集合Kalman滤波同化理论及其数值试验

集合Kalman滤波用于数值试验有着坚实的理论基础.本文介绍了集合Kalman滤波理论及其技术实现, 在此基础上搭建了集合Kalman滤波同化系统, 用MM5模式同化了实测探空资料并作了48 h的预报试验, 并将预报结果与实测值及4D VAR同化的结果作了比较.试验结果表明: 集合Kalman滤波同化探空资料可以改进MM5模式的预报效果, 且集合Kalman滤波同化后模式的预报效果明显优于4D VAR同化后模式的预报效果.     

NUMERICAL EXPERIMENTS AND ANALYSIS OF DIGITAL FILTER INITIALIZATION FOR WRF MODEL

Initialization and initial imbalance problem were discussed in the context of a three-dimensional variational data assimilation system of the new generation “Weather Research and Forecasting Model”. Several options of digital filter initialization have been tested with a rain storm case. It is shown that digital filter initialization, especially diabatic digital filter initialization and twice digital filter initialization, have effectively removed spurious high frequency noise from initial data for numerical weather prediction and produced balanced initial conditions. For six consecutive intermittent data assimilation cycles covering a 3-day period, mean initialization increments and impact on forecast variables are studied. DFI has been demonstrated to provide better adjustment of the hydrometeors and vertical velocity, reduced spin-up time, and improved forecast variables quantity.     

三次谐波对配电系统的危害及抑制措施

为了更好地降低或消除供电系统中的三次谐波，保障供电系统的安全运行，针对三次谐波对供配电系统的影响越来越严重的现状，以国家气象信息中心供配电系统为研究对象，采用理论分析与实际测量相结合的方法，对三次谐波的危害及抑制措施进行了研究。指出了三次谐波产生的原因，分析了三次谐波引起的5种主要危害，并以此为依据，提出了降低或消除三次谐波的6种不同方法。通过对降低或消除三次谐波不同方法进行分析、对比，结合实践的检验，通过在配电系统接近负荷的位置安装D-Yn接法隔离变压器，可以最有效地抑制三次谐波产生的各种危害，明显改善供电系统的供电质量和配电系统及设备的安全运行，是行之有效的三次谐波抑制措施。     

卡尔曼滤波法在西峰雷达估测降水中的应用

应用卡尔曼滤波校准法,利用地面自动雨量计的降水量值,对庆阳市2007年7月和9月2次降水过程中的2个雨量较明显时段的雷达估测值进行了校准分析,并与雨量计测量的雨量值进行了误差比较分析。结果显示：混合降水过程的校准效果要优于均匀降水过程。校准后,混合降水过程的相对误差从166.4%下降到了7.6%,均匀降水过程的相对误差从284.3%下降到了56.9%。由此可以看出卡尔曼滤波校准法能有效消除雷达估测降水过程中的各种随机噪声,并能提高雷达定量估算区域降水量的精度,同时还较好地保留了雷达探测降水的精细结构。     

CHARACTERIZATION OF THE EFFECT OF PEAK SHIFTS ON THE PERFORMANCE OF THE KALMAN FILTER IN MULTICOMPONENT ANALYSES

The effect of peak shifts on the performance of a Kalman filter multicomponent analysis algorithm hasbeen investigated.A series of Gaussian test systems were employed to characterize the concentrationestimation errors and the morphology of the on-line residuals(the innovations sequence).Both forwardand reverse filters were used in the generation of the innovations sequences.It was found that thedifference between the forward and reverse innovations sequences gave an accurate indication of thedirection and magnitude of the peak shift.A series of overlapped two-component systems were alsoinvestigated.Again,a correlation between the difference innovations and the degree of the responseshift was observed.The behavior of the Katman filter in fitting the shifted fluorescence emissionspectrum of benzo[a]pyrene was also examined.The response for henzo[a]pyrene in cyclohexanesolution was compared to that obtained on the surface of a reversed phase thin layer chromatographyplate.A red shift of 4 nm was detected for the surface spectrum by observing the difference innovationssequence.This approach holds promise for correction of response shifts in multicomponent systems.     

以T42产品为因子用卡尔曼滤波方法作温度预报

本文以Ｔ４２数据预报产品为因子，用卡尔曼滤波方法建立可更新系数的预报方程作南京和芜湖的温度预报。从１９９４年１月至４月共８４次试报中，用完全相同的因子卡尔曼滤波方法比回归分析方法预报结果精度提高０．５度。     

Effect of doubling the ensemble size on the performance of ensemble prediction in the warm season using MOGREPS implemented at the KMA

Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

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.     

1013号台风“鲶鱼”的台风环流分离方法比较

台风系统为中α尺度系统,接近大尺度系统.为了研究环境气流与台风的相互作用,需要将台风从环境风场中分离出来.分别采用时间滤波、空间滤波和动力学方法将2010年第13号（1013号）超强台风“鲶鱼”（Megi）从环境风场中分离出来,对比分离得到的台风环境场,结果表明：时间滤波和空间滤波具有相对的随机性,容易留下台风风场的残留,同时也会减弱环境风场,而采用动力学方法可以将台风“Megi”较好地分离出来.3种方法各有优缺点,采用时间滤波可以使风场、高度场和温度场很好地平衡,但是需要较长的时间序列;空间滤波方法,仅采用一个时刻的时间,方法相对简单易用,但是由于台风风场、高度场和温度场的尺度并不相同,采用同一尺度对台风滤波容易造成风场、高度场和温度场的不平衡;动力学滤波方法分离效果较好,但是相对复杂,在低层也容易产生不平衡的现象.