三维变分和物理初始化方法相结合同化多普勒雷达资料的试验研究

以美国新近研发的天气研究预报模式(WRF)配置的三维变分(3D-Var)同化系统WRF 3D-Var为平台,结合物理初始化方法(Physical Initialization,简称PI)来同化多普勒雷达径向风和回波强度观测资料.其基本做法是首先用物理初始化方法由雷达回波资料估计出比湿、云水混合比和垂直速度,然后用估计的比湿和云水混合比对模式的相应变量进行调整,最后再将估计出的垂直速度作为一种新的观测类型添加到现有的WRF 3D-Var目标函数中,同时以WRF 3D-Var提供的方法直接同化径向风.针对2002年6月19日的一次强对流性降水过程和2003年7月5日的一次梅雨锋暴雨过程进行了一组同化多普勒雷达径向风和回波资料的试验研究.同化结果表明:分析变量的增量场和观测的雷达回波有很好的对应关系.在雷达回波区,有正的比湿增量、云水含量增量和垂直速度增量,并且水平风增量在此辐合;在没有雷达回波的地方有负的垂直速度增量.预报结果表明,调整云水含量对降水预报改善不明显,调整比湿对降水预报改进明显,直接用物理初始化估计出的垂直速度替代模式的初始垂直速度,对降水预报改进不明显,但以新的方案同化雷达资料能有效地缩短模式的起转时间(spin-up time),明显改进短时降水预报.     

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.     

Evaluation of All-Sky Assimilation of FY-3C/MWHS-2 on Mei-yuPrecipitation Forecasts over the Yangtze-Huaihe River Basin

正1School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610025, China2International Center for Climate and Environment Sciences, Institute of Atmospheric Physics,Chinese Academy of Sciences, Beijing 100029, China3University of Chinese Academy of Sciences, Beijing 100049, China     

EVALUATION OF CONVECTIVE-STRATIFORM RAINFALL SEPARATION SCHEMES BY PRECIPITATION AND CLOUD STATISTICS

In this study,two convective-stratiform rainfall partitioning schemes are evaluated using precipitation and cloud statistics for different rainfall types categorized by applying surface rainfall equation on grid-scale data from a two-dimensional cloud-resolving model simulation.One scheme is based on surface rainfall intensity whereas the other is based on cloud content information.The model is largely forced by the large-scale vertical velocity derived from the Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment(TOGA COARE).The results reveal that over 40% of convective rainfall is associated with water vapor divergence,which primarily comes from the rainfall type with local atmospheric drying and water hydrometeor loss/convergence,caused by precipitation and evaporation of rain.More than 40% of stratiform rainfall is related to water vapor convergence,which largely comes from the rainfall type with local atmospheric moistening and hydrometeor loss/convergence attributable to water clouds through precipitation and the evaporation of rain and ice clouds through the conversion from ice hydrometeor to water hydrometeor.This implies that the separation methods based on surface rainfall and cloud content may not clearly separate convective and stratiform rainfall.     

用多普勒雷达反射率调整模式大气的云微物理变量

一种简单云分析方案, 用于由多普勒天气雷达反射率反演中尺度大气模式初值分析中的云微物理变量（云水混合比和雨水混合比）和空气湿度变量（比湿），使模式积分初始场反映出观测空间的云微物理特征以及哪些空间位置上的大气处于饱和状态。应用于2002年6月梅雨期安徽省马鞍山市一次降水过程的临近数值预报试验结果表明，模式预报的大气综合反射率与雷达观测的回波图像相近，由云微物理变量变化表示的模式云系演变与雷达观测的回波图像一致, 伴随模拟的中小尺度云系, 模式大气能很快调整出合理的中小尺度流场辐散、辐合结构；它们明显好于模式初始场不引入雷达反射率时的结果，即这种方法对改进临近数值天气预报准确率是有效的。     

The WRF 3DVar System Combined with Physical Initialization for Assimilation of Doppler Radar Data

