共查询到20条相似文献，搜索用时 31 毫秒
1.
Olivier Caumont Anouck Foray Lucas Besson Jacques Parent du Châtelet 《Boundary-Layer Meteorology》2013,148(2):379-397
Weather radar refractivity depends on low-level moisture, temperature, and pressure and is available at high space–time resolutions over large areas. It is of definite meteorological interest for assimilation, verification, and process-study purposes. In this study, the path-averaged refractivity change is simulated from the Arome cloud-resolving atmospheric system analyses and compared with corresponding radar observations over a 35-day period with various meteorological conditions. For that, a novel post-processing procedure is applied to radar data to improve its quality. Also, an observation operator is developed that ingests Arome analyses and simulates a 3-h path-averaged refractivity change. A sensitivity study shows that simulated path-averaged refractivity change is immune to the modelling of the beam height as long as it remains below approximately 60 m above the ground. Comparisons show overall consistency between observed and simulated path-averaged refractivity change, with discrepancies at times that suggest an improvement in analyses once radar refractivity change observations are assimilated. Finally, errors introduced when retrieving local refractivity from path-averaged refractivity are estimated and it is found for our dataset that such retrievals halve the range of usable observations. 相似文献
2.
Dynamical and Microphysical Retrieval from Simulated Doppler Radar Observations Using the 4DVAR Assimilation Technique 总被引：3，自引：0，他引：3
Based on a cloud model and the four-dimensional variational (4DVAR) data assimilation method developed by Sun and Crook (1997), simulated experiments of dynamical and microphysical retrieval from Doppler radar data were performed. The 4DVAR data assimilation technique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields were determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities and their model predictions. The adjoint of the numerical model was used to provide the gradient of the cost function with respect to the control variables. Experiments have demonstrated that the 4DVAR assimilation method is able to retrieve the detailed structure of wind, thermodynamics, and microphysics by using either dual-Doppler or single-Doppler information. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variables. Retrieving the temperature field, cloud water and water vapor is more difficult than the recovery of the wind field and rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. The inclusion of a background term, even mean fields from a single sounding, helped reduce the retrieval errors. Less accurate velocity fields were obtained when single-Doppler data were used. It was found that the retrieved velocity is sensitive to the location of the retrieval domain relative to the radars while the other fields have very little changes. Two radar volumetric scans are generally adequate for providing the evolution, although the use of additional volumes improves the retrieval. As the amount of the observations decreases, the performance of the retrieval is degraded. However, the missing observations can be compensated by adding a background term to the cost function. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. The boundary conditions from the dual-Doppler synthesized winds are sufficient for the retrieval. When the retrieval is mainly controlled by the observations in the regions away from the boundaries, the simple boundary conditions from velocity azimuth display (VAD) analysis are also available. The microphysical retrieval is sensitive to model errors. 相似文献
3.
在分析美国局地分析预报系统 (LAPS) 和GRAPES_Meso数值预报系统,实现GRAPES-LAPS接口的基础上,通过两种数据融合方案,即GRAPES/LAPS方案 (简称LAPS方案) 和GRAPES/3DVAR方案 (3DVAR方案),对2008—2010年华南地区的28个个例的地面、探空常规和加密资料,以及多部多普勒天气雷达数据等融合、同化,开展两种方案的对比模拟试验。结果表明:LAPS方案获得的初始场,水汽条件有所改善,其辐合辐散相耦合触发中小尺度系统发展加强的中尺度环境场,有助于提高模式对强对流天气的预报能力。两种方案的24 h要素场均方根误差检验结果和降水TS评分大体相当,但28个个例中,LAPS方案报出了10个暴雨,而3DVAR方案只报出了5个,LAPS方案的中雨、大雨和暴雨的24 h降水预报TS评分要略好于3DVAR方案相应预报的TS评分,表明LAPS方案对强降水的预报较3DVAR方案有一定改进。 相似文献
4.
