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1.
The impacts of AMSU-A and IASI (Infrared Atmospheric Sounding Interferometer) radiances assimila-tion on the prediction of typhoons Vicente and Saola (2012) are studied by using the ensemble transform ...  相似文献   

2.
为加强国内卫星资料在同化系统中的应用,在自主构建的新一代WRF-EnSRF同化系统中,采用RTTOV辐射传输模式作为观测算子,并建立卫星资料读取、偏差订正及质量控制等子模块,构建出WRF-EnSRF卫星资料同化系统.运用该同化系统,同时同化NOAA-16的AMSU-A和AMSU-B的辐射率资料,进行华南暴雨过程的卫星资料同化数值模拟试验.试验结果表明:偏差订正后亮温资料拟合结果基本位于主对角线上,偏差有所降低.从TS评分看,同化试验对中雨及大雨部分的降水落区以及暴雨级别以上的降水强度的模拟效果有改善.试验证明,建立的卫星同化系统是可运行的.  相似文献   

3.
A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds.  相似文献   

4.
The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) on the track prediction of Typhoon Megi (2010) was studied using the Weather Research and Forecasting (WRF) model and a hybrid ensemble three-dimensional variational (En3DVAR) data assimilation (DA) system. The influences of tuning the length scale and variance scale factors related to the static background error covariance (BEC) on the track forecast of the typhoon were studied. The results show that, in typhoon radiance data assimilation, a moderate length scale factor improves the prediction of the typhoon track. The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts, even when the static BEC was carefully tuned to optimize its performance. When the hybrid DA was employed, the track forecast was significantly improved, especially for the sharp northward turn after crossing the Philippines, with the flow-dependent ensemble covariance. The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically. The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR. Additionally, for 24 h forecasts, the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels.  相似文献   

5.
极轨卫星的高级微波温度计(Advanced Microwave Sounding Unit-A,简称AMSU-A)辐射资料对提高降水定量预报的水平有重要作用.但是极轨卫星的轨道特征导致乘载其上的微波温度计资料在区域同化系统中存在严重缺测.本研究重点分析了晨昏轨道卫星上微波温度计资料同化对墨西哥湾沿岸定量降水预报的重要影响.研究选取了早晨星NOAA-15、上午星MetOp-A和下午星NOAA-18,利用美国NCEP(National Centers for Environmental Prediction)的业务同化系GSI(Gridpoint Statistical Interpolation)资料同化系统,进行了加和不加NOAA-15 AMSU-A资料的两组资料同化和预报试验,来阐明晨昏轨道卫星上微波温度计资料同化对墨西哥湾沿岸降水预报的重要影响.试验结果分析表明如果仅同化NOAA-18和MetOp-A资料,在协调世界时00:00和12:00的同化时间,在墨西哥湾和美国西部大陆就是卫星观测资料缺测区,而早晨星NOAA-15资料正好可以填补这个资料空缺.模式预报也表明,同化NOAA-15的AMSU-A资料可以对墨西哥湾降水有持续的正影响.这一研究证明了保持有搭载着AMSU-A或者相似仪器的早晨星,对区域降水预报的重要性.由于目前NOAA-15是唯一的一颗正在运行的、已远超过其正常运行期的早晨星,通过技术手段维持NOAA-15的AMSU-A仪器更超长期运行也就特别重要.  相似文献   

6.
This study explored the impact of coastal radar observability on the forecast of the track and rainfall of Typhoon Morakot(2009)using a WRF-based ensemble Kalman filter(EnKF)data assimilation(DA)system.The results showed that the performance of radar EnKF DA was quite sensitive to the number of radars being assimilated and the DA timing relative to the landfall of the tropical cyclone(TC).It was found that assimilating radial velocity(Vr)data from all the four operational radars during the 6 h immediately before TC landfall was quite important for the track and rainfall forecasts after the TC made landfall.The TC track forecast error could be decreased by about 43% and the 24-h rainfall forecast skill could be almost tripled.Assimilating Vr data from a single radar outperformed the experiment without DA, though with less improvement compared to the multiple-radar DA experiment.Different forecast performances were obtained by assimilating different radars, which was closely related to the first-time wind analysis increment, the location of moisture transport, the quasi-stationary rainband, and the local convergence line.However, only assimilating Vr data when the TC was farther away from making landfall might worsen TC track and rainfall forecasts.Besides, this work also demonstrated that Vr data from multiple radars, instead of a single radar, should be used for verification to obtain a more reliable assessment of the EnKF performance.  相似文献   

7.
The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me...  相似文献   

8.
An hourly-cycling ensemble Kalman filter (EnKF) working at 2.5?km horizontal grid spacing is implemented over southern Ontario (Canada) to assimilate Meteorological Terminal Aviation Routine Weather Reports (METARs) in addition to the observations assimilated operationally at the Canadian Meteorological Centre. This high-resolution EnKF (HREnKF) system employs ensemble land analyses and perturbed roughness length to prevent an ensemble spread that is too small near the surface. The HREnKF then performs continuously for a four-day period, from which twelve-hour ensemble forecasts are launched every six hours. The impact on analyses and short-term forecasts of assimilating METAR data is given special attention.

