ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT

The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting (WRF) model, the WRF model with a three-dimensional variational (3DVAR) data assimilation system and the WRF model with an ensemble square root filter (EnSRF) data assimilation system. In addition, the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze -Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation. The results show the following. (1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region, enhance the convective activities and reduce excessive simulated precipitation. (2) The 3DVAR assimilation method significantly adjusts the horizontal wind field. The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model. In addition, the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands. (3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model. The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers. In addition, the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation, rain-band areal coverage and the center location and intensity of precipitation.     

EXPERIMENTS ON ASSIMILATION OF INITIAL VALUES IN NUMERICAL PREDICTION OF A WARM-SECTOR PRECIPITATION IN SOUTH CHINA

In order to understand the impact of initial conditions upon prediction accuracy of short-term forecast and nowcast of precipitation in South China, four experiments i.e. a control, an assimilation of conventional sounding and surface data, testing with nudging rainwater data and the assimilation of radar-derived radial wind, are respectively conducted to simulate a case of warm-sector heavy rainfall that occurred over South China, by using the GRAPES_MESO model. The results show that (1) assimilating conventional surface and sounding observations helps improve the 24-h rainfall forecast in both the area and order of magnitude; (2) nudging rainwater contributes to a significant improvement of nowcast, and (3) the assimilation of radar-derived radial winds distinctly improves the 24-h rainfall forecast in both the area and order of magnitude. These results serve as significant technical reference for the study on short-term forecast and nowcast of precipitation over South China in the future.     

A New Approach to Data Assimilation

A significant attempt to design a timesaving and efficient four-dimensional variational data assimilation (4DVar) has been made in this paper, and a new approach to data assimilation, which is noted as 'three-dimensional variational data assimilation of mapped observation (3DVM)' is proposed, based on the new concept of mapped observation and the new idea of backward 4DVar. Like the available 4DVar, 3DVM produces an optimal initial condition (IC) that is consistent with the prediction model due to the inclusion of model constraints and best fits the observations in the assimilation window through the model solution trajectory. Different from the 4DVar, the IC derived from 3DVM is located at the end of the assimilation window rather than at the beginning conventionally. This change greatly reduces the computing cost for the new approach, which is almost the same as that of the three-dimensional variational data assimilation (3DVar). Especially, such a change is able to improve assimilation accuracy because it does not need the tangential linear and adjoint approximations to calculate the gradient of cost function. Therefore, in numerical test, the new approach produces better IC than 4DVar does for 72-h simulation of TY9914 (Dan), by assimilating the three-dimensional fields of temperature and wind retrieved from the Advanced Microwave Sounding Unit-A (AMSU-A) observations. Meanwhile, it takes only 1/7 of the computing cost that the 4DVar requires for the same initialization with the same retrieved data.     

Statistics of the Z–R Relationship for Strong Convective Weather over the Yangtze–Huaihe River Basin and Its Application to Radar Reflectivity Data Assimilation for a Heavy Rain Event

The relationship between the radar reflectivity factor(Z) and the rainfall rate(R) is recalculated based on radar observations from 10 Doppler radars and hourly rainfall measurements at 6529 automatic weather stations over the Yangtze–Huaihe River basin. The data were collected by the National 973 Project from June to July 2013 for severe convective weather events. The Z–R relationship is combined with an empirical qr–R relationship to obtain a new Z–qr relationship, which is then used to correct the observational operator for radar reflectivity in the three-dimensional variational(3 DVar) data assimilation system of the Weather Research and Forecasting(WRF) model to improve the analysis and prediction of severe convective weather over the Yangtze–Huaihe River basin. The performance of the corrected reflectivity operator used in the WRF 3 DVar data assimilation system is tested with a heavy rain event that occurred over Jiangsu and Anhui provinces and the surrounding regions on 23 June 2013. It is noted that the observations for this event are not included in the calculation of the Z–R relationship. Three experiments are conducted with the WRF model and its 3 DVar system, including a control run without the assimilation of reflectivity data and two assimilation experiments with the original and corrected reflectivity operators. The experimental results show that the assimilation of radar reflectivity data has a positive impact on the rainfall forecast within a few hours with either the original or corrected reflectivity operators, but the corrected reflectivity operator achieves a better performance on the rainfall forecast than the original operator. The corrected reflectivity operator extends the effective time of radar data assimilation for the prediction of strong reflectivity. The physical variables analyzed with the corrected reflectivity operator present more reasonable mesoscale structures than those obtained with the original reflectivity operator. This suggests that the new statistical Z–R relationship is more suitable for predicting severe convective weather over the Yangtze–Huaihe River basin than the Z–R relationships currently in use.     

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.     