多普勒雷达资料4DVAR同化反演的模拟研究 总被引：21，自引：5，他引：21
利用Sun等建立的同化模式和四维变分同化方法对多普勒雷达资料反演大气风场、热力场和微物理场进行了模拟试验研究.反演的基本思路是将4DVAR同化方法应用到三维云模式,定义价值函数表征雷达资料与模式预报结果之间的差别,通过极小化价值函数得到反演场,价值函数相对模式控制变量的梯度由伴随模式求取.试验结果表明,4DVAR同化技术能够从单(双)多普勒雷达资料反演大气三维风场、热力场和微物理场.各个变量反演精度高低与同化过程中变量受约束的大小程度呈正相关.速度场和雨水场反演精度较高,温度场、云水和水汽的反演精度次之,温度场的准确反演需要较长的同化时间.价值函数中加入背景场,哪怕是单点探空给出的平均场信息也有利于提高反演精度.在采用单部多普勒雷达资料进行反演时,速度场的反演误差较大.反演区相对雷达站的位置变化对速度场反演结果有一定的影响,而对其他变量的反演影响很小.两个时次的雷达观测资料基本足够提供反演所需的时间演变信息,同化更多时次的雷达资料,反演效果改进很小.雷达观测资料的缺值会显著降低同化效果,甚至可能导致同化失败,引入背景场可以改善这一状况.4DVAR同化技术对于雷达观测资料误差不太敏感.利用双多普勒雷达合成风场提供水平风场边界条件是比较准确可靠的.在反演主体离边界较远时,VAD风场也基本可用作水平风场边界条件.微物理场的反演对模式中的微物理参数化方案较敏感. 相似文献
5.
Observing system simulation experiments are performed using an ensemble Kalman filter to investigate the impact of surface observations in addition to radar data on convective storm analysis and forecasting. A multi-scale procedure is used in which different covariance localization radii are used for radar and surface observations. When the radar is far enough away from the main storm so that the low level data coverage is poor, a clear positive impact of surface observations is achieved when the network spacing is 20?km or smaller. The impact of surface data increases quasi-linearly with decreasing surface network spacing until the spacing is close to the grid interval of the truth simulation. The impact of surface data is sustained or even amplified during subsequent forecasts when their impact on the analysis is significant. When microphysics-related model error is introduced, the impact of surface data is reduced but still evidently positive, and the impact also increases with network density. Through dynamic flow-dependent background error covariance, the surface observations not only correct near-surface errors, but also errors at the mid- and upper levels. State variables different from observed are also positively impacted by the observations in the analysis. 相似文献
6.
In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone ÁILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated. 相似文献
7.
A. Routray U. C. Mohanty Dev Niyogi S. R. H. Rizvi Krishna K. Osuri 《Meteorology and Atmospheric Physics》2010,106(1-2):107-125
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. 相似文献
8.
Four-dimensional variational data assimilation experiments for a heavy rain case during the 2002 IOP in China 总被引：3，自引：0，他引：3
A heavy rainfall event along the mei-yu front during 22-23 June 2002 was chosen for this study. To assess the impact of the routine and additional IOP (intensive observation period) radiosonde observations on the mesoscale heavy rainfall forecast, a series of four-dimensional variational (4DVAR) data assimilation and model simulation experiments was conducted using nonhydrostatic mesoscale model MM5 and the MM5 4DVAR system. The effects of the intensive observations in the different areas on the heavy rainfall forecast were also investigated. The results showed that improvement of the forecast skill for mesoscale heavy rainfall intensity was possible from the assimilation of the IOP radiosonde observations. However,the impact of the IOP observations on the forecast of the rainfall pattern was not significant. Initial conditions obtained through the 4DVAR experiments with a 12-h assimilation window were capable of improving the 24-h forecast. The simulated results after the assimilation showed that it would be best to perform the intensive radiosonde observations in the upstream of the rainfall area and in the moisture passageway area at the same time. Initial conditions created by the 4DVAR led to the low-level moisture convergence over the rainfall area, enhanced frontogenesis and upward motion within the mei-yu front,and intensified middle- and high-level unstable stratification in front of the mei-yu front. Consequently,the heavy rainfall forecast was improved. 相似文献
9.
Four-dimensional variational data assimilation for mesoscale and storm-scale applications 总被引：1，自引：0，他引：1
Summary ?The status and progress of the four-dimensional variational data assimilation (4DVAR) are briefly reviewed focusing on application
to prediction of mesoscale/storm-scale atmospheric phenomena. Theoretical background is provided for each important component
of the 4DVAR system – forecast and adjoint models, observations, background, cost function, preconditioning, and minimization.
