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1.
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. 相似文献
2.
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
3.
The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed. 相似文献
4.
Rainfall prediction remains one of the most challenging problems in weather forecasting. In order to improve high-resolution quantitative precipitation forecasts (QPF), a new procedure for assimilating rainfall rate derived from radar composite reflectivity has been proposed and tested in a numerical simulation of the Chicago floods of 17–18 July 1996. The methodology is based on the one-dimensional variation scheme (1DVAR) assimilation approach introduced by Fillion and Errico but applied here using the Ka... 相似文献
5.
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
6.
多普勒天气雷达资料在暴雨数值模拟中的同化应用 总被引：5，自引：3，他引：2
基于中尺度数值模式WRF及其三维变分同化系统WRF-3DVAR对2008年6月广西地区的一次强降雨过程,进行了多普勒天气雷达的多普勒径向速度和反射率因子的三维变分同化对于暴雨过程模拟效果影响研究。结果表明：(1）同化柳州、桂林和永州多普勒天气雷达观测资料后,模式对广西东北部地区特大暴雨的模拟效果明显改进;(2）WRF-3DVAR能够有效地同化多普勒天气雷达径向速度和雷达反射率因子,同化后使得模式初始场包含有更详尽的中尺度特征信息;(3）在高分辨率中尺度数值模式中有效地利用多普勒天气雷达资料,改善了分析场中尺度结构的描述,从而减轻了spin-up现象,能较好的提高中尺度降雨预报。 相似文献
7.
多普勒雷达径向速度同化在淮河暴雨数值模拟中的应用 总被引：2，自引：1，他引：1
针对2007年7月淮河流域的一次强降雨过程,利用WRF中尺度数值模式及其三维变分同化系统(WRF-3DVAR),开展了多普勒雷达径向速度的三维变分同化对暴雨过程模拟效果的影响研究。结果表明:WRF-3DVAR能够有效地同化多普勒雷达径向速度资料,同化后使得模式初始场出现了一定的调整,包含更详尽的中尺度特征信息,进而显著改善模式对大暴雨过程前12h降水的模拟效果。在高分辨率中尺度数值模式中有效地利用多普勒天气雷达资料,能较好地提高中尺度降雨预报。 相似文献
8.
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. 相似文献
9.
利用新一代中尺度预报模式WRFV3.6及其三维变分同化系统(WRF-3DVAR),对2012年7月21日北京地区的一次暴雨过程进行多普勒天气雷达径向风和反射率的同化试验研究,检验和探讨高时空分辨率多普勒天气雷达资料在改进模式初始场及提高对暴雨过程预报的准确率等方面的应用效果及意义。结果发现雷达资料同化能在初始场中加入反映产生降水的低层风场辐合的动力和锋前暖区充足的水汽条件的物理信息,可以在模式积分开始后改善初始场中水汽和风的分布,较快地模拟出局地对流系统的发生、发展,改善由于中尺度观测资料不足而造成的模式初始场里中尺度信息缺乏的问题。径向速度的同化增加了中尺度信息,对初始流场的调整较为显著,侧重于改进风场。而雷达反射率资料的同化对初始温、湿度场和强回波位置的调整更明显,侧重于改进湿度场。累计降水的预报结果显示,同化径向风资料对雨带的位置、范围有较好的改进,同化雷达反射率资料对暴雨强度的预报有明显的改善。通过降水ETS评分发现,同化常规观测试验相对于控制实验,对于5、15 mm和25 mm降水评分能增加0.1左右,径向风同化试验能增加0.2左右,反射率同化试验能增加0.3左右,而径向风加反射率试验增加的评分介于0.2～0.3。雷达资料对于提高定量降水预报的精确度有着重要作用。 相似文献
10.
利用京津冀区域加密自动气象站、SA多普勒天气雷达、L波段风廓线雷达、NCEP 0.25° 再分析资料及0.03° 高分辨率地形资料研究了北京2018年7月15—16日暖区特大暴雨特征和形成机制。结果表明:（1）这次暖区特大暴雨发生在副热带高压边缘的暖气团（θse高能区）中,无明显冷空气强迫,斜压性弱,有丰沛的水汽,850 hPa以下出现强水汽辐合。（2）暴雨的中尺度对流系统发展有3个过程:带状对流建立和局地强雨团影响、北京北部“列车效应”南部雷暴冷池出流造成对流加强和移动、平原地区线状对流重建。（3）暴雨发生前,低层西南风出现风速脉动,低空急流建立。首先在2500—3500 m高度形成低空急流,2 h后2500 m以下风速显著增大,5 h后急流厚度由边界层伸展到700 hPa。急流出口区降压,低层出现气旋性风场或切变,有利于垂直上升运动发展,触发和加强对流。（4）西南低空急流暖湿输送导致高温、高湿、高能的对流不稳定层结反复重建,这是对流发展加强的重要原因。（5）地面辐合线是对流触发并逐渐组织成带状对流系统的关键影响因素。地面辐合线方向、低空急流轴、回波移动方向三者几乎重叠是造成对流后向传播和“列车效应”的有利条件。（6）太行山和燕山地形对对流触发和暴雨增幅有重要影响。北京最大雨强≥40 mm/h站点中的77.4%位于西南部和东北部200—600 m海拔高度处。偏东风在华北西部太行山局地迎风坡触发对流,西南低空急流在北京北部迎风坡和喇叭口地形处辐合和抬升更为显著,造成局地特大暴雨。 相似文献
11.