A COMPARISON OF THE BLENDING AND CONSTRAINING METHODS TO INTRODUCE LARGE-SCALE INFORMATION INTO GRAPES MESOSCALE ANALYSIS

To solve the problem of mesoscale analysis error accumulation after a period of continuous cycle data assimilation (CCDA), a blending method and a constraining method are compared to introduce global analysis information into the Global/Regional Assimilation and Prediction Enhanced System mesoscale three-dimensional variational data assimilation system (GRAPES-Meso 3Dvar). Based on a spatial filter used to obtain a blended analysis, the blending method is weighted toward the T639 global analysis for scales larger than the cutoff wavelength of 1,200 km and toward the GRAPES mesoscale analysis for wavelengths below that. The constraining method considers the T639 global analysis data as an extra source of information to be added in the 3DVar cost function. The cloud-resolving GRAPES-Meso system (3 km resolution) with a 3 h analysis cycle update is chosen, and forecast experiments on an extreme precipitation event over the eastern part of China are presented. The comparison shows that the inclusion of large-scale information with both methods has a positive impact on the regional model, in which the 3 h background forecasts are slightly closer to the radiosonde observations. The results also show that both methods are effective in improving large-scale analysis while reserving the well-featured mesoscale information, leading to an enhancement in the balance and accuracy of the analysis. Subjective verification reveals that the introduction of large-scale information has a visible beneficial impact on the forecast of precipitation location and intensity. The methodologies and experiences presented in this paper could serve as a reference for ongoing efforts toward the development of multi-scale analysis in GRAPES-Meso.     

Variational data assimilation experiments of mei-yu front rainstorms in China

The numerical forecasts of mei-yu front rainstorms in China has been an important issue. The intensity and pattern of the frontal rainfall are greatly influenced by the initial fields of the numerical model. The 4-dimensional variational data assimilation technology (4DVAR) can effectively assimilate all kinds of observed data, including rainfall data at the observed stations, so that the initial fields and the precipitation forecast can both be greatly improved. The non-hydrostatic meso-scale model (MM5) and its adjoint model are used to study the development of the mei-yu front rainstorm from 1200 UTC 25 June to 0600 UTC 26 June 1999. By numerical simulation experiments and assimilation experiments, the T106 data and the observed 6-hour rainfall data are assimilated. The influences of many factors, such as the choice of the assimilated variables and the weighting coefficient, on the precipitation forecast results are studied. The numerical results show that 4DVAR is valuable and important to mei-yu front rainfall prediction.     

Assimilating surface observations in a four-dimensional variational Doppler radar data assimilation system to improve the analysis and forecast of a squall line case

This paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System(VDRAS).Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational(4DVAR) data assimilation system.A squall-line case observed during a field campaign is selected to investigate the performance of the technique.A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions.The surface-based cold pool,divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation.Three experiments—assimilating radar data only,assimilating radar data with surface data blended in a mesoscale background,and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation.Independent surface and wind profiler observations are used for verification.The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations.It is also shown that the additional surface data can help improve the analysis and forecast at low levels.Surface and low-level features of the squall line—including the surface warm inflow,cold pool,gust front,and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction

A new data insertion approach is applied to the Derber and Rosati ocean data assimilation(ODA) system,a system that uses a variational scheme to analyze ocean temperature and provide ocean model corrections continuously.Utilizing the same analysis component as the original system,the new approach conducts analyses to derive model corrections intermittently at once-daily intervals.A technique similar to the Incremental Analysis Update(IAU) method of Bloom et al.is applied to incorporate the corrections into the model gradually and continuously.This approach is computationally more economical than the original.A 13-year global ocean analysis from 1986 to 1998 is produced using this new approach and compared with an analysis based on the original one.An examination of both analyses in the tropical Pacific Ocean shows that they have qualitatively similar annual and interannual temperature variability.Howerver,the new approach produces smoother monthly analyses.Moreover,compared to the independent observations from current meters,the new equatorial currents are significantly better than the original analyses,not only in maintaining the mean state but also in capturing the annual and interannual variations.     

FY-3A卫星微波资料的集合变分混合同化试验

以2012年"北京7.21暴雨"为例,实现了集合变分混合同化方法对FY-3A的微波温度仪和微波湿度仪资料的直接同化,并与三维变分方法进行了比较。结果表明:虽然两种同化方法同化FY-3A微波资料都能改进降水模拟效果,但是与实况相比,集合变分混合同化方法改进效果更为明显,其能有效减少虚假强降水的模拟,改进强降水中心位置的模拟,SAL评分定量检验也同样表明,集合变分混合同化方法对暴雨的模拟效果要优于三维变分同化方法;无论是热力学变量还是动力学变量,集合变分同化得到的初始场均方根误差均显著小于三维变分同化的结果;两种方法同化FY-3A微波资料均能改变初始场中的各种物理量信息,但不同方法得到的同化增量大小和分布却有明显的差异:三维变分同化方法对初始场的调整区域和强度都要大于混合同化方法,且其同化增量表现出均匀和各向同性的分布特点;而利用集合信息的混合同化方法得到的同化增量分布表现为非均匀性和各向异性,具有"流依赖性"的特征,这使得初始场的分布更合理,有利于改善降水的模拟效果。     

