; and 7) evaporation and precipitation over the Arabian Sea during the monsoon seasons. Dr. Rao's research efforts in recent years had focused on case studies of mesocyclones spawned by tropical cyclones (TCs) in Florida using Doppler radar data and a mesoscale numerical model. These included: 1) research on tornadic mesocyclones spawned by TC Earl in 1998; 2) documentation of subtle differences between tornadic and non-tornadic mesocyclones in TC Floyd in 1999; and 3) numerical simulation of the tornadic environment observed in peninsular Florida during TC Earl in 1998. Preliminary findings show that the supercells' cold pools interacted with an existing boundary resulting in increased baroclinicity and horizontal vorticity, and a maximization of the tornado production potential by the updrafts. The model successfully simulated the mesoscale features of the mesocyclones and the tornadic environment observed during TC Earl. A 24 h simulation of accumulated rainfall within the inner domain agreed well with the observed precipitation pattern over the region.     

Altimeter data assimilation in the tropical Indian Ocean using water property conserving scheme

Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially, while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact on model sea level forecast in a free run after assimilation has been quantified in a representative example.     

云分析预报方法研究进展

云作为地球大气系统的重要组成部分,不仅影响着气候变化和天气系统的发展演变,还与航空活动密切相关,一直以来是空军和民航部门非常关注的气象要素之一。在云探测、资料同化和反演方法发展的基础上,从实际业务保障和数值模式发展需求出发,综述国内外云分析、预报方法和云分析预报系统开发的研究成果,分析各类方法的优势和不足,明确国内外研究的主要差距,并探讨国内未来研究的方向。云分析方法中,探空对云廓线识别较好,卫星可见光和红外资料在云顶信息反演方面优势明显,多普勒雷达能够获取对流层中层和底层的云信息,而毫米波雷达能够很好地反映云三维结构信息,发展潜力巨大。云预报方法中,传统的统计和诊断方法发展较为成熟,而考虑了大气温湿和云微物理状况的大气辐射传输模式正演模拟云顶亮温的方法是未来的发展趋势。加强云探测技术,综合利用云分析预报方法,借鉴国外先进云分析预报系统的设计理念,积极开发我国自主的云分析预报系统,推动天气预报、航空气象保障和数值预报模式的发展将会是我国云研究的重要方面。     

雅鲁藏布江色东普沟冰崩—堵江—溃决灾害数值模拟

为了揭示雅鲁藏布江色东普沟2018年10月17日冰崩—堵江—溃决灾害链的动力演化过程,基于Massflow数值模拟仿真平台,使用Fortran编程语言,根据研究区域地质条件特征对程序进行二次开发以优化Voellmy模型,模拟冰崩—泥石流动力过程;将模拟泥石流得到的堰塞坝体嵌入地形中,运用ArcGIS计算堰塞湖范围及体积,通过Manning模型模拟堰塞湖溃决洪水动力过程。采用分段模拟法再现冰崩—泥石流—堵江—堰塞湖—溃坝的完整动力过程,对泥石流运动过程中的流速、流深,坝体高度,溃决洪水的流深、流速等参数进行定量化研究,为色东普流域的防灾减灾工作提供有效支撑。为了揭示雅鲁藏布江色东普沟2018年10月17日冰崩-堵江-溃决灾害链的动力演化过程,采用Massflow数值模拟仿真平台,以Fortran语言为编程手段,根据研究区域地质条件特征对程序进行二次开发优化Voellmy模型,模拟冰崩-泥石流动力过程;将模拟泥石流所得到的堰塞坝体嵌入地形中,采用ArcGIS计算堰塞湖范围及体积,通过曼宁模型模拟堰塞湖溃决洪水动力过程。采用分段模拟法再现冰崩-泥石流-堵江-堰塞湖-溃坝的完整动力过程,对泥石流运动过程中的流速、流深,坝体高度,溃决洪水的流深、流速等参数进行定量化研究,为色东普流域的防灾减工作提供有效支撑。     

Downscaling the 2D Bénard convection equations using continuous data assimilation

We consider a recently introduced continuous data assimilation (CDA) approach for downscaling a coarse resolution configuration of the 2D Bénard convection equations into a finer grid. In this CDA, a nudging term, estimated as the misfit between some interpolants of the assimilated coarse-grid measurements and the fine-grid model solution, is added to the model equations to constrain the model. The main contribution of this study is a performance analysis of CDA for downscaling measurements of temperature and velocity. These measurements are assimilated either separately or simultaneously, and the results are compared against those resulting from the standard point-to-point nudging approach (NA). Our numerical results suggest that the CDA solution outperforms that of NA, always converging to the true solution when the velocity is assimilated as has been theoretically proven. Assimilation of temperature measurements only may not always recover the true state as demonstrated in the case study. Various runs are conducted to evaluate the sensitivity of CDA to noise in the measurements, the size, and the time frequency of the measured grid, suggesting a more robust behavior of CDA compared to that of NA.     