IMPACTS OF ATOVS DATA ASSIMILATION ON PREDICTION OF A RAINSTORM OVER SOUTHEAST CHINA 总被引：1，自引：1，他引：0
Using the Advanced Research WRF (ARW WRF) model and the Gridpoint Statistical
Interpolation (GSI) three-dimensional variational analysis (3DVAR) system, the impact of assimilating
ATOVS (Advanced TIROS Operational Vertical Sounder) radiance through the prototype Community
Radiative Transfer Model (pCRTM) is evaluated on the forecasting of a heavy rainstorm occurring over the
central Guangdong province in the southeast of China on 20-21 June 2005. A pair of comparison
experiments (NODA and DA) for this case is conducted with multiple configurations, including nesting
domains with 4-km and 12-km grid distances. The results showed that by changing the initial condition
through data assimilation, a modified divergence and moisture field with the structure of dipoles has been
added to the axis of the rainband with a southwest-northeast orientation. When more moisture carried by a
southwesterly low level jet (LLJ) was converged into the northeast portion of the rainband around the
observatory station of Longmen, the amplitude of moisture static energy (MSE) increased substantially at
low levels much more than at middle levels, resulting in the enlarging of differences in MSE between 500
hPa and 850 hPa; the atmosphere became more unstable. Consequently, the convective rainfall increased in
the northeast part of the province around the Longmen station, which was consistent with the observed
distribution of rainfall. 相似文献
12.
Analysis and simulation of mesoscale convective systems accompanying heavy rainfall: The goyang case 总被引：1，自引：0，他引：1
Hyun-Young Choi Ji-Hyun Ha Dong-Kyou Lee Ying-Hwa Kuo 《Asia-Pacific Journal of Atmospheric Sciences》2011,47(3):265-279
We investigated a torrential rainfall case with a daily rainfall amount of 379 mm and a maximum hourly rain rate of 77.5 mm that took place on 12 July 2006 at Goyang in the middlewestern part of the Korean Peninsula. The heavy rainfall was responsible for flash flooding and was highly localized. High-resolution Doppler radar data from 5 radar sites located over central Korea were analyzed. Numerical simulations using the Weather Research and Forecasting (WRF) model were also performed to complement the high-resolution observations and to further investigate the thermodynamic structure and development of the convective system. The grid nudging method using the Global Final (FNL) Analyses data was applied to the coarse model domain (30 km) in order to provide a more realistic and desirable initial and boundary conditions for the nested model domains (10 km, 3.3 km). The mesoscale convective system (MCS) which caused flash flooding was initiated by the strong low level jet (LLJ) at the frontal region of high equivalent potential temperature (θe) near the west coast over the Yellow Sea. The ascending of the warm and moist air was induced dynamically by the LLJ. The convective cells were triggered by small thermal perturbations and abruptly developed by the warm θe inflow. Within the MCS, several convective cells responsible for the rainfall peak at Goyang simultaneously developed with neighboring cells and interacted with each other. Moist absolutely unstable layers (MAULs) were seen at the lower troposphere with the very moist environment adding the instability for the development of the MCS. 相似文献
13.
Impact of 4DVAR Assimilation of Rainfall Data on the Simulation of Mesoscale Precipitation Systems in a Mei-yu Heavy Rainfall Event 总被引：1，自引：0，他引：1
The multi-scale weather systems associated with a mei-yu front and the corresponding heavy precipitation during a particular heavy rainfall event that occurred on 4 5 July 2003 in east China were successfully simulated through rainfall assimilation using the PSU/NCAR non-hydrostatic, mesoscale, numerical model （MM5） and its four-dimensional, variational, data assimilation （4DVAR） system. For this case, the improvement of the process via the 4DVAR rainfall assimilation into the simulation of mesoscale precipitation systems is investigated. With the rainfall assimilation, the convection is triggered at the right location and time, and the evolution and spatial distribution of the mesoscale convective systems （MCSs） are also more correctly simulated. Through the interactions between MCSs and the weather systems at different scales, including the low-level jet and mei-yu front, the simulation of the entire mei-yu weather system is significantly improved, both during the data assimilation window and the subsequent 12-h period. The results suggest that the rainfall assimilation first provides positive impact at the convective scale and the influences are then propagated upscale to the meso- and sub-synoptic scales.
Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance. 相似文献
Through a set of sensitive experiments designed to evaluate the impact of different initial variables on the simulation of mei-yu heavy rainfall, it was found that the moisture field and meridional wind had the strongest effect during the convection initialization stage, however, after the convection was fully triggered, all of the variables at the initial condition seemed to have comparable importance. 相似文献
14.
多普勒雷达资料对暴雨定量预报的同化对比试验 总被引：7，自引：6，他引：1
基于NCEP/NCAR再分析资料和连云港雷达探测资料,利用WRF模式及其三维变分同化V2.1系统,对发生在2008年4月19日连云港地区一次区域性暴雨过程进行了三维变分同化数值模拟对比研究.结果表明,同化了雷达资料后,模式预报效果比单独使用NCEP做初始场效果明显改善,暴雨落区和量值更接近实况.同化了雷达资料后,模式预报的垂直运动区、最大上升区、水汽输送通道和高空涡度分布等更接近强降水区,结构也更精细,说明初始场增加雷达资料后,对初始风场的结构、强度和初始云水分布有实质性的改进,从而提高了对暴雨定量预报的效果. 相似文献
15.
暴雨模拟中多普勒雷达径向速度变分同化的应用 总被引：1，自引：0，他引：1
针对2008年6月广东地区的一次强降雨过程,利用WRF中尺度数值模式及其三维变分同化系统(WRF-3DVAR),进行了多普勒雷达径向速度变分同化对暴雨过程模拟效果影响研究。结果表明:WRF-3DVAR能够有效地同化多普勒雷达径向速度,同化后的主要影响在于改进了初始动力场,使得初始场包含有更详尽的中尺度特征信息,进而显著提高模式对广东局地暴雨过程的模拟效果。在高分辨率中尺度数值模式中有效地利用多普勒天气雷达资料,是提高中尺度降雨预报的关键。 相似文献
16.
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
17.
利用国家气象中心中尺度业务数值预报模式GRAPES-MESO v3.0,以2010年6月1～30日为例,开展地面降水率1DVAR(one-dimensional variational assimilation)同化方案在GRAPES-3DVAR(three-dimensional variational assimilation)同化系统中的应用试验研究(ASSI试验),并以未加降水资料同化的试验为对照试验(CNTL试验),以评估全国1h加密雨量资料在模式中同化应用的效果。结果表明:1)在相对湿度背景误差和降水率观测误差范围内,1DVAR同化方案能够对湿度廓线进行有意义的调整,使分析降水向观测降水靠近;ASSI试验对初始温、压、湿、风场的修正主要为正效果;2)对2010年6月17～21日江南、华南连续性降水过程进行了分析,整体而言ASSI试验对逐日及逐时降水强度的预报普遍强于CNTL试验,与实况更加接近;3)ASSI试验对2010年6月1～30日08时起报的0～24 h模式预报的小雨、中雨、大雨、暴雨、大暴雨各个降水量级TS评分及ETS评分相比CNTL试验均有较明显提高,预报偏差也更接近于1;4)ASSI试验较CNTL试验能更好地模拟雨带的分布、雨带演变特征和降水强度的变化;5)对降水所做的典型个例和统计检验分析从不同角度说明了地面降水资料1DVAR同化方案在GRAPES-3DVAR系统中的应用改善了GRAPES-MESO v3.0的降水模拟效果。 相似文献
18.
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
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Assimilation of Doppler Radar Observations with an Ensemble Square Root Filter：A Squall Line Case Study 
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下载免费PDF全文 The effectiveness of using an Ensemble Square Root Filter（EnSRF） to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12July 2005 in midwest Shandong Province using the Weather Research and Forecasting（WRF） model.The experimental results show that：（1） The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data.The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably.The model spin-up time has been shortened,and the precipitation forecast is improved accordingly.（2） Compared with the control run,the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields.The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms.（3） The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation,but the propagation speed is larger than the observed.The effective forecast period for this squall line is about 5-6 h,probably because of the nonlinear development of the convective storm. 相似文献
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利用WRF（Weather Research Forecast）模式及其3D-Var（Three-Dimensional Variational assimilation）变分系统,针对2017年7月7日一次飑线进行了雷达资料的循环同化敏感性试验。结果表明：以循环同化雷达资料至飑线成熟期时刻的试验预报效果最好,主要原因在于预报的低层西北冷空气较强,从而导致较强的低层切变,再配合强的热力不稳定条件从而使飑线的发展得以维持。通过七组试验对比表明,对于单次雷达资料,同化的时机更为重要。同化飑线成熟阶段的雷达反射率,对低层热力层结有改善作用,为飑线发展提供了不稳定能量;对于循环同化,通过观测的影响和模式自身的热动力调整,对流场也有较好的修正作用,为对流系统的持续发展提供了充分的动力条件。 相似文献