An overview of practical issues specific for mesoscale/storm-scale 4DVAR is then presented in terms of high-resolution observations,
nonlinearity and discontinuity problem, model error, errors from lateral boundary condition, and precipitation assimilation.
Practical strategies for efficient and simplified 4DVAR are also introduced, e.g., incremental 4DVAR, poor man’s 4DVAR, and
inverse 3DVAR. A new concept on hybrid approach is proposed to combine an efficient 4DVAR scheme and the standard 4DVAR scheme
aiming at reducing computational demand required by the standard 4DVAR while improving the accuracy of the simplified 4DVAR.
Applications to both hydrostatic and nonhydrostatic models are illustrated and our vision on opportunities and directions
for future research is provided.
Received March 12, 2001; revised July 24, 2001; accepted September 5, 2001 相似文献
10.
In the present reported study, the vertical distributions of local atmospheric refractivity were retrieved from ground- based GPS observations at low elevation angles. An improved optimization method was implemented at altitudes of 0-10 km to search for a best-fit refractivity profile that resulted in atmospheric delays most similar to the delays calculated from the observations. A ray-tracing model was used to simulate neutral atmospheric delays corresponding to a given refractivity profile. We initially performed a ＂theoretical retrieval＂, in which no observation data were involved, to verify the optimization method. A statistical relative error of this ＂theoretical retrieval＂ （-2% to 2%） indicated that such a retrieval is effective. In a practical retrieval, observations were obtained using a dual-frequency GPS receiver, and its initial value was provided by CIRA86aQ_UoG data. The statistical relative errors of the practical retrieval range from -3% to 5% were compared with co-located radiosonde measurements, Results clearly revealed diurnal variations in local refractivity prc,files, The results also suggest that the general vertical distribution of refractivity can be derived with a high temporal resolution. However, further study is needed to describe the vertical refractivity gradient clearly. 相似文献
11.
This study examines the performance of coupling the deterministic four-dimensional
variational assimilation system (4DVAR) with an ensemble Kalman filter (EnKF) to produce a
superior hybrid approach for data assimilation. The coupled assimilation scheme (E4DVAR)
benefits from using the state-dependent uncertainty provided by EnKF while taking advantage
of 4DVAR in preventing filter divergence: the 4DVAR analysis produces posterior maximum
likelihood solutions through minimization of a cost function about which the ensemble
perturbations are transformed, and the resulting ensemble analysis can be propagated forward
both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility
and effectiveness of this coupled approach are demonstrated in an idealized model with
simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR
and the EnKF under both perfect- and imperfect-model scenarios. The performance of the
coupled scheme is also less sensitive to either the ensemble size or the assimilation window
length than those for standard EnKF or 4DVAR implementations. 相似文献
12.
Summary The increasing use of weather radar quantitative precipitation estimates, particularly in automatic applications such as operational
hydrometeorological modelling or assimilation in numerical weather prediction (NWP) models, has promoted the development of
quality control procedures on radar data.
Anomalous propagation (AP) of the radar beam due to deviation from the standard refractivity vertical profile, is one of the
factors that may affect seriously the quality of radar observations because of the increase in quantity and intensity of non-precipitating
clutter echoes and consequent contamination of the estimated rainfall field. Another undesired effect of AP is the change
in the expected radar echo height, which may be relevant when correcting for beam blockage in radar rainfall estimation in
complex terrain. The aim of this paper is to study the use of NWP mesoscale forecasts to predict and monitor AP events.
A nested 15-km grid resolution version of the MASS model has been used to retrieve refractivity profiles in the coastal area
of Barcelona, near a weather radar and a radiosonde station. Using the refractivity profiles two different magnitudes were
computed: the vertical refractivity profile of the lowest 1000 m layer and a ducting index which describes the existence and
intensity of the most super-refractive layer contained in the lowest 3-km layer. A comparison between model forecasts and
radiosonde diagnostics during a six-month period showed that the model tended to underestimate the degree of super-refraction,
with a bias of 4 km−1 and RMSE of 11 km−1 in the 1-km vertical refractivity gradient. Further analysis of the data showed that a combination of previous observations
and forecasts allowed to produce modified forecasts improving the original direct model output, decreasing substantially the
bias, reducing the RMSE by 20% and improving the skill by 40%, beating also radiosonde observations persistence. 相似文献
13.