中尺度数值模式MM5的四维变分资料同化系统

应用伴随方法求解以数值预报方程作为约束条件的四维变分资料同化方案，关键问题是如何构造伴随模式。以中尺度数值模式MM5为例，讨论了如何用伴随码技术建立MM5伴随模式，以及伴随模式系统中权重、尺度因子的选取；最后对MM5伴随模式系统进行了梯度检验，并利用实际资料进行四维变分资料同化试验。试验表明该系统有较强的同化能力，能够提高MM5降水预报的准确性。     

雷达反射率资料的三维变分同化研究

应用天气研究和预报模式(WRF)三维变分系统中一种新的雷达反射率资料间接同化方法来进行反射率资料的三维变分同化研究,评估雷达反射率资料对夏季短时定量降水预报的作用.该方法不直接同化雷达反射率资料,而是同化由反射率资料反演出的雨水和估计的水汽.以2009年夏季北京地区发生的4次强降水过程为例,考察了北京市气象局业务运行的快速更新循环同化预报系统对京津冀地区雷达网的雷达反射率资料的同化性能以及雷达反射率资料和径向风资料同时同化的效果.数值试验结果表明:(1)同化反演雨水或水汽都能改善降水预报,但同化反演水汽对降水预报效果的改善起了更重要的作用;(2)同化反射率资料能极大地提高短时降水预报的效果,其稳定的正面效果可以延伸到6h的预报时效,而同化径向风资料不能得到稳定的正效果;(3)同化雷达资料时,应用快速更新循环同化预报系统是提高短时定量降水预报的一个有效途径.     

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.     

华中区域LRUC系统的构建与试验

如何将区域观测资料在中尺度模式中快速有效同化是提高区域精细化数值预报准确率和时效性的关键所在。本文简单介绍了基于LAPS和WRF建立的快速循环同化预报系统LRUC,以一次影响长江中游的典型梅雨锋暴雨过程为例,设计几种试验方案,结合实况降水分布,对比分析华中区域多普勒雷达资料循环同化对改善暴雨预报的作用;根据个例试验结果,设计批量后报试验方案,对比分析雷达资料同化对华中区域2007年主汛期(6—8月)多个时效降水预报评分。暴雨个例试验结果表明,华中区域多普勒雷达资料在LAPS中的同化,能为WRF提供更优初值场;同化华中区域地面资料和雷达资料后,比仅同化地面资料更能改进模式初值,改善模式降水预报,且降雨量级越大,24 h降水预报TS评分越高。批量后报试验结果表明,建立的快速循环同化预报系统LRUC,能对区域多源观测资料进行多时次循环同化分析,为模式初值增加观测资料的时间演变信息,提高模式对暴雨量级降水的预报水平;系统具有稳定性和一定的评分水平。     

Doppler Radar Data Assimilation with a Local SVD-En3DVar Method

An observation localization scheme is introduced into an ensemble-based three-dimensional variational (3DVar) assimilation method based on the singular value decomposition technique (SVD-En3DVar) to improve assimilation skill. A point-by-point analysis technique is adopted in which the weight of each observation decreases with increasing distance between the analysis point and the observation point. A set of numerical experiments, in which simulated Doppler radar data are assimilated into the Weather Research and Forecasting (WRF) model, is designed to test the scheme. The results are compared with those obtained using the original global and local patch schemes in SVD-En3DVar, neither of which includes this type of observation localization. The observation localization scheme not only eliminates spurious analysis increments in areas of missing data, but also avoids the discontinuous analysis fields that arise from the local patch scheme. The new scheme provides better analysis fields and a more reasonable short-range rainfall forecast than the original schemes. Additional forecast experiments that assimilate real data from 10 radars indicate that the short-term precipitation forecast skill can be improved by assimilating radar data and the observation localization scheme provides a better forecast than the other two schemes.     

对流天气系统自动站雨量资料同化对降雨预报的影响

利用GRAPES(Global and Regional Assimilation and Prediction Enhanced System,全球/区域同化预报系统)三维变分同化系统,针对对流天气系统特点,用改进的郭晓岚对流参数化方案作为观测算子,同化广东省自动站记录的对流天气系统的雨量资料,并且与同化探空资料进行了比较.在雨带有明显改进的区域,分别同化这两种资料都可以调整大气低层水汽辐合增加(或辐散),对流层中下层增暖增湿(或变冷变干),从而增加(或减少)降水,表明降水的同化方案对初始场的调整在一定