It is shown that using ensemble land surface analyses increases near-surface ensemble spreads for temperature and specific humidity. Perturbing roughness length enlarges the spread for surface wind. Given sufficient ensemble spread, the four-day case study shows that the near-surface model state is brought closer to surface observations during the cycling process. The impact of assimilating surface data can also be seen at higher levels by using aircraft reports for verification. The ensemble forecast verification suggests that METAR data assimilation improves ensemble forecasts of air temperature and dewpoint near the surface up to a lead time of six hours or even longer. However, only minor improvement is found in surface wind forecasts.  相似文献   

9.
Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.  相似文献   

10.
The impact of assimilating radiance data from the advanced satellite sensor GMI(GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational(3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting(WRF) model. The GMI radiance data are assimilated for Typhoon Matmo(2014), Typhoon Chan-hom(2015), Typhoon Meranti(2016), and Typhoon Mangkhut(2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind,temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method.  相似文献   

11.
Assimilating satellite radiances into Numerical Weather Prediction (NWP) models has become an important approach to increase the accuracy of numerical weather forecasting. In this study, the assimilation technique scheme was employed in NOAA’s STMAS (Space-Time Multiscale Analysis System) to assimilate AMSU-A radiances data. Channel selection sensitivity experiments were conducted on assimilated satellite data in the first place. Then, real case analysis of AMSU-A data assimilation was performed. The analysis results showed that, following assimilating of AMSU-A channels 5–11 in STMAS, the objective function quickly converged, and the channel vertical response was consistent with the AMSU-A weighting function distribution, which suggests that the channels can be used in the assimilation of satellite data in STMAS. With the case of the Typhoon Morakot in Taiwan Island in August 2009 as an example, experiments on assimilated and unassimilated AMSU-A radiances data were designed to analyze the impact of the assimilation of satellite data on STMAS. The results demonstrated that assimilation of AMSU-A data provided more accurate prediction of the precipitation region and intensity, and especially, it improved the 0–6h precipitation forecast significantly.  相似文献   

12.
探索了基于WRF模式的集合卡尔曼滤波同化方法（WRF-EnKF,简称EnKF）在近海有可能达到更强台风连续循环同化中国大陆高时空分辨率多普勒天气雷达径向风观测资料的效果,同时检验台风Vicente（2012）的三维结构演变及其动力学特征。通过短期集合预报得到跟随当前流场变化着的背景误差协方差的台风涡旋和动力学结构。研究发现,EnKF同化预报系统能有效地同化高时空分辨率雷达径向速度观测资料,显著改善初始场中台风Vicente的中小尺度内核结构,同时提高对台风Vicente的路径和强度及其相伴随的短期强降水预报。在台风最强时刻同化雷达径向风观测能快速（1~2 h）得到真实的暖核台风结构,同时进一步提高台风路径和强度的预报。另外,EnKF同化雷达径向风观测资料还能有效提高短期降水预报,1 h和3 h累积降水的分布、降水中心以及降水随时间演变都能得到显著改善,这与改善台风路径、结构和强度有密切关系。因此,对中国东南沿海有可能达到较强的台风进行同化雷达径向风观测资料可改善登陆台风的预报水平,这为利用我国地基多普勒天气雷达观测资料改善模式的初始场从而提高台风预报提供一定的指示作用。   相似文献   