The three-dimensional variational data assimilation (3DVar) system of the Weather Research and Forecasting (WRF) model (WRF-Var) is further developed with a physical initialization (PI) procedure to assimilate Doppler radar radial velocity and reflectivity observations. In this updated 3DVar system, specific humidity, cloud water content, and vertical velocity are first derived from reflectivity with PI, then the model fields of specific humidity and cloud water content are replaced with the modified ones, and finally, the estimated vertical velocity is added to the cost-function of the existing WRF-Var (version 2.0) as a new observation type, and radial velocity observations are assimilated directly by the method afforded by WRF-Var. The new assimilation scheme is tested with a heavy convective precipitation event in the middle reaches of Yangtze River on 19 June 2002 and a Meiyu front torrential rain event in the Huaihe River Basin on 5 July 2003. Assimilation results show that the increments of analyzed variables correspond well with the horizontal distribution of the observed reflectivity. There are positive increments of cloud water content, specific humidity, and vertical velocity in echo region and negative increments of vertical velocity in echo-free region where the increments of horizontal winds present a clockwise transition. Results of forecast experiments show that the effects of adjusting cloud water content or vertical velocity directly with PI on forecast are not obvious. Adjusting specific humidity shows better performance in forecasting the precipitation than directly adjusting cloud water content or vertical velocity. Significant improvement in predicting precipitation as well as in reducing the model's spin-up time are achieved when radial velocity and reflectivity observations are assimilated with the new scheme.     

The implementation of reverse Kessler warm rain scheme for radar reflectivity assimilation using a nudging approach in New Zealand

Reverse Kessler warm rain processes were implemented within the Weather Research and Forecasting Model (WRF) and coupled with a Newtonian relaxation, or nudging technique designed to improve quantitative precipitation forecasting (QPF) in New Zealand by making use of observed radar reflectivity and modest computing facilities. One of the reasons for developing such a scheme, rather than using 4D-Var for example, is that radar VAR scheme in general, and 4D-Var in particular, requires computational resources beyond the capability of most university groups and indeed some national forecasting centres of small countries like New Zealand. The new scheme adjusts the model water vapor mixing ratio profiles based on observed reflectivity at each time step within an assimilation time window. The whole scheme can be divided into following steps: (i) The radar reflectivity is firstly converted to rain water, and (ii) then the rain water is used to derive cloud water content according to the reverse Kessler scheme; (iii) The cloud water content associated water vapor mixing ratio is then calculated based on the saturation adjustment processes; (iv) Finally the adjusted water vapor is nudged into the model and the model background is updated. 13 rainfall cases which occurred in the summer of 2011/2012 in New Zealand were used to evaluate the new scheme, different forecast scores were calculated and showed that the new scheme was able to improve precipitation forecasts on average up to around 7 hours ahead depending on different verification thresholds.     

A Modified Double-Moment Bulk Microphysics Scheme Geared toward the East Asian Monsoon Region

Representation of cloud microphysical processes is one of the key aspects of numerical models. An improved doublemoment bulk cloud microphysics scheme(named IMY) was created based on the standard Milbrandt-Yau(MY) scheme in the Weather Research and Forecasting(WRF) model for the East Asian monsoon region(EAMR). In the IMY scheme, the shape parameters of raindrops, snow particles, and cloud droplet size distributions are variables instead of fixed constants.Specifically, the shape parameters of r...     

The Regularized WSM6 Microphysical Scheme and Its Validation in WRF 4D-Var

A cold cloud assimilation scheme was developed that fully considers the water substances, i.e., water vapor, cloud water, rain, ice, snow, and graupel, based on the single-moment WSM6 microphysical scheme and four-dimensional variational(4D-Var) data assimilation in the Weather Research and Forecasting data assimilation(WRFDA) system. The verification of the regularized WSM6 and its tangent linearity model(TLM) and adjoint mode model(ADM) was proven successful. Two groups of single observation a...     