A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over
the Korean Peninsula was selected to investigate the impact of radar data
assimilation on a heavy rainfall forecast. The Weather Research and
Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation
system with tuning of the length scale of the background error covariance
and observation error parameters was used to assimilate radar radial
velocity and reflectivity data. The radar data used in the assimilation
experiments were preprocessed using quality-control procedures and
interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC
packages. Sensitivity experiments were carried out in order to determine the
optimal values of the assimilation window length and the update frequency
used for the rapid update cycle and incremental analysis update experiments.
The assimilation of radar data has a positive influence on the heavy
rainfall forecast. Quantitative features of the heavy rainfall case, such as
the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of
zonal/meridional wind components, were improved by tuning of the length
scale and observation error parameters. Qualitative features of the case,
such as the maximum rainfall position and time series of hourly rainfall,
were enhanced by an incremental analysis update technique. The positive
effects of the radar data assimilation and the tuning of the length scale
and observation error parameters were clearly shown by the 3DVAR increment. 相似文献
14.
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. 相似文献
15.
The impact of Global Positioning System data on the prediction of an extratropical cyclone: an observing system simulation experiment 总被引：1，自引：0，他引：1
In this paper, we report a series of observing system simulation experiments that we conducted to assess the potential impact of Global Positioning System/meteorology (GPS/MET) refractivity data on short-range numerical weather prediction. We first conducted a control experiment using the Penn State/NCAR mesoscale model MM5 at 90-km resolution on an extratropical cyclone known as the ERICA (Experiment on Rapidly Intensifying Cyclones over the Atlantic) IOP 4 storm. The results from the control experiment were then used to simulate GPS/MET refractivity observations with different spatial resolution and measurement characteristics. The simulated refractivity observations were assimilated into an 180-km model during a 6-h period, which was followed by a 48-h forecast integration. Key findings can be summarized as follows:
- • The assimilation of refractivity data at the 180-km resolution can recover important atmospheric structures in temperature and moisture fields both in the upper and lower troposphere, and, through the internal model dynamical processes, also the wind fields. The assimilation of refractivity data led to a considerably more accurate prediction of the cyclone.
- • Distributing the refractivity randomly in space and applying a line averaging did not alter the results significantly, while reducing the spatial resolution from 180 km to 360 km produced a moderately degraded result. Even at the 360-km resolution, the GPS-type refractivity data still have a notable positive impact on cyclone prediction.
- • Restricting the refractivity data to altitude 3 km and above considerably degraded its impact on cyclone prediction. This degradation was greater than the combined effects of distributing the refractivity data randomly, performing line averaging, and reducing the resolution to 360 km.
16.
Recent advances in Global Positioning System （GPS） remote sensing technology allow for a direct estimation of the precipitable water vapor （PWV） from delayed signals transmitted by GPS satellites, which can be assimilated into numerical models with four-dimensional variational （4DVAR） data assimilation. A mesoscale model and its 4DVAR system are used to access the impacts of assimilating GPS-PWV and hourly rainfall observations on the short-range prediction of a heavy rainfall event on 20 June 2002. The heavy precipitation was induced by a sequence of meso-β-scale convective systems （MCS） along the mei-yu front in China. The experiments with GPS-PWV assimilation cluster and also eliminated the erroneous rainfall successfully simulated the evolution of the observed MCS systems found in the experiment without 4DVAR assimilation. Experiments with hourly rainfall assimilation performed similarly both on the prediction of MCS initiation and the elimination of erroneous systems, however the MCS dissipated much sooner than it did in observations. It is found that the assimilation-induced moisture perturbation and mesoscale low-level jet are helpful for the MCS generation and development. It is also discovered that spurious gravity waves may post serious limitations for the current 4DVAR algorithm, which would degrade the assimilation efficiency, especially for rainfall data. Sensitivity experiments with different observations, assimilation windows and observation weightings suggest that assimilating GPS-PWV can be quite effective, even with the assimilation window as short as 1 h. On the other hand, assimilating rainfall observations requires extreme cautions on the selection of observation weightings and the control of spurious gravity waves. 相似文献
17.