A comprehensive study of surface and upper-air characteristics over two stations on the west coast of India during the occurrence of a cyclonic storm

While qualitative information from meteorological satellites has long been recognized as critical for monitoring weather events such as tropical cyclone activity, quantitative data are required to improve the numerical prediction of these events. In this paper, the sea surface winds from QuikSCAT, cloud motion vectors and water vapor winds from KALPANA-1 are assimilated using three-dimensional variational assimilation technique within Weather Research Forecasting (WRF) modeling system. Further, the sensitivity experiments are also carried out using the available cumulus convective parameterizations in WRF modeling system. The model performance is evaluated using available observations, and both qualitative and quantitative analyses are carried out while analyzing the surface and upper-air characteristics over Mumbai (previously Bombay) and Goa during the occurrence of the tropical cyclone PHYAN at the west coast of Indian subcontinent. The model-predicted surface and upper-air characteristics show improvements in most of the situations with the use of the satellite-derived winds from QuikSCAT and KALPANA-1. Some of the model results are also found to be better in sensitivity experiments using cumulus convection schemes as compared to the CONTROL simulation.     

Assimilation of satellite observations of the atmosphere

Measurements of the atmosphere by satellite were first collected in the 1960s. However, it was not until the mid-1990s that these observations were translated into systematic improvements of numerical weather forecasts. We present here the data and methodology of data assimilation that enabled this achievement. Data assimilation is essentially a filtering processing that exploits the (assumed) known error statistical properties of the observations and of the underlying numerical model in which data are assimilated. It is also a process which corrects the state of the numerical model with physical observations of the real world. This part relies on (assumed) known physical laws to relate meteorological quantities (such as temperature, humidity, pressure, and wind) to observables. Atmospheric data collected by satellite all come from the interaction of electromagnetic waves with the atmosphere. Satellite data assimilation has greatly supported the progress in numerical weather prediction and holds promises for climate studies, for example via reanalysis.     

组合式流量在线监测系统在变动回水断面的应用

为解决低流速、受变动回水影响等较复杂监测环境下的流量在线监测问题,依据声学多普勒频移及雷达波测速的方法原理,结合新运粮河入滇池口监测断面复杂的水文监测条件实际,采用非接触式雷达波传感器与接触式水平ADCP传感器组合成一套完整的在线流量监测系统,实现河流水表面流速和水下指标流速的同步实时数据采集,再将同步采集的水表面流速和水下指标流速同时输入模型模拟监测断面流场、流速分布,再计算实时流量后,通过GPRS信道传送到中心站服务器,对数据进行分析处理、存储、转录、发布,实现WEB、手机APP远程实时流量、工作状态等查询功能。经近一年的运行,系统正常稳定。     

All-sky radiance simulation of Megha-Tropiques SAPHIR microwave sensor using multiple scattering radiative transfer model for data assimilation applications

Incorporation of cloud- and precipitation-affected radiances from microwave satellite sensors in data assimilation system has a great potential in improving the accuracy of numerical model forecasts over the regions of high impact weather. By employing the multiple scattering radiative transfer model RTTOV-SCATT, all-sky radiance (clear sky and cloudy sky) simulation has been performed for six channel microwave SAPHIR (Sounder for Atmospheric Profiling of Humidity in the Inter-tropics by Radiometry) sensors of Megha-Tropiques (MT) satellite. To investigate the importance of cloud-affected radiance data in severe weather conditions, all-sky radiance simulation is carried out for the severe cyclonic storm ‘Hudhud’ formed over Bay of Bengal. Hydrometeors from NCMRWF unified model (NCUM) forecasts are used as input to the RTTOV model to simulate cloud-affected SAPHIR radiances. Horizontal and vertical distribution of all-sky simulated radiances agrees reasonably well with the SAPHIR observed radiances over cloudy regions during different stages of cyclone development. Simulated brightness temperatures of six SAPHIR channels indicate that the three dimensional humidity structure of tropical cyclone is well represented in all-sky computations. Improved correlation and reduced bias and root mean square error against SAPHIR observations are apparent. Probability distribution functions reveal that all-sky simulations are able to produce the cloud-affected lower brightness temperatures associated with cloudy regions. The density scatter plots infer that all-sky radiances are more consistent with observed radiances. Correlation between different types of hydrometeors and simulated brightness temperatures at respective atmospheric levels highlights the significance of inclusion of scattering effects from different hydrometeors in simulating the cloud-affected radiances in all-sky simulations. The results are promising and suggest that the inclusion of multiple scattering radiative transfer models into data assimilation system can simulate the cloud-affected microwave radiance data which provide detailed information on three dimensional humidity structure of the atmosphere in the presence of cloud hydrometeors.     