有云环境下MODIS亮温资料的变分同化Ⅱ——对暴雨预报的影响

卫星资料提供了大量关于云和雨的观测信息,在暴雨预报中可发挥巨大的作用,然而在数值模式资料同化中的应用水平仍然不高,特别是红外辐射资料的应用。由于有云环境下辐射传输过程的模拟难度很大,因此通常只同化晴空环境下的红外辐射资料。基于GRAPES-3DVAR(Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统),根据RTTOV辐射传输模式(fast radiative transfer model for TOVS,快速辐射传输模式)的特点,增加云水含量、云冰水含量和云量作为同化系统控制变量,在改进辐射传输模式对红外资料模拟的同时,利用红外资料调整初始云参数和大气参数。针对2007年5月26日南海季风爆发后广东地区的一次暴雨过程,选取MODIS(Moderate Resolution Imaging Spectroradiometer,中分辨成像光谱仪)传感器水汽(第27)和云顶观测(第36)通道进行了同化试验,利用WRF(Weather Research and Forecasting Model,天气研究和预报模式)进行了数值模拟,结果表明同化MODIS资料,可以改进初始场水汽和温度分布,间接调整高空风场,调整趋势符合卫星观测,对短时降水预报有正面影响。     

Preliminary Study on Direct Assimilation of Cloud-affected Satellite Microwave Brightness Temperatures

Direct assimilation of cloud-affected microwave brightness temperatures from AMSU-A into the GSI three-dimensional variational(3D-Var) assimilation system is preliminarily studied in this paper. A combination of cloud microphysics parameters retrieved by the 1D-Var algorithm(including vertical profiles of cloud liquid water content, ice water content, and rain water content) and atmospheric state parameters from objective analysis fields of an NWP model are used as background fields. Three cloud microphysics parameters(cloud liquid water content, ice water content, and rain water content) are applied to the control variable. Typhoon Halong(2014) is selected as an example. The results show that direct assimilation of cloud-affected AMSU-A observations can effectively adjust the structure of large-scale temperature, humidity and wind analysis fields due to the assimilation of more AMSU-A observations in typhoon cloudy areas, especially typhoon spiral cloud belts. These adjustments, with temperatures increasing and humidities decreasing in the movement direction of the typhoon,bring the forecasted typhoon moving direction closer to its real path. The assimilation of cloud-affected satellite microwave brightness temperatures can provide better analysis fields that are more similar to the actual situation. Furthermore, typhoon prediction accuracy is improved using these assimilation analysis fields as the initial forecast fields in NWP models.     

台风“云娜”在近海强度变化及结构特征的数值研究Ⅰ:云微物理参数化对云结构及降水特征的影响

首先对AREM模式模拟的台风基本结构和云结构进行验证,检验了模拟结果的可靠性.在此基础上,设计了5组试验来研究云微物理参数化方案对台风"云娜"云结构及降水特征的影响.试验设计主要突出冰相云微物理过程、云微物理特征引发的冷却效应以及霰下落速度的重要性.结果表明:云微物理参数化过程对云的发展和降水特征的影响更为显著.各试验的水凝物分布和强度不同,降水类型和强度存在较大差异,由此引起的云中热力结构也有较大区别;在所有试验方案中,24 h降水率最大差异为52.5 mm/h.云微物理过程对云和降水特征的具体影响表现在:(1)如果不考虑雨水蒸发冷却效应,此时台风内核上升运动强度最强(达到-19 Pa/s),雨水和霰粒子增长最明显,相对于对照试验增量分别为1.8和2.5 g/kg.(2)霰和雪的融化对于螺旋雨带中雨滴的增长十分重要,但他们可能不是云墙中雨水形成的主导因子.(3)不同方案的降水模拟特征也存在较大差别,采用暖云参数化后,降水区域最小,但其中对流降水比例最大(63.19%);霰落速减半后,降水区域最大,其中非对流降水比例也最大(51.15%).     

Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.     