13.
ATOVS 不同卫星资料在台风模拟中的同化试验研究   总被引：5，自引：1，他引：4  
利用美国国家大气研究中心(NCAR)开发的中尺度模式WRF(ARW)V3.2 及其三维变分同化系统WRF-3DVAR,以1011 号超强台风“ 凡亚比” 为个例,采用连续循环同化的方法对ATOVS 卫星资料进行同化试验,探讨了同化ATOVS 不同卫星资料对“ 凡亚比” 模拟的影响。结果表明,强度影响方面:同化ATOVS不同资料均可有效改善台风强度,台风中心海平面气压平均偏差从42 hPa 下降到18 hPa,但不同资料间的差异并不显著,平均在6 hPa 以内,这表明仅同化ATOVS 资料对台风强度的改善相对有限。路径影响方面:(1)不同卫星的同一种传感器资料效果略有不同,同化NOAA-18 和NOAA-15 的AMSU-A 资料效果较好,NOAA-16 的AMSU-A 效果较差;同化NOAA-15 和NOAA-16 的AMSU-B 资料效果相当,且均优于AMSU-A 资料。(2) 同一颗卫星不同传感器资料的差异较大,同化AMSU-B 资料的改善较为明显,HIRS-3 次之,AMSU-A较差,而同时同化不同资料并没有带来更为明显的改善。(3) 同时同化多颗卫星ATOVS 资料的试验表明,将多种资料引入到同化系统的同时,也带来相应的累积误差,因而仅同化一颗卫星可能比同时同化两颗或三颗卫星ATOVS 资料的效果要好。   相似文献   

14.
An ensemble Kalman filter(EnKF) combined with the Advanced Research Weather Research and Forecasting model(WRF) is cycled and evaluated for western North Pacific(WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone(TC) minimum sea level pressure(SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient v...  相似文献   

15.
本文基于中尺度区域模式WRF,开展模式层顶高度变化对高空气象要素,特别是高空风场数值模拟影响的研究。通过设计模式顶高45、5 hPa两个试验,同化来源于NOAA-15、NOAA-18、NOAA-19和METOP-2的AMSU-A辐射计高通道数据,表明提高模式层顶能够使卫星更多的高通道样本数量进入同化系统,达到减小背景场误差,同时减小高于层顶通道辐射能量对低层通道影响的目的,一定程度上改进了同化效果,从而改善高空气象要素,特别是风场的模拟效果,与观测值的均方根误差减小了约0.4~0.5 m·s-1。  相似文献   

16.
A hybrid GSI (Grid-point Statistical Interpolation)-ETKF (Ensemble Transform Kalman Filter) data assimilation system has been recently developed for the WRF (Weather Research and Forecasting) model and tested with simulated observations for tropical cyclone (TC) forecast. This system is based on the existing GSI but with ensemble background information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution (27 km) is used. A case study of typhoon Muifa (2011) is performed to assimilate real observations including conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble covariance shows significant improvements with respect to track forecast compared to the standard GSI system which in theory is three dimensional variational analysis (3DVAR). By comparing the analyses, analysis increments and forecasts, the hybrid system is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better describe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon initial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is tested and similar results are revealed with those from cycled GSI-ETKF approach.  相似文献   

17.
The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis.  相似文献   

18.
This study introduces the operational data assimilation (DA) system at the Korea Institute of Atmospheric Prediction Systems (KIAPS) to the numerical weather prediction community. Its development history and performance are addressed with experimental illustrations and the authors’ previously published studies. Milestones in skill improvements include the initial operational implementation of three-dimensional variational data assimilation (3DVar), the ingestion of additional satellite observations, and changing the DA scheme to a hybrid four-dimensional ensemble-variational DA using forecasts from an ensemble based on the local ensemble transform Kalman filter (LETKF). In the hybrid system, determining the relative contribution of the ensemble-based covariance to the resultant analysis is crucial, particularly for moisture variables including a variety of horizontal scale spectra. Modifications to the humidity control variable, partial rather than full recentering of the ensemble for humidity further improves moisture analysis, and the inclusion of more radiance observations with higher-level peaking channels have significant impacts on stratosphere temperature and wind performance. Recent update of the operational hybrid DA system relative to the previous 3DVar system is described for detailed improvements with interpretation.  相似文献   

19.
利用ATOVS资料和常规观测资料, 采用GRAPES 3D-Var同化系统和中尺度数值模式MM5设计了仅同化常规观测资料的NOATOVS试验和同化常规观测资料及ATOVS辐射率资料的ATOVS试验, 对2004年6月22—24日长江中下游和西南地区东部的特大暴雨进行了分析和模拟。结果表明:直接同化ATOVS辐射率资料获得的分析场可以有效改进对流层温、湿场分布, 对风场也有一定的影响。对比试验结果表明:ATOVS试验可以较好地模拟出暴雨天气形势、主要影响系统, 对降雨的落区、强度也有较好的反映, 模拟的局地暴雨强度与实际降雨量基本一致, 同化卫星资料的改善效果较为明显。即同化ATOVS资料对于改进中尺度局地暴雨过程模拟效果是可行的。  相似文献   

20.
Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A （AMSU-A） and Unit B （AMSU-B） radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder （ATOVS） microwave radiance assimilation, is employed to simulate some heavy rainfall cases. The experiment results show that the assimilation of AMSU-A （B） microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A （B） microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.  相似文献   

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