Estimating the masses of the spherical and disk components of galaxies via numerical simulations

A series of numerical N-body simulations is performed in order to dynamically model the properties of four galaxies (NGC 5603, NGC 3198, NGC 891, and NGC 1566) with known rotation curves, radial disk scales L, and velocity dispersions of old disk stars at various galactocentric distances r. Each model includes a three-dimensional collisionless disk and rigid spherical components, whose relative mass μ was treated as a free parameter that differed from simulation to simulation. The observed disk stellar velocity dispersions were assumed to be equal to or (in the general case) greater than the corresponding line-of-sight projections of the simulated values for the adopted μ after the initially unstable disk is heated and arrives at a steady state. A comparison of the simulated and observed rotational velocities and velocity dispersions provides evidence for “light” disks with μ≥2 in the disk (r<4L).     

Overview of Researches on All-Sky Satellite Microwave Data Variational Assimilation

As the important components of the earth’s atmospheric system, cloud and precipitation strongly affect the global hydrology and energy cycles through the interaction of solar and infrared radiation with cloud droplets and the release of latent heat in precipitation development. The microwave observations in cloudy and rainy conditions have a large amount of information closely related to the development of weather systems, especially the severe weather systems like typhoon and rainstorm. Nevertheless, satellite microwave observations are usually only assimilated in clear-sky above the ocean and their cloud and precipitation content is discarded. Over the past two decades, several Numerical Weather Prediction (NWP) centers have gradually developed the “all-sky” approach to make use of the cloud- and precipitation-affected microwave radiances. It’s been proved that the all-sky assimilation can be used to improve the first guessed mass, wind, humidity, cloud and precipitation through the tracer effect. For providing an investigated reference for the future research of all-weather assimilation in domestic numerical weather forecast, this paper reviewed the all-sky assimilation methods using microwave observation data, analyzed the advantages and disadvantages of each method, and discussed the key technical problems and the existing difficulties and challenges in this field. With the development and application of the new generation of NWP model in China, advancing the domestic research of all-weather data assimilation technology will bring more scientific and practical benefits in the future.     

颗粒沉降的格子Boltzmann模拟与PIV实验验证

在格子Boltzmann方法中引入大涡模拟,对球形颗粒在静水中沉降引起的紊动流场进行了数值模拟。数值模拟沉速与理论值以及粒子图像测速系统(PIV)实验结果吻合,验证了模型的合理性。同时分析比较了颗粒沉降过程中尾部紊动流场分布以及尾流流速值,发现数值模拟结果与实测结果趋势、数值基本一致,进一步说明了利用格子Boltzmann方法与大涡模拟技术相结合可以合理模拟泥沙颗粒在紊流区的沉降。     

基于土壤水模型及站点资料的土壤湿度同化方法

基于非饱和土壤水模型和扩展卡尔曼滤波(Extended Kalman Filter)同化算法并结合陆面过程模型VIC发展了一个土壤湿度同化方案,并进行了理想试验及同化站点资料的同化试验。理想试验结果表明：扩展卡尔曼滤波方法能完整反演土壤湿度廓线,对土壤湿度的估计有较大改善;观测深度、观测层数和观测资料引入频率对同化结果有一定影响;加大观测频率,可以进一步改善同化效果。利用气象强迫驱动陆面模型VIC算出地表入渗条件而进行的同化站点资料的试验所得土壤湿度分布与观测资料基本吻合,反映了站点土壤湿度的月、季变化,表明该方案是合理的。