Application of a Three-dimensional Variational Method for Radar Reflectivity Data Correction in a Mudslide-inducing Rainstorm Simulation

Various types of radars with different horizontal and vertical detection ranges are deployed in China, particularly over complex terrain where radar blind zones are common. In this study, a new variational method is developed to correct threedimensional radar reflectivity data based on hourly ground precipitation observations. The aim of this method is to improve the quality of observations of various types of radar and effectively assimilate operational Doppler radar observations. A mudslide-inducing local rainstorm is simulated by the WRF model with assimilation of radar reflectivity and radial velocity data using LAPS(Local Analysis and Prediction System). Experiments with different radar data assimilated by LAPS are performed. It is found that when radar reflectivity data are corrected using this variational method and assimilated by LAPS,the atmospheric conditions and cloud physics processes are reasonably described. The temporal evolution of radar reflectivity corrected by the variational method corresponds well to observed rainfall. It can better describe the cloud water distribution over the rainfall area and improve the cloud water analysis results over the central rainfall region. The LAPS cloud analysis system can update cloud microphysical variables and represent the hydrometeors associated with strong convective activities over the rainfall area well. Model performance is improved and the simulation of the dynamical processes and moisture transport is more consistent with observation.     

多普勒天气雷达资料对中尺度模式短时预报的影响

利用中尺度模式ARPS(The Advanced Regional Prediction System)及其资料分析系统ADAS(ARPS Data Analysis System),将国内新一代多普勒雷达(CINRAD)反射率及径向风资料直接用于中尺度数值模拟,通过一次华北地区暴雨过程的模拟对比试验,分析了雷达资料对初始场的改进效果及其对模拟结果的影响,结果表明:(1)利用雷达径向风资料对初始风场进行调整后,自近地面到对流层顶的u,v,w都发生了变化,调整后的初始风场在对流层中层变化最大.(2)利用雷达反射率进行微物理调整和云分析能调整初始场中的云水信息,使得雷达回波附近3 km以下的水汽混合比(qv)增加,4 km以下的雨水混合比(qr)增加,对流层(约10 km以下)的云水混合比(qc)增加,4～9 km的对流层上部云冰混合比(qi)和雪混合比(qs)增加.ADAS通过非绝热初始化调整温度场,从而得到了一个动力和热力上平衡的初始场.(3)模拟的1 h雨量与实况的对比表明,同时利用雷达反射率和径向风改进过的初始场能明显增强3 h内的降水强度和落区预报,改善中尺度数值模式短时定量降水预报.模拟的1 h流场对比分析表明,经雷达径向风调整后,能够在初始场中增加气旋性涡旋等中小尺度风信息,明显减少模式的spin-up时间.(4)通过对雷达径向风和反射率对模式初始场和模拟结果影响的对比分析发现,雷达径向风主要是改进初始风场,而雷达反射率主要是改进初始场中的湿度参数,增加初始场中云水等的含量,调整温度场.通过模拟的6 h降水对比发现,利用雷达径向风调整初始场后,对降水模拟有一定的改进,但效果不甚明显,而雷达反射率资料对定量降水预报改进效果明显,同时使用雷达径向风和反射率资料改进初始场后对降水的模拟效果最明显.     

CAMS复杂云微物理方案与GRAPES模式耦合的数值试验

CAMS复杂云微物理方案是混合相双参数方案, 包括11个云物理变量和31个云物理过程, 能够同时预报水成物的比质量和数浓度。通过在GRAPES非静力中尺度模式中增加预报量并修改相关程序后, 实现了二者的耦合, 耦合后模式运行稳定。选取2005年8月15—17日我国华北地区一次暴雨过程, 利用耦合后的模式进行48 h模拟试验, 同时还选取了GRAPES模式中其他3个比较复杂的微物理方案进行模拟, 着重分析了降水和水成物分布的模拟结果。研究结果表明: CAMS方案能够模拟出与实测相接近的雨带分布特征, 并且对降水演变的模拟结果与其他方案比较一致, 对暴雨中心位置的模拟有待改进。CAMS方案模拟的水成物垂直分布与其他方案相比具有相似的总体特征, 各相态粒子的量级和分布合理, 不同方案的结果在量值上有所差别。个例分析结果显示出CAMS方案对降水和水成物的分布能够合理描述。今后应通过更多个例进行更为精细的模拟试验, 对新方案进行检验。     