利用自主构建的基于风暴尺度的WRF-En SRF系统同化模拟多普勒雷达资料,讨论了微物理方案及其参数的不确定性对同化效果的影响。试验采用组合微物理方案以及扰动微物理方案中的参数的方法,结果表明,模式误差非常小甚至可以忽略时,使用单个微物理方案并扰动参数能够使真实风暴的主要特征在分析场中较未扰动参数得到更好地反映;存在模式误差时,使用单个微物理方案并扰动参数后,分析场中的各要素的分布较未扰动参数更加接近真实风暴,同化效果得到改进,且改进效果比模式误差非常小时更为明显;存在模式误差时,组合微物理方案并扰动参数后,分析场中对流云团的形态较未组合方案或未扰动参数更接近真实风暴,主要要素场的配置最能反映真实风暴的特征,同化效果最为理想。结果也表明,扰动参数时、参数扰动范围较小时,同化效果较优。 相似文献
18.
集合卡尔曼滤波同化多普勒雷达资料的数值试验 总被引：35，自引：10，他引：25
利用集合卡尔曼滤波(EnKF)在云数值模式中同化模拟多普勒雷达资料,并考察了不同条件下EnKF同化方法的性能.结果显示,经过几个同化周期后,EnKF分析结果非常接近真值.单多普勒雷达资料EnKF同化对雷达位置不太敏感,双雷达资料同化结果在同化的初期阶段比单雷达资料同化结果准确.同化由反射率导出的雨水比直接同化反射率资料更有效,联合同化径向速度和雨水有利于提高同化分析效果.协方差对EnKF同化效果起着非常重要的作用,考虑模式全部预报变量与径向速度协方差的同化效果比仅考虑速度场与径向速度协方差的同化效果好.雷达资料缺值降低了同化效果,此时增加地面常规观测资料的同化可以明显提高同化分析效果.EnKF同化技术对雷达观测资料误差不太敏感.初始集合对同化分析有较大影响.EnKF同化受集合大小和观测资料影响半径.同化对模式误差较敏感.利用EnKF同化双多普勒雷达资料,分析了一次梅雨锋暴雨过程的中尺度结构.结果表明,EnKF同化技术能够从双多普勒雷达资料反演暴雨中尺度系统的动力场、热力场和微物理场,反演的风场是较准确的,反演的热力场和微物理场分布也是基本合理的.中低层切变线是此次暴雨的主要动力特征,对流云表现为低层辐合、高层辐散并有垂直上升运动伴随,其热力特征表现为低层是低压区,高层为高压区,中部为暖区而上、下部为冷区,水汽、云水和雨水分别集中在对流云体内、上升气流区和强回波区. 相似文献
19.
针对对流尺度集合卡尔曼滤波（EnKF）雷达资料同化中雷达位置对同化的影响进行研究。为了考察强对流出现在雷达不同方位时集合卡尔曼滤波同化雷达资料的能力,以一个理想风暴为例,设计了8个均匀分布在模拟区域周围的模拟雷达进行试验。单雷达同化试验中,初期同化对雷达位置较敏感,而十几个循环后对雷达方位的敏感性降低。造成初期同化效果较差的雷达观测位于模拟区域正南和正北方向,这两部雷达与模拟区域中心的连线垂直于风暴移动方向（即环境气流的方向）。双雷达试验的结果表明,正东、正南、正西和正北方向的雷达组合观测会使同化初期误差较大,这说明并不是所有与风暴连线成90°的雷达组合都能在短时同化中得到合理的分析结果,还需要都处于模拟区域对角线上（即与环境气流成45°夹角）,同化效果才较好。短时同化后的确定性预报结果表明,较大分析误差也会导致较大预报误差。这些分析误差主要是由于同化初期不准确的集合平均场驱动出的不合理的背景误差协方差造成的。当背景场随着同化循环得到改进后,驱动出的合理的背景误差协方差使得不同位置雷达同化造成的差异逐步减小。基于上述结果,引入迭代集合均方根滤波（iEnSRF）算法,结果显示使用该算法后,雷达位置对同化效果的影响减小,同化不同位置的雷达资料均能有效降低分析和预报误差。 相似文献
20.
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. 相似文献