“非bogus初值”热带气旋数值预报及其性能

从高分辨模式特点及精细预报的需要出发，尝试了用“非bogus初值”开展热带气旋精细数值预报的方案。即直接采用四维同化形成的热带气旋（TC）初值，而不再加入“人造台风”（BOGUS）信息，以图避免人为理想结构带来的虚假信息，对热带气旋路径、强度、结构等有更为精细和准确的预报。用2001年所有影响华南的TC实例，对方案的可行性进行了分析。结果表明，本方案可以较好地预报TC的生成；路径预报准确率较高，对疑难路径及局地效应反映较为细致，路径预报误差随时效增大不显著；强度趋势预报指示意义强，准确率较高；尤其是能很好地反映TC的云、雨、风等的非对称结构、螺旋结构等特征的时空演化，对TC中尺度结构的刻画能力和预报可用性较强。显示了方案的优越性和发展潜力，也意味着“非bogus初值”方案应该作为未来TC数值预报发展的一个主要技术方向。分析表明目前条件下本方案还存在一些不足之处，主要表现在因初始信息的不足，当在TC过弱、初生或远离大陆等情况下，常会出现初始场中TC位置和强度与实况偏差过大，容易造成较大预报误差。     

ON THE SENSITIVITY OF PRECIPITATION FORECASTS TO THE MOIST PHYSICS AND THE HORIZONTAL RESOLUTION OF NUMERICAL MODEL*

The impacts of the enhanced model's moist physics and horizontal resolution upon the QPFs(quantitative precipitation forecasts) are investigated by applying the HIRLAM(high resolution limited area model) to the summer heavy-rain cases in China.The performance of the control run,for which a 0.5°×0.5°grid spacing and a traditional "grid-box supersaturation removal+Kuo type convective paramerization" are used as the moist physics,is compared with that of the sensitivity runs with an enhanced model's moist physics(Sundqvist scheme) and an increased horizontal resolution(0.25°×0.25°),respectively.The results show:(1) The enhanced moist physics scheme(Sundqvist scheme),by introducing the cloud water content as an additional prognostic variable and taking into account briefly of the microphysics involved in the cloud-rain conversion,does bring improvements in the model's QPFs.Although the deteriorated QPFs also occur occasionally,the improvements are found in the majority of the cases,indicating the great potential for the improvement of QPFs by enhancing the model's moist physics.(2) By increasing the model's horizontal resolution from 0.5°×0.5°,which is already quite high compared with that of the conventional atmospheric soundings,to 0.25°×0.25°without the simultaneous enhancement in model physics and objective analysis,the improvements in QPFs are very limited.With higher resolution,although slight amelioration in locating the rainfall centers and in resolving some finer structures of precipitation pattern are made,the number of the mis-predicted fine structures in rainfall field increases with the enhanced model resolution as well.     

基于NLS-4DVar方法的雷达资料同化及其在暴雨预报中的应用

在基于本征正交分解POD（Proper Orthogonal Decomposition）的集合四维变分同化方法（POD4DEnVar）建立的雷达资料同化系统（PRAS）的基础上,本文利用非线性最小二乘法的集合四维变分同化方法（NLS-4DVar）对PRAS进行改进,解决PRAS在高度非线性情况下的适应性问题,建立了新的雷达资料同化系统（NRAS）。通过观测系统模拟试验OSSEs（Observing System Simulation Experiments）和两次实际暴雨同化试验（2010年7月8日,中国中部地区;2014年3月30日,中国华南地区）对NRAS进行检验,并与PRAS的同化结果进行了对比。结果表明:无论是OSSEs还是实际雷达资料的同化,相对于PRAS,NRAS能够进一步提高同化效果。通过增加迭代的次数,NRAS能够有效地调整初始场的风场和水汽场,进一步提高了降水强度和位置的预报精度。但随着迭代次数的增加,对初始场的调整变小,进而对降水预报效果的改进也减小。试验结果表明NRAS能够有效解决PRAS在高度非线性情况下的应用问题,通过有限次数的迭代,即可得到近似收敛的结果。因而NRAS有望在数值预报中更有效地同化雷达资料,提高中小尺度天气的预报水平。     

Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.